A. Cancino, C, Nuñez, A & M. Merigó, J 2019, 'Influence of a seed capital program for supporting high growth firms in Chile', Contaduría y Administración, vol. 64, no. 1, pp. 65-65.
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<p>The main economic development agency in Chile, CORFO, implemented in 2001 a Seed Capital Program (SCP) to promote the development of high-growth firms. The SCP not only provides financial aid to entrepreneurs but also technical and administrative assistance through the support of incubators. Incubators may be universities incubators (UI) or private firms (NUI). The aim of this paper is to know the performance of beneficiaries according to the assistance of UI or NUI. A total of 238 new firms beneficiaries with the CORFO program were surveyed (84 supported by UI and 154 supported by NUI). Two logistic regression models were used, a first model to assess the probability that a new firm achieves positive sales, and a second model to assess the probability that the new firm reaches a high growth during the first five years from its inception. Overall, mixed results were found. SCP’s beneficiaries supported by either UI and NUI have the same probability of having positive sales when starting their operations. However, five years after started their operations, businesses supported by UI have higher probabilities of achieving high growth than businesses supported by NUI. The results highlight a positive interaction between private entrepreneurs, public agencies and university incubators.<strong></strong></p>
Abas, AEP, Yong, J, Mahlia, TMI & Hannan, MA 2019, 'Techno-Economic Analysis and Environmental Impact of Electric Vehicle', IEEE Access, vol. 7, pp. 98565-98578.
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Abas, PE & Mahlia, TMI 2019, 'Techno-Economic and Sensitivity Analysis of Rainwater Harvesting System as Alternative Water Source', Sustainability, vol. 11, no. 8, pp. 2365-2365.
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This paper formulates a rainwater harvesting model, with system and economic measures to determine the feasibility of a rainwater harvesting system, which uses water from the mains to complement the system. Although local meteorological and market data were used to demonstrate the model, it can also be easily adapted for analysis of other localities. Analysis has shown that an optimum tank size exists, which minimizes the cost per unit volume of water. Economic performance measures have indicated that rainwater harvesting system is currently infeasible to be implemented in Brunei; with capital cost and water price being shown to be among the prohibiting factors. To improve feasibility, a combination of rebate scheme on capital cost and raising the current water price has been proposed. It has also been shown that the system is more viable for households with high water demand.
Abbasi, M, Abbasi, E, Tousi, B & Gharehpetian, GB 2019, 'A zero‐current switching switched‐capacitor DC‐DC converter with reduction in cost, complexity, and size', International Journal of Circuit Theory and Applications, vol. 47, no. 10, pp. 1630-1644.
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SummaryThis paper presents a new step‐up switched‐capacitor (SC) DC‐DC converter which has many advantages such as reduction in investment cost, control complexity, number of components, voltage stress on components, and size over traditional topologies. In the proposed structure, power switches are reduced in number which in turn leads to the merits mentioned earlier and makes the converter more suitable for industrial applications. Furthermore, a previously introduced zero‐current switching (ZCS) method is used here which provides soft switching for the devices. There is also a reduction in the number of required inductors to achieve ZCS due to the decreased number of switches in the proposed converter. The proposed converter is validated by comprehensive simulation results in MATLAB Simulink environment and also precise experimental results which show the acceptable performance of the proposed topology.
Abbasi, M, Babaei, E & Tousi, B 2019, 'New family of non‐isolated step‐up/down and step‐up switched‐capacitor‐based DC–DC converters', IET Power Electronics, vol. 12, no. 7, pp. 1706-1720.
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Here, a new family of non‐isolated step‐up/down and step‐up switched‐capacitor (SC)‐based DC–DC converters are proposed possessing many advantages as lower voltage stress on capacitors and higher voltage gain compared to previously introduced DC–DC converters. The proposed converter structures have multiple capacitors which are based on principles of DC–DC SC converters. Therefore, the amount of power transfer from the input to the output is higher in the proposed converters. Generally, the proposed converters are more suitable for industrial applications, especially for generating high‐voltage gains in lower duty‐cycles. For proving the analysis, comprehensive comparisons and precise experiments have been performed which show remarkable performance of the proposed converter topologies.
Abbasi, MH, Taki, M, Rajabi, A, Li, L & Zhang, J 2019, 'Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach', Applied Energy, vol. 239, pp. 1294-1307.
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© 2019 Elsevier Ltd As the number of electric vehicles (EVs) is steadily increasing, their aggregation can offer significant storage to improve the electric system operation in many aspects. To this end, a comprehensive stochastic optimization framework is proposed in this paper for the joint operation of a fleet of EVs with a wind power producer (WPP) in a three-settlement pool-based market. An aggregator procures enough energy for the EVs based on their daily driving patterns, and schedules the stored energy to counterbalance WPP fluctuations. Different sources of uncertainty including the market prices and WPP generation are modeled through proper scenarios, and the risk is hedged by adding a risk measure to the formulation. To obtain more accurate results, the battery degradation costs are also included in the problem formulation. A detailed case study is presented based on the Iberian electricity market data as well as the technical information of three different types of EVs. The proposed approach is benchmarked against the disjoint operation of EVs and WPP. Numerical simulations demonstrate that the proposed strategy can effectively benefit EV owners and WPP by reducing the energy costs and increasing the profits.
Abdelkarim, A, Gaber, AFD, Youssef, AM & Pradhan, B 2019, 'Flood Hazard Assessment of the Urban Area of Tabuk City, Kingdom of Saudi Arabia by Integrating Spatial-Based Hydrologic and Hydrodynamic Modeling', Sensors, vol. 19, no. 5, pp. 1024-1024.
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This study deals with the use of remote sensing (RS), geographic information systems (GISs), hydrologic modeling (water modeling system, WMS), and hydraulic modeling (Hydrologic Engineering Center River Analysis System, HEC-RAS) to evaluate the impact of flash flood hazards on the sustainable urban development of Tabuk City, Kingdom of Saudi Arabia (KSA). Determining the impact of flood hazards on the urban area and developing alternatives for protection and prevention measures were the main aims of this work. Tabuk City is exposed to frequent flash flooding due to its location along the outlets of five major wadis. These wadis frequently carry flash floods, seriously impacting the urban areas of the city. WMS and HEC-HMS models and RS data were used to determine the paths and morphological characteristics of the wadis, the hydrographic flow of different drainage basins, flow rates and volumes, and the expansion of agricultural and urban areas from 1998 to 2018. Finally, hydraulic modeling of the HEC-RAS program was applied to delineate the urban areas that could be inundated with floodwater. Ultimately, the most suitable remedial measures are proposed to protect the future sustainable urban development of Tabuk City from flood hazards. This approach is rarely used in the KSA. We propose a novel method that could help decision-makers and planners in determining inundated flood zones before planning future urban and agricultural development in the KSA.
Abdo, P, Huynh, BP, Irga, PJ & Torpy, FR 2019, 'Evaluation of air flow through an active green wall biofilter', Urban Forestry & Urban Greening, vol. 41, pp. 75-84.
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© 2019 Elsevier GmbH Green walls show promise as active bio-filters to improve indoor air quality by removing both gaseous and particulate air pollutants. The current work represents a detailed assessment of airflow through an active green wall module. Airflow distribution through the module, the effect of wetting the substrate, and the effect of introducing a cover to the module's open top face were investigated, with the aim to improve the module's design and achieve more appropriate and effective airflow. Four cases of both planted and unplanted modules under both dry and wet conditions are considered. This work's primary observation is that more air will pass through a typical green wall substrate, and hence become cleansed, when the substrate is saturated wet more than when it is dry. The increase was substantial at approximately 50% more with 14.9 ± 0.2 L/s total air flow rate passing through the wet planted module versus 10 ± 0.2 L/s when dry. Reducing the 15.5 ± 0.75% of airflow passing through the module's open top face was found to be essential to maximize the bio-filtration capacity. Adding a top cover to the module having six 10 mm holes for irrigation decreased the airflow through the top by 6 ± 0.75%, and directed it through the filter increasing the percentage of air flow passing through the front openings from 79 ± 4% to 85 ± 4%.
Abdollahi, A, Pradhan, B & Shukla, N 2019, 'Extraction of road features from UAV images using a novel level set segmentation approach', International Journal of Urban Sciences, vol. 23, no. 3, pp. 391-405.
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© 2019, © 2019 The Institute of Urban Sciences. A novel hybrid technique for road extraction from UAV imagery is presented in this paper. The suggested analysis begins with image segmentation via Trainable Weka Segmentation. This step uses an immense range of image features, such as detectors for edge detection, filters for texture, filters for noise depletion and a membrane finder. Then, a level set method is performed on the segmented images to extract road features. Next, morphological operators are applied on the images for improving extraction precision. Eventually, the road extraction precision is calculated on the basis of manually digitized road layers. Obtained results indicated that the average proportions of completeness, correctness and quality were 93.52%, 85.79% and 81.01%, respectively. Therefore, experimental results validated the superior performance of the proposed hybrid approach in road extraction from UAV images.
Abdulkareem, JH, Pradhan, B, Sulaiman, WNA & Jamil, NR 2019, 'Development of lag time and time of concentration for a tropical complex catchment under the influence of long-term land use/land cover (LULC) changes', Arabian Journal of Geosciences, vol. 12, no. 3.
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© 2019, Saudi Society for Geosciences. Lag time (t L ) and time of concentration (T c ) are essential time parameters used in hydrological flood-flow design methods for estimating peak discharge and flood hydrograph shape. They form the basis of a number of hydrological models used among the scientists. Kelantan River basin, Malaysia, is a tropical catchment receiving heavy monsoon rainfall coupled with intense land use/land cover (LULC) changes making the area consistently flood prone thereby deteriorating water balance in the area. The most recent is that of December 2014 flood which lead to catastrophic loss of huge amount of properties worth millions of Malaysian ringgit. In view of this, the current research was conducted with the aim of developing (1) t L and T c based on 1984, 2002, and 2013 LULC conditions; (2) a relationship between t L and t L parameters; and (3) a relationship between t L and T c among different LULC conditions. Kelantan River basin was first delineated into four major catchments, viz., Galas, Pergau, Lebir, and Nenggiri, due to its large size (approximately 13,100 km 2 ). Soil map and LULC change maps corresponding to 1984, 2002, and 2013 were used for the calculation of CN values while NRCS lag equation was used for the estimation of t L and T c . The results showed that deforestation for logging activities and agricultural practices were the dominant LULC changes across the watershed. Low values of both t L and T c were obtained across the catchment which are typical for a tropical monsoon catchment characterized with high runoff and short peak discharge. Results of t L and T c in this study are not affected by LULC changes in the basin. Slope was observed to be highly correlated with t L . Correlation coefficient was used to determine the relationship between t L and t L factor, and hydraulic length and slope (L√S). The results showed high correlation in all the catchments from 1984 to 2013 except for Lebir catchment wher...
Abdulkareem, JH, Pradhan, B, Sulaiman, WNA & Jamil, NR 2019, 'Long-term runoff dynamics assessment measured through land use/cover (LULC) changes in a tropical complex catchment', Environment Systems and Decisions, vol. 39, no. 1, pp. 16-33.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The estimation of excess rainfall is critically important in water resource management as it provides the basis for calculating flood peak discharge that results in surface runoff. Kelantan River basin in Malaysia is a tropical catchment receiving heavy monsoon rainfall coupled with intense land use/cover (LULC) changes making the area consistently flood prone. The current study is therefore aimed to achieve the following goals: (1) to develop a curve number (CN) and runoff maps for 1984, 2002, and 2013 LULC conditions and (2) to determine runoff dynamics due to changes in LULC as well as to assess how the extent of LULC change will affect surface runoff generation. To achieve the aforementioned goals, land use maps corresponding to 1984, 2002, and 2014 LULC conditions were analyzed and prepared for the calculation of CN values using Soil Conservation Service (SCS-CN) method. CN and runoff maps corresponding to 1984, 2002, and 2013 LULC changes were successfully developed and the performance of the method was tested. The results indicated that forest was found to be the major land use type to have changed in all the LULC conditions across the watershed leading to intense runoff dynamics in the entire watershed. Higher runoff values were observed under 2013 LULC conditions across the watershed mainly due to intense deforestation relative to those of 1984 and 2002. The results of this study indicated that runoff generation is significantly affected by deforestation instead of changes in the rainfall pattern. The findings may be useful to water resource planners in controlling water loss for future planning.
Abdulkareem, JH, Pradhan, B, Sulaiman, WNA & Jamil, NR 2019, 'Prediction of spatial soil loss impacted by long-term land-use/land-cover change in a tropical watershed', Geoscience Frontiers, vol. 10, no. 2, pp. 389-403.
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© 2017 China University of Geosciences (Beijing) and Peking University. The devastating effect of soil erosion is one of the major sources of land degradation that affects human lives in many ways which occur mainly due to deforestation, poor agricultural practices, overgrazing, wildfire and urbanization. Soil erosion often leads to soil truncation, loss of fertility, slope instability, etc. which causes irreversible effects on the poorly renewable soil resource. In view of this, a study was conducted in Kelantan River basin to predict soil loss as influenced by long-term land use/land-cover (LULC) changes in the area. The study was conducted with the aim of predicting and assessing soil erosion as it is influenced by long-term LULC changes. The 13,100 km 2 watershed was delineated into four sub-catchments Galas, Pergau, Lebir and Nenggiri for precise result estimation and ease of execution. GIS-based Universal Soil Loss Equation (USLE) model was used to predict soil loss in this study. The model inputs used for the temporal and spatial calculation of soil erosion include rainfall erosivity factor, topographic factor, land cover and management factor as well as erodibility factor. The results showed that 67.54% of soil loss is located under low erosion potential (reversible soil loss) or 0-1 t ha -1 yr -1 soil loss in Galas, 59.17% in Pergau, 53.32% in Lebir and 56.76% in Nenggiri all under the 2013 LULC condition. Results from the correlation of soil erosion rates with LULC changes indicated that cleared land in all the four catchments and under all LULC conditions (1984-2013) appears to be the dominant with the highest erosion losses. Similarly, grassland and forest were also observed to regulate erosion rates in the area. This is because the vegetation cover provided by these LULC types protects the soil from direct impact of rain drops which invariably reduce soil loss to the barest minimum. Overall, it was concluded that the results have shown the significan...
Abidi, IH, Mendelson, N, Tran, TT, Tyagi, A, Zhuang, M, Weng, L, Özyilmaz, B, Aharonovich, I, Toth, M & Luo, Z 2019, 'Selective Defect Formation in Hexagonal Boron Nitride', Advanced Optical Materials, vol. 7, no. 13, pp. 1900397-1900397.
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AbstractLuminescent defects in hexagonal boron nitride (hBN) have emerged as promising single photon emitters (SPEs) due to their high brightness and robust operation at room temperature. The ability to create such emitters with well‐defined optical properties is a cornerstone toward their integration into on‐chip photonic architectures. Here, an effective approach is reported to fabricate hBN SPEs with desired emission properties in distinct spectral regions via the manipulation of boron diffusion through copper during atmospheric pressure chemical vapor deposition (CVD)—a process termed gettering. Using the gettering technique the resulting zero‐phonon line is deterministically placed between the regions 550 and 600 nm or from 600 to 650 nm, paving the way for hBN SPEs with tailored emission properties. Additionally, rational control over the observed SPE density in the resulting films is demonstrated. The ability to control defect formation during hBN growth provides a cost effective means to improve the crystallinity of CVD hBN films, and lower defect density making it applicable to hBN growth for a wide‐range of applications. The results are important to understand defect formation of quantum emitters in hBN and deploy them for scalable photonic technologies.
Abidi, S, Piccardi, M, Tsang, IW & Williams, M-A 2019, 'Well-M$^3$N: A Maximum-Margin Approach to Unsupervised Structured Prediction', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 3, no. 6, pp. 427-439.
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Unsupervised structured prediction is of fundamental importance for the clustering and classification of unannotated structured data. To date, its most common approach still relies on the use of structural probabilistic models and the expectation-maximization (EM) algorithm. Conversely, structural maximum-margin approaches, despite their extensive success in supervised and semi-supervised classification, have not raised equivalent attention in the unsupervised case. For this reason, in this paper we propose a novel approach that extends the maximum-margin Markov networks (M3N) to an unsupervised training framework. The main contributions of our extension are new formulations for the feature map and loss function of M3N that decouple the labels from the measurements and support multiple ground-truth training. Experiments on two challenging segmentation datasets have achieved competitive accuracy and generalization compared to other unsupervised algorithms such as k-means, EM and unsupervised structural SVM, and comparable performance to a contemporary deep learning-based approach.
Abnisa, F, Anuar Sharuddin, SD, bin Zanil, MF, Wan Daud, WMA & Indra Mahlia, TM 2019, 'The Yield Prediction of Synthetic Fuel Production from Pyrolysis of Plastic Waste by Levenberg–Marquardt Approach in Feedforward Neural Networks Model', Polymers, vol. 11, no. 11, pp. 1853-1853.
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The conversion of plastic waste into fuel by pyrolysis has been recognized as a potential strategy for commercialization. The amount of plastic waste is basically different for each country which normally refers to non-recycled plastics data; consequently, the production target will also be different. This study attempted to build a model to predict fuel production from different non-recycled plastics data. The predictive model was developed via Levenberg-Marquardt approach in feed-forward neural networks model. The optimal number of hidden neurons was selected based on the lowest total of the mean square error. The proposed model was evaluated using the statistical analysis and graphical presentation for its accuracy and reliability. The results showed that the model was capable to predict product yields from pyrolysis of non-recycled plastics with high accuracy and the output values were strongly correlated with the values in literature.
Acosta, E, Smirnov, V, Szabo, PSB, Buckman, J & Bennett, NS 2019, 'Optimizing Thermoelectric Power Factor in p-Type Hydrogenated Nano-crystalline Silicon Thin Films by Varying Carrier Concentration', Journal of Electronic Materials, vol. 48, no. 4, pp. 2085-2094.
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© 2019, The Minerals, Metals & Materials Society. Most approaches to silicon-based thermoelectrics are focused on reducing the lattice thermal conductivity with minimal deterioration of the thermoelectric power factor. This study investigates the potential of p-type hydrogenated nano-crystalline silicon thin films (μc-Si:H), produced by plasma-enhanced chemical vapor deposition, for thermoelectric applications. We adopt this heterogeneous material structure, known to have a very low thermal conductivity (~ 1 W/m K), in order to obtain an optimized power factor through controlled variation of carrier concentration drawing on stepwise annealing. This approach achieves a best thermoelectric power factor of ~ 3 × 10 −4 W/mK 2 at a carrier concentration of ~ 4.5 × 10 19 cm 3 derived from a significant increase of electrical conductivity ~ × 8, alongside a less pronounced reduction of the Seebeck coefficient, while retaining a low thermal conductivity. These thin films have a good thermal and mechanical stability up to 500°C with appropriate adhesion at the film/substrate interface.
Acuna, P, Rojas, CA, Baidya, R, Aguilera, RP & Fletcher, JE 2019, 'On the Impact of Transients on Multistep Model Predictive Control for Medium-Voltage Drives', IEEE Transactions on Power Electronics, vol. 34, no. 9, pp. 8342-8355.
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© 1986-2012 IEEE. In medium-voltage drives, multistep model predictive control (MPC) can lower the total harmonic distortion of the stator currents and thereby reduce losses and improve efficiency. However, from the point of view of implementation, there is still uncertainty as to whether transients have a major adverse effect on achieving this improved steady-state performance. The time-varying nature of machine drives, initialization of the optimization process, and limited computational resources are identified as key factors. This paper analyzes the link between these key factors in detail, thus a suitable reformulation and selective initialization approach is designed to enable the deterministic use of multistep finite-control-set MPC irrespective of the drive system conditions. Guidelines to select the prediction horizon, weighting factor, and minimum switching frequency considering the control platform limitations are presented. The significant impacts of transients on the design and experimental validation, covering several drive conditions, are evaluated in a scaled-down three-level induction machine drive switching at 350 Hz. This paper is accompanied by a video demonstrating the real-Time implementation of multistep MPC.
Adak, C, Chaudhuri, BB, Lin, C-T & Blumenstein, M 2019, 'Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis', IEEE Transactions on Information Forensics and Security, 2020, vol. 15, pp. 3567-3579.
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In this paper, we work on intra-variable handwriting, where the writingsamples of an individual can vary significantly. Such within-writer variationthrows a challenge for automatic writer inspection, where the state-of-the-artmethods do not perform well. To deal with intra-variability, we analyze theidiosyncrasy in individual handwriting. We identify/verify the writer fromhighly idiosyncratic text-patches. Such patches are detected using a deeprecurrent reinforcement learning-based architecture. An idiosyncratic score isassigned to every patch, which is predicted by employing deep regressionanalysis. For writer identification, we propose a deep neural architecture,which makes the final decision by the idiosyncratic score-induced weightedaverage of patch-based decisions. For writer verification, we propose twoalgorithms for patch-fed deep feature aggregation, which assist inauthentication using a triplet network. The experiments were performed on twodatabases, where we obtained encouraging results.
Adanta, D, ., B, ., W, Quaranta, E & I. Mahlia, TM 2019, 'Investigation of the effect of gaps between the blades of open flume Pico hydro turbine runners', Journal of Mechanical Engineering and Sciences, vol. 13, no. 3, pp. 5493-5512.
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This study will analyze the impact of gap size in two different runners called runner A (five blades) and B (six blades) to provides recommendations in design and manufacture of open flume turbine runners so that maximize the conversion of kinetic and potential energy. There are three methods was used to investigate its: analytical method is used to design the turbine; experimental to determine the actual turbine performance; computational fluid dynamics (CFD) to study the physical phenomena and re-check the velocity triangle on the runner to validate the design and manufacturing process. Using the results obtained, gaps between the blades can alter the velocity vector on the outlet and unbalance the rotation of runner; this imbalance could cause cavitation. Then, the decreasing torque is assumed because water pressure in the draft tube is similar to atmospheric pressure. Two conditions must be satisfied to maximize the performance of the turbine: swirling flow is required after the water flows past the runner in order to minimize the radial velocity on the outlet so that the draft tube can function properly; the dimensions of the blade must be carefully selected to avoid the formation of gaps between the blades.
Afshar, S, Hamilton, TJ, Tapson, J, van Schaik, A & Cohen, G 2019, 'Investigation of Event-Based Surfaces for High-Speed Detection, Unsupervised Feature Extraction, and Object Recognition', Frontiers in Neuroscience, vol. 12.
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Afzal, MK, Khan, WZ, Umer, T, Kim, B-S & Yu, S 2019, 'Editorial of cross-layer design issues, challenges and opportunities for future intelligent heterogeneous networks', Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 11, pp. 4207-4208.
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Agarwal, A, Dowsley, R, McKinney, ND, Wu, D, Lin, C-T, De Cock, M & Nascimento, ACA 2019, 'Protecting Privacy of Users in Brain-Computer Interface Applications', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 8, pp. 1546-1555.
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Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on secure multiparty computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e., such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing-based SMC in general, namely, with 15 players involved in all the computations.
Ahmed II, JB & Pradhan, B 2019, 'Spatial assessment of termites interaction with groundwater potential conditioning parameters in Keffi, Nigeria', Journal of Hydrology, vol. 578, pp. 124012-124012.
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© 2019 Elsevier B.V. Termite mounds are traditionally presumed to be good indicators of groundwater in places they inhabit but this hypothesis is yet to be scientifically substantiated. To confirm this assertion, it is expected that termite mounds would have strong correlations with groundwater conditioning parameters (GCPs). In this study, termite mounds distribution covering an area of about 156 km2 were mapped and their structural characteristics documented with the aim of examining their relationships with twelve (12) chosen GCPs. Other specific objectives were to identify specific mound types with affinity to groundwater and to produce a groundwater potential map of the study area. To achieve this, 12 GCPs including geology, drainage density, lineament density, lineament intersection density, land use/land cover, topographic wetness index (TWI), normalized difference vegetation index (NDVI), slope, elevation, plan curvature, static water level and groundwater level fluctuation were extracted from relevant sources. Frequency ratio (FR) and Spearman's rank correlation were used to find relationships and direction of such relationships. The result revealed a consistent agreement between FR and Spearman's rank correlation that tall (≥1.8 m) and Cathedral designed mounds are good indicators of groundwater. Further, the groundwater potential map produced from the Random Forest (RF) model via Correlation-based Feature Selection (CFS) using best-first algorithm depicted an erratic nature of groundwater distribution in the study area. This was then classified using natural break into very-high, high, moderate, low and very low potential classes and area under curve (AUC) of the receiver operating characteristics (ROC) showed an 86.5% validity of the model. About 75% of mapped termite mounds fell within the very-high to moderate potential classes thereby suggesting that although tall and cathedral mounds in particular showed good correlations with a number of GCPs, hi...
Ahmed II, JB, Pradhan, B, Mansor, S, Yusoff, ZM & Ekpo, SA 2019, 'Aquifer Potential Assessment in Termites Manifested Locales Using Geo-Electrical and Surface Hydraulic Measurement Parameters', Sensors, vol. 19, no. 9, pp. 2107-2107.
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In some parts of tropical Africa, termite mound locations are traditionally used to site groundwater structures mainly in the form of hand-dug wells with high success rates. However, the scientific rationale behind the use of mounds as prospective sites for locating groundwater structures has not been thoroughly investigated. In this paper, locations and structural features of termite mounds were mapped with the aim of determining the aquifer potential beneath termite mounds and comparing the same with adjacent areas, 10 m away. Soil and species sampling, field surveys and laboratory analyses to obtain data on physical, hydraulic and geo-electrical parameters from termite mounds and adjacent control areas followed. The physical and hydraulic measurements demonstrated relatively higher infiltration rates and lower soil water content on mound soils compared with the surrounding areas. To assess the aquifer potential, vertical electrical soundings were conducted on 28 termite mounds sites and adjacent control areas. Three (3) important parameters were assessed to compute potential weights for each Vertical Electrical Sounding (VES) point: Depth to bedrock, aquifer layer resistivity and fresh/fractured bedrock resistivity. These weights were then compared between those of termite mound sites and those from control areas. The result revealed that about 43% of mound sites have greater aquifer potential compared to the surrounding areas, whereas 28.5% of mounds have equal and lower potentials compared with the surrounding areas. The study concludes that termite mounds locations are suitable spots for groundwater prospecting owing to the deeper regolith layer beneath them which suggests that termites either have the ability to locate places with a deeper weathering horizon or are themselves agents of biological weathering. Further studies to check how representative our study area is of other areas with similar termite activities are recommended.
Ahmed, AA & Pradhan, B 2019, 'Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system', Environmental Monitoring and Assessment, vol. 191, no. 3.
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© 2019, Springer Nature Switzerland AG. This study proposes a neural network (NN) model to predict and simulate the propagation of vehicular traffic noise in a dense residential area at the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia. The proposed model comprises of two main simulation steps: that is, the prediction of vehicular traffic noise using NN and the simulation of the propagation of traffic noise emission using a mathematical model. First, the NN model was developed with the following selected noise predictors: the number of motorbikes, the sum of vehicles, car ratio, heavy vehicle ratio (e.g. truck, lorry and bus), highway density and a light detection and ranging (LiDAR)-derived digital surface model (DSM). Subsequently, NN and its hyperparameters were optimised by a systematic optimisation procedure based on a grid search approach. The noise propagation model was then developed in a geographic information system (GIS) using five variables, namely road geometry, barriers, distance, interaction of air particles and weather parameters. The noise measurement was conducted continuously at 15-min intervals and the data were analysed by taking the minimum, maximum and average values recorded during the day. The measurement was performed four times a day (i.e. morning, afternoon, evening, and midnight) over two days of the week (i.e. Sunday and Monday). An optimal radial basis function NN was used with 17 hidden layers. The learning rate and momentum values were 0.05 and 0.9, respectively. Finally, the accuracy of the proposed method achieved 78.4% with less than 4.02 dB (A) error in noise prediction. Overall, the proposed models were found to be promising tools for traffic noise assessment in dense urban areas.
Ahmed, MB, Hasan Johir, MA, Zhou, JL, Ngo, HH, Nghiem, LD, Richardson, C, Moni, MA & Bryant, MR 2019, 'Activated carbon preparation from biomass feedstock: Clean production and carbon dioxide adsorption', Journal of Cleaner Production, vol. 225, pp. 405-413.
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© 2019 Elsevier Ltd The current methods used for the production of activated carbon (AC) are often chemical and energy intensive and produce significant amount of chemical waste. Thus, clean production of AC is important to reduce its overall production cost and to limit the adverse effect on the environment. Therefore, the main aim of this study is to develop a clean method for AC production from woody biomass with low chemical consumption. Herein, this study reports a facile strategy for reducing chemical usages in the production of high-performance AC, by introducing a crucial pre-pyrolysis step before chemical activation of biomass. The ACs prepared were characterised using scanning electron microscopy, Fourier transform infrared spectroscopy, nitrogen and carbon dioxide gas adsorption measurements. All these characterisations indicated that produced ACs have similar physicochemical properties. The strategy reduced chemical use by 70% and produced high-performance ultra-microporous ACs with excellent carbon dioxide adsorption capacity (4.22–5.44 mmol m −2 ). The facile pre-pyrolysis method is recommended for further research as a cleaner activated carbon preparation method from biomass feedstock.
Ajibola, II, Mansor, S, Pradhan, B & Mohd. Shafri, HZ 2019, 'Fusion of UAV-based DEMs for vertical component accuracy improvement', Measurement, vol. 147, pp. 106795-106795.
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© 2019 Elsevier Ltd Most construction projects require data that comply with a certain standard of accuracy both in the horizontal and vertical components. This study aimed to develop a model for improving the quality of Digital Elevation Model (DEM) produced by UAV. UAV is the short form of Unmanned Aerial Vehicle, which is either fixed-wing or rotorcraft type. The study proposes a fusion approach that integrates a weighted averaging and additive median filtering algorithms to improve accuracy of the DEMs derived from fixed-wing UAVs. The low quality DEM was fused with high quality DEM produced by multi-rotor UAVs. Assessment of the DEM produced root mean square error of 1.14 cm and standard vertical accuracy of 2.24 cm at a 95% confidence level. This value represents a decrease in vertical standard error of 18.31 cm to 2.24 cm, which is an improvement of 88%. The result of the study indicates that the method is suitable for improving accuracy of DEM produced by UAVs.
Ajuyah, P, Hill, M, Ahadi, A, Lu, J, Hutvagner, G & Tran, N 2019, 'MicroRNA (miRNA)-to-miRNA Regulation of Programmed Cell Death 4 (PDCD4)', Molecular and Cellular Biology, vol. 39, no. 18, pp. 1-15.
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Copyright © 2019 Ajuyah et al. The regulation of tumor suppressor genes by microRNAs (miRNAs) is often demonstrated as a one-miRNA-to-one-target relationship. However, given the large number of miRNA sites within a 3= untranslated region (UTR), most targets likely undergo miRNA cooperation or combinatorial action. Programmed cell death 4 (PDCD4), an important tumor suppressor, prevents neoplastic events and is commonly downregulated in cancer. This study investigates the relationship between miRNA 21 (miR-21) and miR-499 in regulating PDCD4. This was explored using miRNA overexpression, mutational analysis of the PDCD4 3= UTR to assess regulation at each miRNA site, and 50% inhibitory concentration (IC50) calculations for combinatorial behavior. We demonstrate that the first miR-499 binding site within PDCD4 is inactive, but the two remaining sites are both required for PDCD4 suppression. Additionally, the binding of miR-21 to PDCD4 influenced miR-499 activity through an increase in its silencing potency and stabilization of its mature form. Furthermore, adjoining miRNA sites more than 35 nucleotides (nt) apart could potentially regulate thousands of 3= UTRs, similar to that observed between miR-21 and miR-499. The regulation of PDCD4 serves as a unique example of regulatory action by multiple miRNAs. This relationship was predicted to occur on thousands of targets and may represent a wider mode of miRNA regulation.
Akther, N, Daer, S, Wei, Q, Janajreh, I & Hasan, SW 2019, 'Synthesis of polybenzimidazole (PBI) forward osmosis (FO) membrane and computational fluid dynamics (CFD) modeling of concentration gradient across membrane surface', Desalination, vol. 452, pp. 17-28.
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Akther, N, Lim, S, Tran, VH, Phuntsho, S, Yang, Y, Bae, T-H, Ghaffour, N & Shon, HK 2019, 'The effect of Schiff base network on the separation performance of thin film nanocomposite forward osmosis membranes', Separation and Purification Technology, vol. 217, pp. 284-293.
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© 2019 Elsevier B.V. In this study, Schiff base network-1 (SNW-1) nanoparticles, which are covalent organic frameworks (COFs), were used as fillers in the polyamide (PA) active layer to elevate the performance of thin-film nanocomposite (TFN) forward osmosis (FO) membranes. The TFN membranes were prepared by interfacial polymerization (IP) of m-phenylenediamine (MPD) and trimesoyl chloride (TMC), and the SNW-1 nanoparticles were dispersed in the MPD aqueous solution at various concentrations. The secondary amine groups of SNW-1 nanoparticles reacted with the acyl chloride groups of TMC during the IP reaction to form strong covalent/amide bonds, which facilitated better interface integration of SNW-1 nanoparticles in the PA layer. Additionally, the incorporation of amine-rich SNW-1 nanoparticles into the TFN membranes improved their surface hydrophilicity, and the porous structure of SNW-1 nanoparticles offered additional channels for transport of water molecules. The TFN0.005 membrane with a SNW-1 nanoparticle loading of 0.005 wt% demonstrated a higher water flux than that of pristine TFC membrane in both AL-FS (12.0 vs. 9.3 L m−2 h−1) and AL-DS (25.2 vs. 19.4 L m−2 h−1) orientations when they were tested with deionized water and 0.5 M NaCl as feed and draw solution, respectively.
Akther, N, Phuntsho, S, Chen, Y, Ghaffour, N & Shon, HK 2019, 'Recent advances in nanomaterial-modified polyamide thin-film composite membranes for forward osmosis processes', Journal of Membrane Science, vol. 584, pp. 20-45.
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© 2019 Elsevier B.V. Polyamide thin-film composite (PA TFC)membranes have attained much attention for forward osmosis (FO)applications in separation processes, water and wastewater treatment due to their superior intrinsic properties, such as high salt rejection and water permeability compared to the first-generation cellulose-based FO membranes. Nonetheless, several problems like fouling and trade-off between membrane selectivity and water permeability have hindered the progress of conventional PA TFC FO membranes for real applications. To overcome these issues, nanomaterials or chemical additives have been integrated into the TFC membranes. Nanomaterial-modified membranes have demonstrated significant improvement in their anti-fouling properties and FO performance. In addition, the PA TFC membranes can be designed for specific applications like heavy metal removal and osmotic membrane bioreactor by using nanomaterials to modify their physicochemical properties (porosity, surface charge, hydrophilicity, membrane structure and mechanical strength). This review provides a comprehensive summary of the progress of nanocomposite PA TFC membrane since its first development for FO in the year 2012. The nanomaterial-incorporated TFC membranes are classified into four categories based on the location of nanomaterial in/on the membranes: embedded inside the PA active layer, incorporated within the substrate, coated on the PA layer surface, or deposited as an interlayer between the substrate and the PA active layer. The key challenges still being confronted and the future research directions for nanocomposite PA TFC FO are also discussed.
Alamdari, MM, Dang Khoa, NL, Wang, Y, Samali, B & Zhu, X 2019, 'A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge', Structural Health Monitoring, vol. 18, no. 1, pp. 35-48.
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A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.
Al-Amin Hoque, M, Billah, MM & Pradhan, B 2019, 'Spatio-temporal and demographic distribution of lightning related casualties in northeastern part of Bangladesh', International Journal of Disaster Risk Reduction, vol. 38, pp. 101197-101197.
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© 2019 Lightning is one of the frequent catastrophic hazards to people and properties across the world. Bangladesh is one of the major lightning prone countries in the world. Information regarding the spatial, temporal and demographic distribution of lightning casualties is required to develop mitigation policies to minimize the impacts of lightning. This study aims to analyse the spatial, temporal and demographic distribution of lightning-related casualties in the northeastern part of Bangladesh from 2016 to 2018. The database of lightning casualties was developed from a variety of sources including government and private agencies. Records dating from 2016 to 2018 indicate that about 78 and 60 people have been killed and injured, respectively by lightning strikes. The highest number of lightning fatalities were reported in the districts of Kishoreganj (31%), Habiganj (18%) and Sunamganj (15%). The overall fatality rate is 1.76 per million people per year, and fatality density rate is 0.00388 per million people km−2 year−1. The majority of fatalities and injuries occurred within the early morning 0800 and early evening 1700 at local time. The number of fatalities was higher in April–May during the pre-monsoon season. The maximum number of people died by lightning during farming activities, followed by fishing, boating or bathing in water bodies. The findings of the study are highly beneficial to the administrator and policymakers to develop lightning mitigation plans, improve public awareness and lightning safety campaign to reduce the impacts of lightning hazards.
Alazigha, DP, Vinod, JS, Indraratna, B & Heitor, A 2019, 'Potential use of lignosulfonate for expansive soil stabilisation', Environmental Geotechnics, vol. 6, no. 7, pp. 480-488.
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This study involved the laboratory evaluation of the effectiveness of lignosulfonate (LS) admixture in improving engineering properties (i.e. swell potential, unconfined compressive strength, durability, compaction characteristics, permeability, consolidation characteristics and shrinkage behaviour) of a remoulded expansive soil. Standard geotechnical laboratory tests performed on untreated and LS-treated soil specimens compacted at optimum moisture content and maximum dry unit weight showed significant and consistent improvements in the engineering properties of the soil. The swell potential of the soil decreased by 23% while maintaining its ductility and pH value. The improved soil resistance to repeated freeze–thaw/wet–dry cycles was also observed in the LS-treated specimens. Likewise, the compressive strength, consolidation characteristics and shrinkage limit improved appreciably. However, the compaction characteristics and permeability of the treated soil remained relatively unchanged. With over 50 Mt of global annual production of LS, the successful use of LS as an alternative admixture for expansive soil stabilisation provides viable solutions to the sustainable use of the lignin by-products from paper manufacturing industry.
Alderighi, T, Malomo, L, Giorgi, D, Bickel, B, Cignoni, P & Pietroni, N 2019, 'Volume-aware design of composite molds.', ACM Trans. Graph., vol. 38, no. 4, pp. 110:1-110:1.
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© 2019 Association for Computing Machinery. We propose a novel technique for the automatic design of molds to cast highly complex shapes. The technique generates composite, two-piece molds. Each mold piece is made up of a hard plastic shell and a flexible silicone part. Thanks to the thin, soft, and smartly shaped silicone part, which is kept in place by a hard plastic shell, we can cast objects of unprecedented complexity. An innovative algorithm based on a volumetric analysis defines the layout of the internal cuts in the silicone mold part. Our approach can robustly handle thin protruding features and intertwined topologies that have caused previous methods to fail. We compare our results with state of the art techniques, and we demonstrate the casting of shapes with extremely complex geometry.
Alfaro-García, VG, Merigó, JM, Plata-Pérez, L, Alfaro-Calderón, GG & Gil-Lafuente, AM 2019, 'INDUCED AND LOGARITHMIC DISTANCES WITH MULTI-REGION AGGREGATION OPERATORS', Technological and Economic Development of Economy, vol. 0, no. 0, pp. 1-29.
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This paper introduces the induced ordered weighted logarithmic averaging IOWLAD and multiregion induced ordered weighted logarithmic averaging MR-IOWLAD operators. The distinctive characteristic of these operators lies in the notion of distance measures combined with the complex reordering mechanism of inducing variables and the properties of the logarithmic averaging operators. The main advantage of MR-IOWLAD operators is their design, which is specifically thought to aid in decision-making when a set of diverse regions with different properties must be considered. Moreover, the induced weighting vector and the distance measure mechanisms of the operator allow for the wider modeling of problems, including heterogeneous information and the complex attitudinal character of experts, when aiming for an ideal scenario. Along with analyzing the main properties of the IOWLAD operators, their families and specific cases, we also introduce some extensions, such as the induced generalized ordered weighted averaging IGOWLAD operator and Choquet integrals. We present the induced Choquet logarithmic distance averaging ICLD operator and the generalized induced Choquet logarithmic distance averaging IGCLD operator. Finally, an illustrative example is proposed, including real-world information retrieved from the United Nations World Statistics for global regions.
Alharbi, SK, Nghiem, LD, van de Merwe, JP, Leusch, FDL, Asif, MB, Hai, FI & Price, WE 2019, 'Degradation of diclofenac, trimethoprim, carbamazepine, and sulfamethoxazole by laccase fromTrametes versicolor: Transformation products and toxicity of treated effluent', Biocatalysis and Biotransformation, vol. 37, no. 6, pp. 399-408.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. The degradation of diclofenac (DCF), trimethoprim (TMP), carbamazepine (CBZ), and sulfamethoxazole (SMX) by laccase from Trametes versicolor was investigated. Experiments were conducted using the pharmaceuticals individually, or as a mixture at different initial concentrations (1.25 and 5 mg/L each). The initial enzymatic activity of all the treated samples was around 430–460 U(DMP)/L. The removal of the four selected pharmaceuticals tested individually was more effective than when tested in mixtures under the same conditions. For example, 5 mg DCF/L was completely removed to below its detection limit (1 µg/L) within 8 h in the individual experiment vs. after 24 h when dosed as a mixture with the other pharmaceuticals. A similar trend was visible with other three pharmaceuticals, with 95 vs. 39%, 82 vs. 34% and 56 vs. 49% removal after 48 h with 5 mg/L of TMP, CBZ, and SMX tested individually or as mixtures, respectively. In addition, at the lower initial concentration (1.25 mg/L each), the removal efficiency of TMP, CBZ, and SMX in mixtures was lower than that obtained at the higher initial concentrations (5 mg/L each) during both the individual and combined treatments. Four enzymatic transformation products (TPs) were identified during the individual treatments of DCF and CBZ by T. versicolor. For TMP and SMX, no major TPs were observed under the experimental conditions used. The toxicity of the solution before and after enzymatic treatment of each pharmaceutical was also assessed and all treated effluent samples were verified to be non-toxic.
Ali, A & Lee, JE-Y 2019, 'Fully Differential Piezoelectric Button-Like Mode Disk Resonator for Liquid Phase Sensing', IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 66, no. 3, pp. 600-608.
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Ali, SM, Qamar, A, Kerdi, S, Phuntsho, S, Vrouwenvelder, JS, Ghaffour, N & Shon, HK 2019, 'Energy efficient 3D printed column type feed spacer for membrane filtration', Water Research, vol. 164, pp. 114961-114961.
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© 2019 Elsevier Ltd Modification of the feed spacer design significantly influences the energy consumption of membrane filtration processes. This study developed a novel column type feed spacer with the aim to reduce the specific energy consumption (SEC) of the membrane based water filtration system. The proposed spacer increases the clearance between the filament and the membrane (reducing the spacer filament diameter) while keeping the same flow channel thickness as compared to a standard non-woven symmetric spacer. Since the higher clearance reduces the flow unsteadiness, column type nodes were added in the spacer structure as additional vortex shading bodies. Fluid flow behaviour in the channel for this spacer was numerically simulated by 3D CFD studies and then compared with the standard spacer. The numerical results showed that the proposed spacer substantially reduced the pressure drop, shear stress at the constriction region and shortened the dead zone. Finally, these findings were confirmed experimentally by investigating the filtration performances using the 3D printed prototypes of these spacers in a lab-scale filtration module. It is observed that the column spacer reduced the pressure drop by three times and doubled the specific water flux. 2D OCT (Optical Coherence Tomography) scans of the membrane surface acquired after the filtration revealed much lower biomass accumulation using the proposed spacer. Consequently, the SEC for the column spacer was found about two folds lower than the standard spacer.
Aliyu, A, El-Sayed, H, Abdullah, AH, Alam, I, Li, J & Prasad, M 2019, 'Video Streaming in Urban Vehicular Environments: Junction-Aware Multipath Approach', Electronics, vol. 8, no. 11, pp. 1239-1239.
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In multipath video streaming transmission, the selection of the best vehicle for video packet forwarding considering the junction area is a challenging task due to the several diversions in the junction area. The vehicles in the junction area change direction based on the different diversions, which lead to video packet drop. In the existing works, the explicit consideration of different positions in the junction areas has not been considered for forwarding vehicle selection. To address the aforementioned challenges, a Junction-Aware vehicle selection for Multipath Video Streaming (JA-MVS) scheme has been proposed. The JA-MVS scheme considers three different cases in the junction area including the vehicle after the junction, before the junction and inside the junction area, with an evaluation of the vehicle signal strength based on the signal to interference plus noise ratio (SINR), which is based on the multipath data forwarding concept using greedy-based geographic routing. The performance of the proposed scheme is evaluated based on the Packet Loss Ratio (PLR), Structural Similarity Index (SSIM) and End-to-End Delay (E2ED) metrics. The JA-MVS is compared against two baseline schemes, Junction-Based Multipath Source Routing (JMSR) and the Adaptive Multipath geographic routing for Video Transmission (AMVT), in urban Vehicular Ad-Hoc Networks (VANETs).
Alkalbani, AM, Hussain, W & Kim, JY 2019, 'A Centralised Cloud Services Repository (CCSR) Framework for Optimal Cloud Service Advertisement Discovery From Heterogenous Web Portals', IEEE Access, vol. 7, pp. 128213-128223.
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© 2013 IEEE. A cloud service marketplace is the first point for a consumer to discovery, select and possible composition of different services. Although there are some private cloud service marketplaces, such as Microsoft Azure, that allow consumers to search service advertainment belonging to a given vendor. However, due to an increase in the number of cloud service advertisement, a consumer needs to find related services across the worldwide web (WWW). A consumer mostly uses a search engine such as Google, Bing, for the service advertisement discovery. However, these search engines are insufficient in retrieving related cloud services advertainments on time. There is a need for a framework that effectively and efficiently discovery of the related service advertisement for ordinary users. This paper addresses the issue by proposing a user-friendly harvester and a centralised cloud service repository framework. The proposed Centralised Cloud Service Repository (CCSR) framework has two modules - Harvesting as-a-Service (HaaS) and the service repository module. The HaaS module allows users to extract real-time data from the web and make it available to different file format without the need to write any code. The service repository module provides a centralised cloud service repository that enables a consumer for efficient and effective cloud service discovery. We validate and demonstrate the suitability of our framework by comparing its efficiency and feasibility with three widely used open-source harvesters. From the evaluative result, we observe that when we harvest a large number of services advertisements, the HaaS is more efficient compared with the traditional harvesting tools. Our cloud services advertisements dataset is publicly available for future research at: http://cloudmarketregistry.com/cloud-market-registry/home.html.
Almabrok, M, McLaughlan, R & Vessalas, K 2019, 'Investigation of the Influence of Light Crude Oil on the Performance of Cement Mortar', International Journal of Civil Engineering, vol. 6, no. 4, pp. 34-40.
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Almabrok, MH, McLaughlan, R, Vessalas, K & Thomas, P 2019, 'Effect of oil contaminated aggregates on cement hydration', American Journal of Engineering Research (AJER), vol. 8, no. 5, pp. 81-89.
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Canola oil, refined mineral oil, and crude oil additions up to 10% of the aggregate mass inPortland cement mortars were found to decrease the 28-day compressive strength by 71%, 75% and 50%,respectively, and retard setting times. There was a progressive impact upon cement hydration as the oil contentincreased in mortars. Only in the case of vegetable oil and refined mineral oil could strength loss be attributedin part to cement hydration inhibition, as evidenced by reduced total evolved heat. It is likely thatmicrostructural effects were also a key factor in strength loss for all mortars particularly for those containingcrude oil.
Al-Muhsen, NFO, Huang, Y & Hong, G 2019, 'Effects of direct injection timing associated with spark timing on a small spark ignition engine equipped with ethanol dual-injection', Fuel, vol. 239, pp. 852-861.
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© 2018 Elsevier Ltd Dual injection of ethanol fuel (DualEI) has been in development. DualEI has the potential in increasing the compression ratio and thermal efficiency of spark ignition engines by taking the advantages of ethanol fuel properties and the direct injection. This paper reports an experimental investigation of the effect of direct injection (DI) timing associated with spark timing on the performance of a small DualEI engine. Experiments were conducted with fixed port injection timing and varied DI timing before (early) and after (late) the intake valve closed at 3500 RPM and two load conditions. Results show that the engine performance is enhanced by early DI timing, although the variation of IMEP and indicated thermal efficiency with DI timing is not significant either with early DI timing or in most of the tested conditions with late DI timing. Only in the medium load condition when the DI timing is retarded from 80 to 60 CAD bTDC, the IMEP and thermal efficiency significantly reduced by about 16% due to the increased initial combustion duration, resulting in reduced flame speed and increased combustion instability. The results also show different effects of early and late DI timing associated with the spark timing on engine emissions. With late DI timing, the engine emissions of CO and NO increase with the advance of late DI timing and spark timing. With early DI timing, the engine emissions increase with the advance of spark timing. However, the variation of engine emissions with early DI timing is more complicated than that late.
Al-Najjar, HAH, Kalantar, B, Pradhan, B, Saeidi, V, Halin, AA, Ueda, N & Mansor, S 2019, 'Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks', Remote Sensing, vol. 11, no. 12, pp. 1461-1461.
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In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are no (or only a few) studies that have been done to increase the land cover classification accuracy via unmanned aerial vehicle (UAV)–digital surface model (DSM) fused datasets. Therefore, this study looks at improving the accuracy of these datasets by exploiting convolutional neural networks (CNNs). In this work, we focus on the fusion of DSM and UAV images for land use/land cover mapping via classification into seven classes: bare land, buildings, dense vegetation/trees, grassland, paved roads, shadows, and water bodies. Specifically, we investigated the effectiveness of the two datasets with the aim of inspecting whether the fused DSM yields remarkable outcomes for land cover classification. The datasets were: (i) only orthomosaic image data (Red, Green and Blue channel data), and (ii) a fusion of the orthomosaic image and DSM data, where the final classification was performed using a CNN. CNN, as a classification method, is promising due to hierarchical learning structure, regulating and weight sharing with respect to training data, generalization, optimization and parameters reduction, automatic feature extraction and robust discrimination ability with high performance. The experimental results show that a CNN trained on the fused dataset obtains better results with Kappa index of ~0.98, an average accuracy of 0.97 and final overall accuracy of 0.98. Comparing accuracies between the CNN with DSM result and the CNN without DSM result for the overall accuracy, average accuracy and Kappa index revealed an improvement of 1.2%, 1.8% and 1.5%, respectively. Accordingly, adding the heights of features such as buildings and trees improved the differentiation between vegetation specifically where plants wer...
Alshehri, MD & Hussain, FK 2019, 'A fuzzy security protocol for trust management in the internet of things (Fuzzy-IoT)', Computing, vol. 101, no. 7, pp. 791-818.
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© 2018, Springer-Verlag GmbH Austria, ein Teil von Springer Nature. Recently, the Internet of things (IoT) has received a lot of attention from both industry and academia. A reliable and secure IoT connection and communication is essential for the proper working of the IoT network as a whole. One of the ways to achieve robust security in an IoT network is to enable and build trusted communication among the things (nodes). In this area, the existing IoT literature faces many critical issues, such as the lack of intelligent cluster-based trust approaches for IoT networks and the detection of attacks on the IoT trust system from malicious nodes, such as bad service providers. The existing literature either does not address these issues or only addresses them partially. Our proposed solution can firstly detect on-off attacks using the proposed fuzzy-logic based approach, and it can detect contradictory behaviour attacks and other malicious nodes. Secondly, we develop a fuzzy logic-based approach to detect malicious nodes involved in bad service provisioning. Finally, to maintain the security of the IoT network, we develop a secure messaging system that enables secure communication between nodes. This messaging system uses hexadecimal values with a structure similar to serial communication. We carried out extensive experimentation under varying network sizes to validate the working of our proposed solution and also to test the efficiency of the proposed methods in relation to various types of malicious behavior. The experiment results demonstrate the effectiveness of our approach under various conditions.
Al‐Soeidat, M, Cheng, T, Lu, DD & Agelidis, VG 2019, 'Experimental study of static and dynamic behaviours of cracked PV panels', IET Renewable Power Generation, vol. 13, no. 16, pp. 3002-3008.
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Solar cell power performance is greatly affected by two critical factors ageing and crack. In order to mitigate their negative effects on the solar system, these cells are to be substituted by new cells, thus, replacing the panels. This study presents an active crack detection method that detects the cracked cells within a solar string by using AC parameter characterisation without a need to have a physical inspection. The mathematical module of the solar cell shows that it constitutes of series and parallel resistors in addition to a parallel capacitor and that their values change by ageing and crack. In addition to studying the effects of the crack on the solar cell, it is verified by the experiment that the solar cells behave as a capacitive circuit, and their capacitance increases when the cell gets cracked, getting higher as the crack becomes more serious. The experiment is extended to investigate the effect of series and parallel PV strings, which are affected by cracked and partially shaded cells to evaluate their criticality levels. By monitoring the AC parameter of the solar cell and the change of the capacitance, it is easy to detect the crack when it occurs.
Al-Soeidat, M, Lu, DD-C & Zhu, J 2019, 'An Analog BJT-Tuned Maximum Power Point Tracking Technique for PV Systems', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 66, no. 4, pp. 637-641.
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© 2004-2012 IEEE. In this brief, an analog, bipolar junction transistor (BJT)-tuned voltage reference maximum power point tracking (MPPT) method for photovoltaic modules is proposed. The conventional fixed voltage reference method is the simplest method for tracking, but it does not obtain good MPPT efficiency because the maximum power point (MPP) voltage changes at different insolation levels. In reality, an approximately linear slope is formed when connecting the MPPs measured from the highest insolation level to the lowest. Utilizing this characteristic, a BJT, which has a similar electrical property, is used to implement a variable voltage reference that improves the accuracy of the MPP voltage when the insolation changes. The proposed circuit is simple and easy to implement, and it can track the MPP very quickly without the need for a digital controller or PID controller. Hence, the circuits cost and complexity are reduced. Experimental results are given to verify the feasibility of the proposed MPPT method.
Altaee, A & Cipolina, A 2019, 'Modelling and optimization of modular system for power generation from a salinity gradient', Renewable Energy, vol. 141, pp. 139-147.
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© 2019 Elsevier Ltd Pressure retarded osmosis has been proposed for power generation from a salinity gradient resource. The process has been promoted as a promising technology for power generation from renewable resources, but most of the experimental work has been done on a laboratory size units. To date, pressure retarded osmosis optimization and operation is based on parametric studies performed on laboratory scale units, which leaves a gap in our understanding of the process behaviour in a full-scale modular system. A computer model has been developed to predict the process performance. Process modelling was performed on a full-scale membrane module and impact of key operating parameters such as hydraulic feed pressure and feed and draw solution rates were evaluated. Results showed that the optimum fraction of feed/draw solution in a mixture is less than what has been earlier proposed ratio of 50% and it is entirely dependent on the salinity gradient resource concentration. Furthermore, the optimized pressure retarded osmosis process requires a hydraulic pressure less than that in the normal (unoptimized) process. The results here demonstrate that the energy output from the optimized pressure regarded osmosis process is up to 54% higher than that in the normal (unoptimized) process.
Altaee, A, Braytee, A, Millar, GJ & Naji, O 2019, 'Energy efficiency of hollow fibre membrane module in the forward osmosis seawater desalination process', Journal of Membrane Science, vol. 587, pp. 117165-117165.
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© 2019 This study provided new insights regarding the energy efficiency of hollow fibre forward osmosis modules for seawater desalination; and as a consequence an approach was developed to improve the process performance. Previous analysis overlooked the relationship between the energy efficiency and operating modes of the hollow fibre forward osmosis membrane when the process was scaled-up. In this study, the module length and operating parameters were incorporated in the design of an energy-efficient forward osmosis system. The minimum specific power consumption for seawater desalination was calculated at the thermodynamic limits. Two FO operating modes: (1) draw solution in the lumen and (2) feed solution in the lumen, were evaluated in terms of the desalination energy requirements at a minimum draw solution flow rate. The results revealed that the operating mode of the forward osmosis membrane was important in terms of reducing the desalination energy. In addition, the length of the forward osmosis module was also a significant factor and surprisingly increasing the length of the forward osmosis module was not always advantageous in improving the performance. The study outcomes also showed that seawater desalination by the forward osmosis process was less energy efficient at low and high osmotic draw solution concentration and performed better at 1.2–1.4 M sodium chloride draw solution concentrations. The findings of this study provided a platform to the manufacturers and operators of hollow fibre forward osmosis membrane to improve the energy efficiency of the desalination process.
Altaee, A, Zhou, J, Zaragoza, G & Sharif, AO 2019, 'Impact of membrane orientation on the energy efficiency of dual stage pressure retarded osmosis', Journal of Water Process Engineering, vol. 30, pp. 100621-100621.
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© 2018 Elsevier Ltd The performance of Dual Stage Pressure Retarded Osmosis (DSPRO) was analyzed using a developed computer model. DSPRO process was evaluated on Pressure Retarded Osmosis (PRO) and Forward Osmosis (FO) operating modes for different sodium chloride (NaCl) draw and feed concentrations. Simulation results revealed that the total power generation in the DSPRO process operating on the PRO mode was 2.5–5 times more than that operating on the FO mode. For DSPRO operating on the PRO mode, the higher power generation was in the case of 2 M NaCl-fresh and 32% the contribution of the second stage to the total power generation in the DSPRO. To the contrast, he total power generated in the DSPRO operating on the FO mode was in the following order 5M-0.6M > 5M-0.7M > 2M-0.01 > 2M-0.6 M. Interestingly, single stage process operating on the FO mode performed better than DSPRO process due to the severe concentration polarization effects. The results also showed that power density of the DSPRO reached a maximum amount at a hydraulic pressure less than the average osmotic pressure gradient, Δπ/2, due to the variation of optimum operating pressure of each stage. Moreover, results showed that the effective specific energy in the PRO process was lower than the maximum specific energy. However, the effective specific energy of the DSPRO was larger than that of the single stage PRO due to the rejuvenation of the salinity gradient, emphasizing the high potential of the DSPRO process for power generation.
Alterman, D, Stewart, MG & Netherton, MD 2019, 'Probabilistic assessment of airblast variability and fatality risk estimation for explosive blasts in confined building spaces', International Journal of Protective Structures, vol. 10, no. 3, pp. 306-329.
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Explosive blasts in confined building spaces, such as lobbies or foyers, can amplify blast loads. This article uses the computational fluid dynamics model ProsAir to estimate blast loads in a typical ground floor lobby of a commercial or government building. Monte-Carlo simulation is used to probabilistically model the effect that variability and uncertainty of charge mass and location, net equivalent quantity factor, temperature, atmospheric pressure and model errors have on airblast variability. The analysis then calculates the probability of casualties due to the effects of pressure and impulse, where human vulnerability due to the effects of pressure and impulse is a function of lung rupture, whole-body displacement or skull fracture (or the combination of the three). The terrorist threats considered are improvised explosive devices ranging in mass from 5 kg (backpack bomb) to 23 kg (suitcase bomb) detonated in various locations inside the building. As expected, blast pressure and fatality risks are dependent on the type of facade glazing (e.g. vulnerable glazing allows venting of the blast), improvised explosive device size and location. It was found that the mean fatality risk for a 23 kg terrorist improvised explosive device is 8.6%, but there is a 5% chance that fatality risks can exceed 20%. It was also found that a probabilistic analysis yielded lower mean fatality risks than a deterministic analysis. The effect of venting was also significant. Mean fatality risks increased by up to 10-fold if there was no venting (i.e. a bunker-like structure without windows), but reduced by about 30% for a fully vented structure (i.e. no windows). This probabilistic analysis allows decision-makers to be more aware of terrorism risks to building occupants, and how improved building design and security measures may ameliorate these risks.
Alturki, R & Gay, V 2019, 'The Development of an Arabic Weight-Loss App Akser Waznk: Qualitative Results', JMIR Formative Research, vol. 3, no. 1, pp. e11785-e11785.
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Al-Zubaydi, AYT & Hong, G 2019, 'Experimental study of a novel water-spraying configuration in indirect evaporative cooling', Applied Thermal Engineering, vol. 151, pp. 283-293.
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© 2019 Elsevier Ltd Indirect evaporative cooler (IEC) is a clean cooling device in development for air conditioning systems. It uses the water to partially replace the refrigerant and can be applied as a standalone cooling system or an energy recovery ventilation system. Driven by the concern about the sustainability, IEC has been required to reduce the power to operate and increase its capacity in order to reduce the energy consumption and increase the overall efficiency of the air condition system. The water distribution over the plate walls in the cooler is a key factor affecting the IEC performance and efficiency. This study investigates the effect of a novel suggested water spray configuration on the performance of a ventilation energy recovery IEC. Experiments were conducted to investigate three water spray modes: external spray, internal spray and mixed internal and external sprays. The results show that the mixed mode performs best and internal spraying mode performs better than the external spraying mode does in terms of the wet-bulb efficiency, cooling capacity and the COP of IEC. The mixed mode improves the performance further by increasing wettability.
Amirbagheri, K, Núñez-Carballosa, A, Guitart-Tarrés, L & Merigó, JM 2019, 'Research on green supply chain: a bibliometric analysis', Clean Technologies and Environmental Policy, vol. 21, no. 1, pp. 3-22.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Abstract: Recently, the emergent concept of green supply chain has received increasing attention. Although popular among scholars, many literature reviews have only examined GSC from a general point of view or focused on a specific issue related to GSC. This study presents a comprehensive analysis of the influence and productivity of research on GSC from 1995 to 2017 by reporting trends among authors, countries and institutions based on a bibliometric approach. To this end, the study analyzes around 1900 papers on GSC. This study uses the Web of Science Core Collection database to analyze the bibliometric data and the visualization of similarities viewer method to graphically map those data. The graphical analysis uses bibliographic coupling, co-citation, co-authorship and co-occurrence of keywords. Graphical abstract: [Figure not available: see fulltext.].
Amiri, M, Tofigh, F, Shariati, N, Lipman, J & Abolhasan, M 2019, 'Miniature tri‐wideband Sierpinski–Minkowski fractals metamaterial perfect absorber', IET Microwaves, Antennas & Propagation, vol. 13, no. 7, pp. 991-996.
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With rapidly growing adoption of wireless technologies, requirements for the design of a miniature wideband multi‐resonators are increasing. In this study, a compact fractal‐based metamaterial structure with lumped resistors is described. The structure of the authors proposed absorber is a combination of Sierpinski curve and Minkowski fractal. The new combination provides larger capacitance and inductance in the system enabling perfect absorption at lower frequencies. The final structure with dimensions of 20 × 20 × 1.6 mm3 and an air gap of 12.5 mm provides three main resonances at frequencies of 2.1, 5.1, and 12.8 GHz with bandwidth (absorption ratio over 90%) of 840 MHz, 1.05 GHz, and 910 MHz, respectively.
Amjadipour, M, MacLeod, J, Lipton-Duffin, J, Tadich, A, Boeckl, JJ, Iacopi, F & Motta, N 2019, 'Electron effective attenuation length in epitaxial graphene on SiC', Nanotechnology, vol. 30, no. 2, pp. 025704-025704.
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The inelastic mean free path (IMFP) for carbon-based materials is notoriously challenging to model, and moving from bulk materials to 2D materials may exacerbate this problem, making the accurate measurements of IMFP in 2D carbon materials critical. The overlayer-film method is a common experimental method to estimate IMFP by measuring electron effective attenuation length (EAL). This estimation relies on an assumption that elastic scattering effects are negligible. We report here an experimental measurement of electron EAL in epitaxial graphene on SiC using photoelectron spectroscopy over an electron kinetic energy range of 50-1150 eV. We find a significant effect of the interface between the 2D carbon material and the substrate, indicating that the attenuation length in the so-called 'buffer layer' is smaller than for free-standing graphene. Our results also suggest that the existing models for estimating IMFPs may not adequately capture the physics of electron interactions in 2D materials.
An, L, Cheng, T & Lu, DD-C 2019, 'Single-Stage Boost-Integrated Full-Bridge Converter With Simultaneous MPPT, Wide DC Motor Speed Range, and Current Ripple Reduction', IEEE Transactions on Industrial Electronics, vol. 66, no. 9, pp. 6968-6978.
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© 1982-2012 IEEE. This paper presents a new approach to controlling and optimizing a single-stage boost-integrated full-bridge dc-dc converter for a stand-alone photovoltaic-battery-powered dc motor system by combining pulse-frequency modulation (PFM), pulsewidth modulation (PWM), and phase angle shift (PAS). Unlike most of the existing multiport dc-dc converters, which aim at regulating the output voltage (first-or second-quadrant operation), the dc motor load requires both voltage and current reversals (four-quadrant operation). The converter is able to perform three tasks simultaneously: maximum power point tracking (MPPT), battery charging/discharging, and driving the dc motor at variable speeds including bidirectional and stall motions. To achieve these control objectives, the boost inductor and the motor inductance operate in different modes such that PFM and PWM can be used to achieve MPPT and a wide motor voltage range, respectively. By properly adjusting the PAS of the duty cycles, the capacitor and battery rms current value can be reduced, while the operation of the converter remains unchanged, hence improving the conversion efficiency. Experimental results of a 26-W laboratory prototype converter confirmed the proposed design and operation and the efficiency improvement by 2-6%.
An, Z, Dai, Y, Jiang, Y & He, J 2019, 'Asymmetric Knoevenagel‐Phospha‐Michael Tandem Reaction Synergistically Catalyzed by Achiral Silanols and Grafted Chiral Amines on Mesoporous Silica', Asian Journal of Organic Chemistry, vol. 8, no. 8, pp. 1539-1547.
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AbstractHighly efficient and enantioselective asymmetric Knoevenagel‐phospha‐Michael tandem reactions have been achieved on bifunctional heterogeneous catalysts with inherent silanols as acidic sites and immobilized chiral amines as basic sites. Final products were afforded in yields of up to 99% and ee values of up to 99%. The effects of substituents on benzaldehyde and molecular dimensions of phosphites have also been investigated. Larger substrates can access the catalytic site inside larger mesoporous pores, thereby improving both of yield and ee of final products.
Anaissi, A, Khoa, NLD, Rakotoarivelo, T, Alamdari, MM & Wang, Y 2019, 'Smart pothole detection system using vehicle-mounted sensors and machine learning', Journal of Civil Structural Health Monitoring, vol. 9, no. 1, pp. 91-102.
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Road networks are critical assets supporting economies and communities. Despite budget and time constraints, road authorities strive to maintain them to ensure safety, ongoing service, and economic productivity. This paper proposes a virtual road network inspector (VRNI), which continuously monitors road conditions and provides decision support to managers and engineers. VRNI uses acceleration data from vehicle-mounted sensors to assess road conditions. It proposes a novel road damage detection method based on two adaptive one-class support vector machine models, which were applied on the vertical and lateral acceleration data. We evaluated this method on data from a real deployment on school buses in New South Wales, Australia. Experimental results show that our method consistently detects 97.5% of the road damage with a 4% false alarm rate that relate to benign anomalies such as expansion joints.
Andrade-Valbuena, NA, Merigó-Lindahl, JM, Fernández, LV & Nicolas, C 2019, 'Mapping leading universities in strategy research: Three decades of collaborative networks', Cogent Business & Management, vol. 6, no. 1, pp. 1632569-1632569.
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© 2019, © 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. This paper presents a longitudinal classification of the impact that universities have on strategy research from three decades of publications, between 1987 and 2016, by using bibliometric techniques and distance-based analysis of networks applied at the level of universities. Using the WoS database, this study proposes a general overview of three decades of strategic management research. Using these techniques we (i) categorize the last 30 years of academic production of research institutions in terms of strategy, evaluating their impact; (ii) analyze which universities are publishing the most in journals whose scope of publication covers strategic management; and (iii) map the network of collaboration structures among research organizations, determining its relationship and analyzing its evolution in those three decades. We found that the University of Pennsylvania was the most prominent institution throughout the years, showing the broadest network of citations according to our network analysis. There was also a remarkable presence of international universities from the UK, Canada, France and the Netherlands, however, the citation pattern among them is still low. We also observed evidence of inner knowledge flowing among different fields based on the deliberate multidisciplinary nature of research in strategy, as the strong coincidence with the ranking of the main journals in the marketing field when comparing the bibliometric studies of both fields. This analysis contributes to strategy research, first by delivering insights based on the impact of academic production and secondly through the evolution of collaborative network linkages in terms of strategy investigations undertaken to build collective knowledge.
Ansari, M, Zhu, H, Shariati, N & Guo, YJ 2019, 'Compact Planar Beamforming Array With Endfire Radiating Elements for 5G Applications', IEEE Transactions on Antennas and Propagation, vol. 67, no. 11, pp. 6859-6869.
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© 1963-2012 IEEE. In this paper, a compact 4×6 Butler matrix (BM) based on microstrip lines is designed and applied to a linear antenna array. The proposed design creates four beams in four different directions within the 27.5 and 28.5 GHz band. One of the advantages of this BM is a reduction in the size of the beamforming network (BFN). In order to attain this objective, the basic microstrip-based 4×4 BM is designed, and then modified to a 4×6 BM through a dual-substrate structure to avoid crossing lines using microstrip-to-slotline transitions. The BFN is cascaded with a six-element linear antenna array with endfire radiating elements. The array can be conveniently integrated into the BFN. The resulting design benefits from low-loss characteristics, ease of realization, and low fabrication cost. The array is fabricated and tested, and the experimental results are in good agreement with the simulated ones. The multi-beam antenna size is 5.6 λ × 4.6 λ including feed lines and feed network, while the new BM design is only 3.5λ0 × 1.4λ0 , which is almost half as large as the traditional one. The measured radiation patterns show that the beams cover roughly a spatial range of 90° with a peak active gain of 11 dBi.
Anshu, A, Berta, M, Jain, R & Tomamichel, M 2019, 'A minimax approach to one-shot entropy inequalities', Journal of Mathematical Physics, vol. 60, no. 12, pp. 122201-122201.
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One-shot information theory entertains a plethora of entropic quantities,such as the smooth max-divergence, hypothesis testing divergence andinformation spectrum divergence, that characterize various operational tasksand are used to prove the asymptotic behavior of various tasks in quantuminformation theory. Tight inequalities between these quantities are thus ofimmediate interest. In this note we use a minimax approach (appearingpreviously for example in the proofs of the quantum substate theorem), tosimplify the quantum problem to a commutative one, which allows us to derivesuch inequalities. Our derivations are conceptually different from previousarguments and in some cases lead to tighter relations. We hope that theapproach discussed here can lead to progress in open problems in quantumShannon theory, and exemplify this by applying it to a simple case of the jointsmoothing problem.
Antariksawan, AR, Widodo, S, Juarsa, M, Ismarwanti, S, Saptoadi, D, Kusuma, MH, Ardiyati, T & Mahlia, TMI 2019, 'Experimental and Numerical Simulation Investigation of Single-Phase Natural Circulation in a Large Scale Rectangular Loop', Atom Indonesia, vol. 45, no. 1, pp. 17-17.
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© 2019 Atom Indonesia. In order to anticipate station blackout, the use of safety system based on passive features is highly considered in advanced nuclear power plant designs, especially after the Fukushima Dai-ichi nuclear power station accident. An example is the application of natural circulation in the emergency cooling system. To study the reliability of such an application, a research project on natural circulation was carried out. This paper describes the investigation results on the natural circulation phenomena obtained using a large rectangular experimental loop named FASSIP-01. The experiments were conducted at two different heat source powers. The experimental results are analysed using existing correlation and numerical model simulation. The RELAP5 system code is applied to model the natural circulation. FLUENT computational fluid dynamic code is used to visualize the flow distribution. The experimental results show the establishment of stable natural circulation in all heat power input with the mass flow rate of about 0.0012 kg/s. Calculation using the existing correlation shows that the experimental Reynold numbers are lower than predicted by the correlation. The computational fluid dynamics-based tool could show the three dimensional distribution of the temperature, while the model of RELAP5 predict well the dynamic of the single-phase natural circulation established in the experimental loop. It is concluded that the stable natural circulation have been established in the large rectangular loop and the model of the RELAP5 could simulate the observed natural circulation phenomenon reasonably well.
Arabameri, A, Chen, W, Blaschke, T, Tiefenbacher, JP, Pradhan, B & Tien Bui, D 2019, 'Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran', Water, vol. 12, no. 1, pp. 16-16.
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To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach—bagging-based alternating decision-tree classifier (bagging-ADTree)—and use it to model a landscape’s susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model’s goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models’ analysis and prediction capabilities, 83 head-cuts (70% of the total) and the corresponding measures of the conditioning factors were input into each model. The results from the models were validated using the data pertaining to the remaining 36 gully locations (30%). Next, the frequency ratio is used to identify which conditioning-factor classes have the strongest correlation with gully erosion. Using random-forest modeling, the relative importance of each of the conditioning factors was determined. Based on the random-forest results, the top eight factors in this study area are distance-to-road, drainage density, distance-to-stream, LU/LC, annual precipitation, topographic wetness index, NDVI, and elevation. Finally, based on goodness-of-fit and AUROC of the success rate curve (SRC) and prediction rate curve (PRC), the results indicate that the bagging-ADTree ensemble model had the best performance, with SRC (0.964) and PRC (0.978). RF-ADTree (SRC = 0.952 and PRC = 0.971), ADTree (SRC = 0.926 and PRC = 0.965), and LR (SRC = ...
Arabameri, A, Pradhan, B & Lombardo, L 2019, 'Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling', CATENA, vol. 183, pp. 104223-104223.
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© 2019 Elsevier B.V. The initiation and development of gullies as worldwide features in landscape have resulted in land degradation, soil erosion, desertification, flooding and groundwater level decrease, which in turn, cause severe destruction to infrastructure. Gully erosion susceptibility mapping is the first and most important step in managing these effects and achieving sustainable development. This paper attempts to generate a reliable map using four state-of-the-art models to investigate the Bayazeh Watershed in Iran. These models consists of boosted regression trees (BRT), binary logistic regression (BLR), numerical risk factor (NRF) and frequency ratio (FR), which are based on a geographic information system (GIS). The gully erosion inventory map accounts for 362 gully locations, which were randomly divided into two groups (70% for training and 30% for validation). Sixteen topographical, geological, hydrological and environmental gully-related conditioning factors were selected for modelling. The threshold-independent area under receiver operating characteristic (AUROC) and seed cell area index (SCAI) approaches were used for validation. According to the results of BLR and BRT, the conditioning parameters namely, NDVI and lithology, played a key role in gully occurrence. Validation results showed that the BRT model with AUROC = 0.834 (83.4%) had higher prediction accuracy than other models, followed by FR 0.823 (82.3%), NRF 0.746 (74.6%) and BLR 0.659 (65.9%). SCAI results indicated that the BRT, FR and BLR models had acceptable classification accuracy. The findings, in terms of model and predictor choice, can be used by decision-makers for hazard management and implementation of protective measures in gully erosion-prone areas.
Arabameri, A, Pradhan, B & Rezaei, K 2019, 'Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS', Journal of Environmental Management, vol. 232, pp. 928-942.
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© 2018 Elsevier Ltd Every year, gully erosion causes substantial damage to agricultural land, residential areas and infrastructure, such as roads. Gully erosion assessment and mapping can facilitate decision making in environmental management and soil conservation. Thus, this research aims to propose a new model by combining the geographically weighted regression (GWR) technique with the certainty factor (CF) and random forest (RF) models to produce gully erosion zonation mapping. The proposed model was implemented in the Mahabia watershed of Iran, which is highly sensitive to gully erosion. Firstly, dependent and independent variables, including a gully erosion inventory map (GEIM) and gully-related causal factors (GRCFs), were prepared using several data sources. Secondly, the GEIM was randomly divided into two groups: training (70%) and validation (30%) datasets. Thirdly, tolerance and variance inflation factor indicators were used for multicollinearity analysis. The results of the analysis corroborated that no collinearity exists amongst GRCFs. A total of 12 topographic, hydrologic, geologic, climatologic, environmental and soil-related GRCFs and 150 gully locations were used for modelling. The watershed was divided into eight homogeneous units because the importance level of the parameters in different parts of the watershed is not the same. For this purpose, coefficients of elevation, distance to stream and distance to road parameters were used. These coefficients were obtained by extracting bi-square kernel and AIC via the GWR method. Subsequently, the RF-CF integrated model was applied in each unit. Finally, with the units combined, the final gully erosion susceptibility map was obtained. On the basis of the RF model, distance to stream, distance to road and land use/land cover exhibited a high influence on gully formation. Validation results using area under curve indicated that new GWR–CF–RF approach has a higher predictive accuracy 0.967 (96....
Arabameri, A, Pradhan, B & Rezaei, K 2019, 'Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models', Geosciences Journal, vol. 23, no. 4, pp. 669-686.
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© 2019, The Association of Korean Geoscience Societies and Springer. Remote sensing is recognized as a powerful and efficient tool that provides a comprehensive view of large areas that are difficult to access, and also reduces costs and shortens the timing of projects. The purpose of this study is to introduce effective parameters using remote sensing data and subsequently predict gully erosion using statistical models of Density Area (DA) and Information Value (IV), and data mining based Random Forest (RF) model and their ensemble. The aforementioned models were employed at the Tororud-Najarabad watershed in the northeastern part of Semnan province, Iran. For this purpose, at first using various resources, the map of the distribution of the gullies was prepared with the help of field visits and Google Earth images. In order to analyse the earth’s surface and extraction of topographic parameters, a digital elevation model derived from PALSAR (Phased Array type L-band Synthetic Aperture Radar) radar data with a resolution of 12.5 meters was used. Using literature review, expert opinion and multi-collinearity test, 15 environmental parameters were selected with a resolution of 12.5 meters for the modelling. Results of RF model indicate that parameters of NDVI (normalized difference vegetation index), elevation and land use respectively had the highest effect on the gully erosion. Several techniques such as area under curve (AUC), seed cell area index (SCAI), and Kappa coefficient were used for validation. Results of validation indicated that the combination of bivariate (IV and DA models) with the RF data-mining model has increased their performance. The prediction accuracy of AUC and Kappa values in DA, IV and RF are (0.745, 0.782, and 0.792) and (0.804, 0.852, and 0.860) and these values in ensemble models of DA-RF and IV-RF are (0.845, and 0.911) and (0.872, and 0.951) respectively. Results of SCAI show that ensemble models had a good performance, so that, with...
Arabameri, A, Pradhan, B, Rezaei, K & Conoscenti, C 2019, 'Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques', CATENA, vol. 180, pp. 282-297.
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© 2019 Elsevier B.V. This research introduces a scientific methodology for gully erosion susceptibility mapping (GESM)that employs geography information system (GIS)-based multi-criteria decision analysis. The model was tested in Semnan Province, Iran, which has an arid and semi-arid climate with high susceptibility to gully erosion. The technique for order of preference by similarity to ideal solution (TOPSIS)and the analytic hierarchy process (AHP)multi-criteria decision-making (MCDM)models were integrated. The important aspect of this research is that it did not require gully erosion inventory maps for GESM. Therefore, the proposed methodology could be useful in areas with missing or incomplete data. Fifteen variables reflecting topographic, hydrologic, geologic, environmental and soil characteristics were selected as proxies for gully erosion conditioning factors (GECFs). The experiment was conducted using 200 sample points that were selected randomly in the study area, and the weights of criteria (GECFs)were obtained using the AHP model. In the next step, the TOPSIS model was applied, and the weight of each alternative (sample points)was obtained. Kriging and inverse distance-weighted (IDW)methods were used for interpolation and GESM. Natural break method was used for classifying gully erosion susceptibility into five classes, from very low to very high. The area under the ROC curve (AUC)was used for validation. AHP results showed that distance to stream (0.14), slope degree (0.13)and distance to road (0.12)played major roles in controlling gully erosion in the study area. The values of points obtained by using the TOPSIS model ranged from 0.321 to 0.808. Verification results showed that kriging had higher prediction accuracy than IDW. The GESM results obtained by this methodology can be used by decision makers and managers to plan preventive measures and reduce damages due to gully erosion.
Arabameri, A, Pradhan, B, Rezaei, K & Lee, C-W 2019, 'Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs', Remote Sensing, vol. 11, no. 9, pp. 999-999.
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Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often due to the geological and climatic conditions and tectonic and human activities. To reduce or control the damage caused by landslides, landslide susceptibility mapping (LSM) and landslide risk assessment are necessary. In this study, the efficiency and integration of frequency ratio (FR) and random forest (RF) in statistical- and artificial intelligence-based models and different digital elevation models (DEMs) with various spatial resolutions were assessed in the field of LSM. The experiment was performed in Sangtarashan watershed, Mazandran Province, Iran. The study area, which extends to 1072.28 km2, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most ...
Arabameri, A, Pradhan, B, Rezaei, K, Sohrabi, M & Kalantari, Z 2019, 'GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms', Journal of Mountain Science, vol. 16, no. 3, pp. 595-618.
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© 2019, Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature. In this study, a novel approach of the landslide numerical risk factor (LNRF) bivariate model was used in ensemble with linear multivariate regression (LMR) and boosted regression tree (BRT) models, coupled with radar remote sensing data and geographic information system (GIS), for landslide susceptibility mapping (LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory (70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party (ILWP), Forestry, Rangeland and Watershed Organisation (FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve (AUC), frequency ratio (FR) and seed cell area index (SCAI). Normalised difference vegetation index, land use/ land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models (AUC = 0.912 (91.2%) and 0.907 (90.7%), respectively) had high predictive accuracy than the LNRF model alone (AUC = 0.855 (85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslid...
Arabameri, A, Yamani, M, Pradhan, B, Melesse, A, Shirani, K & Tien Bui, D 2019, 'Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility', Science of The Total Environment, vol. 688, pp. 903-916.
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Gully erosion is considered as a severe environmental problem in many areas of the world which causes huge damages to agricultural lands and infrastructures (i.e. roads, buildings, and bridges); however, gully erosion modeling and prediction with high accuracy are still difficult due to the complex interactions of various factors. The objective of this research was to develop and introduce three new ensemble models, which were based on Complex Proportional Assessment of Alternatives (COPRAS), Logistic Regression (LR), Boosted Regression Tree (BRT), Random Forest (RF), and Frequency Ratio (FR) for spatial prediction of gully erosion with a case study at the Najafabad watershed (Iran). For this purpose, a total of 290 head-cut of gullies and 17 conditioning factors were collected and used to establish a geospatial database. Subsequently, FR was used to determine the spatial relationship between the conditioning factors and the head-cut of gullies, whereas RF, BRT, and LR were used to quantify the relative importance of these factors. In the next step, three ensemble gully erosion models, named COPRAS-FR-RF, COPRAS-FR-BRT, and COPRAS-FR-LR were developed and verified. The Success Rate Curve (SRC), and the Prediction Rate Curve (PRC) and their areas under the curves (AUC) were used to check the performance of the three proposed models. The result showed that Soil group, geomorphology, and drainage density factors played the key role on the occurrence of the gully erosion. All the three models have very high degree-of-fit and the prediction performance, the COPRAS-FR-RF model (AUC-SRC = 0.974 and AUC-PRC = 0.929), the COPRAS-FR-BRT model (AUC-SRC = 0.973 and AUC-PRC = 0.928), and the COPRAS-FR-LR model (AUC-SRC = 0.972 and AUC-PRC = 0.926); therefore, it is concluded that they are efficient and new powerful tools which could be used for predicting gully erosion in prone-areas.
Areerachakul, N, Sakulkhaemaruethai, S, Johir, MAH, Kandasamy, J & Vigneswaran, S 2019, 'Photocatalytic degradation of organic pollutants from wastewater using aluminium doped titanium dioxide', Journal of Water Process Engineering, vol. 27, pp. 177-184.
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Argha, A, Su, SW & Celler, BG 2019, 'Control allocation-based fault tolerant control', Automatica, vol. 103, pp. 408-417.
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© 2019 Elsevier Ltd This paper describes a novel scheme for fault tolerant control using a robust optimal control design method. This scheme can also be employed as actuator redundancy management for over-actuated linear systems. In contrast to many existing methods in the literature, this scheme can be applied to systems whose control input matrix cannot be factorised into two matrices whose ranks are equal and less than the minimum of the number of columns and rows of the input matrix. The so-called virtual control, in this scheme, is calculated using a robust ℋ 2 -based feedback design approach constructed to be robust against uncertainties emanating from visibility of the control allocator to the controller and imperfection in the estimated effectiveness gain. Then, using a new control allocation scheme along with a novel Tikhonov-based re-distributor mechanism, the obtained virtual control signal is re-distributed among remaining (redundant or non-faulty) set of actuators. As the proposed scheme is modular-based, it can be employed as a real-time fault tolerant control scheme with no need to reconfigure the controller in the case of actuator faults or failures. The effectiveness of the proposed scheme is demonstrated by a numerical example.
Argha, A, Su, SW & Celler, BG 2019, 'Static output feedback fault tolerant control using control allocation scheme', International Journal of Robust and Nonlinear Control, vol. 29, no. 1, pp. 98-116.
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SummaryThis paper describes two novel schemes for fault tolerant control using robust suboptimal static output feedback design methods. These schemes can also be employed as actuator redundancy management for overactuated uncertain linear systems. In contrast to many existing methods in the literature that assume the control input matrix (i) is not of full‐rank such that it can be factorized into two matrices and (ii) it does not involve uncertainty, these schemes can be applied to systems whose control input matrix cannot be factorized and/or involve uncertainty. The so‐called virtual control, in these schemes, is calculated using suboptimal ‐based static output feedback design schemes constructed to be robust against uncertainties emanating from inherent input matrix uncertainty and visibility of the control allocator to the controller. Then, using two proposed control allocation schemes (fixed and on‐line), the obtained virtual control signal is redistributed among remaining (redundant or nonfaulty) set of actuators. As the proposed schemes are modular‐based, they can be employed as real‐time fault tolerant control schemes with no need to reconfigure the controller in the case of actuator faults or failures. The effectiveness of the proposed schemes is discussed and compared with numerical examples.
Argha, A, Su, SW, Savkin, A & Celler, B 2019, 'Design of optimal sliding-mode control using partial eigenstructure assignment', International Journal of Control, vol. 92, no. 7, pp. 1511-1523.
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© 2017 Informa UK Limited, trading as Taylor & Francis Group This paper describes a new framework for the design of a sliding surface for a given system while multi-channel (Formula presented.) performances of the closed-loop system are under control. In contrast to most of the current sliding surface design schemes, in this new method, the level of control effort required to maintain sliding is penalised. The proposed method for the design of optimal sliding mode control is implemented in two stages. In the first stage, a state feedback gain is derived using a linear matrix inequality (LMI)-based scheme that can assign a number of the closed-loop eigenvalues to a known value whilst satisfying performance specifications. The sliding function matrix related to the particular state feedback derived in the first stage is obtained in the second stage by using one of the two different methods developed for this goal. The proposed theory is evaluated by using numerical examples including the problem of steady-state output tracking via a state-feedback SMC for flight control.
Argha, A, Su, SW, Zheng, WX & Celler, BG 2019, 'Sliding‐mode fault‐tolerant control using the control allocation scheme', International Journal of Robust and Nonlinear Control, vol. 29, no. 17, pp. 6256-6273.
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SummaryThis paper is devoted to the design of a novel fault‐tolerant control (FTC) using the combination of a robust sliding‐mode control (SMC) strategy and a control allocation (CA) algorithm, referred to as a CA‐based sliding‐mode FTC (SMFTC). The proposed SMFTC can also be considered a modular‐design control strategy. In this approach, first, a high‐level SMC, designed without detailed knowledge of systems' actuators/effectors, commands a vector of virtual control signals to meet the overall control objectives. Then, a CA algorithm distributes the virtual control efforts among the healthy actuators/effectors using the real‐time information obtained from a fault detection and reconstruction mechanism. As the underlying system is not assumed to have a rank‐deficient input matrix, the control allocator module is visible to the SMC module resulting in an uncertainty. Hence, the virtual control, in this scheme, is designed to be robust against uncertainties emanating from the visibility of the control allocator to the controller and imperfections in the estimated effectiveness gain. The proposed CA‐based SMFTC scheme is a unified FTC, which does not need to reconfigure the control system in the case of actuator fault or failure. Additionally, to cope with actuator saturation limits, a novel redistributed pseudoinverse‐based CA mechanism is proposed. The effectiveness of the proposed schemes is discussed with a numerical example.
Argha, A, W. Su, S, Ye, L & G. Celler, B 2019, 'Optimal sparse output feedback for networked systems with parametric uncertainties', Numerical Algebra, Control & Optimization, vol. 9, no. 3, pp. 283-295.
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© 2019, American Institute of Mathematical Sciences. All rights reserved. This paper investigates the design of block row/column-sparse distributed static output H2 feedback control for interconnected systems with polytopic uncertainties. The proposed approach is applicable to the networked systems with publisher/subscriber communication topology. We added two additional terms into the optimisation index function to penalise the number of publishers and subscribers. To optimally select a subset of available publishers and/or subscribers in the network, we introduced both an explicit scheme and an iterative process to handle this problem. We demonstrated the effectiveness by using a numerical example. The example showed that the simultaneous identification of favourable networks topologies and design of controller strategy can be achieved by using the proposed method.
Argha, A, Wu, J, Su, SW & Celler, BG 2019, 'Blood Pressure Estimation From Beat-by-Beat Time-Domain Features of Oscillometric Waveforms Using Deep-Neural-Network Classification Models', IEEE Access, vol. 7, pp. 113427-113439.
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In general, existing machine learning based approaches, developed for systolic and diastolic blood pressure (SBP and DBP) estimation from oscillometric waveforms (OWs), employ features extracted from the OW envelope (OWE) alone and ignore important beat-by-beat (BBB) features which represent fundamental physical properties of the entire non-invasive blood pressure (NIBP) measurement system. Unlike the existing literature, this paper proposes a novel deep-learning based method for BP estimation trained with BBB time-domain features extracted from OWs. First, we extract six time-domain features from each beat of the OW, relative to the preceding beat. Second, using the extracted BBB features along with the corresponding cuff pressures, we form a feature vector for each OW beat and locate it in one of three different classes, namely pre-systolic (PS), between systolic and diastolic (BSD) and after diastolic (AD). We then devise a deep-belief network (DBN)-deep neural network (DNN) classification model as well as a novel artificial feature extraction method for estimating SBP and DBP from feature vectors extracted from OWs and their corresponding deflation curves. The proposed DBN-DNN classification approach can effectively learn the complex nonlinear relationship between the artificial feature vectors and target classes. The SBP and DBP points are then obtained by mapping the beats at which the network output sequence switches from PS phase to BSD phase and from BSD phase to AD phase, respectively, to the deflation curve. Adopting a 5-fold cross-validation scheme and using a data base of 350 NIBP recordings gave an average mean absolute error of 1.1±2.9 mmHg for SBP and 3.0±5.6 mmHg for DBP relative to reference values. We experimentally show that the proposed DBN-DNN-based classification algorithm trained with BBB time-domain features can outperform traditional deep-learning based methods for BP estimation trained with features extracted only from OWEs.
Armaghani, DJ, Koopialipoor, M, Marto, A & Yagiz, S 2019, 'Application of several optimization techniques for estimating TBM advance rate in granitic rocks', Journal of Rock Mechanics and Geotechnical Engineering, vol. 11, no. 4, pp. 779-789.
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Arockia Baskaran, AGR, Nanda, P, Nepal, S & He, S 2019, 'Testbed evaluation of Lightweight Authentication Protocol (LAUP) for 6LoWPAN wireless sensor networks', Concurrency and Computation: Practice and Experience, vol. 31, no. 23.
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Summary6LoWPAN networks involving wireless sensors consist of resource starving miniature sensor nodes. Since secured authentication is one of the important considerations, the use of asymmetric key distribution scheme may not be a perfect choice. Recent research shows that Lucky Thirteen attack has compromised Datagram Transport Layer Security (DTLS) with Cipher Block Chaining (CBC) mode for key establishment. Even though EAKES6Lo and S3 K techniques for key establishment follow the symmetric key establishment method, they strongly rely on a remote server and trust anchor. Our proposed Lightweight Authentication Protocol (LAUP) used a symmetric key method with no preshared keys and comprised of four flights to establish authentication and session key distribution between sensors and Edge Router in a 6LoWPAN environment. Each flight uses freshly derived keys from existing information such as PAN ID (Personal Area Network IDentification) and device identities. We formally verified our scheme using the Scyther security protocol verification tool. We simulated and evaluated the proposed LAUP protocol using COOJA simulator and achieved less computational time and low power consumption compared to existing authentication protocols such as the EAKES6Lo and SAKES. LAUP is evaluated using real‐time testbed and achieved less computational time, which is supportive of our simulated results.
Arshad, B, Ogie, R, Barthelemy, J, Pradhan, B, Verstaevel, N & Perez, P 2019, 'Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review', Sensors, vol. 19, no. 22, pp. 5012-5012.
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Floods are amongst the most common and devastating of all natural hazards. The alarming number of flood-related deaths and financial losses suffered annually across the world call for improved response to flood risks. Interestingly, the last decade has presented great opportunities with a series of scholarly activities exploring how camera images and wireless sensor data from Internet-of-Things (IoT) networks can improve flood management. This paper presents a systematic review of the literature regarding IoT-based sensors and computer vision applications in flood monitoring and mapping. The paper contributes by highlighting the main computer vision techniques and IoT sensor approaches utilised in the literature for real-time flood monitoring, flood modelling, mapping and early warning systems including the estimation of water level. The paper further contributes by providing recommendations for future research. In particular, the study recommends ways in which computer vision and IoT sensor techniques can be harnessed to better monitor and manage coastal lagoons—an aspect that is under-explored in the literature.
Asadabadi, MR, Chang, E & Saberi, M 2019, 'Are MCDM methods useful? A critical review of Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP)', Cogent Engineering, vol. 6, no. 1.
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© 2019, © 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Although Multi Criteria Decision Making (MCDM) methods have been applied in numerous case studies, many companies still avoid employing these methods in making their decisions and prefer to decide intuitively. There are studies claiming that MCDM methods provide better rankings for companies than intuitive approaches. This study argues that this claim may have low validity from a company’s perspective. For this purpose, it focuses on one of the MCDM methods referred to as the Analytic Hierarchy Process (AHP) and shows that AHP is very likely to provide a ranking of options that would not be acceptable by a rational person. The main reason that many companies do not rely on current MCDM methods can be due to the fact that managers intuitively notice ranking errors. Future studies should end the promotion of outdated approaches, pay closer attention to the deficiencies of the current MCDM processes, and develop more useful methods.
Ashok, B, Nanthagopal, K, Darla, S, Chyuan, OH, Ramesh, A, Jacob, A, Sahil, G, Thiyagarajan, S & Geo, VE 2019, 'Comparative assessment of hexanol and decanol as oxygenated additives with calophyllum inophyllum biodiesel', Energy, vol. 173, pp. 494-510.
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© 2019 Elsevier Ltd In this research work, the four ternary blends were prepared with 30% and 40% by volume of higher alcohol (decanol and hexanol) with biodiesel while maintain 50% of diesel concentration. All ternary blends of diesel-biodiesel-higher alcohols were used in single cylinder engine and the results were compared with binary blend of 50%–50% biodiesel, pure diesel and biodiesel. It was revealed that thermal efficiency of ternary blends was higher than biodiesel and in some cases it is closer to pure diesel. In contrary, specific fuel consumption is found to lower with increase in alcohol fractions in ternary blends. Moreover, hydrocarbon, smoke, carbon monoxide emissions from alcohol-infused fuel blends were observed to be lower than both biodiesel and pure diesel. Significant reduction in oxides of nitrogen (NOx) emissions was also observed by the addition of higher alcohols to the fuel blend when compared to biodiesel fuel. It is to be noted that decanol 40% addition with diesel and biodiesel blend has shown better results in emission characteristics. Furthermore, the heat release rate and in-cylinder pressure for biodiesel were significantly lower compared to pure diesel fuels. However, addition of 40% decanol with fuel blend improved the heat release rate and in-cylinder pressure.
Ashournejad, Q, Hosseini, A, Pradhan, B & Hosseini, SJ 2019, 'Hazard zoning for spatial planning using GIS-based landslide susceptibility assessment: a new hybrid integrated data-driven and knowledge-based model', Arabian Journal of Geosciences, vol. 12, no. 4.
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Ashtari, K, Nazari, H, Ko, H, Tebon, P, Akhshik, M, Akbari, M, Alhosseini, SN, Mozafari, M, Mehravi, B, Soleimani, M, Ardehali, R, Ebrahimi Warkiani, M, Ahadian, S & Khademhosseini, A 2019, 'Electrically conductive nanomaterials for cardiac tissue engineering', Advanced Drug Delivery Reviews, vol. 144, pp. 162-179.
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© 2019 Elsevier B.V. Patient deaths resulting from cardiovascular diseases are increasing across the globe, posing the greatest risk to patients in developed countries. Myocardial infarction, as a result of inadequate blood flow to the myocardium, results in irreversible loss of cardiomyocytes which can lead to heart failure. A sequela of myocardial infarction is scar formation that can alter the normal myocardial architecture and result in arrhythmias. Over the past decade, a myriad of tissue engineering approaches has been developed to fabricate engineered scaffolds for repairing cardiac tissue. This paper highlights the recent application of electrically conductive nanomaterials (carbon and gold-based nanomaterials, and electroactive polymers) to the development of scaffolds for cardiac tissue engineering. Moreover, this work summarizes the effects of these nanomaterials on cardiac cell behavior such as proliferation and migration, as well as cardiomyogenic differentiation in stem cells.
Asif, MB, Ansari, AJ, Chen, S-S, Nghiem, LD, Price, WE & Hai, FI 2019, 'Understanding the mechanisms of trace organic contaminant removal by high retention membrane bioreactors: a critical review', Environmental Science and Pollution Research, vol. 26, no. 33, pp. 34085-34100.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. High retention membrane bioreactors (HR-MBR) combine a high retention membrane separation process such as membrane distillation, forward osmosis, or nanofiltration with a conventional activated sludge (CAS) process. Depending on the physicochemical properties of the trace organic contaminants (TrOCs) as well as the selected high retention membrane process, HR-MBR can achieve effective removal (80–99%) of a broad spectrum of TrOCs. An in-depth assessment of the available literature on HR-MBR performance suggests that compared to CAS and conventional MBRs (using micro- or ultra-filtration membrane), aqueous phase removal of TrOCs in HR-MBR is significantly better. Conceptually, longer retention time may significantly improve TrOC biodegradation, but there are insufficient data in the literature to evaluate the extent of TrOC biodegradation improvement by HR-MBR. The accumulation of hardly biodegradable TrOCs within the bioreactor of an HR-MBR system may complicate further treatment and beneficial reuse of sludge. In addition to TrOCs, accumulation of salts gradually increases the salinity in bioreactor and can adversely affect microbial activities. Strategies to mitigate these limitations are discussed. A qualitative framework is proposed to predict the contribution of the different key mechanisms of TrOC removal (i.e., membrane retention, biodegradation, and sorption) in HR-MBR.
Askari, M, Yu, Y, Zhang, C, Samali, B & Gu, X 2019, 'Real-Time Tracking of Structural Stiffness Reduction with Unknown Inputs, Using Self-Adaptive Recursive Least-Square and Curvature-Change Techniques', International Journal of Structural Stability and Dynamics, vol. 19, no. 10, pp. 1950123-1950123.
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In this paper, a new computationally efficient algorithm is developed for online and real-time identification of time, location, and severity of abrupt changes in structural stiffness as well as the unknown inputs such as earthquake signal. The proposed algorithm consists of three stages and is based on self-adaptive recursive least-square (RLS) and curvature-change approaches. In stage 1 (intact structure), a simple compact RLS is hired to estimate the unknown parameters and input of the structure such as stiffness and earthquake. Once the damage has occurred, its time and location are identified in stage 2, using two robust damage indices which are based on the structural jerk response and the error between measured and estimated responses of structure from RLS. Finally, the damage severity as well as the unknown excitations are identified in the third stage (damaged structure), using a self-adaptive multiple-forgetting-factor RLS. The method is validated through numerical and experimental case studies including linear and nonlinear buildings, a truss structure, and a three-story steel frame with different excitations and damage scenarios. Results show that the proposed algorithm can effectively identify the time-varying structural stiffness as well as unknown excitations with high computational efficiency, even when the measured data is contaminated with different levels of noise. In addition, as no optimization method is used here, it can be applied to real-time applications with computational efficiency.
Aslani, F, Hou, L, Nejadi, S, Sun, J & Abbasi, S 2019, 'Experimental analysis of fiber‐reinforced recycled aggregate self‐compacting concrete using waste recycled concrete aggregates, polypropylene, and steel fibers', Structural Concrete, vol. 20, no. 5, pp. 1670-1683.
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AbstractSelf‐compacting concrete (SCC) is a cementitious composite which serves complex formworks without mechanical vibrations with superior deformability and high resistance to segregation. Besides, the recycled aggregate concrete (RAC) is also developing rapidly and along with the ever‐increasing sustainable demand for infrastructure. The combination of the fibers, RAC, and SCC may create advantages for the construction industry. In this study, the polypropylene (PP) fiber at 0.1, 0.15, 0.2, and 0.25% volume fractions and steel fibers at 0.25, 0.5, 0.75, and 1% volume fractions are introduced into fiber‐reinforced recycled aggregate self‐compacting concrete (FR‐RASCC). Both fresh property and hardened mechanical performance, comprising compressive and tensile strengths and modulus of elasticity are analyzed. The fibers validate the optimal 0.1% volume fraction for PP fiber and 0.75% volume fraction for steel fiber. In addition, the results are proved to enhance the mechanical properties and reduce cracking despite the negative impact on the fresh property. Moreover, the experimental outcomes are compared with previous researches to establish the linear model, demonstrating the relationship between fiber fraction and the mechanical properties.
Asteris, PG, Armaghani, DJ, Hatzigeorgiou, GD, Karayannis, CG & Pilakoutas, K 2019, 'Predicting the shear strength of reinforced concrete beams using artificial neural networks', Computers and Concrete, vol. 24, no. 5, pp. 469-488.
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In this research study, the artificial neural networks approach is used to estimate the ultimate shear capacity of reinforced concrete beams with transverse reinforcement. More specifically, surrogate approaches, such as artificial neural network models, have been examined for predicting the shear capacity of concrete beams, based on experime ntal test results available in the pertinent literature. The comparison of the predicted values with the corresponding experimentaones, as we ll as with available formulas from previous research studies or code provisions highlight the ability of artificial neural networks to evaluate the shear capacity of reinforced concrete beams in a trustworthy and effective manner. Furthermore, for the first time, the (quantitative) values of weights for the proposed neural network model, are provided, so that the proposed model can be readily implemented in a spreadsheet and accessible to everyone interested in the procedure of simulation.
Atov, I, Chen, K-C, Kamal, A & Yu, S 2019, 'Data Science and Artificial Intelligence for Communications', IEEE Communications Magazine, vol. 57, no. 11, pp. 82-83.
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Aung, Y, Khabbaz, H & Fatahi, B 2019, 'Mixed hardening hyper-viscoplasticity model for soils incorporating non-linear creep rate – H-creep model', International Journal of Plasticity, vol. 120, pp. 88-114.
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© 2019 Elsevier Ltd. This paper focuses on the deformation of soils considering the time-dependent stress-strain evolution. In this paper, a new mixed hardening hyper-viscoplasticity model is proposed for the derivation of the time-dependent constitutive behaviour of soils, with the intention to capture the variation in the shapes of the yield loci by pursuing non-associated flow rules and accounting for kinematic hardening effects. The distinctive departure from the existing viscoplasticity models is the application of thermodynamics, based upon the use of internal variables, to postulate free-energy and dissipation potential functions, from which the corresponding yield locus, isotropic and kinematic hardening laws, flow rules and the elasticity law are deduced in a systematic procedure. The kinematic hardening behaviour of the yield locus is considered using the shift stress, resulting from the additional plastic component of the free-energy function. A non-linear creep formulation is postulated to address the limitation of over-estimating long-term settlement and incorporated into the model for more reliable predictions. The major parameters required for the model are identified, along with the summary of descriptions on how the model parameters can readily be determined. Non-associated behaviour is found to be a natural consequence of this approach, whenever the division between dissipated and stored plastic work is not equal. This study aims to provide a theoretical background and a numerical implementation for those who are interested in the advancement of constitutive modelling of soil behaviour under the framework of hyperplasticity. Validity and versatility of the proposed constitutive model are evaluated against triaxial and oedometer test results available in literature.
Awwad, S, Tarvade, S, Piccardi, M & Gattas, DJ 2019, 'The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene', International Journal for Quality in Health Care, vol. 31, no. 1, pp. 36-42.
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© 2018 The Author(s). Objectives: (i) To demonstrate the feasibility of automated, direct observation and collection of hand hygiene data, (ii) to develop computer visual methods capable of reporting compliance with moment 1 (the performance of hand hygiene before touching a patient) and (iii) to report the diagnostic accuracy of automated, direct observation of moment 1. Design: Observation of simulated hand hygiene encounters between a healthcare worker and a patient. Setting: Computer laboratory in a university. Participants: Healthy volunteers. Main outcome measures: Sensitivity and specificity of automatic detection of the first moment of hand hygiene. Methods: We captured video and depth images using a Kinect camera and developed computer visual methods to automatically detect the use of alcohol-based hand rub (ABHR), rubbing together of hands and subsequent contact of the patient by the healthcare worker using depth imagery. Results: We acquired images from 18 different simulated hand hygiene encounters where the healthcare worker complied with the first moment of hand hygiene, and 8 encounters where they did not. The diagnostic accuracy of determining that ABHR was dispensed and that the patient was touched was excellent (sensitivity 100%, specificity 100%). The diagnostic accuracy of determining that the hands were rubbed together after dispensing ABHR was good (sensitivity 83%, specificity 88%). Conclusions: We have demonstrated that it is possible to automate the direct observation of hand hygiene performance in a simulated clinical setting. We used cheap, widely available consumer technology and depth imagery which potentially increases clinical application and decreases privacy concerns.
Azadi, S, Aboulkheyr Es, H, Razavi Bazaz, S, Thiery, JP, Asadnia, M & Ebrahimi Warkiani, M 2019, 'Upregulation of PD-L1 expression in breast cancer cells through the formation of 3D multicellular cancer aggregates under different chemical and mechanical conditions', Biochimica et Biophysica Acta (BBA) - Molecular Cell Research, vol. 1866, no. 12, pp. 118526-118526.
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© 2019 Elsevier B.V. Expression of programmed death-ligand 1 (PD-L1) in cancer cells plays an important role in cancer-immune cell interaction. The emerging evidence suggests regulation of PD-L1 expression by several tumor microenvironmental cues. However, the association of PD-L1 expression with chemical and mechanical features of the tumor microenvironment, specifically epidermal growth factor receptor (EGFR) signaling and matrix stiffness, remains elusive. Herein, we determine whether EGFR targeting and substrate stiffness affect the regulation of PD-L1 expression. Breast carcinoma cell lines, MCF7 and MDA-MB-231, were cultured under different conditions targeting EGFR and exposing cells to distinct substrate stiffness to evaluate PD-L1 expression. Furthermore, the ability to form aggregates in short-term culture of breast carcinoma cells and its effect on expression level of PD-L1 was probed. Our results indicated that PD-L1 expression was altered in response to both EGFR inhibition and substrate stiffness. Additionally, a positive association between the formation of multicellular aggregates and PD-L1 expression was observed. MDA-MB-231 cells expressed the highest PD-L1 level on a stiff substrate, while inhibition of EGFR reduced expression of PD-L1. The results suggested that both physical and chemical features of tumor microenvironment regulate PD-L1 expression through alteration of tumor aggregate formation potential. In line with these results, the in-silico study highlighted a positive correlation between PD-L1 expression, EGFR signaling, epithelial to mesenchymal transition related transcription factors (EMT-TFs) and stemness markers in metastatic breast cancer. These findings improve our understanding of regulation of PD-L1 expression by tumor microenvironment leading to evasion of tumor cells from the immune system.
Azadi, S, Tafazzoli‐Shadpour, M, Soleimani, M & Warkiani, ME 2019, 'Modulating cancer cell mechanics and actin cytoskeleton structure by chemical and mechanical stimulations', Journal of Biomedical Materials Research Part A, vol. 107, no. 8, pp. 1569-1581.
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AbstractTo date, a myriad of strategies has been suggested for targeting the chemical signaling of cancer cells. Also, biomechanical features are gaining much more attention. These features can be used as biomarkers which influence cancer progression. Current approaches on cancer treatment are mainly focused on changing the biochemical signaling of cancer cells, whereas less attention was devoted to their biomechanical properties. Herein, we propose targeting of cancer cell mechanics through the microenvironmental mechanical and chemical cues. As such, we examined the role of substrate stiffness as well as the effect of epidermal growth factor receptor (EGFR) blockade in the cell mechanics. As a mechanical stimulus, stiff and soft polydimethylsiloxane substrates were utilized, while as a chemical stimulus, EGFR blockade was considered. Thus, breast cancer cell lines, MCF7 and MDA‐MB‐231, were cultured among chemical and mechanical groups. The local elasticity of cancer cells was assessed by atomic force microscopy nanoindentation method. Furthermore, we evaluated the effect of mentioned mechanical and chemical treatments on the morphology, actin cytoskeleton structures, and cancer cell migration abilities. The stiffness and migration ability of cancer cells increased by substrate stiffening while Cetuximab treatment demonstrated an elevation in the elastic modulus of cells followed by a reduction in the migration ability. These findings indicate that cancer cell mechanics is modulated not only by the mechanical cues but also by the chemical ones through EGFR signaling pathway. Overall, our results illustrate that manipulation of cell mechanics allows for the possible modulation of tumor cell migration. © 2019 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 107A: 1569–1581, 2019.
Azar, APK, Askari, G, Crispini, L, Pour, AB, Zoheir, B & Pradhan, B 2019, 'Field and spaceborne imagery data for evaluation of the paleo-stress regime during formation of the Jurassic dike swarms in the Kalateh Alaeddin Mountain area, Shahrood, north Iran', Arabian Journal of Geosciences, vol. 12, no. 17.
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© 2019, Saudi Society for Geosciences. Dike swarms are commonly linked with extensional structures in diverse geodynamic environments. Mafic dyke swarms are typically used to reconstruct the paleo-stress fields of a given region. These dikes are considered paleo-stress indicators and excellent time marker (if related geochronological data are available) of the local and regional stress fields. In the Middle-Late Jurassic, swarms of mafic dikes emplaced into the Neoproterozoic schists and amphibolites in the Kalateh Alaeddin Mountain area in south Shahrood, north Iran. These dikes with different thicknesses show a general east–west strike direction, with mostly a steep dip angle. In this paper, we present structural data of these dike swarms for the sake of assessing the paleo-stress state and the magma pressure ratio at the time of their emplacement. Field and structural data are integrated with ASTER Global Digital Elevation Model (GDEM) and Centre National d’Etudes Spatiales (CNES)/SPOT imagery data, to extract important parameters of the investigated dikes and controlling fault/joint sets. Orientation of the principal paleo-stress axes, quantification of the stress ratio, and the associated magma pressure ratio (driving stress ratio) were calculated using the stereographic projection and Mohr’s circle reconstruction techniques. The results reveal that the maximum paleo-stress component (σ1) was sub-vertical and the intermediate (σ2) and minimum (σ3) paleo-stresses components were sub-horizontal in N264° E and N173° E trends, respectively. Due to the low value of the driving stress ratio (R = 0.05), these dikes developed perpendicular to the minimum principal stress (in E–W direction). The stress ratio value (ø = 0.66) indicates a moderately oblate stress ellipsoid. The orientation of the principal paleo-stress axes and the oblate ellipsoid are indicative of the dike emplacement during a N–S-directed tectonic extension, in agreement with the Jurassic subsidence...
AzariJafari, H, Taheri Amiri, MJ, Ashrafian, A, Rasekh, H, Barforooshi, MJ & Berenjian, J 2019, 'Ternary blended cement: An eco-friendly alternative to improve resistivity of high-performance self-consolidating concrete against elevated temperature', Journal of Cleaner Production, vol. 223, pp. 575-586.
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© 2019 Elsevier Ltd The unique fresh and hardened properties of high-performance self-consolidating concrete (HPSCC) led to an extensive application of this mixture in high-rise buildings. In this paper, the elevated temperature resistivity of 19 HPSCC mixtures incorporating binary and ternary blends of fly ash, silica fume, natural zeolite, and metakaolin was investigated. Changes in mass, compressive strength and ultrasonic pulse velocity (UPV) of the mixtures were measured at different temperatures (20, 300, 500, and 700 °C). A life cycle assessment (LCA) was also employed to explore the environmental performance of the mixtures. The test results revealed that in ambient temperature, ternary mixtures incorporating natural zeolite and fly ash or natural zeolite and metakaolin have lower compressive strength than that of the control mixture. The residual compressive strengths of fly ash-silica fume-incorporated mixture was similar to those in binary mixtures. The UPV test results revealed a larger than 50% reduction in transition velocity when the temperature was above 500 °C, and there is a strong association between the UPV and compressive strength test results of the mixtures at different temperatures, but the correlation decreased inversely proportional to the exposure temperature. Among the ternary mixtures, those mixtures that incorporate natural zeolite indicate the most significant mass loss after exposing to elevated temperature. The environmental results indicate that the substitution of pozzolanic materials with Portland cement may not always be beneficial. The ecosystem quality results of binary fly ash mixtures were larger than the control mixture due to the extensive transportation distance of import. In addition, metakaolin binary mixture exposes larger damage to the resources. Silica fume-incorporated mixtures had significant damage to human health. The ternary blended mixtures can be a remedy to obtain the optimized fire-resistant results and to...
Azeez, OS, Pradhan, B, Jena, R, Jung, HS & Ahmed, AA 2019, 'Traffic Emission Modelling Using LiDAR Derived Parameters and Integrated Geospatial Model', Korean Journal of Remote Sensing, vol. 35, no. 1, pp. 137-149.
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Traffic emissions are the main cause of environmental pollution in cities and respiratory problems amongst people. This study developed a model based on an integration of support vector regression (SVR) algorithm and geographic information system (GIS) to map traffic carbon monoxide (CO) concentrations and produce prediction maps from micro level to macro level at a particular time gap in a day in a very densely populated area (Utara–Selatan Expressway–NKVE, Kuala Lumpur, Malaysia). The proposed model comprised two models: the first model was implemented to estimate traffic CO concentrations using the SVR model, and the second model was applied to create prediction maps at different times a day using the GIS approach. The parameters for analysis were collected from field survey and remote sensing data sources such as very-high-resolution aerial photos and light detection and ranging point clouds. The correlation coefficient was 0.97, the mean absolute error was 1.401 ppm and the root mean square error was 2.45 ppm. The proposed models can be effectively implemented as decision-making tools to find a suitable solution for mitigating traffic jams near tollgates, highways and road networks.
Azeez, OS, Pradhan, B, Shafri, HZM, Shukla, N, Lee, C-W & Rizeei, HM 2019, 'Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks', Applied Sciences, vol. 9, no. 2, pp. 313-313.
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Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.
Baba, AA, Hashmi, RM, Esselle, KP, Ahmad, Z & Hesselbarth, J 2019, 'Millimeter-Wave Broadband Antennas With Low Profile Dielectric Covers', IEEE Access, vol. 7, pp. 186228-186235.
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Baba, AA, Hashmi, RM, Esselle, KP, Marin, JG & Hesselbarth, J 2019, 'Broadband Partially Reflecting Superstrate-Based Antenna for 60 GHz Applications', IEEE Transactions on Antennas and Propagation, vol. 67, no. 7, pp. 4854-4859.
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© 1963-2012 IEEE. In this communication, a broadband low-profile partially reflecting superstrate (PRS)-based antenna is presented for 60 GHz applications. The PRS with an asymmetric pattern of circular metallic patches printed on one side of a thin dielectric slab improves the feed antenna gain from 6.5 to 18.5 dBi. A prototype antenna exhibits a peak gain of 18.8 dBi with a 3 dB gain bandwidth of 16.7%. Simulated and measured radiation patterns of the proposed antenna are highly directive toward broadside over a large operating bandwidth. The prototype antenna is well matched with a measured voltage standing wave ratio (VSWR) below 2 over a frequency range from 55.6 to 69.6 GHz, corresponding to a matching bandwidth of 22.4%. Fabrication limitations and assembly tolerances are also discussed. Overall, the prototype validates the high gain and wideband performance of the proposed antenna. The total area of the PRS is 5.2\lambda -{0}^{2} and the overall height of the antenna is only 0.66\lambda -{0} at the lowest operating frequency of 55.6 GHz.
Babayan, M, Mazraeh, AE, Yari, M, Niazi, NA & Saha, SC 2019, 'Hydrogen production with a photovoltaic thermal system enhanced by phase change materials, Shiraz, Iran case study', Journal of Cleaner Production, vol. 215, pp. 1262-1278.
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© 2019 Elsevier Ltd Whereas Photovoltaic Thermal Systems (PVT), Phase Change Materials (PCM) and Proton Exchange Membrane (PEM) electrolyzer have been thoroughly studied individually, the effects of their combination need to be more investigated. The current study proposed a new PVT system integrated with PCM and PEM electrolyzer to produce hydrogen in a hydrogen fuel filling station. Based on the energy and exergy balance equations, a mathematical model is developed to analyse the effects of different types of PV and PCM sets on the thermal and electrical performances. Variations in the temperature of system components, generated electricity, hydrogen production as well as the energy/exergy amounts and efficiencies with time are presented for different effective parameters. Based on the obtained results, we found that PV type is one of the most dominant parameters of the system. PCM utilization improves the electrical, thermal energies and exergy efficiencies. The highest daily amount of produced hydrogen is obtained for 16th August 2018 with mono-crystalline semitransparent PV and 120 kg of RT35 PCM type (88.71 gr/day). While the hydrogen production for the same PVT system without PCM is 5.32% less than the case with PCM. Moreover, the maximum diurnal energy efficiency is obtained 35.04% for mono-crystalline semitransparent PV and RT35 PCM during the summer, while the maximum daily exergy efficiency of 15.17% is achieved for the integration of mono-crystalline semitransparent PV and RT28 PCM type in the winter.
Bah, AO, Qin, P-Y, Ziolkowski, RW, Guo, YJ & Bird, TS 2019, 'A Wideband Low-Profile Tightly Coupled Antenna Array With a Very High Figure of Merit', IEEE Transactions on Antennas and Propagation, vol. 67, no. 4, pp. 2332-2343.
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© 1963-2012 IEEE. A wideband, low-profile, tightly coupled antenna array with a simple feed network is presented. The dipole and feed networks in each unit cell are printed on both sides of a single RT/Duroid 6010 substrate with a relative dielectric constant of 10.2. The feed network, composed of meandered impedance transformer and balun sections, is designed based on Klopfenstein tapered microstrip lines. The wide-angle impedance matching is empowered by a novel wideband metasurface superstrate. For the optimum design, scanning to 70° along the E-plane is obtained together with a very high array figure of merit P A = 2.84. The H-plane scan extends to 55°. The broadside impedance bandwidth is 5.5:1 (0.80-4.38) GHz with an active voltage standing-wave ratio value ≤2. The overall height of the array above the ground plane is 0.088λ L, where λ L is the wavelength at the lowest frequency of operation. A prototype was fabricated and tested to confirm the design concepts.
Bai, X, Feng, X, Ni, J, Beretov, J, Deng, J, Zhu, Y, Graham, P & Li, Y 2019, 'Abstract 4754: CHTOP is a novel therapeutic target for chemoresistant epithelial ovarian cancer therapy', Cancer Research, vol. 79, no. 13_Supplement, pp. 4754-4754.
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Abstract Background: Chemotherapy is the mainstay treatment for ovarian cancer (OC). Chemoresistance is a major challenge in epithelial ovarian cancer (EOC) therapy. CHTOP was identified as a potential chemoresistant biomarker in chemoresistant EOC cell lines using label-free LC-MS/MS proteomic technique. However, the role of CHTOP in EOC chemoresistance is still unclear. Aim: In this study, we aimed to investigate whether CHTOP can be used as a therapeutic target in chemoresistant EOC cells and to reveal the mechanism underlying chemosensitization. Methods: The expression difference of CHTOP was detected in chemoresistant and metastatic EOC cell lines by immunofluorescence (IF) and Western blot (WB). The expression of CHTOP in human EOC tissues was examined using immunohistochemistry (IHC). The effect of CHTOP knockdown (KD) on metastasis was examined using the Transwell® matrigel invasion and wound healing assays. Flow cytometry and TUNEL assay were employed to determine the association of CHTOP with apoptosis, while mammary sphere formation assay and IF were used to evaluate its regulation on EOC-cis cell stemness. Results: The higher expression of CHTOP was found in EOC-cis (A2780-cis and IGROV-1-cis) and metastatic EOC (SKOV-3 and OV-90) cells as compared to normal epithelial ovarian cells (HOSE) by IF and WB. Also, high expression of CHTOP was found in human EOC tissues and associated with poor prognosis in patients. In contrast, CHTOP KD significantly reduced the metastatic potential of EOC-cis cells and increased their apoptosis at the presence of cisplatin. Furthermore, CHTOP KD decreased the stemness of EOC-cis cells. Conclusion: Our findings suggest that CHTOP is associated with metastasis, apoptosis, and stemness in chemoresistant EOC cells, the survival in EOC patient...
Baier-Fuentes, H, Merigó, JM, Amorós, JE & Gaviria-Marín, M 2019, 'International entrepreneurship: a bibliometric overview', International Entrepreneurship and Management Journal, vol. 15, no. 2, pp. 385-429.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature The aim of this paper is to provide an overview of the academic research on International Entrepreneurship (IE). To accomplish this, an exhaustive bibliometric analysis was carried out, involving a bibliometric performance analysis and a graphic mapping of the references in this field. Our analysis focuses on journals, papers, authors, institutions and countries. To perform the performance analysis, the work uses a series of bibliometric indicators such as h-index, productivity and citations. Furthermore, the VOS viewer to graphically map the bibliographic material is used. The graphical analysis uses co-citation, bibliographic coupling and co-occurrence of keywords. The results of both analyzes are consistent among them, and show that the USA is the most influential country in IE research as it houses the main authors and institutions in this research field. Moreover, is observed and expected the continued growth of the field globally. Our research plays an informative and complementary role as it presents most of the key aspects in International Entrepreneurship research.
Bakshi, HA, Mishra, V, Satija, S, Mehta, M, Hakkim, FL, Kesharwani, P, Dua, K, Chellappan, DK, Charbe, NB, Shrivastava, G, Rajeshkumar, S, Aljabali, AA, Al-Trad, B, Pabreja, K & Tambuwala, MM 2019, 'Dynamics of Prolyl Hydroxylases Levels During Disease Progression in Experimental Colitis', Inflammation, vol. 42, no. 6, pp. 2032-2036.
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Bano, M, Zowghi, D, Ferrari, A, Spoletini, P & Donati, B 2019, 'Teaching requirements elicitation interviews: an empirical study of learning from mistakes.', Requir. Eng., vol. 24, no. 3, pp. 259-289.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Interviews are the most widely used elicitation technique in requirements engineering (RE). However, conducting a requirements elicitation interview is challenging. The mistakes made in design or conduct of the interviews can create problems in the later stages of requirements analysis. Empirical evidence about effective pedagogical approaches for training novices on conducting requirements elicitation interviews is scarce. In this paper, we present a novel pedagogical approach for training student analysts in the art of elicitation interviews. Our study is conducted in two parts: first, we perform an observational study of interviews performed by novices, and we present a classification of the most common mistakes made; second, we utilize this list of mistakes and monitor the students’ progress in three set of interviews to discover the individual areas for improvement. We conducted an empirical study involving role-playing and authentic assessment in two semesters on two different cohorts of students. In the first semester, we had 110 students, teamed up in 28 groups, to conduct three interviews with stakeholders. We qualitatively analysed the data to identify and classify the mistakes made from their first interview only. In the second semester, we had 138 students in 34 groups and we monitored and analysed their progress in all three interviews by utilizing the list of mistakes from the first study. First, we identified 34 unique mistakes classified into seven high-level themes, namely question formulation, question omission, interview order, communication skills, analyst behaviour, customer interaction, teamwork and planning. In the second study, we discovered that the students struggled mostly in the areas of question formulation, question omission and interview order and did not manage to improve their skills throughout the three interviews. Our study presents a novel and repeatable pe...
Bao, F-H, Bao, J-F, Lee, JE-Y, Bao, L-L, Khan, MA, Zhou, X, Wu, Q-D, Zhang, T & Zhang, X-S 2019, 'Quality factor improvement of piezoelectric MEMS resonator by the conjunction of frame structure and phononic crystals', Sensors and Actuators A: Physical, vol. 297, pp. 111541-111541.
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Bao, T, Damtie, MM, Yu, ZM, Liu, Y, Jin, J, Wu, K, Deng, CX, Wei, W, Wei, XL & Ni, B-J 2019, 'Green Synthesis of Fe3O4@Carbon Filter Media for Simultaneous Phosphate Recovery and Nitrogen Removal from Domestic Wastewater in Biological Aerated Filters', ACS Sustainable Chemistry & Engineering, vol. 7, no. 19, pp. 16698-16709.
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Copyright © 2019 American Chemical Society. Domestic wastewater depth processing and reclamation are essential in the alleviation of global water shortage. In this study, an innovative filter media (i.e., Fe3O4@Carbon filter media [FCM]) was synthesized and subsequently used in a biological aerated filter (BAF) for simultaneous phosphate recovery and nitrogen removal (SPN) from domestic wastewater. The performance of FCM was compared with the commercially available ceramsite (CAC). The results showed that the performance of FCMBAF was better than that of CACBAF; as far as SPN is concerned, the magnetic field of FCMBAF could accelerate the growth rate of biofilm. Moreover, the nitrospira and nirK gene copy numbers of FCMBAF were considerably higher than those of CACBAF. Interestingly, the interconnectivity and uniformity of pores were also suitable for the microdistribution of biofilm, where different aerobic and anaerobic zones of the FCM were formed. This facilitates the microinteraction between the key microorganisms and the filter media that successfully enhanced the nitrogen removal. The phosphate recovery was attained via hydroxyapatite (Ca10(PO4)6(OH)2) formation, which resulted from the reaction between phosphate (PO43-) and FCM. The average effluent concentrations of total organic carbon (TOC), total nitrogen (TN), ammonia nitrogen (NH4+-N), and PO43- were 8.12, 6.18, 0.997, and 0.073 mg/L of FCMBAF, respectively, which were lower than those from the national standard (CODcr ≤ 50 mg L-1, NH4+-N ≤ 5.0 mg L-1, TN ≤ 15 mg L-1, TP ≤ 0.5 mg L-1, GB 18918-2002, first standard). Thus, FCM demonstrated a promising potential for SPN and wastewater recycling of BAF in domestic wastewater treatment.
Baral, P, Rujikiatkamjorn, C, Indraratna, B, Leroueil, S & Yin, J-H 2019, 'Radial Consolidation Analysis Using Delayed Consolidation Approach', Journal of Geotechnical and Geoenvironmental Engineering, vol. 145, no. 10, pp. 04019063-04019063.
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© 2019 American Society of Civil Engineers. The paper offers an analytical solution for radial consolidation that captures isotaches with a strain-rate dependency of preconsolidation pressure. These relationships are obtained based on constant-rate-of-strain (CRS) and long-term consolidation (LTC) tests and then used in the radial consolidation model incorporating the field strain rate, which is generally much lower compared with the typical laboratory environment. In this study, the calculated settlement and associated excess pore-water pressure are obtained using the equivalent preconsolidation pressure from the reference isotache within the (σp′/σp0′)-(ϵv) domain. Moreover, the change in Cα/Cc ratio (i.e., secondary compression index/compression index) with decreasing strain rate is used to calculate the long-term settlement. This method is then validated using various case histories in Australia and Southeast Asia, where excess pore-water pressure is dissipated at a slower rate in relation to the observed settlement.
Barambu, NU, Bilad, MR, Wibisono, Y, Jaafar, J, Mahlia, TMI & Khan, AL 2019, 'Membrane Surface Patterning as a Fouling Mitigation Strategy in Liquid Filtration: A Review', Polymers, vol. 11, no. 10, pp. 1687-1687.
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Membrane fouling is seen as the main culprit that hinders the widespread of membrane application in liquid-based filtration. Therefore, fouling management is key for the successful implementation of membrane processes, and it is done across all magnitudes. For optimum operation, membrane developments and surface modifications have largely been reported, including membrane surface patterning. Membrane surface patterning involves structural modification of the membrane surface to induce secondary flow due to eddies, which mitigate foulant agglomeration and increase the effective surface area for improved permeance and antifouling properties. This paper reviews surface patterning approaches used for fouling mitigation in water and wastewater treatments. The focus is given on the pattern formation methods and their effect on overall process performances.
Barua, P & Rahman, SH 2019, 'Sustainable Livelihood of Vulnerable Communities in Southern Coast of Bangladesh through the Utilization of Mangroves', Asian Journal of Water, Environment and Pollution, vol. 16, no. 1, pp. 59-67.
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It is well known that mangrove forests are enriched with a source of livelihood for coastal communities in developing countries which enhance coastal waters, yield commercial forest products, protect coastlines, and support coastal fisheries. Local communities in the coastal areas of Chittagong highly depends on fisheries and coastal resources for their livings. This study was conducted in three coastal villages, Bagachattar, Ichakhali and Kattoli, at Sitakunda-Mirsarai coast of Chittagong using Participatory rapid appraisal (PRA) tools. Participatory rapid appraisal was utilized to elucidate the mangrove related livelihood activities in the coastal communities. A total of 23,838 ha has plantation and exist in the 6528 ha in study areas. The main causes of destruction are river erosion, cyclone and human encroachment. The Ichakhali and Bagachattar area have no aquaculture practice commercially though the areas are suitable for commercial culture. In Kattoli area commercial aquaculture farm have been grown rapidly which increase the pressure on adjacent mangroves.
Barua, P, Meah, MM & Rahman, SH 2019, 'Abundance of Macrobenthos with Special Reference to Some Physico-Chemical Parameters of South-Eastern Coastal Area, Bangladesh', Asian Journal of Water, Environment and Pollution, vol. 16, no. 4, pp. 51-60.
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Benthic communities are important to any aquatic ecosystem and form important food source for most organisms especially fish. The study about macro benthos was carried out in a canal of south-eastern coast of Bangladesh with some physico-chemical parameters of water and soil during post and pre-monsoon seasons. The canal originates from hilly areas and opens into the Bay of Bengal. Polychaetes were the most abundant group followed by Oligocheates Bivalves crabs during post-monsoon. Oligocheates were the most abundant group followed by Polychaetes, Bivalves during pre-monsoon. Salinity showed positive significant relationship with the Polycheates and as well as Phosphate-Phosphorus. A negative significant relationship was found between Chemical Oxygen Demand (COD) and Oligocheates abundance in the investigated canal and a positive relationship was found between Total Suspended Solids and Oligocheates abundance. There was no relationship among the parameters of water as well as soil and crab abundance. The abundance of macro benthos is useful indicators of the condition of the canal and of the canal habitat as a whole. The effect of anthropogenic induced stressors had resulted in an unstable physically controlled environment characterized by a low density of macrobenthos.
Basnet, S, He, Y, Dutkiewicz, E & Jayawickrama, BA 2019, 'Resource Allocation in Moving and Fixed General Authorized Access Users in Spectrum Access System', IEEE Access, vol. 7, pp. 107863-107873.
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© 2013 IEEE. Spectrum access system (SAS) is a spectrum sharing framework proposed to share the spectrum between the incumbent users and the citizen broadband radio service devices, i.e. Priority access users and general authorized access (GAA) users. In this paper, we propose an interfering angle based method for the joint resource (channel and transmit power) allocation problem to the mobile and fixed GAA users. With mobile GAA users, the set of GAA users that can hear each other will change at different time instants making the resource allocation problem more challenging. The resource allocation of fixed and mobile GAA users is done considering coexistence with priority users, as well as coexistence between mobile and fixed GAA users. For the conflict-free resource allocation to fixed and mobile GAA users, we propose to use the maximum allowed transmit power for the beams of fixed GAA users that lie within the interference range of mobile GAA users. The simulation results show improved capacity from our proposed method while satisfying a predetermined interference constraint.
Bauer, D, Patten, T & Vincze, M 2019, 'VeREFINE: Integrating Object Pose Verification with Physics-guided Iterative Refinement'.
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Accurate and robust object pose estimation for robotics applications requiresverification and refinement steps. In this work, we propose to integratehypotheses verification with object pose refinement guided by physicssimulation. This allows the physical plausibility of individual object poseestimates and the stability of the estimated scene to be considered in aunified optimization. The proposed method is able to adapt to scenes ofmultiple objects and efficiently focuses on refining the most promising objectposes in multi-hypotheses scenarios. We call this integrated approach VeREFINEand evaluate it on three datasets with varying scene complexity. The generalityof the approach is shown by using three state-of-the-art pose estimators andthree baseline refiners. Results show improvements over all baselines and onall datasets. Furthermore, our approach is applied in real-world graspingexperiments and outperforms competing methods in terms of grasp success rate.Code is publicly available at github.com/dornik/verefine.
Bautista, MG, Zhu, H, Zhu, X, Yang, Y, Sun, Y & Dutkiewicz, E 2019, 'Compact Millimeter-Wave Bandpass Filters Using Quasi-Lumped Elements in 0.13-$\mu$ m (Bi)-CMOS Technology for 5G Wireless Systems', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 7, pp. 3064-3073.
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© 1963-2012 IEEE. A design methodology for a compact millimeter-wave on-chip bandpass filter (BPF) is presented in this paper. Unlike the previously published works in the literature, the presented method is based on quasi-lumped elements, which consists of a resonator with enhanced self-coupling and metal-insulator-metal capacitors. Thus, this approach provides inherently compact designs comparing with the conventional distributed elements-based ones. To fully understand the insight of the approach, simplified LC-equivalent circuit models are developed. To further demonstrate the feasibility of using this approach in practice, the resonator and two compact BPFs are designed using the presented models. All three designs are fabricated in a standard 0.13- \mu \text{m} (Bi)-CMOS technology. The measured results show that the resonator can generate a notch at 47 GHz with the attenuation better than 28 dB due to the enhanced self-coupling. The chip size, excluding the pads, is only 0.096 \times 0.294 mm2. In addition, using the resonator for BPF designs, the first BPF has one transmission zero at 58 GHz with a peak attenuation of 23 dB. The center frequency of this filter is 27 GHz with an insertion loss of 2.5 dB, while the return loss is better than 10 dB from 26 to 31 GHz. The second BPF has two transmission zeros, and a minimum insertion loss of 3.5 dB is found at 29 GHz, while the return loss is better than 10 dB from 26 GHz to 34 GHz. Also, more than 20-dB stopband attenuation is achieved from dc to 20.5 GHz and from 48 to 67 GHz. The chip sizes of these two BPFs, excluding the pads, are only 0.076\times 0.296 mm2 and 0.096\times 0.296 mm2, respectively.
Beetson, SJ, Pradhan, S, Gordon, G & Ford, J 2019, 'Building a Digital Entrepreneurial Platform Through Local Community Activity and Digital Skills with Ngemba First Nation, Australia', International Indigenous Policy Journal, vol. 11, no. 1, pp. 1-19.
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In collaboration with Ngemba First Nation in Brewarrina, Australia, this research involves co-designing and co-developing an innovative community digital entrepreneurial platform that includes a mobile app and a website. The methodology is informed by theories of relatedness, Indigenist standpoint, and by the principles of Indigenist research and related ways of being, knowing, and doing research. It uses an Indigenist technology co-design and co-development method (ITCD2). The platform proposes several practical applications, including individual and community entrepreneurship promotion and skills development. This research is motivated by the Australian government’s First Nations priorities through the Close the Gap initiative, including the digital divide, employment and business, and economic development. This research project proposes a paradigm shift from a focus on welfare to a focus on entrepreneurial enterprise.
Behera, TM, Mohapatra, SK, Samal, UC, Khan, MS, Daneshmand, M & Gandomi, AH 2019, 'Residual Energy-Based Cluster-Head Selection in WSNs for IoT Application', IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5132-5139.
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© 2014 IEEE. Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.
Beiranvand Pour, A, Park, Y, Crispini, L, Läufer, A, Kuk Hong, J, Park, T-YS, Zoheir, B, Pradhan, B, Muslim, AM, Hossain, MS & Rahmani, O 2019, 'Mapping Listvenite Occurrences in the Damage Zones of Northern Victoria Land, Antarctica Using ASTER Satellite Remote Sensing Data', Remote Sensing, vol. 11, no. 12, pp. 1408-1408.
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Listvenites normally form during hydrothermal/metasomatic alteration of mafic and ultramafic rocks and represent a key indicator for the occurrence of ore mineralizations in orogenic systems. Hydrothermal/metasomatic alteration mineral assemblages are one of the significant indicators for ore mineralizations in the damage zones of major tectonic boundaries, which can be detected using multispectral satellite remote sensing data. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing data were used to detect listvenite occurrences and alteration mineral assemblages in the poorly exposed damage zones of the boundaries between the Wilson, Bowers and Robertson Bay terranes in Northern Victoria Land (NVL), Antarctica. Spectral information for detecting alteration mineral assemblages and listvenites were extracted at pixel and sub-pixel levels using the Principal Component Analysis (PCA)/Independent Component Analysis (ICA) fusion technique, Linear Spectral Unmixing (LSU) and Constrained Energy Minimization (CEM) algorithms. Mineralogical assemblages containing Fe2+, Fe3+, Fe-OH, Al-OH, Mg-OH and CO3 spectral absorption features were detected in the damage zones of the study area by implementing PCA/ICA fusion to visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Silicate lithological groups were mapped and discriminated using PCA/ICA fusion to thermal infrared (TIR) bands of ASTER. Fraction images of prospective alteration minerals, including goethite, hematite, jarosite, biotite, kaolinite, muscovite, antigorite, serpentine, talc, actinolite, chlorite, epidote, calcite, dolomite and siderite and possible zones encompassing listvenite occurrences were produced using LSU and CEM algorithms to ASTER VNIR+SWIR spectral bands. Several potential zones for listvenite occurrences were identified, typically in association with mafic metavolcanic rocks (Glasgow Volcanic...
Belete, GF, Voinov, A, Arto, I, Dhavala, K, Bulavskaya, T, Niamir, L, Moghayer, S & Filatova, T 2019, 'Exploring Low-Carbon Futures: A Web Service Approach to Linking Diverse Climate-Energy-Economy Models', Energies, vol. 12, no. 15, pp. 2880-2880.
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The use of simulation models is essential when exploring transitions to low-carbon futures and climate change mitigation and adaptation policies. There are many models developed to understand socio-environmental processes and interactions, and analyze alternative scenarios, but hardly one single model can serve all the needs. There is much expectation in climate-energy research that constructing new purposeful models out of existing models used as building blocks can meet particular needs of research and policy analysis. Integration of existing models, however, implies sophisticated coordination of inputs and outputs across different scales, definitions, data and software. This paper presents an online integration platform which links various independent models to enhance their scope and functionality. We illustrate the functionality of this web platform using several simulation models developed as standalone tools for analyzing energy, climate and economy dynamics. The models differ in levels of complexity, assumptions, modeling paradigms and programming languages, and operate at different temporal and spatial scales, from individual to global. To illustrate the integration process and the internal details of our integration framework we link an Integrated Assessment Model (GCAM), a Computable General Equilibrium model (EXIOMOD), and an Agent Based Model (BENCH). This toolkit is generic for similar integrated modeling studies. It still requires extensive pre-integration assessment to identify the ‘appropriate’ models and links between them. After that, using the web service approach we can streamline module coupling, enabling interoperability between different systems and providing open access to information for a wider community of users.
Bell, ME, Murphy, T, Hancock, PJ, Callingham, JR, Johnston, S, Kaplan, DL, Hunstead, RW, Sadler, EM, Croft, S, White, SV, Hurley-Walker, N, Chhetri, R, Morgan, JS, Edwards, PG, Rowlinson, A, Offringa, AR, Bernardi, G, Bowman, JD, Briggs, F, Cappallo, RJ, Deshpande, AA, Gaensler, BM, Greenhill, LJ, Hazelton, BJ, Johnston-Hollitt, M, Lonsdale, CJ, McWhirter, SR, Mitchell, DA, Morales, MF, Morgan, E, Oberoi, D, Ord, SM, Prabu, T, Shankar, NU, Srivani, KS, Subrahmanyan, R, Tingay, SJ, Wayth, RB, Webster, RL, Williams, A & Williams, CL 2019, 'The Murchison Widefield Array Transients Survey (MWATS). A search for low frequency variability in a bright Southern hemisphere sample', Monthly Notices of the Royal Astronomical Society, vol. 482, no. 2, pp. 2484-2501.
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Belotti, Y, Tolomeo, S, Conneely, MJ, Huang, T, McKenna, SJ, Nabi, G & McGloin, D 2019, 'High-Throughput, Time-Resolved Mechanical Phenotyping of Prostate Cancer Cells', Scientific Reports, vol. 9, no. 1.
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AbstractWorldwide, prostate cancer sits only behind lung cancer as the most commonly diagnosed form of the disease in men. Even the best diagnostic standards lack precision, presenting issues with false positives and unneeded surgical intervention for patients. This lack of clear cut early diagnostic tools is a significant problem. We present a microfluidic platform, the Time-Resolved Hydrodynamic Stretcher (TR-HS), which allows the investigation of the dynamic mechanical response of thousands of cells per second to a non-destructive stress. The TR-HS integrates high-speed imaging and computer vision to automatically detect and track single cells suspended in a fluid and enables cell classification based on their mechanical properties. We demonstrate the discrimination of healthy and cancerous prostate cell lines based on the whole-cell, time-resolved mechanical response to a hydrodynamic load. Additionally, we implement a finite element method (FEM) model to characterise the forces responsible for the cell deformation in our device. Finally, we report the classification of the two different cell groups based on their time-resolved roundness using a decision tree classifier. This approach introduces a modality for high-throughput assessments of cellular suspensions and may represent a viable application for the development of innovative diagnostic devices.
Ben, X, Gong, C, Zhang, P, Jia, X, Wu, Q & Meng, W 2019, 'Coupled Patch Alignment for Matching Cross-View Gaits', IEEE Transactions on Image Processing, vol. 28, no. 6, pp. 3142-3157.
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© 1992-2012 IEEE. Gait recognition has attracted growing attention in recent years, as the gait of humans has a strong discriminative ability even under low resolution at a distance. Unfortunately, the performance of gait recognition can be largely affected by view change. To address this problem, we propose a coupled patch alignment (CPA) algorithm that effectively matches a pair of gaits across different views. To realize CPA, we first build a certain amount of patches, and each of them is made up of a sample as well as its intra-class and inter-class nearest neighbors. Then, we design an objective function for each patch to balance the cross-view intra-class compactness and the cross-view inter-class separability. Finally, all the local-independent patches are combined to render a unified objective function. Theoretically, we show that the proposed CPA has a close relationship with canonical correlation analysis. Algorithmically, we extend CPA to 'multi-dimensional patch alignment' that can handle an arbitrary number of views. Comprehensive experiments on CASIA(B), USF, and OU-ISIR gait databases firmly demonstrate the effectiveness of our methods over other existing popular methods in terms of cross-view gait recognition.
Best, G, Cliff, OM, Patten, T, Mettu, RR & Fitch, R 2019, 'Dec-MCTS: Decentralized planning for multi-robot active perception', The International Journal of Robotics Research, vol. 38, no. 2-3, pp. 316-337.
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We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize its own actions by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of their search trees, which are used to update the joint distribution using a distributed optimization approach inspired by variational methods. Our method admits any objective function defined over robot action sequences, assumes intermittent communication, is anytime, and is suitable for online replanning. Our algorithm features a new MCTS tree expansion policy that is designed for our planning scenario. We extend the theoretical analysis of standard MCTS to provide guarantees for convergence rates to the optimal payoff sequence. We evaluate the performance of our method for generalized team orienteering and online active object recognition using real data, and show that it compares favorably to centralized MCTS even with severely degraded communication. These examples demonstrate the suitability of our algorithm for real-world active perception with multiple robots.
Beydoun, G, Abedin, B, Merigó, JM & Vera, M 2019, 'Twenty Years of Information Systems Frontiers.', Inf. Syst. Frontiers, vol. 21, no. 2, pp. 485-494.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Information Systems Frontiers is a leading international journal that publishes research at the interface between information systems and information technology. The journal was launched in 1999. In 2019, the journal celebrates the 20th anniversary. Motivated by this event, this paper aims to review this first twenty years of publication record to uncover trends most influential on ISF. The analysis considers various metics including citation structure of the journal, most-cited papers, the most influential authors, institutions and countries, and citing articles. Importantly, the paper presents a thematic analysis of the publications that appeared in ISF in the past 20 years. The thematic analysis is evidenced by two sources of data: First, a bibliometric analysis highlighting core topics within the past 20 years is presented. Second, a semantic analysis of keywords introduced by the authors themselves is applied.
Bharill, N, Patel, OP, Tiwari, A, Mu, L, Li, D-L, Mohanty, M, Kaiwartya, O & Prasad, M 2019, 'A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering', IEEE Access, vol. 7, pp. 50347-50361.
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© 2013 IEEE. Data clustering is a challenging task to gain insights into data in various fields. In this paper, an Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means (EQIE-FCM) algorithm is proposed for data clustering. In the EQIE-FCM, quantum computing concept is utilized in combination with the FCM algorithm to improve the clustering process by evolving the clustering parameters. The improvement in the clustering process leads to improvement in the quality of clustering results. To validate the quality of clustering results achieved by the proposed EQIE-FCM approach, its performance is compared with the other quantum-based fuzzy clustering approaches and also with other evolutionary clustering approaches. To evaluate the performance of these approaches, extensive experiments are being carried out on various benchmark datasets and on the protein database that comprises of four superfamilies. The results indicate that the proposed EQIE-FCM approach finds the optimal value of fitness function and the fuzzifier parameter for the reported datasets. In addition to this, the proposed EQIE-FCM approach also finds the optimal number of clusters and more accurate location of initial cluster centers for these benchmark datasets. Thus, it can be regarded as a more efficient approach for data clustering.
Biglari, S, Le, TYL, Tan, RP, Wise, SG, Zambon, A, Codolo, G, De Bernard, M, Warkiani, M, Schindeler, A, Naficy, S, Valtchev, P, Khademhosseini, A & Dehghani, F 2019, 'Simulating Inflammation in a Wound Microenvironment Using a Dermal Wound‐on‐a‐Chip Model', Advanced Healthcare Materials, vol. 8, no. 1, pp. 1801307-1801307.
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AbstractConsiderable progress has been made in the field of microfluidics to develop complex systems for modeling human skin and dermal wound healing processes. While microfluidic models have attempted to integrate multiple cell types and/or 3D culture systems, to date they have lacked some elements needed to fully represent dermal wound healing. This paper describes a cost‐effective, multicellular microfluidic system that mimics the paracrine component of early inflammation close to normal wound healing. Collagen and Matrigel are tested as materials for coating and adhesion of dermal fibroblasts and human umbilical vein endothelial cells (HUVECs). The wound‐on‐chip model consists of three interconnecting channels and is able to simulate wound inflammation by adding tumor necrosis factor alpha (TNF‐α) or by triculturing with macrophages. Both the approaches significantly increase IL‐1β, IL‐6, IL‐8 in the supernatant (p < 0.05), and increases in cytokine levels are attenuated by cotreatment with an anti‐inflammatory agent, Dexamethasone. Incorporation of M1 and M2 macrophages cocultured with fibroblasts and HUVECs leads to a stimulation of cytokine production as well as vascular structure formation, particularly with M2 macrophages. In summary, this wound‐on‐chip system can be used to model the paracrine component of the early inflammatory phase of wound healing and has the potential for the screening of anti‐inflammatory compounds.
BINSAWAD, M, SOHAIB, O & HAWRYSZKIEWYCZ, I 2019, 'FACTORS IMPACTING TECHNOLOGY BUSINESS INCUBATOR PERFORMANCE', International Journal of Innovation Management, vol. 23, no. 01, pp. 1950007-1950007.
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Technology business incubators support economic growth by developing innovative technologies. However, assessing the performance of technology business incubators in Saudi Arabia has not been well recognised. This study provides a conceptual framework for assessing technology business incubators based on knowledge sharing practices and sharing, diffusion of innovation and individual creativity. Partial least squares structural equation modelling, such as (PLS-SEM) path modelling was used to test the model. The results provide empirical insights about the performance of technology business incubators. The findings show knowledge donation and collection has positive effects on technology business incubator. The importance–performance map analysis shows additional findings and conclusions for managerial actions.
Biradar, J, Banerjee, S, Shankar, R, Ghosh, P, Mukherjee, S & Fatahi, B 2019, 'Response of square anchor plates embedded in reinforced soft clay subjected to cyclic loading', Geomechanics and Engineering, vol. 17, no. 2, pp. 165-173.
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Plate anchors are generally used for structures like transmission towers, mooring systems etc. where the uplift and lateral forces are expected to be predominant. The capacity of anchor plate can be increased by the use of geosynthetics without altering the size of plates. Numerical simulations have been carried out on three different sizes of square anchor plates. A single layer geosynthetic has been used as reinforcement in the analysis and placed at three different positions from the plate. The effects of various parameters like embedment ratio, position of reinforcement, width of reinforcement, frequency and loading amplitude on the pull out capacity have been presented in this study. The load-displacement behaviour of anchors for various embedment ratios with and without reinforcement has been also observed. The pull out load, corresponding to a displacement equal to each of the considered maximum amplitudes of a given frequency, has been expressed in terms of a dimensionless breakout factor. The pull out load for all anchors has been found to increase by more than 100% with embedment ratio varying from 1 to 6. Finally a semi empirical formulation for breakout factor for square anchors in reinforced soil has also been proposed by carrying out regression analysis on the data obtained from numerical simulations.
Blamires, SJ & Sellers, WI 2019, 'Modelling temperature and humidity effects on web performance: implications for predicting orb-web spider (Argiope spp.) foraging under Australian climate change scenarios', Conservation Physiology, vol. 7, no. 1.
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Lay Summary.How climate change impacts animal extended phenotypes (EPs) is poorly understood. We modelled how temperature and humidity affects the ability of spider webs to intercept prey. We found humidity had negative effects at the extremes. Temperature, however, likely interacts with humidity to affect web tension and prey retention.
Blamires, SJ, Cerexhe, G, White, TE, Herberstein, ME & Kasumovic, MM 2019, 'Spider silk colour covaries with thermal properties but not protein structure', Journal of The Royal Society Interface, vol. 16, no. 156, pp. 20190199-20190199.
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Understanding how and why animal secretions vary in property has important biomimetic implications as desirable properties might covary. Spider major ampullate (MA) silk, for instance, is a secretion earmarked for biomimetic applications, but many of its properties vary among and between species across environments. Here, we tested the hypothesis that MA silk colour, protein structure and thermal properties covary when protein uptake is manipulated in the spider Trichonephila plumipes . We collected silk from adult female spiders maintained on a protein-fed or protein-deprived diet. Based on spectrophotometric quantifications, we classified half the silks as ‘bee visible’ and the other half ‘bee invisible’. Wide angle X-ray diffraction and differential scanning calorimetry were then used to assess the silk's protein structure and thermal properties, respectively. We found that although protein structures and thermal properties varied across our treatments only the thermal properties covaried with colour. This ultimately suggests that protein structure alone is not responsible for MA silk thermal properties, nor does it affect silk colours. We speculate that similar ecological factors act on silk colour and thermal properties, which should be uncovered to inform biomimetic programmes.
Blanco-Mesa, F, León-Castro, E & Merigó, JM 2019, 'A bibliometric analysis of aggregation operators', Applied Soft Computing, vol. 81, pp. 105488-105488.
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© 2019 Elsevier B.V. Aggregation operators consist of mathematical functions that enable the combining and processing of different types of information. The aim of this work is to present the main contributions in this field by a bibliometric review approach. The paper employs an extensive range of bibliometric indicators using the Web of Science (WoS) Core Collection and Scopus datasets. The work considers leading journals, articles, authors, institutions countries and patterns. This paper highlights that Xu is the most productive author and Yager is the most influential author in the field. Likewise, China is leading the field with many new researchers who have entered the field in recent years. This discipline has been strengthening to create a unique theory and will continue to expand with many new theoretical developments and applications.
Blanco‐Mesa, F, León‐Castro, E, Merigó, JM & Herrera‐Viedma, E 2019, 'Variances with Bonferroni means and ordered weighted averages', International Journal of Intelligent Systems, vol. 34, no. 11, pp. 3020-3045.
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© 2019 Wiley Periodicals, Inc. The variance is a statistical measure frequently used for analysis of dispersion in the data. This paper presents new types of variances that use Bonferroni means and ordered weighted averages in the aggregation process of the variance. The main advantage of this approach is that we can underestimate or overestimate the variance according to the attitudinal character of the decision-maker. The work considers several particular cases including the minimum and the maximum variance and presents some numerical examples. The article also develops some extensions and generalizations by using induced aggregation operators and generalized and quasi-arithmetic means. These approaches provide a more general framework that can consider a lot of other particular cases and a complex attitudinal character that could be affected by a wide range of variables. The study ends with an application of the new approach in a business decision-making problem regarding strategic analysis in enterprise risk management.
Blanco-Mesa, F, León-Castro, E, Merigó, JM & Xu, Z 2019, 'Bonferroni means with induced ordered weighted average operators', International Journal of Intelligent Systems, vol. 34, no. 1, pp. 3-23.
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© 2018 Wiley Periodicals, Inc. The induced ordered weighted average is an averaging aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. This paper presents some new generalizations by using Bonferroni means (BM) forming induced BM. The main advantage of this approach is the possibility of reordering the results according to complex ranking processes based on order-inducing variables. The work also presents some additional extensions by using the weighted ordered weighted average, immediate weights, and hybrid averages. Some further generalizations with generalized and quasi-arithmetic means are also developed to consider a wide range of particular cases including quadratic and geometric aggregations. The article also considers the applicability of the new approach in-group decision-making developing an application in sales forecasting.
Bliuc, D, Tran, T, van Geel, T, Adachi, JD, Berger, C, van den Bergh, J, Eisman, JA, Geusens, P, Goltzman, D, Hanley, DA, Josse, R, Kaiser, S, Kovacs, CS, Langsetmo, L, Prior, JC, Nguyen, TV & Center, JR 2019, 'Reduced Bone Loss Is Associated With Reduced Mortality Risk in Subjects Exposed to Nitrogen Bisphosphonates: A Mediation Analysis', Journal of Bone and Mineral Research, vol. 34, no. 11, pp. 2001-2011.
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ABSTRACT Bisphosphonates, potent antiresorptive agents, have been found to be associated with mortality reduction. Accelerated bone loss is, in itself, an independent predictor of mortality risk, but the relationship between bisphosphonates, bone loss, and mortality is unknown. This study aimed to determine whether the association between bisphosphonates and mortality is mediated by a reduction in the rate of bone loss. Participants from the population-based Canadian Multicentre Osteoporosis Study were followed prospectively between1996 and 2011. Comorbidities and lifestyle factors were collected at baseline and bone mineral density (BMD) at baseline and at years 3 (for those aged 40 to 60 years), 5, and 10. Rate of bone loss was calculated using linear regression. Information on medication use was obtained yearly. Bisphosphonate users grouped into nitrogen bisphosphonates (nBP; alendronate or risedronate) and etidronate and non-users (NoRx) were matched by propensity score, including all baseline factors as well as time of treatment. Cox's proportional hazards models, unadjusted and adjusted for annual rate of bone loss, were used to determine the association between nBP and etidronate versus NoRx. For the treatment groups with significant mortality risk reduction, the percent of mortality reduction mediated by a reduction in the rate of bone loss was estimated using a causal mediation analysis. There were 271 pairs of nBP and matched NoRx and 327 pairs of etidronate and matched NoRx. nBP but not etidronate use was associated with significant mortality risk reduction (hazard ratios [HR] = 0.61 [95% confidence interval 0.39–0.96] and 1.35 [95% CI 0.86–2.11] for nBP and etidronate, respectively). Rapid bone loss was associated with more than 2-fold increased mortality risk compared with no loss. Mediation analysis indicated that 39% (95% CI 7%–84%) of the nBP association with mortality was related to a redu...
Bliuc, D, Tran, T, van Geel, T, Adachi, JD, Berger, C, van den Bergh, J, Eisman, JA, Geusens, P, Goltzman, D, Hanley, DA, Josse, RG, Kaiser, S, Kovacs, CS, Langsetmo, L, Prior, JC, Nguyen, TV & Center, JR 2019, 'Mortality risk reduction differs according to bisphosphonate class: a 15-year observational study', Osteoporosis International, vol. 30, no. 4, pp. 817-828.
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© 2019, International Osteoporosis Foundation and National Osteoporosis Foundation. Summary: In this prospective cohort of 6120 participants aged 50+, nitrogen-bisphosphonates but not non-nitrogen bisphosphonates were associated with a significant 34% mortality risk reduction compared to non-treated propensity score matched controls. These findings open new avenues for research into mechanistic pathways. Introduction: Emerging evidence suggests that bisphosphonates (BP), first-line treatment of osteoporosis, are associated with reduced risks for all-cause mortality. This study aimed to determine the association between different BP types and mortality risk in participants with or without a fracture. Methods: A prospective cohort study of users of different BPs matched to non-users by propensity score (age, gender, co-morbidities, fragility fracture status) and time to starting the BP medication from the population-based Canadian Multicentre Osteoporosis Study from nine Canadian centres followed from 1995 to 2013. Mortality risk for bisphosphonate users vs matched non-users was assessed using pairwise multivariable Cox proportional hazards models. Results: There were 2048 women and 308 men on BP and 1970 women and 1794 men who did not receive medication for osteoporosis. The relationship between BP and mortality risk was explored in three separate 1:1 propensity score-matched cohorts of BP users and no treatment (etidronate, n = 599, alendronate, n = 498, and risedronate n = 213). Nitrogen BP (n-BP) (alendronate and risedronate) was associated with lower mortality risks [pairwise HR, 0.66 (95% CI, 0.48–0.91)] while the less potent non-n-BP, etidronate, was not [pairwise HR: 0.89 (95% CI, 0.66–1.20)]. A direct comparison between n-BP and etidronate (n = 340 pairs) also suggested a better survival for n-BP [paired HR, 0.47 (95%CI, (95% CI, 031–0.70)] for n-BP vs. etidronate]. Conclusion: Compared to no treatment, nitrogen but not non-nitrogen bisphosphonat...
Bobba, SS & Agrawal, A 2019, 'Author Correction: Ultra-broad Mid-IR Supercontinuum Generation in Single, Bi and Tri Layer Graphene Nano-Plasmonic waveguides pumping at Low Input Peak Powers', Scientific Reports, vol. 9, no. 1.
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A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.
Braytee, A, Liu, W, Anaissi, A & Kennedy, PJ 2019, 'Correlated Multi-label Classification with Incomplete Label Space and Class Imbalance', ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 5, pp. 1-26.
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Multi-label classification is defined as the problem of identifying the multiple labels or categories of new observations based on labeled training data. Multi-labeled data has several challenges, including class imbalance, label correlation, incomplete multi-label matrices, and noisy and irrelevant features. In this article, we propose an integrated multi-label classification approach with incomplete label space and class imbalance (ML-CIB) for simultaneously training the multi-label classification model and addressing the aforementioned challenges. The model learns a new label matrix and captures new label correlations, because it is difficult to find a complete label vector for each instance in real-world data. We also propose a label regularization to handle the imbalanced multi-labeled issue in the new label, and l 1 regularization norm is incorporated in the objective function to select the relevant sparse features. A multi-label feature selection (ML-CIB-FS) method is presented as a variant of the proposed ML-CIB to show the efficacy of the proposed method in selecting the relevant features. ML-CIB is formulated as a constrained objective function. We use the accelerated proximal gradient method to solve the proposed optimisation problem. Last, extensive experiments are conducted on 19 regular-scale and large-scale imbalanced multi-labeled datasets. The promising results show that our method significantly outperforms the state-of-the-art.
Bródka, P, Musial, K & Jankowski, J 2019, 'Interacting spreading processes in multilayer networks', IEEE Access, volume 8, 2020, vol. 8, pp. 10316-10341.
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The world of network science is fascinating and filled with complex phenomenathat we aspire to understand. One of them is the dynamics of spreadingprocesses over complex networked structures. Building the knowledge-base in thefield where we can face more than one spreading process propagating over anetwork that has more than one layer is a challenging task, as the complexitycomes both from the environment in which the spread happens and fromcharacteristics and interplay of spreads' propagation. As thiscross-disciplinary field bringing together computer science, network science,biology and physics has rapidly grown over the last decade, there is a need tocomprehensively review the current state-of-the-art and offer to the researchcommunity a roadmap that helps to organise the future research in this area.Thus, this survey is a first attempt to present the current landscape of themulti-processes spread over multilayer networks and to suggest the potentialways forward.
Broom, M, Kecskes, Z, Kildea, S & Gardner, A 2019, 'Exploring the Impact of a Dual Occupancy Neonatal Intensive Care Unit on Staff Workflow, Activity, and Their Perceptions', HERD: Health Environments Research & Design Journal, vol. 12, no. 2, pp. 44-54.
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In 2012, a tertiary neonatal intensive care unit (NICU) transitioned from an open plan (OP) to a dual occupancy (DO) NICU. The DO design aimed to provide a developmental appropriate, family-centered environment for neonates and their families. During planning, staff questioned the impact DO would have on staff workflow and activity. To explore the impact of changing from an OP to a DO NICU, a prospective longitudinal study was undertaken from 2011 to 2014, using observational, time and motion, and surveys methods. Main outcome measures included distance walked by staff, minutes of staff activity, and staff perceptions of the DO design. Results highlighted no significant difference in the distances clinical nurses walked nor time spent providing direct clinical care, whereas technical support staff walked further than other staff in both designs. Staff perceived the DO design created a developmentally appropriate, family-centered environment that facilitated communication and collaboration between staff and families. Staff described the main challenges of the DO design such as effective staff communication, gaining educational opportunities, and the isolation of staff and families compared to the OP design. Our study provides new evidence that DO provides an improved developmentally environment and has similar positive benefits to single-family room for neonates and families. Such design may reduce the larger floor plan’s impact on staff walking distance and work practices. Challenges of staff transition can be minimized by planning and leadership throughout the development and move to a new design.
Bryce, T, Far, H & Gardner, A 2019, 'Barriers to career advancement for female engineers in Australia’s civil construction industry and recommended solutions', Australian Journal of Civil Engineering, vol. 17, no. 1, pp. 1-10.
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This study explores the challenges that have emerged from the outdated and inflexible workplace culture of the civil construction industry, and how it is affecting female engineers and women in other functional site roles. The study primarily explores issues such as the strong culture of long work hours, the perception of staff who pursue work-life balance, and the perception of part time and flexible working options within the industry. The study was conducted over three phases where members of the industry answered questionnaires on the workplace issues listed above. The first phase aimed to document a female perspective on the construction workplace culture while the second phase focused on the perceptions of the same respondents regarding the industry’s attitude to work-life balance and whether any of the aspects of workplace culture has discouraged them from staying in the industry. The final phase included men and women in management and employer roles to determine their views on part time and flexible working options within the industry. The results of the study are presented and discussed before recommendations are offered for contractor organisations, their employers and staff. The recommendations have been addressed in a way that a gradual culture change can be accepted and acted on throughout the entire workplace.
Bui, DT, Moayedi, H, Kalantar, B, Osouli, A, Pradhan, B, Nguyen, H & Rashid, ASA 2019, 'A Novel Swarm Intelligence—Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility', Sensors, vol. 19, no. 16, pp. 3590-3590.
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In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO–ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO–ANN = 0.773) the landslide pattern.
Bui, HH, Ha, NH, Nguyen, TND, Nguyen, AT, Pham, TTH, Kandasamy, J & Nguyen, TV 2019, 'Integration of SWAT and QUAL2K for water quality modeling in a data scarce basin of Cau River basin in Vietnam', Ecohydrology & Hydrobiology, vol. 19, no. 2, pp. 210-223.
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© 2019 European Regional Centre for Ecohydrology of the Polish Academy of Sciences Water quality modeling in a river basin often faces the problem of having a large number of parameters yet limited available data. The important inputs to the water quality model are pollution concentrations and discharge from river tributaries, lateral inflows and related pollution load from different sources along the river. In general, such an extensive data set is rarely available, especially for data scarce basins. This makes water quality modeling more challenging. However, integration of models may be able to fill this data gap. Selection of models should be made based on the data that is available for the river basin. For the case of Cau River basin, the SWAT and QUAL2K models were selected. The outputs of SWAT model for lateral inflows and discharges of ungauged tributaries, and the observed pollutant concentrations data and estimated pollution loads of sub-watersheds were used as inputs to the water quality model QUAL2K. The resulting QUAL2K model was calibrated and validated using recent water quality data for two periods in 2014. Four model performance ratings PBIAS, NSE, RSR and R2 were used to evaluate the model results. PBIAS index was chosen for water quality model evaluation because it more adequately accounted for the large uncertainty inherent in water quality data. In term of PBIAS, the calibration and validation results for Cau River water quality model were in the “very good” performance range with ǀPBIASǀ < 15%. The obtained results could be used to support water quality management and control in the Cau River basin.
Buscemi, F, Sutter, D & Tomamichel, M 2019, 'An information-theoretic treatment of quantum dichotomies', Quantum, vol. 3, p. 209.
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Given two pairs of quantum states, we want to decide if there exists aquantum channel that transforms one pair into the other. The theory of quantumstatistical comparison and quantum relative majorization provides necessary andsufficient conditions for such a transformation to exist, but such conditionsare typically difficult to check in practice. Here, by building upon work byMatsumoto, we relax the problem by allowing for small errors in one of thetransformations. In this way, a simple sufficient condition can be formulatedin terms of one-shot relative entropies of the two pairs. In the asymptoticsetting where we consider sequences of state pairs, under some mild convergenceconditions, this implies that the quantum relative entropy is the only relevantquantity deciding when a pairwise state transformation is possible. Moreprecisely, if the relative entropy of the initial state pair is strictly largercompared to the relative entropy of the target state pair, then atransformation with exponentially vanishing error is possible. On the otherhand, if the relative entropy of the target state is strictly larger, then anysuch transformation will have an error converging exponentially to one. As animmediate consequence, we show that the rate at which pairs of states can betransformed into each other is given by the ratio of their relative entropies.We discuss applications to the resource theories of athermality and coherence.
Bykerk, L, Quin, P & Liu, D 2019, 'A Method for Selecting the Next Best Angle-of-Approach for Touch-Based Identification of Beam Members in Truss Structures', IEEE Sensors Journal, vol. 19, no. 10, pp. 3939-3949.
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© 2001-2012 IEEE. A robot designed to climb truss structures such as power transmission towers is expected to have an adequate tactile sensing in the grippers to identify a structural beam member and its properties. Depending on how a gripper grasps a structural member, defined as the Angle-of-Approach (AoA), the extracted tactile data can result in erroneous identifications due to the similarities in beam cross-sectional shapes and sizes. In these cases, further grasps at favorable Angles-of-Approach (AoAs) are required to correctly identify the beam member and its properties. This paper presents an information-based method which uses tactile data to determine the next best AoA for the identification of beam members in truss structures. The method is used in conjunction with a state estimate of beam shape, dimension, and AoA calculated by a Random Forest classifier. The method is verified through simulation by using the data collected using a soft gripper retrofitted with simple tactile sensors. The results show that this method can correctly identify a structural beam member and its properties with a small number of grasps (typically fewer than 4). This method can be applied to other adaptive robotic gripper designs fitted with suitable tactile sensors, regardless of the number of sensors used and their layout.
Cagno, E, Accordini, D & Trianni, A 2019, 'A Framework to Characterize Factors Affecting the Adoption of Energy Efficiency Measures Within Electric Motors Systems', Energy Procedia, vol. 158, pp. 3352-3357.
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© 2019 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-review under responsibility of the scientific committee of ICAE2018 The 10th International Conference on Applied Energy. Electric motor systems account for a remarkable share of total industrial power consumption (even more than 70% in some countries). Despite the wide set of effective opportunities to improve energy efficiency in this cross-cutting technology, the implementation rate is still quite low. Among the barriers affecting the adoption of such measures - identified by previous literature -, little knowledge of the factors that should be taken into account when deciding to undertake an action in this area emerges. Therefore, in the present study we present an innovative framework representing factors affecting the adoption of measures for improved efficiency in electric motor systems. Such factors have been classified according to several categories as follows: compatibility, economic, energy benefits, production-related and operations-related non-energy benefits and losses, synergies, complexity, personnel, and additional technical features, so to fully describe the relevant elements to be considered when considering the adoption of energy efficiency measures (EEMs) in electric motor systems (EMS). The framework may represent a valuable instrument to support industrial decision-makers in the adoption of EEMs for EMS.
Cagno, E, Moschetta, D & Trianni, A 2019, 'Only non-energy benefits from the adoption of energy efficiency measures? A novel framework', Journal of Cleaner Production, vol. 212, pp. 1319-1333.
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© 2018 Elsevier Ltd Industrial energy efficiency has been widely recognized as a major contributor to the reduction of greenhouse gases emissions and improvement of industrial competitiveness. Nevertheless, a broad set of studies have pointed out the existence of barriers limiting the adoption of promising Energy Efficiency Measures (EEMs). Recently, scholars have shown the relevance of the so-called “non-energy benefits” (NEBs) coming from the adoption of EEMs for overcoming those barriers. Still, the existence of such benefits has been pointed out from specific studies and manuals for practitioners, but an overall framework describing them in terms of savings and benefits, as well as technical and management implications, is missing yet. Moreover, a considerable part of the scholars and of the practitioners just focuses on the identification and definition of the positive benefits deriving from these measures after they have been completely adopted, thus neglecting to describe the full set of both positive and negative effects occurring also during the implementation phase. Thus, starting from a literature review of scientific as well as practitioners’ studies, we have proposed a novel framework and characterization of the relevant items to be considered by an industrial decision-maker when deciding whether to adopt an EEM considering both the implementation and service phases. Hence, by taking this perspective, we have tested and validated the framework and the characterization in a two-step process: firstly, considering a set of EEMs well diffused and adopted in industry; secondly, investigating benefits and losses in ad-hoc selected manufacturing companies. Finally, considerations and implications are drawn from the preliminary validation and suggestion for further research are proposed, for both industrial decision-making as well as policy-making purposes.
Cagno, E, Neri, A, Howard, M, Brenna, G & Trianni, A 2019, 'Industrial sustainability performance measurement systems: A novel framework', Journal of Cleaner Production, vol. 230, pp. 1354-1375.
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© 2019 Elsevier Ltd Improved sustainability of industrial activities and measurement of its performance are becoming prime topics of discussion among policy-makers and industrial decision-makers. The current literature proposes a number of performance measurement systems and related indicators, but mainly lacks a real capability to address all sustainability pillars and their intersections, as well as scalability to firms of different sizes, availability of internal resources, and maturity over sustainability issues, suggesting that further research is needed in this area. Building on the literature, our work develops a new framework for the evaluation of industrial sustainability performance, proposing three different Industrial Sustainability Performance Measurement Systems (ISPMSs), with a decreasing number of indicators suitable in different contexts of application. In the framework, selection mechanisms have been conceived and used to reduce the number of indicators considered, while still guaranteeing complete and adequate coverage of all sustainability pillars, as well as their intersections. The framework has been tested through semi-structured case studies in heterogeneous Northern Italian manufacturing firms. The preliminary results are sound as the different ISPMSs proved to be complete, useful, and easy to use. The proposed ISPMSs provide industrial decision-makers with a scalable framework applicable in different contexts, allowing benchmarking and development of specific implementation strategies for increased sustainability, and provide policy-makers with a framework to develop a more effective regulatory policy, better understanding how sustainability performance can be addressed in an integrated manner across industrial firms.
Cai, Z, Yang, Y, Tang, X, Li, Z, Lu, D & Liu, Y 2019, 'Ultralow Phase-Noise Differential Oscillator Using Quarter Stepped-Impedance Resonator', IEEE Microwave and Wireless Components Letters, vol. 29, no. 12, pp. 806-809.
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© 2019 IEEE. In this letter, an ultralow phase-noise differential oscillator is proposed using a balanced feedback filter based on quarter stepped-impedance resonators (QSIRs). Phase-noise performance of the designed oscillators can be significantly improved by taking advantage of high peak group delay, improved stopband suppression, and differential configuration of the proposed feedback filter. To verify the hypothesis and compare the phase-noise performance among single-ended and differential oscillators, both the prototypes are designed, fabricated, and measured. The measured results show that the designed single-ended and differential oscillators are operating at 1.953 GHz. The differential oscillator shows not only a good balanced output power of 8.29 dBm with a measured high suppression of 40.07 dB on the second harmonic but also a superior low phase-noise performance of −130.19 dBc/Hz at a frequency offset of 100 kHz. To the best of our knowledge, this is the best phase-noise performance among the state-of-the-art works designed on planar hybrid integrated circuits at a similar frequency range.
Cancino, CA, Amirbagheri, K, Merigó, JM & Dessouky, Y 2019, 'A bibliometric analysis of supply chain analytical techniques published in Computers & Industrial Engineering', Computers & Industrial Engineering, vol. 137, pp. 106015-106015.
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© 2019 Elsevier Ltd Computers & Industrial Engineering (CAIE) is a leading international journal that publishes manuscripts in the field of supply chain. Due to the recent advances of different analytical techniques applied in order to address supply chain related problems, the aim of this work is to study CAIE publications with a focus on the supply chain using a bibliometric approach that can identify the leading trends in this area by analysing the most significant papers, keywords, authors, institutions and countries. The work also develops a graphical mapping of the bibliographic material by using the visualization of similarities (VOS) viewer software. With this software, the study analyses bibliographic coupling, co-occurrence of author keywords and how the journal is connected with other journals through co-citation analysis. The results indicate that Computers and Industrial Engineering has the fourth highest publications in this area among leading journals that publish in Supply Chain, and China and Iran are the leading publishing countries while Taiwan and Singapore have the highest publications per capita. Finally, supply chain optimization modelling received the highest number of publications in the study.
Cao, L 2019, 'Data Science: Profession and Education', IEEE Intelligent Systems, vol. 34, no. 5, pp. 35-44.
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Cao, X, Qiu, B & Xu, G 2019, 'BorderShift: toward optimal MeanShift vector for cluster boundary detection in high-dimensional data', Pattern Analysis and Applications, vol. 22, no. 3, pp. 1015-1027.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. We present a cluster boundary detection scheme that exploits MeanShift and Parzen window in high-dimensional space. To reduce the noises interference in Parzen window density estimation process, the kNN window is introduced to replace the sliding window with fixed size firstly. Then, we take the density of sample as the weight of its drift vector to further improve the stability of MeanShift vector which can be utilized to separate boundary points from core points, noise points, isolated points according to the vector models in multi-density data sets. Under such circumstance, our proposed BorderShift algorithm doesn’t need multi-iteration to get the optimal detection result. Instead, the developed Shift value of each data point helps to obtain it in a liner way. Experimental results on both synthetic and real data sets demonstrate that the F-measure evaluation of BorderShift is higher than that of other algorithms.
Cao, X, Qiu, B, Li, X, Shi, Z, Xu, G & Xu, J 2019, 'Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data', IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 6, pp. 1867-1880.
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© 2018 IEEE. The balance of neighborhood space around a central point is an important concept in cluster analysis. It can be used to effectively detect cluster boundary objects. The existing neighborhood analysis methods focus on the distribution of data, i.e., analyzing the characteristic of the neighborhood space from a single perspective, and could not obtain rich data characteristics. In this paper, we analyze the high-dimensional neighborhood space from multiple perspectives. By simulating each dimension of a data point's k nearest neighbors space (k NNs) as a lever, we apply the lever principle to compute the balance fulcrum of each dimension after proving its inevitability and uniqueness. Then, we model the distance between the projected coordinate of the data point and the balance fulcrum on each dimension and construct the DHBlan coefficient to measure the balance of the neighborhood space. Based on this theoretical model, we propose a simple yet effective cluster boundary detection algorithm called Lever. Experiments on both low- and high-dimensional data sets validate the effectiveness and efficiency of our proposed algorithm.
Cao, Y, Cao, Y, Wen, S, Huang, T & Zeng, Z 2019, 'Passivity analysis of delayed reaction–diffusion memristor-based neural networks', Neural Networks, vol. 109, pp. 159-167.
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This paper discusses the passivity of delayed reaction-diffusion memristor-based neural networks (RDMNNs). By exploiting inequality techniques and by constructing appropriate Lyapunov functional, several sufficient conditions are obtained in the form of linear matrix inequalities (LMIs), which can be used to ascertain the passivity, output and input strict passivity of delayed RDMNNs. In addition, the passivity of RDMNNs without any delay is also considered. These conditions, represented by LMIs, can be easily verified by virtue of the Matlab toolbox. Finally, some illustrative examples are provided to substantiate the effectiveness and validity of the theoretical results, and to present an application of RDMNN in pseudo-random number generation.
Cao, Y, Wang, S, Guo, Z, Huang, T & Wen, S 2019, 'Synchronization of memristive neural networks with leakage delay and parameters mismatch via event-triggered control', Neural Networks, vol. 119, pp. 178-189.
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In this paper, we investigate the synchronization problem on delayed memristive neural networks (MNNs) with leakage delay and parameters mismatch via event-triggered control. We divide MNNs with parameters mismatch into two categories for discussion. One is state-dependent and can achieve synchronization by designing a suitable controller. A novel Lyapunov functional is constructed to analyze the synchronization problem. Moreover, the triggering conditions are independent from the delay boundaries and can be static or dynamic. Another category of parameters mismatch is structure-dependent and can only achieve quasi-synchronization by appropriate controller. By using matrix measure method and generalized Halanay inequality, a quasi-synchronization criterion is established. The controllers in this paper are discrete state-dependent and can be updated under the event-based triggering condition, which is more simpler than the previous results. In the end of our paper, two illustrative examples are given to support our results.
Cao, Z, Chuang, C-H, King, J-K & Lin, C-T 2019, 'Multi-channel EEG recordings during a sustained-attention driving task', Scientific Data, vol. 6, no. 1, p. 19.
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AbstractWe describe driver behaviour and brain dynamics acquired from a 90-minute sustained-attention task in an immersive driving simulator. The data included 62 sessions of 32-channel electroencephalography (EEG) data for 27 subjects driving on a four-lane highway who were instructed to keep the car cruising in the centre of the lane. Lane-departure events were randomly induced to cause the car to drift from the original cruising lane towards the left or right lane. A complete trial included events with deviation onset, response onset, and response offset. The next trial, in which the subject was instructed to drive back to the original cruising lane, began 5–10 seconds after finishing the previous trial. We believe that this dataset will lead to the development of novel neural processing methodology that can be used to index brain cortical dynamics and detect driving fatigue and drowsiness. This publicly available dataset will be beneficial to the neuroscience and brain-computer interface communities.
Cao, Z, Lin, C-T, Ding, W, Chen, M-H, Li, C-T & Su, T-P 2019, 'Identifying Ketamine Responses in Treatment-Resistant Depression Using a Wearable Forehead EEG', IEEE Transactions on Biomedical Engineering, vol. 66, no. 6, pp. 1668-1679.
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© 2019 IEEE. This study explores responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited and randomly assigned 55 outpatients with TRD into three approximately equal-sized groups (A: 0.5-mg/kg ketamine; B: 0.2-mg/kg ketamine; and C: normal saline) under double-blind conditions. The ketamine responses were measured by EEG signals and Hamilton depression rating scale scores. At baseline, the responders showed significantly weaker EEG theta power than the non-responders (p < 0.05). Compared to the baseline, the responders exhibited higher EEG alpha power but lower EEG alpha asymmetry and theta cordance post-treatment (p < 0.05). Furthermore, our baseline EEG predictor classified the responders and non-responders with 81.3 ± 9.5% accuracy, 82.1 ± 8.6% sensitivity, and 91.9 ± 7.4% specificity. In conclusion, the rapid antidepressant effects of mixed doses of ketamine are associated with prefrontal EEG power, asymmetry, and cordance at baseline and early post-treatment changes. Prefrontal EEG patterns at baseline may serve as indicators of ketamine effects. Our randomized double-blind placebo-controlled study provides information regarding the clinical impacts on the potential targets underlying baseline identification and early changes from the effects of ketamine in patients with TRD.
Castel, A, François, R, Santisi d’Avila, MP & Jenkins, D 2019, 'New service limit state criteria for reinforced concrete in chloride environments', Corrosion Reviews, vol. 37, no. 1, pp. 21-29.
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Abstract In chloride environments, reinforcement stress limits, intended to control flexural cracking, are one of the most important requirements for service limit state (SLS) design. However, concrete damage at the steel-concrete interface between bending cracks, so called cover-controlled cracking, is always correlated to areas of severe steel reinforcement corrosion. Based on the assumption that cover-controlled cracking should be limited, a model has been developed to provide alternative reinforcement stress limits in marine exposure conditions such as concrete in sea water, including permanently submerged, spray zone and tidal/splash zone, as well as coastal constructions located within 1 km of the shoreline. In this paper, the new reinforcement stress limitation is compared to the Australian Standards AS3600 concrete building code and AS5100.5 concrete bridge code provisions. Analysis shows that the new model is very sensitive to the reinforcement percentage of the cross-section. As a result, the existing AS3600 and AS5100.5 code provisions are more conservative than the new limitation for lightly to normally reinforced concrete cross-section. In this case, crack width control governs the SLS design. However, for normally to heavily reinforced concrete cross-section, the new model provides more conservative results suggesting that cover-controlled cracking governs the SLS design.
Castillo, EHC, Thomas, N, Al-Ketan, O, Rowshan, R, Abu Al-Rub, RK, Nghiem, LD, Vigneswaran, S, Arafat, HA & Naidu, G 2019, '3D printed spacers for organic fouling mitigation in membrane distillation', Journal of Membrane Science, vol. 581, pp. 331-343.
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Catchpoole, DR 2019, 'Expert's Response: Daniel R. Catchpoole', BIOPRESERVATION AND BIOBANKING, vol. 17, no. 3, pp. 206-208.
Catchpoole, DR, Parry-Jones, A & Kozlakidis, Z 2019, 'ISBER's Global Outlook: A Summary of Recent International Activities', Biopreservation and Biobanking, vol. 17, no. 1, pp. 91-92.
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Chacon, A, Guatelli, S, Rutherford, H, Bolst, D, Mohammadi, A, Ahmed, A, Nitta, M, Nishikido, F, Iwao, Y, Tashima, H, Yoshida, E, Akamatsu, G, Takyu, S, Kitagawa, A, Hofmann, T, Pinto, M, Franklin, DR, Parodi, K, Yamaya, T, Rosenfeld, A & Safavi-Naeini, M 2019, 'Comparative study of alternative Geant4 hadronic ion inelastic physics models for prediction of positron-emitting radionuclide production in carbon and oxygen ion therapy', Physics in Medicine & Biology, vol. 64, no. 15, pp. 155014-155014.
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Chacon, A, Safavi-Naeini, M, Bolst, D, Guatelli, S, Franklin, DR, Iwao, Y, Akamatsu, G, Tashima, H, Yoshida, E, Nishikido, F, Kitagawa, A, Mohammadi, A, Gregoire, M-C, Yamaya, T & Rosenfeld, AB 2019, 'Monte Carlo investigation of the characteristics of radioactive beams for heavy ion therapy', Scientific Reports, vol. 9, no. 1.
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AbstractThis work presents a simulation study evaluating relative biological effectiveness at 10% survival fraction (RBE10) of several different positron-emitting radionuclides in heavy ion treatment systems, and comparing these to the RBE10s of their non-radioactive counterparts. RBE10 is evaluated as a function of depth for three positron-emitting radioactive ion beams (10C, 11C and 15O) and two stable ion beams (12C and 16O) using the modified microdosimetric kinetic model (MKM) in a heterogeneous skull phantom subject to a rectangular 50 mm × 50 mm × 60 mm spread out Bragg peak. We demonstrate that the RBE10 of the positron-emitting radioactive beams is almost identical to the corresponding stable isotopes. The potential improvement in PET quality assurance image quality which is obtained when using radioactive beams is evaluated by comparing the signal to background ratios of positron annihilations at different intra- and post-irradiation time points. Finally, the incidental dose to the patient resulting from the use of radioactive beams is also quantified and shown to be negligible.
Chan, QN, Fattah, IMR, Zhai, G, Yip, HL, Chen, TBY, Yuen, ACY, Yang, W, Wehrfritz, A, Dong, X, Kook, S & Yeoh, GH 2019, 'Color-ratio pyrometry methods for flame–wall impingement study', Journal of the Energy Institute, vol. 92, no. 6, pp. 1968-1976.
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© 2018 Energy Institute The use of color-ratio pyrometry (CRP) methods, with variable or prescribed soot content (KL) to image flame–wall interactions was examined, with results compared with that obtained using the more mature two-color pyrometry (TCP) technique. The CRP and TCP methods were applied to flame–wall impingement images recorded in a optically-accessible constant volume combustion chamber (CVCC) under compression-ignition (CI) engine conditions. Good correlation in the result trends were observed for the CRP method with fixed KL output and that generated using TCP. Slight discrepancies in the predicted absolute temperature values were observed, which were linked to the difference in the KL value prescribed to the CRP method, and the KL value predicted using TCP. No useful output was obtained with CRP method with variable soot output because of channel noise. A simplified flame transparency modeling was performed to assess the inherent errors associated with the pyrometry methods. The results indicated that the uncertainties arising from the fixing of the KL output appeared acceptable.
Chang, L, Ni, J, Zhu, Y, Pang, B, Graham, P, Zhang, H & Li, Y 2019, 'Liquid biopsy in ovarian cancer: recent advances in circulating extracellular vesicle detection for early diagnosis and monitoring progression', Theranostics, vol. 9, no. 14, pp. 4130-4140.
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The current biomarkers available in the clinic are not enough for early diagnosis or for monitoring disease progression of ovarian cancer. Liquid biopsy is a minimally invasive test and has the advantage of early diagnosis and real-time monitoring of treatment response. Although significant progress has been made in the usage of circulating tumor cells and cell-free DNA for ovarian cancer diagnosis, their potential for early detection or monitoring progression remains elusive. Extracellular vesicles (EVs) are a heterogeneous group of lipid membranous particles released from almost all cell types. EVs contain proteins, mRNA, DNA fragments, non-coding RNAs, and lipids and play a critical role in intercellular communication. Emerging evidence suggests that EVs have crucial roles in cancer development and metastasis, thus holding promise for liquid biopsy-based biomarker discovery for ovarian cancer diagnosis. In this review, we discuss the advantages of EV-based liquid biopsy, summarize the protein biomarkers identified from EVs in ovarian cancer, and highlight the utility of new technologies recently developed for EV detection with an emphasis on their use for diagnosing ovarian cancer, monitoring cancer progression, and developing personalized medicine.
Cheah, R, Billa, L, Chan, A, Teo, FY, Pradhan, B & Alamri, AM 2019, 'Geospatial Modelling of Watershed Peak Flood Discharge in Selangor, Malaysia', Water, vol. 11, no. 12, pp. 2490-2490.
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Conservative peak flood discharge estimation methods such as the rational method do not take into account the soil infiltration of the precipitation, thus leading to inaccurate estimations of peak discharges during storm events. The accuracy of estimated peak flood discharge is crucial in designing a drainage system that has the capacity to channel runoffs during a storm event, especially cloudbursts and in the analysis of flood prevention and mitigation. The aim of this study was to model the peak flood discharges of each sub-watershed in Selangor using a geographic information system (GIS). The geospatial modelling integrated the watershed terrain model, the developed Soil Conservation Service Curve Cumber (SCS-CN) and precipitation to develop an equation for estimation of peak flood discharge. Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) was used again to simulate the rainfall-runoff based on the Clark-unit hydrograph to validate the modelled estimation of peak flood discharge. The estimated peak flood discharge showed a coefficient of determination, r2 of 0.9445, when compared with the runoff simulation of the Clark-unit hydrograph. Both the results of the geospatial modelling and the developed equation suggest that the peak flood discharge of a sub-watershed during a storm event has a positive relationship with the watershed area, precipitation and Curve Number (CN), which takes into account the soil bulk density and land-use of the studied area, Selangor in Malaysia. The findings of the study present a comparable and holistic approach to the estimation of peak flood discharge in a watershed which can be in the absence of a hydrodynamic simulation model.
Chellappan, DK, Yee, NJ, Kaur Ambar Jeet Singh, BJ, Panneerselvam, J, Madheswaran, T, Chellian, J, Satija, S, Mehta, M, Gulati, M, Gupta, G & Dua, K 2019, 'Formulation and Characterization of Glibenclamide and quercetin-loaded Chitosan Nanogels Targeting Skin Permeation', Therapeutic Delivery, vol. 10, no. 5, pp. 281-293.
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Aim: Our aim was to develop and characterize a nanogel formulation containing both glibenclamide and quercetin and to explore the permeation profile of this combination. Methods: Drug-loaded nanogel was prepared by ionic gelation. In addition, optimum encapsulation efficiencies of glibenclamide and quercetin were also obtained. The average nanoparticle size at optimum conditions was determined by Zetasizer. Results: The particle size of the nanogel was found to be 370.4 ± 4.78 nm with a polydispersity index of 0.528 ± 0.04, while the λ potential was positive in a range of 17.6 to 24.8 mV. The percentage cumulative drug release also showed favorable findings. Conclusion: The chitosan nanogel could be a potential alternative for delivering glibenclamide and quercetin through skin.
Chen, B, Huang, J & Ji, JC 2019, 'Control of flexible single-link manipulators having Duffing oscillator dynamics', Mechanical Systems and Signal Processing, vol. 121, pp. 44-57.
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© 2018 Elsevier Ltd Unwanted vibrations caused by the commanded motions lower the positioning accuracy and degrade the control performance of flexible link manipulators. Much work has been devoted to the dynamics and control of flexible link manipulators. However, there are few studies dedicated to the flexible link manipulators having Duffing oscillator dynamics. This paper develops a new model for a flexible single-link manipulator by assuming large mechanical impedance in the drives or large inertia of the motor hub and by considering the flexibility of the single-link manipulator. The derived model includes an infinite number of uncoupled Duffing oscillators by ignoring the modal coupling effects. Two new methods are presented for controlling the vibrations of the flexible manipulator governed by Duffing oscillators. One is designed for the single-mode Duffing oscillator, and the other is for the multi-mode Duffing oscillators. A comparison of these two methods is made using the results of numerical simulations and experimental measurements. Experimental investigations are also performed on a flexible single-link manipulator to validate the dynamic behavior of Duffing oscillators and the effectiveness of the new control methods.
Chen, B, Yu, S, Yu, Y & Guo, R 2019, 'Nonlinear active noise control system based on correlated EMD and Chebyshev filter', Mechanical Systems and Signal Processing, vol. 130, pp. 74-86.
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© 2019 Elsevier Ltd Investigations into active noise control (ANC)have been widely conducted with the aim of effective control of low-frequency noise. This paper proposes a novel nonlinear ANC system to control non-stationary noise produced by rotating machinery under nonlinear primary path. A real-time correlated empirical mode decomposition (CEMD)is first presented to decompose non-stationary noise into intrinsic mode functions (IMFs), some of which are chosen by the correlation analysis between IMFs and primary noise. Subsequently, the second-order Chebyshev nonlinear filter is applied to expand selected IMFs that are controlled individually by the filtered-x LMS. The convergence of proposed nonlinear ANC system is also investigated. Simulation results demonstrate that proposed method outperforms the filtered-x LMS, filtered-s LMS and Volterra filtered-x LMS algorithms with respect to noise reduction and convergence rate.
Chen, C, Guo, W & Ngo, HH 2019, 'Pesticides in stormwater runoff—A mini review', Frontiers of Environmental Science & Engineering, vol. 13, no. 5.
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© 2019, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature. Recently, scientific interest has grown in harvesting and treating stormwater for potable water use, in order to combat the serious global water scarcity issue. In this context, pesticides have been identified as the key knowledge gap as far as reusing stormwater is concerned. This paper reviewed the presence of pesticides in stormwater runoff in both rural and urban areas. Specifically, the sources of pesticide contamination and possible pathways were investigated in this review. Influential factors affecting pesticides in stormwater runoff were critically identified as: 1) characteristics of precipitation, 2) properties of pesticide, 3) patterns of pesticides use, and 4) properties of application surface. The available pesticide mitigation strategies including best management practice (BMP), low impact development (LID), green infrastructure (GI) and sponge city (SC) were also included in this paper. In the future, large-scale multi-catchment studies that directly evaluate pesticide concentrations in both urban and rural stormwater runoff will be of great importance for the development of effective pesticides treatment approaches and stormwater harvesting strategies.[Figure not available: see fulltext.].
Chen, D, Luo, Q, Meng, M, Li, Q & Sun, G 2019, 'Low velocity impact behavior of interlayer hybrid composite laminates with carbon/glass/basalt fibres', Composites Part B: Engineering, vol. 176, pp. 107191-107191.
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© 2019 Elsevier Ltd This work investigates the effects of carbon/glass/basalt hybridization and fabric structure on the low velocity impact resistance of fibre reinforced plastic composites. Interply hybrid specimens used in the study were fabricated in a sandwich-like stacking sequence using a vacuum assisted resin infusion molding technique. Low velocity impact tests were carried out to study effects of hybridization and fabric structure on the impact resistance of composite laminates. A continuum damage mechanical model was developed and validated for non-hybrid woven fabric laminates at different impact energy levels. Residual damage characteristics in the cross-sectional view were identified using a 3D surface scanning system and an X-ray computed tomography (CT) method. On the basis of experimental results, numerical simulation was conducted to analyse the damage mechanisms of the hybrid laminates. Experimental results showed that: (a) hybrid laminates with carbon fibre as the core exhibited superior impact resistance for sandwich-like stacking sequence; (b) similar impact behaviors appeared for carbon laminates hybrid with either basalt or glass fibre; (c) for basalt fibre, weave fabric composite laminates exhibited better energy absorption capability and deformation resistance than cross-ply laminates reinforced by unidirectional fabrics.
Chen, F, Duarte, C & Fu, W-T 2019, 'Special Issue on Highlights of ACM Intelligent User Interface (IUI) 2017', ACM Transactions on Interactive Intelligent Systems, vol. 9, no. 2-3, pp. 1-3.
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Chen, F, Lu, S, Hu, X, He, Q, Feng, C, Xu, Q, Chen, N, Ngo, HH & Guo, H 2019, 'Multi-dimensional habitat vegetation restoration mode for lake riparian zone, Taihu, China', Ecological Engineering, vol. 134, pp. 56-64.
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© 2019 Elsevier B.V. The riparian zones that were surround bodies of fresh water have been extensively degraded by human influence. Their restoration strength and management are an issue of urgent. Particularly, the knowledge based for the restoration of riparian zone has expanded in recent years. However, progress on a global scale has been limited, because little is known about its complex and diverse functions and structures. A national ecological restoration project of Taihu Lake (China)provided a case study for classifying and restoring the riparian zone. In this work, we quantified the classification of riparian zone, vegetation-zone in the ecotones and a recommended suite of introduced riparian vegetation communities. Taking the structures of the ecotones, soil conditions, vegetation configurations, and anthropogenic disturbances into account, six types of vegetation-zone were used to classify riparian zone, which are as follow: reefs, islands, dokdo–island, island–shore, dike–shore, and shoreland. Then a multi-dimensional habitat vegetation restoration mode for each type based on six vegetation-zones were also recommended. The water ecological quality was developed to a healthy state under this implement. Therefore, results suggest that profound division of habitat-vegetation is important in the modern ecological engineering theories for riparian zone. In order to provide a key parameter for Taihu Lake and other worldwide lakes with similar characteristics, an eco-restoration model and a reconstruction scheme for the vegetation community have been presented. In a conclusion, these recommendations could largely assist further development for lake management.
Chen, F, Wen, L, Qiao, L, Shi, Z, Xue, T, Chen, X & Li, X 2019, 'Impact of Allergy and Eosinophils on the Morbidity of Chronic Rhinosinusitis with Nasal Polyps in Northwest China', International Archives of Allergy and Immunology, vol. 179, no. 3, pp. 209-214.
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<b><i>Background:</i></b> Nasal polyps are a common health problem that can significantly impact the quality of life. <b><i>Objective:</i></b> To analyze the impact of allergy and peripheral eosinophils (EOS) on the morbidity of chronic rhinosinusitis with nasal polyps (CRSwNP) in Northwest China. <b><i>Methods:</i></b> A retrospective cohort of 323 patients who underwent endoscopic sinus surgery (ESS) for chronic rhinosinusitis without nasal polyps (CRSsNP) and CRSwNP in Xijing Hospital was studied between January 5, 2011, and January 4, 2015. All of the patients underwent an allergen skin prick test and peripheral blood EOS inspection. Detailed information regarding the impact of allergy and EOS on the morbidity of CRSwNP was collected. Potential risk factors associated with nasal polyps were explored using logistic regression analysis. Multivariate logistic regression was performed to identify independent risk factors. <b><i>Results:</i></b> The results revealed that EOS is an important risk factor for nasal polyps. In the univariate analysis, the adjusted OR was 2.01 (95% CI 1.08–3.72; <i>p</i> = 0.027). In the multivariate analysis, the adjusted OR was 2.02 (95% CI 1.08–3.76; <i>p</i> = 0.027). Compared to allergic rhinitis and normal EOS levels, nonallergic rhinitis and elevated EOS levels constituted a risk factor for CRSwNP (OR = 2.70; 95% CI 1.32–5.50). Compared to allergen-positive and EOS-normal status, allergen-negative and elevated-EOS status constituted a risk factor for CRSwNP (OR = 2.95; 95% CI 1.38–6.33). <b><i>Conclusion:</i></b> EOS is a significant factor related to the morbidity of CRSwNP in Northwest China. Elevated EOS levels occurring in the context of nonallergic rhinitis constitute a risk factor for CRSwNP. Similarly, elevated EOS levels occurring in the context of allergen-nega...
Chen, H, Li, A, Cui, C, Ma, F, Cui, D, Zhao, H, Wang, Q, Ni, B & Yang, J 2019, 'AHL-mediated quorum sensing regulates the variations of microbial community and sludge properties of aerobic granular sludge under low organic loading', Environment International, vol. 130, pp. 104946-104946.
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© 2019 The Authors Aerobic granular sludge (AGS) is promising in wastewater treatment. However, the formation and existence of AGS under low organic loading rate (OLR) is still not fully understood due to a knowledge gap in the variations and correlations of N-acyl-homoserine lactones (AHLs), the microbial community, extracellular polymeric substances (EPS) and other physiochemical granule properties. This study comprehensively investigated the AHL-mediated quorum sensing (QS) and microbial community characters in the AGS fed with ammonium-rich wastewater under a low OLR of 0.15 kg COD (m3 d)−1. The results showed that the AGS appeared within 90 days, and the size of mature granules was over 700 μm with strong settleability and ammonium removal performance. More tightly-bound extracellular polysaccharide and tightly-bound extracelluar protein were produced in the larger AGS. C10-HSL and C12-HSL gradually became dominant in sludge, and short-chain AHLs dominated in water. EPS producers and autotrophic nitrifiers were successfully retained in the AGS under low OLR. AHL-mediated QS utilized C10-HSL, C12-HSL and 3OC6-HSL as the critical AHLs to regulate the TB-EPS in aerobic granulation, and autotrophic nitrifiers may perform interspecific communication with C10-HSL. The correlations of bacterial genera with AGS properties and AHLs were complex due to the dynamic fluctuations of microbial composition and other variable factors in the mixed-culture system. These findings confirmed the participation of AHL-mediated QS in the regulation of microbial community characters and AGS properties under low OLR, which may provide guidance for the operation of AGS systems under low OLR from a microbiological viewpoint.
Chen, H, Li, L, Hu, J, Zhao, Z, Ji, L, Cheng, C, Zhang, G, zhang, T, Li, Y, Chen, H, Pan, S & Sun, B 2019, 'UBL4A inhibits autophagy-mediated proliferation and metastasis of pancreatic ductal adenocarcinoma via targeting LAMP1', Journal of Experimental & Clinical Cancer Research, vol. 38, no. 1.
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CHEN, J, SU, S & WANG, X 2019, 'Towards Privacy-Preserving Location Sharing over Mobile Online Social Networks', IEICE Transactions on Information and Systems, vol. E102.D, no. 1, pp. 133-146.
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Chen, J, Tian, Z, Cui, X, Yin, L & Wang, X 2019, 'Trust architecture and reputation evaluation for internet of things', Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 8, pp. 3099-3107.
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Chen, S, Wang, Y, Lin, C-T, Ding, W & Cao, Z 2019, 'Semi-supervised feature learning for improving writer identification', Information Sciences, vol. 482, pp. 156-170.
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© 2019 Elsevier Inc. Data augmentation is typically used by supervised feature learning approaches for offline writer identification, but such approaches require a mass of additional training data and potentially lead to overfitting errors. In this study, a semi-supervised feature learning pipeline is proposed to improve the performance of writer identification by training with extra unlabeled data and the original labeled data simultaneously. Specifically, we propose a weighted label smoothing regularization (WLSR) method for data augmentation, which assigns a weighted uniform label distribution to the extra unlabeled data. The WLSR method regularizes the convolutional neural network (CNN) baseline to allow more discriminative features to be learned to represent the properties of different writing styles. The experimental results on well-known benchmark datasets (ICDAR2013 and CVL) showed that our proposed semi-supervised feature learning approach significantly improves the baseline measurement and perform competitively with existing writer identification approaches. Our findings provide new insights into offline writer identification.
Chen, S-L, Karmokar, DK, Li, Z, Qin, P-Y, Ziolkowski, RW & Guo, YJ 2019, 'Circular-Polarized Substrate-Integrated-Waveguide Leaky-Wave Antenna With Wide-Angle and Consistent-Gain Continuous Beam Scanning', IEEE Transactions on Antennas and Propagation, vol. 67, no. 7, pp. 4418-4428.
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© 2019 IEEE. Circularly polarized (CP) antennas are in high demand for use in future wireless communications. To advance the development of CP substrate-integrated-waveguide (SIW) leaky-wave antennas (LWAs) with the intent to meet this demand, a novel benzene-ring-shaped slot-loaded LWA with partially reflecting wall (PRW) vias is investigated and verified to realize wide-angle continuous beam scanning with consistent gain. The dispersion features of slot-loaded SIW LWAs with PRW vias are theoretically explored using an equivalent circuit model. The CP radiation feature is investigated numerically utilizing the E- A nd H-field distributions of an initial design and its equivalent magnetic currents. The results of these studies are used to demonstrate that improved CP performance over a wide-angle scan range can be attained with a change from a standard slot shape to a benzene-ring-shaped slot. The resulting benzene-ring-shaped slot-loaded CP SIW LWA was optimized, fabricated, and measured. The measured results verify that a CP beam was continuously scanned through a wide angle from backward to forward directions with a consistent gain. The prototype exhibits a continuous 97.1° CP beam scan with a gain variation between 8 and 11.3 dBic when the source frequency is swept from 9.35 to 11.75 GHz.
Chen, S-L, Karmokar, DK, Li, Z, Qin, P-Y, Ziolkowski, RW & Guo, YJ 2019, 'Continuous Beam Scanning at a Fixed Frequency With a Composite Right-/Left-Handed Leaky-Wave Antenna Operating Over a Wide Frequency Band', IEEE Transactions on Antennas and Propagation, vol. 67, no. 12, pp. 7272-7284.
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© 1963-2012 IEEE. Fixed-frequency beam scanning leaky-wave antennas (LWAs) that can scan their main beam over a specific frequency band are highly desired for future wireless communication systems. A composite right-/left-handed (CRLH) LWA is developed in this article that facilitates fixed-frequency continuous beam scanning over a wide operational frequency band. A variation of a simple single-layer nonreconfigurable frequency-based continuous beam scanning CRLH LWA is considered first. Its dispersion properties are approximately investigated using an equivalent circuit model. It is reported how two groups of varactor diodes can be incorporated into its basic circuit model to electronically control its dispersion behavior. An optimized reconfigurable CRLH LWA with practical dc biasing lines is then realized from this nonreconfigurable design. Fixed-frequency continuous beam scanning, from backward to forward directions through broadside, is reported over a wide operational frequency band. Simulations predict that the antenna can operate from 4.75 to 5.25 GHz with the main beam being continuously scannable at each frequency point. A prototype was fabricated, assembled, and tested. The measured results confirm its simulated performance characteristics.
Chen, W, Pradhan, B, Li, S, Shahabi, H, Rizeei, HM, Hou, E & Wang, S 2019, 'Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis', Natural Resources Research, vol. 28, no. 4, pp. 1239-1258.
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© 2019, International Association for Mathematical Geosciences. Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of Fisher’s linear discriminant function (FLDA) with rotation forest (RFLDA) and bagging (BFLDA) ensembles was used for groundwater potential assessment at the Ningtiaota area in Shaanxi, China. A spatial database with 66 groundwater spring locations and 14 groundwater spring contributing factors was prepared; these factors were elevation, aspect, slope, plan and profile curvatures, sediment transport index, stream power index, topographic wetness index, distance to roads and streams, land use, lithology, soil and normalized difference vegetation index. The classifier attribute evaluation method based on the FLDA model was implemented to test the predictive competence of the mentioned contributing factors. The area under curve, confidence interval at 95%, standard error, Friedman test and Wilcoxon signed-rank test were used to compare and validate the success and prediction competence of the three applied models. According to the achieved results, the BFLDA model showed the most prediction competence, followed by the RFLDA and FLDA models, respectively. The resulting groundwater spring potential maps can be used for groundwater development plans and land use planning.
Chen, W, Yan, X, Zhao, Z, Hong, H, Bui, DT & Pradhan, B 2019, 'Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China)', Bulletin of Engineering Geology and the Environment, vol. 78, no. 1, pp. 247-266.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation...
Chen, W, Zhang, Y, Wang, D & Wu, C 2019, 'Investigation on damage development of AP1000 nuclear power plant in strong ground motions with numerical simulation', Nuclear Engineering and Technology, vol. 51, no. 6, pp. 1669-1680.
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© 2019 Seismic safety is considered to be one of the key design objectives of AP1000 nuclear power plant (NPP) in strong earthquakes. Dynamic behavior, damage development and aggravation effect are studied in this study for the three main components of AP1000 NPP, namely reinforced concrete shield building (RCSB), steel vessel containment (SVC) and reinforced concrete auxiliary building (RCAB). Characteristics including nonlinear concrete tension and compressive constitutions with plastic damage are employed to establish the numerical model, which is further validated by existing studies. The author investigates three earthquakes and eight input levels with the maximum magnitude of 2.4 g and the results show that the concrete material of both RCSB and RCAB have suffered serious damage in intense earthquakes. Considering RCAB in the whole NPP, significant damage aggravation effect can be detected, which is mainly concentrated at the upper intersection between RCSB and RCAB. SVC and reinforcing bar demonstrate excellent seismic performance with no obvious damage.
Chen, W-H, Cheng, C-L, Show, P-L & Ong, HC 2019, 'Torrefaction performance prediction approached by torrefaction severity factor', Fuel, vol. 251, pp. 126-135.
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Chen, W-H, Lee, KT & Ong, HC 2019, 'Biofuel and Bioenergy Technology', Energies, vol. 12, no. 2, pp. 290-290.
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Biomass is considered as a renewable resource because of its short life cycle, and biomass-derived biofuels are potential substitutes to fossil fuels [...]
Chen, W-H, Lin, Y-Y, Liu, H-C, Chen, T-C, Hung, C-H, Chen, C-H & Ong, HC 2019, 'A comprehensive analysis of food waste derived liquefaction bio-oil properties for industrial application', Applied Energy, vol. 237, pp. 283-291.
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© 2019 Elsevier Ltd Hydrothermal liquefaction is a promising technology to convert wet biomass into bio-oil with high calorific value and without drying process. To evaluate the potential application of liquefaction bio-oil in industry, the present study aims to provide a comprehensive analysis on the properties of liquefaction bio-oil derived from food waste. The food waste is pretreated with K2CO3 at 100 °C for 1 h, followed by liquefaction in a semi-pilot reactor at 320 °C for 30 min. The higher heating value of produced bio-oil is 34.79 MJ kg−1, accounting for 53% increase when compared to the feedstock (22.74 MJ kg−1). The ignition and burnout temperatures of the bio-oil are lower than other liquefaction bio-oils, reflecting its higher reactivity and combustibility. Meanwhile, the bio-oil has a higher oxidation onset temperature than pyrolysis bio-oils, showing its higher thermal stability. The independent parallel reaction model in association with the particle swarm optimization indicates that the pyrolysis kinetics of the bio-oil can be approximated by four groups. The component analysis further reveals two important groups of fatty acids and amides in the bio-oil, stemming from the conversion of carbohydrate and protein in the food waste. The comprehensive analysis shows that the liquefaction bio-oil from food waste, characterized by higher energy density and better combustibility, is a potential substitute to the fossil fuels.
Chen, W-H, Wang, C-W, Ong, HC, Show, PL & Hsieh, T-H 2019, 'Torrefaction, pyrolysis and two-stage thermodegradation of hemicellulose, cellulose and lignin', Fuel, vol. 258, pp. 116168-116168.
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Chen, X, Chen, J, Liang, J, Li, Y, Courtney, CA & Yang, Y 2019, 'Entropy-Based Surface Electromyogram Feature Extraction for Knee Osteoarthritis Classification', IEEE Access, vol. 7, pp. 164144-164151.
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Chen, X, Kong, X, Xu, M, Sandrasegaran, K & Zheng, J 2019, 'Road Vehicle Detection and Classification Using Magnetic Field Measurement', IEEE Access, vol. 7, pp. 52622-52633.
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Chen, X, Lai, C-Y, Fang, F, Zhao, H-P, Dai, X & Ni, B-J 2019, 'Model-based evaluation of selenate and nitrate reduction in hydrogen-based membrane biofilm reactor', Chemical Engineering Science, vol. 195, pp. 262-270.
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© 2018 Elsevier Ltd A biofilm model was developed to describe the simultaneous NO3− and SeO42− reduction in a H2-based membrane biofilm reactor (MBfR). Model calibration and validation was conducted using the experimental data of a reported H2-based MBfR. With a good level of identifiability, the SeO42− affinity constant and the SeO32− affinity constant were estimated at 9.80 ± 0.51 g Se m−3 and 1.83 ± 0.38 g Se m−3, respectively. The model was then applied to evaluate the effects of key operating conditions on the single-stage H2-based MBfR and the role of reactor configuration through comparing two-stage to single-stage MBfR systems. The results showed that (i) high SeO42− or low NO3− concentration in the influent favored the growth of selenate-reducing bacteria (SeRB) and therefore benefited the Se removal, (ii) the influent dissolved oxygen slightly inhibited the Se removal through enhancing the aerobic microbial respiration on H2, (iii) the H2 supply should be controlled at a proper level to avoid SeRB suppression and H2 wastage, (iv) thin biofilm should be avoided to ensure a protected niche for SeRB and therefore a promising Se removal, and (v) the two-stage MBfR configuration offered relatively higher efficiency in removing Se and NO3− simultaneously under the same loading condition.
Chen, X, Ni, B & Sin, G 2019, 'Nitrous oxide production in autotrophic nitrogen removal granular sludge: A modeling study', Biotechnology and Bioengineering, vol. 116, no. 6, pp. 1280-1291.
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AbstractThe sustainability of autotrophic granular system performing partial nitritation and anaerobic ammonium oxidation (anammox) for complete nitrogen removal is impaired by the production of nitrous oxide (N2O). A systematic analysis of the pathways and affecting parameters is, therefore, required for developing N2O mitigation strategies. To this end, a mathematical model capable of describing different N2O production pathways was defined in this study by synthesizing relevant mechanisms of ammonium‐oxidizing bacteria (AOB), nitrite‐oxidizing bacteria, heterotrophic bacteria (HB), and anammox bacteria. With the model validity reliably tested and verified using two independent sets of experimental data from two different autotrophic nitrogen removal biofilm/granular systems, the defined model was applied to reveal the underlying mechanisms of N2O production in the granular structure as well as the impacts of operating conditions on N2O production. The results show that: (a) in the aerobic zone close to the granule surface where AOB contribute to N2O production through both the AOB denitrification pathway and the NH2OH pathway, the co‐occurring HB consume N2O produced by AOB but indirectly enhance the N2O production by providing NO from NO2− reduction for the NH2OH pathway, (b) the inner anoxic zone of granules with the dominance of anammox bacteria acts as a sink for NO2− diffusing from the outer aerobic zone and, therefore, reduces N2O production from the AOB denitrification pathway, (c) operating parameters including bulk DO, influent NH4+, and granu...
Chen, X, Ni, W, Chen, T, Collings, IB, Wang, X, Liu, RP & Giannakis, GB 2019, 'Multi-Timescale Online Optimization of Network Function Virtualization for Service Chaining', IEEE Transactions on Mobile Computing, vol. 18, no. 12, pp. 2899-2912.
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IEEE Network Function Virtualization (NFV) can cost-efficiently provide network services by running different virtual network functions (VNFs) at different virtual machines (VMs) in a correct order. This can result in strong couplings between the decisions of the VMs on the placement and operations of VNFs. This paper presents a new fully decentralized online approach for optimal placement and operations of VNFs. Building on a new stochastic dual gradient method, our approach decouples the real-time decisions of VMs, asymptotically minimizes the time-average cost of NFV, and stabilizes the backlogs of network services with a cost-backlog tradeoff of [1], for any 0. Our approach can be relaxed into multiple timescales to have VNFs (re)placed at a larger timescale and hence alleviate service interruptions. While proved to preserve the asymptotic optimality, the larger timescale can slow down the optimal placement of VNFs. A learn-and-adapt strategy is further designed to speed the placement up with an improved tradeoff. Numerical results show that the proposed method is able to reduce the time-average cost of NFV by 23% and reduce the queue length (or delay) by 74%, as compared to existing benchmarks.
Chen, X, Ni, W, Collings, IB, Wang, X & Xu, S 2019, 'Automated Function Placement and Online Optimization of Network Functions Virtualization', IEEE Transactions on Communications, vol. 67, no. 2, pp. 1225-1237.
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Chen, X, Sin, G & Ni, B-J 2019, 'Impact of granule size distribution on nitrous oxide production in autotrophic nitrogen removal granular reactor', Science of The Total Environment, vol. 689, pp. 700-708.
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© 2019 Elsevier B.V. This work applied an approach with reactor compartmentation and artificial diffusion to study the impact of granule size distribution on the autotrophic granular reactor performing partial nitritation and anaerobic ammonium oxidation with focus on the nitrous oxide (N2O) production. The results show that the microbial community and the associated N2O production rates in the granular structure are significantly influenced by the granule size distribution. Heterotrophic bacteria growing on microbial decay products tend to be retained and contribute to N2O consumption in relatively small granules. Ammonium-oxidizing bacteria are mainly responsible for N2O production via two pathways in granules of different sizes. Under the conditions studied, such heterogeneity in the granular structure disappears when the number of granule size classes considered reaches >4, where heterotrophic bacteria are completely outcompeted in the granules. In general, larger granules account for a higher portion of the net N2O production, while the trend regarding the volumetric contribution of each granule size class changes with a varied number of granule size classes, due to the different contributions of relevant N2O production pathways (with the heterotrophic denitrification pathway being the most decisive). Overall, with the increasing extent of granule size distribution, the nitrogen removal efficiency decreases slightly but consistently, whereas the N2O production factor increases until the number of granule size classes reaches 4 or above. Practical implications of this work include: i) granules should be controlled as well-distributed as possible in order to obtain high nitrogen removal while minimizing N2O production; ii) granule size distribution should be considered carefully and specifically when modelling N2O production/emission from the autotrophic nitrogen removal granular reactor.
Chen, X, Yang, L, Sun, J, Dai, X & Ni, B-J 2019, 'Modelling of simultaneous nitrogen and thiocyanate removal through coupling thiocyanate-based denitrification with anaerobic ammonium oxidation', Environmental Pollution, vol. 253, pp. 974-980.
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© 2019 Elsevier Ltd Thiocyanate (SCN−)-based autotrophic denitrification (AD) has recently been demonstrated as a promising technology that could be integrated with anaerobic ammonium oxidation (Anammox) to achieve simultaneous removal of nitrogen and SCN−. However, there is still a lack of a complete SCN−-based AD model, and the potential microbial competition/synergy between AD bacteria and Anammox bacteria under different operating conditions remains unknown, which significantly hinders the possible application of coupling SCN−-based AD with Anammox. To this end, a complete SCN−-based AD model was firstly developed and reliably calibrated/validated using experimental datasets. The obtained SCN−-based AD model was then integrated with the well-established Anammox model and satisfactorily verified with experimental data from a system coupling AD with Anammox. The integrated model was lastly applied to investigate the impacts of influent NH4+-N/NO2−-N ratio and SCN− concentration on the steady-state microbial composition as well as the removal of nitrogen and SCN−. The results showed that the NH4+-N/NO2−-N ratio in the presence of a certain SCN− level should be controlled at a proper value so that the maximum synergy between AD bacteria and Anammox bacteria could be achieved while their competition for NO2− would be minimized. For the simultaneous maximum removal (>95%) of nitrogen and SCN−, there existed a negative relationship between the influent SCN− concentration and the optimal NH4+-N/NO2−-N ratio needed. High-level (>95%) simultaneous removal of nitrogen and thiocyanate could be achieved through combining thiocyanate-based autotrophic denitrification and Anammox.
Chen, Y, Alanezi, AA, Zhou, J, Altaee, A & Shaheed, MH 2019, 'Optimization of module pressure retarded osmosis membrane for maximum energy extraction', Journal of Water Process Engineering, vol. 32, pp. 100935-100935.
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© 2019 Elsevier Ltd A full-scale Pressure Retarded Osmosis process (PRO) is optimized in non-ideal operating conditions using Grey Wolf Optimization (GWO) algorithms. Optimization process included the classical parameters that previous studies recommended such as operating pressure, and feed and draw fractions in the mixture solution. The study has revealed that the recommended operating pressure ΔP=Δπ/2 and the ratio of feed or draw solution to the total mixture solution, ̴ 0.5, in a laboratory scale unit or in an ideal PRO process are not valid in a non-ideal full-scale PRO module. The optimization suggested that the optimum operating pressure is less than the previously recommended value of ΔP=Δπ/2. The optimization of hydraulic pressure resulted in 4.4% increase of the energy output in the PRO process. Conversely, optimization of feed fraction in the mixture has resulted in 28%–70% higher energy yield in a single-module PRO process and 9%–54% higher energy yield in a four-modules PRO process. The net energy generated in the optimized PRO process is higher than that in the unoptimized (normal) PRO process. The findings of this study reveal the significance of incorporating machine-learning algorithms in the optimization of PRO process and identifying the preferable operating conditions.
Chen, Y, Ju, LA, Zhou, F, Liao, J, Xue, L, Su, QP, Jin, D, Yuan, Y, Lu, H, Jackson, SP & Zhu, C 2019, 'An integrin αIIbβ3 intermediate affinity state mediates biomechanical platelet aggregation', Nature Materials, vol. 18, no. 7, pp. 760-769.
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© 2019, The Author(s), under exclusive licence to Springer Nature Limited. Integrins are membrane receptors that mediate cell adhesion and mechanosensing. The structure–function relationship of integrins remains incompletely understood, despite the extensive studies carried out because of its importance to basic cell biology and translational medicine. Using a fluorescence dual biomembrane force probe, microfluidics and cone-and-plate rheometry, we applied precisely controlled mechanical stimulations to platelets and identified an intermediate state of integrin αIIbβ3 that is characterized by an ectodomain conformation, ligand affinity and bond lifetimes that are all intermediate between the well-known inactive and active states. This intermediate state is induced by ligand engagement of glycoprotein (GP) Ibα via a mechanosignalling pathway and potentiates the outside-in mechanosignalling of αIIbβ3 for further transition to the active state during integrin mechanical affinity maturation. Our work reveals distinct αIIbβ3 state transitions in response to biomechanical and biochemical stimuli, and identifies a role for the αIIbβ3 intermediate state in promoting biomechanical platelet aggregation.
Chen, Y, Su, QP & Yu, L 2019, 'Studying Autophagic Lysosome Reformation in Cells and by an In Vitro Reconstitution System', vol. 1880, pp. 163-172.
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Autophagic lysosome reformation (ALR) is the terminal step of autophagy. ALR functions to recycle lysosomal membranes and maintain lysosome homeostasis. Maintaining a functional lysosome pool is critical for generating autolysosomes, in which cellular components are degraded and turned over during autophagy. This unit describes methods to visualize ALR in cells. In addition, this unit provides detailed protocols to establish in vitro systems which can be used to reconstitute ALR as well as to reconstitute mitochondrial tubulation/network formation, another process that is driven by motor proteins.
Chen, Y-S, Mosiman, DS, Yang, L, Pham, TH, Hawkett, B, Mariñas, BJ & Cairney, JM 2019, 'Atomic-scale Observation of Hydroxyapatite Nanoparticle', Microscopy and Microanalysis, vol. 25, no. S2, pp. 2528-2529.
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Chen, Z, Duan, X, Wei, W, Wang, S & Ni, B-J 2019, 'Recent advances in transition metal-based electrocatalysts for alkaline hydrogen evolution', Journal of Materials Chemistry A, vol. 7, no. 25, pp. 14971-15005.
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Transition metal-based electrocatalysts for alkaline hydrogen evolution reaction.
Chen, Z, Li, J & You, X 2019, 'Learn to focus on objects for visual detection', Neurocomputing, vol. 348, pp. 27-39.
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© 2018 State-of-art visual detectors utilize object proposals as the reference of objects to achieve higher efficiency. However, the number of the proposal to ensure full coverage of potential objects is still large because the proposals are generated with thread and thrum, exposing proposal computation as a bottleneck. This paper presents a complementary technique that aims to work with any existing proposal generating system, amending the work-flow from “propose-assess” to “propose-adjust-assess”. Inspired by the biological processing, we propose to improve the quality of object proposals by analyzing visual contexts and gradually focusing proposals on targets. In particular, the proposed method can be employed with existing proposals generation algorithms based on both hand-crafted features and Convolutional Neural Network (CNN) features. For the former, we realize the focusing function by two learning-based transformation models, which are trained for identifying generic objects using image cues. For the latter, a Focus Proposal Net (FoPN) with cascaded layers, which can be directly injected into CNN models in an end-to-end manner, is developed as the implementation of focusing operation. Experiments on real-life image data sets demonstrate that the quality of the proposal is improved by the proposed technique. Besides, it can reduce the number of proposals to achieve high recall rate of the objects based on both hand-crafted features and CNN-features, and can boost the performance of state-of-art detectors.
Chen, Z, Liu, Y, Wei, W & Ni, B-J 2019, 'Recent advances in electrocatalysts for halogenated organic pollutant degradation', Environmental Science: Nano, vol. 6, no. 8, pp. 2332-2366.
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Advanced electrocatalysts for halogenated organic pollutant degradation.
Chenari, RJ, Fatahi, B, Ghoreishi, M & Taleb, A 2019, 'Physical and numerical modelling of the inherent variability of shear strength in soil mechanics', Geomechanics and Engineering, vol. 17, no. 1, pp. 31-45.
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In this study the spatial variability of soils is substantiated physically and numerically by using random field theory. Heterogeneous samples are fabricated by combining nine homogeneous soil clusters that are assumed to be elements of an adopted random field. Homogeneous soils are prepared by mixing different percentages of kaolin and bentonite at water contents equivalent to their respective liquid limits. Comprehensive characteristic laboratory tests were carried out before embarking on direct shear experiments to deduce the basic correlations and properties of nine homogeneous soil clusters that serve to reconstitute the heterogeneous samples. The tests consist of Atterberg limits, and Oedometric and unconfined compression tests. The undrained shear strength of nine soil clusters were measured by the unconfined compression test data, and then correlations were made between the water content and the strength and stiffness of soil samples with different consistency limits. The direct shear strength of heterogeneous samples of different stochastic properties was then evaluated by physical and numerical modelling using FISH code programming in finite difference software of FLAC 3D . The results of the experimental and stochastic numerical analyses were then compared. The deviation of numerical simulations from direct shear load-displacement profiles taken from different sources were discussed, potential sources of error was introduced and elaborated. This study was primarily to explain the mathematical and physical procedures of sample preparation in stochastic soil mechanics. It can be extended to different problems and applications in geotechnical engineering discipline to take in to account the variability of strength and deformation parameters.
Cheng, DL, Ngo, HH, Guo, WS, Chang, SW, Nguyen, DD & Kumar, SM 2019, 'Microalgae biomass from swine wastewater and its conversion to bioenergy', Bioresource Technology, vol. 275, pp. 109-122.
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© 2018 Elsevier Ltd Ever-increasing swine wastewater (SW) has become a serious environmental concern. High levels of nutrients and toxic contaminants in SW significantly impact on the ecosystem and public health. On the other hand, swine wastewater is considered as valuable water and nutrient source for microalgae cultivation. The potential for converting the nutrients from SW into valuable biomass and then generating bioenergy from it has drawn increasing attention. For this reason, this review comprehensively discussed the biomass production, SW treatment efficiencies, and bioenergy generation potentials through cultivating microalgae in SW. Microalgae species grow well in SW with large amounts of biomass being produced, despite the impact of various parameters (e.g., nutrients and toxicants levels, cultivation conditions, and bacteria in SW). Pollutants in SW can effectively be removed by harvesting microalgae from SW, and the harvested microalgae biomass elicits high potential for conversion to valuable bioenergy.
Cheng, E-J, Chou, K-P, Rajora, S, Jin, B-H, Tanveer, M, Lin, C-T, Young, K-Y, Lin, W-C & Prasad, M 2019, 'Deep Sparse Representation Classifier for facial recognition and detection system', Pattern Recognition Letters, vol. 125, pp. 71-77.
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© 2019 Elsevier B.V. This paper proposes a two-layer Convolutional Neural Network (CNN) to learn the high-level features which utilizes to the face identification via sparse representation. Feature extraction plays a vital role in real-world pattern recognition and classification tasks. The details description of the given input face image, significantly improve the performance of the facial recognition system. Sparse Representation Classifier (SRC) is a popular face classifier that sparsely represents the face image by a subset of training data, which is known as insensitive to the choice of feature space. The proposed method shows the performance improvement of SRC via a precisely selected feature exactor. The experimental results show that the proposed method outperforms other methods on given datasets.
Cheng, EJ, Young, K-Y & Lin, C-T 2019, 'Temporal EEG Imaging for Drowsy Driving Prediction', Applied Sciences, vol. 9, no. 23, pp. 5078-5078.
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As a major cause of vehicle accidents, the prevention of drowsy driving has received increasing public attention. Precisely identifying the drowsy state of drivers is difficult since it is an ambiguous event that does not occur at a single point in time. In this paper, we use an electroencephalography (EEG) image-based method to estimate the drowsiness state of drivers. The driver’s EEG measurement is transformed into an RGB image that contains the spatial knowledge of the EEG. Moreover, for considering the temporal behavior of the data, we generate these images using the EEG data over a sequence of time points. The generated EEG images are passed into a convolutional neural network (CNN) to perform the prediction task. In the experiment, the proposed method is compared with an EEG image generated from a single data time point, and the results indicate that the approach of combining EEG images in multiple time points is able to improve the performance for drowsiness prediction.
Cheng, H, Zhang, J, Wu, Q & An, P 2019, 'A Computational Model for Stereoscopic Visual Saliency Prediction', IEEE Transactions on Multimedia, vol. 21, no. 3, pp. 678-689.
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© 2018 IEEE. Depth information plays an important role in human vision as it provides additional cues that distinguish objects from their backgrounds. This paper explores depth information for analyzing stereoscopic saliency and presents a computational model that predicts stereoscopic visual saliency based on three aspects of human vision: 1) the pop-out effect; 2) comfort zones; and 3) background effects. Through an analysis of these three phenomena, we find that most of the stereoscopic saliency region can be explained. Our model comprises three modules, each describing one aspect of saliency distribution, and a control function that can be used to adjust the three models independently. The relationship between the three models is not mutually exclusive. One, two, or three phenomena may appear in one image. Therefore, to accurately determine which phenomena the image conforms to, we have devised a selection strategy that chooses the appropriate combination of models based on the content of the image. Our approach is implemented within a framework based on the multifeature analysis. The framework considers surrounding regions, color/depth contrast, and points of interest. The selection strategy can improve the performance of the framework. A series of experiments on two recent eye-tracking datasets shows that our proposed method outperforms several state-of-the-art saliency models.
Cheng, Q, Shi, Z, Nguyen, DN & Dutkiewicz, E 2019, 'Sensing OFDM Signal: A Deep Learning Approach', IEEE Transactions on Communications, vol. 67, no. 11, pp. 7785-7798.
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© 1972-2012 IEEE. Spectrum sensing plays a critical role in dynamic spectrum sharing, a promising technology to address the radio spectrum shortage. In particular, sensing of orthogonal frequency division multiplexing (OFDM) signals, a widely accepted multi-carrier transmission paradigm, has received paramount interest. Despite various efforts, noise uncertainty, timing delay and carrier frequency offset (CFO) still remain as challenging problems, significantly degrading the sensing performance. In this work, we develop two novel OFDM sensing frameworks utilizing the properties of deep learning networks. Specifically, we first propose a stacked autoencoder based spectrum sensing method (SAE-SS), in which a stacked autoencoder network is designed to extract the hidden features of OFDM signals for classifying the user's activities. Compared to the conventional OFDM sensing methods, SAE-SS is significantly superior in the robustness to noise uncertainty, timing delay, and CFO. Moreover, SAE-SS requires neither any prior information of signals (e.g., signal structure, pilot tones, cyclic prefix) nor explicit feature extraction algorithms which however are essential for the conventional OFDM sensing methods. To further improve the sensing accuracy of SAE-SS, especially under low SNR conditions, we propose a stacked autoencoder based spectrum sensing method using time-frequency domain signals (SAE-TF). SAE-TF achieves higher sensing accuracy than SAE-SS using the features extracted from both time and frequency domains, at the cost of higher computational complexity. Through extensive simulation results, both SAE-SS and SAE-TF are shown to achieve notably higher sensing accuracy than that of state of the art approaches.
Cheng, R, Omidvar, MN, Gandomi, AH, Sendhoff, B, Menzel, S & Yao, X 2019, 'Solving Incremental Optimization Problems via Cooperative Coevolution', IEEE Transactions on Evolutionary Computation, vol. 23, no. 5, pp. 762-775.
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© 1997-2012 IEEE. Engineering designs can involve multiple stages, where at each stage, the design models are incrementally modified and optimized. In contrast to traditional dynamic optimization problems, where the changes are caused by some objective factors, the changes in such incremental optimization problems (IOPs) are usually caused by the modifications made by the decision makers during the design process. While existing work in the literature is mainly focused on traditional dynamic optimization, little research has been dedicated to solving such IOPs. In this paper, we study how to adopt cooperative coevolution to efficiently solve a specific type of IOPs, namely, those with increasing decision variables. First, we present a benchmark function generator on the basis of some basic formulations of IOPs with increasing decision variables and exploitable modular structure. Then, we propose a contribution-based cooperative coevolutionary framework coupled with an incremental grouping method for dealing with them. On one hand, the benchmark function generator is capable of generating various benchmark functions with various characteristics. On the other hand, the proposed framework is promising in solving such problems in terms of both optimization accuracy and computational efficiency. In addition, the proposed method is further assessed using a real-world application, i.e., the design optimization of a stepped cantilever beam.
Cheng, S, Dong, H, Yu, L, Zhang, D & Ji, J 2019, 'Consensus of Second-order Multi-agent Systems with Directed Networks Using Relative Position Measurements Only', International Journal of Control, Automation and Systems, vol. 17, no. 1, pp. 85-93.
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© 2019, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature. This brief paper studies the consensus problem of second-order multi-agent systems when the agents’ velocity measurements are unavailable. Firstly, two simple consensus protocols which do not need velocity measurements of the agents are derived to guarantee that the multi-agent systems achieve consensus in directed networks. Secondly, a key constant which is determined by the complex eigenvalue of the nonsymmetric Laplacian matrix and an explicit expression of the consensus state are respectively developed based on matrix theory. The obtained results show that all the agents can reach consensus if the feedback parameter is bigger than the key constant. Thirdly, the theoretical analysis shows that the followers can track the position and velocity of the leader provided that the leader has a directed path to all other followers and the feedback parameter is bigger enough. Finally, numerical simulations are given to illustrate the effectiveness of the proposed protocols.
Cheng, T, Al‐Soeidat, M, Lu, DD & Agelidis, VG 2019, 'Experimental study of PV strings affected by cracks', The Journal of Engineering, vol. 2019, no. 18, pp. 5124-5128.
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Crack is one critical factor that degrades the performance of photovoltaic (PV) panels. To gain a better understanding of the impacts of cracks appeared on PVs and also to mitigate it, its failure mechanism, detrimental effects, criticality, and potential risks on independent PV panels are firstly reviewed in this study. An experimental study which investigates the degree of series connected and parallel connected PV strings which are affected by cracked cells are presented. A comparison of impacts of the partially shaded PV panel string and cracked cells happened to the PV panel string is given to evaluate their criticality levels. The experimental results show that the series connected PV panel string is strongly affected once the cell is seriously cracked, as the current generation capability is clamped. Partial shading, however, shows better performance. In addition, though the overall power the parallel connected PV string is reduced, it is less affected by the cracked cells compared to the series connected one. Lastly, a bypass diode is added to a series connected PV panel string with cracked cells, and the experimental results show that it can be an effective way to minimise the negative impacts of cracks.
Cheng, X, Jiang, Z, Wei, D, Wu, H & Jiang, L 2019, 'Adhesion, friction and wear analysis of a chromium oxide scale on a ferritic stainless steel', Wear, vol. 426-427, pp. 1212-1221.
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Cheng, Z, Chang, X, Zhu, L, Kanjirathinkal, RC & Kankanhalli, M 2019, 'MMALFM', ACM Transactions on Information Systems, vol. 37, no. 2, pp. 1-28.
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Personalized rating prediction is an important research problem in recommender systems. Although the latent factor model (e.g., matrix factorization) achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for individual user-item pairs. In this article, we exploit textual reviews and item images together with ratings to tackle these limitations. Specifically, we first apply a proposed multi-modal aspect-aware topic model (MATM) on text reviews and item images to model users’ preferences and items’ features from different aspects , and also estimate the aspect importance of a user toward an item. Then, the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user’s and item’s latent factors based on ratings. In particular, ALFM introduces a weight matrix to associate those latent factors with the same set of aspects in MATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, every aspect rating is weighted by its aspect importance, which is dependent on the targeted user’s preferences and the targeted item’s features. Therefore, it is expected that the proposed method can model a user’s preferences on an item more accurately for each user-item pair. Comprehensive experimental studies have been conducted on the Yelp 2017 Challenge dataset and Amazon product datasets. Results show that (1) our method achieves significant improvement compared to strong baseline methods, especially for users with only few ratings; (2) item v...
Cheng, Z, Xiong, G, Liu, Y, Zhang, T, Tian, J & Guo, YJ 2019, 'High‐efficiency Doherty power amplifier with wide OPBO range for base station systems', IET Microwaves, Antennas & Propagation, vol. 13, no. 7, pp. 926-929.
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A high‐efficiency, S‐band Doherty power amplifier (DPA) with wide output power back‐off (OPBO) range is presented. A novel parasitic capacitance compensation approach is applied at the output of Cree's GaN high‐electron‐mobility transistor to achieve high saturation efficiency in a wide OPBO range. Specifically, a parallel shorting microstrip line between the transistor output and its match network is adopted to realise parasitic capacitance compensation. The measurement results indicate good Doherty behaviour with 10 dB back‐off efficiency of 40.6–44.2% and saturation efficiency of 70.2–73.3% over 2.9–3.3 GHz. When stimulated by a 20‐MHz LTE signal with 7.5 dB PAPR, the proposed Doherty amplifier power, combined with digital pre‐distortion, achieved adjacent channel leakage ratios below −47.2 dBc. The DPA demonstrate superior performance in OPBO range and efficiency, which makes it an ideal component for base station communication systems.
Cheung, NW, Campbell, LV, Fulcher, GR, McElduff, P, Depczynski, B, Acharya, S, Carter, J, Champion, B, Chen, R, Chipps, D, Flack, J, Kinsella, J, Layton, M, McLean, M, Moses, RG, Park, K, Poynten, AM, Pollock, C, Scadden, D, Tonks, KT, Webber, M, White, C, Wong, V & Middleton, S 2019, 'Routine glucose assessment in the emergency department for detecting unrecognised diabetes: a cluster randomised trial', Medical Journal of Australia, vol. 211, no. 10, pp. 454-459.
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Chih, Y-K, Chen, W-H, Ong, HC & Show, PL 2019, 'Product Characteristics of Torrefied Wood Sawdust in Normal and Vacuum Environments', Energies, vol. 12, no. 20, pp. 3844-3844.
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To investigate the efficacy of torrefaction in a vacuum environment, wood sawdust was torrefied at various temperatures (200–300 °C) in different atmospheres (nitrogen and vacuum) with different residence times (30 and 60 min). It was found that the amount of biochar reduced at the same rate—regardless of atmosphere type—throughout the torrefaction process. In terms of energy density, the vacuum system produced biochar with better higher heating value (HHV, MJ/kg) than the nitrogen system below 250 °C. This was the case because the moisture and the high volatility compounds such as aldehydes diffused more easily in a vacuum. Over 250 °C, however, a greater amount of low volatility compounds evaded from the vacuum system, resulting in lower higher heating value in the biochar. Despite the mixed results with the solid products, the vacuum system increased the higher heating value of its liquid products more significantly than did the nitrogen system regardless of torrefaction temperature. It was found that 23% of the total energy output came from the liquid products in the vacuum system; the corresponding ratio was 19% in the nitrogen system. With liquid products contributing to a larger share of the total energy output, the vacuum system outperformed the nitrogen system in terms of energy density.
Chiu, SK, Orive, SL, Moon, MJ, Saw, J, Ellis, S, Kile, BT, Huang, Y, Chacon, D, Pimanda, JE, Beck, D, Hamilton, JR, Tremblay, CS & Curtis, DJ 2019, 'Shared roles for Scl and Lyl1 in murine platelet production and function', Blood, vol. 134, no. 10, pp. 826-835.
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Abstract The stem cell leukemia (Scl or Tal1) protein forms part of a multimeric transcription factor complex required for normal megakaryopoiesis. However, unlike other members of this complex such as Gata1, Fli1, and Runx1, mutations of Scl have not been observed as a cause of inherited thrombocytopenia. We postulated that functional redundancy with its closely related family member, lymphoblastic leukemia 1 (Lyl1) might explain this observation. To determine whether Lyl1 can substitute for Scl in megakaryopoiesis, we examined the platelet phenotype of mice lacking 1 or both factors in megakaryocytes. Conditional Scl knockout (KO) mice crossed with transgenic mice expressing Cre recombinase under the control of the mouse platelet factor 4 (Pf4) promoter generated megakaryocytes with markedly reduced but not absent Scl. These Pf4Sclc-KO mice had mild thrombocytopenia and subtle defects in platelet aggregation. However, Pf4Sclc-KO mice generated on an Lyl1-null background (double knockout [DKO] mice) had severe macrothrombocytopenia, abnormal megakaryocyte morphology, defective pro-platelet formation, and markedly impaired platelet aggregation. DKO megakaryocytes, but not single-knockout megakaryocytes, had reduced expression of Gata1, Fli1, Nfe2, and many other genes that cause inherited thrombocytopenia. These gene expression changes were significantly associated with shared Scl and Lyl1 E-box binding sites that were also enriched for Gata1, Ets, and Runx1 motifs. Thus, Scl and Lyl1 share functional roles in platelet production by regulating expression of partner proteins including Gata1. We propose that this functional redundancy provides one explanation for the absence of Scl and Lyl1 mutations in inherited thrombocytopenia.
Choi, J, Dorji, P, Shon, HK & Hong, S 2019, 'Applications of capacitive deionization: Desalination, softening, selective removal, and energy efficiency', Desalination, vol. 449, pp. 118-130.
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© 2018 Elsevier B.V. Capacitive deionization (CDI) has attracted a great attention as a promising desalination technology, and studies on CDI have increased significantly in the last ten years. However, there have been no guidelines for developing strategies involving CDI technology for specific applications. Therefore, our work presents a critical review of the recent advances in CDI to meet the technical requirements of various applicable areas, with an emphasis on hybrid systems. This paper first summarizes the major developments made on novel electrode materials for CDI for brackish water desalination. Then, CDI and reverse osmosis (RO) integrated systems are critically reviewed for both ultrapure water production and wastewater treatment. Additionally, the applicability of CDI on various industrial processes is discussed, covering two distinct topics: (1) water softening and (2) selective removal of valuable heavy metals and nutrients (nitrate/phosphate). Lastly, recent improvements on the energy efficiency of CDI processes are delineated, specifically focusing on energy recovery and hybridization with energy producing technology, such as reverse electrodialysis (RED) and microbial fuel cells (MFC). This review paper is expected to share the practical experience of CDI applications as well as to provide guidelines for electrode material development for each specific application.
Choi, Y, Naidu, G, Lee, S & Vigneswaran, S 2019, 'Effect of inorganic and organic compounds on the performance of fractional-submerged membrane distillation-crystallizer', Journal of Membrane Science, vol. 582, pp. 9-19.
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© 2019 Elsevier B.V. A novel approach - fractional-submerged membrane distillation crystallizer (F-SMDC) was evaluated for treating brine. F-SMDC is based on creating concentration gradient (CG) and temperature gradient (TG) in a reactor containing submerged hollow-fiber membrane. This enables water and salt recovery to occur simultaneously in a single reactor. The influence of inorganic and organic compounds present in brine solutions on the development and stability of CG/TG in F-SMDC was evaluated in detail in this study. The results of the study showed that properties of inorganic compounds - molecular weight and electronegativity played a significant role in influencing CG/TG in F-SMDC. A high CG ratio (between 1.51 and 1.83 after crystallization) was observed when using feed solutions with inorganic compounds such as KCl, MgSO4, and Na2SO4. However, only low CG ratio (between 0.94 and 1.46) was achieved in the case of feed solutions containing lower molecular weight compounds, NH4Cl and NaCl. The high CG ratio with KCl resulted in the occurrence of salt crystallization at a faster rate (from VCF 2.4 onwards) compared to the predicted theoretical salt saturation point of VCF 3.0. On the other hands, Na2SO4 showed lower flux decline (12.56% flux decline) compared to MgSO4 (55.93% flux decline) This was attributed to lower cation electronegativity of Na+. The presence of CG in F-SMDC by concentrated inorganic compounds also enhanced organic compounds to gravitate downwards to the bottom of the reactor, potentially mitigating organic deposition on the membrane.
Choi, Y, Naidu, G, Nghiem, LD, Lee, S & Vigneswaran, S 2019, 'Membrane distillation crystallization for brine mining and zero liquid discharge: opportunities, challenges, and recent progress', Environmental Science: Water Research & Technology, vol. 5, no. 7, pp. 1202-1221.
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This review outlines all the work done on the membrane distillation crystallization process.
Choi, Y, Ryu, S, Naidu, G, Lee, S & Vigneswaran, S 2019, 'Integrated submerged membrane distillation-adsorption system for rubidium recovery', Separation and Purification Technology, vol. 218, pp. 146-155.
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© 2019 Elsevier B.V. Seawater reverse osmosis (SWRO) brine management is essential for desalination. Improving brine recovery rate with resource recovery can enhance the overall desalination process. In this study, an integrated submerged membrane distillation (S-MD) with adsorption (granular potassium copper hexacyanoferrate (KCuFC)) was evaluated for improving water recovery from brine while extracting valuable Rb. The thermal S-MD process (55 °C) with a continuous supply of Rb-rich SWRO brine enabled Rb to be concentrated (99% rejection) while producing fresh water. Concentrated Rb in thermal condition enhanced Rb extraction by granular KCuFC. An optimum dose (0.24 g/L) KCuFC was identified based on 98% Rb mass adsorption (9.78 mg as Rb). The integrated submerged MD-adsorption system was able to achieve more than 85% water recovery and Rb extraction in continuous feed supply (in two cycles). Ca in SWRO brine resulted in CaSO4 deposition onto the membrane and surface of KCuFC, reducing recovery rate and Rb adsorption. MD water recovery significantly improved upon Ca removal while achieving a total of 6.65 mg of Rb extraction. In comparing the performance of different KCuFC forms (granular, particle and powder), the particle form of KCuFC exhibited 10–47% higher capacity in terms of total adsorbed Rb mass and adsorption rate.
Chong, CT, Mong, GR, Ng, J-H, Chong, WWF, Ani, FN, Lam, SS & Ong, HC 2019, 'Pyrolysis characteristics and kinetic studies of horse manure using thermogravimetric analysis', Energy Conversion and Management, vol. 180, pp. 1260-1267.
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Chong, W-T, Muzammil, WK, Ong, H-C, Sopian, K, Gwani, M, Fazlizan, A & Poh, S-C 2019, 'Performance analysis of the deflector integrated cross axis wind turbine', Renewable Energy, vol. 138, pp. 675-690.
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Chu Van, T, Zare, A, Jafari, M, Bodisco, TA, Surawski, N, Verma, P, Suara, K, Ristovski, Z, Rainey, T, Stevanovic, S & Brown, RJ 2019, 'Effect of cold start on engine performance and emissions from diesel engines using IMO-Compliant distillate fuels', Environmental Pollution, vol. 255, no. Pt 2, pp. 113260-113260.
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© 2019 Elsevier Ltd Emissions from ships at berth are small compared to the total ship emissions; however, they are one of the main contributors to pollutants in the air of densely-populated areas, consequently heavily affecting public health. This is due to auxiliary marine engines being used to generate electric power and steam for heating and providing services. The present study has been conducted on an engine representative of a marine auxiliary, which was a heavy duty, six-cylinder, turbocharged and after-cooled engine with a high pressure common rail injection system. Engine performance and emission characterisations during cold start are the focus of this paper, since cold start is significantly influential. Three tested fuels were used, including the reference diesel and two IMO (International Maritime Organization) compliant spiked fuels. The research engine was operated at a constant speed and 25% load condition after 12 h cooled soak. Results show that during cold start, significant heat generated from combustion is used to heat the engine block, coolant and lubricant. During the first minute, compared to the second minute, emissions of particle number (PN), carbon monoxide (CO), particulate matter (PM), and nitrogen oxides (NOx) were approximately 10, 4, 2 and 1.5 times higher, respectively. The engine control unit (ECU) plays a vital role in reducing engine emissions by changing the engine injection strategy based on the engine coolant temperature. IMO-compliant fuels, which were higher viscosity fuels associated with high sulphur content, resulted in an engine emission increase during cold start. It should be taken into account that auxiliary marine diesel engines, working at partial load conditions during cold start, contribute considerably to emissions in coastal areas. It demonstrates a need to implement practical measures, such as engine pre-heating, to obtain both environmental and public health advantages in coastal areas.
Chu, L, Shi, J & Braun, R 2019, 'The equivalent Young's modulus prediction for vacancy defected graphene under shear stress', Physica E: Low-dimensional Systems and Nanostructures, vol. 110, pp. 115-122.
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© 2019 Elsevier B.V. The uncertain and unavoidable vacancy defects in graphene have the inevitable influence in the extraordinary intrinsic in-plane strength. In this paper, the equivalent Young's modulus is derived from the strain energy as an important factor to evaluate the stiffness of the entire graphene based on the mechanical molecular theory. The location of vacancy defects in graphene is discussed in the regular deterministic and uncertain patterns. In terms of the boundary condition, shear stress is loaded in armchair and zigzag edges, respectively. The results show that the center concentrated vacancy defects evidently deteriorate the elastic stiffness under shear stress. The influences of periodic and regular vacancy defects are sensitive to the boundary condition. By the Monte Carlo based finite element method, vacancy defects are dispersed randomly and propagated. The results of the equivalent Young's modulus are compared with the original values in pristine graphene. The interval and mean values of Young's modulus, total strain and energy density are also provided and discussed. Compared with the results of graphene with vacancy defects under uniaxial tension, the enhancement effects of vacancy defects are less evident in the graphene under shear stress.
Chu, VW, Wong, RK, Chen, F, Ho, I & Lee, J 2019, 'Enhancing portfolio return based on sentiment-of-topic', Data & Knowledge Engineering, vol. 123, pp. 101601-101601.
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© 2017 While time-series analysis is commonly used in financial forecasting, a key source of market-sentiments is often omitted. Financial news is known to be making persuasive impact on the markets. Without considering this additional source of signals, only sub-optimal predictions can be made. This paper proposes a notion of sentiment-of-topic (SoT) to address the problem. It is achieved by considering sentiment-linked topics, which are retrieved from time-series with heterogeneous dimensions (i.e., numbers and texts). Using this approach, we successfully improve the prediction accuracy of a proprietary trade recommendation platform. Different from traditional sentiment analysis and unsupervised topic modeling methods, topics associated with different sentiment levels are used to quantify market conditions. In particular, sentiment levels are learned from historical market performances and commentaries instead of using subjective interpretations of human expressions. By capturing the domain knowledge of respective industries and markets, an impressive double-digit improvement in portfolio return is obtained as shown in our experiments.
Clarke, SL, Hubble, TCT, Miao, G, Airey, DW & Ward, SN 2019, 'Eastern Australia’s submarine landslides: implications for tsunami hazard between Jervis Bay and Fraser Island', Landslides, vol. 16, no. 11, pp. 2059-2085.
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Condina, MR, Dilmetz, BA, Razavi Bazaz, S, Meneses, J, Ebrahimi Warkiani, M & Hoffmann, P 2019, 'Rapid separation and identification of beer spoilage bacteria by inertial microfluidics and MALDI-TOF mass spectrometry', Lab on a Chip, vol. 19, no. 11, pp. 1961-1970.
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Microfluidics and MALDI-TOF MS is a rapid, high-throughput, and accurate method for the identification of beer spoilage bacteria.
Crowther, CA, Ashwood, P, Andersen, CC, Middleton, PF, Tran, T, Doyle, LW, Robinson, JS, Harding, JE, Crowther, C, Ashwood, P, Andersen, C, Middleton, P, Tran, T, Ball, V, Holst, C, Robinson, K, Zhang, S, Robinson, J, Khong, Y, McPhee, A, Groom, K, Alsweiler, J, Eaglen, D, Harding, J, Hauch, H, Vallely, A, Angus, S, Chenia, F, Drew, A, Gavranich, J, Green, A, Jack, S, Mahomed, K, Sebastian, R, Turner, L, Baldwin, M, Dennis, A, Fisher, E, Gee, K, Gee, M, Strong, D, Boord, D, Edge, N, Marsh, M, Staehr, C, Chaplin, J, Gardener, G, Gray, P, Hurrion, E, Jardine, L, Kan, J, Lynn, L, Poulsen, L, Tremellen, A, Codner, T, Cubis, W, Downward, S, Dunn, C, Furey, J, Hansen, D, Lampropoulos, B, Masson, E, Peek, M, Sellar, S, Butterley, K, Chadwick, M, Davis, C, DePaoli, T, Green, L, Matzolic, T, Woodhead, G, Biggs, V, Henry, A, Lainchbury, A, Nesbitt-Hawes, E, Oei, JL, Rodrigues, C, Shand, A, Sutton, L, Welsh, A, Bowen, J, Hayes-Cameron, L, Howard, G, Jacobs, C, Milligan, J, Morris, J, Rickard, K, Sedgley, J, White-Matthews, K, Blandthorn, J, Brownfoot, F, Burnett, A, Callanan, K, Davis, N, Deluca, C, Doyle, L, Duff, J, Howard, K, Hutchinson, E, Kelly, E, Kornman, L, Kuschel, C, Maxwell, D, McDonald, M, Poth, M, Co, J, Davis, G, Fonsesca, B, Khouri, J, Roberts, L, Rowe, C, Boniface, C, Boynton, C, Davies, C, Dickinson, C, Edmonds, L, Ireland, S, Koh, G, Kumar, P, Lawrence, A, Lock, R, Watson, D, Bahtia, V, Cash, S, Gagliardi, D, Gooding, M, Gowling, K, Grivell, R, Haslam, R, Headley, B, Johnson, M, Kobayashi, N, Kochar, A, Nikpoor, P, Simmonds, L, Siwicki, K, Stark, M & Trenowden, S 2019, 'Maternal intramuscular dexamethasone versus betamethasone before preterm birth (ASTEROID): a multicentre, double-blind, randomised controlled trial', The Lancet Child & Adolescent Health, vol. 3, no. 11, pp. 769-780.
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BACKGROUND: Antenatal corticosteroids given to women before preterm birth improve infant survival and health. However, whether dexamethasone or betamethasone have better maternal, neonatal, and childhood health outcomes remains unclear. We therefore aimed to assess whether administration of antenatal dexamethasone to women at risk of preterm birth reduced the risk of death or neurosensory disability in their children at age 2 years compared with betamethasone. We also aimed to assess whether dexamethasone reduced neonatal morbidity, had benefits for the mother, or affected childhood body size, blood pressure, behaviour, or general health compared with betamethasone. METHODS: In this multicentre, double-blind, randomised controlled trial, we recruited pregnant women from 14 maternity hospitals in Australia and New Zealand that could provide care to preterm babies. Women were eligible for study inclusion if they were at risk of preterm birth before 34 weeks of gestation, had a singleton or twin pregnancy, and had no contraindications to antenatal corticosteroids. We randomly assigned women (1:1) to receive two intramuscular injections of either 12 mg dexamethasone (dexamethasone sodium phosphate) or 11·4 mg betamethasone (Celestone Chronodose), 24 h apart. The randomisation schedule used balanced, variable blocks that were stratified by hospital, gestational age, and number of fetuses (singleton or twins). We masked all participants, staff, and assessors to treatment groups. Analyses were by intention to treat. The primary outcome was death or neurosensory disability at age 2 years (corrected for prematurity). This study is registered with ANZCTR, ACTRN12608000631303. FINDINGS: Between Jan 28, 2009, and Feb 1, 2013, we randomly assigned 1346 (78%) women who were pregnant with 1509 fetuses to groups: 679 (50%) women were assigned to receive dexamethasone and 667 (50%) women were assigned to receive betamethasone. 27 (4%) fetuses, infants, or children in th...
Cui, L, Qu, Y, Nosouhi, MR, Yu, S, Niu, J-W & Xie, G 2019, 'Improving Data Utility Through Game Theory in Personalized Differential Privacy', Journal of Computer Science and Technology, vol. 34, no. 2, pp. 272-286.
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© 2019, Springer Science+Business Media, LLC & Science Press, China. Due to dramatically increasing information published in social networks, privacy issues have given rise to public concerns. Although the presence of differential privacy provides privacy protection with theoretical foundations, the trade-off between privacy and data utility still demands further improvement. However, most existing studies do not consider the quantitative impact of the adversary when measuring data utility. In this paper, we firstly propose a personalized differential privacy method based on social distance. Then, we analyze the maximum data utility when users and adversaries are blind to the strategy sets of each other. We formalize all the payoff functions in the differential privacy sense, which is followed by the establishment of a static Bayesian game. The trade-off is calculated by deriving the Bayesian Nash equilibrium with a modified reinforcement learning algorithm. The proposed method achieves fast convergence by reducing the cardinality from n to 2. In addition, the in-place trade-off can maximize the user’s data utility if the action sets of the user and the adversary are public while the strategy sets are unrevealed. Our extensive experiments on the real-world dataset prove the proposed model is effective and feasible.
Cui, P-F, Zhang, JA, Lu, W-J, Guo, YJ & Zhu, H 2019, 'Statistical Sparse Channel Modeling for Measured and Simulated Wireless Temporal Channels', IEEE Transactions on Wireless Communications, vol. 18, no. 12, pp. 5868-5881.
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© 2002-2012 IEEE. Time-domain wireless channels are generally modeled by Tapped Delay Line (TDL) model and its variants. These models are not effective for channel representation and estimation when the number of multipath taps is large. Compressive sensing (CS) provides a powerful tool for sparse channel modeling and estimation. Most of the research has been focusing on sparse channel estimation, while sparse channel modeling (SCM) is rarely considered for centimetre-wave channels. In this paper, we investigate statistical sparse channel modeling, using both measured and simulated channels over a frequency range of 6 to 8.5 GHz. We first introduce the triple equilibrium principle to explore the trade-off between sparsity, modeling accuracy, and algorithm complexity in SCM, and provide a methodology for characterizing the sparsity of time-domain channels using single-measurement-vector compressive sensing algorithms. Using mainly the selected wavelet dictionary and various CS reconstruction (aka recovery) algorithms, we then present comprehensive statistical sparse channel models, including channel sparsity, magnitude decaying profile, sparse coefficient distribution and atomic index distribution. Connections between the parameters of conventional TDL and sparse channel models are mathematically established. We also propose three methods for generating simulated channels from the developed sparse channel models, which validates their effectiveness.
Cui, Q, Gong, Z, Ni, W, Hou, Y, Chen, X, Tao, X & Zhang, P 2019, 'Stochastic Online Learning for Mobile Edge Computing: Learning from Changes', IEEE Communications Magazine, vol. 57, no. 3, pp. 63-69.
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© 1979-2012 IEEE. ML has been increasingly adopted in wireless communications, with popular techniques, such as supervised, unsupervised, and reinforcement learning, applied to traffic classification, channel encoding/ decoding, and cognitive radio. This article discusses a different class of ML technique, stochastic online learning, and its promising applications to MEC. Based on stochastic gradient descent, stochastic online learning learns from the changes of dynamic systems (i.e., the gradient of the Lagrange multipliers) rather than training data, decouples tasks between time slots and edge devices, and asymptotically minimizes the time-averaged operational cost of MEC in a fully distributed fashion with the increase of the learning time. By taking the widely adopted big data analytic framework MapReduce as an example, numerical studies show that the network throughput can increase by eight times through adopting stochastic online learning as compared to existing offline implementations.
Cui, Q, Wang, Y, Chen, K-C, Ni, W, Lin, I-C, Tao, X & Zhang, P 2019, 'Big Data Analytics and Network Calculus Enabling Intelligent Management of Autonomous Vehicles in a Smart City', IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2021-2034.
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Cui, Y, Poon, J, Miro, JV, Yamazaki, K, Sugimoto, K & Matsubara, T 2019, 'Environment-adaptive interaction primitives through visual context for human–robot motor skill learning', Autonomous Robots, vol. 43, no. 5, pp. 1225-1240.
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© 2018, The Author(s). In situations where robots need to closely co-operate with human partners, consideration of the task combined with partner observation maintains robustness when partner behavior is erratic or ambiguous. This paper documents our approach to capture human–robot interactive skills by combining their demonstrative data with additional environmental parameters automatically derived from observation of task context without the need for heuristic assignment, as an extension to overcome shortcomings of the interaction primitives framework. These parameters reduce the partner observation period required before suitable robot motion can commence, while also enabling success in cases where partner observation alone was inadequate for planning actions suited to the task. Validation in a collaborative object covering exercise with a humanoid robot demonstrate the robustness of our environment-adaptive interaction primitives, when augmented with parameters directly drawn from visual data of the task scene.
Cutler, RL, Torres-Robles, A, Wiecek, E, Drake, B, Van der Linden, N, Benrimoj, SIC & Garcia-Cardenas, V 2019, '<p>Pharmacist-led medication non-adherence intervention: reducing the economic burden placed on the Australian health care system</p>', Patient Preference and Adherence, vol. Volume 13, pp. 853-862.
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© 2019 Cutler et al. Background: Scarcity of prospective medication non-adherence cost measurements for the Australian population with no directly measured estimates makes determining the burden medication non-adherence places on the Australian health care system difficult. This study aims to indirectly estimate the national cost of medication non-adherence in Australia comparing the cost prior to and following a community pharmacy-led intervention. Methods: Retrospective observational study. A de-identified database of dispensing data from 20,335 patients (n=11,257 on rosuvastatin, n=6,797 on irbesartan and n=2,281 on desvenlafaxine) was analyzed and average adherence rate determined through calculation of PDC. Included patients received a pharmacist-led medication adherence intervention and had twelve months dispensing records; six months before and six months after the intervention. The national cost estimate of medication non-adherence in hypertension, dyslipidemia and depression pre-and post-intervention was determined through utilization of disease prevalence and comorbidity, non-adherence rates and per patient disease-specific adherence-related costs. Results: The total national cost of medication non-adherence across three prevalent conditions, hypertension, dyslipidemia and depression was $10.4 billion equating to $517 per adult. Following enrollment in the pharmacist-led intervention medication non-adherence costs per adult decreased $95 saving the Australian health care system and patients $1.9 billion annually. Conclusion: In the absence of a directly measured national cost of medication non-adherence, this estimate demonstrates that pharmacists are ideally placed to improve patient adherence and reduce financial burden placed on the health care system due to non-adherence. Funding of medication adherence programs should be considered by policy and decision makers to ease the current burden and improve patient health outcomes moving forward.
Dackermann, U, Smith, WA, Alamdari, MM, Li, J & Randall, RB 2019, 'Cepstrum-based damage identification in structures with progressive damage', Structural Health Monitoring, vol. 18, no. 1, pp. 87-102.
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This article aims at developing a new framework to identify and assess progressive structural damage. The method relies solely on output measurements to establish the frequency response functions of a structure using cepstrum-based operational modal analysis. Two different damage indicative features are constructed using the established frequency response functions. The first damage feature takes the residual frequency response function, defined as the difference in frequency response function between evolving states of the structure, and then reduces its dimension using principle component analysis; while in the second damage indicator, a new feature based on the area under the residual frequency response function curve is proposed. The rationale behind this feature lies in the fact that damage often affects a number of modes of the system, that is, it affects the frequency response function over a wide range of frequencies; as a result, this quantity has higher sensitivity to any structural change by combining all contributions from different frequencies. The obtained feature vectors serve as inputs to a novel multi-stage neural network ensemble designed to assess the severity of damage in the structure. The proposed method is validated using extensive experimental data from a laboratory four-girder timber bridge structure subjected to gradually progressing damage at various locations with different severities. In total, 13 different states of the structure are considered, and it is demonstrated that the new damage feature outperforms the conventional principle component analysis–based feature. The contribution of the work is threefold: first, the application of cepstrum-based operational modal analysis in structural health monitoring is further validated, which has potential for real-life applications where only limited knowledge of the input is available; second, a new damage feature is proposed and its superior performance is demonstrated;...
Dadzie, J, Runeson, G & Ding, G 2019, 'Assessing determinants of sustainable upgrade of existing buildings', Journal of Engineering, Design and Technology, vol. 18, no. 1, pp. 270-292.
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PurposeEstimates show that close to 90% of the buildings we will need in 2050 are already built and occupied. The increase in the existing building stock has affected energy consumption thereby negatively impacting the environment. The purpose of this paper is to assess determinants of sustainable upgrade of existing buildings through the adoption and application of sustainable technologies. The study also ranks sustainable technologies adopted by the professionals who participated in the survey with an in-built case study.Design/methodology/approachAs part of the overall methodology, a detailed literature review on the nature and characteristics of sustainable upgrade and the sustainable technologies adopted was undertaken. A survey questionnaire with an in-built case study was designed to examine all the sustainable technologies adopted to improve energy consumption in Australia. The survey was administered to sustainability consultants, architects, quantity surveyors, facility managers and engineers in Australia.FindingsThe results show a total of 24 technologies which are mostly adopted to improve energy consumption in existing buildings. A factor analysis shows the main components as: lighting and automation, heating, ventilation and air conditioning (HAVC) systems and equipment, envelope, renewable energy and passive technologies.Originality/valueThe findings bridge the gap in the literature on the adoption and application of sustainable technologies to upgrade existing buildings. The technologies can be adopted to reduce the excessive energy consumption patterns in ex...
Dai, Y, Chen, S-R, Chai, L, Zhao, J, Wang, Y & Wang, Y 2019, 'Overview of pharmacological activities of Andrographis paniculata and its major compound andrographolide', Critical Reviews in Food Science and Nutrition, vol. 59, no. sup1, pp. S17-S29.
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Damanik, N, Ong, HC, Mofijur, M, Tong, CW, Silitonga, AS, Shamsuddin, AH, Sebayang, AH, Mahlia, TMI, Wang, C-T & Jang, J-H 2019, 'The Performance and Exhaust Emissions of a Diesel Engine Fuelled with Calophyllum inophyllum—Palm Biodiesel', Processes, vol. 7, no. 9, pp. 597-597.
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Nowadays, increased interest among the scientific community to explore the Calophyllum inophyllum as alternative fuels for diesel engines is observed. This research is about using mixed Calophyllum inophyllum-palm oil biodiesel production and evaluation that biodiesel in a diesel engine. The Calophyllum inophyllum–palm oil methyl ester (CPME) is processed using the following procedure: (1) the crude Calophyllum inophyllum and palm oils are mixed at the same ratio of 50:50 volume %, (2) degumming, (3) acid-catalysed esterification, (4) purification, and (5) alkaline-catalysed transesterification. The results are indeed encouraging which satisfy the international standards, CPME shows the high heating value (37.9 MJ/kg) but lower kinematic viscosity (4.50 mm2/s) due to change the fatty acid methyl ester (FAME) composition compared to Calophyllum inophyllum methyl ester (CIME). The average results show that the blended fuels have higher Brake Specific Fuel Consumption (BSFC) and NOx emissions, lower Brake Thermal Efficiency (BTE), along with CO and HC emissions than diesel fuel over the entire range of speeds. Among the blends, CPME5 offered better performance compared to other fuels. It can be recommended that the CPME blend has great potential as an alternative fuel because of its excellent characteristics, better performance, and less harmful emission than CIME blends.
Damtie, MM, Woo, YC, Kim, B, Hailemariam, RH, Park, K-D, Shon, HK, Park, C & Choi, J-S 2019, 'Removal of fluoride in membrane-based water and wastewater treatment technologies: Performance review', Journal of Environmental Management, vol. 251, pp. 109524-109524.
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The presence of excess fluoride in aqueous media above local environmental standards (e.g., the U.S. Environmental Protection Agency (EPA) standard of 4 mg/L) affects the health of aquatic life. Excess fluoride in drinking water above the maximum contaminant level (e.g., the World Health Organization (WHO) standard of 1.5 mg/L) also affects the skeletal and nervous systems of humans. Fluoride removal from aqueous solutions is difficult using conventional electrochemical, precipitation, and adsorption methods owing to its ionic size and reactivity. Thus, new technologies have been introduced to reduce the fluoride concentration in industrial wastewater effluents and various drinking water sources. Membrane technology is one of the newer technologies found to be very effective in significantly reducing fluoride to desired standards levels; however, it has received less attention than other technologies because it is perceived as a costly process. This study critically reviewed the performance of various membrane process and compared it with effluent and zero liquid discharge (ZLD) standards. The performance review has been conducted with the consideration of the theoretical background, rejection mechanisms, technical viability, and parameters affecting flux and rejection performance. This review includes membrane systems investigated for the defluoridation process but operated under pressure (i.e., reverse osmosis [RO] and nanofiltration [NF]), temperature gradients (i.e., membrane distillation [MD]), electrical potential gradients (i.e., electrodialysis [ED] and Donnan dialysis [DD]), and concentration differences (i.e., forward osmosis [FO]). Moreover, the study also addressed the advantages, limitations, & applicable conditions of each membrane based defluoridation process.
Damtie, MM, Woo, YC, Kim, B, Park, K-D, Hailemariam, RH, Shon, HK & Choi, J-S 2019, 'Analysis of mass transfer behavior in membrane distillation: Mathematical modeling under various conditions', Chemosphere, vol. 236, pp. 124289-124289.
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Dang, C, Zhang, J, Kwong, C-P & Li, L 2019, 'Demand Side Load Management for Big Industrial Energy Users Under Blockchain-Based Peer-to-Peer Electricity Market', IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 6426-6435.
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© 2010-2012 IEEE. Blockchain is the key technology of Bitcoin and other cryptocurrencies, and it is one of the most exciting technologies changing the world as of late. Targeting at big industrial energy users, this paper first presents a new market structure (i.e., transaction rules) under existing blockchain-based electricity transaction platforms to cover popular types of markets such as contract, day-ahead, adjustment and balancing markets; and then focuses on the optimal load management problem for a particular industrial user. The proof-of-work cost from blockchain is also modeled. A key feature of this load management problem is that the user has direct control on its own load. The obtained load control model is much more accurate than existing approaches in which system operators or demand aggregators cannot control load directly and have to rely on inaccurate estimations. As a case study, the pumping load of a water supply plant is investigated to illustrate how the demand load is managed under this blockchain-based market. From the case study, it is found that 18.9% of total cost can be saved under this new market structure.
Daniel, J, Naderpour, M & Lin, C-T 2019, 'A Fuzzy Multilayer Assessment Method for EFQM', IEEE Transactions on Fuzzy Systems, vol. 27, no. 6, pp. 1252-1262.
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© 1993-2012 IEEE. Although the European Foundation for Quality Management (EFQM) is one of the best-known business excellence frameworks, its inherent self-assessment approaches have several limitations. A critical review of self-assessment models reveals that most models are ambiguous and limited to precise data. In addition, the impact of expert knowledge on scoring is overly subjective, and most methodologies assume the relationships between variables are linear. This paper presents a new fuzzy multilayer assessment method that relies on fuzzy inference systems to accommodate imprecise data and varying assessor experiences to overcome uncertainty and complexity in the EFQM model. The method was implemented, tested, and verified under real conditions at a regional electricity company. The case was assessed by internal company experts and external assessors from an EFQM business excellence organization and the model was implemented using MATLAB software. When comparing the classical model with the new model, assessors and experts favored outputs from the new model.
Dano, U, Balogun, A-L, Matori, A-N, Wan Yusouf, K, Abubakar, I, Said Mohamed, M, Aina, Y & Pradhan, B 2019, 'Flood Susceptibility Mapping Using GIS-Based Analytic Network Process: A Case Study of Perlis, Malaysia', Water, vol. 11, no. 3, pp. 615-615.
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Understanding factors associated with flood incidence could facilitate flood disaster control and management. This paper assesses flood susceptibility of Perlis, Malaysia for reducing and managing their impacts on people and the environment. The study used an integrated approach that combines geographic information system (GIS), analytic network process (ANP), and remote sensing (RS) derived variables for flood susceptibility assessment and mapping. Based on experts’ opinion solicited via ANP survey questionnaire, the ANP mathematical model was used to calculate the relative weights of the various flood influencing factors. The ArcGIS spatial analyst tools were used in generating flood susceptible zones. The study found zones that are very highly susceptible to flood (VHSF) and those highly susceptible to flood (HSF) covering 38.4% (30,924.6 ha) and 19.0% (15,341.1 ha) of the study area, respectively. The results were subjected to one-at-a-time (OAT) sensitivity analysis to verify their stability, where 6 out of the 22 flood scenarios correlated with the simulated spatial assessment of flood susceptibility. The findings were further validated using real-life flood incidences in the study area obtained from satellite images, which confirmed that most of the flooded areas were distributed over the VHSF and HSF zones. This integrated approach enables network model structuring, and reflects the interdependences among real-life flood influencing factors. This accurate identification of flood prone areas could serve as an early warning mechanism. The approach can be replicated in cities facing flood incidences in identifying areas susceptible to flooding for more effective flood disaster control.
Daqamseh, S, Al-Fugara, A, Pradhan, B, Al-Oraiqat, A & Habib, M 2019, 'MODIS Derived Sea Surface Salinity, Temperature, and Chlorophyll-a Data for Potential Fish Zone Mapping: West Red Sea Coastal Areas, Saudi Arabia', Sensors, vol. 19, no. 9, pp. 2069-2069.
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In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.
Darabi, H, Choubin, B, Rahmati, O, Torabi Haghighi, A, Pradhan, B & Kløve, B 2019, 'Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques', Journal of Hydrology, vol. 569, pp. 142-154.
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© 2018 Elsevier B.V. Flood risk mapping and modeling is important to prevent urban flood damage. In this study, a flood risk map was produced with limited hydrological and hydraulic data using two state-of-the-art machine learning models: Genetic Algorithm Rule-Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST). The flood conditioning factors used in modeling were: precipitation, slope, curve number, distance to river, distance to channel, depth to groundwater, land use, and elevation. Based on available reports and field surveys for Sari city (Iran), 113 points were identified as flooded areas (with each flooded zone assigned a value of 1). Different conditioning factors, including urban density, quality of buildings, age of buildings, population density, and socio-economic conditions, were taken into account to analyze flood vulnerability. In addition, the weight of these conditioning factors was determined based on expert knowledge and Fuzzy Analytical Network Process (FANP). An urban flood risk map was then produced using flood hazard and flood vulnerability maps. The area under the receiver-operator characteristic curve (AUC-ROC) and Kappa statistic were applied to evaluate model performance. The results demonstrated that the GARP model (AUC-ROC = 93.5%, Kappa = 0.86) had higher performance accuracy than the QUEST model (AUC-ROC = 89.2%, Kappa = 0.79). The results also indicated that distance to channel, land use, and elevation played major roles in flood hazard determination, whereas population density, quality of buildings, and urban density were the most important factors in terms of vulnerability. These findings demonstrate that machine learning models can help in flood risk mapping, especially in areas where detailed hydraulic and hydrological data are not available.
Dash, SK, Saikia, R & Nimbalkar, S 2019, 'Contact Pressure Distribution on Subgrade Soil Underlying Geocell Reinforced Foundation Beds', Frontiers in Built Environment, vol. 5.
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© Copyright © 2019 Dash, Saikia and Nimbalkar. High contact stresses generated in the foundation soil, owing to increased load, causes distress, instability, and large settlements. Present days, geocell reinforcement is being widely used for the performance improvement of foundation beds. Pressure distribution on subgrade soil in geocell reinforced foundation beds is studied through model tests and numerical analysis. The test data indicates that with provision of geocell reinforcement the contact pressure on the subgrade soil reduces significantly. Consequently, the subgrade soil tends to remain intact until large loadings on the foundation leading to significant performance improvement. Through numerical analysis it is observed that the geocells in the region under the footing were subjected to compression and beyond were in tension. This indicates that the geocell reinforcement right under the footing directly sustains the footing loading through mobilization of its compressive stiffness and bending rigidity. Whereas, the end portions of the geocell reinforcement, contribute to the performance improvement in a secondary manner through mobilization of anchorage derived from soil passive resistance and friction.
De Carvalho Gomes, S, Zhou, JL, Li, W & Long, G 2019, 'Progress in manufacture and properties of construction materials incorporating water treatment sludge: A review', Resources, Conservation and Recycling, vol. 145, pp. 148-159.
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© 2019 Elsevier B.V. Water treatment sludge (WTS) management is a growing global problem for water treatment plants (WTPs) and governments. Considering the scarcity of raw materials in many parts of the planet and unique properties of WTS, extensive research has been conducted on the application of WTS in the production of construction materials such as roof tiles, bricks, lightweight aggregates, cement, concrete and geopolymers. This paper critically reviews the progress in the application of WTS in construction materials, by synthesizing results from recent studies. Research findings have revealed that incorporation of ≤10% alum-based sludge in ceramic bricks is satisfactory with a small reduction of mechanical performance. Using the iron-based sludge, the bricks presented better mechanical strength than the reference clay-bricks. Concerning WTS application in concrete, 5% replacement of cement or sand by WTS was considered as the ideal value for the application in a variety of structural and non-structural concrete without adverse effect on concrete mechanical performance. Furthermore, this paper discusses sludge-amended concrete in terms of durability, potential leaching of toxic elements and cost, and suggests topics for future research on the sustainable management of WTS.
de Moura, PK, Cavalli, CB & da Rocha, CG 2019, 'Interface design for in-home displays', Sustainable Production and Consumption, vol. 18, pp. 130-144.
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© 2018 Institution of Chemical Engineers In-home displays can support behavioral changes by providing users with real time information (or feedback) on their energy consumption. Previous studies have shown that the design of such devices plays an important role in effectively communicating such information to users and that preferences are culturally-depend. This paper examines information elements best suited to the Brazilian context for children, adults, and elderly. It explores the preference, ranking, and understanding in addition to the ideal in-home display for each user profile. Lastly, display prototypes suited to each user profile are also proposed based on the results of seven focus groups totaling fifty participants. Real-time consumption was one of the most important information whereas penalty was the least. All participants preferred historical comparison to normative comparison. Numerical formats were better understood than ambient formats for real-time consumption. In addition, children and adults preferred and designed ambient formats whereas elderly preferred numerical information in monetary unit. Elderly would like to keep the display simple whereas adults and children need more interactive designs. These findings contribute to more effective in-home display design particularly for the Brazilian context, where smart meter and in home-displays are not extensively adopted.
Dekhtyar, A, Huffman Hayes, J, Hadar, I, Combs, E, Ferrari, A, Gregory, S, Horkoff, J, Levy, M, Nayebi, M, Paech, B, Payne, J, Primrose, M, Spoletini, P, Clarke, S, Brophy, C, Amyot, D, Maalej, W, Ruhe, G, Cleland-Huang, J & Zowghi, D 2019, 'Requirements Engineering (RE) for Social Good: RE Cares [Requirements]', IEEE Software, vol. 36, no. 1, pp. 86-94.
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© 1984-2012 IEEE. As researchers and teachers and practitioners, we software types excel at multitasking. This, in part, led us to ask the question: Can one attend a software engineering conference and do something good for society? We found the answer to be a resounding yes. In this article, we present our first experience of running RE Cares, a conference collocated event. This event included a workshop, conference sessions, and a hackathon for developing an application to support emergency field activity for Mutual Aid Alberta, a nonprofit organization coordinating natural disaster responses in the Canadian province.
Deng, F, Lu, J, Wang, S-Y, Pan, J & Zhang, L-Y 2019, 'A distributed PDP model based on spectral clustering for improving evaluation performance', World Wide Web, vol. 22, no. 4, pp. 1555-1576.
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Deng, L, Ngo, H-H, Guo, W & Zhang, H 2019, 'Pre-coagulation coupled with sponge-membrane filtration for organic matter removal and membrane fouling control during drinking water treatment', Water Research, vol. 157, pp. 155-166.
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© 2019 Elsevier Ltd A new hybrid system was developed in this study for the treatment of drinking water consisting of pre-coagulation using polyaluminium chloride (PACl) and membrane filtration (MF) with sponge cubes acting as biomass carriers (P-SMF). When compared to a conventional MF (CMF) and a MF after coagulation by utilizing PACl (P-MF), better removal of nutrients, UV254 and dissolved organic carbon (DOC) (>65%) was obtained from the P-SMF. The accumulation of biopolymers (including polysaccharides and proteins), humic substances, hydrophilic organics, and other small molecular weight (MW) organic matter in the CMF led to the most severe membrane fouling coupled with the highest pore blocking and cake resistance. Pre-coagulation was ineffective in eliminating small MW and hydrophilic organic matter. Conversely, the larger MW organics (i.e. biopolymers and humic substances), small MW organics and hydrophilic organic compounds could be removed in significantly larger quantities in the P-SMF by PACl coagulation. This was achieved via adsorption and the biodegradation by attached biomass on these sponges and by the suspended sludge. Further analyses of the microbial community indicated that the combined addition of PACl and sponges generated a high enrichment of Zoolgloea, Amaricoccus and Reyranella leading to the reduction of biopolymers, and Flexibacter and Sphingobium were linked to the degradation of humic substances. Moreover, some members of Alphaproteobacteria in the P-SMF may be responsible for the removal of low MW organics. These results suggest that the pre-coagulation process coupled with adding sponge in the MF system is a promising technology for mitigating membrane fouling.
Deng, S, Ji, J, Yin, S & Wen, G 2019, 'Multistability in the Centrifugal Governor System Under a Time-Delay Control Strategy', Journal of Computational and Nonlinear Dynamics, vol. 14, no. 11.
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Abstract The centrifugal governor system plays an indispensable role in maintaining the near-constant speed of engines. Although different arrangements have been developed, the governor systems are still applied in many machines for its simple mechanical structure. Therefore, the large-amplitude vibrations of the governor system which can lead to the function failure of the system should be attenuated to guarantee reliable operation. This paper adopts a time-delay control strategy to suppress the undesirable large-amplitude motions in the centrifugal governor system, which can be regarded as the practical application of the delayed feedback controller in this system. The stability region of the trivial equilibrium of the controlled system is determined by investigating the characteristic equation and generic Hopf bifurcations. It is found that the dynamic behavior of multistability can be induced by the Bautin bifurcation, arising on the stability boundary of the trivial equilibrium with a constant delay. More specifically, a coexistence of two desirable stable motions, i.e., an equilibrium or a small-amplitude periodic motion, can be observed in the controlled centrifugal governor system without changing the physical parameters. This is a new feature of the motion control in the centrifugal governor systems, which has not yet been reported in the existing studies. Finally, the results of theoretical analyses are verified by numerical simulations.
Deng, W-Y, Lendasse, A, Ong, Y-S, Tsang, IW-H, Chen, L & Zheng, Q-H 2019, 'Domain Adaption via Feature Selection on Explicit Feature Map', IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 4, pp. 1180-1190.
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© 2018 IEEE. In most domain adaption approaches, all features are used for domain adaption. However, often, not every feature is beneficial for domain adaption. In such cases, incorrectly involving all features might cause the performance to degrade. In other words, to make the model trained on the source domain work well on the target domain, it is desirable to find invariant features for domain adaption rather than using all features. However, invariant features across domains may lie in a higher order space, instead of in the original feature space. Moreover, the discriminative ability of some invariant features such as shared background information is weak, and needs to be further filtered. Therefore, in this paper, we propose a novel domain adaption algorithm based on an explicit feature map and feature selection. The data are first represented by a kernel-induced explicit feature map, such that high-order invariant features can be revealed. Then, by minimizing the marginal distribution difference, conditional distribution difference, and the model error, the invariant discriminative features are effectively selected. This problem is NP-hard to be solved, and we propose to relax it and solve it by a cutting plane algorithm. Experimental results on six real-world benchmarks have demonstrated the effectiveness and efficiency of the proposed algorithm, which outperforms many state-of-the-art domain adaption approaches.
Deplano, I, Yazdani, D & Nguyen, TT 2019, 'The Offline Group Seat Reservation Knapsack Problem With Profit on Seats', IEEE Access, vol. 7, pp. 152358-152367.
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In this paper we present the Group Seat Reservation Knapsack Problem with Profit on Seat. This is an extension of the the Offline Group Seat Reservation Knapsack Problem. In this extension we introduce a profit evaluation dependant on not only the space occupied, but also on the individual profit brought by each reserved seat. An application of the new features introduced in the proposed extension is to influence the distribution of passengers, such as assigning seats near the carriage centre for long journeys, and close to the door for short journeys. Such distribution helps to reduce the excess of dwelling time on platform. We introduce a new GRASP based algorithm that solves the original problem and the newly proposed one. In the experimental section we show that such algorithm can be useful to provide a good feasible solution very rapidly, a desirable condition in many real world systems. Another application could be to use the algorithm solution as a startup for a successive branch and bound procedure when optimality is desired. We also add a new class of problem with five test instances that represent some challenging real-world scenarios that have not been considered before. Finally, we evaluate both the existing model, the newly proposed model, and analyse the pros and cons of the proposed algorithm.
Derakhshani, M, Abbaszadeh, H, Movassaghpour, AA, Mehdizadeh, A, Ebrahimi-Warkiani, M & Yousefi, M 2019, 'Strategies for elevating hematopoietic stem cells expansion and engraftment capacity', Life Sciences, vol. 232, pp. 116598-116598.
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© 2019 Elsevier Inc. Hematopoietic stem cells (HSCs) are a rare cell population in adult bone marrow, mobilized peripheral blood, and umbilical cord blood possessing self-renewal and differentiation capability into a full spectrum of blood cells. Bone marrow HSC transplantation has been considered as an ideal option for certain disorders treatment including hematologic diseases, leukemia, immunodeficiency, bone marrow failure syndrome, genetic defects such as thalassemia, sickle cell anemia, autoimmune disease, and certain solid cancers. Ex vivo proliferation of these cells prior to transplantation has been proposed as a potential solution against limited number of stem cells. In such culture process, MSCs have also been shown to exhibit high capacity for secretion of soluble mediators contributing to the principle biological and therapeutic activities of HSCs. In addition, endothelial cells have been introduced to bridge the blood and sub tissues in the bone marrow, as well as, HSCs regeneration induction and survival. Cell culture in the laboratory environment requires cell growth strict control to protect against contamination, symmetrical cell division and optimal conditions for maximum yield. In this regard, microfluidic systems provide culture and analysis capabilities in micro volume scales. Moreover, two-dimensional cultures cannot fully demonstrate extracellular matrix found in different tissues and organs as an abstract representation of three dimensional cell structure. Microfluidic systems can also strongly describe the effects of physical factors such as temperature and pressure on cell behavior.
Deuse, J, Schmitt, J, Bönig, J & Beitinger, G 2019, 'Dynamische Röntgenprüfung in der Elektronikproduktion', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 114, no. 5, pp. 264-267.
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Kurzfassung In diesem Beitrag wird ein Konzept zur dynamischen Röntgenprüfung in der Elektronikproduktion vorgestellt, das durch die Auswertung von Prozessdaten mithilfe von Data-Mining-Verfahren die Prognose der finalen Produktqualität im laufenden Prozess erlaubt. Dies ermöglicht die Reduzierung von Röntgenprüfumfängen durch die Entwicklung dynamischer Prüfpläne.
Devereux, L, Watson, PH, Mes-Masson, A-M, Luna-Crespo, F, Thomas, G, Pitman, H, Speirs, V, Hall, AG, Bollinger, N, Posada, M, Lochmüller, H, Thorne, H, Eng, CB, Riegman, PHJ, Ng, W & Parry-Jones, A 2019, 'A Review of International Biobanks and Networks: Success Factors and Key Benchmarks—A 10-Year Retrospective Review', Biopreservation and Biobanking, vol. 17, no. 6, pp. 512-519.
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Dhingra, S, Madda, RB, Gandomi, AH, Patan, R & Daneshmand, M 2019, 'Internet of Things Mobile–Air Pollution Monitoring System (IoT-Mobair)', IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5577-5584.
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© 2014 IEEE. Internet of Things (IoT) is a worldwide system of 'smart devices' that can sense and connect with their surroundings and interact with users and other systems. Global air pollution is one of the major concerns of our era. Existing monitoring systems have inferior precision, low sensitivity, and require laboratory analysis. Therefore, improved monitoring systems are needed. To overcome the problems of existing systems, we propose a three-phase air pollution monitoring system. An IoT kit was prepared using gas sensors, Arduino integrated development environment (IDE), and a Wi-Fi module. This kit can be physically placed in various cities to monitoring air pollution. The gas sensors gather data from air and forward the data to the Arduino IDE. The Arduino IDE transmits the data to the cloud via the Wi-Fi module. We also developed an Android application termed IoT-Mobair, so that users can access relevant air quality data from the cloud. If a user is traveling to a destination, the pollution level of the entire route is predicted, and a warning is displayed if the pollution level is too high. The proposed system is analogous to Google traffic or the navigation application of Google Maps. Furthermore, air quality data can be used to predict future air quality index (AQI) levels.
Dikshit, A, Sarkar, R, Pradhan, B, Acharya, S & Dorji, K 2019, 'Estimating Rainfall Thresholds for Landslide Occurrence in the Bhutan Himalayas', Water, vol. 11, no. 8, pp. 1616-1616.
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Consistently over the years, particularly during monsoon seasons, landslides and related geohazards in Bhutan are causing enormous damage to human lives, property, and road networks. The determination of thresholds for rainfall triggered landslides is one of the most effective methods to develop an early warning system. Such thresholds are determined using a variety of rainfall parameters and have been successfully calculated for various regions of the world at different scales. Such thresholds can be used to forecast landslide events which could help in issuing an alert to civic authorities. A comprehensive study on the determination of rainfall thresholds characterizing landslide events for Bhutan is lacking. This paper focuses on defining event rainfall–duration thresholds for Chukha Dzongkhag, situated in south-west Bhutan. The study area is chosen due to the increase in frequency of landslides during monsoon along Phuentsholing-Thimphu highway, which passes through it and this highway is a major trade route of the country with the rest of the world. The present threshold method revolves around the use of a power law equation to determine event rainfall–duration thresholds. The thresholds have been established using available rainfall and landslide data for 2004–2014. The calculated threshold relationship is fitted to the lower boundary of the rainfall conditions leading to landslides and plotted in logarithmic coordinates. The results show that a rainfall event of 24 h with a cumulated rainfall of 53 mm can cause landslides. Later on, the outcome of antecedent rainfall varying from 3–30 days was also analysed to understand its effect on landslide incidences based on cumulative event rainfall. It is also observed that a minimum 10-day antecedent rainfall of 88 mm and a 20-day antecedent rainfall of 142 mm is required for landslide occurrence in the area. The thresholds presented can be improved with the availability of hourly rainfall data a...
Dikshit, A, Satyam, N & Pradhan, B 2019, 'Estimation of Rainfall-Induced Landslides Using the TRIGRS Model', Earth Systems and Environment, vol. 3, no. 3, pp. 575-584.
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© 2019, King Abdulaziz University and Springer Nature Switzerland AG. Rainfall-induced landslides have become the biggest threat in the Indian Himalayas and their increasing frequency has led to serious calamities. Several models have been built using various rainfall characteristics to determine the minimum rainfall amount for landslide occurrences. The utilisation of such models depends on the quality of available landslide and rainfall data. However, these models do not consider the effect of local soil, geology, hydrology and topography, which varies spatially. This study is to analyse the triggering process for shallow landslides using physical-based models for the Indian Himalayan region. This research focuses on the utilisation and dependability of physical models in the Kalimpong area of Darjeeling Himalayas, India. The approach utilised the transient rainfall infiltration and grid-based regional slope-stability (TRIGRS) model, which is a widely used model in assessing the variations in pore water pressure and determining the change in the factor of safety. TRIGRS uses an infinite slope model to calculate the change in the factor of safety for every pixel. Moreover, TRIGRS is used to compare historical rainfall scenarios with available landslide database. This study selected the rainfall event from 30th June to 1st July 2015 as input for calibration because the amount of rainfall in this period was higher than the monthly average and caused 18 landslides. TRIGRS depicted variations in the factor of safety with duration before, during and after the heavy rainfall event in 2015. This study further analysed the landslide event and evaluated the predictive capability using receiver operating characteristics. The model was able to successfully predict 71.65% of stable pixels after the landslide event, however, the availability of more datasets such as hourly rainfall, accurate time of landslide event would further improve the results. The results from this stu...
Ding, A, Lin, D, Zhao, Y, Ngo, HH, Guo, W, Bai, L, Luo, X, Li, G, Ren, N & Liang, H 2019, 'Effect of metabolic uncoupler, 2,4‑dinitrophenol (DNP) on sludge properties and fouling potential in ultrafiltration membrane process', Science of The Total Environment, vol. 650, no. Pt 2, pp. 1882-1888.
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© 2018 Elsevier B.V. Energy uncoupling technology was applied to the membrane process to control the problem of bio-fouling. Different dosages of uncoupler (2,4‑dinitrophenol, DNP) were added to the activated sludge, and a short-term ultrafiltration test was systematically investigated for analyzing membrane fouling potential and underlying mechanisms. Ultrafiltration membrane was used and made of polyether-sulfone with a molecular weight cut off (MWCO) of 150 kDa. Results indicated that low DNP concentration (15–30 mg/g VSS) aggravated membrane fouling because more soluble microbial products were released and then rejected by the membrane, which significantly increased cake layer resistance compared with the control. Conversely, a high dosage of DNP (45 mg/g VSS) retarded membrane fouling owing to the high inhibition of extracellular polymeric substances (proteins and polysaccharides) of the sludge, which effectively prevented the formation of cake layer on the membrane surface. Furthermore, analyses of fouling model revealed that a high dosage of DNP delayed the fouling model from pore blocking transition to cake filtration, whereas this transition process was accelerated in the low dosage scenario.
Ding, G & Ying, X 2019, 'Embodied and operating energy assessment of existing buildings – Demolish or rebuild', Energy, vol. 182, pp. 623-631.
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© 2019 Addressing climate change and energy efficiency of buildings challenge governments. Research studies to improve the efficiency of new buildings are many, but the potential of existing buildings to alleviate environmental problems is yet to be recognised. The economic development in China triggered the rapid growth of population and urbanisation. The government has experienced severe environmental problems due, among other things, to an increasing demand for housing. The demand for housing and environmental degradation have compelled the government to demolish historic houses for the construction of more efficient residential buildings. Nevertheless, the consumption of natural resources is essential considerations for redevelopment. The research has selected a south China town to conduct multiple case studies to analyse and compare the energy efficiency of historic and modern dwellings. The research reveals that modern building overall outperforms the historic houses in energy consumption for heating but consumes much higher energy for cooling over a 12-month period. However, the historic houses outperform the modern building in the embodied energy and carbon analysis. If these historic houses are to be replaced with energy efficient buildings, it will take approximately 18–41 years to recover the embodied energy invested in the materials for the new buildings.
Ding, G, Zhang, S, Khan, S, Tang, Z, Zhang, J & Porikli, F 2019, 'Feature Affinity-Based Pseudo Labeling for Semi-Supervised Person Re-Identification', IEEE Transactions on Multimedia, vol. 21, no. 11, pp. 2891-2902.
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© 1999-2012 IEEE. Vision-based person re-identification aims to match a person's identity across multiple images, which is a fundamental task in multimedia content analysis and retrieval. Deep neural networks have recently manifested great potential in this task. However, a major bottleneck of existing supervised deep networks is their reliance on a large amount of annotated training data. Manual labeling for person identities in large-scale surveillance camera systems is quite challenging and incurs significant costs. Some recent studies adopt generative model outputs as training data augmentation. To more effectively use these synthetic data for an improved feature learning and re-identification performance, this paper proposes a novel feature affinity-based pseudo labeling method with two possible label encodings. To the best of our knowledge, this is the first study that employs pseudo-labeling by measuring the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks. We propose training the network with the joint supervision of cross-entropy loss together with a center regularization term, which not only ensures discriminative feature representation learning but also simultaneously predicts pseudo-labels for unlabeled data. We show that both label encodings can be learned in a unified manner and help improve the overall performance. Our extensive experiments on three person re-identification datasets: Market-1501, DukeMTMC-reID, and CUHK03, demonstrate significant performance boost over the state-of-the-art person re-identification approaches.
Ding, W, Jin, W, Cao, S, Zhou, X, Wang, C, Jiang, Q, Huang, H, Tu, R, Han, S-F & Wang, Q 2019, 'Ozone disinfection of chlorine-resistant bacteria in drinking water', Water Research, vol. 160, pp. 339-349.
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© 2019 Elsevier Ltd The wide application of chlorine disinfectant for drinking water treatment has led to the appearance of chlorine-resistant bacteria, which pose a severe threat to public health. This study was performed to explore the physiological-biochemical characteristics and environmental influence (pH, temperature, and turbidity)of seven strains of chlorine-resistant bacteria isolated from drinking water. Ozone disinfection was used to investigate the inactivation effect of bacteria and spores. The DNA concentration and cell surface structure variations of typical chlorine-resistant spores (Bacillus cereus spores)were also analysed by real-time qPCR, flow cytometry, and scanning electron microscopy to determine their inactivation mechanisms. The ozone resistance of bacteria (Aeromonas jandaei < Vogesella perlucida < Pelomonas < Bacillus cereus < Aeromonas sobria)was lower than that of spores (Bacillus alvei < Lysinibacillus fusiformis < Bacillus cereus)at an ozone concentration of 1.5 mg/L. More than 99.9% of Bacillus cereus spores were inactivated by increasing ozone concentration and treatment duration. Moreover, the DNA content of Bacillus cereus spores decreased sharply, but approximately 1/4 of the target genes remained. The spore structure exhibited shrinkage and folding after ozone treatment. Both cell structures and gene fragments were damaged by ozone disinfection. These results showed that ozone disinfection is a promising method for inactivating chlorine-resistant bacteria and spores in drinking water.
Ding, W, Lin, C-T & Cao, Z 2019, 'Deep Neuro-Cognitive Co-Evolution for Fuzzy Attribute Reduction by Quantum Leaping PSO With Nearest-Neighbor Memeplexes', IEEE Transactions on Cybernetics, vol. 49, no. 7, pp. 2744-2757.
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© 2013 IEEE. Attribute reduction with many patterns and indicators has been regarded as an important approach for large-scale data mining and machine learning tasks. However, it is extremely difficult for researchers to inadequately extract knowledge and insights from multiple overlapping and interdependent fuzzy datasets from the current changing and interconnected big data sources. This paper proposes a deep neuro-cognitive co-evolution for fuzzy attribute reduction (DNCFAR) that contains a combination of quantum leaping particle swarm optimization with nearest-neighbor memeplexes. A key element of DNCFAR resides in its deep neuro-cognitive cooperative co-evolution structure, which is explicitly permitted to identify interdependent variables and adaptively decompose them in the same neuro-subpopulation, with minimizing the complexity and nonseparability of interdependent variables among different fuzzy attribute subsets. Next DNCFAR formalizes to the different types of quantum leaping particles with nearest-neighbor memeplexes to share their respective solutions and deeply cooperate to evolve the assigned fuzzy attribute subsets. The experimental results demonstrate that DNCFAR can achieve competitive performance in terms of average computational efficiency and classification accuracy while reinforcing noise tolerance. Furthermore, it can be well applied to clearly identify different longitudinal surfaces of infant cerebrum regions, which indicates its great potential for brain disorder prediction based on fMRI.
Ding, W, Lin, C-T & Cao, Z 2019, 'Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction With Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces', IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 7, pp. 2013-2027.
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© 2012 IEEE. The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces.
Ding, X, Wei, D, Guo, W, Wang, B, Meng, Z, Feng, R, Du, B & Wei, Q 2019, 'Biological denitrification in an anoxic sequencing batch biofilm reactor: Performance evaluation, nitrous oxide emission and microbial community', Bioresource Technology, vol. 285, pp. 121359-121359.
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Do, T-TN, Chuang, C-H, Hsiao, S-J, Lin, C-T & Wang, Y-K 2019, 'Neural Comodulation of Independent Brain Processes Related to Multitasking', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 6, pp. 1160-1169.
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© 2001-2011 IEEE. Distracted driving is regarded as an integrated task requiring different regions of the brain to receive sensory data, coordinate information, make decisions, and synchronize movements. In this paper, we applied an independent modulator analysis (IMA) method to temporally independent electroencephalography (EEG) components to understand how the human executive control system coordinates different brain regions to simultaneously perform multiple tasks with distractions presented in different modalities. The behavioral results showed that the reaction time (RT) in response to traffic events increased while multitasking. Moreover, the RT was longer when the distractor was presented in an auditory form versus a visual form. The IMA results showed that there were performance-related IMs coordinating different brain regions during distracted driving. The component spectral fluctuations affected by the modulators were distinct between the single- and dual-task conditions. Specifically, more modulatory weight was projected to the occipital region to address the additional distracting stimulus in both visual and auditory modality in the dual-task conditions. A comparison of modulatory weights between auditory and visual distractors showed that more modulatory weight was projected to the frontal region during the processing of the auditory distractor. This paper provides valuable insights into the temporal dynamics of attentional modulation during multitasking as well as an understanding of the underlying brain mechanisms that mediate the synchronization across brain regions and govern the allocation of attention in distracted driving.
Doborjeh, M, Kasabov, N, Doborjeh, Z, Enayatollahi, R, Tu, E & Gandomi, AH 2019, 'Personalised modelling with spiking neural networks integrating temporal and static information', Neural Networks, vol. 119, pp. 162-177.
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This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction and pattern recognition when both static and dynamic/spatiotemporal features are presented in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal selection of neighbouring samples to an individual with respect to the integration of both static (vector-based) and temporal individual data. The most relevant samples to an individual are selected to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to capture the space and time association patterns. The generated time-dependant patterns resulted in a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling and conventional methods. In addition, the PSNN models can support interpretability by creating personalised profiling of an individual. This contributes to a better understanding of the interactions between features. Therefore, an end-user can comprehend what interactions in the model have led to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several real-life health applications, where different data domains describe a person's health condition. The system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the investigation of individual response to treatment and (2) prediction of risk of stroke with respect to temporal environmental data. For both datasets, besides the temporal data, static health data were also available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN clustering parameters, are optimised for each individual.
Dong, B, Xia, Z, Sun, J, Dai, X, Chen, X & Ni, B-J 2019, 'The inhibitory impacts of nano-graphene oxide on methane production from waste activated sludge in anaerobic digestion', Science of The Total Environment, vol. 646, pp. 1376-1384.
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© 2018 The wide application of graphene oxide nanoparticles inevitably leads to their discharge into wastewater treatment plants and combination with the activated sludge. However, to date, it is largely unknown if the nano-graphene oxide (NGO) has potential impacts on the anaerobic digestion of waste activated sludge (WAS). Therefore, this work aims to fill the knowledge gap through comprehensively investigating the effects of NGO on carbon transformation and methane production in the anaerobic digestion of WAS. Biochemical methane potential tests demonstrated the methane production dropped with increasing NGO additions, the cumulative methane production decreasing by 7.6% and 12.6% at the NGO dosing rates of 0.054 mg/mg-VS and 0.108 mg/mg-VS, respectively. Model-based analysis indicated NGO significantly reduced biochemical methane potential, with the highest biochemical methane potential decrease being approximately 10% at the highest NGO dosing rate. Further experimental analysis suggested that the decreased methane production was firstly related to a decrease in soluble organic substrates availability during the process of sludge disintegration, potentially attributing to the strong absorption of organic substrates by NGO. Secondly, NGO significantly inhibited the methanogenesis by negatively affecting the corresponding enzyme activity (i.e. coenzyme F420), which could also resulted in a decreased methane production.
Dong, M, Wen, S, Zeng, Z, Yan, Z & Huang, T 2019, 'Sparse fully convolutional network for face labeling', Neurocomputing, vol. 331, pp. 465-472.
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© 2018 Elsevier B.V. This paper proposes a sparse fully convolutional network (FCN) for face labeling. FCN has demonstrated strong capabilities in learning representations for semantic segmentation. However, it often suffers from heavy redundancy in parameters and connections. To ease this problem, group Lasso regularization and intra-group Lasso regularization are utilized to sparsify the convolutional layers of the FCN. Based on this framework, parameters that correspond to the same output channel are grouped into one group, and these parameters are simultaneously zeroed out during training. For the parameters in groups that are not zeroed out, intra-group Lasso provides further regularization. The essence of the regularization framework lies in its ability to offer better feature selection and higher sparsity. Moreover, a fully connected conditional random fields (CRF) model is used to refine the output of the sparse FCN. The proposed approach is evaluated on the LFW face dataset with the state-of-the-art performance. Compared with a non-regularized FCN, the sparse FCN reduces the number of parameters by 91.55% while increasing the segmentation performance by 11% relative error reduction.
Dong, W, Li, W, Long, G, Tao, Z, Li, J & Wang, K 2019, 'Electrical resistivity and mechanical properties of cementitious composite incorporating conductive rubber fibres', Smart Materials and Structures, vol. 28, no. 8, pp. 085013-085013.
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© 2019 IOP Publishing Ltd. Conductive cementitious composites with excellent conductivity and piezoresistivity can be potentially used for pavement deicing, concrete corrosion evaluations or structural health monitoring. Inspired by the practice of recycling rubber wastes for concrete manufacturing, the conductive rubbers are first added as enhanced fillers to improve the electrical conductivity of cementitious composite in this study. Based on the experimental investigations on electrical resistivity, mechanical properties and microstructure, the results show that cementitious composites containing conductive rubber fibres exhibit relatively low resistivity with nearly one order of magnitude to approximately 1 × 104 Ω cm. On the other hand, cementitious composites with aluminium/silver filled rubber (AR) exhibit better conductivity than the counterparts with carbon black filled rubber (CR). For CR reinforced composites (CRC) and AR reinforced composite (ARC) with more than 40 rubber fibres (0.64 vol%), the higher the rubber fibre content, the better is the conductivity but the slightly lower the compressive strength. The cementitious composites reinforced by 100 conductive rubber fibres (1.6 vol%) not only display excellent conductivity but also provides acceptable mechanical properties, with up to 30.6% increase in ultimate strain but only 17.3% reduction in compressive strength. Furthermore, cementitious composites with rubber fibres demonstrate better damping capacity by enlarging stress-strain hysteresis loops compared to the counterpart without rubber. Such promising conductivity and damping properties provide the cementitious composites with great potentials for being used as cementitious composite sensors and smart composites to self-monitor the structural health or traffic load of various transportation infrastructures, such as bridges, highways and pavements.
Dong, W, Li, W, Lu, N, Qu, F, Vessalas, K & Sheng, D 2019, 'Piezoresistive behaviours of cement-based sensor with carbon black subjected to various temperature and water content', Composites Part B: Engineering, vol. 178, pp. 107488-107488.
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© 2019 Elsevier Ltd Cement-based sensor possesses unique properties for structural health monitoring (SHM) applications, such as low cost, high durability, adaptability and excellent sensitivity. The piezoresistivity of cement-based sensor possesses is often affected by working environments, which may limit its real potentials. In this study, the piezoresistive sensitivity and repeatability of cement-based sensors with carbon black (CB) under various environmental conditions were investigated. Under various temperatures ranging from −20 °C to 100 °C, the piezoresistive sensitivity and repeatability were almost unchanged when eliminating the effects by thermal exchanges. The water content of cementitious composites caused significant fluctuations on the resistivity and piezoresistivity, and the optimal water content for cement-based sensor possesses was found to be approximately 8%. Subjected to freeze-thaw cycles, dry CB/cementitious composites slightly reduced the piezoresistive sensitivity. However, the saturated composites presented dramatic piezoresistivity reduction by 30.7%, due to the microstructural damages caused by the volume expansion and shrinkage of pore solution. The related outcomes provide scientific framework for the adoption of CB/cementitious composites sensors for the SHM of concrete infrastructures under various environmental conditions.
Dong, W, Li, W, Shen, L & Sheng, D 2019, 'Piezoresistive behaviours of carbon black cement-based sensors with layer-distributed conductive rubber fibres', Materials & Design, vol. 182, pp. 108012-108012.
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© 2019 Conductive rubber fibres filled carbon black (CB)/cementitious composites were developed to achieve the cement-based sensors with excellent piezoresistivity in this study. Ameliorations on the conductivity and piezoresistive sensitivity of CB filled composites were mainly explored with conductive rubber fibres embedded. Their compressive strengths were investigated to evaluate the practical application possibility. The results indicated that the composites with CB content <4.0 wt% possessed acceptable compressive strengths. In terms of conductivity and piezoresistivity, both conductivity and piezoresistivity of composites filled with 0.5 wt% CB increased with the rubber content, and their gauge factor raised to 91 when embedded with 80 rubber fibres (1.27 vol%). Moreover, phenomenon of “piezoresistive percolation” was observed by sharp fractional changes of resistivity for the composites filled with 1.0 wt% CB, where existed highest gauge factor reaching 482 when embedded with same rubber fibres. However, because of the excellent conductivity of 2.0 wt% CB filled composites, the gauge factor firstly increased but then slightly decreased around 100 with increase of rubber fibre content. Overall, conductive rubber fibres can significantly improve the piezoresistivity of CB/cementitious composites by the increased gauge factor.
Dong, X, Qiu, P, Lu, J, Cao, L & Xu, T 2019, 'Mining Top-${k}$ Useful Negative Sequential Patterns via Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 9, pp. 2764-2778.
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As an important tool for behavior informatics, negative sequential patterns (NSPs) (such as missing a medical treatment) are sometimes much more informative than positive sequential patterns (PSPs) (e.g., attending a medical treatment) in many applications. However, NSP mining is at an early stage and faces many challenging problems, including 1) how to mine an expected number of NSPs; 2) how to select useful NSPs; and 3) how to reduce high time consumption. To solve the first problem, we propose an algorithm Topk-NSP to mine the k most frequent negative patterns. In Topk-NSP, we first mine the top- k PSPs using the existing methods, and then we use an idea which is similar to top- k PSPs mining to mine the top- k NSPs from these PSPs. To solve the remaining two problems, we propose three optimization strategies for Topk-NSP. The first optimization strategy is that, in order to consider the influence of PSPs when selecting useful top- k NSPs, we introduce two weights, wP and wN , to express the user preference degree for NSPs and PSPs, respectively, and select useful NSPs by a weighted support wsup. The second optimization strategy is to merge wsup and an interestingness metric to select more useful NSPs. The third optimization strategy is to introduce a pruning strategy to reduce the high computational costs of Topk-NSP. Finally, we propose an optimization algorithm Topk-NSP+. To the best of our knowledge, Topk-NSP+ is the first algorithm that can mine the top- k useful NSPs. The experimental results on four synthetic and two real-life data sets show that the Topk-NSP+ is very efficient in mining the top- k NSPs in the sense of computational cost and scalability.
Dong, X, Yan, Y, Tan, M, Yang, Y & Tsang, IW 2019, 'Late Fusion via Subspace Search With Consistency Preservation', IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 518-528.
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© 1992-2012 IEEE. In many real-world applications, data can be represented by multiple ways or multi-view features to describe various characteristics of data. In this sense, the prediction performance can be significantly improved by taking advantages of these features together. Late fusion, which combines the predictions of multiple features, is a commonly used approach to make the final decision for a test instance. However, it is ubiquitous that different features dispute the prediction on the same data with each other, leading to performance degeneration. In this paper, we propose an efficient and effective matrix factorization-based approach to fuse predictions from multiple sources. This approach leverages a hard constraint on the matrix rank to preserve the consistency of predictions by various features, and we thus named it as Hard-rank Constraint Matrix Factorization-based fusion (HCMF). HCMF can avoid the performance degeneration caused by the controversy of multiple features. Extensive experiments demonstrate the efficacy of HCMF for outlier detection and the performance improvement, which outperforms the state-of-the-art late fusion algorithms on many data sets.
Dorji, P, Kim, DI, Jiang, J, Choi, J, Phuntsho, S, Hong, S & Shon, HK 2019, 'Bromide and iodide selectivity in membrane capacitive deionisation, and its potential application to reduce the formation of disinfection by-products in water treatment', Chemosphere, vol. 234, pp. 536-544.
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© 2019 Elsevier Ltd The formation of toxic disinfection by-products during water disinfection due to the presence of bromide and iodide is a major concern. Current treatment technologies such as membrane, adsorption and electrochemical processes have been known to have limitations such as high energy demand and excessive chemical use. In this study, the selectivity between bromide and iodide, and their removal in membrane capacitive deionisation (MCDI) was evaluated. The results showed that iodide was more selectively removed over bromide from several binary feed waters containing bromide and iodide under various initial concentrations and applied voltages. Even in the presence of significant background concentration of sodium chloride, definite selectivity of iodide over bromide was observed. The high partial-charge transfer coefficient of iodide compared to bromide could be a feasible explanation for high iodide selectivity since both bromide and iodide have similar ionic charge and hydrated radius. The result also shows that MCDI can be a potential alternative for the removal of bromide and iodide during water treatment.
Dorji, U, Tenzin, UM, Dorji, P, Wangchuk, U, Tshering, G, Dorji, C, Shon, H, Nyarko, KB & Phuntsho, S 2019, 'Wastewater management in urban Bhutan: Assessing the current practices and challenges', Process Safety and Environmental Protection, vol. 132, pp. 82-93.
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© 2019 Institution of Chemical Engineers This study reviews the current wastewater management practices and their challenges in urban Bhutan. The study data was collected from the local authorities of 35 classified towns, and the field survey was conducted for the two representative towns of Thimphu City and Khuruthang town. The study observed that only eight of the 35 classified towns (22.8%) have public sewerage systems, with an average coverage of 19.7% of Bhutan's total urban population, or 7.4% of Bhutan's entire population. The imported modular wastewater treatment technology was significantly more expensive than alternative options; however, approximately six towns have already adopted this technology, due to a lack of space for a much cheaper waste stabilisation ponds. Currently, over 80% of Bhutan's urban population depends on the on-site sanitation system for their domestic wastewater disposal; however, over 40% of these properties lacked a soak-pit system for the safe disposal of septic tank effluent. Therefore, this study indicates that urban settlements in Bhutan are potentially subjected to overflow of significant amount of hazardous septic tank effluents directly into the environment posing significant risk to public and the environment. A critical urban plot space analysis indicates that the current system of on-site sanitation is inadequate and unsuitable for the current urban settings. Since it is impractical for the government to provide public sewerage system to all the towns, a low-cost public sewerage system, or an alternative and improved on-site treatment system, needs to be explored and promoted to achieve long-term environmental objectives.
Dou, W, Tang, W, Li, S, Yu, S & Raymond Choo, K-K 2019, 'A heuristic line piloting method to disclose malicious taxicab driver’s privacy over GPS big data', Information Sciences, vol. 483, pp. 247-261.
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© 2018 While privacy preservation is important, there are occasions when an individual's privacy should not be preserved (e.g., those involved in the case of a terrorist attack). Existing works do not generally make such a distinction. We posit the importance of classifying an individual's privacy as positive and negative, say in the case of a misbehaving driver (e.g., a driver involved in a hit-and-run or terrorist attack). This will allow us to revoke the right of the misbehaving driver's right to privacy to facilitate investigation. Hence, we propose a heuristic line piloting method, hereafter referred to as HelpMe. Using taxi services as a case study, we explain how the proposed method constantly accumulates the knowledge of taxi routes from related historical GPS datasets using machine-learning techniques. Hence, a taxi deviating from the typical route could be detected in real-time, which may be used to raise an alert (e.g., the taxi may be hijacked by criminals). We also evaluate the utility of our method on real-life GPS datasets.
Douglas, ANJ, Irga, PJ & Torpy, FR 2019, 'Determining broad scale associations between air pollutants and urban forestry: A novel multifaceted methodological approach', Environmental Pollution, vol. 247, pp. 474-481.
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© 2019 Elsevier Ltd Global urbanisation has resulted in population densification, which is associated with increased air pollution, mainly from anthropogenic sources. One of the systems proposed to mitigate urban air pollution is urban forestry. This study quantified the spatial associations between concentrations of CO, NO₂ SO₂ and PM₁₀ and urban forestry, whilst correcting for anthropogenic sources and sinks, thus explicitly testing the hypothesis that urban forestry is spatially associated with reduced air pollution on a city scale. A Land Use Regression (LUR) model was constructed by combining air pollutant concentrations with environmental variables, such as land cover type and use, to develop predictive models for air pollutant concentrations. Traffic density and industrial air pollutant emissions were added to the model as covariables to permit testing of the main effects after correcting for these air pollutant sources. It was found that the concentrations of all air pollutants were negatively correlated with tree canopy cover and positively correlated with dwelling density, population density and traffic count. The LUR models enabled the establishment of a statistically significant spatial relationship between urban forestry and air pollution mitigation. These findings further demonstrate the spatial relationships between urban forestry and reduced air pollution on a city-wide scale, and could be of value in developing planning policies focused on urban greening.
Du, J, Jing, H, Castro-Lacouture, D & Sugumaran, V 2019, 'Multi-agent simulation for managing design changes in prefabricated construction projects', Engineering, Construction and Architectural Management, vol. 27, no. 1, pp. 270-295.
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PurposeThe purpose of this paper is to develop a multi-agent-based model for quantitatively measuring how the design change management strategies improve project performance.Design/methodology/approachBased on questionnaires and interviews, this paper investigates the coordination mechanism of risks due to design changes in prefabricated construction (PC) projects. Combined with all the variables related with design change risks, a multi-agent-based simulation model is proposed to evaluate the design change management effect.FindingsThe coordination mechanism between design change factors, design change events, risk consequence and management strategy in PC projects is described and then the simulation-based design change management mechanism in PC projects is used to assess the effect of management strategies under dynamic scenarios.Originality/valuePC projects have rapidly increased in recent years due to the advantages of fast construction, high quality and labor savings. Different from traditional on-site construction, the impact and risk from design changes are likely to be greater due to the prefabricated project being multi-stage, highly interactive and complex. The simulations presented in this paper make it possible to test different design change management strategies in order to study their effectiveness and support managerial decision making.
Du, W & Su, QP 2019, 'Single-molecule in vitro reconstitution assay for kinesin-1-driven membrane dynamics', Biophysical Reviews, vol. 11, no. 3, pp. 319-325.
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© 2019, International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature. Intracellular membrane dynamics, especially the nano-tube formation, plays important roles in vesicle transportation and organelle biogenesis. Regarding the regulation mechanisms, it is well known that during the nano-tube formation, motor proteins act as the driven force moving along the cytoskeleton, lipid composition and its associated proteins serve as the linkers and key mediators, and the vesicle sizes play as one of the important regulators. In this review, we summarized the in vitro reconstitution assay method, which has been applied to reconstitute the nano-tube dynamics during autophagic lysosomal regeneration (ALR) and the morphology dynamics during mitochondria network formation (MNF) in a mimic and pure in vitro system. Combined with the single-molecule microscopy, the advantage of the in vitro reconstitution system is to study the key questions at a single-molecule or single-vesicle level with precisely tuned parameters and conditions, such as the motor mutation, ion concentration, lipid component, ATP/GTP concentration, and even in vitro protein knockout, which cannot easily be achieved by in vivo or intracellular studies.
Du, Z, Ge, L, Ng, AH, Zhu, Q, Zhang, Q, Kuang, J & Dong, Y 2019, 'Long‐term subsidence in Mexico City from 2004 to 2018 revealed by five synthetic aperture radar sensors', Land Degradation & Development, vol. 30, no. 15, pp. 1785-1801.
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AbstractAnthropogenic land subsidence is an example of changes to the natural environment due to human activities and is one of the key factors in causing land degradation at a range of scales. Previous studies assessing land subsidence in the Valley of Mexico either focused on regional scale or short (noncontinuous) temporal scale. In this study, long‐term land subsidence (~15 years) is mapped in Mexico City (Mexico) using two interferometric synthetic aperture radar (InSAR) methods, namely, GEOS (Geoscience and Earth Observing Systems Group)‐Advance Time‐series Analysis and GEOS‐Small Baseline Subset. An inverse distance weighted‐based integration module and maximum likelihood regression‐based M estimator are introduced to further enhance these two methods. The land subsidence was continuously mapped using ENVISAT (2004–2007), ALOS‐1 (2007–2011), COSMO‐SkyMed (2011–2014), ALOS‐2 (2014–2018), and SENTINEL‐1 (2015–2017) data sets. A comparison between InSAR time‐series and GPS measurement shows that the subsidence rates are consistent over 2004–2018. The subsidence map over 15 years was generated finding a maximum subsidence over 4.5 m. By comparing our InSAR results with a land use map, we find that the subsidence centre in Mexico City is mostly located in the residential regions with the consumption of groundwater contributing considerably to the local subsidence rate. A total volume of 1.20 × 108 m3 of the land in Ciudad Nezahualcoyotl subsided/degraded. A continuing subsidence process limits the potential land use causing serious land degradation. Our results may be used to assist disaster reduction plans.
Dua, K, Malyla, V, Singhvi, G, Wadhwa, R, Krishna, RV, Shukla, SD, Shastri, MD, Chellappan, DK, Maurya, PK, Satija, S, Mehta, M, Gulati, M, Hansbro, N, Collet, T, Awasthi, R, Gupta, G, Hsu, A & Hansbro, PM 2019, 'Increasing complexity and interactions of oxidative stress in chronic respiratory diseases: An emerging need for novel drug delivery systems', Chemico-Biological Interactions, vol. 299, pp. 168-178.
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© 2018 Elsevier B.V. Oxidative stress is intensely involved in enhancing the severity of various chronic respiratory diseases (CRDs) including asthma, chronic obstructive pulmonary disease (COPD), infections and lung cancer. Even though there are various existing anti-inflammatory therapies, which are not enough to control the inflammation caused due to various contributing factors such as anti-inflammatory genes and antioxidant enzymes. This leads to an urgent need of novel drug delivery systems to combat the oxidative stress. This review gives a brief insight into the biological factors involved in causing oxidative stress, one of the emerging hallmark feature in CRDs and particularly, highlighting recent trends in various novel drug delivery carriers including microparticles, microemulsions, microspheres, nanoparticles, liposomes, dendrimers, solid lipid nanocarriers etc which can help in combating the oxidative stress in CRDs and ultimately reducing the disease burden and improving the quality of life with CRDs patients. These carriers improve the pharmacokinetics and bioavailability to the target site. However, there is an urgent need for translational studies to validate the drug delivery carriers for clinical administration in the pulmonary clinic.
Dua, K, Wadhwa, R, Singhvi, G, Rapalli, V, Shukla, SD, Shastri, MD, Gupta, G, Satija, S, Mehta, M, Khurana, N, Awasthi, R, Maurya, PK, Thangavelu, L, S, R, Tambuwala, MM, Collet, T, Hansbro, PM & Chellappan, DK 2019, 'The potential of siRNA based drug delivery in respiratory disorders: Recent advances and progress', Drug Development Research, vol. 80, no. 6, pp. 714-730.
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AbstractLung diseases are the leading cause of mortality worldwide. The currently available therapies are not sufficient, leading to the urgent need for new therapies with sustained anti‐inflammatory effects. Small/short or silencing interfering RNA (siRNA) has potential therapeutic implications through post‐transcriptional downregulation of the target gene expression. siRNA is essential in gene regulation, so is more favorable over other gene therapies due to its small size, high specificity, potency, and no or low immune response. In chronic respiratory diseases, local and targeted delivery of siRNA is achieved via inhalation. The effectual delivery can be attained by the generation of aerosols via inhalers and nebulizers, which overcomes anatomical barriers, alveolar macrophage clearance and mucociliary clearance. In this review, we discuss the different siRNA nanocarrier systems for chronic respiratory diseases, for safe and effective delivery. siRNA mediated pro‐inflammatory gene or miRNA targeting approach can be a useful approach in combating chronic respiratory inflammatory conditions and thus providing sustained drug delivery, reduced therapeutic dose, and improved patient compliance. This review will be of high relevance to the formulation, biological and translational scientists working in the area of respiratory diseases.
Duan, H, Ye, L, Wang, Q, Zheng, M, Lu, X, Wang, Z & Yuan, Z 2019, 'Nitrite oxidizing bacteria (NOB) contained in influent deteriorate mainstream NOB suppression by sidestream inactivation', Water Research, vol. 162, pp. 331-338.
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© 2019 Sidestream sludge treatment approaches have been developed in recent years to achieve mainstream nitrite shunt or partial nitritation, where NOB are selectively inactivated by biocidal factors such as free nitrous acid (FNA) or free ammonium (FA) in a sidestream reactor. The existence of NOB in raw wastewater has been increasingly realized and could pose critical challenge to stable NOB suppressions in those systems. This study, for the first time, evaluated the impact of influent NOB on the NOB suppressions in a mainstream nitrite shunt system achieved through sidestream sludge treatment. An over 500-day sequential batch reactor operation with six experimental phases rigorously demonstrated the negative effects of influent NOB on mainstream NOB control. Continuously seeding of NOB contained in influent stimulated NOB community shifts, leading to different extents of ineffective NOB suppression. The role of primary wastewater treatment in NOB removal from raw wastewater was also investigated. Results suggest primary settling and High Rate Activated Sludge system could remove a large part of NOB contained in raw wastewater. Primary treatment for raw wastewater is necessary for ensuring stable mainstream NOB suppressions.
Duan, L, Sun, C-A, Zhang, Y, Ni, W & Chen, J 2019, 'A Comprehensive Security Framework for Publish/Subscribe-Based IoT Services Communication', IEEE Access, vol. 7, pp. 25989-26001.
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Duong, HC, Ansari, AJ, Nghiem, LD, Cao, HT, Vu, TD & Nguyen, TP 2019, 'Membrane Processes for the Regeneration of Liquid Desiccant Solution for Air Conditioning', Current Pollution Reports, vol. 5, no. 4, pp. 308-318.
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© 2019, Springer Nature Switzerland AG. Purpose of Review: Regeneration of liquid desiccant solutions is critical for the liquid desiccant air conditioning (LDAC) process. In most LDAC systems, the weak desiccant solution is regenerated using the energy-intensive thermal evaporation method which suffers from desiccant carry-over. Recently, membrane processes have gained increasing interest as a promising method for liquid desiccant solution regeneration. This paper provides a comprehensive review on the applications of membrane processes for regeneration of liquid desiccant solutions. Fundamental knowledge, working principles, and the applications of four key membrane processes (e.g., reverse osmosis (RO), forward osmosis (FO), electrodialysis (ED), and membrane distillation (MD)) are discussed to shed light on their feasibility for liquid desiccant solution regeneration and the associated challenges. Recent Findings: RO is effective at preventing desiccant carry-over; however, current RO membranes are not compatible with hypersaline liquid desiccant solutions. FO deploys a concentrated draw solution to overcome the high osmotic pressure of liquid desiccant solutions; hence, it is feasible for their regeneration despite the issues with internal/external concentration polarization and reverse salt flux. ED has proven its technical feasibility for liquid desiccant solution regeneration; nevertheless, more research into the process energy efficiency and the recycling of spent solution are recommended. Finally, as a thermally driven process, MD is capable of regenerating liquid desiccant solutions, but it is adversely affected by the polarization effects associated with the hypersalinity of the solutions. Summary: Extensive studies are required to realize the applications of membrane processes for the regeneration of liquid desiccant solutions used for LDAC systems.
Duong, HC, Pham, TM, Luong, ST, Nguyen, KV, Nguyen, DT, Ansari, AJ & Nghiem, LD 2019, 'A novel application of membrane distillation to facilitate nickel recovery from electroplating wastewater', Environmental Science and Pollution Research, vol. 26, no. 23, pp. 23407-23415.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. In many years, the nickel electroplating technique has been applied to coat nickel on other materials for their increased properties. Nickel electroplating has played a vital role in our modern society but also caused considerable environmental concerns due to the mass discharge of its wastewater (i.e. containing nickel and other heavy metals) to the environment. Thus, there is a growing need for treating nickel electroplating wastewater to protect the environment and, in tandem, recover nickel for beneficial use. This study explores a novel application of membrane distillation (MD) for the treatment of nickel electroplating wastewater for a dual purpose: facilitating the nickel recovery and obtaining fresh water. The experimental results demonstrate the technical capability of MD to pre-concentrate nickel in the wastewater (i.e. hence pave the way for subsequent nickel recovery via chemical precipitation or electrodeposition) and extract fresh water. At a low operating feed temperature of 60 °C, the MD process increased the nickel content in the wastewater by more than 100-fold from 0.31 to 33 g/L with only a 20% reduction in the process water flux and obtained pure fresh water. At such high concentration factors, the membrane surface was slightly fouled by inorganic precipitates; however, membrane pore wetting was not evident, confirmed by the purity of the obtained fresh water. The fouled membrane was effectively cleaned using a 3% HCl solution to restore its surface morphology. Finally, the preliminary thermal energy analysis of the combined MD–chemical precipitation/electrodeposition process reveals a considerable reduction in energy consumption of the nickel recovery process.
Duong, HC, Tran, TL, Ansari, AJ, Cao, HT, Vu, TD & Do, K-U 2019, 'Advances in Membrane Materials and Processes for Desalination of Brackish Water', Current Pollution Reports, vol. 5, no. 4, pp. 319-336.
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Duong, NMH, Glushkov, E, Chernev, A, Navikas, V, Comtet, J, Nguyen, MAP, Toth, M, Radenovic, A, Tran, TT & Aharonovich, I 2019, 'Facile Production of Hexagonal Boron Nitride Nanoparticles by Cryogenic Exfoliation', Nano Letters, vol. 19, no. 8, pp. 5417-5422.
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© 2019 American Chemical Society. Fluorescent nanoparticles with optically robust luminescence are imperative to applications in imaging and labeling. Here we demonstrate that hexagonal boron nitride (hBN) nanoparticles can be reliably produced using a scalable cryogenic exfoliation technique with sizes below 10 nm. The particles exhibit bright fluorescence generated by color centers that act as atomic-size quantum emitters. We analyze their optical properties, including emission wavelength, photon-statistics, and photodynamics, and show that they are suitable for far-field super-resolution fluorescence nanoscopy. Our results provide a foundation for exploration of hBN nanoparticles as candidates for bioimaging, labeling, as well as biomarkers that are suitable for quantum sensing.
Duong, NMH, Regan, B, Toth, M, Aharonovich, I & Dawes, J 2019, 'A Random Laser Based on Hybrid Fluorescent Dye and Diamond Nanoneedles', physica status solidi (RRL) – Rapid Research Letters, vol. 13, no. 2, pp. 1800513-1800513.
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Random lasers use radiative gain and multiple scatterers in disordered media to generate light amplification. In this study, a random laser based on diamond nanoneedles that act as scatterers in combination with fluorescent dye molecules that serve as a gain medium has been demonstrated. Random lasers realized using diamond possess high spectral radiance with angle‐free emission and thresholds of 0.16 mJ. The emission dependence on the pillar diameter and density is investigated, and optimum lasing conditions are measured for pillars with spacing and density of ≈336 ± 40 nm and ≈2.9 × 1010 cm−2. Our results expand the application space of diamond as a material platform for practical, compact photonic devices, and sensing applications.
Eager, D & Hayati, H 2019, 'Additional Injury Prevention Criteria for Impact Attenuation Surfacing Within Children's Playgrounds', ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, vol. 5, no. 1.
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More than four decades have passed since the introduction of safety standards for impact attenuation surfaces (IAS) used in playgrounds. Falls in children's playground are a major source of injuries and IAS is one of the best methods of preventing severe head injuries. However, the ability of IAS in prevention of other types of injuries, such as upper limb fractures, is unclear. Accordingly, in this paper, ten synthetic playground surfaces were tested to examine their performance beyond the collected head injury criterion (HIC) and maximum G-force (Gmax) outputs recommended by ASTM F1292. The aim of this work was to investigate any limitations with current safety criteria and proposing additional criteria to filter hazardous IAS that technically comply with the current 1000 HIC and 200 Gmax thresholds. The proposed new criterion is called the impulse force criterion (If). If combines two important injury predictor characteristics, namely: HIC duration that is time duration of the most severe impact; and the change in momentum that addresses the IAS properties associated with bounce. Additionally, the maximum jerk (Jmax), the bounce, and the IAS absorbed work are presented. HIC, Gmax, If, and Jmax followed similar trends regarding material thickness and drop height. Moreover, the bounce and work done by the IAS on the falling missile at increasing drop heights was similar for all surfaces apart from one viscoelastic foam sample. The results presented in this paper demonstrate the limitations of current safety criteria and should, therefore, assist future research to reduce long-bone injuries in playgrounds.
Eder, K, Otter, LM, Yang, L, Jacob, DE & Cairney, JM 2019, 'Overcoming Challenges Associated with the Analysis of Nacre by Atom Probe Tomography', Geostandards and Geoanalytical Research, vol. 43, no. 3, pp. 385-395.
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In this study atom probe tomography was used to study nacre, an important biocomposite material that is challenging to prepare and analyse by atom probe and, when successful, yields data that is challenging to interpret. It was found that these challenges mainly arise from the insulating and heterogeneous nano‐scale properties of nacre. We outline our current best practice for preparing and running atom probe tips, such as using a low acceleration voltage (< 3 kV) and current (≤ 50 pA) to avoid damage to the microstructure, and using transmission electron microscopy to confirm that the region of interest is located close to the apex of the atom probe tip. Optimisation of the preparation parameters led to several successful atom probe experiments, with one of the data sets containing part of an organic membrane and others showing organic inclusions within the reconstruction.
Eeshwarasinghe, D, Loganathan, P & Vigneswaran, S 2019, 'Simultaneous removal of polycyclic aromatic hydrocarbons and heavy metals from water using granular activated carbon', Chemosphere, vol. 223, pp. 616-627.
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© 2019 Elsevier Ltd Polycyclic aromatic hydrocarbons (PAHs) and heavy metals are dangerous pollutants that commonly co-occur in water. An adsorption study conducted on the simultaneous removal of PAHs (acenaphthylene, phenanthrene) and heavy metals (Cd, Cu, Zn) by granular activated carbon (GAC) showed that, when these pollutants are present together, their adsorption was less than when they were present individually. The adsorptive removal percentage of PAHs (initial concentration 1 mg/L) was much higher than that of heavy metals (initial concentration (20 mg/L). The reduction in adsorption of PAHs by heavy metals followed the heavy metals' adsorption capacity and reduction in the negative zeta potential of GAC order (Cu > Zn > Cd). In contrast, PAHs had little effect on the zeta potential of GAC. The Langmuir adsorption capacities of acenaphthylene (0.31–2.63 mg/g) and phenanthrene (0.74–7.36 mg/g) on GAC decreased with increased metals' concentration with the reduction following the order of the metals’ adsorption capacity. The kinetic adsorption data fitted to Weber and Morris plots, indicating intra-particle diffusion of both PAHs and heavy metals into the mesopores and micropores in GAC with the diffusion rates. This depended on the type of PAH and metal and whether the pollutants were present alone or together.
Eggler, D, Karimi, M & Kessissoglou, N 2019, 'Active acoustic cloaking in a convected flow field', The Journal of the Acoustical Society of America, vol. 146, no. 1, pp. 586-594.
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Acoustic cloaking has mostly been considered within a stationary fluid. The authors herein show that accounting for the effects of convection in the presence of fluid flow is critical for cloaking in the acoustic domain. This work presents active acoustic cloaking in a convected flow field for two different incident fields, corresponding to a plane wave and a single monopole source, impinging on a rigid body. Monopole control sources circumferentially arranged around the rigid body are used to generate a secondary acoustic field to destructively interfere with the primary scattered field arising from the incident excitation cases. The authors show that for sound waves in a moving fluid, active cloaking can only be achieved using a convected cloak, which is dependent on Mach number.
Eickelmann, M, Wiegand, M, Deuse, J & Bernerstätter, R 2019, 'Bewertungsmodell zur Analyse der Datenreife', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 114, no. 1-2, pp. 29-33.
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Kurzfassung Die digitale Transformation der Unternehmensprozesse führt zu einem stetigen Anstieg verfügbarer Daten. Zur effizienten Nutzung des in den Daten verborgenen Wissens streben Unternehmen den Einsatz maschineller Lernverfahren an. Die Datenqualität hat eine herausragende Bedeutung für die Anwendbarkeit maschineller Lernverfahren sowie die resultierende Güte der Ergebnisse. Dieser Beitrag präsentiert ein Modell zur Bewertung der Datenreife, das die Evaluierung der Erfolgs-chancen industrieller Datenanalyseprojekte ermöglicht und Hinweise auf erforderliche Schritte zur Verbesserung der Datenreife gibt.
Eisman, JA & White, CP 2019, 'Dispelling confusion about de‐prescribing bisphosphonates', Medical Journal of Australia, vol. 210, no. 1, pp. 17-19.
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Ejeian, F, Azadi, S, Razmjou, A, Orooji, Y, Kottapalli, A, Ebrahimi Warkiani, M & Asadnia, M 2019, 'Design and applications of MEMS flow sensors: A review', Sensors and Actuators A: Physical, vol. 295, pp. 483-502.
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© 2019 Elsevier B.V. There is an indispensable need for fluid flow rate and direction sensors in various medical, industrial and environmental applications. Besides the critical demands on sensing range of flow parameters (such as rate, velocity, direction and temperature), the properties of different target gases or liquids to be sensed pose challenges to the development of reliable, inexpensive and low powered sensors. This paper presents an overview of the work done on design and development of Microelectromechanical system (MEMS)-based flow sensors in recent years. In spite of using some similar principles, diverse production methods, analysis strategies, and different sensing materials, MEMS flow sensors can be broadly categorized into three main types, namely thermal sensors, piezoresistive sensors and piezoelectric sensors. Additionally, some key challenges and future prospects for the use of the MEMS flow sensors are discussed briefly.
Ekanayake, D, Aryal, R, Hasan Johir, MA, Loganathan, P, Bush, C, Kandasamy, J & Vigneswaran, S 2019, 'Interrelationship among the pollutants in stormwater in an urban catchment and first flush identification using UV spectroscopy', Chemosphere, vol. 233, pp. 245-251.
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© 2019 Elsevier Ltd Assessing urban stormwater quality by investigation and characterisation of pollutants is a prerequisite for its effective management, for reuse and safe discharge. The stochastic nature of rainfall, dry weather periods, topology, human activities and climatic conditions generate and wash-off pollutants differently from event to event. This study investigated the major physico-chemical pollutants in stormwater runoff collected from an urban catchment over a period of two years. The aim of this study was to explore the use of UV spectroscopy to identify the first flush. In this study, the variation of pollutants during the passage of a rain event and the relationships among the measured pollutants was analysed to help broaden the application of UV spectroscopy beyond the detection of organic matter. Correlation analysis and principal component analysis (PCA) were performed to identify the possible relationship among measured pollutants. Although correlation analysis revealed some relationships between pollutants, in general they were not strong enough and was not helpful. PCA biplots suggested a few groups and revealed that the two components model could explain nearly 72% of the variability between pollutants. Pollutants in the group that included dissolved organic carbon (DOC) behaved in a similar manner. UV spectroscopy was applied to identify the first flush by comparing the recorded spectrum of consecutive samples that were collected in an event. Analysis of the spectra was able to isolate the point when first flush ends for DOC and pollutants that behave similar to it.
Ekberg, S, Danby, S, Theobald, M, Fisher, B & Wyeth, P 2019, 'Using physical objects with young children in ‘face-to-face’ and telehealth speech and language therapy', Disability and Rehabilitation, vol. 41, no. 14, pp. 1664-1675.
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Entezari, A, Roohani, I, Li, G, Dunstan, CR, Rognon, P, Li, Q, Jiang, X & Zreiqat, H 2019, 'Architectural Design of 3D Printed Scaffolds Controls the Volume and Functionality of Newly Formed Bone', Advanced Healthcare Materials, vol. 8, no. 1, pp. 1801353-1801353.
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AbstractThe successful regeneration of functional bone tissue in critical‐size defects remains a significant clinical challenge. To address this challenge, synthetic bone scaffolds are widely developed, but remarkably few are translated to the clinic due to poor performance in vivo. Here, it is demonstrated how architectural design of 3D printed scaffolds can improve in vivo outcomes. Ceramic scaffolds with different pore sizes and permeabilities, but with similar porosity and interconnectivity, are implanted in rabbit calvaria for 12 weeks, and then the explants are harvested for microcomputed tomography evaluation of the volume and functionality of newly formed bone. The results indicate that scaffold pores should be larger than 390 µm with an upper limit of 590 µm to enhance bone formation. It is also demonstrated that a bimodal pore topology—alternating large and small pores—enhances the volume and functionality of new bone substantially. Moreover, bone formation results indicate that stiffness of new bone is highly influenced by the scaffold's permeability in the direction concerned. This study demonstrates that manipulating pore size and permeability in a 3D printed scaffold architecture provides a useful strategy for enhancing bone regeneration outcomes.
Entezari, A, Zhang, Z, Sue, A, Sun, G, Huo, X, Chang, C-C, Zhou, S, Swain, MV & Li, Q 2019, 'Nondestructive characterization of bone tissue scaffolds for clinical scenarios', Journal of the Mechanical Behavior of Biomedical Materials, vol. 89, pp. 150-161.
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Eskandari, M & Li, L 2019, 'Microgrid operation improvement by adaptive virtual impedance', IET Renewable Power Generation, vol. 13, no. 2, pp. 296-307.
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Microgrids (MGs) are regarded as the best solution for optimal integration of the renewable energy sources into power systems. However, novel control strategies should be developed because of the distinct inherent feature of MG components in comparison to conventional power systems. Although the droop‐based control method is adopted in the MG to share power among distributed generation units, its dependency to grid parameters makes its implementation not as convenient as that in conventional power systems. Virtual impedance has been proposed as the complementary part of droop control in MGs. In this study, adaptive virtual impedance is designed considering its effects on the system performance in the MG including: (i) decoupling active and reactive power control by making the grid X/R ratio high, (ii) maximum transferable power through the feeder, (iii) stability concern and (iv) precise reactive power sharing in different operating modes as well as smooth transition from connected mode to islanded mode (IM). To this end, a novel method is proposed to determine the reactive power reference of distributed generation (DG) units according to their contribution in reactive power sharing in IM. In addition, simulation in MATLAB/Simulink environment is conducted to assess the performance of the control system.
Eskandari, M, Li, L, Moradi, MH, Siano, P & Blaabjerg, F 2019, 'Active Power Sharing and Frequency Restoration in an Autonomous Networked Microgrid', IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 4706-4717.
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© 1969-2012 IEEE. Microgrid (MG) concept is considered as the best solution for future power systems, which are expected to receive a considerable amount of power through renewable energy resources and distributed generation units. Droop control systems are widely adopted in conventional power systems and recently in MGs for power sharing among generation units. However, droop control causes frequency fluctuations, which leads to poor power quality. This paper deals with frequency fluctuation and stability concerns of f-P droop control loop in MGs. Inspired from conventional synchronous generators, virtual damping is proposed to diminish frequency fluctuation in MGs. Then, it is demonstrated that the conventional frequency restoration method inserts an offset to the phase angle, which is in conflict with accurate power sharing. To this end, a novel control method, based on phase angle feedback, is proposed for frequency restoration in conjunction with a novel method for adaptively tuning the feedback gains to preserve precise active power sharing. Nonlinear stability analysis is conducted by drawing the phase variations of the nonlinear second-order equation of the δ-P droop loop and it is proved that the proposed method improves the stability margin of f-P control loop. Simulation results demonstrate the effectiveness of the proposed method.
Esselle, K 2019, 'Call for IEEE AP-S Distinguished Lecturer Nominations', IEEE Antennas and Propagation Magazine, vol. 61, no. 3, pp. C2-C2.
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Esselle, KP 2019, 'Distinguished Lecturer Program Update [Distinguished Lecturers]', IEEE Antennas and Propagation Magazine, vol. 61, no. 4, pp. 120-120.
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Etchebarne, MS, Cancino, CA & Merigó, JM 2019, 'Evolution of the business and management research in Chile', International Journal of Technology, Policy and Management, vol. 19, no. 2, pp. 108-108.
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Copyright © 2019 Inderscience Enterprises Ltd. Different aspects have enhanced the development of scientific research in business and management in Chile. The aim of this paper is to analyse the characterisation of this scientific evolution. The method used is a Bibliometric analysis. Our sample examines any paper published between 1991 and 2015 in the Web of Science (WoS) database in the area of business and management. The main results show that the publications have had a significant increase. Scientific productivity increase may be related, among other factors: to the efforts of the Chilean universities that reward and incentivise publications in WoS; the participation of academics in competitive grants (Fondecyt); and international accreditations that demand more productive universities in terms of research. The results of the study could be interesting for universities from developing countries wishing to generate policies to increase the productivity in the areas of business and management.
Fahmideh, M & Beydoun, G 2019, 'Big data analytics architecture design - An application in manufacturing systems.', Comput. Ind. Eng., vol. 128, pp. 948-963.
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© 2018 Elsevier Ltd Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This needs a systematic re-architecting approach incorportaitng careful and thorough evaluation of goals for integrating manufacturing legacy information systems with data analytics platforms. Furthermore, ameliorating the uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing phases. Objective: We propose an approach for goal-obstacle analysis and selecting suitable big data solution architectures that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. Next, it combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems: (i) A goal-oriented modelling for exploring goals and obstacles in integrating systems with data analytics platforms at the requirement level and (ii) An analysis of the architectural decisions under uncertainty. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected ma...
Fahmideh, M, Beydoun, G & Low, G 2019, 'Experiential probabilistic assessment of cloud services.', Inf. Sci., vol. 502, pp. 510-524.
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© 2019 Elsevier Inc. Substantial difficulties in adopting cloud services are often encountered during upgrades of existing software systems. A reliable early stage analysis can facilitate an informed decision process of moving systems to cloud platforms. It can also mitigate risks against system quality goals. Towards this, we propose an interactive goal reasoning approach which is supported by a probabilistic layer for the precise analysis of cloud migration risks to improve the reliability of risk control. The approach is illustrated using a commercial scenario of integrating a digital document processing system to Microsoft Azure cloud platform.
Fahmideh, M, Daneshgar, F, Rabhi, FA & Beydoun, G 2019, 'A generic cloud migration process model.', Eur. J. Inf. Syst., vol. 28, no. 3, pp. 233-255.
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© 2018, © 2018 Operational Research Society. The cloud computing literature provides various ways to utilise cloud services, each with a different viewpoint and focus and mostly using heterogeneous technical-centric terms. This hinders efficient and consistent knowledge flow across the community. Little, if any, research has aimed on developing an integrated process model which captures core domain concepts and ties them together to provide an overarching view of migrating legacy systems to cloud platforms that is customisable for a given context. We adopt design science research guidelines in which we use a metamodeling approach to develop a generic process model and then evaluate and refine the model through three case studies and domain expert reviews. This research benefits academics and practitioners alike by underpinning a substrate for constructing, standardising, maintaining, and sharing bespoke cloud migration models that can be applied to given cloud adoption scenarios.
Fan, X, Li, C, Yuan, X, Dong, X & Liang, J 2019, 'An interactive visual analytics approach for network anomaly detection through smart labeling', Journal of Visualization, vol. 22, no. 5, pp. 955-971.
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© 2019, The Visualization Society of Japan. Abstract: Network anomaly detection is an important means for safeguarding network security. On account of the difficulties encountered in traditional automatic detection methods such as lack of labeled data, expensive retraining costs for new data and non-explanation, we propose a novel smart labeling method, which combines active learning and visual interaction, to detect network anomalies through the iterative labeling process of the users. The algorithms and the visual interfaces are tightly integrated. The network behavior patterns are first learned by using the self-organizing incremental neural network. Then, the model uses a Fuzzy c-means-based algorithm to do classification on the basis of user feedback. After that, the visual interfaces are updated to present the improved results of the model, which can help users to choose meaningful candidates, judge anomalies and understand the model results. The experiments show that compared to labeling without our visualizations, our method can achieve a high accuracy rate of anomaly detection with fewer labeled samples. Graphic abstract: [Figure not available: see fulltext.].
Fan, Y, Lin, X, Liang, W, Tan, G & Nanda, P 2019, 'A secure privacy preserving deduplication scheme for cloud computing', Future Generation Computer Systems, vol. 101, pp. 127-135.
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© 2019 Elsevier B.V. Data deduplication is a key technique to improve storage efficiency in cloud computing. By pointing redundant files to a single copy, cloud service providers greatly reduce their storage space as well as data transfer costs. Despite of the fact that the traditional deduplication approach has been adopted widely, it comes with a high risk of losing data confidentiality because of the data storage models in cloud computing. To deal with this issue in cloud storage, we first propose a TEE (trusted execution environment) based secure deduplication scheme. In our scheme, each cloud user is assigned a privilege set; the deduplication can be performed if and only if the cloud users have the correct privilege. Moreover, our scheme augments the convergent encryption with users’ privileges and relies on TEE to provide secure key management, which improves the ability of such cryptosystem to resist chosen plaintext attacks and chosen ciphertext attacks. A security analysis indicates that our scheme is secure enough to support data deduplication and to protect the confidentiality of sensitive data. Furthermore, we implement a prototype of our scheme and evaluate the performance of our prototype, experiments show that the overhead of our scheme is practical in realistic environments.
Fang, G, Lu, H, Law, A, Gallego-Ortega, D, Jin, D & Lin, G 2019, 'Gradient-sized control of tumor spheroids on a single chip', Lab on a Chip, vol. 19, no. 24, pp. 4093-4103.
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Gradient-sized spheroids can be simultaneously generated on a single chip using a liquid-dome method assisted by the surface tension. The facile method can be used for investigation of the size-dependent behaviors of spheroids in biomedical research.
Fang, J, Wu, C, Li, J, Liu, Q, Wu, C, Sun, G & Li, Q 2019, 'Phase field fracture in elasto-plastic solids: Variational formulation for multi-surface plasticity and effects of plastic yield surfaces and hardening', International Journal of Mechanical Sciences, vol. 156, pp. 382-396.
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© 2019 Elsevier Ltd The phase field modelling has been extended from brittle fracture to ductile fracture by incorporating plasticity. However, the effects of plastic yield functions and hardening on the fracture behaviour have not been examined systematically to date. The phase field fracture coupled with multi-surface plasticity is formulated in the variational framework for the unified yield criterion, which is able to facilitate the study on different yield surfaces. First, the homogeneous solutions of fracture in elasto-plastic solids are derived analytically for 1D and 2D cases. The results show that a greater hardening modulus would lead to an ascending branch of the stress versus strain curve; and the yield function may significantly affect the stress state and phase field damage. Second, the finite element (FE) technique is implemented for modelling the phase field fracture in elasto-plastic solids, in which the stress update and consistent tangent modular matrix are derived for the unified yield criterion. Finally, three numerical examples are presented to explore the effects of the yield function and material hardening. It is found that the yield function and material hardening could significantly affect the crack propagation and the final fracture pattern. In particular, the Tresca yield function tends to create a straight crack path orthogonal to the first principal stress, while the other yield functions show no sizeable difference in their crack paths.
Fang, J, Wu, C, Liu, Q, Sun, G & Li, Q 2019, 'Implicit Integration of the Unified Yield Criterion in the Principal Stress Space', Journal of Engineering Mechanics, vol. 145, no. 7, pp. 04019041-04019041.
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© 2019 American Society of Civil Engineers. An implicit numerical integration algorithm is presented for the unified yield criterion, which could encompass most other yield criteria. The modification matrix, which is used to convert the continuum tangent modular matrix into the consistent tangent modular matrix, is derived for the return to planes, lines, and the apex of the unified yield criterion with multisurface plasticity with discontinuities. The stress update and consistent tangent modular matrix are first derived in closed form in the principal stress space, and then they are transformed back into the general stress space by coordinate transformation. Three numerical examples are used to demonstrate the effectiveness of the presented algorithm. The correctness of the developed algorithm is validated by the analytical solution and ABAQUS solution with the built-in Mohr-Coulomb model. The developed algorithm is also demonstrated to be least twice more efficient than the ABAQUS built-in algorithm. The presented algorithm for the unified yield criterion can facilitate the understanding of the effect the intermediate principal stress. With the increase in b, the force versus deflection curve at the midspan increases for the beam under three-point bending, and the critical radius of the elastoplastic interface decreases (i.e., the plastic zone becomes small) for the circular tunnel under hydrostatic pressure.
Fang, J, Wu, C, Rabczuk, T, Wu, C, Ma, C, Sun, G & Li, Q 2019, 'Phase field fracture in elasto-plastic solids: Abaqus implementation and case studies', Theoretical and Applied Fracture Mechanics, vol. 103, pp. 102252-102252.
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© 2019 Elsevier Ltd Phase field modelling for fracture has been extended from elastic solids to elasto-plastic solids. In this study, we present the implementation procedures of a staggered scheme for phase field fracture of elasto-plastic solids in commercial finite element software Abaqus using subroutines UEL and UMAT. The UMAT is written for the constitutive behaviour of elasto-plastic solids, while the UEL is written for the phase field fracture. The phase field and displacement field are solved separately using the Newton-Raphson iteration method. In each iteration, one field is computed by freezing the other field at the last loading increment. A number of benchmark examples are tested from one single element up to 3D problems. The correctness of the staggered scheme is verified analytically in terms of the stress-strain curve and the evolution of the phase field in the one single element example. In the 2D and 3D problems, the fracture behaviour of elasto-plastic solids can be reproduced in terms of reaction force curve and crack propagation, which exhibit good agreement with the experimental observations and numerical results in literature. Not only can the proposed implementation help attract more academic researchers, but also engineering practitioners to take the advantages of phase field modelling for fracture in elasto-plastic solids. The Abaqus subroutine codes can be downloaded online from Mendeley data repository linked to this work (The link is provided in Supplementary material).
Fang, XS, Sheng, QZ, Wang, X, Chu, D & Ngu, AHH 2019, 'SmartVote: a full-fledged graph-based model for multi-valued truth discovery', World Wide Web, vol. 22, no. 4, pp. 1855-1885.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. In the era of Big Data, truth discovery has emerged as a fundamental research topic, which estimates data veracity by determining the reliability of multiple, often conflicting data sources. Although considerable research efforts have been conducted on this topic, most current approaches assume only one true value for each object. In reality, objects with multiple true values widely exist and the existing approaches that cope with multi-valued objects still lack accuracy. In this paper, we propose a full-fledged graph-based model, SmartVote, which models two types of source relations with additional quantification to precisely estimate source reliability for effective multi-valued truth discovery. Two graphs are constructed and further used to derive different aspects of source reliability (i.e., positive precision and negative precision) via random walk computations. Our model incorporates four important implications, including two types of source relations, object popularity, loose mutual exclusion, and long-tail phenomenon on source coverage, to pursue better accuracy in truth discovery. Empirical studies on two large real-world datasets demonstrate the effectiveness of our approach.
Fang, Y, Huang, X, Qin, L, Zhang, Y, Zhang, W, Cheng, R & Lin, X 2019, 'A Survey of Community Search Over Big Graphs.', CoRR, vol. abs/1904.12539, no. 1, pp. 353-392.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently, a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus, it is desirable to have a comprehensive review of these works. In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze and compare the quality of communities under their models, and the performance of different solutions. Furthermore, we point out new research directions. This survey does not only help researchers to have better understanding of existing community search solutions, but also provides practitioners a better judgment on choosing the proper solutions.
Fanos, AM & Pradhan, B 2019, 'A Novel Hybrid Machine Learning-Based Model for Rockfall Source Identification in Presence of Other Landslide Types Using LiDAR and GIS', Earth Systems and Environment, vol. 3, no. 3, pp. 491-506.
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© 2019, King Abdulaziz University and Springer Nature Switzerland AG. Abstract: Rockfall is a common phenomenon in mountainous and hilly areas worldwide, including Malaysia. Rockfall source identification is a challenging task in rockfall hazard assessment. The difficulty rise when the area of interest has other landslide types with nearly similar controlling factors. Therefore, this research presented and assessed a hybrid model for rockfall source identification based on the stacking ensemble model of random forest (RF), artificial neural network, Naive Bayes (NB), and logistic regression in addition to Gaussian mixture model (GMM) using high-resolution airborne laser scanning data (LiDAR). GMM was adopted to automatically compute the thresholds of slope angle for various landslide types. Chi square was utilised to rank and select the conditioning factors for each landslide type. The best fit ensemble model (RF–NB) was then used to produce probability maps, which were used to conduct rockfall source identification in combination with the reclassified slope raster based on the thresholds obtained by the GMM. Next, landslide potential area was structured to reduce the sensitivity and the noise of the model to the variations in different conditioning factors for improving its computation performance. The accuracy assessment of the developed model indicates that the model can efficiently identify probable rockfall sources with receiver operating characteristic curve accuracies of 0.945 and 0.923 on validation and training datasets, respectively. In general, the proposed hybrid model is an effective model for rockfall source identification in the presence of other landslide types with a reasonable generalisation performance. Graphic Abstract: [Figure not available: see fulltext.].
Fanos, AM & Pradhan, B 2019, 'A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS', IEEE Access, vol. 7, pp. 74570-74585.
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Fanos, AM, Pradhan, B, Mansor, S, Yusoff, ZM, Abdullah, AFB & Jung, HS 2019, 'Rockfall Source Identification Using a Hybrid Gaussian Mixture-Ensemble Machine Learning Model and LiDAR Data', Korean Journal of Remote Sensing, vol. 35, no. 1, pp. 93-115.
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The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms (ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.
Far, C & Far, H 2019, 'Improving energy efficiency of existing residential buildings using effective thermal retrofit of building envelope', Indoor and Built Environment, vol. 28, no. 6, pp. 744-760.
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Upgrading the energy efficiency of existing buildings is a well-known issue around the globe. Given the very low renewal rate of the building stock, thermal retrofit of the existing buildings seems to be a good solution to improve the environmental performance of the building sector. Several studies have acknowledged the lack of knowledge, experience and best-practice examples as barriers in thermal retrofit of existing buildings. Therefore, this study has focused on developing recommendations on the most effective and feasible retrofitting techniques for existing buildings and performing financial analysis of initial investment vs. return based on the quantitative results of the energy modelling. Thermal comfort modelling software FirstRate5 has been used to simulate the annual heating and cooling energy consumption of nine benchmark buildings through a range of retrofitting techniques. Dwellings of varying construction materials including weatherboard, cavity brick and brick veneer have been simulated to improve accuracy. Examining seven different thermal retrofitting options in this study, it has become apparent that there is significant heating and cooling energy reduction, with payback period of less than three years, by implementing two options of the examined retrofitting cases to existing residential dwellings.
Fasugba, O, Cheng, AC, Gregory, V, Graves, N, Koerner, J, Collignon, P, Gardner, A & Mitchell, BG 2019, 'Chlorhexidine for meatal cleaning in reducing catheter-associated urinary tract infections: a multicentre stepped-wedge randomised controlled trial', The Lancet Infectious Diseases, vol. 19, no. 6, pp. 611-619.
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Fasugba, O, Das, A, Mnatzaganian, G, Mitchell, BG, Collignon, P & Gardner, A 2019, 'Incidence of single-drug resistant, multidrug-resistant and extensively drug-resistant Escherichia coli urinary tract infections: An Australian laboratory-based retrospective study', Journal of Global Antimicrobial Resistance, vol. 16, pp. 254-259.
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Fatahi, B 2019, 'Editorial', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 172, no. 1, pp. 1-2.
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Fatimah, I, Sahroni, I, Fadillah, G, Musawwa, MM, Mahlia, TMI & Muraza, O 2019, 'Glycerol to Solketal for Fuel Additive: Recent Progress in Heterogeneous Catalysts', Energies, vol. 12, no. 15, pp. 2872-2872.
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Biodiesel has been successfully commercialized in numerous countries. Glycerol, as a byproduct in biodiesel production plant, has been explored recently for fuel additive production. One of the most prospective fuel additives is solketal, which is produced from glycerol and acetone via an acetalization reaction. This manuscript reviewed recent progress on heterogeneous catalysts used in the exploratory stage of glycerol conversion to solketal. The effects of acidity strength, hydrophobicity, confinement effect, and others are discussed to find the most critical parameters to design better catalysts for solketal production. Among the heterogeneous catalysts, resins, hierarchical zeolites, mesoporous silica materials, and clays have been explored as effective catalysts for acetalization of glycerol. Challenges with each popular catalytic material are elaborated. Future works on glycerol to solketal will be improved by considering the stability of the catalysts in the presence of water as a byproduct. The presence of water and salt in the feed is certainly destructive to the activity and the stability of the catalysts.
Fattah, IMR, Yip, HL, Jiang, Z, Yuen, ACY, Yang, W, Medwell, PR, Kook, S, Yeoh, GH & Chan, QN 2019, 'Effects of flame-plane wall impingement on diesel combustion and soot processes', Fuel, vol. 255, pp. 115726-115726.
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© 2019 Elsevier Ltd This work aims to assess the effects of flame-wall impingement on the combustion and soot processes of diesel flames. For this work, experimental measurements were performed in a constant-volume combustion chamber (CVCC) at ambient conditions that are representative of compression-ignition engines. The characteristics of impinging and free flames were compared at two identical ambient and injector conditions (20.8 kg/m3 ambient density, 6 MPa ambient pressure, 1000 K bulk temperature, 15 and 10 vol% ambient O2 concentration, and 100 MPa injection pressure). To simulate flame-wall impingement, a flat plane steel wall, normal to the injector axis, was initially placed at 53 mm from nozzle, but was varied from 53 to 35 mm during the experiments. Under the test conditions of this work, it was found that wall impingement resulted in lower soot temperature and soot content, in addition to a loss of momentum for the wall jet. The results also revealed that decreasing impingement distance from the nozzle resulted in reduced soot temperature and soot level for the wall jet. The reduced soot content observed for the wall jet appeared to be mainly driven by enhanced mixing. Flame transparency modeling was also performed to assess the uncertainties of two-color measurements for flame-plane wall impingement. The analysis indicated that the derived soot temperature and concentration values would be affected by the actual temperature profiles, rendering the technique useful to reveal trends, but not reliable for absolute soot concentration measurements.
Feng, B, Li, G, Li, G, Zhang, Y, Zhou, H & Yu, S 2019, 'Enabling Efficient Service Function Chains at Terrestrial-Satellite Hybrid Cloud Networks', IEEE Network, vol. 33, no. 6, pp. 94-99.
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The great improvements in both satellite and terrestrial networks have motivated the academic and industrial communities to rethink their integration. As a result, there is an increasing interest in new-generation hybrid satellite-terrestrial networks, where sufficient flexibility should be enabled to deploy customized SFCs to satisfy the growing diversity of user needs. However, it is still challenging to achieve such a nice vision, since many key issues remain unaddressed comprehensively such as framework design, communication procedures and resource optimization. Therefore, in this article, we focus on how to efficiently deploy customized SFCs at terrestrial- satellite hybrid cloud networks. In particular, we first propose an elastic framework used for SFC deployment at clouds, and second propose an efficient SFC mapping approach for improvement of system resource utilization. Finally, we verify the proposed framework at a proof-ofconcept prototype via a number of use cases, and evaluate the proposed mapping approach through extensive simulations based on a realworld topology. Related experimental and simulation results have confirmed the feasibility and benefits of our proposed framework and mapping approach.
Feng, J, Liu, L, Wu, D, Li, G, Beer, M & Gao, W 2019, 'Dynamic reliability analysis using the extended support vector regression (X-SVR)', Mechanical Systems and Signal Processing, vol. 126, pp. 368-391.
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© 2019 Elsevier Ltd For engineering applications, the dynamic system responses can be significantly affected by uncertainties in the system parameters including material and geometric properties as well as by uncertainties in the excitations. The reliability of dynamic systems is widely evaluated based on the first-passage theory. To improve the computational efficiency, surrogate models are widely used to approximate the relationship between the system inputs and outputs. In this paper, a new machine learning based metamodel, namely the extended support vector regression (X-SVR), is proposed for the reliability analysis of dynamic systems via utilizing the first-passage theory. Furthermore, the capability of X-SVR is enhanced by a new kernel function developed from the vectorized Gegenbauer polynomial, especially for solving complex engineering problems. Through the proposed approach, the relationship between the extremum of the dynamic responses and the input uncertain parameters is approximated by training the X-SVR model such that the probability of failure can be efficiently predicted without using other computational tools for numerical analysis, such as the finite element analysis (FEM). The feasibility and performance of the proposed surrogate model in dynamic reliability analysis is investigated by comparing it with the conventional ε-insensitive support vector regression (ε-SVR) with Gaussian kernel and Monte Carlo simulation (MSC). Four numerical examples are adopted to evidently demonstrate the practicability and efficiency of the proposed X-SVR method.
Feng, S, Shen, S, Huang, L, Champion, AC, Yu, S, Wu, C & Zhang, Y 2019, 'Three-dimensional robot localization using cameras in wireless multimedia sensor networks', Journal of Network and Computer Applications, vol. 146, pp. 102425-102425.
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© 2019 Elsevier Ltd We consider three-dimensional (3D) localization in wireless multimedia sensor networks (WMSNs) and seek optimal localization accuracy in order to ensure real-time data fusion of mobile robots in WMSNs. To this end, we propose a real-time 3D localization algorithm realized by a distributed architecture with various smart devices to overcome network instability and the bottleneck channel at the coordinator. We then employ the recursive least squares (RLS) algorithm to fuse the 2D image coordinates from multiple views synchronously in WMSNs and determine the mobile robot's 3D location in an indoor environment. To minimize wireless data transmission, we also develop a distributed architecture that combines various smart devices by defining the data content transmitted from multiple wireless visual sensors. Moreover, we analyze the factors influencing the network instability of various smart devices, and factors influencing the localization performance of mobile robots in a multiple-view system. Experimental results show the proposed algorithm can achieve reliable, efficient, and real-time 3D localization in indoor WMSNs.
Feng, X, Bai, X, Ni, J, Wasinger, VC, Beretov, J, Zhu, Y, Graham, P & Li, Y 2019, 'CHTOP in Chemoresistant Epithelial Ovarian Cancer: A Novel and Potential Therapeutic Target', Frontiers in Oncology, vol. 9, no. JUN, pp. 1-13.
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Objective: Chemoresistance is a major challenge in epithelial ovarian cancer (EOC) treatment. Chromatin target of protein arginine methyltransferase (CHTOP) was identified as a potential biomarker in chemoresistant EOC cell lines using label-free LC-MS/MS quantitative proteomics. Thus, the aim of this study is to investigate the role of CHTOP in chemoresistant EOC and the underlying mechanism. Methods: The expression of CHTOP in human ovarian cancer cells and tissues was detected using immunofluorescence (IF), western blot (WB), and immunohistochemistry (IHC), respectively. Flow cytometry and TUNEL assay were employed to detect the effect of CHTOP knockdown (KD) in chemoresistant EOC cell apoptosis, while colony and sphere formation assays were used to evaluate its effect on cell stemness. The association of CHTOP with cell metastasis was determined using Matrigel invasion and wound-healing assays. Results: The higher level expression of CHTOP protein was found in chemoresistant EOC cells as compared to their sensitive parental cells or normal epithelial ovarian cells. Results from IHC and bioinformatic analysis showed CHTOP was highly expressed in human ovarian cancer tissues and associated with a poor progression-free survival in patients. In addition, CHTOP KD significantly enhanced cisplatin-induced apoptosis, reduced the stemness of chemoresistant EOC cells, and decreased their metastatic potential. Conclusion: Our findings suggest that CHTOP is associated with apoptosis, stemness, and metastasis in chemoresistant EOC cells and might be a promising target to overcome chemoresistance in EOC treatment.
Feng, Y, Gao, W, Wu, D & Tin-Loi, F 2019, 'Machine learning aided stochastic elastoplastic analysis', Computer Methods in Applied Mechanics and Engineering, vol. 357, pp. 112576-112576.
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© 2019 Elsevier B.V. The stochastic elastoplastic analysis is investigated for structures under plane stress/strain conditions. A novel uncertain nonlinear analysis framework, namely the machine leaning aided stochastic elastoplastic analysis (MLA-SEPA), is presented herein via finite element method (FEM). The proposed MLA-SEPA is a favourable alternative to determine structural reliability when full-scale testing is not achievable, thus leading to significant eliminations of manpower and computational efforts spent in practical engineering applications. Within the MLA-SEPA framework, an extended support vector regression (X-SVR) approach is introduced and then incorporated for the subsequent uncertainty quantification. By successfully establishing the governing relationship between the uncertain system parameters and any concerned structural output, a comprehensive probabilistic profile including means, standard deviations, probability density functions (PDFs), and cumulative distribution functions (CDFs) of the structural output can be effectively established through a sampling scheme. Consequently, the nonlinear performance of the structure against both serviceability and strength limit states can be effectively investigated with the consideration of various system uncertainties. Three numerical examples are thoroughly investigated to illustrate the accuracy, applicability and effectiveness of the proposed MLA-SEPA approach.
Figlioli, G, Bogliolo, M, Catucci, I, Caleca, L, Lasheras, SV, Pujol, R, Kiiski, JI, Muranen, TA, Barnes, DR, Dennis, J, Michailidou, K, Bolla, MK, Leslie, G, Aalfs, CM, Balleine, R, Baxter, R, Braye, S, Carpenter, J, Dahlstrom, J, Forbes, J, Lee, CS, Marsh, D, Morey, A, Pathmanathan, N, Scott, R, Simpson, P, Spigelman, A, Wilcken, N, Yip, D, Zeps, N, Adank, MA, Adlard, J, Agata, S, Cadoo, K, Agnarsson, BA, Ahearn, T, Aittomäki, K, Ambrosone, CB, Andrews, L, Anton-Culver, H, Antonenkova, NN, Arndt, V, Arnold, N, Aronson, KJ, Arun, BK, Asseryanis, E, Auber, B, Auvinen, P, Azzollini, J, Balmaña, J, Barkardottir, RB, Barrowdale, D, Barwell, J, Beane Freeman, LE, Beauparlant, CJ, Beckmann, MW, Behrens, S, Benitez, J, Berger, R, Bermisheva, M, Blanco, AM, Blomqvist, C, Bogdanova, NV, Bojesen, A, Bojesen, SE, Bonanni, B, Borg, A, Brady, AF, Brauch, H, Brenner, H, Brüning, T, Burwinkel, B, Buys, SS, Caldés, T, Caliebe, A, Caligo, MA, Campa, D, Campbell, IG, Canzian, F, Castelao, JE, Chang-Claude, J, Chanock, SJ, Claes, KBM, Clarke, CL, Collavoli, A, Conner, TA, Cox, DG, Cybulski, C, Czene, K, Daly, MB, de la Hoya, M, Devilee, P, Diez, O, Ding, YC, Dite, GS, Ditsch, N, Domchek, SM, Dorfling, CM, dos-Santos-Silva, I, Durda, K, Dwek, M, Eccles, DM, Ekici, AB, Eliassen, AH, Ellberg, C, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, J, Flyger, H, Foulkes, WD, Friebel, TM, Friedman, E, Gabrielson, M, Gaddam, P, Gago-Dominguez, M, Gao, C, Gapstur, SM, Garber, J, García-Closas, M, García-Sáenz, JA, Gaudet, MM, Gayther, SA, Belotti, M, Bertrand, O, Birot, A-M, Buecher, B, Caputo, S, Dupré, A, Fourme, E, Gauthier-Villars, M, Golmard, L, Le Mentec, M, Moncoutier, V, de Pauw, A, Saule, C, Boutry-Kryza, N, Calender, A, Giraud, S, Léone, M, Bressac-de-Paillerets, B, Caron, O, Guillaud-Bataille, M, Bignon, Y-J, Uhrhammer, N, Bonadona, V, Lasset, C, Berthet, P, Castera, L, Vaur, D, Bourdon, V, Noguès, C, Noguchi, T, Popovici, C, Remenieras, A, Sobol, H, Coupier, I, Pujol, P, Adenis, C, Dumont, A, Révillion, F, Muller, D, Barouk-Simonet, E, Bonnet, F, Bubien, V, Longy, M, Sevenet, N, Gladieff, L, Guimbaud, R, Feillel, V, Toulas, C, Dreyfus, H, Leroux, CD, Peysselon, M, Rebischung, C, Legrand, C, Baurand, A, Bertolone, G, Coron, F, Faivre, L, Jacquot, C, Lizard, S, Kientz, C, Lebrun, M, Prieur, F, Fert-Ferrer, S, Mari, V, Vénat-Bouvet, L, Bézieau, S & et al. 2019, 'The FANCM:p.Arg658* truncating variant is associated with risk of triple-negative breast cancer', npj Breast Cancer, vol. 5, no. 1.
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AbstractBreast cancer is a common disease partially caused by genetic risk factors. Germline pathogenic variants in DNA repair genes BRCA1, BRCA2, PALB2, ATM, and CHEK2 are associated with breast cancer risk. FANCM, which encodes for a DNA translocase, has been proposed as a breast cancer predisposition gene, with greater effects for the ER-negative and triple-negative breast cancer (TNBC) subtypes. We tested the three recurrent protein-truncating variants FANCM:p.Arg658*, p.Gln1701*, and p.Arg1931* for association with breast cancer risk in 67,112 cases, 53,766 controls, and 26,662 carriers of pathogenic variants of BRCA1 or BRCA2. These three variants were also studied functionally by measuring survival and chromosome fragility in FANCM−/− patient-derived immortalized fibroblasts treated with diepoxybutane or olaparib. We observed that FANCM:p.Arg658* was associated with increased risk of ER-negative disease and TNBC (OR = 2.44, P = 0.034 and OR = 3.79; P = 0.009, respectively). In a country-restricted analysis, we confirmed the associations detected for FANCM:p.Arg658* and found that also FANCM:p.Arg1931* was associated with ER-negative breast cancer risk (OR = 1.96; P = 0.006). The functional results indicated that all three variants were deleterious affecting cell survival and chromosome stability with FANCM:p.Arg658* causing more severe phenotypes. In conclusion, we confirmed that the two rare FANCM
Fleming, C, Gunawan, C, Golzan, M, Torpy, F, Irga, P & Mcgrath, K 2019, 'Investigating the effects of air pollutant nanoparticles on the onset or progression of Alzheimer's disease', IBRO Reports, vol. 6, pp. S329-S330.
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Fraietta, A, Bown, O, Ferguson, S, Gillespie, S & Bray, L 2019, 'Rapid Composition for Networked Devices: HappyBrackets', Computer Music Journal, vol. 43, no. 2-3, pp. 89-108.
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Abstract This article introduces an open-source Java-based programming environment for creative coding of agglomerative systems using Internet-of-Things (IoT) technologies. Our software originally focused on digital signal processing of audio—including synthesis, sampling, granular sample playback, and a suite of basic effects—but composers now use it to interface with sensors and peripherals through general-purpose input/output and external networked systems. This article examines and addresses the strategies required to integrate novel embedded musical interfaces and creative coding paradigms through an IoT infrastructure. These include: the use of advanced tooling features of a professional integrated development environment as a composition or performance interface rather than just as a compiler; techniques to create media works using features such as autodetection of sensors; seamless and serverless communication among devices on the network; and uploading, updating, and running of new compositions to the device without interruption. Furthermore, we examined the difficulties many novice programmers experience when learning to write code, and we developed strategies to address these difficulties without restricting the potential available in the coding environment. We also examined and developed methods to monitor and debug devices over the network, allowing artists and programmers to set and retrieve current variable values to or from these devices during the performance and composition stages. Finally, we describe three types of art work that demonstrate how the software, called HappyBrackets, is being used in live-coding and dance performances, in interactive sound installations, and as an advanced composition and performance tool for multimedia works.
Franzò, S, Frattini, F, Cagno, E & Trianni, A 2019, 'A multi-stakeholder analysis of the economic efficiency of industrial energy efficiency policies: Empirical evidence from ten years of the Italian White Certificate Scheme', Applied Energy, vol. 240, pp. 424-435.
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© 2019 There is growing interest worldwide in more effective policies to promote industrial energy efficiency and mitigate climate change. The White Certificates Scheme is a market-based mechanism aimed at stimulating the adoption of Energy Efficiency Measures. The Italian White Certificates scheme - one of the most long-standing and articulated - is a successful example of industrial energy efficiency policies, considered an interesting and remarkable case by other countries, especially due to its robustness in terms of the volume of certificates traded. Despite the considerable interest in White Certificates, an in-depth analysis of the economic efficiency of the mechanism from the perspective of different stakeholders is still lacking. To address this gap, this study develops a cost-benefit evaluation framework and a multi-stakeholder economic efficiency analysis of the Italian White Certificates scheme focusing on the Italian State, utilities, players in the energy efficiency value chain, and energy users. Our findings (also corroborated with sensitivity analyses) show that the White Certificates Scheme has led to several positive impacts for almost all stakeholders involved, with the exception of energy utilities that have suffered a major economic loss mainly due to a reduction of energy sold to end users. Such loss is likely to promote a deep change in the role of utilities in the energy market in terms of the services they offer and their business models. Our findings, in addition to providing useful directions for future research, offer interesting insights and implications for policymakers who may take inspiration from the pros and cons of the Italian White Certificates scheme when promoting energy efficiency through incentive mechanisms.
Fu, J, Liu, Q, Liufu, K, Deng, Y, Fang, J & Li, Q 2019, 'Design of bionic-bamboo thin-walled structures for energy absorption', Thin-Walled Structures, vol. 135, pp. 400-413.
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© 2018 Elsevier Ltd Bio-inspired engineering design has drawn considerable attention in the recent years for its great structural and mechanical features. This study aimed to explore the energy absorption characteristics of a novel bionic-bamboo tube (BBT) structure subjected to axial crushing. The tubes with six different cross-sectional configurations were devised with inspiration of bamboo microstructure. The effects of rib shape and rib number were analyzed by using the finite element code LS-DYNA. The numerical results indicated that the BBT structures with the rib shape of “X” and the rib number of six exhibited the best crashworthiness. To further improve the energy absorption capabilities of these BBT structures, the multiobjective optimization was employed with respect to design variables of configurational structure, such as the rib angle of the “X” shaped cross-section, center distance and rib thickness. The response surface method (RSM) and multiobjective particle swarm optimization (MOPSO) algorithm were adopted to maximize specific energy absorption (SEA) while minimizing peak crushing force (PCF). The optimization results demonstrated that compared to the baseline design, the SEA value of the optimized BBT structure was further increased by 6.84% without sacrificing in peak crushing force.
Fujioka, T, Hoang, AT, Ueyama, T & Nghiem, LD 2019, 'Integrity of reverse osmosis membrane for removing bacteria: new insight into bacterial passage', Environmental Science: Water Research & Technology, vol. 5, no. 2, pp. 239-245.
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Fluorescent microspheres (surrogates for bacteria) allowed identification of the fact that even an intact O-ring seal can allow for some bacterial passage through the reverse osmosis membrane element.
Fujioka, T, Kodamatani, H, Nghiem, LD & Shintani, T 2019, 'Transport of N-Nitrosamines through a Reverse Osmosis Membrane: Role of Molecular Size and Nitrogen Atoms', Environmental Science & Technology Letters, vol. 6, no. 1, pp. 44-48.
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© 2018 American Chemical Society. Reliable and adequate removal of small and uncharged trace organic chemicals, particularly N-nitrosodimethylamine (NDMA) that is carcinogenic and known to occur in treated effluent, is essential for implementing direct potable water use. This study provides new insights to explain the low rejection of NDMA and other N-nitrosamines by reverse osmosis (RO) membranes by examining the role of molecular size and polarity in their molecular structure. The results show that molecular weight is not a suitable molecular property for evaluating the rejection of small uncharged chemicals. In this study, NDMA and two other uncharged chemicals have similar molecular weights (i.e., 72-74 g/mol), but their rejection by the ESPA2 RO membrane varied considerably from 30 to 88%. Instead, a minimum projection area was identified as a more suitable molecular property, indicating that size exclusion plays a primary role in their rejection. It was also determined that chemicals with more nitrogen atoms in their chemical structure consistently showed rejections lower than those of their similarly sized counterparts. The results suggest that chemicals bearing more nitrogen atoms (e.g., NDMA) have higher affinity to amide or amine functional group of a polyamide RO membrane possibly through hydrogen bonding interactions.
Gabela, J, Kealy, A, Li, S, Hedley, M, Moran, W, Ni, W & Williams, S 2019, 'The Effect of Linear Approximation and Gaussian Noise Assumption in Multi-Sensor Positioning Through Experimental Evaluation', IEEE Sensors Journal, vol. 19, no. 22, pp. 10719-10727.
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© 2001-2012 IEEE. Assumptions of Gaussianity in describing the errors of ranging data and linearization of the measurement models are well-accepted techniques for wireless tracking multi-sensor fusion. The main contribution of this paper is the empirical study on the effect of these assumptions on positioning accuracy. A local positioning system (LPS) was set up and raw data were collected using both the global satellite navigation system (GNSS) and the LPS. These data were fused to estimate position using both an extended Kalman filter (EKF) and a particle filter (PF). For these data, it was shown that the PF had an improvement in accuracy over the EKF of 67 cm (72%) with achieved accuracy of 26 cm. This improvement was attributed to the PF handling the non-linear system dynamics, rather than a linear approximation as in the EKF. Furthermore, when the PF used the fitted three-component Gaussian mixture model as the better approximation of the actual LPS ranging error distribution, rather than a Gaussian approximation, a further 3 cm (13%) reduction in positioning error was observed. Overall, the average accuracy of 23 cm was achieved for the proposed multi-sensor positioning system when the assumptions of Gaussianity are not made and the non-linear measurement model is not linearized.
Gamble, LD, Purgato, S, Murray, J, Xiao, L, Yu, DMT, Hanssen, KM, Giorgi, FM, Carter, DR, Gifford, AJ, Valli, E, Milazzo, G, Kamili, A, Mayoh, C, Liu, B, Eden, G, Sarraf, S, Allan, S, Di Giacomo, S, Flemming, CL, Russell, AJ, Cheung, BB, Oberthuer, A, London, WB, Fischer, M, Trahair, TN, Fletcher, JI, Marshall, GM, Ziegler, DS, Hogarty, MD, Burns, MR, Perini, G, Norris, MD & Haber, M 2019, 'Inhibition of polyamine synthesis and uptake reduces tumor progression and prolongs survival in mouse models of neuroblastoma', Science Translational Medicine, vol. 11, no. 477.
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MYCN regulates polyamines in neuroblastoma, and combined inhibition of polyamine synthesis and transport has therapeutic effects in mouse models.
Gan, YY, Ong, HC, Ling, TC, Chen, W-H & Chong, CT 2019, 'Torrefaction of de-oiled Jatropha seed kernel biomass for solid fuel production', Energy, vol. 170, pp. 367-374.
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Gandomi, AH, Daneshmand, M, Jha, R, Kaur, D, Ning, H, Robinson, C & Schilling, H 2019, 'Guest Editorial Nature-Inspired Approaches for IoT and Big Data', IEEE Internet of Things Journal, vol. 6, no. 6, pp. 9213-9216.
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Gandomi, AH, Deb, K, Averill, RC, Rahnamayan, S & Omidvar, MN 2019, 'Using semi-independent variables to enhance optimization search', Expert Systems with Applications, vol. 120, pp. 279-297.
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In this study, the concept of a semi-independent variable (SIV) problem representation is investigated that embodies a set of expected or desired relationships among the original variables, with the goal of increasing search effectiveness and efficiency. The proposed approach intends to eliminate the generation of infeasible solutions associated with the known relationships among the variables and cutting the search space, thereby potentially improving a search algorithm's convergence rate and narrowing down the search space. However, this advantage does not come for free. The issue is the multiplicity of SIV formulations and their varying degree of complexity, especially with respect to variable interaction. In this paper, we propose the use of automatic variable interaction analysis methods to compare and contrast different SIV formulations. The performance of the proposed approach is demonstrated by implementing it within a number of classical and evolutionary optimization algorithms (namely, interior-point algorithm, simulated annealing, particle swarm optimization, genetic algorithm and differential evolution) in the application to several practical engineering problems. The case study results clearly show that the population-based algorithms can significantly benefit from the proposed SIV formulation resulting in better solutions with fewer function evaluations than in the original approach. The results also indicate that an automatic variable interaction analysis is capable of estimating the difficulty of the resultant SIV formulations prior to any optimization attempt.
Gao, C, Ji, J, Yan, F & Liu, H 2019, 'Oscillation induced by Hopf bifurcation in the p53–Mdm2 feedback module', IET Systems Biology, vol. 13, no. 5, pp. 251-259.
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This study develops an integrated model of the p53–Mdm2 interaction composed of five basic components and time delay in the DNA damage response based on the existing research work. Some critical factors, including time delay, system parameters, and their interactions in the p53–Mdm2 system are investigated to examine their effects on the oscillatory behaviour induced by Hopf bifurcation. It is shown that the positive feedback formed between p53 and the activity of Mdm2 in the cytoplasm can cause a slight decrease in the amplitude of the p53 oscillation. The length of the time delay plays an important role in determining the amplitude and period of the oscillation and can significantly extend the parameter range for the system to demonstrate oscillatory behaviour. The numerical simulation results are found to be in good agreement with the published experimental observation. It is expected that the results of this research would be helpful to better understand the biological functions of p53 pathway and provide some clues in the treatment of cancer.
Gao, H, Liu, Z, Yang, Y, Wu, C & Geng, J 2019, 'Blast-resistant performance of aluminum foam-protected reinforced concrete slabs', Baozha Yu Chongji/Explosion and Shock Waves, vol. 39, no. 2.
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n order to study the blast-resistant protective effect of the aluminum foam slab as porous energy absorbing material on the engineering structure, using an outdoor explosion test, the dynamic response and failure modes of reinforced concrete (RC) slabs with different aluminum foam protective layers under blast loading were studied, and the finite element model was established by using the LS-DYNA software. Through comparison with the test, the feasibility of the model was verified. The dynamic responses of RC slabs with or without aluminum foam protective layers were compared and analyzed, and the effects of aluminum foam density gradient distribution and longitudinal reinforcement ratio were analyzed. The results show that the finite element model can accurately describe the dynamic response of RC slabs with aluminum foam protective layers. Aluminum foam protective layers can effectively reduce the deflection of reinforced concrete slabs and reduce the damage of specimens. The aluminum foam density increases from bottom to top, which has the best blast-resistant performance on RC slabs. Moreover, increasing the reinforcement ratio can improve the blast-resistant performance of aluminum foam-protected RC slabs.
Gao, J, Gao, L, Luo, Z & Li, P 2019, 'Isogeometric topology optimization for continuum structures using density distribution function', International Journal for Numerical Methods in Engineering, vol. 119, no. 10, pp. 991-1017.
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SummaryThis paper will propose a more effective and efficient topology optimization method based on isogeometric analysis, termed as isogeometric topology optimization (ITO), for continuum structures using an enhanced density distribution function (DDF). The construction of the DDF involves two steps. (1) Smoothness: the Shepard function is firstly utilized to improve the overall smoothness of nodal densities. Each nodal density is assigned to a control point of the geometry. (2) Continuity: the high‐order NURBS basis functions are linearly combined with the smoothed nodal densities to construct the DDF for the design domain. The nonnegativity, partition of unity, and restricted bounds [0, 1] of both the Shepard function and NURBS basis functions can guarantee the physical meaning of material densities in the design. A topology optimization formulation to minimize the structural mean compliance is developed based on the DDF and isogeometric analysis to solve structural responses. An integration of the geometry parameterization and numerical analysis can offer the unique benefits for the optimization. Several 2D and 3D numerical examples are performed to demonstrate the effectiveness and efficiency of the proposed ITO method, and the optimized 3D designs are prototyped using the Selective Laser Sintering technique.
Gao, J, Luo, Z, Li, H & Gao, L 2019, 'Topology optimization for multiscale design of porous composites with multi-domain microstructures', Computer Methods in Applied Mechanics and Engineering, vol. 344, pp. 451-476.
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© 2018 Elsevier B.V. This paper proposes a new multiscale topology optimization method for the design of porous composites composed of the multi-domain material microstructures considering three design elements: the topology of the macrostructure, the topologies of multiple material microstructures and their overall distribution in the macrostructure. The multiscale design involves two optimization stages: the free material distribution optimization and the concurrent topology optimization. Firstly, the variable thickness sheet (VTS) method with the regularization mechanism is used to generate multiple element density distributions in the macro design domain. Hence, different groups of elements with the identical densities can be uniformly arranged in their corresponding domains, and each domain in the space will be periodically configured by a unique representative microstructure. Secondly, with the discrete material distributions achieved in the macro domain, the topology of the macrostructure and topologies of multiple representative microstructures are concurrently optimized by a parametric level set method combined with the numerical homogenization method. Finally. Several 2D and 3D numerical examples are provided to demonstrate the effectiveness of the proposed multiscale topology optimization method.
Gao, J, Luo, Z, Li, H, Li, P & Gao, L 2019, 'Dynamic multiscale topology optimization for multi-regional micro-structured cellular composites', Composite Structures, vol. 211, pp. 401-417.
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© 2018 Elsevier Ltd In this paper, a new dynamic multiscale topology optimization method for cellular composites with multi-regional material microstructures is proposed to improve the structural performance. Firstly, a free-material distribution optimization method (FMDO) is developed to generate the overall configuration for the discrete element densities distributed within a multi-regional pattern. The macrostructure is divided into several sub regions, and each of them consists of a number of elements but with the same densities. Secondly, a dynamic topology optimization formulation is developed to perform the concurrent design of the macrostructure and material microstructures, subject to the multi-regional distributed element densities. A parametric level set method is employed to optimize the topologies of the macrostructure and material microstructures, with the effective macroscopic properties evaluated by the homogenization. In the numerical implementation, the quasi-static Ritz vector (QSRV) method is incorporated into the finite element analysis so as to reduce the computational cost in numerical analysis, and some kinematical connectors are introduced to make sure the connectivity between adjacent material microstructures. Finally, 2D and 3D numerical examples are tested to demonstrate the effectiveness of the proposed dynamic multiscale topology optimization method for the material-structural composites.
Gao, J, Luo, Z, Xia, L & Gao, L 2019, 'Concurrent topology optimization of multiscale composite structures in Matlab', Structural and Multidisciplinary Optimization, vol. 60, no. 6, pp. 2621-2651.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents the compact and efficient Matlab codes for the concurrent topology optimization of multiscale composite structures not only in 2D scenario but also considering 3D cases. A modified SIMP approach (Sigmund 2007) is employed to implement the concurrent topological design, with an energy-based homogenization method (EBHM) to evaluate the macroscopic effective properties of the microstructure. The 2D and 3D Matlab codes in the paper are developed, using the 88-line 2D SIMP code (Struct Multidisc Optim 43(1): 1–16, 2011) and the 169-line 3D topology optimization code (Struct Multidisc Optim 50(6): 1175–1196, 2014), respectively. This paper mainly contributes to the following four aspects: (1) the code architecture for the topology optimization of cellular composite structures (ConTop2D.m and ConTop3D.m), (2) the code to compute the 3D iso-parametric element stiffness matrix (elementMatVec3D.m), (3) the EBHM to predict the macroscopic effective properties of 2D and 3D material microstructures (EBHM2D.m and EBHM3D.m), and (4) the code to calculate the sensitivities of the objective function with respect to the design variables at two scales. Several numerical examples are tested to demonstrate the effectiveness of the Matlab codes, which are attached in the Appendix, also offering an entry point for new comers in designing cellular composites using topology optimization.
Gao, J, Xue, H, Gao, L & Luo, Z 2019, 'Topology optimization for auxetic metamaterials based on isogeometric analysis', Computer Methods in Applied Mechanics and Engineering, vol. 352, pp. 211-236.
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© 2019 Elsevier B.V. In this paper, an effective and efficient topology optimization method, termed as Isogeometric Topology Optimization (ITO), is proposed for systematic design of both 2D and 3D auxetic metamaterials based on isogeometric analysis (IGA). Firstly, a density distribution function (DDF)with the desired smoothness and continuity, to represent the topological changes of structures, is constructed using the Shepard function and non-uniform rational B-splines (NURBS)basis functions. Secondly, an energy-based homogenization method (EBHM)to evaluate material effective properties is numerically implemented by IGA, with the imposing of the periodic boundary formulation on material microstructure. Thirdly, a topology optimization formulation for 2D and 3D auxetic metamaterials is developed based on the DDF, where the objective function is defined as a combination of the homogenized elastic tensor and the IGA is applied to solve the structural responses. A relaxed optimality criteria (OC)method is used to update the design variables, due to the non-monotonic property of the problem. Finally, several numerical examples are used to demonstrate the effectiveness and efficiency of the proposed method. A series of auxetic microstructures with different deformation mechanisms (e.g. the re-entrant and chiral)can be obtained. The auxetic behavior of material microstructures are numerically validated using ANSYS, and the optimized designs are prototyped using the Selective Laser Sintering (SLS)technique.
Gao, P, Wang, X, Huang, Z & Yu, H 2019, '11B NMR Chemical Shift Predictions via Density Functional Theory and Gauge-Including Atomic Orbital Approach: Applications to Structural Elucidations of Boron-Containing Molecules', ACS Omega, vol. 4, no. 7, pp. 12385-12392.
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© 2019 American Chemical Society. 11B nuclear magnetic resonance (NMR) spectroscopy is a useful tool for studies of boron-containing compounds in terms of structural analysis and reaction kinetics monitoring. A computational protocol, which is aimed at an accurate prediction of 11B NMR chemical shifts via linear regression, was proposed based on the density functional theory and the gauge-including atomic orbital approach. Similar to the procedure used for carbon, hydrogen, and nitrogen chemical shift predictions, a database of boron-containing molecules was first compiled. Scaling factors for the linear regression between calculated isotropic shielding constants and experimental chemical shifts were then fitted using eight different levels of theory with both the solvation model based on density and conductor-like polarizable continuum model solvent models. The best method with the two solvent models yields a root-mean-square deviation of about 3.40 and 3.37 ppm, respectively. To explore the capabilities and potential limitations of the developed protocols, classical boron-hydrogen compounds and molecules with representative boron bonding environments were chosen as test cases, and the consistency between experimental values and theoretical predictions was demonstrated.
Gao, X, Du, J, Zhang, T & Guo, YJ 2019, 'High-<italic>T<sub>c</sub> </italic> Superconducting Fourth-Harmonic Mixer Using a Dual-Band Terahertz On-Chip Antenna of High Coupling Efficiency', IEEE Transactions on Terahertz Science and Technology, vol. 9, no. 1, pp. 55-62.
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© 2019 IEEE. This paper presents a dual-band on-chip antenna-coupled high-Tc superconducting (HTS) Josephson-junction subterahertz (THz) fourth-harmonic mixer. The antenna utilizes a couple of different structured twin slots to enable the resonant radiations at two frequencies, and integrates a well-designed coplanar waveguide network for achieving good radiation coupling and signal isolation characteristics. The electromagnetic simulations show that coupling efficiencies as high as -4 and -3.5 dB are achieved for the 160- and 640-GHz operating frequency bands, respectively. Based on this dual-band antenna, a 640-GHz HTS fourth-harmonic mixer is developed and characterized in a range of operating temperatures. The mixer exhibits a measured conversion gain of around -18 dB at 20 K and -22 dB at 40 K, respectively. The achieved intermediate frequency bandwidth is larger than 23 GHz. These are the best results reported for HTS harmonic mixers at comparable sub-THz frequency bands to date.
Gao, X, Xu, G, Li, S, Wu, Y, Dancigs, E & Du, J 2019, 'Particle Filter-Based Prediction for Anomaly Detection in Automatic Surveillance', IEEE Access, vol. 7, pp. 107550-107559.
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Gao, Y, Xu, Y, Wu, C & Fang, J 2019, 'Topology Optimization of Metal and Carbon Fiber Reinforced Plastic (CFRP) Structures under Loading Uncertainties', SAE Technical Paper Series, vol. 2019-April, no. April.
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© 2019 SAE International. All Rights Reserved. Carbon fiber reinforced plastic (CFRP) composite materials have gained particular interests due to their high specific modulus, high strength, lightweight and perfect corrosion resistance. However, in reality, CFRP composite materials cannot be used alone in some critical places such as positions of joints with hinges, locks. Therefore, metal reinforcements are usually necessary in local positions to prevent structure damage. Besides, if uncertainties present, obtained optimal structures may experience in failures as the optimization usually pushes solutions to the boundaries of constraints and has no room for tolerance and uncertainties, so robust optimization should be considered to accommodate the uncertainties in practice. This paper proposes a mixed topology method to optimize metal and carbon fiber reinforced plastic composite materials simultaneously under nondeterministic load with random magnitude and direction. A joint cost function is employed to contain both the mean and standard deviations of compliance in the robust optimization. The sensitivities of the cost function are derived with respect to the design variables in a nondeterministic context. The discrete material and thickness optimization (DMTO) technique is applied to undertake robust topology optimization for CFRP composites and metal material while the casting constraint to prevent intermediate void was introduced. In this study, two examples are presented to demonstrate the effectiveness of the proposed methods. The robust topology optimization results exhibit that the composite structures with proper distribution of materials and orientations are of more stable performance when the load fluctuates.
Garcia Jaime A. 2019, 'A Virtual Reality Game-Like Tool for Assessing the Risk of Falling in the Elderly', Stud Health Technol Inform, vol. 266, pp. 63-69.
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In recent years, the use of interactive game technology has gained much interest in the research community as a means to measure indicators associated with the risk of falling in the elderly. Input devices used for gaming offer an inexpensive but yet reliable alternative to the costly apparatuses used in clinics and medical centers. In this paper, we explore the feasibility of using virtual reality technology as a tool to assess the risk of falling in the senior community in a more immersive, intuitive and descriptive manner. Our VR-based tool captures stepping performance parameters in order to fulfill the requirements of a well-established clinical test for fall risk assessment. The use of virtual reality allows for an immersive experience where elderly users can fully concentrate on the motor and cognitive functions being assessed rather than the technology being used.
Gardner, A & Willey, K 2019, 'The role of peer review in identity development for engineering education researchers', European Journal of Engineering Education, vol. 44, no. 3, pp. 347-359.
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Gardner, A, Bernhard, J, Male, S & Turns, J 2019, 'EJEE Editorial for Special Issue: Research Methodologies that link theory and practice', European Journal of Engineering Education, vol. 44, no. 1-2, pp. 1-5.
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Gargiulo, GD, Bifulco, P, Cesarelli, M, McEwan, A, Nikpour, A, Jin, C, Gunawardana, U, Sreenivasan, N, Wabnitz, A & Hamilton, TJ 2019, 'Fully Open-Access Passive Dry Electrodes BIOADC: Open-Electroencephalography (EEG) Re-Invented', Sensors, vol. 19, no. 4, pp. 772-772.
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The Open-electroencephalography (EEG) framework is a popular platform to enable EEG measurements and general purposes Brain Computer Interface experimentations. However, the current platform is limited by the number of available channels and electrode compatibility. In this paper we present a fully configurable platform with up to 32 EEG channels and compatibility with virtually any kind of passive electrodes including textile, rubber and contactless electrodes. Together with the full hardware details, results and performance on a single volunteer participant (limited to alpha wave elicitation), we present the brain computer interface (BCI)2000 EEG source driver together with source code as well as the compiled (.exe). In addition, all the necessary device firmware, gerbers and bill of materials for the full reproducibility of the presented hardware is included. Furthermore, the end user can vary the dry-electrode reference circuitry, circuit bandwidth as well as sample rate to adapt the device to other generalized biopotential measurements. Although, not implemented in the tested prototype, the Biomedical Analogue to Digital Converter BIOADC naturally supports SPI communication for an additional 32 channels including the gain controller. In the appendix we describe the necessary modification to the presented hardware to enable this function.
Gaviria-Marin, M, Merigó, JM & Baier-Fuentes, H 2019, 'Knowledge management: A global examination based on bibliometric analysis', Technological Forecasting and Social Change, vol. 140, pp. 194-220.
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© 2018 Knowledge management (KM) is a field of research that has gained wide acceptance in the scientific community and management literature. This article presents a bibliometric overview of the academic research on KM in the business and management areas. Various bibliometric methods are used to perform this overview, including performance analysis and science mapping of the KM field. The performance analysis uses a series of bibliometric indicators, such as the h-index, productivity and citations. In addition, the VOSviewer software is used to map the bibliographic material. Science mapping uses co-citations and the concurrency of keywords. References were obtained from the Web of Science database. We identified and classified the most relevant research in the field according to journals, articles, authors, institutions and countries. The results show that research in this field has increased significantly in the last ten years and that the USA is the most influential country in all aspects in this field. It is important to consider, however, that science continues to advance in this and in all fields and that data rapidly change over time. Therefore, this paper fulfills an informational role that shows that most of the fundamental research of KM is in business and management areas.
Gay Valerie C., Garcia Jaime A. & Leong Tuck W. 2019, 'Using Asynchronous Exergames to Encourage an Active Ageing Lifestyle: Solitaire Fitness Study Protocol', Stud Health Technol Inform, vol. 266, pp. 70-75.
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A healthy and active lifestyle can significantly improve the well-being and quality of life; however, some elderly people struggle to stay motivated and engaged with any form of exercise. The project Elaine (Elderly, AI and New Experiences) addresses this problem by seeking to improve the quality of life of the elderly through exergames. Currently, the project explores a novel approach in the field of health informatics called asynchronous exergaming. This approach, a new trend in games in the health domain, allows the elderly to workout at their own pace, and in their own time, with their physical activity linked asynchronously to a game. This paper presents the study protocol for Solitaire Fitness, a new asynchronous exergame developed by the team. The game aims at increasing the motivation of the elderly to engage in physical exercise whilst helping to maintain their cognitive abilities. It also describes the protocol for the trial. The result of this research has the potential to benefit elderly that need nudging to be motivated to exercise, health care providers treating people with sedentary lifestyles and researchers investigating ways to encourage the elderly to exercise.
Gentil, CL, Vidal-Calleja, T & Huang, S 2019, 'IN2LAAMA: INertial Lidar Localisation Autocalibration And MApping', IEEE Transactions on Robotics, vol. 37, no. 1, pp. 275-290.
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In this paper, we present INertial Lidar Localisation Autocalibration AndMApping (IN2LAAMA): an offline probabilistic framework for localisation,mapping, and extrinsic calibration based on a 3D-lidar and a 6-DoF-IMU. Most oftoday's lidars collect geometric information about the surrounding environmentby sweeping lasers across their field of view. Consequently, 3D-points in onelidar scan are acquired at different timestamps. If the sensor trajectory isnot accurately known, the scans are affected by the phenomenon known as motiondistortion. The proposed method leverages preintegration with a continuousrepresentation of the inertial measurements to characterise the system's motionat any point in time. It enables precise correction of the motion distortionwithout relying on any explicit motion model. The system's pose, velocity,biases, and time-shift are estimated via a full batch optimisation thatincludes automatically generated loop-closure constraints. The autocalibrationand the registration of lidar data rely on planar and edge features matchedacross pairs of scans. The performance of the framework is validated throughsimulated and real-data experiments.
Gentile, C, Kesteven, S, Wu, J, Bursill, C, Davies, MJ & Figtree, G 2019, 'Abstract 138: A Novel Cellular and Genetic Approach to Investigate the Cardioprotective Role Played by Endothelial Nitric Oxide Synthase in Myocardial Infarction', Circulation Research, vol. 125, no. Suppl_1.
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The loss of regenerative properties in adult cardiomyocytes (CMs) is directly linked to their inability to proliferate. Following an extensive ischaemic event in an aged heart, fibrotic scar formation is the only repair process and eventually heart failure develops. However, molecular and cellular cues in the neonatal heart support that cardiac regeneration is possible in presence of proliferating CMs. Based on previous studies demonstrating that endothelial nitric oxide synthase (eNOS) regulates proliferation in both endothelial cells (ECs) and CMs, we hypothesized that eNOS signaling could play a cardioprotective role. To test our hypothesis, we injected different combinations of co-cultured ECs and CMs in the LV muscle wall of MI mice (permanent LAD ligation). First, injected cells were isolated from either WT or KO eNOS neonatal mice and then co-cultured to form 3D vascularized cardiac spheroids (VCSs), which were eventually transplanted in adult MI mice on the day of the procedure. Control infarcted animals received media-only (vehicle). Other mice received a suspension of co-cultured VCSs in media as follows: i ) WT CMs and ECs; ii ) WT CMs and KO ECs; iii ) KO CMs and WT ECs. Following 28 days, injection of WT cells increased the ejection fraction (EF%) by 20% compared with control animals (61%±4% and 41%±11%, respectively). When eNOS was absent in either CMs or ECs, the EF% was 40%±5% and 46%±2%, respectively, suggesting that the eNOS-mediated protection is dependent on its presence in both cells. Histological analyses confirmed the presence of WT VCSs in MI mice, contributing to a thicker wall thickness compared to vehicle MI mice. No VCSs were observed in the LV wall when KO cells were injected. Therefore, our results strongly suggest that eNOS may play a major role via bo...
Gerami, A, Alzahid, Y, Mostaghimi, P, Kashaninejad, N, Kazemifar, F, Amirian, T, Mosavat, N, Ebrahimi Warkiani, M & Armstrong, RT 2019, 'Microfluidics for Porous Systems: Fabrication, Microscopy and Applications', Transport in Porous Media, vol. 130, no. 1, pp. 277-304.
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© 2018, Springer Nature B.V. No matter how sophisticated the structures are and on what length scale the pore sizes are, fluid displacement in porous media can be visualized, captured, mimicked and optimized using microfluidics. Visualizing transport processes is fundamental to our understanding of complex hydrogeological systems, petroleum production, medical science applications and other engineering applications. Microfluidics is an ideal tool for visual observation of flow at high temporal and spatial resolution. Experiments are typically fast, as sample volume is substantially low with the use of miniaturized devices. This review first discusses the fabrication techniques for generating microfluidics devices, experimental setups and new advances in microfluidic fabrication using three-dimensional printing, geomaterials and biomaterials. We then address multiphase transport in subsurface porous media, with an emphasis on hydrology and petroleum engineering applications in the past few decades. We also cover the application of microfluidics to study membrane systems in biomedical science and particle sorting. Lastly, we explore how synergies across different disciplines can lead to innovations in this field. A number of problems that have been resolved, topics that are under investigation and cutting-edge applications that are emerging are highlighted.
Ghadi, MJ, Ghavidel, S, Rajabi, A, Azizivahed, A, Li, L & Zhang, J 2019, 'A review on economic and technical operation of active distribution systems', Renewable and Sustainable Energy Reviews, vol. 104, pp. 38-53.
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© 2019 Elsevier Ltd Along with the advent of restructuring in power systems, considerable integration of renewable energy resources has motivated the transition of traditional distribution networks (DNs) toward new active ones. In the meanwhile, rapid technology advances have provided great potentials for future bulk utilization of generation units as well as the energy storage (ES) systems in the distribution section. This paper aims to present a comprehensive review of recent advancements in the operation of active distribution systems (ADSs) from the viewpoint of operational time-hierarchy. To be more specific, this time-hierarchy consists of two stages, and at the first stage of this time-hierarchy, four major economic factors, by which the operation of traditional passive DNs is evolved to new active DNs, are described. Then the second stage of the time-hierarchy refers to technical management and power quality correction of ADSs in terms of static, dynamic and transient periods. In the end, some required modeling and control developments for the optimal operation of ADSs are discussed. As opposed to previous review papers, potential applications of devices in the ADS are investigated considering their operational time-intervals. Since some of the compensating devices, storage units and generating sources may have different applications regarding the time scale of their utilization, this paper considers real scenario system operations in which components of the network are firstly scheduled for the specified period ahead; then their deviations of operating status from reference points are modified during three time-intervals covering static, dynamic and transient periods.
Ghadi, MJ, Rajabi, A, Ghavidel, S, Azizivahed, A, Li, L & Zhang, J 2019, 'From active distribution systems to decentralized microgrids: A review on regulations and planning approaches based on operational factors', Applied Energy, vol. 253, pp. 113543-113543.
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© 2019 Elsevier Ltd Restructuring of power systems along with the integration of renewable energy resources in electricity networks have transformed traditional distribution networks (DNs) into new active distribution systems (ADSs). In addition, rapid advancement of technology has enabled the bulk utilization of power generation units and energy storage (ES) systems in distribution networks. The next step in this trend is to decentralize ADSs to microgrids (MGs). This paper aims to present a review on the recent advancements in the development of ADSs and MGs. In this respect, the regulatory requirements and economic concepts, by which the traditional passive DNs are evolved into ADSs, are categorized and illustrated first. Then, the state-of-the-art of ADS formation is detailed based on the novel standpoint of grid operation factors which are involved in deregulated electricity markets at the distribution level. After presenting highlighted projects of MGs across the world, a similar review approach has been adopted to explain the formation of MGs which play a vital role in the decentralization of ADSs. This survey can provide both policy makers and distribution system planners with new perspectives to establish or participate in day-ahead wholesale markets.
Ghaffari Jadidi, M, Valls Miro, J & Dissanayake, G 2019, 'Sampling-based incremental information gathering with applications to robotic exploration and environmental monitoring', The International Journal of Robotics Research, vol. 38, no. 6, pp. 658-685.
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We propose a sampling-based motion-planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a constrained maximization problem. Our approach is built on rapidly exploring information-gathering algorithms and benefits from the advantages of sampling-based optimal motion-planning algorithms. We propose two information functions and their variants for fast and online computations. We prove an information-theoretic convergence for an entire exploration and information-gathering mission based on the least upper bound of the average map entropy. A natural automatic stopping criterion for information-driven motion control results from the convergence analysis. We demonstrate the performance of the proposed algorithms using three scenarios: comparison of the proposed information functions and sensor configuration selection, robotic exploration in unknown environments, and a wireless signal strength monitoring task in a lake from a publicly available dataset collected using an autonomous surface vehicle.
Ghantous, GB & Gill, AQ 2019, 'An Agile-DevOps Reference Architecture for Teaching Enterprise Agile', International Journal of Learning, Teaching and Educational Research, vol. 18, no. 7, pp. 128-144.
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©2019 The authors and IJLTER.ORG. All rights reserved. DevOps emerged as an important extension to support the Agile development for frequent and continuous software delivery. The adoption of Agile-DevOps for large scale enterprise agility depends on the most important human capability such as people competency and experience. Hence, academic education and professional training is key to the successful adoption of Agile-DevOps approach. Thus, education and training providers need to teach Agile-DevOps. However, the challenge is: how to establish and simulate an effective Agile-DevOps technology environment for teaching Enterprise Agile? This paper introduces the integrated Adaptive Enterprise Project Management (AEPM) and DevOps Reference Architecture (DRA) approach for adopting and teaching the Agile-DevOps with the help of a teaching case study from the University of Technology - Sydney (UTS), Australia. These learnings can be utilised by educators to develop and teach practice-oriented Agile-DevOps for software engineering courses. Furthermore, the experience and observations can be employed by researchers and practitioners aiming to integrate Agile-DevOps at the large enterprise scale.
Ghasemi, M, Akbari, E, Rahimnejad, A, Razavi, SE, Ghavidel, S & Li, L 2019, 'Phasor particle swarm optimization: a simple and efficient variant of PSO', Soft Computing, vol. 23, no. 19, pp. 9701-9718.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Particle swarm optimizer is a well-known efficient population and control parameter-based algorithm for global optimization of different problems. This paper focuses on a new and primary sample for PSO, which is named phasor particle swarm optimization (PPSO) and is based on modeling the particle control parameters with a phase angle (θ), inspired from phasor theory in the mathematics. This phase angle (θ) converts PSO algorithm to a self-adaptive, trigonometric, balanced, and nonparametric meta-heuristic algorithm. The performance of PPSO is tested on real-parameter optimization problems including unimodal and multimodal standard test functions and traditional benchmark functions. The optimization results show good and efficient performance of PPSO algorithm in real-parameter global optimization, especially for high-dimensional optimization problems compared with other improved PSO algorithms taken from the literature. The phasor model can be used to expand different types of PSO and other algorithms. The source codes of the PPSO algorithms are publicly available at https://github.com/ebrahimakbary/PPSO.
Ghasemi, M, Akbari, E, Zand, M, Hadipour, M, Ghavidel, S & Li, L 2019, 'An Efficient Modified HPSO-TVAC-Based Dynamic Economic Dispatch of Generating Units', Electric Power Components and Systems, vol. 47, no. 19-20, pp. 1826-1840.
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© 2020, © 2020 Taylor & Francis Group, LLC. This paper proposes a novel particle swarm optimization (PSO) algorithm with population reduction, which is called modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients (MNHPSO-JTVAC). The proposed method is used for solving well-known benchmark functions, as well as non-convex and non-smooth dynamic economic dispatch (DED) problems for a 24 h time interval in two different test systems. Operational constraints including the prohibited operating zones (POZs), the transmission losses, the ramp-rate limits and the valve-point effects are considered in solving the DED problem. The obtained numerical results show that the MNHPSO-JTVAC algorithm is very suitable and competitive compared to other algorithms and have the capacity to obtain better optimal solutions in solving the non-convex and non-smooth DED problems compared to the other variants of PSO and the state of the art optimization algorithms proposed in recent literature. The source codes of the HPSO-TVAC algorithms and supplementary data for this paper are publicly available at https://github.com/ebrahimakbary/MNHPSO-JTVAC.
Ghasemi, M, Rasekh, H, Berenjian, J & AzariJafari, H 2019, 'Dealing with workability loss challenge in SCC mixtures incorporating natural pozzolans: A study of natural zeolite and pumice', Construction and Building Materials, vol. 222, pp. 424-436.
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Ghavidel, S, Rajabi, A, Ghadi, MJ, Azizivahed, A, Li, L & Zhang, J 2019, 'Risk‐constrained demand response and wind energy systems integration to handle stochastic nature and wind power outage', IET Energy Systems Integration, vol. 1, no. 2, pp. 114-120.
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Participation of wind generation in electricity markets is mainly restricted by the intermittent nature of wind and their possible outages. The great potential of flexible loads from demand response (DR) can be seen as a cost‐effective option to handle such issues. In this regard, this study investigates the operation of a virtual power plant (VPP) that is constructed by a DR aggregator and wind power aggregator to handle the inherent volatility of wind generators as well as the possible wind power outage. A stochastic programming formulation in three stages is offered for the VPP that participates in the balancing, intraday and day‐ahead markets. The model for DR is developed based on the elasticity concept, and the scenarios related to severe outages of the wind generators are considered. In order to manage the risk of the problem, conditional value‐at‐risk has also been employed in offering strategy. Case studies demonstrate that the VPP offering strategy can efficiently solve the balancing problem as well as outage risk of the wind generation while increasing the net profit in case of joint operation.
Ghodousi, M, Alesheikh, AA, Saeidian, B, Pradhan, B & Lee, C-W 2019, 'Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data', Sustainability, vol. 11, no. 17, pp. 4595-4595.
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Citizen Relationship Management (CiRM) is one of the important matters in citizen-centric e-government. In fact, the most important purpose of e-government is to satisfy citizens. The ‘137 system’ is one of the most important ones based on the citizen-centric that is a municipality phone based request/response system. The aim of this research is a data-mining of a ‘137 system’ (citizens’ complaint system) of the first district of Bojnourd municipality in Iran, to prioritize the urban needs and to estimate citizens’ satisfaction. To reach this, the K-means and Bees Algorithms (BA) were used. Each of these two algorithms was executed using two different methods. In the first method, prioritization and estimation of satisfaction were done separately, whereas in the second method, prioritization and estimation of satisfaction were done simultaneously. To compare the clustering results in the two methods, an index was presented quantitatively. The results showed the superiority of the second method. The index of the second method for the first needs in K-means was 0.299 more than the first method and it was the same in two methods in BA. Also, the results of the BA clustering were better at it because of the S (silhouette) and CH (Calinski-Harabasz) indexes. Considering the final prioritization done by the two algorithms in two methods, the primary needs included asphalt, so specific schemes should be considered.
Gholidoust, A, Maina, JW, Merenda, A, Schütz, JA, Kong, L, Hashisho, Z & Dumée, LF 2019, 'CO2 sponge from plasma enhanced seeded growth of metal organic frameworks across carbon nanotube bucky-papers', Separation and Purification Technology, vol. 209, pp. 571-579.
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In this study, a novel hybrid metal organic framework (MOF) – bucky-paper was developed by seeding crystal growth of Mg-MOF-74 from the surface of carbon nanotube (CNT) mats, called bucky-papers (BP). The seeding density and growth kinetics of the MOF crystals across the CNT-BPs was enhanced through continuous discharge plasma treatments with either O2/Ar or NH3 gas streams. X ray Photo-electron Spectroscopy measurements were used to demonstrate the impact of the plasma treatment on the anchoring of hydroxyl, amine and carbonyl functional groups to the graphitic surfaces, resulting in higher surface wettability and greater MOF seeding densities. CO2 adsorption isotherms at 25 °C showed a large increase in CO2 uptake (maximum of 10.70 mmol CO2/g) for MOF-CNT-BP samples compared to virgin CNT-BP (0.35 mmol CO2/g) or parent Mg-MOF-74 (maximum of 3.13 mmol CO2/g). Higher CO2 adsorption of MOF-CNT-BP sponges were attributed to the synergistic effect between the MOF and CNT due to increased porosity at the interface, better MOF crystal distribution, and enhanced dispersive forces which improve structural integrity of MOF component.
Ghorbani, F, Abbaszadeh, H, Mehdizadeh, A, Ebrahimi-Warkiani, M, Rashidi, M-R & Yousefi, M 2019, 'Biosensors and nanobiosensors for rapid detection of autoimmune diseases: a review', Microchimica Acta, vol. 186, no. 12.
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© 2019, Springer-Verlag GmbH Austria, part of Springer Nature. This review (with 77 refs.) describes the progress that has been made in biosensors for the detection of autoimmune diseases, mainly via detection of autoantibodies. In addition, specific proteins, cytokines and ions have also been introduced as promising diagnostic biomarkers. Following an introduction into the various kinds of autoimmune diseases, we first discuss the state of the art in respective electrochemical biosensors and nanobiosensors (with subsections on amperometric, impedimetric, voltammetric and photoelectrochemical methods). The next large chapter covers optical methods (with subsections on electrochemiluminescence, fluorescence and surface plasmon resonance). We then make a critical comparison between commercially available kits used for detection of autoimmune diseases with the established biosensors. Several Tables are also presented that give an overview on the wealth of methods and nanomaterials. Finally, in the conclusion part, we summarize the current status, addresse present issues, and give an outlook on potential future opportunities. [Figure not available: see fulltext.].
Ghorbani, S, Eyni, H, Khosrowpour, Z, Salari Asl, L, Shabani, R, Nazari, H, Mehdizadeh, M, Ebrahimi Warkiani, M & Amjadi, F 2019, 'Spermatogenesis induction of spermatogonial stem cells using nanofibrous poly(l‐lactic acid)/multi‐walled carbon nanotube scaffolds and naringenin', Polymers for Advanced Technologies, vol. 30, no. 12, pp. 3011-3025.
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Spermatogenesis is a process in which animals generate spermatozoa from spermatogonial stem cells (SSCs). Successful in vitro differentiation of SSCs towards spermatids holds a significant promise for regeneration of impaired spermatogenesis. The present study aims to evaluate the efficiency of a 3D culture containing naringenin on proliferation and differentiation potentials of mouse SSCs. In this study, multi‐walled carbon nanotubes (MWCNTs) were incorporated into poly(l‐lactic acid) (PLLA) fibers via electrospinning technique. The fibrous PLLA/MWCNTs were studied by Fourier‐transform infrared spectroscopy (FTIR), transmission electron microscope (TEM), water contact angle measurements, electrical conductivity, and mechanical properties. Next, the SSCs were seeded into the PLLA/MWCNTs scaffolds and exhibited preferable survival and differentiation efficiency to subsequent cell lines. To shed more light on this matter, the immunocytochemistry, reverse‐transcription polymerase chain reaction (RT‐PCR), and qRT‐PCR results showed that the aforementioned cells on the 3D fabrics overexpressed the C‐kit and SYCP3 proteins. In addition, the reactive oxygen species (ROS) measurement data demonstrated that naringenin, an effective antioxidant, plays an important role in in vitro spermatogenesis. Taken together, the results of this study revealed the synergistic effects of 3D scaffolds and naringenin for efficient spermatogenesis in laboratories.
Ghosh, S & Lee, JE-Y 2019, 'Piezoelectric-on-Silicon MEMS Lorentz Force Lateral Field Magnetometers', IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 66, no. 5, pp. 965-974.
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Ghosh, SS, Nathan, KS, Siwakoti, YP & Long, T 2019, 'Dual polarity DC–DC converter integrated grid‐tied single‐phase transformer less inverter for solar application', The Journal of Engineering, vol. 2019, no. 17, pp. 3962-3966.
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This study proposes a novel single‐phase transformer‐less inverter, using the principle of combined Ćuk SEPIC (CCS) converter for grid‐connected photovoltaic (PV) systems. The new inverter has a common ground between the grid and the PV source, which helps to eliminate the leakage current for the grid‐connected PV application. Unlike common ground‐type charge‐pump‐based transformer‐less inverter, this topology eliminates inrush current and hence reduces the current stress on the components. The CCS allows voltage control with both step‐up and step‐down abilities, along with more robustness against solar panel side fault. Further, application of wide band‐gap devices, such as SiC MOSFETs allows higher switching frequency to be achieved, and thus reduction of the passive components. A novel switching strategy, proposed here allows current in both directions, positive and negative (to the load/grid or from the load/grid, for reactive loads), making the converter suitable for grid connection (unity power factor), as well as stand‐alone operation. The proposed concept has been discussed in detail, along with simulation results. Finally, a prototype hardware has been fabricated and the experimental results are reported.
Gill, AQ & Chew, E 2019, 'Configuration information system architecture: Insights from applied action design research.', Inf. Manag., vol. 56, no. 4, pp. 507-525.
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© 2018 Elsevier B.V. One of the critical information systems that enables service resilience is the service configuration information system (CiS). The fundamental challenge for organisations is the effective designing and implementation of the CiS architecture. This paper addresses this important research problem and reports insights from a completed applied action design research (ADR) project in an Australian financial services organisation. This paper aims to provide guidance to researchers and practitioners contemplating ADR, rooted in the organisational context, for practice-oriented academia-industry collaborative research. This research also contributes in terms of the CiS reference architecture design knowledge and demonstrates the applicability of the ADR method.
Giri, P, Kharkovsky, S, Zhu, X, Clark, SM & Samali, B 2019, 'Debonding detection in a carbon fibre reinforced concrete structure using guided waves', Smart Materials and Structures, vol. 28, no. 4, pp. 045020-045020.
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© 2019 IOP Publishing Ltd. Guided waves are traditionally used in different non-destructive testing applications because of their cost-effectiveness and piezoelectric patches that are easy to incorporate into the structure as transducers. The non-destructive evaluation of interfacial defects such as debonding in a composite structure is critical for safety and long-term use. A new guided wave technique to detect a variety of debondings in carbon fibre reinforced concrete structure has been developed and experimental testing has been carried out to verify the proposed approach. Five composite specimens with different debondings have been prepared. The received guided wave in the specimen with a perfect bonding is taken as a reference. This signal is compared with the received signal under different debonding conditions. The debonding is quantified using three damage indices: correlation coefficient, change in peak-to-peak and root mean square deviation. The results demonstrated that these indices could be a good indicator of the debond conditions as they correlated linearly with the extent of the debonding. The proposed method is effective in detecting interfacial defects in an existing structure without special preparation.
Giri, P, Kharkovsky, S, Zhu, X, Clark, SM, Taheri, S & Samali, B 2019, 'Characterization of carbon fiber reinforced polymer strengthened concrete and gap detection with a piezoelectric-based sensory technique', Structural Health Monitoring, vol. 18, no. 1, pp. 172-179.
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In this article, a piezoelectric-based sensory technique is proposed for detection of the gap between surfaces of a carbon fiber reinforced polymer plate and a concrete specimen and characterization of shrinkage of early-age concrete. The proposed technique uses the propagation properties of the guided waves in the carbon fiber reinforced polymer plate excited and received by piezoelectric transducers attached to an external surface of the carbon fiber reinforced polymer–strengthened concrete specimen. Measurements are conducted with fresh and hardened early-age concrete specimens and two carbon fiber reinforced polymer plates at different gaps. A piezoelectric actuator is excited using a sine burst signal, and the generated wave is received by a sensor after propagation along the specimen. The received signal at different gap values is used to detect a gap. To quantify the gap, damage indices, including correlation coefficient, peak-to-peak amplitude of resultant signal, and root-mean-square deviation, are used. The shrinkage of concrete is detected and predicted by comparing the damage indices at different gaps with the indices at different stages of early-age concrete. The proposed technique is relatively simple method using small transducers. It is one-sided, non-destructive, and cost-effective solution for gap detection and concrete characterization.
Goh, BHH, Ong, HC, Cheah, MY, Chen, W-H, Yu, KL & Mahlia, TMI 2019, 'Sustainability of direct biodiesel synthesis from microalgae biomass: A critical review', Renewable and Sustainable Energy Reviews, vol. 107, pp. 59-74.
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© 2019 Elsevier Ltd Microalgae has been identified as a potential feedstock for biodiesel production since its cultivation requires less cropland compared to conventional oil crops and the high growth rate of microalgae. Research on microalgae oils often are focused on microalgae oil extraction and biomass harvesting techniques. However, energy intensive and costly lipid extraction methods are the major obstacles hampering microalgae biodiesel commercialisation. Direct biodiesel synthesis avoids such problems as it combines lipid extraction techniques and transesterification into a single step. In this review, the potential of direct biodiesel synthesis from microalgae biomass was comprehensively analysed. The various species of microalgae commonly used as biodiesel feedstock was critically assessed, particularly on high lipid content species. The production of microalgae biodiesel via direct conversion from biomass was systematically discussed, covering major enhancements such as heterogeneous catalysts, the use of ultrasonic and microwave- techniques and supercritical alcohols that focus on the overall improvement of biodiesel production. In addition, this review illustrates the cultivation conditions for biomass growth and lipid productivity improvement, the available harvesting and lipid extraction technologies, as well as the key challenges and future prospect of microalgae biodiesel production. This review serves as a basis for future research on direct biodiesel synthesis from modified microalgae biomass to improve profitability of microalgae biodiesel.
Goldsmith, R, Willey, K & Boud, D 2019, 'Investigating invisible writing practices in the engineering curriculum using practice architectures', European Journal of Engineering Education, vol. 44, no. 1-2, pp. 71-84.
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Writing practices are seen to be essential for professional engineers, yet many engineering students and academics struggle with written communication, despite years of interventions to improve student writing. Much has been written about the importance of getting engineering students to write, but there has been a little investigation of engineering academics’ perceptions of writing practices in the curriculum, and the extent to which these practices are visible to their students and to the academics. This paper draws on research from an ongoing study into the invisibility of writing practices in the engineering curriculum using a practice architectures lens. The paper uses examples from the sites of practice of two participants in the study to argue that prevailing practices in engineering education constrain more than enable the development and practice of writing in the engineering curriculum.
Golhani, K, Balasundram, SK, Vadamalai, G & Pradhan, B 2019, 'Estimating chlorophyll content at leaf scale in viroid-inoculated oil palm seedlings (Elaeis guineensis Jacq.) using reflectance spectra (400 nm–1050 nm)', International Journal of Remote Sensing, vol. 40, no. 19, pp. 7647-7662.
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Golhani, K, Balasundram, SK, Vadamalai, G & Pradhan, B 2019, 'Selection of a Spectral Index for Detection of Orange Spotting Disease in Oil Palm (Elaeis guineensis Jacq.) Using Red Edge and Neural Network Techniques', Journal of the Indian Society of Remote Sensing, vol. 47, no. 4, pp. 639-646.
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© 2019, Indian Society of Remote Sensing. Spectral screening can play an important role in successful detection of viroid-infected oil palm seedlings from nursery stage prior to transplanting into the field. Coconut cadang–cadang viroid (CCCVd) is the main causal agent of orange spotting (OS) disease. OS disease is an emerging disease in Malaysian plantation. In this study, a glasshouse experiment was conducted with fifteen CCCVd-inoculated and five healthy oil palm seedlings in the growing season of 2015. Spectral screening was performed using a hyperspectral spectroradiometer, Analytic Spectral Device HandHeld 2 (325–1075 nm). The red edge, a steep gradient in reflectance between red and near-infrared bands (680–780 nm), was used for selection of red edge bands. A maximum point (i.e., 700 nm) and minimum point (i.e., 768 nm) of red edge were selected from healthy and inoculated spectra. Shifts of red edge inflection point from healthy to inoculated spectra were also studied. Four well-known spectral indices, namely simple ratio, red edge normalized difference vegetation index, two-band enhanced vegetation index 2 (EVI2), and chlorophyll index red edge, were evaluated using selected red edge bands. The multilayer perceptron neural network model was used to establish a nonlinear relationship between selected spectral bands and each spectral index. EVI2 was selected as a best spectral index which resulted in zero errors at the training, testing, and validation datasets. The highest coefficient of correlation (r = 1) was recorded between spectral bands (input values) and EVI2 (target values).
Golsorkhi, MS, Shafiee, Q, Lu, DD-C & Guerrero, JM 2019, 'Distributed Control of Low-Voltage Resistive AC Microgrids', IEEE Transactions on Energy Conversion, vol. 34, no. 2, pp. 573-584.
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IEEE This paper proposes a distributed control strategy for coordination of distributed energy resources (DERs) in low-voltage resistive microgrids. The proposed framework consists of two level structure; primary and secondary control. Unlike the existing distributed control solutions, the proposed method is based upon the practical assumption of resistive network impedance. The primary control level consists of a V-I droop mechanism, where GPS timing is used to synchronize the control agents. A consensus-based distributed secondary control method is introduced to improve the voltage regulation and load sharing accuracy of the V-I droop method. In the proposed approach, the d-axis component of the voltage is altered so as to regulate the average microgrid voltage to the rated value while guarantying proper sharing of active power among the DERs. Additionally, the q-axis component of voltage is adjusted to perform proper current and, accordingly reactive power sharing. The proposed control methodology accounts for the distribution line impedances. It features a plug-and-play environment; prior system knowledge is not required, and an arbitrary DER can enter the microgrid without any need for additional synchronization. An AC microgrid is prototyped to experimentally demonstrate the efficacy of the proposed method.
Gong, B, Wang, S, Sloan, SW, Sheng, D & Tang, C 2019, 'Modelling Rock Failure with a Novel Continuous to Discontinuous Method', Rock Mechanics and Rock Engineering, vol. 52, no. 9, pp. 3183-3195.
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© 2019, Springer-Verlag GmbH Austria, part of Springer Nature. The original discontinuous deformation and displacement (DDD) method is greatly refined using the statistical damage theory and contact mechanics. Next, a novel coupled method is proposed to model the continuous to discontinuous failure process of rocks. By hybridizing the finite element method (FEM) and discontinuous deformation analysis (DDA) method, the proposed method inherits the advantages of both and is able to provide a complete and unified description of rock deformation, crack initiation and propagation, and rock body translation, rotation and interaction. Moreover, to improve the deformation results and refine the stress distribution within the model blocks, finite elements are introduced into the blocks. The ability of an intact block to fracture is included as well, i.e., the deformable blocks that contain several finite elements may split into smaller blocks if the strength criteria are satisfied continuously. The boundaries of damaged elements represent newly formed joints, and sliding and opening may occur along these joints, i.e., mechanical interaction is allowed between adjacent blocks. The correctness and validity of the proposed method are verified through a series of benchmark tests. The simulated results are consistent with the analytical solutions, previous studies and experimental observations. Overall, the coupled method is an effective and reliable approach for modelling the entire rock failure process with satisfactory accuracy and shows considerable potential in geotechnical engineering.
Gong, C, Tao, D, Chang, X & Yang, J 2019, 'Ensemble Teaching for Hybrid Label Propagation', IEEE Transactions on Cybernetics, vol. 49, no. 2, pp. 388-402.
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© 2013 IEEE. Label propagation aims to iteratively diffuse the label information from labeled examples to unlabeled examples over a similarity graph. Current label propagation algorithms cannot consistently yield satisfactory performance due to two reasons: one is the instability of single propagation method in dealing with various practical data, and the other one is the improper propagation sequence ignoring the labeling difficulties of different examples. To remedy above defects, this paper proposes a novel propagation algorithm called hybrid diffusion under ensemble teaching (HyDEnT). Specifically, HyDEnT integrates multiple propagation methods as base 'learners' to fully exploit their individual wisdom, which helps HyDEnT to be stable and obtain consistent encouraging results. More importantly, HyDEnT conducts propagation under the guidance of an ensemble of 'teachers'. That is to say, in every propagation round the simplest curriculum examples are wisely designated by a teaching algorithm, so that their labels can be reliably and accurately decided by the learners. To optimally choose these simplest examples, every teacher in the ensemble should comprehensively consider the examples' difficulties from its own viewpoint, as well as the common knowledge shared by all the teachers. This is accomplished by a designed optimization problem, which can be efficiently solved via the block coordinate descent method. Thanks to the efforts of the teachers, all the unlabeled examples are logically propagated from simple to difficult, leading to better propagation quality of HyDEnT than the existing methods. Experiments on six popular datasets reveal that HyDEnT achieves the highest classification accuracy when compared with six state-of-the-art propagation methodologies such as harmonic functions, Fick's law assisted propagation, linear neighborhood propagation, semisupervised ensemble learning, bipartite graph-based consensus maximization, and teaching-to-lea...
Gong, S, Gao, L, Xu, J, Guo, Y, Hoang, DT & Niyato, D 2019, 'Exploiting Backscatter-Aided Relay Communications With Hybrid Access Model in Device-to-Device Networks', IEEE Transactions on Cognitive Communications and Networking, vol. 5, no. 4, pp. 835-848.
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© 2015 IEEE. The backscatter and active RF radios can complement each other and bring potential performance gain. In this paper, we envision a dual-mode radio structure that allows each device to make smart decisions on mode switch between backscatter communications (i.e., the passive mode) or RF communications (i.e., the active mode), according to the channel and energy conditions. The flexibility in mode switching also makes it more complicated for transmission control and network optimization. To exploit the radio diversity gain, we consider a wireless powered device-to-device network of hybrid radios and propose a sum throughput maximization by jointly optimizing energy beamforming and transmission scheduling in two radio modes. We further exploit the user cooperation gain by allowing the passive radios to relay for the active radios. As such, the sum throughput maximization is reformulated into a non-convex. We first present a sub-optimal algorithm based on successive convex approximation, which optimizes the relays' reflection coefficients by iteratively solving semi-definite programs. We also devise a set of heuristic algorithms with reduced computational complexity, which are shown to significantly improve the sum throughput and amenable for practical implementation.
Gong, S, Guo, Z, Wen, S & Huang, T 2019, 'Synchronization control for memristive high-order competitive neural networks with time-varying delay', Neurocomputing, vol. 363, pp. 295-305.
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This paper concerns the synchronization problem of memristive high-order competitive neural networks with time-varying delay. First, a novel control scheme with a linear term and a discontinuous term is proposed. Then, based on the Lyapunov stability theory, several criteria with algebraic form or matrix form are derived to ensure global exponential synchronization of the networks by adopting some inequality techniques. Finally, two numerical examples are presented to substantiate the effectiveness of the results.
Gonzales, RR, Park, MJ, Bae, T-H, Yang, Y, Abdel-Wahab, A, Phuntsho, S & Shon, HK 2019, 'Melamine-based covalent organic framework-incorporated thin film nanocomposite membrane for enhanced osmotic power generation', Desalination, vol. 459, pp. 10-19.
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© 2019 A melamine-based covalent organic framework (COF) nanomaterial, Schiff base network-1 (SNW-1), was incorporated into the polyamide layer of a novel thin film nanocomposite (TFN) pressure retarded osmosis (PRO) membrane. The deposition of SNW-1 was made on an open mesh fabric-reinforced polyamide-imide (PAI) support substrate through interfacial polymerization (IP). SNW-1 loading influence on the water permeability and osmotic power density during PRO operation was investigated. The porous and highly hydrophilic SNW-1 nanomaterial facilitated the flow of water molecules across the membranes, while maintaining satisfactory salt rejection ability of the polyamide selective layer. The membranes exhibited significantly enhanced surface hydrophilicity, water permeability, and power density. The mode of incorporation of SNW-1 during IP was also investigated and it was observed that the secondary amine groups of SNW-1 react with the carbonyl groups of 1,3,5-benzenetricarbonyl trichloride, the acyl halide precursor in polyamide formation; thus, SNW-1 was incorporated through the amine precursor, 1,3-phenylenediamine. Testing with 1.0 M NaCl as the draw solution, the TFN membrane with a loading of 0.02 wt% SNW-1 exhibited the highest water flux of 42.5 Lm−2 h−1 and power density of 12.1 Wm−2, while withstanding hydraulic pressure over 24 bar. This study suggests that COF-incorporation can be a promising method in PRO membrane fabrication to improve both osmotic performance and energy harvesting capability for the PRO process.
Gonzalez Marin, J, Baba, AA, Lopez Cuenca, D, Hesselbarth, J, Hashmi, RM & Esselle, KP 2019, 'High-Gain Low-Profile Chip-Fed Resonant Cavity Antennas for Millimeter-Wave Bands', IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 11, pp. 2394-2398.
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Gowripalan, N, Cao, J, Sirivivatnanon, V & South, W 2019, 'Accelerated autoclave test for determining alkali silica reaction of concrete', Concrete in Australia, vol. Volume 45, no. No 2, pp. 37-40.
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Alkali silica reaction (ASR) in concrete is a deleterious reaction which occurs due to the reaction between alkalis in the pore solution and reactive forms of silica found in some aggregates. ASR results in expansion and cracking which reduce the mechanical properties of the concrete. An ultra-accelerated autoclave test method has been used to test concrete prisms with and without alkali boosting. In this method, expansion and deterioration caused by ASR in concrete was investigated using an autoclave to simulate long-term deterioration. Test parameters such as temperature, pressure, duration of autoclaving and alkali boosting were investigated. Results obtained within a short period, clearly show large expansions and deterioration levels for concrete made with reactive aggregates.
Gowripalan, N, Nguyen, T, Yang, Y, Li, J & Sirivivatnanon, V 2019, 'Evaluation of elastic modulus reduction due to ASR', Concrete in Australia, vol. 45, no. 2, pp. 47-52.
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Evaluation of reduction in modulus of elasticity of concrete undergoing alkali silica reaction is carried out using an artificial neural network
Gracia, L, Solanes, JE, Muñoz-Benavent, P, Miro, JV, Perez-Vidal, C & Tornero, J 2019, 'Human-robot collaboration for surface treatment tasks', Interaction Studies. Social Behaviour and Communication in Biological and Artificial Systems, vol. 20, no. 1, pp. 148-184.
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Abstract This paper presents a human-robot closely collaborative solution to cooperatively perform surface treatment tasks such as polishing, grinding, finishing, deburring, etc. The proposed scheme is based on task priority and non-conventional sliding mode control. Furthermore, the proposal includes two force sensors attached to the manipulator end-effector and tool: one sensor is used to properly accomplish the surface treatment task, while the second one is used by the operator to guide the robot tool. The applicability and feasibility of the proposed collaborative solution for robotic surface treatment are substantiated by experimental results using a redundant 7R manipulator: the Sawyer collaborative robot.
Grant, M & Stewart, MG 2019, 'Postal IEDs and risk assessment of work health and safety considerations for postal workers', International Journal of Risk Assessment and Management, vol. 22, no. 2, pp. 152-152.
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Postal improvised explosive devices (IEDs) provide criminals and terrorists with a convenient mechanism for delivering an energetic payload to an intended victim with little operational risk. Postal IEDs formed 7% of IED attacks reported in the West between 1998-2015, are often dispatched in groups and can bring postal systems to a standstill. Nearly 30% of postal IED explosions occur in the postal worker environment and a third of the casualties caused by postal IEDs are postal workers. Postal IEDs are debatably a reasonably foreseeable cause of harm to postal workers and should be considered under the work health and safety (WHS) constructs of many Western nations. This paper considers this problem, using a probabilistic risk assessment model to inform a cost-benefit analysis considering potential risk reduction options for postal workers. It identifies that the control measures identified were not cost-effective where only the direct WHS costs pertaining to unintentional postal IED detonation within the mail delivery system were considered given the risk levels identified.
Gravina da Rocha, C, El Ghoz, HBC & Jr Guadanhim, S 2019, 'A model for implementing product modularity in buildings design', Engineering, Construction and Architectural Management, vol. 27, no. 3, pp. 680-699.
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PurposeThe purpose of this paper is to examine the fundamental underpinnings of product modularity and how these can be adapted to construction and its specificities (e.g. one-off products delivered by temporary supply chains) to create a model to design modular buildings.Design/methodology/approachThis research adopts a design science research approach. Explanation I (substantive theory devising based on the analysis of an artefact ‒ a low-income housing project) is used, followed by Solution Incubation (a model to implement product modularity in buildings design).FindingsThe model allows product modularity to be implemented at distinct levels (i.e. building, systems and components) at a single stage (building design), different from manufacturing where each level is considered at a distinct stage. This is in line with the project investigated: modularity was considered for house layouts, roof types and gable formats.Practical implicationsThe model provides a hands-on tool for practitioners to design modular buildings. The low-income project is also extensively detailed: three-dimensional models, floor plans and conceptual diagrams (outlining how fundamental underpinnings were applied at each level) are presented. There is a lack of comprehensive accounts such as the one presented here to demonstrate the application of product modularity in real-world projects.Originality/valueThis paper identifies and adapts the fundamental underpinnings of product modularity to constructi...
Gu, Q, Qi, S, Yue, Y, Shen, J, Zhang, B, Sun, W, Qian, W, Islam, MS, Saha, SC & Wu, J 2019, 'Structural and functional alterations of the tracheobronchial tree after left upper pulmonary lobectomy for lung cancer', BioMedical Engineering OnLine, vol. 18, no. 1.
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Abstract Background Pulmonary lobectomy has been a well-established curative treatment method for localized lung cancer. After left upper pulmonary lobectomy, the upward displacement of remaining lower lobe causes the distortion or kink of bronchus, which is associated with intractable cough and breathless. However, the quantitative study on structural and functional alterations of the tracheobronchial tree after lobectomy has not been reported. We sought to investigate these alterations using CT imaging analysis and computational fluid dynamics (CFD) method. Methods Both preoperative and postoperative CT images of 18 patients who underwent left upper pulmonary lobectomy are collected. After the tracheobronchial tree models are extracted, the angles between trachea and bronchi, the surface area and volume of the tree, and the cross-sectional area of left lower lobar bronchus are investigated. CFD method is further used to describe the airflow characteristics by the wall pressure, airflow velocity, lobar flow rate, etc. Results It is found that the angle between the trachea and the right main bronchus increases after operation, but the angle with the left main bronchus decreases. No significant alteration is observed for the surface area or volume of the tree between pre-operation and post-operation. After left upper pulmonary lobectomy, the cross-sectional area of left lower lobar bronchus is reduced for most of the patients (15/18) by 15–75%, especially for 4 patients by more than 50%. The wall pressure, airflow velocity and pressure drop significantly increase after the operation. The flow rat...
Gu, X, Yu, Y, Li, Y, Li, J, Askari, M & Samali, B 2019, 'Experimental study of semi-active magnetorheological elastomer base isolation system using optimal neuro fuzzy logic control', Mechanical Systems and Signal Processing, vol. 119, pp. 380-398.
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© 2018 Elsevier Ltd In this paper, a “smart” base isolation strategy is proposed in this study utilising a semi-active magnetorheological elastomer (MRE) isolator whose stiffness can be controlled in real-time and reversible fashion. By modulating the applied current, the horizontal stiffness of the MRE isolator can be controlled and thus the control action can be generated for the isolated structure. To overcome the inherent nonlinearity and hysteresis of the MRE isolator, radial basis function neural network based fuzzy logic control (RBF-NFLC) was developed due to its inherent robustness and capability in coping with uncertainties. The NFLC was optimised by a non-dominated sorting genetic algorithm type II (NSGA-II) for better suited fuzzy control rules as well as most appropriate parameters for the membership functions. To evaluate the effectiveness of the proposed smart base isolation system, four scenarios are tested under various historical earthquake excitations, i.e. bare building with no isolation, passive isolated structure, MRE isolated structure with Bang-Bang control, MRE isolated structure with proposed NFLC. A three-storey shear building model was adopted as the testing bed. Through the testing results, limited performance of passive isolation system was revealed. In contrast, the adaptability of the proposed isolation strategy was demonstrated and it is proven that the smart MRE base isolation system is able to provide satisfactory protection for both structural and non-structural elements of the system over a wide range of hazard dynamic loadings.
Guan, J, Feng, Y, Turrini, A & Ying, M 2019, 'Model Checking Applied to Quantum Physics.', CoRR, vol. abs/1902.03218.
Guan, Z, Zhang, Y, Zhu, L, Wu, L & Yu, S 2019, 'EFFECT: an efficient flexible privacy-preserving data aggregation scheme with authentication in smart grid', Science China Information Sciences, vol. 62, no. 3.
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© 2019, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature. Smart grid is considered as a promising approach to solve the problems of carbon emission and energy crisis. In smart grid, the power consumption data are collected to optimize the energy utilization. However, security issues in communications still present practical concerns. To cope with these challenges, we propose EFFECT, an efficient flexible privacy-preserving aggregation scheme with authentication in smart grid. Specifically, in the proposed scheme, we achieve both data source authentication and data aggregation in high efficiency. Besides, in order to adapt to the dynamic smart grid system, the threshold for aggregation is adjusted according to the energy consumption information of each particular residential area and the time period, which can support fault-tolerance while ensuring individual data privacy during aggregation. Detailed security analysis shows that our scheme can satisfy the desired security requirements of smart grid. In addition, we compare our scheme with existing schemes to demonstrate the effectiveness of our proposed scheme in terms of low computational complexity and communication overhead.
Guertler, M, Sick, N & Kriz, A 2019, 'A Discipline-Spanning Overview of Action Research and Its Implications for Technology and Innovation Management', Technology Innovation Management Review, vol. 9, no. 4, pp. 48-65.
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The iterative and learning character of action research is particularly beneficial for exploring complex socio-technical problems in technology and innovation management (TIM). In this respect, action research allows both rigorous and relevant research due to parallel solving of real-world problems, capability building, and gaining scientific insights. However, the use of action research within TIM research is surprisingly limited. Action research also is not a homogeneous research methodology since each research discipline, such as education and organizational science, has its own action research streams, which are often only loosely linked. A systematic overview of those action research traditions and specific best practices is still missing, which complicates a systematic transfer and use of action research in TIM. This article addresses this essential gap by building a cross-disciplinary overview of action research streams based on a bibliometric analysis using Scopus. The analysis includes relevant disciplines with action research traditions, their development over time, and the most influential journals, authors, institutions, and countries. Along with this discipline-spanning analysis, the article investigates particular TIM benefits and challenges of action research. The two key contributions of this article are: 1) a discipline-spanning overview of action research and its evolution and 2) an analysis of its implications for TIM research. These contributions build the basis for strengthening the use of action research in TIM. In the medium-term, action research has the capacity to link academia and industry more closely and, in doing so, assists important endeavours of translating more of our research outcomes into practice.
Guo, B, Ouyang, Y, Guo, T, Cao, L & Yu, Z 2019, 'Enhancing Mobile App User Understanding and Marketing With Heterogeneous Crowdsourced Data: A Review', IEEE Access, vol. 7, pp. 68557-68571.
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© 2013 IEEE. The mobile app market has been surging in recent years. It has some key differentiating characteristics which make it different from traditional markets. To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback mining, and so on. This paper reviews CrowdApp, a research field that leverages heterogeneous crowdsourced data for mobile app user understanding and marketing. We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. We further discuss the open issues and future trends of the CrowdApp. Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented.
Guo, D, Lui, GYL, Lai, SL, Wilmott, JS, Tikoo, S, Jackett, LA, Quek, C, Brown, DL, Sharp, DM, Kwan, RYQ, Chacon, D, Wong, JH, Beck, D, van Geldermalsen, M, Holst, J, Thompson, JF, Mann, GJ, Scolyer, RA, Stow, JL, Weninger, W, Haass, NK & Beaumont, KA 2019, 'RAB27A promotes melanoma cell invasion and metastasis via regulation of pro‐invasive exosomes', International Journal of Cancer, vol. 144, no. 12, pp. 3070-3085.
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Despite recent advances in targeted and immune‐based therapies, advanced stage melanoma remains a clinical challenge with a poor prognosis. Understanding the genes and cellular processes that drive progression and metastasis is critical for identifying new therapeutic strategies. Here, we found that the GTPase RAB27A was overexpressed in a subset of melanomas, which correlated with poor patient survival. Loss of RAB27A expression in melanoma cell lines inhibited 3D spheroid invasion and cell motility in vitro, and spontaneous metastasis in vivo. The reduced invasion phenotype was rescued by RAB27A‐replete exosomes, but not RAB27A‐knockdown exosomes, indicating that RAB27A is responsible for the generation of pro‐invasive exosomes. Furthermore, while RAB27A loss did not alter the number of exosomes secreted, it did change exosome size and altered the composition and abundance of exosomal proteins, some of which are known to regulate cancer cell movement. Our data suggest that RAB27A promotes the biogenesis of a distinct pro‐invasive exosome population. These findings support RAB27A as a key cancer regulator, as well as a potential prognostic marker and therapeutic target in melanoma.
Guo, G, Sun, Y, Fu, Q, Ma, Y, Zhou, Y, Xiong, Z & Liu, Y 2019, 'Sol-gel synthesis of ternary conducting polymer hydrogel for application in all-solid-state flexible supercapacitor', International Journal of Hydrogen Energy, vol. 44, no. 12, pp. 6103-6115.
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© 2019 Hydrogen Energy Publications LLC In this contribution, we reported the preparation of a novel conducting polymer hydrogel (CPH) by a sol-gel method, which was subsequently employed to fabricate a flexible all-solid-state supercapacitor device. Taking advantage of the synergistic effects of the different components in the conducting polymer hydrogel and the merits of the proposed synthesis strategies, the prepared supercapacitor device with CPH as electrode exhibited high area-normalized capacitance (2.2 F cm −2 ), high gravimetric capacitance (1573.6 F g −1 ) as well as high energy density of 0.18 mWh cm −2 (or 128.7 Wh Kg −1 ) at 0.08 mW cm −2 (or 55.1 W kg −1 ). This study did not only represent a novel all-solid-state, high performance, flexible supercapacitor with potential applications in flexible energy-related devices, but also developed a new method for enhancing capacitances and mechanical stability of all-solid-state flexible supercapacitor.
Guo, G-C & Ying, M 2019, 'Preface to special topic on quantum computing', National Science Review, vol. 6, no. 1, pp. 20-20.
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Guo, H, Hu, J, Li, J, Gao, M-T, Wang, Q, Guo, W & Ngo, HH 2019, 'Systematic insight into the short-term and long-term effects of magnetic microparticles and nanoparticles on critical flux in membrane bioreactors', Journal of Membrane Science, vol. 582, pp. 284-288.
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© 2019 This study aims to systematically investigate the short-term and long-term effects of magnetic microparticles (MPs) and nanoparticles (NPs) on critical flux in membrane bioreactors (MBRs). Comparison among six MBRs was carried out with different activated sludge samples. Results showed that the short-term adsorption and flocculation contributed only minimally, however, the long-term magnetic induced bio-effect improved the critical flux by conditioning sludge properties. Additional molecular weight distribution of soluble microbial product (SMP) indicated that long-term magnetic induced bio-effect declined the content of macromolecules (>500 kDa and 300–500 kDa), but promoted the content of small molecules (<100 kDa), consequently reduced the free energy of SMP gelling foulants, and further promoted the higher critical flux. Moreover, the magnetic MPs presented the better performance than NPs. This study illustrated that sufficient pre-acclimatization of magnetic activated sludge is significantly necessary to improve the critical flux in MBRs.
Guo, H, Wang, F, Li, L, Zhang, L & Luo, J 2019, 'A Minimum Loss Routing Algorithm Based on Real-Time Transaction in Energy Internet', IEEE Transactions on Industrial Informatics, vol. 15, no. 12, pp. 6446-6456.
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© 2005-2012 IEEE. Due to capacity constraints and the uneven distribution of resources, it becomes a trend that multiple microgrids are interconnected as a net structure through energy routers. With the large-scale penetration of distributed energy, the supply mode of energy networks has been gradually transformed into multisource, multipath, and networked supply. In order to adapt to the energy supply mode and the real-time power transaction, a minimum loss routing (MLR) algorithm based on real-time transaction is proposed to realize the end-to-end energy transmission. On the basis of bidding information, the real-time transaction is introduced to determine the source and destination address of power transmission and the amount and transmission time of power flows. Then, an MLR is selected for transaction power, which minimizes the power loss caused by conversion and transmission. As the key of power real-time transaction, the solutions to transmission time difference, single-loop or double-loop power supply modes, and congestion managements are incorporated with the Dijkstra algorithm to find a no-congestion MLR in this paper. Finally, the effectiveness of the proposed optimization algorithm in the selection of MLR and congestion managements is verified by simulations.
Guo, K, Chai, R, Candra, H, Guo, Y, Song, R, Nguyen, H & Su, S 2019, 'A Hybrid Fuzzy Cognitive Map/Support Vector Machine Approach for EEG-Based Emotion Classification Using Compressed Sensing', International Journal of Fuzzy Systems, vol. 21, no. 1, pp. 263-273.
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© 2018, Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany, part of Springer Nature. Due to the high dimensional, non-stationary and non-linear properties of electroencephalogram (EEG), a significant portion of research on EEG analysis remains unknown. In this paper, a novel approach to EEG-based human emotion study is presented using Big Data methods with a hybrid classifier. An EEG dataset is firstly compressed using compressed sensing, then, wavelet transform features are extracted, and a hybrid Support Vector Machine (SVM) and Fuzzy Cognitive Map classifier is designed. The compressed data is only one-fourth of the original size, and the hybrid classifier has the average accuracy by 73.32%. Comparing to a single SVM classifier, the average accuracy is improved by 3.23%. These outcomes show that psychological signal can be compressed without the sparsity identity. The stable and high accuracy classification system demonstrates that EEG signal can detect human emotion, and the findings further prove the existence of the inter-relationship between various regions of the brain.
Guo, L, Zhu, H & Abbosh, A 2019, 'Phase Reconfigurable Microwave Power Divider', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 66, no. 1, pp. 21-25.
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© 2004-2012 IEEE. A reconfigurable power divider (PD) that can operate either in an in-phase mode or out-of-phase mode is presented. To that end, the novel concept of using tunable phase shifter, which is a reflection-type loaded coupled lines, to emulate an effective variable length transmission line (TL) is utilized. The proposed PD uses a quarter-wavelength TL, a 100 Ω isolation resistor and two tunable phase shifters. The presented theoretical analysis shows that by properly selecting the parameters of the phase shifter, it can be used to approximate the performance of variable length TL. To validate the design, a prototype of dimensions 50 mm × 25 mm is built, using Rogers RO3010 substrate, and tested. The results indicate that the device can operate as an in-phase and out-of-phase by using suitable biasing voltages. Across the band 0.9-1.1 GHz, the device has more than 12 dB return loss at all the ports and more than 15 dB isolation between the two output ports with less than 5° phase deviation for both of the in-phase and out-of-phase states.
Guo, Q, Zhang, Y, Celler, BG & Su, SW 2019, 'Neural Adaptive Backstepping Control of a Robotic Manipulator With Prescribed Performance Constraint', IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 12, pp. 3572-3583.
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IEEE This paper presents an adaptive neural network (NN) control of a two-degree-of-freedom manipulator driven by an electrohydraulic actuator. To restrict the system output in a prescribed performance constraint, a weighted performance function is designed to guarantee the dynamic and steady tracking errors of joint angle in a required accuracy. Then, a radial-basis-function NN is constructed to train the unknown model dynamics of a manipulator by traditional backstepping control (TBC) and obtain the preliminary estimated model, which can replace the preknown dynamics in the backstepping iteration. Furthermore, an adaptive estimation law is adopted to self-tune every trained-node weight, and the estimated model is online optimized to enhance the robustness of the NN controller. The effectiveness of the proposed control is verified by comparative simulation and experimental results with Proportional-integral-derivative and TBC methods.
Guo, T, Pan, S, Zhu, X & Zhang, C 2019, 'CFOND: Consensus Factorization for Co-Clustering Networked Data', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 4, pp. 706-719.
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© 1989-2012 IEEE. Networked data are common in domains where instances are characterized by both feature values and inter-dependency relationships. Finding cluster structures for networked instances and discovering representative features for each cluster represent a special co-clustering task usefully for many real-world applications, such as automatic categorization of scientific publications and finding representative key-words for each cluster. To date, although co-clustering has been commonly used for finding clusters for both instances and features, all existing methods are focused on instance-feature values, without leveraging valuable topology relationships between instances to help boost co-clustering performance. In this paper, we propose CFOND, a consensus factorization based framework for co-clustering networked data. We argue that feature values and linkages provide useful information from different perspectives, but they are not always consistent and therefore need to be carefully aligned for best clustering results. In the paper, we advocate a consensus factorization principle, which simultaneously factorizes information from three aspects: network topology structures, instance-feature content relationships, and feature-feature correlations. The consensus factorization ensures that the final cluster structures are consistent across information from the three aspects with minimum errors. Experiments on real-life networks validate the performance of our algorithm.
Guo, W, Lei, Z, Wang, J & Wei, D 2019, 'Special issue on challenges in biological wastewater treatment and resource recovery', Bioresource Technology Reports, vol. 7, pp. 100243-100243.
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Guo, Z, Gong, S, Wen, S & Huang, T 2019, 'Event-Based Synchronization Control for Memristive Neural Networks With Time-Varying Delay', IEEE Transactions on Cybernetics, vol. 49, no. 9, pp. 3268-3277.
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In this paper, we investigate the global synchronization control problem for memristive neural networks (MNNs) with time-varying delay. A novel event-triggered controller is introduced with the linear diffusive term and discontinuous sign term. In order to greatly reduce the computation cost of the controller under certain event-triggering condition, two event-based control schemes are proposed with static event-triggering condition and dynamic event-triggering condition. Some sufficient conditions are derived by these control schemes to ensure the response MNN to be synchronized with the driving one. Furthermore, under certain event-triggering conditions, a positive lower bound is achieved for the interexecution time to guarantee that Zeno behavior cannot be executed. Finally, numerical simulations are provided to substantiate the effectiveness of the proposed theoretical results.
Guo, Z, Yang, C, Maritz, MF, Wu, H, Wilson, P, Warkiani, ME, Chien, C, Kempson, I, Aref, AR & Thierry, B 2019, 'Validation of a Vasculogenesis Microfluidic Model for Radiobiological Studies of the Human Microvasculature', Advanced Materials Technologies, vol. 4, no. 4, pp. 1800726-1800726.
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AbstractThe therapeutic ratio of radiotherapy is limited by acute or chronic side effects with often severe consequences to patients. The microvasculature is a central player involved in both tumor responses and healthy tissue/organ radiological injuries. However, current preclinical vascular models based on 2D culture offer only limited radiobiological insight due to their failure in recapitulating the 3D nature experienced by endothelial cells within the human microvasculature. To address this issue, the use of a 3D microvasculature‐on‐a‐chip microfluidic technology is demonstrated in radiobiological studies. Within this vasculogenesis model a perfusable network that structurally mimics the human microvasculature is formed and the biological response to ionizing radiation including cellular apoptosis, vessel tight adherens junction breakage, DNA double strand break, and repair is systematically investigated. In comparison to cells grown in a 2D environment, human umbilical vein endothelial cells in the 3D microvasculature‐on‐a‐chip displays significant differences in biological responses, especially at high X‐ray dose. This data confirms the feasibility of using microvascular‐on‐a‐chip models for radiobiological studies. Such vasculogenesis models have strong potential to yield more accurate prediction of healthy tissue responses to ionizing radiation as well as to guide the development of risk‐reducing strategies to prevent radiation‐induced acute and long‐term side‐effects.
Gupta, D, Pratama, M, Ma, Z, Li, J & Prasad, M 2019, 'Financial time series forecasting using twin support vector regression', PLOS ONE, vol. 14, no. 3, pp. e0211402-e0211402.
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© 2019 Gupta et al. Financial time series forecasting is a crucial measure for improving and making more robust financial decisions throughout the world. Noisy data and non-stationarity information are the two key factors in financial time series prediction. This paper proposes twin support vector regression for financial time series prediction to deal with noisy data and nonstationary information. Various interesting financial time series datasets across a wide range of industries, such as information technology, the stock market, the banking sector, and the oil and petroleum sector, are used for numerical experiments. Further, to test the accuracy of the prediction of the time series, the root mean squared error and the standard deviation are computed, which clearly indicate the usefulness and applicability of the proposed method. The twin support vector regression is computationally faster than other standard support vector regression on the given 44 datasets.
Gurjar, DS, Nguyen, HH & Tuan, HD 2019, 'Wireless Information and Power Transfer for IoT Applications in Overlay Cognitive Radio Networks', IEEE Internet of Things Journal, vol. 6, no. 2, pp. 3257-3270.
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IEEE This paper proposes and investigates an overlay spectrum sharing system in conjunction with the simultaneous wireless information and power transfer (SWIPT) to enable communications for the Internet of Things (IoT) applications. Considered is a cooperative cognitive radio network, where two IoT devices (IoDs) exchange their information and also provide relay assistance to a pair of primary users (PUs). Different from most existing works, in this paper, both IoDs can harvest energy from the radio-frequency (RF) signals received from the PUs. By utilizing the harvested energy, they provide relay cooperation to PUs and realize their own communications. For harvesting energy, a time-switching (TS) based approach is adopted at both IoDs. With the proposed scheme, one round of bidirectional information exchange for both primary and IoT systems is performed in four phases, i.e., one energy harvesting (EH) phase and three information processing (IP) phases. Both IoDs rely on the decode-and-forward operation to facilitate relaying, whereas the PUs employ selection combining (SC) technique. For investigating the performance of the considered network, this paper first provides exact expressions of user outage probability (OP) for the primary and IoT systems under Nakagami-m fading. Then, by utilizing the expressions of user OP, the system throughput and energy efficiency are quantified together with the average end-to-end transmission time. Numerical and simulation results are provided to give useful insights into the system behavior and to highlight the impact of various system/channel parameters.
Ha, Q & Phung, MD 2019, 'IoT‐enabled dependable control for solar energy harvesting in smart buildings', IET Smart Cities, vol. 1, no. 2, pp. 61-70.
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Efficiency and reliability have been essential requirements for energy generation in smart cities. This study presents the design and development of dependable control schemes for microgrid management, which can be seamlessly integrated into the management system of smart buildings. Here, to recover from failures in the solar energy system of a building microgrid, dependable controllers are proposed along with their hardware implementation. The system features the use of Internet of Things (IoT) as its core to coordinate the operation of multiple subsystems in a scalable manner. The control scheme uses a number of controllers cooperatively functioning via a token‐based mechanism within the network to provide redundancy and thus reliability in solar tracking. The system exploits data from not only local in‐situ sensors but also online sources via IoT networks for fault‐tolerant control. Experiments conducted in a 12‐storey building indicate that the harvested solar energy meets the design requirement while the control reliability is maintained in face of communication or hardware disruptions. The results confirmed the validity of the proposed approach and its applicability to energy management in smart buildings.
Ha, QP, Yen, L & Balaguer, C 2019, 'Robotic autonomous systems for earthmoving in military applications', Automation in Construction, vol. 107, pp. 102934-102934.
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© 2019 Elsevier B.V. Along with increasing innovations in frontier engineering sciences, the advancement in Robotic Autonomous Systems (RAS) has brought about a new horizon in earthmoving processes for construction. In the military domain, there is also an increasing interest in utilising RAS technologies. In particular, ground-based forces are frequently called upon to conduct earthmoving tasks as part of military operations, tasks which could be partially or fully aided by the employment of RAS technologies. There have been rapid developments in military construction automation using high-mobility ground-based platforms, human-machine and machine-machine interfaces, teleoperation and control systems, data transmission systems, machine perception and manipulation capabilities, as well as advances in networked robotics and cyberphysical systems. Given these developments it is timely to undertake a comprehensive overview on the topic of interest to the research community and the authority. This paper presents an overview of the RAS development for platform-centric earthworks together with an analysis of the technical feasibility, maturity, key technical challenges, and future directions for the application of RAS technologies to earthmoving tasks of interest to the army.
Habib, M, Alfugara, A & Pradhan, B 2019, 'A LOW-COST SPATIAL TOOL FOR TRANSFORMING FEATURE POSITIONS OF CAD-BASED TOPOGRAPHIC MAPPING', Geodesy and cartography, vol. 45, no. 4, pp. 161-168.
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In fact, Computer Aided Design (CAD) offers powerful design tools to produce digital large scale topographic mapping that is considered the backbone for construction projects, urban planning and landscape architecture. Nowadays local agencies in small communities and developing countries are facing some difficulties in map to map transformation and handling discrepancies between the physical reality and represented spatial data due to the need for implementing high cost systems such as GIS and the experienced staff required. Therefore, the require for providing a low-cost tool based on the most common CAD system is very important to guarantee a quality and positional accuracy of features. The main aim of this study is to describe a mathematical relationship to fulfil the coordinate conversion between two different grid references applying two-dimensional conformal polynomial models built on control points and a least squares fitting algorithm. In addition, the automation of this model was performed in the Microsoft Visual Studio environment to calculate polynomial coefficients and convert the positional property of entities in AutoCAD by developing spatial CAD tool. To evaluate the proposed approach the extracted coordinates of check points from the interpolation surface are compared with the known ones.
Hadzhiev, Y, Qureshi, HK, Wheatley, L, Cooper, L, Jasiulewicz, A, Van Nguyen, H, Wragg, JW, Poovathumkadavil, D, Conic, S, Bajan, S, Sik, A, Hutvàgner, G, Tora, L, Gambus, A, Fossey, JS & Müller, F 2019, 'A cell cycle-coordinated Polymerase II transcription compartment encompasses gene expression before global genome activation', Nature Communications, vol. 10, no. 1.
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AbstractMost metazoan embryos commence development with rapid, transcriptionally silent cell divisions, with genome activation delayed until the mid-blastula transition (MBT). However, a set of genes escapes global repression and gets activated before MBT. Here we describe the formation and the spatio-temporal dynamics of a pair of distinct transcription compartments, which encompasses the earliest gene expression in zebrafish. 4D imaging of pri-miR430and zinc-finger-gene activities by a novel, native transcription imaging approach reveals transcriptional sharing of nuclear compartments, which are regulated by homologous chromosome organisation. These compartments carry the majority of nascent-RNAs and active Polymerase II, are chromatin-depleted and represent the main sites of detectable transcription before MBT. Transcription occurs during the S-phase of increasingly permissive cleavage cycles. It is proposed, that the transcription compartment is part of the regulatory architecture of embryonic nuclei and offers a transcriptionally competent environment to facilitate early escape from repression before global genome activation.
Haes Alhelou, H, Golshan, MEH & Hatziargyriou, ND 2019, 'A Decentralized Functional Observer Based Optimal LFC Considering Unknown Inputs, Uncertainties, and Cyber-Attacks', IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 4408-4417.
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Hafiz, MA, Hawari, AH & Altaee, A 2019, 'A hybrid forward osmosis/reverse osmosis process for the supply of fertilizing solution from treated wastewater', Journal of Water Process Engineering, vol. 32, pp. 100975-100975.
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© 2019 Elsevier Ltd This work investigates the application of a hybrid system that combines forward osmosis (FO) and reverse osmosis (RO) processes for the supply of a fertilizing solution that could be used directly for irrigation purposes. In the FO process the feed solution is treated sewage effluent (TSE) and two different types of draw solutions were investigated. The impact of the feed solution and the draw solution flowrates and the membrane orientation on the membrane flux were investigated in the forward osmosis process. RO was used for the regeneration of the draw solution. In the forward osmosis process it was found that the highest membrane flux was 13.2 LMH. The FO process had high rejection rates for total phosphorus and ammonium which were 99% and 97%, respectively. RO achieved 99% total salts rejection rate. Seawater RO (SW30HR) and brackish water RO (BW30LE) membranes were used for the regeneration of the draw solution. The specific power consumption for the regeneration of the draw solution was 2.58 kW h/m3 and 2.18 kW h/m3 for SW30HR and BW30LE membranes, respectively. The final product water had high quality in terms of total dissolved solids concentration but the concentration of phosphorus was slightly higher than recommended due to adding 0.1 M of diammonium phosphate in the draw solution.
Haider, N, Ali, A, Suarez-Rodriguez, C & Dutkiewicz, E 2019, 'Optimal Mode Selection for Full-Duplex Enabled D2D Cognitive Networks.', IEEE Access, vol. 7, pp. 57298-57311.
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Hakdaoui, S, Emran, A, Pradhan, B, Lee, C-W & Nguemhe Fils, SC 2019, 'A Collaborative Change Detection Approach on Multi-Sensor Spatial Imagery for Desert Wetland Monitoring after a Flash Flood in Southern Morocco', Remote Sensing, vol. 11, no. 9, pp. 1042-1042.
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This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event were used. To achieve the objectives, this study used Sentinel-1 data to discriminate water body and soil roughness, and optical data to monitor the soil moisture after the event. The proposed method combines two approaches: one based on spectral processing, and the other based on categorical processing. The first step was to extract four spectral indices and utilize change vector analysis on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI images acquired before and after the event. The second step was performed using pattern classification techniques, namely, linear classifiers based on support vector machines (SVM) with Gaussian kernels. The results of these two approaches were fused to generate a collaborative wetland change map. The application of co-registration and supervised classification based on textural and intensity information from Radar Sentinel-1 images taken before and after the event completes this work. The results obtained demonstrate the importance of the complementarity of multi-sensor images and a multi-approach methodology to better monitor changes to a wetland area after a flash-flood disaster.
Halasi, Z, Maróti, A, Pyber, L & Qiao, Y 2019, 'An improved diameter bound for finite simple groups of Lie type', Bulletin of the London Mathematical Society, vol. 51, no. 4, pp. 645-657.
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© 2019 London Mathematical Society For a finite group (Formula presented.), let (Formula presented.) denote the maximum diameter of a connected Cayley graph of (Formula presented.). A well-known conjecture of Babai states that (Formula presented.) is bounded by (Formula presented.) in case (Formula presented.) is a non-abelian finite simple group. Let (Formula presented.) be a finite simple group of Lie type of Lie rank (Formula presented.) over the field (Formula presented.). Babai's conjecture has been verified in case (Formula presented.) is bounded, but it is wide open in case (Formula presented.) is unbounded. Recently, Biswas and Yang proved that (Formula presented.) is bounded by (Formula presented.). We show that in fact (Formula presented.) holds. Note that our bound is significantly smaller than the order of (Formula presented.) for (Formula presented.) large, even if (Formula presented.) is large. As an application, we show that more generally (Formula presented.) holds for any subgroup (Formula presented.) of (Formula presented.), where (Formula presented.) is a vector space of dimension (Formula presented.) defined over the field (Formula presented.).
Hamzehei, A, Wong, RK, Koutra, D & Chen, F 2019, 'Collaborative topic regression for predicting topic-based social influence', Machine Learning, vol. 108, no. 10, pp. 1831-1850.
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© 2019, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature. The rapid growth of social networks and their strong presence in our lives have attracted many researchers in social networks analysis. Users of social networks spread their opinions, get involved in discussions, and consequently, influence each other. However, the level of influence of different users is not the same. It varies not only among users, but also for one user across different topics. The structure of social networks and user-generated content can reveal immense information about users and their topic-based influence. Although many studies have considered measuring global user influence, measuring and estimating topic-based user influence has been under-explored. In this paper, we propose a collaborative topic-based social influence model that incorporates both network structure and user-generated content for topic-based influence measurement and prediction. We predict topic-based user influence on unobserved topics, based on observed topic-based user influence through their generated contents and activities in social networks. We perform experimental analysis on Twitter data, and show that our model outperforms state-of-the-art approaches on recall, accuracy, precision, and F-score for predicting topic-based user influence.
Han, B, Tsang, IW, Chen, L, Zhou, JT & Yu, CP 2019, 'Beyond Majority Voting: A Coarse-to-Fine Label Filtration for Heavily Noisy Labels', IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 12, pp. 3774-3787.
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Crowdsourcing has become the most appealing way to provide a plethora of labels at a low cost. Nevertheless, labels from amateur workers are often noisy, which inevitably degenerates the robustness of subsequent learning models. To improve the label quality for subsequent use, majority voting (MV) is widely leveraged to aggregate crowdsourced labels due to its simplicity and scalability. However, when crowdsourced labels are "heavily" noisy (e.g., 40% of noisy labels), MV may not work well because of the fact "garbage (heavily noisy labels) in, garbage (full aggregated labels) out." This issue inspires us to think: if the ultimate target is to learn a robust model using noisy labels, why not provide partial aggregated labels and ensure that these labels are reliable enough for learning models? To solve this challenge by improving MV, we propose a coarse-to-fine label filtration model called double filter machine (DFM), which consists of a (majority) voting filter and a sparse filter serially. Specifically, the DFM refines crowdsourced labels from coarse filtering to fine filtering. In the stage of coarse filtering, the DFM aggregates crowdsourced labels by voting filter, which yields (quality-acceptable) full aggregated labels. In the stage of fine filtering, DFM further digs out a set of high-quality labels from full aggregated labels by sparse filter, since this filter can identify high-quality labels by the methodology of support selection. Based on the insight of compressed sensing, DFM recovers a ground-truth signal from heavily noisy data under a restricted isometry property. To sum up, the primary benefits of DFM are to keep the scalability by voting filter, while improve the robustness by sparse filter. We also derive theoretical guarantees for the convergence and recovery of DFM and reveal its complexity. We conduct comprehensive experiments on both the UCI simulated and the AMT crowdsourced datasets. Empirical results show that partial aggregated labels...
Han, B, Yao, Q, Pan, Y, Tsang, IW, Xiao, X, Yang, Q & Sugiyama, M 2019, 'Millionaire: a hint-guided approach for crowdsourcing', Machine Learning, vol. 108, no. 5, pp. 831-858.
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© 2018, The Author(s). Modern machine learning is migrating to the era of complex models, which requires a plethora of well-annotated data. While crowdsourcing is a promising tool to achieve this goal, existing crowdsourcing approaches barely acquire a sufficient amount of high-quality labels. In this paper, motivated by the “Guess-with-Hints” answer strategy from the Millionaire game show, we introduce the hint-guided approach into crowdsourcing to deal with this challenge. Our approach encourages workers to get help from hints when they are unsure of questions. Specifically, we propose a hybrid-stage setting, consisting of the main stage and the hint stage. When workers face any uncertain question on the main stage, they are allowed to enter the hint stage and look up hints before making any answer. A unique payment mechanism that meets two important design principles for crowdsourcing is developed. Besides, the proposed mechanism further encourages high-quality workers less using hints, which helps identify and assigns larger possible payment to them. Experiments are performed on Amazon Mechanical Turk, which show that our approach ensures a sufficient number of high-quality labels with low expenditure and detects high-quality workers.
Han, L, Zhou, M, Han, S, Jia, W, Sun, C & Fu, C 2019, 'Targeting malware discrimination based on reversed association task', Concurrency and Computation: Practice and Experience, vol. 31, no. 23.
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SummaryRegarding the current situation that the recognition rate of malware is decreasing, the article points out that the reason for this dilemma is that more and more targeting malware have emerged, which share little or no common feature with traditional malware. The premise of malware recognition judging whether a software is malicious or benign is actually a decision problem. We propose that malware discrimination should resort to the corresponding task or purpose. We first present a formal definition of a task and then provide further classifications of malicious tasks. Based on the decidable theory, we prove that task performed by any software is recursive and determinable. By establishing a mapping from software to task, we prove that software is many‐to‐one reducible to corresponding tasks. Thus, we demonstrate that software, including malware, is also recursive and can be determined by the corresponding tasks. Finally, we present the discrimination process of our method. Nine real malwares are presented, which were firstly discriminated by our method but at that time could not be identified by Kaspersky, McAfee, Symantec Norton, or Kingsoft Antivirus.
Han, L, Zhou, M, Jia, W, Dalil, Z & Xu, X 2019, 'Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model', Information Sciences, vol. 476, pp. 491-504.
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© 2018 Elsevier Inc. An effective security strategy for Wireless Sensor Networks (WSNs) is imperative to counteract security threats. Meanwhile, energy consumption directly affects the network lifetime of a wireless sensor. Thus, an attempt to exploit a low-consumption Intrusion Detection System (IDS) to detect malicious attacks makes a lot of sense. Existing Intrusion Detection Systems can only detect specific attacks and their network lifetime is short due to their high energy consumption. For the purpose of reducing energy consumption and ensuring high efficiency, this paper proposes an intrusion detection model based on game theory and an autoregressive model. The paper not only improves the autoregressive theory model into a non-cooperative, complete-information, static game model, but also predicts attack pattern reliably. The proposed approach improves on previous approaches in two main ways: (1) it takes energy consumption of the intrusion detection process into account, and (2) it obtains the optimal defense strategy that balances the system's detection efficiency and energy consumption by analyzing the model's mixed Nash equilibrium solution. In the simulation experiment, the running time of the process is regarded as the main indicator of energy consumption of the system. The simulation results show that our proposed IDS not only effectively predicts the attack time and the next targeted cluster based on the game theory, but also reduces energy consumption.
Han, M, Bao, Y, Sun, Z, Wen, S, Xia, L, Zhao, J, Du, J & Yan, Z 2019, 'Automatic Segmentation of Human Placenta Images With U-Net', IEEE Access, vol. 7, pp. 180083-180092.
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© 2013 IEEE. Placenta is closely related to the health of the fetus. Abnormal placental function will affect the normal development of the fetus, and in severe cases, even endanger the life of the fetus. Therefore, accurate and quantitative evaluation of placenta has important clinical significance. It is a common method to segment human placenta with semantic segmentation. However, manual segmentation relies too much on the professional knowledge and clinical experience of the staff, and it will also consume a lot of time. Therefore, based on u-net, we propose an automatic segmentation method of human placenta, which reduces manual intervention and greatly speeds up the segmentation, making large-scale segmentation possible. The human placenta data set we used was labeled by experts, which was obtained from prenatal examinations of 11 pregnant women, about 1,110 images. It was a comprehensive and clinically significant data set. By training the network with such data set, the robustness of the model will be better. After testing on the data set, the segmentation effect is basically consistent with the manual segmentation effect.
Han, R, Khan, MH, Angeloski, A, Casillas, G, Yoon, CW, Sun, X & Huang, Z 2019, 'Hexagonal Boron Nitride Nanosheets Grown via Chemical Vapor Deposition for Silver Protection', ACS Applied Nano Materials, vol. 2, no. 5, pp. 2830-2835.
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© 2019 American Chemical Society. In this study, hexagonal boron nitride nanosheets (h-BNNS) have been grown on polycrystalline silver substrates via chemical vapor deposition (CVD) using ammonia borane as a precursor. The h-BNNS are of few-atomic-layer thickness and form continuous coverage over the whole Ag substrate. The atomically thin coating poses negligible interference to the reflectivity in the UV-visible range. The nanosheet coating also proves very effective in protecting Ag foil chemically. In contrast to bare Ag foil, the coated foil displayed only minor decolorization under high concentration of H2S. The study indicates that h-BNNS can be a promising protective coating for Ag based items such as jewelry or mirrors used in astronomical telescopes.
Han, S-F, Jin, W, Abomohra, AE-F, Zhou, X, Tu, R, Chen, C, Chen, H, Gao, S-H & Wang, Q 2019, 'Enhancement of Lipid Production of Scenedesmus obliquus Cultivated in Municipal Wastewater by Plant Growth Regulator Treatment', Waste and Biomass Valorization, vol. 10, no. 9, pp. 2479-2485.
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© 2018, Springer Science+Business Media B.V., part of Springer Nature. Effects of four different plant growth regulators including indole-3-acetic acid (IAA), 1-triacontanol (TRIA), 2,4-dichlorophenoxyacetic acid (2,4-D) and 6-benzylaminopurine (6-BA) on biomass and lipid productivity of microalga Scenedesmus obliquus cultured in municipal wastewater were primarily studied. The results showed that the lipid productivity of S. obliquus was significantly increased by 30.5 and 23.6% after the treatment by IAA and TRIA, respectively. According to the GC analysis of the lipids, the addition of IAA and TRIA could increase the content of monounsaturated fatty acid in S. obliquus, and thus improving the grade of biodiesel. After the addition of IAA and TRIA, the nitrogen content of S. obliquus significantly decreased, while bacterial diversity in wastewater increased, which could enhance the stability of microbial system in the wastewater medium. Meanwhile, significant increase were also found in the abundances of β-Proteobacteria and α-Proteobacteria.
Han, S-F, Jin, W, Yang, Q, El-Fatah Abomohra, A, Zhou, X, Tu, R, Chen, C, Xie, G-J & Wang, Q 2019, 'Application of pulse electric field pretreatment for enhancing lipid extraction from Chlorella pyrenoidosa grown in wastewater', Renewable Energy, vol. 133, pp. 233-239.
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© 2018 Elsevier Ltd Lipid extraction is a key step of biodiesel production from microalgae, however, the application of traditional extraction methods was limited due to the difficulties of cell disruption as well as solvent toxicity. In this work, pretreatment method using pulsed electric field (PEF), was primarily applied to lipid extraction process from microalgae Chlorella pyrenoidosa grown in wastewater. After the pretreatment with PEF, the yields of fatty acid methyl esters from C.pyrenoidosa was 12.0% higher than traditional pretreatment with ultrasonic. The results indicated that PEF was an effective method for cell disruption. Fluorescence staining and scanning electron microscopy showed that the integrity of the cell membrane of microalgae was damaged under pulsed electric field, which enhanced the penetration of solvents and lipid extraction.
Han, Z, Wu, M, Zhu, Q & Yang, J 2019, 'Three-dimensional wave-domain acoustic contrast control using a circular loudspeaker array', The Journal of the Acoustical Society of America, vol. 145, no. 6, pp. EL488-EL493.
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This paper proposes a three-dimensional wave-domain acoustic contrast control method to reproduce a multizone sound field using a circular loudspeaker array. In this method, sound field analysis is based on spherical harmonic decomposition, and the loudspeaker weights are obtained by maximizing the acoustic energy contrast between the predefined bright zone and dark zone. Simulation results show that the proposed method provides good multizone separation performance over a large spatial region and requires lower-order spherical harmonics, resulting in a much lower number of microphones required to measure the acoustic transfer functions.
Hao, P, Zhang, G, Martinez, L & Lu, J 2019, 'Regularizing Knowledge Transfer in Recommendation With Tag-Inferred Correlation', IEEE Transactions on Cybernetics, vol. 49, no. 1, pp. 83-96.
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© 2013 IEEE. Traditional recommender systems suffer from the data sparsity problem. However, user knowledge acquired in one domain can be transferred and exploited in several other relevant domains. In this context, cross-domain recommender systems have been proposed to create a new and effective recommendation paradigm in which to exploit rich data from auxiliary domains to assist recommendations in a target domain. Before knowledge transfer takes place, building reliable and concrete domain correlation is the key ensuring that only relevant knowledge will be transferred. Social tags are used to explicitly link different domains, especially when neither users nor items overlap. However, existing models only exploit a subset of tags that are shared by heterogeneous domains. In this paper, we propose a complete tag-induced cross-domain recommendation (CTagCDR) model, which infers interdomain and intradomain correlations from tagging history and applies the learned structural constraints to regularize joint matrix factorization. Compared to similar models, CTagCDR is able to fully explore knowledge encoded in both shared and domain-specific tags. We demonstrate the performance of our proposed model on three public datasets and compare it with five state-of-the-art single and cross-domain recommendation approaches. The results show that CTagCDR works well in both rating prediction and item recommendation tasks, and can effectively improve recommendation performance.
Hao, Q, Liu, Y, Chen, T, Guo, Q, Wei, W & Ni, B-J 2019, 'Bi2O3@Carbon Nanocomposites for Solar-Driven Photocatalytic Degradation of Chlorophenols', ACS Applied Nano Materials, vol. 2, no. 4, pp. 2308-2316.
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Copyright © 2019 American Chemical Society. Chlorophenols are corrosive and toxic in a water environment, which have caused increasing concerns and encourage the development of solar-driven techniques with highly efficient photocatalysts for green remediation. Coupling photocatalysis with the surface plasmon resonance (SPR) effect is a practical solution for boosting the utilization of solar light in the IR region while improving the overall performance of the photocatalysts. However, a facile and green strategy to synthesize metallic non-noble bismuth (Bi0)-based photocatalysts is still lacking. Herein, we report smart Bi/Bi2O3/C composites with high performance for the photocatalytic degradation of 2,4-dichlorophenol. Advanced characterizations such as X-ray diffraction, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and high-resolution transmission electron microscopy are applied to analyze the morphology and structure of the prepared materials. The photodegradation rate of the hybrid is significantly enhanced compared with the sole counterparts, which are 1.60-fold of Bi2O3 and 2.47-fold of g-C3N4. The synthesized Bi/C-2 exhibits excellent stability without a decline in activity after four cycles. The SPR effect of Bi is identified to account for the strengthened photoreactivity. Moreover, the relatively high utilization efficiency of solar energy and the rapid separation rate of photogenerated electron and hole pairs helped to enhance the photocatalytic performance synergistically. ©
Hao, S, Shi, C, Niu, Z & Cao, L 2019, 'Modeling positive and negative feedback for improving document retrieval', Expert Systems with Applications, vol. 120, pp. 253-261.
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© 2018 Elsevier Ltd Pseudo-relevance feedback (PRF) has evident potential for enriching the representation of short queries. Traditional PRF methods treat top-ranked documents as feedback, since they are assumed to be relevant to the query. However, some of these feedback documents may actually distract from the query topic for a range of reasons and accordingly downgrade PRF system performance. Such documents constitute negative examples (negative feedback) but could also be valuable in retrieval. In this paper, a novel framework of query language model construction is proposed in order to improve retrieval performance by integrating both positive and negative feedback. First, an improvement-based method is proposed to automatically identify the types of feedback documents (i.e. positive or negative) according to whether the document enhances the retrieval's effectiveness. Subsequently, based on the learned positive and negative examples, the positive feedback models and the negative feedback models are estimated using an Expectation-Maximization algorithm with the assumptions: the positive term distribution is affected by the context term distribution and the negative term distribution is affected by both the positive term distribution and the context term distribution (such that the positive feedback model upgrades the rankings of relevant documents and the negative feedback model prunes the irrelevant documents from a query). Finally, a content-based representativeness criterion is proposed in order to obtain the representative negative feedback documents. Experiments conducted on the TREC collections demonstrate that our proposed approach results in better retrieval accuracy and robustness than baseline methods.
Hasan, ASMM & Ammenberg, J 2019, 'Biogas potential from municipal and agricultural residual biomass for power generation in Hazaribagh, Bangladesh – A strategy to improve the energy system', Renewable Energy Focus, vol. 29, pp. 14-23.
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Energy is considered as one of the significant benchmarks towards sustainable growth. Due to the recent economic growth, energy demand is increasing day by day in Bangladesh. The power generation mainly relies on fossil fuels though there are plans to increase the renewable energy share by the concern stakeholders. Considering the global warming, energy generation from renewable sources is considered as a sustainable way to mitigate the anthropogenic emission. This study, therefore, addresses the potentiality of biogas production from municipal waste and agricultural residues in a city territory of Dhaka namely Hazaribagh. The potential sources include wastes from two markets, six slaughterhouses, domestic wastes, one poultry farm and three croplands. The calculations made in this study to estimate the amount of biogas and electricity from the described sources are done in a simple way, just to illustrate the potential. This study suggests that there is a good potentiality of biogas production and electricity generation from municipal wastes and agricultural residues of Hazaribagh. Moreover, this study also mentions the significant actors like government, future owners, people and so on that are needed to be incorporated to implement biogas solution in a city territory.
Hasan, ASMM, Hossain, R, Tuhin, RA, Sakib, TH & Thollander, P 2019, 'Empirical Investigation of Barriers and Driving Forces for Efficient Energy Management Practices in Non-Energy-Intensive Manufacturing Industries of Bangladesh', Sustainability, vol. 11, no. 9, pp. 2671-2671.
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Improved energy efficiency is being considered as one of the significant challenges to mitigating climate change all over the world. While developed countries have already adopted energy management and auditing practices to improve energy efficiency, the developing countries lag far behind. There are a limited number of studies which have been conducted in the context of developing countries, which mostly revolve around highly energy-intensive sectors. This study looks into the existence and importance of the challenges to and motivating forces for the adoption of energy management practices in Bangladesh, a developing country, focusing on the non-energy-intensive manufacturing industries. Conducted as a multiple case study, the results indicate the existence of several barriers towards adopting and implementing the management of energy practices in the non-energy-intensive industries of Bangladesh, where among them, “other preferences for capital venture” and “inadequate capital expenditure” are the most dominant. This study also identified a number of driving forces that can accelerate the acceptance of energy efficiency practices, such as the demands from the owner, loans, subsidies, and a lowered cost–benefit ratio. Findings of this study could assist the concerned stakeholders to develop beneficial policies and a proper regulatory framework for the non-energy-intensive industries of developing countries like Bangladesh.
Hasan, ASMM, Rokonuzzaman, M, Tuhin, RA, Salimullah, SM, Ullah, M, Sakib, TH & Thollander, P 2019, 'Drivers and Barriers to Industrial Energy Efficiency in Textile Industries of Bangladesh', Energies, vol. 12, no. 9, pp. 1775-1775.
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Bangladesh faced a substantial growth in primary energy demand in the last few years. According to several studies, energy generation is not the only means to address energy demand; efficient energy management practices are also very critical. A pertinent contribution in the energy management at the industrial sector ensures the proper utilization of energy. Energy management and its efficiency in the textile industries of Bangladesh are studied in this paper. The outcomes demonstrate several barriers to energy management practices which are inadequate technical cost-effective measures, inadequate capital expenditure, and poor research and development. However, this study also demonstrates that the risk of high energy prices in the future, assistance from energy professionals, and an energy management scheme constitute the important drivers for the implementation of energy efficiency measures in the studied textile mills. The studied textile industries seem unaccustomed to the dedicated energy service company concept, and insufficient information regarding energy service companies (ESCOs) and the shortage of trained professionals in energy management seem to be the reasons behind this. This paper likewise finds that 3–4% energy efficiency improvements can be gained with the help of energy management practices in these industries.
Hasan, M, Zhao, J, Huang, Z, Wei, D & Jiang, Z 2019, 'Analysis and characterisation of WC-10Co and AISI 4340 steel bimetal composite produced by powder–solid diffusion bonding', The International Journal of Advanced Manufacturing Technology, vol. 103, no. 9-12, pp. 3247-3263.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Cermet and steel material bonding is a challenging task, due to their large difference of physical properties, e.g. coefficient of thermal expansion. In this study, a hot compaction diffusion bonding method was employed to fabricate a small-dimensional bimetallic composite of WC-10Co and high strength AISI 4340 steel, where the cermet was used in powder form and the steel as solid. The bimetal composite was characterised by microstructural analysis and mechanical properties evaluation. The interface microstructure reveals a successful metallurgical bonding between the cermet and steel materials. The influence of sintering temperature (1050–1250 °C) was examined at intervals of 50 °C. This study shows that the properties of sintered powder and the bonding quality with the steel improve with an increase in sintering temperature. A bonding beneficiary reaction layer was observed to grow at the joining interface by mutual diffusion of the alloying elements, which increases with the increasing temperature. The maximum width of the reaction layer observed was 4.13 μm and consists mainly of intermetallic ternary carbides. The bonding shear strength of the interface is found to be slightly higher than claimed in previous studies. The developed bimetal composite could be used in applications where a combination of high strength and hardness is required.
Hassan, M, Liu, D & Xu, D 2019, 'A Two-Stage Approach to Collaborative Fiber Placement through Coordination of Multiple Autonomous Industrial Robots', Journal of Intelligent & Robotic Systems, vol. 95, no. 3-4, pp. 915-933.
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© 2018, Springer Nature B.V. The use of multiple Autonomous Industrial Robots (AIRs) as opposed to a single AIR to perform fiber placement brings about many challenges which have not been addressed by researchers. These challenges include optimal division and allocation of the work and performing path planning in a coordinated manner while considering the requirements and constraints that are unique to the fiber placement task. To solve these challenges, a two-stage approach is proposed in this paper. The first stage considers multiple objectives to optimally allocate each AIR with surface areas, while the second stage aims to generate coordinated paths for the AIRs. Within each stage, mathematical models are developed with several unique objectives and constraints that are specific to the multi-AIR collaborative fiber placement. Several case studies are presented to validate the approach and the proposed mathematical models. Comparison studies with different number of AIRs and variations of the developed mathematical models are also presented.
Hassan, W, Lu, DD & Xiao, W 2019, 'Analysis and experimental verification of a single‐switch high‐voltage gain ZCS DC–DC converter', IET Power Electronics, vol. 12, no. 8, pp. 2146-2153.
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This study proposes and analyses the integration of soft‐switching technique with a single switch, non‐isolated, coupled inductor DC–DC converter to achieve high efficiency and high step‐up conversion ratio. The topology optimally integrates the coupled inductor and soft‐switching technique using a parallel LC resonant tank circuit to maintain zero‐current switching (ZCS) for on/off switching. The leakage inductance of the coupled inductor alleviates the reverse‐recovery issue of diodes, and diodes can operate under ZCS condition. Moreover, the converter operates in a lower frequency range due to high step‐up voltage gain. The principle of operation and steady‐state analyses of the proposed converter are presented. A prototype validates the theoretical analysis and demonstrates a higher peak efficiency of 96.43% than the corresponding hard‐switched converter.
Hassan, W, Lu, DD-C & Xiao, W 2019, 'Single-Switch High Step-Up DC–DC Converter With Low and Steady Switch Voltage Stress', IEEE Transactions on Industrial Electronics, vol. 66, no. 12, pp. 9326-9338.
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© 1982-2012 IEEE. In this paper, a new high voltage gain step-up dc-dc converter is proposed for interfacing renewable power generation. The configuration optimally integrates both the coupled-inductor and switched-capacitor techniques to achieve an ultra-high step-up gain of voltage conversion with low voltage stress and high efficiency. It consists of a voltage boost unit, a passive clamp circuit, and a symmetrical voltage multiplier network. The structure becomes modular and extendable without adding any extra winding for ultra-high step-up voltage gain. The proposed topology not only reduces the voltage stress on the main switch but also maintains it steady for the entire duty cycle range. Furthermore, the reverse recovery issue of the diodes is alleviated through the leakage inductance of the coupled inductor. The operation principle and steady-state analysis are presented in detail. Experimental evaluation validates the claimed advantages and demonstrates a well-distributed efficiency curve and the peak of 96.70%.
Hassoun, M & Fatahi, B 2019, 'Novel integrated ground anchor technology for the seismic protection of isolated segmented cantilever bridges', Soil Dynamics and Earthquake Engineering, vol. 125, pp. 105709-105709.
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© 2019 Elsevier Ltd An external restraining system which is anchoring the bridge superstructure to the embankment backfill is proposed in this study for the seismic protection of isolated bridges. The restraining system is employed to reduce the seismic demands of the bridge deck by utilising the otherwise inactive ground behind the abutment back-walls. The system can be described as fastening the bridge end-diaphragms to the rocky strata that lie beneath the abutment backfill. The anchoring is achieved through a series of steel strands grouted to the rock to achieve a strong anchoring capacity. Indeed, the proposed anchor is flexible enough to allow the thermal, creep and shrinkage serviceability movements of the deck. A parametric study conducted in this paper shows that the ground anchor external restraining system is truly effective in reducing the seismic demands of the bridge deck.
Häußler, S, Benedikter, J, Bray, K, Regan, B, Dietrich, A, Twamley, J, Aharonovich, I, Hunger, D & Kubanek, A 2019, 'Diamond photonics platform based on silicon vacancy centers in a single-crystal diamond membrane and a fiber cavity', Physical Review B, vol. 99, no. 16.
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© 2019 American Physical Society. We realize a potential platform for an efficient spin-photon interface, namely negatively-charged silicon-vacancy centers in a diamond membrane coupled to the mode of a fully-tunable, fiber-based, optical resonator. We demonstrate that introducing the thin (∼200nm), single crystal diamond membrane into the mode of the resonator does not change the cavity properties, which is one of the crucial points for an efficient spin-photon interface. In particular, we observe constantly high Finesse values of up to 3000 and a linear dispersion in the presence of the membrane. We observe cavity-coupled fluorescence from an ensemble of SiV- centers with an enhancement factor of ∼1.9. Furthermore from our investigations we extract the ensemble absorption and extrapolate an absorption cross section of (2.9±2)×10-12cm2 for a single SiV- center, much higher than previously reported.
Hayat, T, Afzal, MU, Lalbakhsh, A & Esselle, KP 2019, '3-D-Printed Phase-Rectifying Transparent Superstrate for Resonant-Cavity Antenna', IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 7, pp. 1400-1404.
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© 2019 IEEE. A three-dimensional (3-D)-printed nonplanar highly transmitting superstrate is presented to improve the directive radiation characteristics of a resonant-cavity antenna (RCA). Classical RCAs are reported with nonuniform aperture-field distribution that compromises their far-field directivity. The concept of near-field phase correction has been used here to design a phase-rectifying transparent superstrate (PRTS), which was fabricated using the 3-D printing technology. The PRTS is printed using easily accessible polylactic acid filament. It has a significantly lower cost and weight compared to its recently published counterparts, while its performance is comparable. The 3-D printing technology yielded the prototype in less than 4 h, which is considerably less compared to the traditional machining methods. Measurements of the prototype indicated close correspondence between the predicted and the measured results. Significant increase in the antenna performance has been achieved, due to the rectification of the aperture phase distribution. Notable aspects encompass 7.3 dB increase in the antenna peak directivity (from 13-20.3 dBi), significant sidelobe level suppression, and an improvement of aperture efficiency by 36.1%, with a PRTS that costs less than 2.5 USD.
Hayati, H, Eager, D & Walker, P 2019, 'The effects of surface compliance on greyhound galloping dynamics', Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, vol. 233, no. 4, pp. 1033-1043.
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Greyhounds are the fastest breed of dog and can reach a speed up to 68 km/h. These racing animals sustain unique injuries seldom seen in other breeds of dog. The highest rate of life-threatening injuries in these dogs is hock fracture, mostly of the right hind-leg. One of the main injury contributing factors in this sport is the track surface. There are some studies into the ideal track surface composition for greyhound racing but almost no study has investigated the body–surface interaction. Accordingly, the purpose of this work is to study the effect of surface compliance on the galloping dynamics of greyhounds during the hind-leg single-support phase which is a critical phase in hock injuries. Thus, a three degrees-of-freedom model for the greyhound body and substrate surface is designed using spring-loaded inverted pendulum method. The results showed that forces acting on the hind-leg were substantially affected when the surface compliance altered from the relatively hard (natural grass) to a relatively soft surface (synthetic rubber). The main contribution of this work is designing a mathematical model to predict the dynamics of the hock and the hind-leg as the most vulnerable body parts in greyhounds. Furthermore, this model can be used to optimise the greyhound track surface composition and therefore improve the safety and welfare within the greyhound racing industry.
Hayati, H, Mahdavi, F & Eager, D 2019, 'Analysis of Agile Canine Gait Characteristics Using Accelerometry', Sensors, vol. 19, no. 20, pp. 4379-4379.
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The high rate of severe injuries associated with racing greyhounds poses a significant problem for both animal welfare and the racing industry. Using accelerometry to develop a better understanding of the complex gait of these agile canines may help to eliminate injury contributing factors. This study used a single Inertial Measurement Unit (IMU) equipped with a tri-axial accelerometer to characterise the galloping of thirty-one greyhounds on five different race tracks. The dorsal-ventral and anterior-posterior accelerations were analysed in both the time and frequency domains. The fast Fourier transform (FFT) and Morlet wavelet transform were applied to signals. The time-domain signals were synced with the corresponding high frame rate videos of the race. It was observed that the acceleration peaks in the dorsal-ventral accelerations correspond to the hind-leg strikes which were noted to be fifteen times the greyhound’s weight. The FFT analysis showed that the stride frequencies in all tracks were around 3.5 Hz. The Morlet wavelet analysis also showed a reduction in both the frequency and magnitude of signals, which suggests a speed reduction throughout the race. Also, by detecting abrupt changes along the track, the wavelet analysis highlighted potentially hazardous locations on the track. In conclusion, the methods applied in this research contribute to animal safety and welfare by eliminating the factors leading to injuries through optimising the track design and surface type.
He, L-X, Wu, C & Li, J 2019, 'Post-earthquake evaluation of damage and residual performance of UHPSFRC piers based on nonlinear model updating', Journal of Sound and Vibration, vol. 448, pp. 53-72.
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© 2019 Elsevier Ltd This paper presents an innovative approach for damage and residual performance evaluation of ultra-high performance steel fiber reinforced concrete (UHPSFRC) piers after earthquakes utilizing low-level vibration tests. A nonlinear fiber section element model is constructed in OpenSees to simulate the hysteretic behavior of a UHPSFRC bridge pier. Experimental data from a UHPSFRC column is utilized to verify the accuracy of the nonlinear numerical model. Based on the nonlinear fiber section element model, a new technique of nonlinear finite element model updating involving two updating stages is developed. This new method is designed to incorporate the maximum and minimum strains of section fibers as the updating parameters. By forming the objective function from the modal information, the damage parameters related to the nonlinear material model can be updated by solving the constrained optimization problem. To validate the efficiency of this updating approach, it has been applied to a numerically simulated UHPSFRC pier. With using the updated nonlinear finite element model, the residual axial loading capacity and post-seismic performance of the UHPSFRC pier are examined. The numerical results indicate that the updated nonlinear finite element model can be used not only to assess the current damage state of the UHPSFRC pier but also to predict its future performance after an earthquake. Finally, the noise effect on the proposed method is also investigated. The results reveal that the post-earthquake evaluation approach for UHPSFRC piers based on this study's updating algorithm is robust to noise.
He, T, Wu, M, Lu, DD-C, Aguilera, RP, Zhang, J & Zhu, J 2019, 'Designed Dynamic Reference With Model Predictive Control for Bidirectional EV Chargers', IEEE Access, vol. 7, pp. 129362-129375.
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© 2013 IEEE. This paper presents a finite control set model predictive control (MPC) using a designed dynamic reference for bidirectional electric vehicle (EV) chargers. In the conventional MPC scheme, a PI controller is involved to generate an active power reference from the DC voltage reference. It is hard to find one fixed set of coefficients for all working conditions. In this paper, a designed dynamic reference based MPC strategy is proposed to replace the PI control loop. In the proposed method, a DC voltage dynamic reference is developed to formulate the inherent relationship between the DC voltage reference and the active power reference. Multi-objective control can be achieved in the proposed scheme, including controlling of the DC voltage, battery charging/discharging current, active power and reactive power, independently. Bidirectional power flow is operated effectively between the EV- and the grid-side. Experimental results are obtained from a laboratory three-phase two-stage bidirectional EV charger controlled by dSPACE DS1104. The results show that fast dynamic and good steady state performance of tracking the above objectives can be achieved with the proposed method. Compared with the system performance obtained by the conventional MPC method, the proposed method generates less active power ripples and produces a better grid current performance.
He, W, Sun, C, Wunsch, DC & Xu, RYD 2019, 'Guest Editorial Special Issue on Intelligent Control Through Neural Learning and Optimization for Human–Machine Hybrid Systems', IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 12, pp. 3530-3533.
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He, X, Wu, W & Wang, S 2019, 'A constitutive model for granular materials with evolving contact structure and contact forces—Part I: framework', Granular Matter, vol. 21, no. 2.
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He, X, Wu, W & Wang, S 2019, 'A constitutive model for granular materials with evolving contact structure and contact forces—part II: constitutive equations', Granular Matter, vol. 21, no. 2.
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© 2019, The Author(s). This and the companion paper present a constitutive model for granular materials with evolving contact structure and contact forces, where the contact structure and contact forces are characterised by some statistics of grain-scale entities such as contact normals and contact forces. And these statistics are actually the “fabric” or “force” terms in the “stress–force–fabric” (SFF) equation. The stress–strain response is obtained by inserting the predicted “fabric” or “force” terms from evolution equations into the SFF equation. In the model, the critical state is characterised by two fitting equations and three critical state parameters. A semi-mechanistic analysis is conducted about the change of the contact number and the obtained results are combined with observed phenomena in DEM virtual experiments to give the constitutive equations for the “fabric” terms. The change of fabric anisotropy is related to the strain rate, current fabric anisotropy and also contact forces. The change of coordination number is induced by two terms related to volumetric and shear deformations, and also an additional term related to the change of fabric anisotropy. The constitutive equations regarding the “force” terms are also proposed. All the “fabric” or “force” terms are modelled to tend toward their critial state value, which agrees with Li and Dafalias’s (J Eng Mech 138(3):263–275, 2012. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000324) basic philosophy in their evolution equation for the fabric tensor. These equations along with the SFF equation form a constitutive model.
He, Y, Liu, P, Zhu, L & Yang, Y 2019, 'Filter Pruning by Switching to Neighboring CNNs with Good Attributes'.
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Filter pruning is effective to reduce the computational costs of neuralnetworks. Existing methods show that updating the previous pruned filter wouldenable large model capacity and achieve better performance. However, during theiterative pruning process, even if the network weights are updated to newvalues, the pruning criterion remains the same. In addition, when evaluatingthe filter importance, only the magnitude information of the filters isconsidered. However, in neural networks, filters do not work individually, butthey would affect other filters. As a result, the magnitude information of eachfilter, which merely reflects the information of an individual filter itself,is not enough to judge the filter importance. To solve the above problems, wepropose Meta-attribute-based Filter Pruning (MFP). First, to expand theexisting magnitude information based pruning criteria, we introduce a new setof criteria to consider the geometric distance of filters. Additionally, toexplicitly assess the current state of the network, we adaptively select themost suitable criteria for pruning via a meta-attribute, a property of theneural network at the current state. Experiments on two image classificationbenchmarks validate our method. For ResNet-50 on ILSVRC-2012, we could reducemore than 50% FLOPs with only 0.44% top-5 accuracy loss.
Hectors, SJ, Bane, O, Kennedy, P, El Salem, F, Menon, M, Segall, M, Khaim, R, Delaney, V, Lewis, S & Taouli, B 2019, 'T1ρ mapping for assessment of renal allograft fibrosis', Journal of Magnetic Resonance Imaging, vol. 50, no. 4, pp. 1085-1091.
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BackgroundThere is an unmet need for noninvasive methods to diagnose and stage renal allograft fibrosis.PurposeTo investigate the utility of T1ρ measured with MRI for the assessment of fibrosis in renal allografts.Study TypeInstitutional Review Board (IRB)‐approved prospective.SubjectsFifteen patients with stable functional allograft (M/F 9/6, mean age 56 years) and 12 patients with allograft dysfunction and established fibrosis (M/F 6/6, mean age 51 years).Field Strength/SequenceT1ρ imaging at 1.5T using a custom‐developed sequence.AssessmentAverage T1ρ in the cortex and medulla was quantified and T1ρ repeatability (expressed by the coefficient of variation [CV]) was measured in four patients.Statistical TestsDifferences in T1ρ values between the 2 groups were assessed using Mann–Whitney U‐tests. Diagnostic performance of T1ρ for differentiation between functional and fibrotic allografts was evaluated using receiver operating characteristic (ROC) analysis. Spearman correlations of T1ρ with Masson's trichrome‐stained fractions and serum estimated glomerular filtration rate (eGFR) were assessed.ResultsHigher T1ρ repeatability was found for cortex compared with medulla (mean CV T1ρ cortex 7.4%, medulla 13.3%). T1ρ values were significantly higher i...
Heitor, A & Ngo, T 2019, 'Editorial', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 172, no. 4, pp. 211-212.
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Henke, T & Deuse, J 2019, 'Arbeitsfortschrittssynchrone Materialbereitstellung in der Großgerätemontage', ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 114, no. 5, pp. 243-246.
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Since the assembly of large-scale products is strongly influenced by the customer, the products have an unique character. Contract manufacturing is characterized by a low level of standardization and a high share of non-value adding activities with negative effects on throughput times and on-time delivery. In the research project SySMaG the IPS (Dortmund) therefore developed a planning framework to standardize the material supply in large scale assembly and to reduce non-value adding activities.
Hesamian, MH, Jia, W, He, X & Kennedy, P 2019, 'Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges', Journal of Digital Imaging, vol. 32, no. 4, pp. 582-596.
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© 2019, The Author(s). Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.
Hoang, DT, Nguyen, DN, Alsheikh, MA, Gong, S, Dutkiewicz, E, Niyato, D & Han, Z 2019, ''Borrowing Arrows with Thatched Boats': The Art of Defeating Reactive Jammers in IoT Networks', IEEE Wireless Communications Magazine, vol. 27, no. 3, pp. 79-87.
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In this article, we introduce a novel deception strategy which is inspired bythe 'Borrowing Arrows with Thatched Boats', one of the most famous militarytactics in the history, in order to defeat reactive jamming attacks forlow-power IoT networks. Our proposed strategy allows resource-constrained IoTdevices to be able to defeat powerful reactive jammers by leveraging their ownjamming signals. More specifically, by stimulating the jammer to attack thechannel through transmitting fake transmissions, the IoT system can not onlyundermine the jammer's power, but also harvest energy or utilize jammingsignals as a communication means to transmit data through using RF energyharvesting and ambient backscatter techniques, respectively. Furthermore, wedevelop a low-cost deep reinforcement learning framework that enables thehardware-constrained IoT device to quickly obtain an optimal defense policywithout requiring any information about the jammer in advance. Simulationresults reveal that our proposed framework can not only be very effective indefeating reactive jamming attacks, but also leverage jammer's power to enhancesystem performance for the IoT network.
Hoang, LM, Kim, M & Kong, S-H 2019, 'Automatic Recognition of General LPI Radar Waveform Using SSD and Supplementary Classifier', IEEE Transactions on Signal Processing, vol. 67, no. 13, pp. 3516-3530.
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© 1991-2012 IEEE. For low probability of intercept (LPI) radars, frequency-modulated and phase-modulated continuous waveforms are widely used because of their low peak power compared to that of pulse waves (PW). However, there has been a limited number of studies on recognizing continuous wave (CW) LPI radar, in spite of its importance and popularity. In this paper, in order to recognize both PW and CW LPI radar waveforms, we propose an LPI radar waveform recognition technique (LWRT) based on a single-shot multi-box detector (SSD) and a supplementary classifier. It is demonstrated with Monte Carlo simulations that the proposed LWRT achieves classification performance similar to that of the current LWRT with the highest classification performance for PW LPI radar waveforms, even without the prior condition used in existing LWRTs. For CW LPI radar waveforms, on the other hand, with the combination of the SSD and the supplementary classifier, the proposed LWRT achieves extraordinary recognition performance for all 12 LPI radar modulation schemes (i.e., BPSK, Costas, LFM, Frank, P1, P2, P3, P4, T1, T2, T3, and T4) considered in the literature.
Hodges, J, Attia, T, Arukgoda, J, Kang, C, Cowden, M, Doan, L, Ranasinghe, R, Abdelatty, K, Dissanayake, G & Furukawa, T 2019, 'Multistage bayesian autonomy for high‐precision operation in a large field', Journal of Field Robotics, vol. 36, no. 1, pp. 183-203.
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AbstractThis paper presents a generalized multistage bayesian framework to enable an autonomous robot to complete high‐precision operations on a static target in a large field. The proposed framework consists of two multistage approaches, capable of dealing with the complexity of high‐precision operation in a large field to detect and localize the target. In the multistage localization, locations of the robot and the target are estimated sequentially when the target is far away from the robot, whereas these locations are estimated simultaneously when the target is close. A level of confidence (LOC) for each detection criterion of a sensor and the associated probability of detection (POD) of the sensor are defined to make the target detectable with different LOCs at varying distances. Differential entropies of the robot and target are used as a precision metric for evaluating the performance of the proposed approach. The proposed multistage observation and localization approaches were applied to scenarios using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Results with the UGV in simulated environments and then real environments show the effectiveness of the proposed approaches to real‐world problems. A successful demonstration using the UAV is also presented.
Ho-Pham, LT & Nguyen, TV 2019, 'Association between trabecular bone score and type 2 diabetes: a quantitative update of evidence', Osteoporosis International, vol. 30, no. 10, pp. 2079-2085.
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© 2019, International Osteoporosis Foundation and National Osteoporosis Foundation. Summary: Patients with type 2 diabetes have an increased risk of fracture despite having a higher areal bone mineral density. This meta-analysis showed that compared with controls, diabetic patients had a lower trabecular bone score (TBS) than non-diabetic individuals, suggesting that TBS can be a useful measurement for the assessment of fracture risk in diabetic patients. Introduction: The association between type 2 diabetes and trabecular bone score (TBS) has not been clear. The present study sought to answer the specific question of whether patients with type 2 diabetes have a lower TBS than those without diabetes. Methods: Using electronic and manual search, we identified 12 studies that had examined the association between type 2 diabetes and TBS between 2013 and 2019. These studies involved 35,546 women and 4962 men aged 30 years and older. We extracted the mean and standard deviation of TBS for patients with and without diabetes. The synthesis of effect sizes was done by the random effects meta-analysis model. Results: Patients with diabetes had significantly lower TBS than those without diabetes, with standardized mean difference being − 0.31 (95% CI, − 0.45 to − 0.16). The difference was greater in women (− 0.50; 95% CI, − 0.69 to − 0.32) than in men (− 0.04; 95% CI, − 0.17 to 0.10). Compared with normal individuals, those with prediabetes had significantly lower TBS (d = − 0.13; 95% CI, − 0.23 to − 0.04; P = 0.005). There was heterogeneity between the studies, with the index of inconsistency (I2) ranging from 92% (in women) to 69.5% (in men). Conclusion: Patients with type 2 diabetes have a lower TBS than non-diabetic individuals, suggesting that TBS can be a useful measurement for the assessment of fracture risk in diabetic patients.
Ho-Pham, LT, Tran, B, Do, AT & Nguyen, TV 2019, 'Association between pre-diabetes, type 2 diabetes and trabecular bone score: The Vietnam Osteoporosis Study', Diabetes Research and Clinical Practice, vol. 155, pp. 107790-107790.
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© 2019 Aims: Trabecular bone score (TBS) is a surrogate indicator of bone microarchitecture. The present study sought to examine the association between type 2 diabetes (T2D) and trabecular bone score (TBS) in adult Vietnamese men and women. Methods: The study was part of the Vietnam Osteoporosis Study, in which 2702 women and 1398 men aged ≥30 years were recruited from the general community in Ho Chi Minh City. HbA1c levels were measured by the ADAMS™ A1c HA-8160 (Arkray, Kyoto, Japan), and classified into 3 groups: normal if HbA1c < 5.7%; pre-diabetes (5.7–6.4%); and diabetes (>6.4%). TBS was evaluated by iNsight Software, version 2.1 (Medimaps, Merignac, France) on lumbar spine BMD scan (Hologic Horizon). Differences in TBS between diabetic status were analyzed by the multivariable regression model with adjustment for age and body mass index. Results: The prevalence of pre-diabetes and diabetes in men and women was 30.2% and 8.3%, respectively. In women, TBS was lower in pre-diabetes (−0.02; P < 0.001) and diabetes (−0.02; P < 0.001) compared with normal individuals. In men, there was no statistically significant difference in TBS between diabetic status. Moreover, TBS was significantly inversely correlated with HbA1c levels in women (P = 0.01), but not in men (P = 0.89). Conclusion: Women, but not men, with type 2 diabetes and pre-diabetes have lower TBS than individuals without diabetes. These data suggest that diabetes and prediabetes are associated with deterioration of bone microarchitecture.
Hoque, MA-A, Ahmed, N, Pradhan, B & Roy, S 2019, 'Assessment of coastal vulnerability to multi-hazardous events using geospatial techniques along the eastern coast of Bangladesh', Ocean & Coastal Management, vol. 181, pp. 104898-104898.
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© 2019 Elsevier Ltd The eastern coastal region of Bangladesh, which has a 377 km-long coastline, is highly vulnerable to multi-hazardous events, such as tropical cyclones, coastal floods, coastal erosion and salinity intrusion. The vulnerability of this coastal region is likely to increase under the future climate change context. This research aims to develop a coastal vulnerability index (CVI) of multi-hazardous events for the eastern coastal region of Bangladesh. Eight parameters, mostly focused on physical vulnerability, were considered in this study. Various thematic layers were prepared for each parameter using spatial techniques, and all parameters were assigned a vulnerability ranking. Finally, a CVI was developed and the related values were categorised into five distinct classes (i.e., very high, high, moderate, low, and very low). Results indicate that approximately 121 km (32%) of the coastline of the study area is in high-to very high-vulnerability zones. Low elevations, gentle slopes, high storm surge impacts, sandy coastlines, high shoreline erosion rates and high sea-level changes are the most important factors of high to very-high vulnerability zones. The moderately vulnerable area covers approximately 119 km (32%) of the coastline. Meanwhile, 78 (21%) and 59 (16%) km of the coastlines are in low-to very low-vulnerability zones, respectively. These coastlines are characterised by steep slopes with high elevations, low tide range and storm surge heights as well as less erosion. The CVI results were validated by qualitative observations acquired from the field. The findings of this study can be applied by policymakers and administrators to develop effective mitigation plans and minimise the likely impacts of coastal multi-hazards.
Hoque, MA-A, Pradhan, B, Ahmed, N & Roy, S 2019, 'Tropical cyclone risk assessment using geospatial techniques for the eastern coastal region of Bangladesh', Science of The Total Environment, vol. 692, pp. 10-22.
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© 2019 Elsevier B.V. Tropical cyclones frequently affect millions of people, damaging properties, livelihoods and environments in the coastal region of Bangladesh. The intensity and extent of tropical cyclones and their impacts are likely to increase in the future due to climate change. The eastern coastal region of Bangladesh is one of the most cyclone-affected coastal regions. A comprehensive spatial assessment is therefore essential to produce a risk map by identifying the areas under high cyclone risks to support mitigation strategies. This study aims to develop a comprehensive tropical cyclone risk map using geospatial techniques and to quantify the degree of risk in the eastern coastal region of Bangladesh. In total, 14 spatial criteria under three risk components, namely, vulnerability and exposure, hazard, and mitigation capacity, were assessed. A spatial layer was created for each criterion, and weighting was conducted following the Analytical Hierarchy Process. The individual risk component maps were generated from their indices, and subsequently, the overall risk map was produced by integrating the indices through a weighted overlay approach. Results demonstrate that the very-high risk zone covered 9% of the study area, whereas the high-risk zone covered 27%. Specifically, the south-western (Sandwip and Sonagazi), western (Patiya, Kutubdia, Maheshkhali, Chakaria, Cox's Bazar and Chittagong Sadar) and south-western (Teknaf) regions of the study site are likely to be under a high risk of tropical cyclone impacts. Low and very-low hazard zones constitute 11% and 28% of the study area, respectively, and most of these areas are located inland. The results of this study can be used by the concerned authorities to develop and apply effective cyclone impact mitigation plans and strategies.
Hoque, MA-A, Tasfia, S, Ahmed, N & Pradhan, B 2019, 'Assessing Spatial Flood Vulnerability at Kalapara Upazila in Bangladesh Using an Analytic Hierarchy Process', Sensors, vol. 19, no. 6, pp. 1302-1302.
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Floods are common natural disasters worldwide, frequently causing loss of lives and huge economic and environmental damages. A spatial vulnerability mapping approach incorporating multi-criteria at the local scale is essential for deriving detailed vulnerability information for supporting flood mitigation strategies. This study developed a spatial multi-criteria-integrated approach of flood vulnerability mapping by using geospatial techniques at the local scale. The developed approach was applied on Kalapara Upazila in Bangladesh. This study incorporated 16 relevant criteria under three vulnerability components: physical vulnerability, social vulnerability and coping capacity. Criteria were converted into spatial layers, weighted and standardised to support the analytic hierarchy process. Individual vulnerability component maps were created using a weighted overlay technique, and then final vulnerability maps were produced from them. The spatial extents and levels of vulnerability were successfully identified from the produced maps. Results showed that the areas located within the eastern and south-western portions of the study area are highly vulnerable to floods due to low elevation, closeness to the active channel and more social components than other parts. However, with the integrated coping capacity, western and south-western parts are highly vulnerable because the eastern part demonstrated particularly high coping capacity compared with other parts. The approach provided was validated by qualitative judgement acquired from the field. The findings suggested the capability of this approach to assess the spatial vulnerability of flood effects in flood-affected areas for developing effective mitigation plans and strategies.
Hossain, MA, Pota, HR, Hossain, MJ & Blaabjerg, F 2019, 'Evolution of microgrids with converter-interfaced generations: Challenges and opportunities', International Journal of Electrical Power & Energy Systems, vol. 109, pp. 160-186.
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© 2019 Elsevier Ltd Although microgrids facilitate the increased penetration of distributed generations (DGs) and improve the security of power supplies, they have some issues that need to be better understood and addressed before realising the full potential of microgrids. This paper presents a comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids. The discussion in this paper presented with a view to establishing microgrids as distinct from the existing distribution systems. This is accomplished by, firstly, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones. Also, the definitions, classifications and characteristics of microgrids are summarised to provide a sound basis for novice researchers to undertake ongoing research on microgrids.
Hossain, N & Mahlia, TMI 2019, 'Progress in physicochemical parameters of microalgae cultivation for biofuel production', Critical Reviews in Biotechnology, vol. 39, no. 6, pp. 835-859.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Microalgae have been exploited for biofuel generation in the current era due to its enormous energy content, fast cellular growth rate, inexpensive culture approaches, accumulation of inorganic compounds, and CO2 sequestration. Currently, research is ongoing towards the advancement of the microalgae cultivation parameters to enhance the biomass yield. The main objective of this study was to delineate the progress of physicochemical parameters for microalgae cultivation such as gaseous transfer, mixing, light demand, temperature, pH, nutrients and the culture period. This review demonstrates the latest research trends on mass transfer coefficient of different microalgae culturing reactors, gas velocity optimization, light intensity, retention time, and radiance effects on microalgae cellular growth, temperature impact on chlorophyll production, and nutrient dosage ratios for cellulosic metabolism to avoid nutrient deprivation. Besides that, cultivation approaches for microalgae associated with mathematical modeling for different parameters, mechanisms of microalgal growth rate and doubling time have been elaborately described. Along with that, this review also documents potential lipid-carbohydrate-protein enriched microalgae candidates for biofuel, biomass productivity, and different cultivation conditions including open-pond cultivation, closed-loop cultivation, and photobioreactors. Various photobioreactor types, the microalgae strain, productivity, advantages, and limitations were tabulated. In line with microalgae cultivation, this study also outlines in detail numerous biofuels from microalgae.
Hossain, N, Mahlia, TMI & Saidur, R 2019, 'Latest development in microalgae-biofuel production with nano-additives', Biotechnology for Biofuels, vol. 12, no. 1.
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© 2019 The Author(s). Background: Microalgae have been experimented as a potential feedstock for biofuel generation in current era owing to its' rich energy content, inflated growth rate, inexpensive culture approaches, the notable capacity of CO2 fixation, and O2 addition to the environment. Currently, research is ongoing towards the advancement of microalgal-biofuel technologies. The nano-additive application has been appeared as a prominent innovation to meet this phenomenon. Main text: The main objective of this study was to delineate the synergistic impact of microalgal biofuel integrated with nano-additive applications. Numerous nano-additives such as nano-fibres, nano-particles, nano-tubes, nano-sheets, nano-droplets, and other nano-structures' applications have been reviewed in this study to facilitate microalgae growth to biofuel utilization. The present paper was intended to comprehensively review the nano-particles preparing techniques for microalgae cultivation and harvesting, biofuel extraction, and application of microalgae-biofuel nano-particles blends. Prospects of solid nano-additives and nano-fluid applications in the future on microalgae production, microalgae biomass conversion to biofuels as well as enhancement of biofuel combustion for revolutionary advancement in biofuel technology have been demonstrated elaborately by this review. This study also highlighted the potential biofuels from microalgae, numerous technologies, and conversion processes. Along with that, the study recounted suitability of potential microalgae candidates with an integrated design generating value-added co-products besides biofuel production. Conclusions: Nano-additive applications at different stages from microalgae culture to end-product utilization presented strong possibility in mercantile approach as well as positive impact on the environment along with valuable co-products generation into the near future.
Hossain, N, Mahlia, TMI, Zaini, J & Saidur, R 2019, 'Techno‐economics and Sensitivity Analysis of Microalgae as Commercial Feedstock for Bioethanol Production', Environmental Progress & Sustainable Energy, vol. 38, no. 5, pp. 13157-13157.
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The foremost purpose of this techno‐economic analysis (TEA) modeling was to predict a harmonized figure of comprehensive cost analysis for commercial bioethanol generation from microalgae species in Brunei Darussalam based on the conventional market scenario. This model was simulated to set out economic feasibility and probabilistic assumption for large‐scale implementations of a tropical microalgae species, Chlorella vulgaris, for a bioethanol plant located in the coastal area of Brunei Darussalam. Two types of cultivation systems such as closed system (photobioreactor—PBR) and open pond approaches were anticipated for a total approximate biomass of 220 t year−1 on 6 ha coastal areas. The biomass productivity was 56 t ha−1 for PBR and 28 t ha−1 for pond annually. The plant output was 58.90 m3 ha−1 for PBR and 24.9 m3 ha−1 for pond annually. The total bioethanol output of the plant was 57,087.58 gal year−1 along with the value added by‐products (crude bio‐liquid and slurry cake). The total production cost of this project was US$2.22 million for bioethanol from microalgae and total bioethanol selling price was US$2.87 million along with the by‐product sale price of US$1.6 million. A sensitivity analysis was conducted to forecast the uncertainty of this conclusive modeling. Different data sets through sensitivity analysis also presented positive impacts of economical and environmental views. This TEA model is expected to be initialized to determine an alternative energy and also minimize environmental pollution. With this current modeling, microalgal‐bioethanol utilization mandated with gasoline as well as microalgae cultivation, biofuel production integrated with existing complementary industries, are strongly recommended for future applications. © 2019 A...
Hossain, N, Razali, AN, Mahlia, TMI, Chowdhury, T, Chowdhury, H, Ong, HC, Shamsuddin, AH & Silitonga, AS 2019, 'Experimental Investigation, Techno-Economic Analysis and Environmental Impact of Bioethanol Production from Banana Stem', Energies, vol. 12, no. 20, pp. 3947-3947.
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Banana stem is being considered as the second largest waste biomass in Malaysia. Therefore, the environmental challenge of managing this huge amount of biomass as well as converting the feedstock into value-added products has spurred the demand for diversified applications to be implemented as a realistic approach. In this study, banana stem waste was experimented for bioethanol generation via hydrolysis and fermentation methods with the presence of Saccharomyces cerevisiae (yeast) subsequently. Along with the experimental analysis, a realistic pilot scale application of electricity generation from the bioethanol has been designed by HOMER software to demonstrate techno-economic and environmental impact. During sulfuric acid and enzymatic hydrolysis, the highest glucose yield was 5.614 and 40.61 g/L, respectively. During fermentation, the maximum and minimum glucose yield was 62.23 g/L at 12 h and 0.69 g/L at 72 h, respectively. Subsequently, 99.8% pure bioethanol was recovered by a distillation process. Plant modeling simulated operating costs 65,980 US$/y, net production cost 869347 US$ and electricity cost 0.392 US$/kWh. The CO2 emission from bioethanol was 97,161 kg/y and SO2 emission was 513 kg/y which is much lower than diesel emission. The overall bioethanol production from banana stem and application of electricity generation presented the approach economically favorable and environmentally benign.
Hossain, N, Zaini, J & Indra Mahlia, TM 2019, 'Life cycle assessment, energy balance and sensitivity analysis of bioethanol production from microalgae in a tropical country', Renewable and Sustainable Energy Reviews, vol. 115, pp. 109371-109371.
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© 2019 Elsevier Ltd Overuse of petroleum and ongoing carbon-di-oxide (CO2) rise in the air of Brunei Darussalam has been emerged as a major environmental concern in this country. To resolve this issue, a comprehensive life cycle assessment (LCA) of alternative biofuel, bioethanol production from microalgae was demanded for realistic implementation. Therefore, LCA of bioethanol production from microalgae in terms of CO2 emission and energy balance was investigated based on the scenario of industrial-scale in Brunei Darussalam. This study demonstrated that 220 tons microalgae biomass was cultivated on 6 ha offshore lands for commercial bioethanol generation. The annual outcome of this commercial bioethanol plant has revealed net CO2 balance 218.86 ton. From the energy perspective, this study manifested itself as favourable with net energy ratio, 0.45 and net energy balance, −2749.6 GJ y−1. Apart from CO2 balance and energy generation aspect, the project demanded low water and land footprints. For photobioreactor cultivation, water and land footprints were 2 m3 GJ−1 and 2 m2 GJ−1, respectively as well as for open pond approach, they were 87 m3 GJ−1 and 13 m2 GJ−1, respectively. The project also presented microalgae growth supplements (phosphorus and nitrogen) accumulation possibilities from wastewater of manure and industries which is another positive aspect for benign environment. Overall, the commercial plant presented low CO2 emission, low land and water demand for microalgae cultivation, alternative eco-friendly and cheaper nutrients sources, quite high energy generation with main product and by-products. Thus, this study projected positive impact on energy and environmental aspects of microalgae-to-bioethanol conversion.
Hossain, N, Zaini, J & Mahlia, TMI 2019, 'Experimental investigation of energy properties forStigonematalessp. microalgae as potential biofuel feedstock', International Journal of Sustainable Engineering, vol. 12, no. 2, pp. 123-130.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. Microalgae has been considered potential biofuel source from the last decade owing to its versatile perspectives such as excellent capability of CO 2 capture and sequestration, water treatment, prolific growth rate and enormous energy content. Thus, energy research on microalgae is being harnessed to mitigate CO 2 and meet future energy demands. This study investigated the bioenergy potential of native blue-green microalgae consortium as initial energy research on microalgae in Brunei Darussalam. The local species of microalgae were assembled from rainwater drains, the species were identified as Stigonematales sp. and physical properties were characterised. Sundried biomass with moisture content ranging from 6.5% to 7.37% was measured to be used to determine the net and gross calorific value and they were 7.98 MJ/kg-8.57 MJ/kg and 8.70 MJ/kg-9.45 MJ/kg, respectively. Besides that, the hydrogen content, ash content, volatile matter, and bulk density were also experimented and they were 2.56%-3.15%, 43.6%-36.71%, 57–38%-63.29% and 661.2 kg/m 3 -673.07 kg/m 3 , respectively. Apart from experimental values, other physical bioenergy parameters were simulated and they were biomass characteristic index 61,822.29 kg/m 3 -62,341.3 kg/m 3 , energy density 5.27 GJ/m 3 -5.76G J/m 3 and fuel value index 86.19–88.54. With these experimental results, microalgae manifested itself a potential source of biofuel feedstock for heat and electricity generation, a key tool to bring down the escalated atmospheric greenhouse gases and an alternation for fossil fuel.
Hossain, N, Zaini, J, Mahlia, TMI & Azad, AK 2019, 'Elemental, morphological and thermal analysis of mixed microalgae species from drain water', Renewable Energy, vol. 131, pp. 617-624.
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© 2018 Elsevier Ltd In this study, Stigonematales sp. microalgae were collected from drain water and characterized for its’ morphological edifice, elemental composition, thermal condition and energy generation capacity by using scanning electron microscopy, energy dispersive X-ray, thermogravimetric analyzer and bomb calorimeter, respectively. Scanning electron micrographs revealed the top view of microalgae and ash pellet with carbon coated specimens at low voltage (5.0 kV) through the secondary electron image detector. Elemental analysis revealed all the major and minor constituents of this microalgae species and its’ ash in terms of dry weight (%) and atomic weight (%). Thermogravimetric analysis was conducted at heating rate, 10 °C/min and this experimental results determined moisture content, volatile matter, ash content and fixed carbon of the sample with 4.5%, 35%, 39.5% and 21%, respectively. Microalgae powder blended with bituminous coal by 75%, 50% and 25% measured calorific value 14.07 MJ/kg, 19.88 MJ/kg and 26.42 MJ/kg, respectively. Microalgae (75%) -coal (25%) blend showed excellent amount of energy content, 24.59 MJ/kg. Microalgae blended with coal unveiled an outstanding outcome with elevation of the volatile matter and drop of the ash content. Optimization of microalgae-coal blend in large-scale application can initiate bright future in renewable energy exploration.
Hossain, SI, Gandhi, NS, Hughes, ZE, Gu, YT & Saha, SC 2019, 'Molecular insights on the interference of simplified lung surfactant models by gold nanoparticle pollutants', Biochimica et Biophysica Acta (BBA) - Biomembranes, vol. 1861, no. 8, pp. 1458-1467.
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Inhaled nanoparticles (NPs) are experienced by the first biological barrier inside the alveolus known as lung surfactant (LS), a surface tension reducing agent, consisting of phospholipids and proteins in the form of the monolayer at the air-water interface. The monolayer surface tension is continuously regulated by the alveolus compression and expansion and protects the alveoli from collapsing. Inhaled NPs can reach deep into the lungs and interfere with the biophysical properties of the lung components. The interaction mechanisms of bare gold nanoparticles (AuNPs) with the LS monolayer and the consequences of the interactions on lung function are not well understood. Coarse-grained molecular dynamics simulations were carried out to elucidate the interactions of AuNPs with simplified LS monolayers at the nanoscale. It was observed that the interactions of AuNPs and LS components deform the monolayer structure, change the biophysical properties of LS and create pores in the monolayer, which all interfere with the normal lungs function. The results also indicate that AuNP concentrations >0.1 mol% (of AuNPs/lipids) hinder the lowering of the LS surface tension, a prerequisite of the normal breathing process. Overall, these findings could help to identify the possible consequences of airborne NPs inhalation and their contribution to the potential development of various lung diseases.
Hossain, SM, Park, MJ, Park, HJ, Tijing, L, Kim, J-H & Shon, HK 2019, 'Preparation and characterization of TiO2 generated from synthetic wastewater using TiCl4 based coagulation/flocculation aided with Ca(OH)2', Journal of Environmental Management, vol. 250, pp. 109521-109521.
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This study focused on the preparation of undoped and Ca-doped titania from flocculation generated sludge. Initially, TiCl4 was utilised to perform coagulation and flocculation in synthetic wastewater and an optimised dose of coagulant was determined by evaluating the turbidity, dissolved organic carbon (DOC) and zeta potential of the treated water. Later, using Ca(OH)2 as a coagulant aid, the effects on effluent pH, turbidity and DOC removal were investigated. Both Ca-doped and undoped anatase TiO2 were prepared from the flocculated sludge for morphological and photocatalytic evaluation. During the standalone use of TiCl4, maximum turbidity and DOC removal were found at 11.63 and 14.54 mg Ti/L, respectively. At the corresponding coagulant dose, rapid deprotonation of water caused the pH of the effluent to reach below 3.77 mg Ti/L. Whereas, when using Ca(OH)2 as a coagulant aid, a neutral pH (7.26) was attained at a simultaneous dosing of 32.40 mg Ca/L and 14.54 mg Ti/L. When aided with Ca(OH)2, the turbidity removal was further increased by 54.28% and the DOC removal was somewhat similar to the standalone use of TiCl4. TiO2 was prepared by incinerating the collected sludge at 600 °C for 2 h. Both XRD and SEM analysis were conducted to observe the morphology of the prepared titania. The XRD pattern of the TiO2 showed only an anatase phase along with the presence of a high atomic proportion of Ca (4.14%). Consequently, a high amount of Ca atoms inhibited the level of TiO2 phase and no obvious presence of CaO was observed. The prepared Ca-doped TiO2 at the optimised dose of Ca(OH)2 was found to be inferior to the undoped TiO2 during the photodegradation of acetaldehyde. However, a reduced dose of Ca(OH)2 (<15 mg Ca/L) exhibited a substantial increase in photoactivity under UV irradiance.
Hosseini, SM & Al-Jumaily, A 2019, 'Analytical solution for forced vibration of piezoelectrically actuated Timoshenko beam', Journal of Intelligent Material Systems and Structures, vol. 30, no. 8, pp. 1276-1284.
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Forced vibrations of a Timoshenko beam covered with a piezoelectric actuator on its top surface were investigated in this article. As the proposed beam model complied with Timoshenko beam theory, the effects of both rotary inertia and shear deformation were considered. Hamilton principle in conjunction with the Galerkin procedure were applied to derive the governing equation of motion resulting in a second-order ordinary differential equation in time. A sinusoidal electric voltage was applied to the piezoelectric actuator, and a spatially distributed harmonic mechanical force was exerted to the beam. The response of the system to the force stimulation gave an analytical relation between natural frequency and amplitude of the vibration. Using the obtained analytical relation, the effects of different factors and material properties including the modulus of elasticity of the piezoelectric layer and the piezoelectric coefficient on the vibrational response of the beam were examined. The results indicated that the piezoelectric layer as an actuator provided an effective tool for active control of vibration. Increasing the piezoelectric coefficient as well as the electric voltage applied on the piezoelectric actuator increased the amplitude of vibration, while the amplitude decreased by increasing the modulus of elasticity of the piezoelectric actuator. The results were also verified by finite element analysis.
Hou, Z, Tang, J-F, Ferrie, C, Xiang, G-Y, Li, C-F & Guo, G-C 2019, 'Experimental realization of self-guided quantum process tomography', Phys. Rev. A, vol. 101, no. 2, p. 022317.
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Characterization of quantum processes is a preliminary step necessary in thedevelopment of quantum technology. The conventional method uses standardquantum process tomography, which requires $d^2$ input states and $d^4$ quantummeasurements for a $d$-dimensional Hilbert space. These experimentalrequirements are compounded by the complexity of processing the collected data,which can take several orders of magnitude longer than the experiment itself.In this paper we propose an alternative self-guided algorithm for quantumprocess tomography, tuned for the task of finding an unknown unitary process.Our algorithm is a fully automated and adaptive process characterizationtechnique. The advantages of our algorithm are: inherent robustness to bothstatistical and technical noise; requires less space and time since there is nopost-processing of the data; requires only a single input state andmeasurement; and, provides on-the-fly diagnostic information while theexperiment is running. Numerical results show our algorithm achieves the same$1/n$ scaling as standard quantum process tomography when $n$ uses of theunknown process are used. We also present experimental results wherein thealgorithm, and its advantages, are realized for the task of finding an elementof $SU(2)$.
Houshyar, S, Kumar, GS, Rifai, A, Tran, N, Nayak, R, Shanks, RA, Padhye, R, Fox, K & Bhattacharyya, A 2019, 'Nanodiamond/poly-ε-caprolactone nanofibrous scaffold for wound management', Materials Science and Engineering: C, vol. 100, pp. 378-387.
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How, HG, Teoh, YH, Masjuki, HH, Nguyen, H-T, Kalam, MA, Chuah, HG & Alabdulkarem, A 2019, 'Impact of two-stage injection fuel quantity on engine-out responses of a common-rail diesel engine fueled with coconut oil methyl esters-diesel fuel blends', Renewable Energy, vol. 139, pp. 515-529.
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Howard, D, Macsween, K, Edwards, GC, Desservettaz, M, Guérette, E-A, Paton-Walsh, C, Surawski, NC, Sullivan, AL, Weston, C, Volkova, L, Powell, J, Keywood, MD, Reisen, F & (Mick) Meyer, CP 2019, 'Investigation of mercury emissions from burning of Australian eucalypt forest surface fuels using a combustion wind tunnel and field observations', Atmospheric Environment, vol. 202, pp. 17-27.
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© 2018 Environmental cycling of the toxic metal mercury (Hg) is ubiquitous, and still not completely understood. Volatilisation and emission of mercury from vegetation, litter and soil during burning represents a significant return pathway for previously-deposited atmospheric mercury. Rates of such emission vary widely across ecosystems as they are dependent on species-specific uptake of atmospheric mercury as well as fire return frequencies. Wildfire burning in Australia is currently thought to contribute between 1 and 5% of the global total of mercury emissions, yet no modelling efforts to date have utilised local mercury emission factors (mass of emitted mercury per mass of dry fuel) or local mercury emission ratios (ratio of emitted mercury to another emitted species, typically carbon monoxide). Here we present laboratory and field investigations into mercury emission from burning of surface fuels in dry sclerophyll forests, native to the temperate south-eastern region of Australia. From laboratory data we found that fire behaviour — in particular combustion phase — has a large influence on mercury emission and hence emission ratios. Further, emission of mercury was predominantly in gaseous form with particulate-bound mercury representing <1% of total mercury emission. Importantly, emission factors and emission ratios with respect to carbon monoxide and carbon dioxide, from both laboratory and field data all show that gaseous mercury emission from biomass burning in Australian dry sclerophyll forests is currently overestimated by around 60%. Based on these results, we recommend a mercury emission factor of 28.7 ± 8.1 μg Hg kg−1 dry fuel, and emission ratio of gaseous elemental mercury relative to carbon monoxide of 0.58 ± 0.01 × 10−7, for estimation of mercury release from the combustion of Australian dry sclerophyll litter.
Hu, B, Sheng, J, Li, J, Pan, P-Z, Zhang, G & Ye, Z 2019, '“Wireline + Wireless” Networking Remote Monitoring Technology for Analysing the Unloading Deformation Characteristics of the Fractured Surrounding Rock Mass Induced by Underground Excavation', Advances in Civil Engineering, vol. 2019, no. 1.
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Collapse or large deformation of fractured surrounding rock mass occurs frequently in underground tunnelling and results in many casualties and extensive property damage. This paper proposed a new type of remote telemetry system for monitoring the mechanical responses of underground tunnels during unloading. This system adopted both wired and wireless networking schemes, including a signal collection and transmission subsystem, a management analysis subsystem, and a remote receiving subsystem, in the tunnels. The application of this new approach in a subway tunnel indicated that the complete unloading performance of a surrounding rock mass can be captured in real time and high frequency using this method, recording the deformation of the surrounding rock, the stress in the bolts, and the stress in the shotcrete between the surrounding rock and steel arch. The in situ experimental study also found that deformation of the fractured surrounding rock mass in the Dashizi Tunnel showed a step‐like fluctuating growth pattern. Additionally, the mechanical response of the surrounding rock mass during unloading tended to stabilize when the opening face was approximately 35 m away from the monitoring section, providing new ways to optimize the excavation process and support measure.
Hu, C, Liu, X & Lu, J 2019, 'Robust trading strategies for a waste-to-energy combined heat and power plant in a day-ahead electricity market', IFAC-PapersOnLine, vol. 52, no. 13, pp. 1108-1113.
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© 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Waste-to-energy (WtE) technologies have been used all over the world as they can solve the dilemma of waste management, energy demand, and global warming. Many modern WtE plants are built and operated in a combined heat and power (CHP) mode due to the high overall energy efficiency. This paper studies robust trading strategies for a WtE CHP plant which sells electricity in a day-ahead electricity market and exports heat to a district heating network. Owing to the requirements of the day-ahead electricity market, plant operators must determine the trading strategy one day before real delivery of electricity. However, many key problem parameters including electricity price, heat demand, and the amount of waste delivered to the plant are uncertain at the day-ahead stage. To derive robust electricity trading strategies for the WtE CHP plant under different types of uncertainty, a two-stage robust optimization model is developed and a solution procedure based on the column-and-constraint generation method is designed. A case study is also performed to illustrate the effectiveness of the robust model and the solution procedure.
Hu, L, Chen, Q, Cao, L, Jian, S, Zhao, H & Cao, J 2019, 'Evolving Coauthorship Modeling and Prediction via Time-Aware Paired Choice Analysis', IEEE Access, vol. 7, pp. 98639-98651.
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Coauthorship prediction is challenging yet important for academic collaboration and novel research topics discovery. The challenges lie in the dynamics of social or organizational relationships, changing preferences of suitable collaborators, and the evolution of research interests or topics. However, most current approaches and systems developed so far are mainly based on past coauthorships from a static viewpoint and do not capture the above evolving characteristics in coauthoring. Accordingly, this paper proposes a time-aware approach to capture the evolving coauthorships from online academic databases in terms of capturing the dynamics of social relationships and research interests. In particular, in order to understand the underlying factors influencing researchers to make choices of coauthors, we incorporate choice modeling based on utility theory. More specifically, our model conducts a series of pairwise choices over a poset induced by a utility function so as to learn the preference over all candidate coauthors. To complete the model inference, a gradient-based algorithm is devised to efficiently learn the model parameters for large-scale data. Finally, extensive experiments conducted on a real-world dataset show that our approach consistently outperforms other state-of-the-art methods.
Hu, M, Liu, Y, Sugumaran, V, Liu, B & Du, J 2019, 'Automated structural defects diagnosis in underground transportation tunnels using semantic technologies', Automation in Construction, vol. 107, pp. 102929-102929.
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Hu, S, Xu, M, Zhang, H, Xiao, C & Gui, C 2019, 'Affective Content-aware Adaptation Scheme on QoE Optimization of Adaptive Streaming over HTTP', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 15, no. 3s, pp. 1-18.
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The article presents a novel affective content-aware adaptation scheme (ACAA) to optimize Quality of Experience (QoE) for dynamic adaptive video streaming over HTTP (DASH). Most of the existing DASH adaptation schemes conduct video bit-rate adaptation based on an estimation of available network resources, which ignore user preference on affective content (AC) embedded in video data streaming over the network. Since the personal demands to AC is very different among all viewers, to satisfy individual affective demand is critical to improve the QoE in commercial video services. However, the results of video affective analysis cannot be applied into a current adaptive streaming scheme directly. Correlating the AC distributions in user's viewing history to each being streamed segment, the affective relevancy can be inferred as an affective metric for the AC related segment. Further, we have proposed an ACAA scheme to optimize QoE for user desired affective content while taking into account both network status and affective relevancy. We have implemented the ACAA scheme over a realistic trace-based evaluation and compared its performance in terms of network performance, QoE with that of Probe and Adaptation (PANDA), buffer-based adaptation (BBA), and Model Predictive Control (MPC). Experimental results show that ACAA can preserve available buffer time for future being delivered affective content preferred by viewer's individual preference to achieve better QoE in affective contents than those normal contents while remain the overall QoE to be satisfactory.
Hu, X, Zheng, W, Zhu, Q, Gu, L, Du, Y, Han, Z, Zhang, X, Carter, DR, Cheung, BB, Qiu, A & Jiang, C 2019, 'Increase in DNA Damage by MYCN Knockdown Through Regulating Nucleosome Organization and Chromatin State in Neuroblastoma', Frontiers in Genetics, vol. 10, no. JUL.
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© 2019 Hu, Zheng, Zhu, Gu, Du, Han, Zhang, Carter, Cheung, Qiu and Jiang. As a transcription factor, MYCN regulates myriad target genes including the histone chaperone FACT. Moreover, FACT and MYCN expression form a forward feedback loop in neuroblastoma. It is unclear whether MYCN is involved in chromatin remodeling in neuroblastoma through regulation of its target genes. We showed here that MYCN knockdown resulted in loss of the nucleosome-free regions through nucleosome assembly in the promoters of genes functionally enriched for DNA repair. The active mark H3K9ac was removed or replaced by the repressive mark H3K27me3 in the promoters of double-strand break repair-related genes upon MYCN knockdown. Such chromatin state alterations occurred only in MYCN-bound promoters. Consistently, MYCN knockdown resulted in a marked increase in DNA damage in the treatment with hydroxyurea. In contrast, nucleosome reorganization and histone modification changes in the enhancers largely included target genes with tumorigenesis-related functions such as cell proliferation, cell migration, and cell-cell adhesion. The chromatin state significantly changed in both MYCN-bound and MYCN-unbound enhancers upon MYCN knockdown. Furthermore, MYCN knockdown independently regulated chromatin remodeling in the promoters and the enhancers. These findings reveal the novel epigenetic regulatory role of MYCN in chromatin remodeling and provide an alternative potential epigenetic strategy for MYCN-driven neuroblastoma treatment.
Hu, Y, Manzoor, A, Ekparinya, P, Liyanage, M, Thilakarathna, K, Jourjon, G & Seneviratne, A 2019, 'A Delay-Tolerant Payment Scheme Based on the Ethereum Blockchain', IEEE Access, vol. 7, pp. 33159-33172.
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Hu, Y, Tang, Z, Li, W, Li, Y & Tam, VWY 2019, 'Physical-mechanical properties of fly ash/GGBFS geopolymer composites with recycled aggregates', Construction and Building Materials, vol. 226, pp. 139-151.
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© 2019 Elsevier Ltd The properties of fly ash and ground granulated blast furnace slag (GGBFS) combination based geopolymer composites containing recycled aggregate are investigated in this study, which obtained from construction and demolition wastes. The effects of recycled aggregate replacement and GGBFS inclusion on the physical and mechanical properties of geopolymer composites were investigated in this study. The scanning electron microscopic (SEM) were conducted to provide a thorough insight into the characterization of microstructures. The results reveal that using recycled aggregate has an insignificant impact on workability and setting time, while it causes a reduction in physical and mechanical properties. The inclusion of GGBFS reduces workability and setting time. However, improved physical and mechanical properties can be achieved in the geopolymer composites after the incorporation of GGBFS, and this effect is more prominent in the geopolymer composites containing recycled aggregates. The water absorption and sorptivity exhibit a strong correlation with the volume of permeable voids of geopolymer composites. Besides, very good relationships were established between the compressive strength and other mechanical properties, and these relationships fitted reasonably well with the other predictions.
Huang, J, Fei, Z, Wang, T, Wang, X, Liu, F, Zhou, H, Zhang, JA & Wei, G 2019, 'V2X-communication assisted interference minimization for automotive radars', China Communications, vol. 16, no. 10, pp. 100-111.
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With the development of automated driving vehicles, more and more vehicles will be fitted with more than one automotive radars, and the radar mutual interference will become very significant. Vehicle to everything (V2X) communication is a potential way for coordinating automotive radars and reduce the mutual interference. In this paper, we analyze the positional relation of the two radars that interfere with each other, and evaluate the mutual interference for different types of automotive radars based on Poisson point process (PPP). We also propose a centralized framework and the corresponding algorithm, which relies on V2X communication systems to allocate the spectrum resources for automotive radars to minimize the interference. The minimum spectrum resources required for zero-interference are analyzed for different cases. Simulation results validate the analysis and show that the proposed framework can achieve near-zero-interference with the minimum spectrum resources.
Huang, K, Chuang, C, Wang, Y, Hsieh, C, King, J & Lin, C 2019, 'The effects of different fatigue levels on brain–behavior relationships in driving', Brain and Behavior, vol. 9, no. 12, p. e01379.
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AbstractBackgroundIn the past decade, fatigue has been regarded as one of the main factors impairing task performance and increasing behavioral lapses during driving, even leading to fatal car crashes. Although previous studies have explored the impact of acute fatigue through electroencephalography (EEG) signals, it is still unclear how different fatigue levels affect brain–behavior relationships.MethodsA longitudinal study was performed to investigate the brain dynamics and behavioral changes in individuals under different fatigue levels by a sustained attention task. This study used questionnaires in combination with actigraphy, a noninvasive means of monitoring human physiological activity cycles, to conduct longitudinal assessment and tracking of the objective and subjective fatigue levels of recruited participants. In this study, degrees of effectiveness score (fatigue rating) are divided into three levels (normal, reduced, and high risk) by the SAFTE fatigue model.ResultsResults showed that those objective and subjective indicators were negatively correlated to behavioral performance. In addition, increased response times were accompanied by increased alpha and theta power in most brain regions, especially the posterior regions. In particular, the theta and alpha power dramatically increased in the high‐fatigue (high‐risk) group. Additionally, the alpha power of the occipital regions showed an inverted U‐shaped change.ConclusionOur results help to explain the inconsistent findings among existing studies, which considered the effects of only acute fatigue on driving performance while ignoring different levels of resident fatigue, and potentially lead to practical and precise biomathematical mode...
Huang, L, Liu, Z, Wu, C & Liang, J 2019, 'The scattering of plane P, SV waves by twin lining tunnels with imperfect interfaces embedded in an elastic half-space', Tunnelling and Underground Space Technology, vol. 85, pp. 319-330.
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© 2018 Elsevier Ltd A viscous-slip interface model is employed to simulate the contact between the tunnels lining and the surrounding rock, and the scattering of P, SV waves by twin shallowly buried lining tunnels is investigated with the indirect boundary integral equation method (IBIEM). The amplification effect of the dynamic stress concentration of the lining and the surface displacement near the tunnels is examined. It is evident that the slipping-stiffness coefficient and viscosity coefficient at the lining-surrounding rock interface have a significant influence on the dynamic stress distribution and the nearby surface displacement response of the lining tunnel, while the influence characteristics strongly depend on the incident wave type, frequency and angle. Under the incidence of low frequency wave, as a whole, with the increase of the sliding stiffness, the hoop stress increases gradually for plane P and SV waves; while in the resonance frequency (the incident wave frequency is consistent with the natural frequency of the soil column above the tunnels), specially for high-frequency band, the dynamic stress concentration effect is more significant for smaller sliding stiffness. With the increase of viscosity coefficient, the dynamic stress concentration factor inside the lining gradually decreases. Also, the tunnels with viscous-slip interfaces have a more significant amplification effect on the nearby surface displacement amplitude. Moreover, the hoop stress of the twin tunnels may be obviously larger than that of single tunnel in most cases. The dynamic analysis of the underground structure under the actual strong dynamic loading should consider the influence of the slip effect between the lining and surrounding rock interface.
Huang, L, Zhang, G, Yu, S, Fu, A & Yearwood, J 2019, 'SeShare: Secure cloud data sharing based on blockchain and public auditing', Concurrency and Computation: Practice and Experience, vol. 31, no. 22, pp. 1-15.
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SummaryIn a data sharing group, each user can upload, modify, and access group files and a user is required to generate a new signature for the modified file after modification. There is a situation that two or more users modify the same file at almost the same time, which should be avoided as it gives rise to a signature conflict. However, the existing schemes do not take it into consideration. In this paper, we proposed a new mechanism SeShare for data storing based on blockchain to realize signature uniqueness, which solves the problem of generating signatures for the same file meanwhile by different group users. Specifically, we record every signature of a file in a blockchain in chronological order, and only one user is allowed to add new signature at the end of the blockchain when modification conflicts occur. On the other hand, to provide a secure data sharing service, SeShare introduces an efficient public auditing scheme for file integrity verification when a group user leaves the group. We also prove the security of the proposed scheme and evaluate the performance at the end of this paper. Our experimental results demonstrate the efficiency of public auditing for user leaving.
Huang, L, Zhang, J, Zuo, Y & Wu, Q 2019, 'Pyramid-Structured Depth MAP Super-Resolution Based on Deep Dense-Residual Network', IEEE Signal Processing Letters, vol. 26, no. 12, pp. 1723-1727.
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© 1994-2012 IEEE. Although deep convolutional neural networks (DCNN) show significant improvement for single depth map (SD) super-resolution (SR) over the traditional counterparts, most SDSR DCNNs do not reuse the hierarchical features for depth map SR resulting in blurred high-resolution (HR) depth maps. They always stack convolutional layers to make network deeper and wider. In addition, most SDSR networks generate HR depth maps at a single level, which is not suitable for large up-sampling factors. To solve these problems, we present pyramid-structured depth map super-resolution based on deep dense-residual network. Specially, our networks are made up of dense residual blocks that use densely connected layers and residual learning to model the mapping between high-frequency residuals and low-resolution (LR) depth map. Furthermore, based on the pyramid structure, our network can progressively generate depth maps of various levels by taking advantages of features from different levels. The proposed network adopts a deep supervision scheme to reduce the difficulty of model training and further improve the performance. The proposed method is evaluated on Middlebury datasets which shows improved performance compared with 6 state-of-the-art methods.
Huang, L, Zhe, T, Wu, J, Wu, Q, Pei, C & Chen, D 2019, 'Robust Inter-Vehicle Distance Estimation Method Based on Monocular Vision', IEEE Access, vol. 7, pp. 46059-46070.
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© 2013 IEEE. Advanced driver assistance systems (ADAS) based on monocular vision are rapidly becoming a popular research subject. In ADAS, inter-vehicle distance estimation from an in-car camera based on monocular vision is critical. At present, related methods based on a monocular vision for measuring the absolute distance of vehicles ahead experience accuracy problems in terms of the ranging result, which is low, and the deviation of the ranging result between different types of vehicles, which is large and easily affected by a change in the attitude angle. To improve the robustness of a distance estimation system, an improved method for estimating the distance of a monocular vision vehicle based on the detection and segmentation of the target vehicle is proposed in this paper to address the vehicle attitude angle problem. The angle regression model (ARN) is used to obtain the attitude angle information of the target vehicle. The dimension estimation network determines the actual dimensions of the target vehicle. Then, a 2D base vector geometric model is designed in accordance with the image analytic geometric principle to accurately recover the back area of the target vehicle. Lastly, area-distance modeling based on the principle of camera projection is performed to estimate distance. The experimental results on the real-world computer vision benchmark, KITTI, indicate that our approach achieves superior performance compared with other existing published methods for different types of vehicles (including front and sideway vehicles).
Huang, Q-S, Wei, W, Sun, J, Mao, S & Ni, B-J 2019, 'Hexagonal K2W4O13 Nanowires for the Adsorption of Methylene Blue', ACS Applied Nano Materials, vol. 2, no. 6, pp. 3802-3812.
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© 2019 American Chemical Society. In this study, novel hexagonal K2W4O13 (h-K2W4O13) nanowires were strategically synthesized via a facial hydrothermal method, which exhibited excellent adsorption capacities for wastewater treatment. The inorganic agent K2SO4 was used as a structure-directing agent to scaffold the tunnel structure of h-K2W4O13 and form the one-dimensional structure. Through increasing the relative molar ratio of K2SO4 to Na2WO4 precursor, the pure-phase h-WO3 nanorods and h-K2W4O13 nanowires were obtained, attributing to the competitive electrostatic adsorption between K+ ions and Na+ ions on h-WO3 nuclei. With a smaller hydrated radius in the solution (dK+ = 3.31 Å, dNa+= 3.58 Å), K+ exhibited superior affinity compared to Na+ with the negatively charged h-WO3 nuclei because of a larger charge density, resulting in the formation of h-K2W4O13. Adsorption experimental results showed that 89.4% of methylene blue was removed by h-K2W4O13 in the first 5 min (99% in 1 h) and the maximum uptake capacity reached 204.08 mg g-1. In addition, the novel h-K2W4O13 exhibited acid or alkali resistance and good reusability, revealed by the stable adsorption capacity in a wide pH range of 3.0-11.0 and five-run recycle tests. The large specific area, high proportion of effective pore volume, and abundant hydroxyl groups of the synthesized h-K2W4O13 resulted in excellent adsorption performance for methylene blue.
Huang, Q-S, Wu, W, Wei, W & Ni, B-J 2019, 'Polyethylenimine modified potassium tungsten oxide adsorbent for highly efficient Ag+ removal and valuable Ag0 recovery', Science of The Total Environment, vol. 692, pp. 1048-1056.
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Elemental Ag0 is well known for its remarkable catalytic and antibacterial properties, thus the regeneration of valuable Ag0 metal from Ag+ wastewater is of great significance. In this study, a novel polyethylenimine (PEI) modified potassium tungsten oxide (N-K2W4O13) adsorbent was prepared for Ag+ removal and reduction to Ag0 using glutaraldehyde as crosslinking agent. XPS and FT-IR spectra verified PEI successfully anchored on the surface O and W atoms of K2W4O13 through aldehyde bridges. The content of PEI in N-K2W4O13 was calculated as 8.74wt% by TG curve. A heterogeneous PEI coating was observed in the SEM and TEM images. The N-K2W4O13 exhibited larger Ag+ uptake (48.25mg/g) than the raw K2W4O13 (42.50mg/g) though required a longer equilibrium time. This was due to the combined results of strong chelation and weak electrostatic repulsion that meanwhile occurring on the positive-charged surface of N-K2W4O13. The maximum Ag+ uptake on N-K2W4O13 was 72.5mg/g, which was larger than many of the reported adsorbents. Furthermore, the prepared N-K2W4O13 displayed good anti-interference toward background ions (Na+, K+) and hold a stable Ag+ removal (>95%) after five runs of recycling tests. The mechanism studies elucidated that NH/N groups from the PEI modified N-K2W4O13 mainly accounted for the Ag+ adsorption and Ag0 recovery in the adsorption-reduction process. Ion-exchange between Ag+ and K+ from the N-K2W4O13 lattice also occurred. This work provided a facile method to synthesize a promising adsorbent for Ag+ wastewater remediation and valuable Ag0 recovery.
Huang, S 2019, 'A review of optimisation strategies used in simultaneous localisation and mapping', Journal of Control and Decision, vol. 6, no. 1, pp. 61-74.
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© 2018, © 2018 Northeastern University, China. This paper provides a brief review of the different optimisation strategies used in mobile robot simultaneous localisation and mapping (SLAM) problem. The focus is on the optimisation-based SLAM back end. The strategies are classified based on their purposes such as reducing the computational complexity, improving the convergence and improving the robustness. It is clearly pointed out that some approximations are made in some of the methods and there is always a trade-off between the computational complexity and the accuracy of the solution. The local submap joining is a strategy that has been used to address both the computational complexity and the convergence and is a flexible tool to be used in the SLAM back end. Although more research is needed to further improve the SLAM back end, nowadays there are quite a few relatively mature SLAM back end algorithms that can be used by SLAM researchers and users.
Huang, S, Kang, Z, Tsang, IW & Xu, Z 2019, 'Auto-weighted multi-view clustering via kernelized graph learning', Pattern Recognition, vol. 88, pp. 174-184.
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© 2018 Datasets are often collected from different resources or comprised of multiple representations (i.e., views). Multi-view clustering aims to analyze the multi-view data in an unsupervised way. Owing to the efficiency of uncovering the hidden structures of data, graph-based approaches have been investigated widely for various multi-view learning tasks. However, similarity measurement in these methods is challenging since the construction of similarity graph is impacted by several factors such as the scale of data, neighborhood size, choice of similarity metric, noise and outliers. Moreover, nonlinear relationships usually exist in real-world datasets, which have not been considered by most existing methods. In order to address these challenges, a novel model which simultaneously performs multi-view clustering task and learns similarity relationships in kernel spaces is proposed in this paper. The target optimal graph can be directly partitioned into exact c connected components if there are c clusters. Furthermore, our model can assign ideal weight for each view automatically without additional parameters as previous methods do. Since the performance is often sensitive to the input kernel matrix, the proposed model is further extended with multiple kernel learning ability. With the proposed joint model, three subtasks including construct the most accurate similarity graph, automatically allocate optimal weight for each view and find the cluster indicator matrix can be simultaneously accomplished. By this joint learning, each subtask can be mutually enhanced. Experimental results on benchmark datasets demonstrate that our model outperforms other state-of-the-art multi-view clustering algorithms.
Huang, W, Kim, S, Billinghurst, M & Alem, L 2019, 'Sharing hand gesture and sketch cues in remote collaboration', Journal of Visual Communication and Image Representation, vol. 58, pp. 428-438.
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© 2018 Elsevier Inc. Many systems have been developed to support remote guidance, where a local worker manipulates objects under guidance of a remote expert helper. These systems typically use speech and visual cues between the local worker and the remote helper, where the visual cues could be pointers, hand gestures, or sketches. However, the effects of combining visual cues together in remote collaboration has not been fully explored. We conducted a user study comparing remote collaboration with an interface that combined hand gestures and sketching (the HandsInTouch interface) to one that only used hand gestures, when solving two tasks; Lego assembly and repairing a laptop. In the user study, we found that (1) adding sketch cues improved the task completion time, only with the repairing task as this had complex object manipulation but (2) using gesture and sketching together created a higher task load for the user.
Huang, W-Y, Ngo, H-H, Lin, C, Vu, C-T, Kaewlaoyoong, A, Boonsong, T, Tran, H-T, Bui, X-T, Vo, T-D-H & Chen, J-R 2019, 'Aerobic co-composting degradation of highly PCDD/F-contaminated field soil. A study of bacterial community', Science of The Total Environment, vol. 660, pp. 595-602.
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This study investigated bacterial communities during aerobic food waste co-composting degradation of highly PCDD/F-contaminated field soil. The total initial toxic equivalent quantity (TEQ) of the soil was 16,004 ng-TEQ kg-1 dry weight. After 42-day composting and bioactivity-enhanced monitored natural attenuation (MNA), the final compost product's TEQ reduced to 1916 ng-TEQ kg-1 dry weight (approximately 75% degradation) with a degradation rate of 136.33 ng-TEQ kg-1 day-1. Variations in bacterial communities and PCDD/F degraders were identified by next-generation sequencing (NGS). Thermophilic conditions of the co-composting process resulted in fewer observed bacteria and PCDD/F concentrations. Numerous organic compound degraders were identified by NGS, supporting the conclusion that PCDD/Fs were degraded during food waste co-composting. Bacterial communities of the composting process were defined by four phyla (Proteobacteria, Actinobacteria, Bacteroidetes and Firmicutes). At the genus level, Bacillus (Firmicutes) emerged as the most dominant phylotype. Further studies on specific roles of these bacterial strains are needed, especially for the thermophiles which contributed to the high degradation rate of the co-co-composting treatment's first 14 days.
Huang, X, An, P, Cao, F, Liu, D & Wu, Q 2019, 'Light-field compression using a pair of steps and depth estimation', Optics Express, vol. 27, no. 3, pp. 3557-3557.
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© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement. Advanced handheld plenoptic cameras are being rapidly developed to capture information about light fields (LFs) from the 3D world. Rich LF data can be used to develop dense sub-aperture images (SAIs) that can provide a more immersive experience for users. Unlike conventional 2D images, 4D SAIs contain both the positional and directional information of light rays; the practical applications of handheld plenoptic cameras are limited by the huge volume of data required to capture this information. Therefore, an efficient LF compression method is vital for further application of the cameras. To this end, the pair of steps and depth estimation (PoS&DE) method is proposed in this paper, and the multiview video and depth (MVD) coding structure is used to relieve the LF coding burden. More specifically, a precise depth-estimation approach is presented for SAIs based on the cost function, and an SAI-guided depth optimization algorithm is designed to refine the initial depth map based on pixel variation tendency. Meanwhile, to reduce running time, intermediate SAI synthesis quality and coding bitrates, including the key SAIs selected and cost-computation steps, are set via extensive statistical experiments. In this way, only a limited number of optimally selected SAIs and their corresponding depth maps must be encoded. The experimental results demonstrate that our proposed LF compression solution using PoS&DE can obtain a satisfied coding performance.
Huang, X, Zhang, JA, Liu, RP, Guo, YJ & Hanzo, L 2019, 'Airplane-Aided Integrated Networking for 6G Wireless: Will It Work?', IEEE Vehicular Technology Magazine, vol. 14, no. 3, pp. 84-91.
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© 2019 IEEE. As demand for wireless connectivity increases, communication technology is moving toward integrating terrestrial networks with space networks. Creating this integrated space and terrestrial network (ISTN) is critically important for industries such as logistics, mining, agriculture, fisheries, and defense. However, a number of significant technological challenges must be overcome for ISTN through low-cost airborne platforms and high-data-rate backbone links.
Huang, Y, Fu, J, Liu, A, Rao, R, Wu, D & Shen, J 2019, 'Model Test and Optimal Design of the Joint in a Sunflower Arch Bridge', Journal of Bridge Engineering, vol. 24, no. 2, pp. 04018121-04018121.
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© 2018 American Society of Civil Engineers. The sunflower arch bridge is a new type of reinforced concrete arch bridge that has been developed recently. Because of the complex constructional details, the stress distribution at the joint between the main arch and spandrel arch is very complicated. To explore the mechanical behavior of this new type of arch bridge, particularly the stress state at the joint of the arch, a 1:5-scaled model of a segment for a sunflower arch bridge was tested. The displacements and stresses at key locations of the tested model were recorded. The experimental results showed that the displacements of the main arch and spandrel arch under dead loads were notably small, which indicated that the global stiffness of the arch was sufficiently large. Moreover, the maximum tensile stress at the end of the spandrel arch subjected to dead loads was larger than the tensile strength of the concrete; therefore, the concrete in these regions is vulnerable to cracking. To avoid cracks at the end of the spandrel arch, an optimized design scheme was proposed for the joint using a steel I-beam to replace the concrete at the end of the spandrel arch. Design parameters were also suggested through a comprehensive parametric investigation based on finite-element analysis (FEA).
Huang, Y, Ng, ECY, Yam, Y-S, Lee, CKC, Surawski, NC, Mok, W-C, Organ, B, Zhou, JL & Chan, EFC 2019, 'Impact of potential engine malfunctions on fuel consumption and gaseous emissions of a Euro VI diesel truck', Energy Conversion and Management, vol. 184, pp. 521-529.
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© 2019 Elsevier Ltd Although new vehicles are designed to comply with specific emission regulations, their in-service performance would not necessarily achieve them due to wear-and-tear and improper maintenance, as well as tampering or failure of engine control and exhaust after-treatment systems. In addition, there is a lack of knowledge on how significantly these potential malfunctions affect vehicle performance. This study was therefore conducted to simulate the effect of various engine malfunctions on the fuel consumption and gaseous emissions of a 16-tonne Euro VI diesel truck using transient chassis dynamometer testing. The simulated malfunctions included those that would commonly occur in the intake, fuel injection, exhaust after-treatment and other systems. The results showed that all malfunctions increased fuel consumption except for the malfunction of EGR fully closed which reduced fuel consumption by 31%. The biggest increases in fuel consumption were caused by malfunctions in the intake system (16%–43%), followed by the exhaust after-treatment (6%–30%), fuel injection (4%–24%) and other systems (6%–11%). Regarding pollutant emissions, the effect of engine malfunctions on HC and CO emissions was insignificant, which remained unchanged or even reduced for most cases. An exception was EGR fully open which increased HC and CO emissions by 343% and 1124%, respectively. Contrary to HC and CO emissions, NO emissions were significantly increased by malfunctions. The largest increases in NO emissions were caused by malfunctions in the after-treatment system, ranging from 38% (SCR) to 1606% (DPF pressure sensor). Malfunctions in the fuel injection system (24%–1259%) and intercooler (438%–604%) could also increase NO emissions markedly. This study demonstrated clearly the importance of having properly functioning engine control and exhaust after-treatment systems to achieve the required performance of fuel consumption and pollutant emissions.
Huang, Y, Organ, B, Zhou, JL, Surawski, NC, Yam, Y-S & Chan, EFC 2019, 'Characterisation of diesel vehicle emissions and determination of remote sensing cutpoints for diesel high-emitters', Environmental Pollution, vol. 252, no. Part A, pp. 31-38.
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© 2019 Elsevier Ltd Diesel vehicles are a major source of air pollutants in cities and have caused significant health risks to the public globally. This study used both on-road remote sensing and transient chassis dynamometer to characterise emissions of diesel light goods vehicles. A large sample size of 183 diesel vans were tested on a transient chassis dynamometer to evaluate the emission levels of in-service diesel vehicles and to determine a set of remote sensing cutpoints for diesel high-emitters. The results showed that 79% and 19% of the Euro 4 and Euro 5 diesel vehicles failed the transient cycle test, respectively. Most of the high-emitters failed the NO limits, while no vehicle failed the HC limits and only a few vehicles failed the CO limits. Vehicles that failed NO limits occurred in both old and new vehicles. NO/CO2 ratios of 57.30 and 22.85 ppm/% were chosen as the remote sensing cutpoints for Euro 4 and Euro 5 high-emitters, respectively. The cutpoints could capture a Euro 4 and Euro 5 high-emitter at a probability of 27% and 57% with one snapshot remote sensing measurement, while only producing 1% of false high-emitter detections. The probability of high-emitting events was generally evenly distributed over the test cycle, indicating that no particular driving condition produced a higher probability of high-emitting events. Analysis on the effect of cutpoints on real-driving diesel fleet was carried out using a three-year remote sensing program. Results showed that 36% of Euro 4 and 47% of Euro 5 remote sensing measurements would be detected as high-emitting using the proposed cutpoints. In-service diesel vehicles emit low CO and HC but high NO.
Huang, Y, Porter, A, Zhang, Y & Barrangou, R 2019, 'Author Correction: Collaborative networks in gene editing', Nature Biotechnology, vol. 37, no. 12, pp. 1522-1522.
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© 2019, Springer Nature America, Inc. In the version of this article initially published, the affiliation for Eugene V. Koonin was given in Table 2 as Korea Centers for Disease Control & Prevention. The correct affiliation is US National Center for Biotechnology Information, National Institutes of Health. The error has been corrected in the HTML and PDF versions of the article.
Huang, Y, Porter, A, Zhang, Y & Barrangou, R 2019, 'Collaborative networks in gene editing', Nature Biotechnology, vol. 37, no. 10, pp. 1107-1109.
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Huang, Y, Porter, AL, Zhang, Y, Lian, X & Guo, Y 2019, 'An assessment of technology forecasting: Revisiting earlier analyses on dye-sensitized solar cells (DSSCs)', Technological Forecasting and Social Change, vol. 146, pp. 831-843.
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The increasingly uncertain dynamics of technological change pose special challenges to traditional technology forecasting tools, which facilitates future-oriented technology analysis (FTA) tools to support the policy processes in the fields of science, technology & innovation (ST&I) and the management of technology (MOT), rather than merely forecasting incremental advances via analyses of continuous trends. Dye-sensitized solar cells are a promising third-generation photovoltaic technology that can add functionality and lower costs to enhance the value proposition of solar power generation in the early years of the 21st century. Through a series of technological forecasting studies analyzing the R&D patterns and trends in Dye-sensitized solar cells technology over the past several years, we have come to realize that validating previous forecasts is useful for improving ST&I policy processes. Yet, rarely do we revisit forecasts or projections to ascertain how well they fared. Moreover, few studies pay much attention to assessing FTA techniques. In this paper, we compare recent technology activities with previous forecasts to reveal the influencing factors that led to differences between past predictions and actual performance. Beyond our main aim of checking accuracy, in this paper we also wish to gain some sense of how valid those studies were and whether they proved useful to others in some ways.
Huang, Y, Surawski, NC, Organ, B, Zhou, JL, Tang, OHH & Chan, EFC 2019, 'Fuel consumption and emissions performance under real driving: Comparison between hybrid and conventional vehicles', Science of The Total Environment, vol. 659, pp. 275-282.
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© 2018 Elsevier B.V. Hybrid electric vehicles (HEVs) are perceived to be more energy efficient and less polluting than conventional internal combustion engine (ICE) vehicles. However, increasing evidence has shown that real-driving emissions (RDE) could be much higher than laboratory type approval limits and the advantages of HEVs over their conventional ICE counterparts under real-driving conditions have not been studied extensively. Therefore, this study was conducted to evaluate the real-driving fuel consumption and pollutant emissions performance of HEVs against their conventional ICE counterparts. Two pairs of hybrid and conventional gasoline vehicles of the same model were tested simultaneously in a novel convoy mode using two portable emission measurement systems (PEMSs), thus eliminating the effect of vehicle configurations, driving behaviour, road conditions and ambient environment on the performance comparison. The results showed that although real-driving fuel consumption for both hybrid and conventional vehicles were 44%–100% and 30%–82% higher than their laboratory results respectively, HEVs saved 23%–49% fuel relative to their conventional ICE counterparts. Pollutant emissions of all the tested vehicles were lower than the regulation limits. However, HEVs showed no reduction in HC emissions and consistently higher CO emissions compared to the conventional ICE vehicles. This could be caused by the frequent stops and restarts of the HEV engines, as well as the lowered exhaust gas temperature and reduced effectiveness of the oxidation catalyst. The findings therefore show that while achieving the fuel reduction target, hybridisation did not bring the expected benefits to urban air quality.
Huang, Y, Xu, J, Wu, Q, Zheng, Z, Zhang, Z & Zhang, J 2019, 'Multi-Pseudo Regularized Label for Generated Data in Person Re-Identification', IEEE Transactions on Image Processing, vol. 28, no. 3, pp. 1391-1403.
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© 1992-2012 IEEE. Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for a labeling large number of images (i.e., annotation), the amount of available training data (i.e., real data) is always limited. To produce more data for training a deep network, generative adversarial network can be used to generate artificial sample data (i.e., generated data). However, the generated data usually does not have annotation labels. To solve this problem, in this paper, we propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign it to the generated data. With MpRL, the generated data will be used as the supplementary of real training data to train a deep neural network in a semi-supervised learning fashion. To build the corresponding relationship between the real data and generated data, MpRL assigns each generated data a proper virtual label which reflects the likelihood of the affiliation of the generated data to pre-defined training classes in the real data domain. Unlike the traditional label which usually is a single integral number, the virtual label proposed in this paper is a set of weight-based values each individual of which is a number in (0,1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data. A comprehensive evaluation is carried out by adopting two state-of-the-art convolutional neural networks (CNNs) in our experiments to verify the effectiveness of MpRL. Experiments demonstrate that by assigning MpRL to generated data, we can further improve the person re-ID performance on five re-ID datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, VIPeR, and CUHK01. The proposed method obtains +6.29%, +6.30%, +5.58%, +5.84%, and +3.48% improvements in rank-1 accuracy over a strong CNN baseline on the five datasets, respectively, and outperforms state-of-the-art methods.
Huang, Y, Zhong, Y, Wu, Q, Dutkiewicz, E & Jiang, T 2019, 'Cost-Effective Foliage Penetration Human Detection Under Severe Weather Conditions Based on Auto-Encoder/Decoder Neural Network', IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6190-6200.
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© 2014 IEEE. Military surveillance events and rescue activities are vital missions for the Internet-of-Things. To this end, foliage penetration for human detection plays an important role. However, although the feasibility of that mission has been validated, we observe that it still cannot perform promisingly under severe weather conditions, such as rainy, foggy, and snowy days. Therefore, in this paper, experiments are conducted under severe weather conditions based on a proposed deep learning approach. We present an auto-encoder/decoder (Auto-ED) deep neural network that can learn the deep representation and conduct classification task concurrently. Since the property of cost-effective, the device-free sensing techniques are used to address human detection in our case. As we pursue the signal-based mission, two components are involved in the proposed Auto-ED approach. First, an encoder is utilized that encode signal-based inputs into higher dimensional tensors by fractionally strided convolution operations. Then, a decoder is leveraged with convolution operations to extract deep representations and learn the classifier simultaneously. To verify the effectiveness of the proposed approach, we compare it with several machine learning approaches under different weather conditions. Also, a simulation experiment is conducted by adding additive white Gaussian noise to the original target signals with different signal to noise ratios. Experimental results demonstrate that the proposed approach can best tackle the challenge of human detection under severe weather conditions in the high-clutter foliage environment, which indicates its potential application values in the near future.
Huang, YQ, Fu, JY, Liu, AR, Pi, YL, Wu, D & Gao, W 2019, 'Effect of concrete creep on dynamic stability behavior of slender concrete-filled steel tubular column', Composites Part B: Engineering, vol. 157, pp. 173-181.
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© 2018 Elsevier Ltd An analytical procedure for dynamic stability of CFST column accounting for the creep of concrete core is proposed. The long-term effect of creep of concrete core is formulated based on the creep model by the ACI 209 committee and the age-adjusted effective modulus method (AEMM). The equations of boundary frequencies accounting for the effects of concrete creep are derived by the Bolotin's theory and solved as a quadratic eigenvalue problem. The effectiveness of the proposed method and the characteristics of time-varying distribution of instability regions are numerically surveyed. It is shown that the CFST column becomes dynamically unstable even when the sum of the sustained static load and the amplitude of the dynamic excitation is much lower than the static instability load. It is also found that due to the time effects of concrete creep under the sustained static load, the same excitation, that cannot induce dynamic instability in the early stage of sustained loading, can induce the dynamic instability in a few days later. The critical amplitude and frequency of the dynamic excitation can decrease by 6% and 3% in 5 days, and 11% and 6% in 100 days.
Hung, S-H, Hietala, K, Zhu, S, Ying, M, Hicks, M & Wu, X 2019, 'Quantitative robustness analysis of quantum programs.', Proc. ACM Program. Lang., vol. 3, no. POPL, pp. 31:1-31:1.
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Quantum computation is a topic of significant recent interest, with practical advances coming from both research and industry. A major challenge in quantum programming is dealing with errors (quantum noise) during execution. Because quantum resources (e.g., qubits) are scarce, classical error correction techniques applied at the level of the architecture are currently cost-prohibitive. But while this reality means that quantum programs are almost certain to have errors, there as yet exists no principled means to reason about erroneous behavior. This paper attempts to fill this gap by developing a semantics for erroneous quantum while-programs, as well as a logic for reasoning about them. This logic permits proving a property we have identified, called є-robustness, which characterizes possible “distance” between an ideal program and an erroneous one. We have proved the logic sound, and showed its utility on several case studies, notably: (1) analyzing the robustness of noisy versions of the quantum Bernoulli factory (QBF) and quantum walk (QW); (2) demonstrating the (in)effectiveness of different error correction schemes on single-qubit errors; and (3) analyzing the robustness of a fault-tolerant version of QBF.
Huo, S, Liu, M, Wu, L, Liu, M, Xu, M, Ni, W & Yan, Y-M 2019, 'Synthesis of ultrathin and hierarchically porous carbon nanosheets based on interlayer-confined inorganic/organic coordination for high performance supercapacitors', Journal of Power Sources, vol. 414, pp. 383-392.
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Hussain, R, Raza, A, Khan, MU, Shammim, A & Sharawi, MS 2019, 'Miniaturized frequency reconfigurable pentagonal MIMO slot antenna for interweave CR applications', International Journal of RF and Microwave Computer-Aided Engineering, vol. 29, no. 9.
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Hussain, W & Sohaib, O 2019, 'Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a Dataset.', IEEE Access, vol. 7, pp. 82649-82671.
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© 2019 IEEE. service level agreement (SLA) management is one of the key issues in cloud computing. The primary goal of a service provider is to minimize the risk of service violations, as these results in penalties in terms of both money and a decrease in trustworthiness. To avoid SLA violations, the service provider needs to predict the likelihood of violation for each SLO and its measurable characteristics (QoS parameters) and take immediate action to avoid violations occurring. There are several approaches discussed in the literature to predict service violation; however, none of these explores how a change in control parameters and the freshness of data impact prediction accuracy and result in the effective management of an SLA of the cloud service provider. The contribution of this paper is two-fold. First, we analyzed the accuracy of six widely used prediction algorithms - simple exponential smoothing, simple moving average, weighted moving average, Holt-Winter double exponential smoothing, extrapolation, and the autoregressive integrated moving average - by varying their individual control parameters. Each of the approaches is compared to 10 different datasets at different time intervals between 5 min and 4 weeks. Second, we analyzed the prediction accuracy of the simple exponential smoothing method by considering the freshness of a data; i.e., how the accuracy varies in the initial time period of prediction compared to later ones. To achieve this, we divided the cloud QoS dataset into sets of input values that range from 100 to 500 intervals in sets of 1-100, 1-200, 1-300, 1-400, and 1-500. From the analysis, we observed that different prediction methods behave differently based on the control parameter and the nature of the dataset. The analysis helps service providers choose a suitable prediction method with optimal control parameters so that they can obtain accurate prediction results to manage SLA intelligently and avoid violation penalties.
Huynh, NV, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2019, 'Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks', IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1-1.
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Effective network slicing requires an infrastructure/network provider to dealwith the uncertain demand and real-time dynamics of network resource requests.Another challenge is the combinatorial optimization of numerous resources,e.g., radio, computing, and storage. This article develops an optimal and fastreal-time resource slicing framework that maximizes the long-term return of thenetwork provider while taking into account the uncertainty of resource demandfrom tenants. Specifically, we first propose a novel system model which enablesthe network provider to effectively slice various types of resources todifferent classes of users under separate virtual slices. We then capture thereal-time arrival of slice requests by a semi-Markov decision process. Toobtain the optimal resource allocation policy under the dynamics of slicingrequests, e.g., uncertain service time and resource demands, a Q-learningalgorithm is often adopted in the literature. However, such an algorithm isnotorious for its slow convergence, especially for problems with largestate/action spaces. This makes Q-learning practically inapplicable to our casein which multiple resources are simultaneously optimized. To tackle it, wepropose a novel network slicing approach with an advanced deep learningarchitecture, called deep dueling that attains the optimal average reward muchfaster than the conventional Q-learning algorithm. This property is especiallydesirable to cope with real-time resource requests and the dynamic demands ofusers. Extensive simulations show that the proposed framework yields up to 40%higher long-term average return while being few thousand times faster, comparedwith state of the art network slicing approaches.
Huynh, NV, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2019, ''Jam Me If You Can'': Defeating Jammer with Deep Dueling Neural Network Architecture and Ambient Backscattering Augmented Communications', IEEE Journal on Selected Areas in Communications, vol. 37, no. 11, pp. 2603-2620.
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With conventional anti-jamming solutions like frequency hopping or spreadspectrum, legitimate transceivers often tend to 'escape' or 'hide' themselvesfrom jammers. These reactive anti-jamming approaches are constrained by thelack of timely knowledge of jamming attacks. Bringing together the latestadvances in neural network architectures and ambient backscatteringcommunications, this work allows wireless nodes to effectively 'face' thejammer by first learning its jamming strategy, then adapting the rate ortransmitting information right on the jamming signal. Specifically, to dealwith unknown jamming attacks, existing work often relies on reinforcementlearning algorithms, e.g., Q-learning. However, the Q-learning algorithm isnotorious for its slow convergence to the optimal policy, especially when thesystem state and action spaces are large. This makes the Q-learning algorithmpragmatically inapplicable. To overcome this problem, we design a novel deepreinforcement learning algorithm using the recent dueling neural networkarchitecture. Our proposed algorithm allows the transmitter to effectivelylearn about the jammer and attain the optimal countermeasures thousand timesfaster than that of the conventional Q-learning algorithm. Through extensivesimulation results, we show that our design (using ambient backscattering andthe deep dueling neural network architecture) can improve the averagethroughput by up to 426% and reduce the packet loss by 24%. By augmenting theambient backscattering capability on devices and using our algorithm, it isinteresting to observe that the (successful) transmission rate increases withthe jamming power. Our proposed solution can find its applications in bothcivil (e.g., ultra-reliable and low-latency communications or URLLC) andmilitary scenarios (to combat both inadvertent and deliberate jamming).
Iacopi, F & McIntosh, M 2019, 'Opportunities and perspectives for green chemistry in semiconductor technologies', Green Chemistry, vol. 21, no. 12, pp. 3250-3255.
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Semiconductor technologies offer a plethora of technological challenges and opportunities for a more extensive implementation of green chemistry principles.
Ibrar, I, Naji, O, Sharif, A, Malekizadeh, A, Alhawari, A, Alanezi, AA & Altaee, A 2019, 'A Review of Fouling Mechanisms, Control Strategies and Real-Time Fouling Monitoring Techniques in Forward Osmosis', Water, vol. 11, no. 4, pp. 695-695.
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Forward osmosis has gained tremendous attention in the field of desalination and wastewater treatment. However, membrane fouling is an inevitable issue. Membrane fouling leads to flux decline, can cause operational problems and can result in negative consequences that can damage the membrane. Hereby, we attempt to review the different types of fouling in forward osmosis, cleaning and control strategies for fouling mitigation, and the impact of membrane hydrophilicity, charge and morphology on fouling. The fundamentals of biofouling, organic, colloidal and inorganic fouling are discussed with a focus on recent studies. We also review some of the in-situ real-time online fouling monitoring technologies for real-time fouling monitoring that can be applicable to future research on forward osmosis fouling studies. A brief discussion on critical flux and the coupled effects of fouling and concentration polarization is also provided.
Idroes, R, Yusuf, M, Saiful, S, Alatas, M, Subhan, S, Lala, A, Muslem, M, Suhendra, R, Idroes, GM, Marwan, M & Mahlia, TMI 2019, 'Geochemistry Exploration and Geothermometry Application in the North Zone of Seulawah Agam, Aceh Besar District, Indonesia', Energies, vol. 12, no. 23, pp. 4442-4442.
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A geochemistry study has been done in four geothermal manifestations—Ie-Seu’um, Ie-Brôuk, Ie-Jue and the Van-Heutz crater—located in the north zone of Seulawah Agam mountain (Aceh Besar District, Indonesia). The study was performed through water and gas analysis. Water analysis were done for all geothermal manifestations, but gas analysis was only done for the Ie-Jue manifestation that has fumaroles. Cation and anion contents were analyzed by ion chromatography, ICP-OES, alkalimetry titrations, and spectrophotometry, meanwhile isotopes were measured by a Liquid Water Isotope Analyzer. The resulting data were used for fluid and gas geothermometry calculations, and plotted in a FT-CO2 Cross-Plot and a CH4-CO2-H2S triangle diagram to obtain reservoir temperatures. The data were also plotted by a Cl-HCO3-SO4 triangle and Piper diagram to obtain the water type and dominant chemical composition, a Na-K-Mg triangle diagram to obtain fluid equilibria, the isotope ratio in the stable isotope plot to obtain the origin of water, and a N2-He-Ar triangle diagram to establish the origin of fumaroles. The water analysis results showed that (1) Ie-Seu’um has an average reservoir temperature of 241.9 ± 0.3 °C, a chloride water type, a dominant Na-K-Cl chemical composition, a mature water fluid equilibrium, and water of meteoric origin; (2) Ie-Brôuk has an average reservoir temperature of 321.95 ± 13.4 °C, a bicarbonate water type, a dominant Na-Ca-HCO3chemical composition, an immature water fluid equilibrium, and water of meteoric origin; (3) Ie-Jue has an average reservoir temperature of 472.4 ± 91.4 °C, a sulphate water type, a dominant Ca-SO4 chemical composition, an immature water fluid equilibrium and water of meteoric origin; and (4) the Van-Heutz crater has an average reservoir temperature of 439.3 ± 95.3 °C, a sulphate water type, a dominant Ca-SO4 chemical composition, an immature water fluid equilibrium and water of magmatic origin. The results of our ...
Imdad, U, Asif, M, Ahmad, MT, Sohaib, O, Hanif, MK & Chaudary, MH 2019, 'Three Dimensional Point Cloud Compression and Decompression Using Polynomials of Degree One.', Symmetry, vol. 11, no. 2, pp. 209-209.
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© 2019 by the authors. The availability of cheap depth range sensors has increased the use of an enormous amount of 3D information in hand-held and head-mounted devices. This has directed a large research community to optimize point cloud storage requirements by preserving the original structure of data with an acceptable attenuation rate. Point cloud compression algorithms were developed to occupy less storage space by focusing on features such as color, texture, and geometric information. In this work, we propose a novel lossy point cloud compression and decompression algorithm that optimizes storage space requirements by preserving geometric information of the scene. Segmentation is performed by using a region growing segmentation algorithm. The points under the boundary of the surfaces are discarded that can be recovered through the polynomial equations of degree one in the decompression phase. We have compared the proposed technique with existing techniques using publicly available datasets for indoor architectural scenes. The results show that the proposed novel technique outperformed all the techniques for compression rate and RMSE within an acceptable time scale.
Indraratna, B, Babar Sajjad, M, Ngo, T, Gomes Correia, A & Kelly, R 2019, 'Improved performance of ballasted tracks at transition zones: A review of experimental and modelling approaches', Transportation Geotechnics, vol. 21, pp. 100260-100260.
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© 2019 Elsevier Ltd Track transitions such as bridge approaches, road crossings and shifts from slab track to ballasted track are common locations where track degradation accelerates due to dynamic and high impact forces; as a consequence there is higher differential settlement. These types of discontinuities cause an abrupt change in the structural response of the track due mainly to variations in stiffness and track damping. Track transition zones are prone to an accelerated deterioration of track material and geometry that leads to increased maintenance costs. Track deterioration also leads to vehicle degradation due to enhanced acceleration, low frequency oscillation, and high frequency vibrations. While ballast deterioration is a major factor affecting the stability and longevity of rail tracks, the cost of tackling transition related problems that detract from passenger comfort is also high. A good transition zone lessens the impact of dynamic load of moving trains by minimising the abrupt variations in track stiffness and ensuring a smooth and gradual change from a less stiff (ballasted track) to a stiff (slab track) structure. This paper presents a critical review of various problems associated with transition zones and the measures adopted to mitigate them; it also includes critical review of research work carried out using large-scale laboratory testing, mathematical and computational modelling and field measurements on track transition zones.
Indraratna, B, Qi, Y, Heitor, A & Vinod, JS 2019, 'The influence of rubber crumbs on the critical state behavior of waste mixtures', E3S Web of Conferences, vol. 92, pp. 06004-06004.
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The practical application of waste materials such as steel furnace slag (SFS) and coal wash (CW) is becoming more prevalent in many geotechnical projects. It was found that the inclusion of rubber crumbs (RCs) from recycled tyres into mixtures of SFS and CW not only solves the problem of large stockpiles of waste tyres, it also can provide an energy-absorbing medium that will reduce track degradation. In order to investigate the influence of RC on the geotechnical properties of the granular waste matrix (SFS+CW+RC), a series of monotonic consolidated drained triaxial tests were conducted on waste mixtures. The test results reveal that the inclusion of RC significantly affects the geotechnical properties of the waste mixtures, especially their critical state behaviour. Specifically, the waste matrix can achieve a critical state with a low RC content (<20%), whereas those mixtures with higher RC contents (20-40%) cannot attain a critical state within the ultimate strain capacity that can be applied to specimens using the traditional triaxial equipment. Therefore, for the waste matrix with higher RC contents extrapolation of the measured volumetric strains had to be adopted to obtain the appropriate critical state parameters. Moreover, the influence of energy absorbing property by adding RC on the critical state behaviour has also been captured through an empirical equation.
Indraratna, B, Qi, Y, Ngo, TN, Rujikiatkamjorn, C, Neville, T, Ferreira, FB & Shahkolahi, A 2019, 'Use of Geogrids and Recycled Rubber in Railroad Infrastructure for Enhanced Performance', Geosciences, vol. 9, no. 1, pp. 30-30.
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Railway tracks are conventionally built on compacted ballast and structural fill layers placed above the natural (subgrade) foundation. However, during train operations, track deteriorations occur progressively due to ballast degradation. The associated track deformation is usually accompanied by a reduction in both load bearing capacity and drainage, apart from imposing frequent track maintenance. Suitable ground improvement techniques involving plastic inclusions (e.g., geogrids) and energy absorbing materials (e.g., rubber products) to enhance the stability and longevity of tracks have become increasingly popular. This paper presents the outcomes from innovative research and development measures into the use of plastic and rubber elements in rail tracks undertaken at the University of Wollongong, Australia, over the past twenty years. The results obtained from laboratory tests, mathematical modelling and numerical modelling reveal that track performance can be improved significantly by using geogrid and energy absorbing rubber products (e.g., rubber crumbs, waste tire-cell and rubber mats). Test results show that the addition of rubber materials can efficiently improve the energy absorption of the structural layer and also reduce ballast breakage. Furthermore, by incorporating the work input parameters, the energy absorbing property of the newly developed synthetic capping layer is captured by correct modelling of dilatancy. In addition, the laboratory behavior of tire cells and geogrids has been validated by numerical modelling (i.e., Finite Element Modelling-FEM, Discrete Element—DEM), and a coupled DEM-FEM modelling approach is also introduced to simulate ballast deformation.
Indraratna, B, Rujikiatkamjorn, C, Baral, P & Ameratunga, J 2019, 'Performance of marine clay stabilised with vacuum pressure: Based on Queensland experience', Journal of Rock Mechanics and Geotechnical Engineering, vol. 11, no. 3, pp. 598-611.
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© 2018 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences Stabilising soft marine clay and estuarine soils via vacuum preloading has become very popular in Australasia over the past decades because it is a cost-effective and time-efficient approach. In recent times, new land on areas outside but adjacent to existing port amenities, the Fisherman Islands at the Port of Brisbane (POB), was reclaimed to cater for an increase in trade activities. A vacuum preloading method combined with surcharge to stabilise the deep layers of soil was used to enhance the application of prefabricated vertical drains (PVDs). This paper describes the performance of this combined surcharge fill and vacuum system under the embankment and also compares it with a surcharge loading system to demonstrate the benefits of vacuum pressure over conventional fill. The performance of this embankment is also presented in terms of field monitoring data, and the relative performance of the vacuum together with non-vacuum systems is evaluated. An analytical solution to radial consolidation with time-dependent surcharge loading and vacuum pressure is also presented in order to predict the settlement and associated excess pore water pressure (EPWP) of deposits of thick soft clay.
Indraratna, B, Rujikiatkamjorn, C, Tawk, M & Heitor, A 2019, 'Compaction, degradation and deformation characteristics of an energy absorbing matrix', Transportation Geotechnics, vol. 19, pp. 74-83.
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© 2019 Elsevier Ltd The reuse of waste materials as an alternative to natural aggregates is becoming more popular in engineering projects. It offers a sustainable and economical solution to address the environmental concerns arising from the scarcity of natural quarries as well as the increase in waste generation. Coal wash (CW) and rubber crumbs (RC) are industrial by-products that could potentially be used in railway substructures. In this study, different RC levels are introduced into CW (i.e. CWRC mixture) to reduce potential breakage of CW and increase the ductility and energy absorbing capacity of the matrix. The compaction and degradation characteristics of CWRC mixtures to be used as a construction fill are investigated under five energy levels ranging from standard to modified Proctor compaction. An optimum compaction energy is determined so as to minimize breakage but still yield an acceptable void ratio (compact packing) to avoid excessive settlements. The compressibility of rubber particles and the induced change in the volume of solids is addressed with regard to the overall void ratio of the matrix. Furthermore, the results of triaxial tests on four CWRC mixtures compacted to the same void ratio under three different confining pressures (25, 50 and 75 kPa) are presented, and the effect of RC content on the stress-strain relationship is elucidated.
Irfansyah, AN, Lehmann, T, Jenkins, J, Tong, T & Hamilton, TJ 2019, 'A resistive DAC for a multi-stage sigma-delta modulator DAC with dynamic element matching', Analog Integrated Circuits and Signal Processing, vol. 98, no. 1, pp. 109-123.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. This paper presents a study and implementation of a shunt–shunt resistive voltage divider digital-to-analog converter (DAC) for use as a multibit DAC in a multi-stage noise shaping sigma-delta modulator DAC design with dynamic element matching. This resistive DAC structure is employed to address the problem of code-dependent finite output impedance and thus aims to improve systematic linearity, while still being suitable for scaled CMOS processes. Chip measurement results from an implementation in CMOS 180 nm technology are presented. At low sampling clock frequencies, an SFDR of 71.81 dB is achieved, while at a higher sampling clock frequency of 600 MHz the SFDR is measured to be 59.73 dB, all for an OSR of 32. Our results show that low systematic nonlinearity can be achieved with this resistive DAC at low sampling frequencies, and we discuss potential enhancements to our prototype to obtain better SFDR at higher sampling rate.
Irga, PJ, Pettit, T, Irga, RF, Paull, NJ, Douglas, ANJ & Torpy, FR 2019, 'Does plant species selection in functional active green walls influence VOC phytoremediation efficiency?', Environmental Science and Pollution Research, vol. 26, no. 13, pp. 12851-12858.
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© Springer-Verlag GmbH Germany, part of Springer Nature 2019. Volatile organic compounds (VOCs) are of public concern due to their adverse health effects. Botanical air filtration is a promising technology for reducing indoor air contaminants, but the underlying mechanisms are not fully understood. This study assessed active botanical biofilters for their single-pass removal efficiency (SPRE) for benzene, ethyl acetate and ambient total volatile organic compounds (TVOCs), at concentrations of in situ relevance. Biofilters containing four plant species (Chlorophytum orchidastrum, Nematanthus glabra, Nephrolepis cordifolia ‘duffii’ and Schefflera arboricola) werecompared to discern whether plant selection influenced VOC SPRE. Amongst all tested plant species, benzene SPREs were between 45.54 and 59.50%, with N. glabra the most efficient. The botanical biofilters removed 32.36–91.19% of ethyl acetate, with C. orchidastrum and S. arboricola recording significantly higher ethyl acetate SPREs than N. glabra and N. cordifolia. These findings thus indicate that plant type influences botanical biofilter VOC removal. It is proposed that ethyl acetate SPREs were dependent on hydrophilic adsorbent sites, with increasing root surface area, root diameter and root mass all associated with increasing ethyl acetate SPRE. The high benzene SPRE of N. glabra is likely due to the high wax content in its leaf cuticles. The SPREs for the relatively low levels of ambient TVOCs were consistent amongst plant species, providing no evidence to suggest that in situ TVOC removal is influenced by plant choice. Nonetheless, as inter-species differences do exist for some VOCs, botanical biofilters using a mixture of plants is proposed.
Islam, M, Mithulananthan, N, Hossain, J & Shah, R 2019, 'Dynamic voltage stability of unbalanced distribution system with high penetration of single‐phase PV units', The Journal of Engineering, vol. 2019, no. 17, pp. 4074-4080.
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Dynamic voltage instability (DVI) issues are the primary concern in low‐voltage distribution network (DN) due to growing integration of low‐inertia compressor motor loads such as air‐conditioner and refrigerator. The concern of DVI is likely to increase owing to high penetration of rooftop type single‐phase photovoltaic (PV) units in DN. On the other hand, DNs are inherently unbalanced as a result of load and line characteristics along with unbalanced PV penetration. This paper examines the impact of imbalance on the dynamic voltage stability (DVS) in DN and provides solutions to mitigate any adverse effects. Dynamic models of the single‐phase PV units are developed and utilised in the paper. The degree of unbalanced is defined first, and then its impact on the DVS is investigated. From the investigation, it is observed that degree of instability is increased with the increment of imbalance. The paper has also proposed a mitigation strategy i.e. reactive power injection by PV inverter. Case studies are conducted on modified IEEE 4 bus system which represents a low‐voltage DN. Results reveal that reactive power injection by PV inverter can improve the DVS by mitigating the impact of unbalance.
Islam, M, Nadarajah, M & Hossain, MJ 2019, 'Short-Term Voltage Stability Enhancement in Residential Grid With High Penetration of Rooftop PV Units', IEEE Transactions on Sustainable Energy, vol. 10, no. 4, pp. 2211-2222.
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© 2010-2012 IEEE. Short-term voltage instability (STVI) imposes a severe threat to modern distribution networks (DNs) where a large number of intermittent distributed generator (DG) units, like rooftop photovoltaic (PV), is being integrated. Consequently, most of the international standards have been revised by incorporating the requirement of the dynamic voltage support (DVS) through DG units, which is a promising approach to alleviate the STVI. In this paper, a novel DVS strategy is proposed to improve the short-term voltage stability (STVS) in residential grids. In comparison with other DVS strategies, the proposed DVS scheme maximizes the active power support from PV units following a contingency utilizing the maximum allowable current of the PV inverters. Moreover, the inverter design margin is taken into account in designing the proposed scheme to limit the injected grid current within the maximum allowable inverter current. The impact of the inverter design margin on the STVS is explained, and the effectiveness of the proposed strategy compared with the conventional DVS is demonstrated. The feasibility of the DVS control strategies in practical application is studied. Several case studies are carried out on benchmark IEEE 4 bus and IEEE 13 node test feeder systems, and finally, on a ring-type DN. The results show that the proposed DVS scheme is feasible, and achieved superior performance compared to the other strategies. Furthermore, it has been shown that implementation of the proposed DVS scheme can avoid the installation of an expensive 1200-kVA D-STATCOM for STVS improvement in the target system.
Islam, MR, Lu, H, Hossain, MJ & Li, L 2019, 'Mitigating unbalance using distributed network reconfiguration techniques in distributed power generation grids with services for electric vehicles: A review', Journal of Cleaner Production, vol. 239, pp. 117932-117932.
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© 2019 Elsevier Ltd With rapid movement to combat climate change by reducing greenhouse gases, there is an increasing trend to use more electric vehicles (EVs) and renewable energy sources (RES). With more EVs integration into electricity grid, this raises many challenges for the distribution service operators (DSOs) to integrate such RES-based, distributed generation (DG) and EV-like distributed loads into distribution grids. Effective management of distribution network imbalance is one of the challenges. The distribution network reconfiguration (DNR) techniques are promising to address the issue of imbalance along with other techniques such as the optimal distributed generation placement and allocation (OPDGA) method. This paper presents a systematic and thorough review of DNR techniques for mitigating unbalance of distribution networks, based on papers published in peer-reviewed journals in the last three decades. It puts more focus on how the DNR techniques have been used to manage network imbalance due to distributed loads and DG units. To the best of our knowledge, this is the first attempt to review the research works in the field using DNR techniques to mitigate unbalanced distribution networks. Therefore, this paper will serve as a prime source of the guidance for mitigating network imbalance using the DNR techniques to the new researchers in this field.
Islam, MS, Saha, SC, Gemci, T, Yang, IA, Sauret, E, Ristovski, Z & Gu, YT 2019, 'Euler-Lagrange Prediction of Diesel-Exhaust Polydisperse Particle Transport and Deposition in Lung: Anatomy and Turbulence Effects', Scientific Reports, vol. 9, no. 1, p. 12423.
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AbstractIn clinical assessments, the correlation between atmospheric air pollution and respiratory damage is highly complicated. Epidemiological studies show that atmospheric air pollution is largely responsible for the global proliferation of pulmonary disease. This is particularly significant, since most Computational Fluid Dynamics (CFD) studies to date have used monodisperse particles, which may not accurately reflect realistic inhalation patterns, since atmospheric aerosols are mostly polydisperse. The aim of this study is to investigate the anatomy and turbulent effects on polydisperse particle transport and deposition (TD) in the upper airways. The Euler-Lagrange approach is used for polydisperse particle TD prediction in both laminar and turbulent conditions. Various anatomical models are adopted to investigate the polydisperse particle TD under different flow conditions. Rossin-Rammler diameter distribution is used for the distribution of the initial particle diameter. The numerical results illustrate that airflow rate distribution at the right lung of a realistic model is higher than a non-realistic model. The CFD study also shows that turbulence effects on deposition are higher for larger diameter particles than with particles of smaller diameter. A significant amount of polydisperse particles are also shown to be deposited at the tracheal wall for CT-based model, whereas particles are mostly deposited at the carinal angle for the non-realistic model. A comprehensive, polydisperse particle TD analysis would enhance understanding of the realistic deposition pattern and decrease unwanted therapeutic aerosol deposition at the extrathoracic airways.
Islam, MS, Saha, SC, Sauret, E, Ong, H, Young, P & Gu, YT 2019, 'Euler–Lagrange approach to investigate respiratory anatomical shape effects on aerosol particle transport and deposition', Toxicology Research and Application, vol. 3, pp. 239784731989467-239784731989467.
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An accurate knowledge of the pulmonary aerosol particle transport in the realistic lung is essential to deliver the therapeutic particle to the targeted site of the bifurcating airways. The available in silico studies have enriched the knowledge of the aerosol transport and deposition (TD) in the lung; however, the absolute TD data in the realistic lung airway are still elusive. Therefore, in this study, a 3-D geometry of the human lung central airway is developed from the computed tomography (CT) images. A CT scan-based modified lung geometry with a smooth surface and nonrealistic Weibel’s lung geometry is also generated. The coal mine exhausted aerosol TD in the upper airway is investigated. The Euler–Lagrange (E-L) method for particle tracking and ANSYS Fluent solver are used to carry out the entire investigation. The effective diameter method is employed to define the shape-specific particles and is integrated with the E-L method. The anatomical shape effects on the deposition patterns are investigated for different deposition parameter. The numerical results illustrated that the airway geometry, particle shape, particle diameter, and breathing flow rates significantly influence the aerosol TD pattern in the upper airway. The present study reports that airway tracheal wall is the new deposition hot spot for the CT-based geometry instead of bifurcating area for the idealized model, which might be helpful for zone-specific drug delivery to the respiratory airways.
Isola, A, Mansor, S, Shafri, HM, Pradhan, B & Mansor, Y 2019, 'Impact of externai forces on the quality of digital elevation model derived from drone technology', International Journal of Geoinformatics, vol. 15, no. 1, pp. 81-91.
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Platform instability is one of the sources of error of Digital Elevation Model (DEM) derived from a low altitude aircraft. This paper examines the influence of atmospheric pressure (AP) on the DEM produced by drone system. To achieve the research objective, an experimental-based ftxed-wing drone platform was set up at the Universiti Putra Malaysia Campus. First, Ground Control Points (GCPs) and CheckPoints (CPs) were established within the study area by a real-time kinematic differential global positioning system. The drone flew seven times at different altitudes over the study area. In the process, an on-board canon digital camera took a series of overlapping photos. The photos were processed with an image-matching algorithm. Then orthorectified the photos using the GCPs. Photo orthorectification entails orientation of aerial photos with respect to the ground control points. It helps to remove distortions that might occur while acquiring or Processing the aerial photographs. In the end, seven DEMs were exported in tiff file format. Analysis of impact of AP on the resulting DEMs was conducted using a proposed model and obtained 0.072m, 0.05m, 0.014m, 0.0lm, 0.004m, 0.003m, and 0.002m for lOOm, I50m, 200m, 250m, 350m, 400m, and 500m altitudes, respectively. To confirm the efficiency of the proposed model, the results were tested using the CPs and their corresponding points on the DEMs and obtained root mean square error of 0.03m, O.OSm, 0.07m, O.lm, 13m, 0.14m, and 0.16m. On a final note, a close look at the validation and impact of AP results unveils a small gap. Hence, suggests that platform instability should be ignored amidst of other externai forces that can influence the performance of drone system.
Israr, J & Indraratna, B 2019, 'Study of Critical Hydraulic Gradients for Seepage-Induced Failures in Granular Soils', Journal of Geotechnical and Geoenvironmental Engineering, vol. 145, no. 7, pp. 04019025-04019025.
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© 2019 American Society of Civil Engineers. This paper reports on a series of laboratory hydraulic tests on a select range of granular soils compacted at relative densities between 0% and 100%. The critical hydraulic gradient at the onset of seepage failure (i.e., heave and suffusion) is considerably smaller than unity for internally unstable (i.e., nonuniform) sand-gravel mixtures due to stress reduction in their finer fraction. For example, stable uniform fine sands have been shown to exhibit heave at hydraulic gradients ≥1.0, whereas sand-gravel mixtures suffer from suffusion at hydraulic gradients ≥1.0. The boundary friction from the cell walls of test equipment would influence the development of heave, while suffusion is controlled by interparticle friction. In this study, the critical hydraulic gradient is modeled theoretically by considering the effects of interparticle and boundary frictions, and stress reduction in the soil. The experimental results from both this and past studies are used to verify the proposed model, which showed good agreement with experimental observations with less than 5% standard error.
Ivanyos, G & Qiao, Y 2019, 'Algorithms Based on *-Algebras, and Their Applications to Isomorphism of Polynomials with One Secret, Group Isomorphism, and Polynomial Identity Testing', SIAM Journal on Computing, vol. 48, no. 3, pp. 926-963.
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© 2019 Society for Industrial and Applied Mathematics. We consider two basic algorithmic problems concerning tuples of (skew-)symmetric matrices. The first problem asks us to decide, given two tuples of (skew-)symmetric matrices (B1,..., Bm) and (C1,..., Cm), whether there exists an invertible matrix A such that for every i ∈ {1,..., m}, AtBiA = Ci. We show that this problem can be solved in randomized polynomial time over finite fields of odd size, the reals, and the complex numbers. The second problem asks us to decide, given a tuple of square matrices (B1,..., Bm), whether there exist invertible matrices A and D, such that for every i ∈ {1,..., m}, ABiD is (skew-)symmetric. We show that this problem can be solved in deterministic polynomial time over fields of characteristic not 2. For both problems we exploit the structure of the underlying ∗-algebras (algebras with an involutive antiautomorphism) and utilize results and methods from the module isomorphism problem. Applications of our results range from multivariate cryptography to group isomorphism and to polynomial identity testing. Specifically, these results imply efficient algorithms for the following problems. (1) Test isomorphism of quadratic forms with one secret over a finite field of odd size. This problem belongs to a family of problems that serves as the security basis of certain authentication schemes proposed by Patarin [J. Patarin, in Advances in Cryptology, EUROCRYPT'96, Springer, Berlin, 1996, pp. 33-48]. (2) Test isomorphism of p-groups of class 2 and exponent p (p odd) with order p in time polynomial in the group order, when the commutator subgroup is of order pO(). (3) Deterministically reveal two families of singularity witnesses caused by the skew-symmetric structure. This represents a natural next step for the polynomial identity testing problem, in the direction set up by the recent resolution of the noncommutative rank problem [A. Garg et al., in Proceedings of the 57th Annu...
Jafari, M, Malekjamshidi, Z, Lu, DD-C & Zhu, J 2019, 'Development of a Fuzzy-Logic-Based Energy Management System for a Multiport Multioperation Mode Residential Smart Microgrid', IEEE Transactions on Power Electronics, vol. 34, no. 4, pp. 3283-3301.
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IEEE In this paper a grid-tied residential smart micro-grid topology is proposed which integrates energies of a PV, a fuel cell and a battery bank to supply the local loads through a combination of electric and magnetic buses. In contrast to multiple-converter based micro-grids with a common electric bus, using a multi-port converter with a common magnetic bus can effectively reduce the number of voltage conversion stages, size and cost of the micro-grid and isolates the conversion ports. The resultant topology utilizes a centralized system level control which leads to the faster and more flexible energy management. The proposed micro-grid is able to operate in multiple grid-connected and off-grid operation modes. A fuzzy controlled energy management unit is designed to select the appropriate operation mode considering both real-time and long-term-predicted data of the system. A mode transition process is designed to smooth the mode variation by using a state transition diagram and bridging modes. To improve the micro-grid operation performance, appropriate control techniques such as synchronized bus-voltage balance are used. A prototype of the proposed micro-grid is developed and experimentally tested for three different energy management scenarios. Energy distribution and energy cost analysis are performed to validate the proposed control method.
Jafari, M, Verma, P, Bodisco, TA, Zare, A, Surawski, NC, Borghesani, P, Stevanovic, S, Guo, Y, Alroe, J, Osuagwu, C, Milic, A, Miljevic, B, Ristovski, ZD & Brown, RJ 2019, 'Multivariate analysis of performance and emission parameters in a diesel engine using biodiesel and oxygenated additive', Energy Conversion and Management, vol. 201, pp. 112183-112183.
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© 2019 Elsevier Ltd Rising concerns over environmental and health issues of internal combustion engines, along with growing energy demands, have motivated investigation into alternative fuels derived from biomasses, such as biodiesel. Investigating engine and exhaust emission behaviour of such alternative fuels is vital in order to assess suitability for further utilisation. Since many parameters are relevant, an effective multivariate analysis tool is required to identify the underlying factors that affect the engine performance and exhaust emissions. This study utilises principal component analysis (PCA) to present a comprehensive correlation of various engine performance and emission parameters in a compression ignition engine using diesel, biodiesel and triacetin. The results show that structure-borne acoustic emission is strongly correlated with engine parameters. Brake specific NOx, primary particle diameter and fringe length increases by increasing the rate of pressure rise. Longer ignition delay and higher engine speeds can increase the nucleation particle emissions. Higher air-fuel equivalence ratio can increase the oxidative potential of the soot by increasing fringe distance and tortuosity. The availability of oxygen in the cylinder, from the intake air or fuel, can increase soot aggregate compactness. Fuel oxygen content reduces particle mass and particle number in the accumulation mode; however, they increase the proportion of oxygenated organic species. PCA results for particle chemical and physical characteristics show that soot particles reactivity increases with fuel oxygen content.
Jahandari, S, Saberian, M, Tao, Z, Mojtahedi, SF, Li, J, Ghasemi, M, Rezvani, SS & Li, W 2019, 'Effects of saturation degrees, freezing-thawing, and curing on geotechnical properties of lime and lime-cement concretes', Cold Regions Science and Technology, vol. 160, pp. 242-251.
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© 2019 Elsevier B.V. There are very limited researches carried out to investigate the influence of saturation degrees, freezing-thawing, and curing times on geotechnical properties of lime concrete (LC) and lime-cement concrete (LCC) due to the capillary action and changes in groundwater table. Subsequently, the primary goal of this research is to investigate the influence of these parameters on mechanical properties of LC and LCC using unconfined compression tests, namely uniaxial compressive strength (UCS), stress-strain behavior, deformability index (I D ), secant modulus (E S ), failure strain, bulk modulus (K), resilient modulus (M R ), brittleness index (I B ), and shear modulus (G). At first, the mechanical and chemical characteristics of the utilized materials were measured. Then, samples were made with an optimal amount of cement, lime, coarse-grained soil, fine-grained soil, and water. The samples were then exposed to saturation points extending from 0 to 100% after 14, 28, 45 and 60 curing days. Then, to consider the effect of amount of saturation on the mechanical properties, UCS tests were performed on some of the samples. Other LCC specimens were exposed to freezing-thawing conditions to consider the effect of this phenomenon on the mechanical properties as well. The results of more than 250 UCS tests demonstrated that the curing times significantly affected the strength of LC and LCC specimens. Moreover, it is not ideal and logical to utilize LC and LC columns at a profundity underneath or near the groundwater level, though it is reasonable to adopt LCC and LCC columns at a profundity beneath or near the groundwater level because of the immaterial effect of degrees of saturation on LCC. In addition, this study showed that extending the curing period and diminishing the saturation degree would increase the strength and mechanical properties of the LCC specimens. The results of freezing-thawing demonstrated a negligible increase in the stre...
Jamaluddin, NAM, Riayatsyah, TMI, Silitonga, AS, Mofijur, M, Shamsuddin, AH, Ong, HC, Mahlia, TMI & Rahman, SMA 2019, 'Techno-Economic Analysis and Physicochemical Properties of Ceiba pentandra as Second-Generation Biodiesel Based on ASTM D6751 and EN 14214', Processes, vol. 7, no. 9, pp. 636-636.
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Processing biodiesel from non-edible sources of feedstock seems to be thriving in recent years. It also has also gathered more attention than in the past, mainly because the biodiesel product is renewable and emits lower pollution compared to fossil fuels. Researchers have started their work on various kinds of biodiesel product, especially from a non-edible feedstock. Non-edible feedstocks such as Ceiba pentandra show great potential in the production of biodiesel, especially in the Southeast Asia region because the plants seem to be abundant in that region. Ceiba pentandra, also known as the Kapok tree, produces hundreds of pods with a length of 15 cm (5.9 in) and diameter 2–5 cm (1–2 in). The pods consist of seeds and fluff in the surrounding areas inside the pod, which itself contains yellowish fibre, a mixture of cellulose and lignin. The seeds of Ceiba pentandra can be used as feedstock for biodiesel production. The study for Ceiba pentandra will involve techno-economic, as well as a sensitivity analysis. Moreover, the study also shows that the techno-economic analysis of a biodiesel processing plant for 50 ktons Ceiba pentandra with a life span of 20 years is around $701 million with 3.7 years of the payback period. Besides that, this study also shows the differences in operating cost and oil conversion yield, which has the least impact on running cost. By improving the conversion processes continuously and by increasing the operational efficiency, the cost of production will decrease. In addition, the study also explains the differences of final price biodiesel and diesel fossil fuel, both showing dissimilar scenarios subsidy and taxation. Biodiesel has a subsidy of $0.10/L and $0.18/L with a total tax exemption of 15%. The value was obtained from the latest subsidy cost and diesel in Malaysia. Finally, further research is needed in order to fully utilize the use of Ceiba pentandra as one of the non-edible sources of biodiesel.
Jamborsalamati, P, Fernandez, E, Moghimi, M, Hossain, MJ, Heidari, A & Lu, J 2019, 'MQTT-Based Resource Allocation of Smart Buildings for Grid Demand Reduction Considering Unreliable Communication Links', IEEE Systems Journal, vol. 13, no. 3, pp. 3304-3315.
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© 2018 IEEE. This paper proposes an autonomous resource allocation system (RAS) for smart neighborhood areas in presence of distributed energy resources and storage systems, with the purpose of grid demand reduction (GDR). Different from the past research on RAS, most of which are not broken down into resource allocation of individual appliances, do not address practical implementation of RAS with communication systems, and do not consider realistic case scenarios with network latency and communication link failure, this paper presents an improved appliance-level RAS developed in four operational modes with a designed bidding mechanism to exchange energy among neighborhood members through a common storage facility, a hierarchical cloud-based two-layered communication architecture founded on message queuing telemetry transport protocol to implement local and global messaging required for the proposed RAS, and realistic case scenarios by considering data from a real-world residential area and utilizing a virtual wide area network emulator to emulate characteristics of a real network in order to investigate the effects of network latency or communication link failure on the implemented RAS. From the results of diverse scenarios, it could be observed that the proposed system performs effectively to achieve GDR, even if the communication system fails partially in the smart community under test.
Jamil, S, Loganathan, P, Kandasamy, J, Listowski, A, Khourshed, C, Naidu, R & Vigneswaran, S 2019, 'Removal of dissolved organic matter fractions from reverse osmosis concentrate: Comparing granular activated carbon and ion exchange resin adsorbents', Journal of Environmental Chemical Engineering, vol. 7, no. 3, pp. 103126-103126.
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© 2019 Elsevier Ltd. All rights reserved. Reverse osmosis (RO) generates a concentrate (ROC) containing dangerous levels of pollutants including dissolved organic carbon (DOC). Adsorption experiments were conducted to study the effectiveness of removing DOC and its fractions from ROCs produced in a water reclamation plant using three adsorbents tested individually and in sequential combination. The ROCs had 23-42 mg/L DOC which contained 83-90% hydrophilics. These hydrophilics comprised 72-76% humics, 2-3% biopolymers, 5-7% building blocks, and 16-18% low molecular weight neutrals. Granular activated carbon (GAC) removed a larger amount of DOC than two strong base anion exchange resins (Purolite A502PS, Purolite A860S). In both batch and column experiments, the adsorptive removal of the hydrophobic fraction was greater for GAC than for the Purolites. Humics present in hydrophilic fraction was completely removed by Purolites but only partially by GAC. In the sequential adsorption batch experiment, GAC followed by Purolite treatment removed more hydrophobics, however, Purolite followed by GAC removed more humics. Almost 100% of humics was removed for all doses of adsorbents when Purolite served as the first treatment. It is concluded that the order of adsorbent use for effectively treating ROC depends on the target DOC fraction intended to be removed.
Jamil, S, Loganathan, P, Listowski, A, Kandasamy, J, Khourshed, C & Vigneswaran, S 2019, 'Simultaneous removal of natural organic matter and micro-organic pollutants from reverse osmosis concentrate using granular activated carbon', Water Research, vol. 155, pp. 106-114.
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© 2019 Elsevier Ltd Although reverse osmosis produces high quality reusable water from wastewater the rejected concentrate (ROC) poses potentially serious health hazards to non-target species. This is especially the case when it is disposed into aquatic environments due to the presence of high concentrations of dissolved natural organics, micro-organic pollutants (MOPs) and other pollutants. In batch and column studies we found that granular activated carbon (GAC) was very effective in simultaneously removing dissolved organic carbon (DOC) and 18 MOPs from ROC. The amounts of all DOC fractions adsorbed (0.01–3 mg/g) were much higher than those of the MOPs (0.01–2.5 μg/g) mainly because ROC contained larger concentrations of DOC fractions than MOPs. However, the partition coefficient which is a measure of the adsorbability was higher for most of the MOPs (0.21–21.6 L/g) than for the DOC fractions (0.01–0.45 L/g). The amount of DOC fraction adsorbed was in the order: humics > low molecular weights > building blocks > biopolymers (following mostly their concentrations in ROC). The partition coefficient was in the order: low molecular weigth nuetrals > humics > building blocks > biopolymers. The MOPs were classified into four groups based on their hydrophobicity (log Kow) and charge. The four positively charged MOPs with high hydrophobicity had the highest amounts adsorbed and partition coefficient, with 95–100% removal in the GAC column. The MOPs that are negatively charged, regardless of their hydrophobicity, had the lowest amounts adsorbed and partition coefficient with 73–94% removal.
Jamilu Bala Ahmed, II, Pradhan, B, Mansor, S, Tongjura, JDC & Yusuf, B 2019, 'Multi-criteria evaluation of suitable sites for termite mounds construction in a tropical lowland', CATENA, vol. 178, pp. 359-371.
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© 2019 Elsevier B.V. Termite mounds influence ecosystem heterogeneity and contribute to the stabilization of the system under global change. A number of environmental factors influence the distribution, height, diameter and designs of termite mounds but these factors are not only poorly understood, they cannot be extrapolated for everywhere. In this study, we employed a ground based survey and Geographical Information System (GIS) technique to map 156 km 2 study area in Keffi, Nigeria. The aims were to (1) estimate the density and area covered by termite mounds, (2) sample and identify species types and how they are distributed, and (3) use five environmental factors (elevation, geology, surface water drainage, land use/land cover and static water level) to model suitable sites for mounds construction. A total of 361 mounds were mapped representing a density of about 0.8 mounds ha −1 and covering only about 0.31% of the studied area. Next, the effect of the five chosen environmental factors on the geographic distribution, life status, height and diameter of mounds and species diversity were analysed and their relationships plotted in pairwise comparison matrices using the Saaty's Analytical Hierarchy Process. Normalized rates for classes in each factor and corresponding weights were computed and aggregated using the Weighted Linear Combination method. The result depicted that moderate to low elevation (270–330 m amsl), rock cover types that are more susceptible to weathering (schist), cultivated areas and shallow water table zones are most favourable for termites to build mounds. The result obtained in this study shows a promising correlation between the environmental factors and termite mounds distribution. The proposed model can easily be replicated in a different but similar multi-land use and rock cover types.
Jamshaid, M, Masjuki, HH, Kalam, MA, Zulkifli, NWM, Arslan, A, Alwi, A, Khuong, LS, Alabdulkarem, A & Syahir, AZ 2019, 'Production optimization and tribological characteristics of cottonseed oil methyl ester', Journal of Cleaner Production, vol. 209, pp. 62-73.
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Jamshidi Chenari, R, Alaie, R & Fatahi, B 2019, 'Constrained Compression Models for Tire-Derived Aggregate-Sand Mixtures Using Enhanced Large Scale Oedometer Testing Apparatus', Geotechnical and Geological Engineering, vol. 37, no. 4, pp. 2591-2610.
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© 2018, Springer Nature Switzerland AG. Tire derived aggregates have recently been in wide use both in industry and engineering applications depending on the size and the application sought. Five different contents of tire derived aggregates (TDA) were mixed with sand thoroughly to ensure homogeneity. A series of large scale oedometer experiments were conducted to investigate the compressibility properties of the mixtures. Tire shreds content, TDA aspect ratio, skeletal relative density and overburden pressure are studied parameters. Constrained deformation modulus and coefficient of earth pressure at rest are measured parameters. All tests were conducted at seven overburden pressure levels. It was concluded that deformability of TDA-sand mixture increases with soft inclusion. Overburden pressure and skeletal relative density are also important parameters which render more rigidity and less lateral earth pressure coefficient accordingly. TDA size or aspect ratio was shown to have minor effect at least for the constrained strain conditions encountered in current study. An EPR-based parametric study and also sensitivity analyses based on cosine amplitude method revealed quantitative evaluation of the relative importance of each input parameter in varying deformation and lateral earth pressure coefficient as the outputs.
Javaheri, F, Kheshti, Z, Ghasemi, S & Altaee, A 2019, 'Enhancement of Cd2+ removal from aqueous solution by multifunctional mesoporous silica: Equilibrium isotherms and kinetics study', Separation and Purification Technology, vol. 224, pp. 199-208.
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© 2019 In this work, a novel amino-functionalized mesoporous microsphere was synthesized to remove cadmium ions from water. The Fe3O4@SiO2@m-SiO2–NH2 micro-spheres were successfully prepared via a facile two-stage process by coating of the as-synthesized magnetic cores with a silica shell followed by increasing the porosity of the structure using a cationic surfactant as structure-directing agents. The template removal from the structure has been performed following the method of solvent extraction and methanol-enhanced supercritical fluid CO2 (SCF-CO2)extraction. This novel approach provides the multifunctional microspheres with a high surface area, which improves the adsorption capacity of adsorbent. Characterization of the as-synthesized adsorbent were analytically determined showing that as-prepared adsorbent has a significant surface area of 637.38 m2 g−1. The kinetic data agreed with pseudo-second-order model and Langmuir isotherm. The maximum adsorption capacity of the synthesized adsorbent was about 884.9 mg g−1, and can be easily separated from solution under an external magnetic field. The synthesized microspheres were recycled using HCl and cadmium removal was over 92% after 6 cycles, which confirms the chemical stability and reusability of the manufactured particles.
Javdanian, H & Pradhan, B 2019, 'Assessment of earthquake-induced slope deformation of earth dams using soft computing techniques', Landslides, vol. 16, no. 1, pp. 91-103.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Evaluating behavior of earth dams under dynamic loads is one of the most important problems associated with the initial design of such massive structures. This study focuses on prediction of deformation of earth dams due to earthquake shaking. A total number of 103 real cases of deformation in earth dams due to earthquakes that has occurred over the past years were gathered and analyzed. Using soft computing methods, including feed-forward back-propagation and radial basis function based neural networks, two models were developed to predict slope deformations in earth dams under variant earthquake shaking. Earthquake magnitude (Mw), yield acceleration ratio (ay/amax), and fundamental period ratio (Td/Tp) were considered as the most important factors contributing to the level of deformation in earth dams. Subsequently, a sensitivity analysis was conducted to assess the performance of the proposed model under various conditions. Finally, the accuracy of the developed soft computing model was compared with the conventional relationships and models to estimate seismic deformations of earth dams. The results demonstrate that the developed neural model can provide accurate predictions in comparison to the available practical charts and recommendations.
Jayasinghe, J, Saraereh, O, Khokle, R & Esselle, K 2019, 'Design and analysis of m‐segment fractal boundary antennas', Microwave and Optical Technology Letters, vol. 61, no. 9, pp. 2119-2125.
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AbstractThis article investigates the resonant behavior of a novel family of fractal boundary antennas at the fundamental mode of operation. The miniaturization patterns over iterations as well as over the number of segments on the boundary have been studied by simulating the fractal antennas in High Frequency Structure Simulator (HFSS). The antennas are fed by a 50‐Ω coaxial probe, which is placed at the best position on the patch, with impedance matching and S11 < −10 dB. Analysis of the resonant frequency with respect to the square‐shaped fractal generator resulted in curve‐fit expressions that vary with a single variable of either iteration or number of segments on the boundary. The derived equations are independent from the substrate thickness. They are useful to design miniature patch antennas within a specified area in order to resonate at a desired frequency by simply changing the boundary or the fractal iteration.
Jayasuriya, C, Indraratna, B & Ngoc Ngo, T 2019, 'Experimental study to examine the role of under sleeper pads for improved performance of ballast under cyclic loading', Transportation Geotechnics, vol. 19, pp. 61-73.
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© 2019 Elsevier Ltd The degradation and deformation of ballast critically affect the track geometry, safety, and passenger comfort. The increase in axle loads and train speed increases the stress applied on the ballast and exacerbates the rate of ballast degradation. This situation is more critical when tracks are built on stiff subgrades (e.g. bridges, tunnels and crossings), hence the use of energy absorbing (damping) layers in track substructure is a countermeasure to minimize track damage. In this study, a series of large-scale laboratory tests using the track process simulation testing apparatus (TPSA) is carried out to assess the performance of under sleeper pads (USP) to reduce ballast degradation and to decrease permanent deformation. When placed beneath the sleeper, the energy absorbing nature of USP reduces the energy transferred to the ballast and other substructure components. Subsequently, the ballast layer experiences less deformation and degradation. Innovative tactile surface sensors (matrix-based) are used to measure the pressure and contact area between sleeper and ballast. The measured data show that an increase in contact area between sleeper and ballast decreases the stress applied on ballast, and thus a reduction in ballast breakage and corresponding reduced ballast deformation can be achieved. Furthermore, the influence of the USP stiffness is examined and the measured data offer an insightful understanding of the role of USP for given track and loading conditions in terms of energy dissipation and reduced ballast deformation.
Jayawardana, D, Liyanapathirana, R & Zhu, X 2019, 'RFID-Based Wireless Multi-Sensory System for Simultaneous Dynamic Acceleration and Strain Measurements of Civil Infrastructure', IEEE Sensors Journal, vol. 19, no. 24, pp. 12389-12397.
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© 2001-2012 IEEE. In this paper, we develop a radio frequency identification (RFID)-based wireless multi-sensory infrastructure health monitoring (IHM) system that can simultaneously measure dynamic acceleration and strain. The system consists of a novel multi-sensor integrated semi-passive ultra-high frequency (UHF) tag antenna that can be mounted on civil infrastructure elements; even made out of metal. The system is capable of measuring 3-axis dynamic acceleration and strain with spectral bandwidths of 40 Hz and 26.5 Hz, respectively. The natural frequency determination of infrastructure by the dynamic acceleration and strain measurements of the proposed system is accurate to 60 mHz. Benchmarking of the RFID-based wireless multi-sensory system is provided by comprehensive comparison of the results with measurements from a commercial wireless strain measurement system. The proposed system has 30 mHz natural frequency determination error when compared with dynamic strain measurement from the commercial system.
Jeevanantham, AK, Nanthagopal, K, Ashok, B, Al-Muhtaseb, AH, Thiyagarajan, S, Geo, VE, Ong, HC & Samuel, KJ 2019, 'Impact of addition of two ether additives with high speed diesel- Calophyllum Inophyllum biodiesel blends on NOx reduction in CI engine', Energy, vol. 185, pp. 39-54.
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Jeong, SY, Chang, SW, Ngo, HH, Guo, W, Nghiem, LD, Banu, JR, Jeon, B-H & Nguyen, DD 2019, 'Influence of thermal hydrolysis pretreatment on physicochemical properties and anaerobic biodegradability of waste activated sludge with different solids content', Waste Management, vol. 85, pp. 214-221.
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© 2018 Elsevier Ltd The influence of thermal hydrolysis pretreatment (THP) on physicochemical properties (pH, total solids, volatile solids, chemical oxygen demand, total nitrogen, ammonium nitrogen, volatile fatty acids, viscosity, and cell morphology) and anaerobic biodegradability of highly concentrated waste activated sludge (WAS) with TS content ranging from 1 to 7% was evaluated at different temperatures ranging from 100 to 220 °C. The biomethane potential (BMP) of the WAS was systematically analyzed and evaluated. Images of its cellular structure were also analyzed. The results indicated that THP is a useful method for solubilizing volatile solids and enhancing CH 4 production regardless of the TS content of the WAS feed. The ultimate CH 4 production determined from the BMP analysis was 313–348 L CH 4 /kg VS (72.6–74.1% CH 4 ) at the optimum THP temperature of 180 °C. The results showed that THP could improve both the capacity and efficiency of anaerobic digestion, even at a high TS content, and could achieve the dual purpose of sludge reduction and higher energy recovery.
JHANG, J-Y, LIN, C-J, YOUNG, K-Y & LIN, C-T 2019, 'A hybrid of fuzzy theory and quadratic function for estimating and refining transmission map', TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, vol. 27, no. 5, pp. 3791-3803.
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© TÜBİTAK In photographs captured in outdoor environments, particles in the air cause light attenuation and degrade image quality. This effect is especially obvious in hazy environments. In this study, a fuzzy theory is proposed to estimate the transmission map of a single image. To overcome the problem of oversaturation in dehazed images, a quadratic-function-based method is proposed to refine the transmission map. In addition, the color vector of the atmospheric light is estimated using the top 1% of the brightest light area. Finally, the dehazed image is reconstructed using the transmission map and the estimated atmospheric light. Experimental results demonstrate that the proposed hybrid method performs better than the other existing methods in terms of color oversaturation, visibility, and quantitative evaluation.
Ji, L-Y, Qin, P-Y, Li, J-Y & Zhang, L-X 2019, '1-D Electronic Beam-Steering Partially Reflective Surface Antenna', IEEE Access, vol. 7, pp. 115959-115965.
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A 1-D electronic beam-steering partially reflective surface (PRS) antenna using a new reconfigurable PRS unit cell is proposed in this paper. The proposed work addresses the challenge to achieve a large beam steering angle with small gain variation and a small number of active/lumped elements by using a reconfigurable PRS superstrate only. The PRS unit cell consists of two back-to-back T-shaped strips with one PIN diode inserted between them and a pair of trapezoid patches (a rectangular patch and a pair of triangle parasitic patches). Beam steering is achieved by controlling the different states of PIN diodes. Thanks to the trapezoid patches, the proposed unit cell can generate a larger phase difference between different states, thereby leading to a larger beam steering angle. Furthermore, due to the addition of more degrees of freedom in the proposed unit cell, the phase difference can be easily manipulated. Moreover, since the T-shaped strips in each unit cell is connected with adjacent ones, the biasing network is very simple without needing a large number of lumped elements and dc biasing lines. The beam steering property is analyzed by using phased array theory. An antenna prototype with a main beam direction towards 0°, -18° and 18° operating at 5.5 GHz in the H-plane is fabricated and measured. Good agreement between the predicted simulation and measurement results for the input reflection coefficients and radiation patterns is achieved, which validates the feasibility of the design. The measured realized gains are over 11 dBi for all states with a 0.8 dBi gain variation.
Ji, Z 2019, 'Classical verification of quantum proofs', Theory of Computing, vol. 15, no. 1, pp. 1-42.
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© 2019 Zhengfeng Ji. We present a classical interactive protocol that checks the validity of a quantum witness state for the local Hamiltonian problem. It follows from this protocol that approximating the nonlocal value of a multi-player one-round game to inverse polynomial precision is QMA-hard. Our result makes a connection between the theory of QMA-completeness and Hamiltonian complexity on one hand and the study of nonlocal games and Bell inequalities on the other.
Jia, H, Feng, F, Wang, J, Ngo, H-H, Guo, W & Zhang, H 2019, 'On line monitoring local fouling behavior of membrane filtration process by in situ hydrodynamic and electrical measurements', Journal of Membrane Science, vol. 589, pp. 117245-117245.
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© 2019 The hollow fiber ultrafiltration (UF) membrane has been widely applied in the water treatment industry, however, the membrane fouling is the core reason and limiting factor in terms of its industrial application. In the constant flux process, hollow fiber membranes (HFM) non-uniform fouling varies along the axis direction, which is the basic mechanism of HFM fouling. In this paper, the local membrane fouling behaviors and verities are investigated using electrical impedance (EI) and zeta potential (ZP) to capture the feedback signals of membrane fouling behaviors. The results are then, integrated with Hermia's model and an equivalent circuit model. As the fitting results show, both the EI and ZP can be employed as indicators of different membrane fouling states. This work defines the different stages of membrane fouling depending on the alternating relationship between EI and ZP in the membrane filtration process. Furthermore, the behavior of cake layer compaction is defined from the perspective of the membrane fouling mechanism. Therefore, this study provides an effective means for accurate identification of membrane fouling behavior. In addition, the EI and ZP exhibit great potential to identify the fouling distributions and proceedings in HFM fouling. Doing so successfully confirms that the characteristics of non-uniform fouling of HFM are reflected in the spatiotemporal difference of the fouling process.
Jia, M, Srinivasan, RS, Ries, R, Weyer, N & Bharathy, G 2019, 'A systematic development and validation approach to a novel agent-based modeling of occupant behaviors in commercial buildings', Energy and Buildings, vol. 199, pp. 352-367.
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Jia, Y, Tang, L, Xu, B & Zhang, S 2019, 'Crack Detection in Concrete Parts Using Vibrothermography', Journal of Nondestructive Evaluation, vol. 38, no. 1.
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Jia, Z, Xiu, P, Roohani-Esfahani, S-I, Zreiqat, H, Xiong, P, Zhou, W, Yan, J, Cheng, Y & Zheng, Y 2019, 'Triple-Bioinspired Burying/Crosslinking Interfacial Coassembly Strategy for Layer-by-Layer Construction of Robust Functional Bioceramic Self-Coatings for Osteointegration Applications', ACS Applied Materials & Interfaces, vol. 11, no. 4, pp. 4447-4469.
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Jian, S, Pang, G, Cao, L, Lu, K & Gao, H 2019, 'CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 5, pp. 853-866.
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IEEE The representation of categorical data with hierarchical coupling relationships (i.e., value to value cluster interactions) is very critical yet challenging for capturing data characteristics in learning tasks. This paper proposes a novel and flexible coupled unsupervised categorical data representation (CURE) framework which not only captures the hierarchical couplings but also is flexible to be instantiated for contrastive learning tasks. Based on two complementary value coupling functions, CURE is instantiated into two instances: the coupled data embedding (CDE) for clustering and the coupled outlier scoring of high-dimensional data (COSH) for outlier detection, by customizing the ways of value clustering and coupling learning between value clusters. CDE embeds categorical data into a new space in which features are independent and semantics are rich. COSH represents data with an outlying vector to capture complex outlying behaviors of objects in high-dimensional data. Substantial experiments show that CDE significantly outperforms three popular unsupervised embedding methods and three state-of-the-art similarity-based representation methods, and COSH performs significantly better than five state-of-the-art outlier detection methods on high-dimensional data sets. CDE and COSH are scalable and stable, linear to data size and quadratic to the number of features, and are insensitive to their parameters.
Jiang, J, Gao, L, Jin, J, Luan, TH, Yu, S, Xiang, Y & Garg, S 2019, 'Sustainability Analysis for Fog Nodes With Renewable Energy Supplies', IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6725-6735.
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© 2014 IEEE. There is a growing interest in the use of renewable energy sources to power fog networks in order to mitigate the detrimental effects of conventional energy production. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a fog node powered by renewable energy sources is studied. We develop a generic analytical model to study the energy sustainability of fog nodes powered by renewable energy sources, by generalizing the leaky bucket model to shape and police traffic source for rate-based congestion control in high-speed fog networks. Based on the closed-form solutions of energy buffer analysis, i.e., the energy depletion probability and mean energy length, we study the energy sustainability in two special but real-happening scenarios. The experimental results show that with proper design the leaky bucket model effectively reflects the energy sustainability of data traffic in fog networks. Numerical results also reveal that the model performance is sensitive to certain traffic source characteristics in fog networks.
Jiang, J, Kim, DI, Dorji, P, Phuntsho, S, Hong, S & Shon, HK 2019, 'Phosphorus removal mechanisms from domestic wastewater by membrane capacitive deionization and system optimization for enhanced phosphate removal', Process Safety and Environmental Protection, vol. 126, pp. 44-52.
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© 2019 Institution of Chemical Engineers Membrane capacitive deionization (MCDI) is an emerging technology for effective removal of charged pollutants from the water sources including domestic wastewater. In this work, a lab-scale MCDI system was employed to investigate its feasibility for effective phosphorus removal from domestic wastewater. The effect of phosphate equilibrium reactions on the ion sorption behaviour was studied in sodium phosphate buffer solution at typical pH range maintained in a real domestic raw wastewater effluent (between 6.5 and 8.5). The results demonstrated that phosphate equilibrium system has positive impact on the degree of inorganic phosphorus (P) adsorption capacity in aqueous solution. In addition, the ion selectivity of P over other co-existing anions (Cl-, SO42-) were experimentally studied using a synthetic wastewater solution. And it was found that the preferential electrosorption sequence of the competitive anions is: Cl-> SO42- > P, while the initial ion concentration order in the synthetic feed solution is: Cl- 1.90 mM> P (0.40 mM) > SO42- (0.32 mM). The experiments with diverse operating conditions revealed that the optimal adsorption of inorganic phosphorus over chloride and sulphate can be achieved in some extent with slower flow rates and higher applied potentials (less than 1.23 V).
Jiang, J, Zhang, H, Pi, D & Dai, C 2019, 'A novel multi-module neural network system for imbalanced heartbeats classification', Expert Systems with Applications: X, vol. 1, pp. 100003-100003.
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Jiang, P, Wang, B, Li, H & Lu, H 2019, 'Modeling for chaotic time series based on linear and nonlinear framework: Application to wind speed forecasting', Energy, vol. 173, pp. 468-482.
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© 2019 Elsevier Ltd Wind-speed forecasting plays a crucial part in improving the operational efficiency of wind power generation. However, accurate forecasts are difficult owing to the uncertainty of the wind speed. Although numerous investigations of wind-speed forecasting have been performed, many of the previous studies used wind-speed data directly to make forecasts, which were rarely based on the structural characteristics of the data. Therefore, in this study, a hybrid linear-nonlinear modeling method based on the chaos theory was successfully employed to capture the linear and nonlinear factors hidden in chaotic time series. Before the forecast, the noise in the data was removed using a decomposition algorithm. Then, through the phase-space reconstruction, the one-dimensional time series were extended to the multi-dimensional space to determine the utilization form of the data. Finally, Holt's exponential smoothing based on the firefly optimization algorithm and support vector regression were combined to predict the wind speed. The experimental results show that the proposed model is not only better than the comparison models but also has great application potential in the wind power generation system.
Jiang, S, Li, K & Da Xu, RY 2019, 'Relative Pairwise Relationship Constrained Non-Negative Matrix Factorisation', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 8, pp. 1595-1609.
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IEEE Non-negative Matrix Factorisation (NMF) has been extensively used in machine learning and data analytics applications. Most existing variations of NMF only consider how each row/column vector of factorised matrices should be shaped, and ignore the relationship among pairwise rows or columns. In many cases, such pairwise relationship enables better factorisation, for example, image clustering and recommender systems. In this paper, we propose an algorithm named, Relative Pairwise Relationship constrained Non-negative Matrix Factorisation (RPR-NMF), which places constraints over relative pairwise distances amongst features by imposing penalties in a triplet form. Two distance measures, squared Euclidean distance and Symmetric divergence, are used, and exponential and hinge loss penalties are adopted for the two measures respectively. It is well known that the so-called "multiplicative update rules" result in a much faster convergence than gradient descend for matrix factorisation. However, applying such update rules to RPR-NMF and also proving its convergence is not straightforward. Thus, we use reasonable approximations to relax the complexity brought by the penalties, which are practically verified. Experiments on both synthetic datasets and real datasets demonstrate that our algorithms have advantages on gaining close approximation, satisfying a high proportion of expected constraints, and achieving superior performance compared with other algorithms.
Jiang, X, Pan, S, Long, G, Xiong, F, Jiang, J & Zhang, C 2019, 'Cost-Sensitive Parallel Learning Framework for Insurance Intelligence Operation', IEEE Transactions on Industrial Electronics, vol. 66, no. 12, pp. 9713-9723.
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IEEE Recent advancements in artificial intelligence (AI) are providing the insurance industry with new opportunities to create tailored solutions and services based on newfound knowledge of consumers, and the execution of enhanced operations and business functions. However, insurance data is heterogeneous, and imbalanced class distribution with low frequency and high dimensions presents four major challenges to machine learning in real-world business. Traditional machine learning algorithms can typically only be applied to standard data sets, which are normally homogeneous and balanced. In this paper, we focus on an efficient cost-sensitive parallel learning framework (CPLF) to enhance insurance operations with a deep learning approach that does not require pre-processing. Our approach comprises a novel, unified, end-to-end cost-sensitive parallel neural network that learns real-world heterogeneous data. A specifically-designed cost-sensitive matrix then automatically generates a robust model for learning minority classifications, and the parameters of both the cost-sensitive matrix and the hybrid neural network are alternately but jointly optimized during training. We also study the CPLF-based architecture for a real-world insurance intelligence operation system, and demonstrate fraud detection experiments on this system. The results of comparative experiments on real-world insurance data sets reflecting actual business cases demonstrate the effectiveness of our design.
Jiang, Y & Nimbalkar, S 2019, 'Finite Element Modeling of Ballasted Rail Track Capturing Effects of Geosynthetic Inclusions', Frontiers in Built Environment, vol. 5.
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© 2019 Jiang and Nimbalkar. This paper presents a two dimensional finite element (FE) approach to investigating beneficial aspects of geogrids in the railway track. The influences of different factors including the subgrade strength, the geogrid stiffness, the placement depth of geogrid, the effective width of geogrid, the strength of ballast-geogrid interface and the combination of double geogrid layers were investigated under the monotonic loading. The results indicated the role of geogrid reinforcement is more pronounced over the weak compressible subgrade. A stiffer geogrid reduces ballast settlement and produces a more uniform stress distribution along a track. The placement location of a geogrid is suggested at the ballast-sub-ballast interface to achieve better reinforcement results. Although the width of a geogrid layer should be sufficient to cover an entire loaded area, excessive width does not guarantee additional benefits. Higher interface strength between a ballast and a geogrid is beneficial for effective reinforcement. Increasing the number of geogrid layers is an effective way to reinforce the ballast over weak subgrades. The results of the limited cyclic FE simulations revealed the consistency of the reinforcement effect of the geogrids under monotonic and cyclic loads.
Jiao, S & Liu, RP 2019, 'A survey on physical authentication methods for smart objects in IoT ecosystem', Internet of Things, vol. 6, pp. 100043-100043.
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Jiao, S, Tan, X, Sui, Y & You, F 2019, 'Muscle fibre type composition in the lateral muscle of olive flounder Paralichthys olivaceus', Acta Histochemica, vol. 121, no. 1, pp. 1-6.
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Jin, X, Gu, F, Niu, J, Yu, S & Ouyang, Z 2019, 'HRCal: An effective calibration system for heart rate detection during exercising', Journal of Network and Computer Applications, vol. 136, pp. 1-10.
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© 2019 Elsevier Ltd Heart rate directly reflects heart health and the detection of heart rate contributes to finding the abnormal performance of heart activity in a timely manner. Nevertheless, there is scope for a significant improvement in current heart rate detection systems and devices, especially during strenuous exercise. Motion compensation algorithm is used in most current systems to improve the monitoring accuracy, but it is limited by sensors and its performance is not satisfactory. In this paper, we propose HRCal, a novel Heart Rate Calibration System, which establishes a Long Short-Term Memory (LSTM) model to calibrate the detection of heart rate based on multisensor data fusion. Specifically, HRCal utilizes the built-in sensors (e.g. accelerometer, gyroscope and magnetometer) from smart devices (smartphones and sports watches) to collect users' motion data. Then a LSTM model is proposed and trained with different features to improve the accuracy and reliability of heart rate detection. In addition, we also elaborately design an evaluation scheme to compare HRCal with other approaches. We have fully implemented HRCal on Android platform and the experimental results (8 subjects) demonstrate that HRCal has a remarkable effect on common sports watches, to improve their accuracy of heart rate detection in physical training (up to 12.5% for moto 360 and 6.8% for Mio Alpha).
Jin, Y, Wu, H, Merigó, JM & Peng, B 2019, 'Generalized Hamacher Aggregation Operators for Intuitionistic Uncertain Linguistic Sets: Multiple Attribute Group Decision Making Methods', Information, vol. 10, no. 6, pp. 206-206.
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In this paper, we consider multiple attribute group decision making (MAGDM) problems in which the attribute values take the form of intuitionistic uncertain linguistic variables. Based on Hamacher operations, we developed several Hamacher aggregation operators, which generalize the arithmetic aggregation operators and geometric aggregation operators, and extend the algebraic aggregation operators and Einstein aggregation operators. A number of special cases for the two operators with respect to the parameters are discussed in detail. Also, we developed an intuitionistic uncertain linguistic generalized Hamacher hybrid weighted average operator to reflect the importance degrees of both the given intuitionistic uncertain linguistic variables and their ordered positions. Based on the generalized Hamacher aggregation operator, we propose a method for MAGDM for intuitionistic uncertain linguistic sets. Finally, a numerical example and comparative analysis with related decision making methods are provided to illustrate the practicality and feasibility of the proposed method.
Jo, Y, Johir, MAH, Cho, Y, Naidu, G, Rice, SA, McDougald, D, Kandasamy, J, Vigneswaran, S & Sun, S 2019, 'A comparative study on nitric oxide and hypochlorite as a membrane cleaning agent to minimise biofilm growth in a membrane bioreactor (MBR) process', Biochemical Engineering Journal, vol. 148, pp. 9-15.
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© 2019 Elsevier B.V. Reverse osmosis concentrates (ROC) produced from water reclamation plants can threaten the environment if it is not appropriately treated before discharge. A membrane bioreactor (MBR) process to treat ROC was used in this project. In an MBR, fouling is an essential and inevitable phenomenon which leads to higher operational and capital costs. A comparative study on chemical cleaning, such as sodium hypochlorite (NaOCl) and nitric oxide (NO), was experimentally evaluated together with the influence of filtration flux. Exposure to a low concentration of NO reduced biofilms in an MBR system. NO treatment delayed the formation of new biofilm biomass on the membrane. NO also showed good performance in reducing membrane fouling and had no adverse effect on activated sludge and the environment. In MBR, the bacterial community was dominated by Proteobacteria (61%), with Alpha and Beta-proteobacteria representing approximately 54% of the community. After NO treatment, the relative abundance of the Proteobacteria decreased to 44%, and this was also reflected in a reduction in Alpha and Beta-proteobacteria, to 30% and 5% respectively. Thus, NO treatment resulted in the decrease of the relative biofilms associated with reduced MBR performance.
Ju, M, Ding, C, Guo, YJ & Zhang, D 2019, 'Remote Sensing Image Haze Removal Using Gamma-Correction-Based Dehazing Model', IEEE Access, vol. 7, pp. 5250-5261.
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© 2013 IEEE. Haze is evident in most remote sensing (RS) images, particularly for the RS scenes captured in inclement weather, which severely hinders image interpretation. In this paper, two simple yet effective visibility restoration formulas are proposed for RGB-channel RS (RRS) images and multi-spectral RS (MSRS) images, respectively. More specifically, a robust gamma-correction-based dehazing model (RGDM) is first defined, which can better address the non-uniform illumination problem in hazy images. Then, the scene albedo restoration formula (SARF) used for the RRS images is obtained by imposing the existing prior knowledge on this RGDM, which enables us to simultaneously eliminate the interferences of haze and non-uniform illumination. In subsequence, according to Rayleigh's law, an expanded restoration formula (E-SARF) is further developed for MSRS data. Using the proposed E-SARF, the spatially varying haze in each band can be thoroughly removed without using any extra information. The experiments are conducted on the challenging RRS and MSRS images, including images with non-uniform illumination, non-uniform haze distribution, and heavy haze. The results reveal that the SARF and the E-SARF are superior to most other state-of-the-art techniques in terms of both the recover quality and the implementation efficiency.
Ju, M, Ding, C, Zhang, D & Guo, YJ 2019, 'BDPK: Bayesian Dehazing Using Prior Knowledge', IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 8, pp. 2349-2362.
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IEEE Atmospheric scattering model (ASM) has been widely used in hazy image restoration. However, the recovered albedo might deviate from the real scene once the input hazy image cannot fully satisfy the model’s assumptions such as the homogeneous atmosphere and even illumination. In this paper, we break these limitations and redefine a more reliable atmospheric scattering model (RASM) that is extremely adaptable for various practical scenarios. Benefiting from RASM, a simple yet effective Bayesian dehazing algorithm (BDPK) is further proposed based on the prior knowledge. Our strategy is to convert the single image dehazing problem into a maximum a-posteriori probability (MAP) one that can be approximated as an optimization function using the existing priori constraints. To efficiently solve this optimization function, the alternating minimizing technique (AMT) is introduced, which enables us to directly restore the scene albedo. Experiments on a number of challenging images reveal the power of BDPK on removing haze and verify its superiority over several state-of-the-art techniques in terms of quality and efficiency.
Jung, JY, Kang, P-W, Kim, E, Chacon, D, Beck, D & McNevin, D 2019, 'Ancestry informative markers (AIMs) for Korean and other East Asian and South East Asian populations', International Journal of Legal Medicine, vol. 133, no. 6, pp. 1711-1719.
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Inference of ancestry from biological evidence can provide investigative information, especially for unknown DNA donors. Although tools for predicting ancestry have been developing, ancestry research focusing on populations relevant for South Korea is not common and markers are seldom chosen specifically to differentiate Koreans from other East Asian and South East Asian populations. Here, we report ancestry informative markers (AIMs) for distinguishing six East/South East Asian regional populations: China, Japan, Indonesia, Philippines, South Korea and Thailand. Individual genotypes from these six populations were available in PanSNPdb: The HUGO Pan-Asian SNP Database. To select AIMs, we calculated four population divergence metrics for each SNP: Nei's FST, Rosenberg's Informativeness (In), the average absolute allele frequency difference between populations (δFmean) and the maximum allele frequency difference between populations (δFmax). Based on these values, we selected 100 single nucleotide polymorphisms (SNPs) for distinguishing the six populations, 13 of which exhibited large allele frequency differences between Koreans and non-Koreans. To assess the performance of the AIMs, we performed principal coordinates analysis (PCoA) on the individuals from all six populations and inferred ancestral population clusters using the STRUCTURE program. In conclusion, we found that the selected AIMs can be applied to distinguish the six East/South East Asian groups and we suggest the markers in this study will be helpful to establish ancestry panels for Korea and neighbouring populations.
Kacprzyk, J, Yager, RR & Merigo, JM 2019, 'Towards Human-Centric Aggregation via Ordered Weighted Aggregation Operators and Linguistic Data Summaries: A New Perspective on Zadeh's Inspirations', IEEE Computational Intelligence Magazine, vol. 14, no. 1, pp. 16-30.
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© 2005-2012 IEEE. This work presents a new perspective on how Zadeh's ideas related to fuzzy logic and computing with words have influenced the crucial issue of information aggregation and have led to what may be called a human-centric aggregation. We indicate a need to develop tools and techniques to reflect some fine shades of meaning regarding what can be considered the very purpose of human-centric aggregation, notably stated by various modalities in natural language specifications, in particular the usuality. We advocate the use of the ordered weighted average (OWA) operator, which is a formidable tool that can easily be tailored to a user?s intention as to the purpose and method of aggregation, generalizing many simple and natural aggregation types, such as the arithmetic mean, maximum and minimum, and probability. We show some of the most representative extensions and generalizations, including the induced OWA, the generalized OWA, the probabilistic OWA, and the OWA distance. We show their use in the basic case of the aggregation of numerical values and in social choice (voting) results. Then, we claim that linguistic data summaries in Yager?s sense can be considered an »ultimately human consistent» form of human-centric aggregation and show how the OWA operators can be used therein.
Kalantar, B, Al-Najjar, HAH, Pradhan, B, Saeidi, V, Halin, AA, Ueda, N & Naghibi, SA 2019, 'Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping', Water, vol. 11, no. 9, pp. 1909-1909.
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Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis methods—Variance Inflation Factor (VIF), Chi-Square Factor Optimization, and Gini Importance—to measure the significance of GCFs. From a total of 15 frequently used GCFs, 11 most effective ones (i.e., altitude, slope angle, plan curvature, profile curvature, topographic wetness index, distance from river, distance from fault, river density, fault density, land use, and lithology) were finally selected. In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). The resultant trained models were then applied for groundwater potential prediction and mapping in the Haraz basin of Mazandaran province, Iran. MDA has been successfully applied for soil erosion and landslide mapping, but has not yet been fully explored for groundwater potential mapping (GPM). Although other discriminant methods, such as LDA, exist, MDA is worth exploring due to its capability to model multivariate nonlinear relationships between variables; it also undertakes a mixture of unobserved subclasses with regularization of non-linear decision boundaries, which could potentially provide more accurate classification. For the validation, areas under Receiver Operating Characteristics (ROC) curves (AUC) were calculated for the three algorithms. RF performed better with AUC value of 84.4%, while MDA and LDA yielded 75.2% and 74.9%, respectively. Although MDA performance is lower than RF, the result is satisfactory, because it is within the acceptable standard of environmental modeling. The outcome of factor analysis and groundwater maps emphasizes on optimization of multicolinearity factors for faster spatial m...
Kalaruban, M, Loganathan, P, Nguyen, TV, Nur, T, Hasan Johir, MA, Nguyen, TH, Trinh, MV & Vigneswaran, S 2019, 'Iron-impregnated granular activated carbon for arsenic removal: Application to practical column filters', Journal of Environmental Management, vol. 239, pp. 235-243.
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© 2019 Elsevier Ltd Arsenic is a major drinking water contaminant in many countries causing serious health hazards, and therefore, attempts are being made to remove it so that people have safe drinking water supplies. The effectiveness of arsenic removal from As(V) solutions using granular activated carbon (GAC) (zero point of charge (ZPC) pH 3.2) and iron incorporated GAC (GAC-Fe) (ZPC pH 8.0) was studied at 25 ± 1 °C. The batch study confirmed that GAC-Fe had higher Langmuir adsorption capacity at pH 6 (1.43 mg As/g) than GAC (1.01 mg As/g). Adsorption data of GAC-Fe fitted the Freundlich model better than the Langmuir model, thus indicating the presence of heterogeneous adsorption sites. Weber and Morris plots of the kinetic adsorption data suggested intra-particle diffusion into meso and micro pores in GAC. The column adsorption study revealed that 2–4 times larger water volumes can be treated by GAC-Fe than GAC, reducing the arsenic concentration from 100 μg/L to the WHO guideline of 10 μg/L. The volume of water treated increased with a decrease in flow velocity and influent arsenic concentration. The study indicates the high potential of GAC-Fe to remove arsenic from contaminated drinking waters in practical column filters.
Kamal, MS, Sarowar, MG, Dey, N, Ashour, AS, Ripon, SH, Panigrahi, BK & Tavares, JMRS 2019, 'Self-organizing mapping based swarm intelligence for secondary and tertiary proteins classification', International Journal of Machine Learning and Cybernetics, vol. 10, no. 2, pp. 229-252.
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Kandoli, SJ, Alidadi, H, Najafpoor, AA, Mehrabpour, M, Hosseinzadeh, A & Momeni, F 2019, 'Assessment of cemetery effects on groundwater quality using GIS', Desalination and Water Treatment, vol. 168, pp. 235-242.
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Kang, Y, Xie, H, Li, B, Zhang, J, Hao Ngo, H, Guo, W, Guo, Z, Kong, Q, Liang, S, Liu, J, Cheng, T & Zhang, L 2019, 'Performance of constructed wetlands and associated mechanisms of PAHs removal with mussels', Chemical Engineering Journal, vol. 357, pp. 280-287.
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Karimi, M, Croaker, P, Skvortsov, A, Moreau, D & Kessissoglou, N 2019, 'Numerical prediction of turbulent boundary layer noise from a sharp‐edged flat plate', International Journal for Numerical Methods in Fluids, vol. 90, no. 10, pp. 522-543.
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SummaryAn efficient hybrid uncorrelated wall plane waves–boundary element method (UWPW‐BEM) technique is proposed to predict the flow‐induced noise from a structure in low Mach number turbulent flow. Reynolds‐averaged Navier‐Stokes equations are used to estimate the turbulent boundary layer parameters such as convective velocity, boundary layer thickness, and wall shear stress over the surface of the structure. The spectrum of the wall pressure fluctuations is evaluated from the turbulent boundary layer parameters and by using semi‐empirical models from literature. The wall pressure field underneath the turbulent boundary layer is synthesized by realizations of uncorrelated wall plane waves (UWPW). An acoustic BEM solver is then employed to compute the acoustic pressure scattered by the structure from the synthesized wall pressure field. Finally, the acoustic response of the structure in turbulent flow is obtained as an ensemble average of the acoustic pressures due to all realizations of uncorrelated plane waves. To demonstrate the hybrid UWPW‐BEM approach, the self‐noise generated by a flat plate in turbulent flow with Reynolds number based on chord Rec = 4.9 × 105 is predicted. The results are compared with those obtained from a large eddy simulation (LES)‐BEM technique as well as with experimental data from literature.
Karmokar, DK, Chen, S-L, Bird, TS & Guo, YJ 2019, 'Single-Layer Multi-Via Loaded CRLH Leaky-Wave Antennas for Wide-Angle Beam Scanning With Consistent Gain', IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 2, pp. 313-317.
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© 2018 IEEE. Achieving continuous backward-to-forward wide-angle beam scanning with consistent gain by employing composite right/left-handed (CRLH) leaky-wave antennas (LWAs) is reported. For structural and design simplicity, a single-layer one-dimensional structure is considered in which each unit cell consists of a patch shorted centrally to the ground plane. It was found that, when using only one via at the center of a unit cell, a continuous beam scan requires a large-diameter via when all other parameters remain unchanged. To eliminate this limitation while maintaining a single-layer structure, novel unit cells are proposed using multiple vias in each unit cell. An LWA design for continuous beam scan with three vias in each unit cell is investigated, and the results show good performance and design flexibility. A prototype antenna has been realized, and the measured results show that the antenna can scan the radiation beam continuously in a wide range, from -60° to +66° with a consistent gain. The measured gain variation within the scan range is only 2.9 dB, and the 3 dB gain bandwidth is 58.6%.
Karthickeyan, V, Thiyagarajan, S, Geo, VE, Ashok, B, Nanthagopal, K, Chyuan, OH & Vignesh, R 2019, 'Simultaneous reduction of NOx and smoke emissions with low viscous biofuel in low heat rejection engine using selective catalytic reduction technique', Fuel, vol. 255, pp. 115854-115854.
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© 2019 Elsevier Ltd The present work offered a comprehensive investigation on engine characteristics of single cylinder Direct Injection (DI) diesel engine fuelled with Lemon oil (LO) biofuel. LO was obtained from the peels of lemon using steam distillation process. The physio-chemical properties of LO were analysed based ASTM biodiesel standard and compared with diesel. The chemical composition of LO was observed with Fourier Transform Infrared Spectroscopy (FTIR) and Gas Chromatography and Mass Spectrometry (GC–MS). In-order to enhance the properties of LO, a cetane enhancer namely Pyrogallol (PY) was added. The engine combustion chamber components namely piston head, cylinder head and intake and exhaust valves were thermally coated with Partially Stabilized Zirconia (PSZ) which converted the conventional engine into low heat rejection engine. In the PSZ coated engine, enhanced performance and combustion characteristics were observed with LO and PY blend. Declined carbon monoxide (CO), hydrocarbon (HC) and smoke emissions were observed with LO and PY blend in coated engine. Further, the work was extended with the application of Selective catalytic reduction (SCR) and Catalytic Converter (CC) as post treatment system for the reduction of NOx emission. With post treatment, LO and pyrogallol in PSZ coated engine showed lower NOx emission than diesel and LO. Consequently, LO and pyrogallol in PSZ coated engine with post treatment was considered as more advantageous than other fuel samples on account of its performance, combustion and emission characteristics.
Kasinathan, G, Jayakumar, S, Gandomi, AH, Ramachandran, M, Fong, SJ & Patan, R 2019, 'Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier', Expert Systems with Applications, vol. 134, pp. 112-119.
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© 2019 Elsevier Ltd The World Health Organization (WHO) recently reported that the lung tumor was the leading cause of death worldwide. In this study, a practical computer-aided diagnosis (CAD) system is developed to increase a patient's chance of survival. Segmentation is acritical analysis tool for dividing a lung image into several sub-regions. This work characterized an automated 3-D lung segmentation tool modeled by an active contour model for computed tomography (CT) images. The proposed segmentation model is used to integrate the local image bias field formulation with the active contour model (ACM). Here, a local energy term is specified by using the mean squared error to reconcile severely in homogeneous CT images and used to detect and segment tumor regions efficiently with intensity inhomogeneity. In addition, a Multiscale Gaussian distribution was applied to the CT images for smoothening the evolution process, and features were determined. For proposed model evaluation, were used the Lung Image Database Consortium (LIDC-IDRI) data set that consisted of 850 lung nodule-lesion images that were segmented and refined to generate accurate 3D lesions of lung tumor CT images. Tumor portions were extracted with 97% accuracy. Using continuous feature extraction of 3-D images leads to attributing the deformation and quantifies the centroid displacement. In this work, predict the centroid displacement and contour points by a curve evolution method which results in more accurate predictions of contour changes and than the extracted images were classified using an Enhanced Convolutional Neural Network (CNN) Classifier. The experimental result shows that the modified Computer Aided Diagnosis (CAD) system has a high ability to acquire good accuracy and assures automated diagnosis of a lung tumor.
Katz, A, Shon, HK, Chekli, L & Kim, J-H 2019, 'TiO2-Coated Optical Fibres for Groundwater Remediation', Journal of Nanoscience and Nanotechnology, vol. 19, no. 2, pp. 1086-1089.
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In this study, polyethylene glycol (PEG) was tested as an alternative polymer to improve the coating of TiO₂ particles onto optical fibres. The addition of PEG helped dispersing effectively the particles in solution to control their deposition and therefore achieving better properties of the coating film. Results showed that PEG increased the effectiveness of the coating and the prepared fibres showed better performance for the removal of methylene blue (MB). This was attributed to the morphological changes induced by PEG. EDX mapping of the fibre surface showed that the addition of PEG lead to a better coverage of the fibre surface; increasing the active surface area for subsequent photocatalytic degradation. This study also showed that the light intensity, pH and initial concentration of MB have a significant influence. Finally, it was demonstrated that the coatings using PEG were better ordered and structured; showing a distinct layer-by-layer deposition.
Kavehei, O, Hamilton, TJ, Truong, ND & Nikpour, A 2019, 'Opportunities for Electroceuticals in Epilepsy', Trends in Pharmacological Sciences, vol. 40, no. 10, pp. 735-746.
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© 2019 Elsevier Ltd Epilepsy is a neurological disorder that affects ∼1% of the world population. Nearly 30% of epilepsy patients suffer from pharmacoresistant epilepsy that cannot be treated with antiepileptic drugs. Depending on seizure type, a diverse range of therapies are available, including surgery, vagus nerve stimulation, and deep brain stimulation. We review the sensing and stimulation technologies most used in neurological disorders, and provide a vision of minimally invasive electroceuticals to enable accurate forecasting of epileptic seizures and therapy. The use of such systems could potentially help patients to prevent injuries and, in combination with an intervention mechanism, could provide a method of suppressing seizures in epileptic patients.
Kern, ML, McCarthy, PX, Chakrabarty, D & Rizoiu, M-A 2019, 'Social media-predicted personality traits and values can help match people to their ideal jobs', Proceedings of the National Academy of Sciences, vol. 116, no. 52, pp. 26459-26464.
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Work is thought to be more enjoyable and beneficial to individuals and society when there is congruence between one’s personality and one’s occupation. We provide large-scale evidence that occupations have distinctive psychological profiles, which can successfully be predicted from linguistic information unobtrusively collected through social media. Based on 128,279 Twitter users representing 3,513 occupations, we automatically assess user personalities and visually map the personality profiles of different professions. Similar occupations cluster together, pointing to specific sets of jobs that one might be well suited for. Observations that contradict existing classifications may point to emerging occupations relevant to the 21st century workplace. Findings illustrate how social media can be used to match people to their ideal occupation.
Keshavarz, S, Abdipour, A, Mohammadi, A & Keshavarz, R 2019, 'Design and implementation of low loss and compact microstrip triplexer using CSRR loaded coupled lines', AEU - International Journal of Electronics and Communications, vol. 111, pp. 152913-152913.
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Khabbaz, H, Gibson, R & Fatahi, B 2019, 'Effect of constructing twin tunnels under a building supported by pile foundations in the Sydney central business district', Underground Space, vol. 4, no. 4, pp. 261-276.
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© 2019 Tongji University and Tongji University Press In congested cities such as Sydney, competition for underground space escalates within the built environment because various assets require finite geotechnical strength and support. Specific problems such as damage to buildings may develop when high-rise buildings on piled foundations are subject to ground movements as tunnels are constructed. This paper focuses on the risks of tunneling beneath Sydney's Martin Place and how buildings are subject to additional loads caused by tunneling. The key objective of this study is to improve the understanding of tunnel–rock–pile interactions and to encourage sustainable development. A finite element model is developed to predict the interaction between tunnel construction and piled foundations. The tunnel, rock, and pile components are studied separately and are then combined into a single model. The ground model is based on the characteristics of Hawkesbury Sandstone and is developed through a desktop study. The piles are designed using Australian Standards and observations of high-rise buildings. The tunnel construction is modeled based on the construction sequence of a tunnel boring machine. After combining the components, a parametric study on the relationship between tunnel location, basements, and piles is conducted. Our findings, thus far, show that tunneling can increase the axial and flexural loads of piles, where the additional loading exceeds the structural capacity of some piles, especially those that are close to basement walls. The parametric study reveals a strong relationship between tunnel depth and lining stresses, while the relationship between tunnel depth and induced pile loads is less convincing. Furthermore, the horizontal tunnel position relative to piles shows a stronger relationship with pile loads. Further research into tunnel–rock–pile interactions is recommended, especially beneath basements, to substantiate the results of this study.
Khalilpour, KR, Grossmann, IE & Vassallo, A 2019, 'Integrated Power-to-Gas and Gas-to-Power with Air and Natural-Gas Storage', Industrial & Engineering Chemistry Research, vol. 58, no. 3, pp. 1322-1340.
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© 2018 American Chemical Society. Compressed-air energy storage (CAES) is an energy-storage option with a history of almost half a century. The concept of CAES is formed around the integration of air storage with a gas-fired power generator. Here, we introduce a methodology that a gas power-generating plant installs both air- and natural-gas-storage systems to utilize the stored energy as well as the real economic value of natural gas, following the market dynamics. The energy-storage hybridization increases the operation complexity substantially. We present a detailed mixed-integer techno-economic formulation for operation scheduling of such a system. An example is also provided for a 180 MW gas generator in Australia with results showing how the storage facilities could improve the revenues of the plant. The analyses also show the existence of optimal conditions for a mix of natural gas and storage sizes in a given regional market jurisdiction in order to achieve the highest economic revenue.
Khan, AA, Abolhasan, M, Ni, W, Lipman, J & Jamalipour, A 2019, 'A Hybrid-Fuzzy Logic Guided Genetic Algorithm (H-FLGA) Approach for Resource Optimization in 5G VANETs', IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 6964-6974.
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© 2019 IEEE. To support diversified quality of service demands and dynamic resource requirements of users in 5G driven VANETs, network resources need flexible and scalable resource allocation strategies. Current heterogeneous vehicular networks are designed and deployed with a connection-centric mindset with fixed resource allocation to a cell regardless of traffic conditions, static coverage, and capacity. In this paper, we propose a hybrid-fuzzy logic guided genetic algorithm (H-FLGA) approach for the software defined networking controller, to solve a multi-objective resource optimization problem for 5G driven VANETs. Realizing the service oriented view, the proposed approach formulates five different scenarios of network resource optimization in 5G VANETs. Furthermore, the proposed fuzzy inference system is used to optimize weights of multi-objectives, depending on the type of service requirements of customers. The proposed approach shows the minimized value of multi-objective cost function when compared with the GA. The simulation results show the minimized value of end-to-end delay as compared to other schemes. The proposed approach will help the network service providers to implement a customer-centric network infrastructure, depending on dynamic customer needs of users.
Khan, HA, Castel, A, Khan, MSH & Mahmood, AH 2019, 'Durability of calcium aluminate and sulphate resistant Portland cement based mortars in aggressive sewer environment and sulphuric acid', Cement and Concrete Research, vol. 124, pp. 105852-105852.
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© 2019 Elsevier Ltd This study aims to compare the performance of sulphate resisting (SR) Portland cement mortar (SRm) and calcium aluminate cement mortars (CACm) in both natural sewer environment and sulphuric acid. Specimens were extracted after 12 and 24 months from field exposure, and were also removed from 1.5% sulphuric acid (H2SO4) after 6 months to investigate the deterioration caused by chemically induced corrosion. Visual, physical and extensive microstructural analyses were performed to evaluate the degradation of CACm and SRm matrix using techniques such as Scanning Electron Microscopy (SEM), Energy Dispersive X-Ray (EDX), X-Ray Diffraction (XRD) and Fourier Transform Infrared (FTIR) Spectroscopy. Surface pH was estimated after 12 and 24 months of field exposure to identify the initiation of biotic film development due to microbial induced corrosion (MIC). Material properties such as mass loss, compressive strength, linear expansion, and pH profile with respect to neutralization depth were also measured. The difference in mechanism of deterioration was also highlighted based on microstructural investigations between in field experimentation and acid exposure. The results showed that overall CACm performed significantly better than SRm in onsite sewer environment and sulphuric acid solution in terms of visual observations, loss in mass, compressive strength reduction, depth of neutralization, reduction in pH and penetration of sulphur. Crystallization of gypsum within the matrix of both mixes was the main factor behind the deterioration observed using XRD and FTIR from both in field and acid attack exposure, with higher deterioration within the matrix of SRm as compared to CACm. Moreover, sulphuric acid testing is suitable for screening the mixes rapidly against acidic environment, but due to the major differences observed in deterioration processes with natural field conditions this method is unsuitable for service life design of sewage structures.
Khan, I, Xu, T, Castel, A & Gilbert, RI 2019, 'Early-age tensile creep and shrinkage-induced cracking in internally restrained concrete members', Magazine of Concrete Research, vol. 71, no. 22, pp. 1167-1179.
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Experiments were carried out under ambient conditions and in a temperature–humidity control room to assess the influences of early-age shrinkage and tensile creep on cracking in reinforced-concrete (RC) members subjected to internal restraint. Two concrete mixes were considered, with compressive strengths of 36 MPa and 47 MPa. The evolution of the tensile creep coefficients was measured using unreinforced dog-bone-shaped specimens subjected to sustained axial tension. The shrinkage-induced stress tests were performed on RC prisms internally restrained by one concentrically placed reinforcement. Free shrinkage and restrained shrinkage were measured on companion plain concrete prisms and on unloaded RC prisms, respectively, to determine the degree of restraint. The results show that the 36 MPa concrete had a higher tensile creep coefficient than the 47 MPa concrete, but that there were no significant differences in early-age free shrinkage. A lower humidity results in more free shrinkage strain, but leads to more tensile creep and, consequently, increased relaxation of the tensile stresses. The magnitude of the restrained shrinkage depends on the reinforcement ratio. The development of the tensile strength of concrete is a governing factor influencing the time to cracking. The tensile ageing coefficient was calibrated for the two concrete mixes.
Khan, I, Xu, T, Castel, A, Gilbert, RI & Babaee, M 2019, 'Risk of early age cracking in geopolymer concrete due to restrained shrinkage', Construction and Building Materials, vol. 229, pp. 116840-116840.
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© 2019 Elsevier Ltd In this paper, experimental tests were carried out in order to measure early-age shrinkage and tensile creep of geopolymer concrete and assess their influence on early age cracking in reinforced concrete members. Two mixes of geopolymer concrete were tested. For the first mix, the specimens were heat-cured at a temperature of either 60 °C or 90 °C. For the second mix, the specimens were cured under ambient conditions. Tensile creep was directly measured using unreinforced dog-bone shaped specimens subjected to sustained axial tension. The shrinkage induced stress tests were performed on restrained geopolymer concrete rings. The results show that the tensile creep coefficient and shrinkage strains in geopolymer concrete are affected by the curing temperature and duration. Higher curing temperature leads to less tensile creep and shrinkage strains. Heat-cured geopolymer concrete demonstrated a lower early-age shrinkage and higher tensile creep coefficient, compared to the control ordinary Portland cement (OPC) concrete. Both restrained ring test and simulations confirm that heat-cured geopolymer concrete can relax undesirable stresses in concrete caused by restrained shrinkage, and reduce the risk of early-age cracking.
Khan, JA, Shon, HK & Nghiem, LD 2019, 'From the Laboratory to Full-Scale Applications of Forward Osmosis: Research Challenges and Opportunities', Current Pollution Reports, vol. 5, no. 4, pp. 337-352.
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© 2019, Springer Nature Switzerland AG. Forward osmosis (FO) has recently emerged as a new separation platform for a range of applications that are currently not possible for other membrane processes. This review paper covers key aspects of FO development with a specific emphasis on current technical challenges for practical applications. Main hurdles in the transition of FO from a lab-scale process to large scale applications include low-performance membranes, development of suitable draw solute, inherent transport phenomena (e.g. concentration polarization and reverse solute flux), membrane fouling and subsequent membrane cleaning. Several new FO membranes have been developed with some improved performances but no membrane has yet been found convincing in all of the key performance indicators. Draw solutes have been broadly investigated but mainly at the lab-scale. There have only been very few pilot-scale studies, most of them using inorganic salts as draw solutes. Development of thermo-responsive draw solutes and TFC membranes have been reported to be most effective in reducing reverse solute flux while altering the hydrodynamic conditions and the use of ultrasonication along with exploring other viable options have been suggested to tackle external and internal concentration polarization respectively. Although membrane fouling types and mitigation strategies have been extensively explored, this review also highlights the need for further research in biofouling for long-term FO operation.
Khan, MA, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Varjani, S, Liu, Y, Deng, L & Cheng, C 2019, 'Selective production of volatile fatty acids at different pH in an anaerobic membrane bioreactor', Bioresource Technology, vol. 283, pp. 120-128.
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© 2019 Elsevier Ltd This study investigated the production of major volatile fatty acid (VFA) components in an anaerobic membrane bioreactor (AnMBR) to treat low-strength synthetic wastewater. No selective inhibition was applied for methane production and solvent-extraction method was used for VFA extraction. The results showed acetic and propionic acid were the predominant VFA components at pH 7.0 and 6.0 with concentrations of 1.444 ± 0.051 and 0.516 ± 0.032 mili-mol/l respectively. At pH 12.0 isobutyric acid was the major VFA component with a highest concentration of 0.712 ± 0.008 mili-mol/l. The highest VFA yield was 48.74 ± 1.5 mg VFA/100 mg CODfeed at pH 7.0. At different pH, AnMBR performance was evaluated in terms of COD, nutrient removal and membrane fouling rate. It was observed that the membrane fouled at a faster rate in both acidic and alkaline pH conditions, the slowest rate in membrane fouling was observed at pH 7.0.
Khan, MA, Ngo, HH, Guo, W, Liu, Y, Nghiem, LD, Chang, SW, Nguyen, DD, Zhang, S, Luo, G & Jia, H 2019, 'Optimization of hydraulic retention time and organic loading rate for volatile fatty acid production from low strength wastewater in an anaerobic membrane bioreactor', Bioresource Technology, vol. 271, pp. 100-108.
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This study aims to investigate the production of volatile fatty acids (VFAs) from low strength wastewater at various hydraulic retention time (HRT) and organic loading rate (OLR) in a continuous anaerobic membrane bioreactor (AnMBR) using glucose as carbon source. This experiment was performed without any selective inhibition of methanogens and the reactor pH was maintained at 7.0 ± 0.1. 48, 24, 18, 12, 8 and 6 h-HRTs were applied and the highest VFA concentration was recorded at 8 h with an overall VFA yield of 48.20 ± 1.21 mg VFA/100 mg CODfeed. Three different ORLs were applied (350, 550 and 715 mg CODfeed) at the optimum 8 h-HRT. The acetic and propanoic acid concentration maximums were (1.1845 ± 0.0165 and 0.5160 ± 0.0141 mili-mole/l respectively) at 550 mg CODfeed. The isobutyric acid concentration was highest (0.3580 ± 0.0407 mili-mole/l) at 715 mg CODfeed indicating butyric-type fermentation at higher organic loading rate.
Khan, TA & Ling, SH 2019, 'Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications', Algorithms, vol. 12, no. 5, pp. 88-88.
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Electrical impedance tomography (EIT) has been a hot topic among researchers for the last 30 years. It is a new imaging method and has evolved over the last few decades. By injecting a small amount of current, the electrical properties of tissues are determined and measurements of the resulting voltages are taken. By using a reconstructing algorithm these voltages then transformed into a tomographic image. EIT contains no identified threats and as compared to magnetic resonance imaging (MRI) and computed tomography (CT) scans (imaging techniques), it is cheaper in cost as well. In this paper, a comprehensive review of efforts and advancements undertaken and achieved in recent work to improve this technology and the role of artificial intelligence to solve this non-linear, ill-posed problem are presented. In addition, a review of EIT clinical based applications has also been presented.
Khari, M, Dehghanbanadaki, A, Motamedi, S & Jahed Armaghani, D 2019, 'Computational estimation of lateral pile displacement in layered sand using experimental data', Measurement, vol. 146, pp. 110-118.
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Khawwaf, J, Zheng, J, Chai, R, Lu, R & Man, Z 2019, 'Adaptive Microtracking Control for an Underwater IPMC Actuator Using New Hyperplane-Based Sliding Mode', IEEE/ASME Transactions on Mechatronics, vol. 24, no. 5, pp. 2108-2117.
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Kheshti, Z, Azodi Ghajar, K, Altaee, A & Kheshti, MR 2019, 'High-Gradient Magnetic Separator (HGMS) combined with adsorption for nitrate removal from aqueous solution', Separation and Purification Technology, vol. 212, pp. 650-659.
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© 2018 Elsevier B.V. This paper investigates the adsorption of nitrate anions from aqueous solutions on ammonium-functionalized magnetic mesoporous silica. The adsorbent was prepared via two-step coating process of silica on magnetic core (Fe3O4). The resultant structure was modified by 3-aminopropyl triethoxysilane (APTES), and finally acidified in HCl solution to convert the grafted amino groups to ammonium ones. Field-emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), vibration sample magnetometer (VSM), Energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared spectroscopy (FT-IR), and N2 adsorption/desorption were used to characterize the obtained samples. Experimental results showed that several factors affected the uptake behavior such as pH, contact time, and initial concentration of nitrate. The amount of sorbent loading were examined and the adsorbent shows great adsorption capacity for NO3¯ (ca.51.28 mg g−1 at 25 °C). The nitrate loaded multifunctional microsphere can be easily regenerated with NaOH solution. The separation of multifunctional magnetic microspheres from solution by novel high gradient magnetic separation (HGMS), using the collection of rods, was also investigated in details. Contrast to other methods based on filter and batch conditions, large volumes of water can be easily handled by the new designed HGMS due to the decreasing pressure drop and retention times. The effect of a set of two different experimental variables, i.e. flowrate and magnetic field strength, were investigated to identify the best working conditions for the separation of adsorbent from treated water. The most efficient backwash system was offered to reuse the magnetic particles, too. The removal efficiency of NO3¯ from solution was around 86.24% by the constructed HGMS under the optimal experimental conditions of 7.5 mL s −1 flowrate and: 3.49 mT magnitude of the magnetic field.
Khoa, NLD, Wang, Y & Chawla, S 2019, 'Incremental commute time and its online applications', Pattern Recognition, vol. 88, pp. 101-112.
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Khokle, RP, Franco, F, de Freitas, SC, Esselle, KP, Heimlich, MC & Bokor, DJ 2019, 'Eddy Current–Tunneling Magneto-Resistive Sensor for Micromotion Detection of a Tibial Orthopaedic Implant', IEEE Sensors Journal, vol. 19, no. 4, pp. 1285-1292.
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© 2001-2012 IEEE. In this paper, an eddy current loop coupled with the tunneling magneto resistor (EC-TMR) is used as a displacement sensor to detect the micromotion of an orthopaedic implant. The high sensitivity and signal to noise ratio of the TMR sensor are used to achieve high resolution at a large standoff distance. First, a small three-turn rectangular eddy current loop of dimension 2.5, text {mm}times 10$ mm is designed and simulated inside the human body using a full-wave EM simulator. Then, it is fabricated and tested using vector network analyzer. The magnetic tunnel junction stack is optimized and a six-element TMR sensor is fabricated and characterized. The eddy current and tunnelling magneto resistive sensor are integrated and heterodyne detection technique is used to obtain the high-resolution micromotion detection at an extended standoff range. This technique can be used for in-vivo detection of the micromotion of the orthopaedic implant which will be useful in reducing the revision surgeries due to the mechanical failures of the implant.
Khoo, KS, Chew, KW, Ooi, CW, Ong, HC, Ling, TC & Show, PL 2019, 'Extraction of natural astaxanthin from Haematococcus pluvialis using liquid biphasic flotation system', Bioresource Technology, vol. 290, pp. 121794-121794.
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Khoo, WH, Ledergor, G, Weiner, A, Roden, DL, Terry, RL, McDonald, MM, Chai, RC, De Veirman, K, Owen, KL, Opperman, KS, Vandyke, K, Clark, JR, Seckinger, A, Kovacic, N, Nguyen, A, Mohanty, ST, Pettitt, JA, Xiao, Y, Corr, AP, Seeliger, C, Novotny, M, Lasken, RS, Nguyen, TV, Oyajobi, BO, Aftab, D, Swarbrick, A, Parker, B, Hewett, DR, Hose, D, Vanderkerken, K, Zannettino, ACW, Amit, I, Phan, TG & Croucher, PI 2019, 'A niche-dependent myeloid transcriptome signature defines dormant myeloma cells', Blood, vol. 134, no. 1, pp. 30-43.
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AbstractThe era of targeted therapies has seen significant improvements in depth of response, progression-free survival, and overall survival for patients with multiple myeloma. Despite these improvements in clinical outcome, patients inevitably relapse and require further treatment. Drug-resistant dormant myeloma cells that reside in specific niches within the skeleton are considered a basis of disease relapse but remain elusive and difficult to study. Here, we developed a method to sequence the transcriptome of individual dormant myeloma cells from the bones of tumor-bearing mice. Our analyses show that dormant myeloma cells express a distinct transcriptome signature enriched for immune genes and, unexpectedly, genes associated with myeloid cell differentiation. These genes were switched on by coculture with osteoblastic cells. Targeting AXL, a gene highly expressed by dormant cells, using small-molecule inhibitors released cells from dormancy and promoted their proliferation. Analysis of the expression of AXL and coregulated genes in human cohorts showed that healthy human controls and patients with monoclonal gammopathy of uncertain significance expressed higher levels of the dormancy signature genes than patients with multiple myeloma. Furthermore, in patients with multiple myeloma, the expression of this myeloid transcriptome signature translated into a twofold increase in overall survival, indicating that this dormancy signature may be a marker of disease progression. Thus, engagement of myeloma cells with the osteoblastic niche induces expression of a suite of myeloid genes that predicts disease progression and that comprises potential drug targets to eradicate dormant myeloma cells.
Khorsand, M, Tavakoli, J, Kamanya, K & Tang, Y 2019, 'Simulation of high-output and lightweight sliding-mode triboelectric nanogenerators', Nano Energy, vol. 66, pp. 104115-104115.
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In light of the rapid growth in microelectronic technology, triboelectric nanogenerators (TENGs) have been exploited as securely sustainable substitutes for energy scavenging purposes as well as self-powered sensory utilization. In essence, TENGs’ energy output and average power distribution depend highly on certain key parameters including contact area, the thickness of electric films and external resistance. This study attempts to predict the behavior of TENGs based on variation of those key parameters and tries to optimize the associated characteristics leading to high-output and light-weight sliding-mode TENGs. To meet this problem, an artificial intelligence approach is taken into consideration and solutions for load resistance and geometry are presented. Furthermore, an experimental setup is designed to evaluate the accuracy of the simulation results, demonstrating the precision of the applied theory. The results revealed that the predefined sliding-mode TENG can harvest 0.25 mJ at each cycle in an open-circuit condition where the weight is almost 42.91 g. Moreover, simulation proves that an appropriate value for the external resistor can increase the scavenged energy up to 3.65 mJ at each reciprocal movement. Finally, temporal responses for charge, current, voltage, power output, and harvested energy are plotted and discussed, facilitating understanding of the relationship between scavenged energy and optimized parameters.
Khosoussi, K, Giamou, M, Sukhatme, GS, Huang, S, Dissanayake, G & How, JP 2019, 'Reliable Graphs for SLAM', The International Journal of Robotics Research, vol. 38, no. 2-3, pp. 260-298.
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Estimation-over-graphs (EoG) is a class of estimation problems that admit a natural graphical representation. Several key problems in robotics and sensor networks, including sensor network localization, synchronization over a group, and simultaneous localization and mapping (SLAM) fall into this category. We pursue two main goals in this work. First, we aim to characterize the impact of the graphical structure of SLAM and related problems on estimation reliability. We draw connections between several notions of graph connectivity and various properties of the underlying estimation problem. In particular, we establish results on the impact of the weighted number of spanning trees on the D-optimality criterion in 2D SLAM. These results enable agents to evaluate estimation reliability based only on the graphical representation of the EoG problem. We then use our findings and study the problem of designing sparse SLAM problems that lead to reliable maximum likelihood estimates through the synthesis of sparse graphs with the maximum weighted tree connectivity. Characterizing graphs with the maximum number of spanning trees is an open problem in general. To tackle this problem, we establish several new theoretical results, including the monotone log-submodularity of the weighted number of spanning trees. We exploit these structures and design a complementary greedy–convex pair of efficient approximation algorithms with provable guarantees. The proposed synthesis framework is applied to various forms of the measurement selection problem in resource-constrained SLAM. Our algorithms and theoretical findings are validated using random graphs, existing and new synthetic SLAM benchmarks, and publicly available real pose-graph SLAM datasets.
Khosravi, K, Shahabi, H, Pham, BT, Adamowski, J, Shirzadi, A, Pradhan, B, Dou, J, Ly, H-B, Gróf, G, Ho, HL, Hong, H, Chapi, K & Prakash, I 2019, 'A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods', Journal of Hydrology, vol. 573, pp. 311-323.
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© 2019 Elsevier B.V. Floods around the world are having devastating effects on human life and property. In this paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW), along with two machine learning methods (NBT and NB), were tested for their ability to model flood susceptibility in one of China's most flood-prone areas, the Ningdu Catchment. Twelve flood conditioning factors were used as input parameters: Normalized Difference Vegetation Index (NDVI), lithology, land use, distance from river, curvature, altitude, Stream Transport Index (STI), Topographic Wetness Index (TWI), Stream Power Index (SPI), soil type, slope and rainfall. The predictive capacity of the models was evaluated and validated using the Area Under the Receiver Operating Characteristic curve (AUC). While all models showed a strong flood prediction capability (AUC > 0.95), the NBT model performed best (AUC = 0.98), suggesting that, among the models studied, the NBT model is a promising tool for the assessment of flood-prone areas and can allow for proper planning and management of flood hazards.
Khuat, TT & Gabrys, B 2019, 'A comparative study of general fuzzy min-max neural networks for pattern classification problems', Neurocomputing, 2019, vol. 386, pp. 110-125.
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General fuzzy min-max (GFMM) neural network is a generalization of fuzzyneural networks formed by hyperbox fuzzy sets for classification and clusteringproblems. Two principle algorithms are deployed to train this type of neuralnetwork, i.e., incremental learning and agglomerative learning. This paperpresents a comprehensive empirical study of performance influencing factors,advantages, and drawbacks of the general fuzzy min-max neural network onpattern classification problems. The subjects of this study include (1) theimpact of maximum hyperbox size, (2) the influence of the similarity thresholdand measures on the agglomerative learning algorithm, (3) the effect of datapresentation order, (4) comparative performance evaluation of the GFMM withother types of fuzzy min-max neural networks and prevalent machine learningalgorithms. The experimental results on benchmark datasets widely used inmachine learning showed overall strong and weak points of the GFMM classifier.These outcomes also informed potential research directions for this class ofmachine learning algorithms in the future.
Khuat, TT & Le, MH 2019, 'Binary teaching–learning-based optimization algorithm with a new update mechanism for sample subset optimization in software defect prediction', Soft Computing, vol. 23, no. 20, pp. 9919-9935.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Software defect prediction has gained considerable attention in recent years. A broad range of computational methods has been developed for accurate prediction of faulty modules based on code and design metrics. One of the challenges in training classifiers is the highly imbalanced class distribution in available datasets, leading to an undesirable bias in the prediction performance for the minority class. Data sampling is a widespread technique to tackle this problem. However, traditional sampling methods, which depend mainly on random resampling from a given dataset, do not take advantage of useful information available in training sets, such as sample quality and representative instances. To cope with this limitation, evolutionary undersampling methods are usually used for identifying an optimal sample subset for the training dataset. This paper proposes a binary teaching–learning- based optimization algorithm employing a distribution-based solution update rule, namely BTLBOd, to generate a balanced subset of highly valuable examples. This subset is then applied to train a classifier for reliable prediction of potentially defective modules in a software system. Each individual in BTLBOd includes two vectors: a real-valued vector generated by the distribution-based update mechanism, and a binary vector produced from the corresponding real vector by a proposed mapping function. Empirical results showed that the optimal sample subset produced by BTLBOd might ameliorate the classification accuracy of the predictor on highly imbalanced software defect data. Obtained results also demonstrated the superior performance of the proposed sampling method compared to other popular sampling techniques.
Khuat, TT & Le, MH 2019, 'Ensemble learning for software fault prediction problem with imbalanced data', International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 4, pp. 3241-3241.
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Fault prediction problem has a crucial role in the software development process because it contributes to reducing defects and assisting the testing process towards fault-free software components. <span lang='EN-US'>Therefore, there are a lot of efforts aiming to address this type of issues, in which static code characteristics are usually adopted to construct fault classification models. </span> One of the challenging problems influencing the performance of predictive classifiers is the high imbalance among patterns belonging to different classes. This paper aims to integrate the sampling techniques and common classification techniques to form a useful ensemble model for the software defect prediction problem. The empirical results conducted on the benchmark datasets of software projects have shown the promising performance of our proposal in comparison with individual classifiers.
Khuat, TT, Chen, F & Gabrys, B 2019, 'An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network', IEEE Transactions on Fuzzy Systems, pp. 1-1, 2019, vol. 29, no. 2, pp. 427-441.
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Motivated by the practical demands for simplification of data towards beingconsistent with human thinking and problem solving as well as tolerance ofuncertainty, information granules are becoming important entities in dataprocessing at different levels of data abstraction. This paper proposes amethod to construct classifiers from multi-resolution hierarchical granularrepresentations (MRHGRC) using hyperbox fuzzy sets. The proposed approach formsa series of granular inferences hierarchically through many levels ofabstraction. An attractive characteristic of our classifier is that it canmaintain relatively high accuracy at a low degree of granularity based onreusing the knowledge learned from lower levels of abstraction. In addition,our approach can reduce the data size significantly as well as handling theuncertainty and incompleteness associated with data in real-world applications.The construction process of the classifier consists of two phases. The firstphase is to formulate the model at the greatest level of granularity, while thelater stage aims to reduce the complexity of the constructed model and deduceit from data at higher abstraction levels. Experimental outcomes conductedcomprehensively on both synthetic and real datasets indicated the efficiency ofour method in terms of training time and predictive performance in comparisonto other types of fuzzy min-max neural networks and common machine learningalgorithms.
Khuat, TT, Ruta, D & Gabrys, B 2019, 'Hyperbox based machine learning algorithms: A comprehensive survey', Soft Computing, vol. 25, no. 2, pp. 1325-1363.
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With the rapid development of digital information, the data volume generatedby humans and machines is growing exponentially. Along with this trend, machinelearning algorithms have been formed and evolved continuously to discover newinformation and knowledge from different data sources. Learning algorithmsusing hyperboxes as fundamental representational and building blocks are abranch of machine learning methods. These algorithms have enormous potentialfor high scalability and online adaptation of predictors built using hyperboxdata representations to the dynamically changing environments and streamingdata. This paper aims to give a comprehensive survey of literature onhyperbox-based machine learning models. In general, according to thearchitecture and characteristic features of the resulting models, the existinghyperbox-based learning algorithms may be grouped into three major categories:fuzzy min-max neural networks, hyperbox-based hybrid models, and otheralgorithms based on hyperbox representations. Within each of these groups, thispaper shows a brief description of the structure of models, associated learningalgorithms, and an analysis of their advantages and drawbacks. Mainapplications of these hyperbox-based models to the real-world problems are alsodescribed in this paper. Finally, we discuss some open problems and identifypotential future research directions in this field.
Kieferová, M, Scherer, A & Berry, DW 2019, 'Simulating the dynamics of time-dependent Hamiltonians with a truncated Dyson series', Physical Review A, vol. 99, no. 4.
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Kildashti, K, Samali, B, Mortazavi, M, Ronagh, H & Sharafi, P 2019, 'Seismic collapse assessment of a hybrid cold-formed hot-rolled steel building', Journal of Constructional Steel Research, vol. 155, pp. 504-516.
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This paper investigates seismic collapse potential of a hybrid cold-formed hot-rolled system in order to quantify the response modification factor (R-factor) through a procedural method proposed in FEMA-P695. A series of hot-rolled steel (HRS) knee-braced frames in conjunction with cold-formed steel (CFS) stud walls are proposed to resist lateral and gravity loads. ASCE7-16 does not provide seismic performance factors for this hybrid HRS/CFS structural topology in lightweight steel construction and as a result, more sophisticated assessment is needed to measure reasonable seismic performance. A nonlinear numerical model that simulates post-peak response of HRS knee-braced frames is calibrated with experimental data. Post-buckling behaviour of CFS studs are measured according to various techniques in terms of finite strip method (FSM), finite element method (FEM) and AISI-S136-16 analytical formulations. The modelling approach is implemented into nonlinear analytical models of a six-storey steel building which is designed in accordance with ASCE7-16, ANSI/AISC360-16, and AISI-S316-16. A suite of twenty-two bidirectional far-field ground motions are chosen from PEER/NGA database subset and scaled to conditional mean spectrum (CMS) relevant to Urban California region. A set of nonlinear static analysis as well as incremental dynamic analysis (IDA) is conducted to measure collapse fragility and seismic performance of the building. It is concluded that initially assumed R-factor for the proposed structural system maintains the collapse prevention criterion as recommended by FEMA-P695 and is appropriate to be considered for design purposes.
Kim, B-J, Piao, G, Kim, S, Yang, SY, Park, Y, Han, DS, Shon, HK, Hoffmann, MR & Park, H 2019, 'High-Efficiency Solar Desalination Accompanying Electrocatalytic Conversions of Desalted Chloride and Captured Carbon Dioxide', ACS Sustainable Chemistry & Engineering, vol. 7, no. 18, pp. 15320-15328.
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Kim, D, Graham, T, Wan, Z & Rizoiu, M-A 2019, 'Analysing user identity via time-sensitive semantic edit distance (t-SED): A case study of Russian trolls on Twitter', Journal of Computational Social Science, vol. 2, no. 2, pp. 331-351.
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In the digital era, individuals are increasingly profiled and grouped basedon the traces they leave behind in online social networks such as Twitter andFacebook. In this paper, we develop and evaluate a novel text analysis approachfor studying user identity and social roles by redefining identity as asequence of timestamped items (e.g. tweet texts). We operationalise this ideaby developing a novel text distance metric, the time-sensitive semantic editdistance (t-SED), which accounts for the temporal context across multipletraces. To evaluate this method we undertake a case study of Russianonline-troll activity within US political discourse. The novel metric allows usto classify the social roles of trolls based on their traces, in this casetweets, into one of the predefined categories left-leaning, right-leaning, andnews feed. We show the effectiveness of the t-SED metric to measure thesimilarities between tweets while accounting for the temporal context, and weuse novel data visualisation techniques and qualitative analysis to uncover newempirical insights into Russian troll activity that have not been identified inprevious work. Additionally, we highlight a connection with the field ofActor-Network Theory and the related hypotheses of Gabriel Tarde, and wediscuss how social sequence analysis using t-SED may provide new avenues fortackling a longstanding problem in social theory: how to analyse societywithout separating reality into micro versus macro levels.
Kim, DI, Dorji, P, Gwak, G, Phuntsho, S, Hong, S & Shon, H 2019, 'Effect of Brine Water on Discharge of Cations in Membrane Capacitive Deionization and Its Implications on Nitrogen Recovery from Wastewater', ACS Sustainable Chemistry & Engineering, vol. 7, no. 13, pp. 11474-11484.
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© 2019 American Chemical Society. We examined the desorption behavior of cations in membrane capacitive deionization (MCDI) from the cathode into high-concentration brine through a cation-exchange membrane (CEM) brine, during mineral recovery. Several major issues were explored to demonstrate the suitability of the mineral recovery process: discharge behavior using different solution chemistries, desorption efficiencies of various regeneration methods for the enrichment of ions, and desorption selectivity among selected cations. The desorption efficiency was hampered when the adsorbed cations migrated toward the brine solution against a higher ionic-strength gradient and was further lowered by the enhanced membrane resistance under the low concentration of the adsorbed ions on the cathode. Furthermore, the electrochemically adsorbed ions were limitedly discharged by the cost-effective regeneration method (short-circuiting). The cations were preferentially released in the order of K+ > Na+ > Mg2+, as mainly determined by their physiochemical properties such as diffusion rate and charge valence, whereas the influence of permselectivity through the CEM was insignificant. Furthermore, through the ammonium recovery tests, a high concentration of ammonium brine was obtained from wastewater through a successive five-cycle-operation due to its selective desorption over the sodium ions present. However, the incomplete discharge of ions from the electrode was a challenging issue to overcome for the use of MCDI for ammonium recovery.
Kim, DI, Dorji, P, Gwak, G, Phuntsho, S, Hong, S & Shon, H 2019, 'Reuse of municipal wastewater via membrane capacitive deionization using ion-selective polymer-coated carbon electrodes in pilot-scale', Chemical Engineering Journal, vol. 372, pp. 241-250.
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© 2019 Elsevier B.V. This study investigated membrane capacitive deionization (MCDI) at a pilot-scale using ion-selective polymer-coated carbon electrodes for wastewater reuse. Several issues have been addressed to verify the suitability of MCDI for wastewater reclamation: electrosorption performance, removal efficiency and selectivity of ions present in wastewater, optimization of operating conditions, and performance degradation in long-term caused by the accumulation of organic contaminants. The coated electrodes had better adsorption capacities and charge efficiencies than the conventional MCDI system, which was attributed to their low electrical resistance induced by the thin coated layer. The pilot-scale MCDI test cell involved 50 pairs of anion- and cation-selective electrodes and achieved good removal efficiency of ions from the wastewater effluent, particularly for problematic charged impurities, such as nitrate (NO3−) (up to 91.08% of NO3− was removed). Increasing the flow rate and reducing the applied potential were shown to be efficient for achieving better water quality by enhancing the NO3− selectivity. Last, the 15 d operation showed good reproducibility in electrosorption and regeneration for the coated electrodes, despite the fact that high concentrations of organics were contained in the wastewater feed solution (12.4 mg/L of dissolved organic carbon).
Kim, H, Nerse, C, Lee, J & Wang, S 2019, 'Multidisciplinary Analysis and Multiobjective Design Optimization of a Switched Reluctance Motor for Improving Sound Quality', IEEE Access, vol. 7, pp. 66020-66027.
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In this study, the design optimization method for improving sound quality (SQ) of a switched reluctance motor (SRM) is proposed. The multidisciplinary finite element analysis (FEA) of an SRM is performed to evaluate both average torque and the SQ metrics to design the rotor configuration of the SRM. Specifically, the magneto-static FEA of the SRM is used to evaluate the average torque which is the most important performance of motors, and the local force distribution applied to the stator. Also, the transient structural FEA of the stator excited by the local force distribution, and the transient acoustic FEA are conducted to get the sound pressure radiated by the vibration of the stator. Then, the SQ metrics of loudness, sharpness, fluctuation strength and roughness can be obtained from the radiated sound pressure. We define the correlation function between the SQ metrics and the jury test results of the different types of SRMs. The weighted sum of the torque and the correlation function is set as an objective function. After that, design optimization method of the SRM using a design of experiments is discussed. This study does not consider the nonlinear material properties and is based on 2D analysis; however, with all these limitations, note that this is the first study to propose an overall procedure to increase the SQ of an SRM.
Kim, H-G, Nerse, C & Wang, S 2019, 'Topography optimization of an enclosure panel for low-frequency noise and vibration reduction using the equivalent radiated power approach', Materials & Design, vol. 183, pp. 108125-108125.
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An enclosure panel is widely used in industrial applications. The panel under a dynamic loading excites the surrounding air medium and noise is radiated into the acoustic space. The radiated sound can be suppressed by having changes in the structure. The noise reduction performance can be further improved by a design optimization. In this study, a topography optimization is conducted to design an enclosure panel. Topography optimization results in a bead pattern, which helps maintain the thickness at a constant level throughout the structure. The final optimized structure can be manufactured using a stamping process. Compared with other optimization methods, topography optimization requires minimal manufacturing effort and cost, with no additional increase in mass. Moreover, this type of optimization is effective for noise reduction problems because no holes are created in the structure. In this study, the objective function selected to minimize the low-frequency noise is the equivalent radiated power. The topography optimization of the enclosure panel has been conducted using the commercial software Altair OptiStruct, with loads and constraints considered. In order to verify the optimization result, in-situ experiment was performed with panels produced by the stamping process.
Kim, JE, Kuntz, J, Jang, A, Kim, IS, Choi, JY, Phuntsho, S & Shon, HK 2019, 'Techno-economic assessment of fertiliser drawn forward osmosis process for greenwall plants from urban wastewater', Process Safety and Environmental Protection, vol. 127, pp. 180-188.
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© 2019 Institution of Chemical Engineers Pressure-assisted osmosis (PAO) has been suggested to integrate with fertiliser driven forward osmosis (FDFO) to improve the overall efficiency of simultaneous wastewater reuse and fertiliser osmotic dilution. This study aims to demonstrate the techno-economic feasibility of pressure-assisted fertiliser driven forward osmosis (PAFDO) hybrid system compared to the existing ultraviolet and reverse osmosis (UV–RO) process. The results showed that coupling FDFO with PAO (i.e. PAFDO) could help fulfill the water quality required for greenwall fertigation. An economic analysis on capital and operational costs for the PAFDO showed that the PAO mode application at a lower FDFO dilution stage could significantly reduce the costs. However, when considering the different applied pressures in PAO (i.e. 2, 4, and 6 bar), the increase in the total water cost was not significant. This indicates that the dilution stage for applying PAO is more sensitive to the total water cost of the PAFDO than the applied pressure. A coupling of higher average water flux (>10 L/m2h) and lower draw solution (DS) dilution factor (DF < 60) is recommended. Therefore, this could make the PAFDO system economically viable compared to the benchmark for the UV-RO disinfection system.
Kim, T, Alnahhal, MF, Nguyen, QD, Panchmatia, P, Hajimohammadi, A & Castel, A 2019, 'Initial sequence for alkali-silica reaction: Transport barrier and spatial distribution of reaction products', Cement and Concrete Composites, vol. 104, pp. 103378-103378.
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© 2019 Elsevier Ltd Alkali-silica reaction (ASR) is the result of complex chemical reactions. The exact sequence of ASR gel formation has yet to be fully understood. One promising hypothesis is that ASR gel starts to form only in localized regions where the availability of calcium is restricted. A transport barrier (C–S–H) around the aggregate has been hypothetically suggested. However, the existence of this physical barrier and the formation of ASR gel inside this barrier has been questioned because it has never been observed experimentally. This paper firstly presents a direct observation of the physical barrier, spatial distribution of reaction products, and crack formations in a reactive aggregate exposed to a model reactant system. Combined analyses using X-ray micro tomography and other chemical characterization techniques shows that ASR gel preferentially starts to form at localized areas covered by C–S–H, loosely packed reaction products with pores, and pre-existing defects.
Kim, Y, Li, S, Phuntsho, S, Xie, M, Shon, HK & Ghaffour, N 2019, 'Understanding the organic micropollutants transport mechanisms in the fertilizer-drawn forward osmosis process', Journal of Environmental Management, vol. 248, pp. 109240-109240.
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© 2019 Elsevier Ltd We systematically investigated the transport mechanisms of organic micropollutants (OMPs) in a fertilizer-drawn forward osmosis (FDFO) membrane process. Four representative OMPs, i.e., atenolol, atrazine, primidone, and caffeine, were chosen for their different molecular weights and structural characteristics. All the FDFO experiments were conducted with the membrane active layer on the feed solution (FS) side using three different fertilizer draw solutions (DS): potassium chloride (KCl), monoammonium phosphate (MAP), and diammonium phosphate (DAP) due to their different properties (i.e., osmotic pressure, diffusivity, viscosity and solution pH). Using KCl as the DS resulted in both the highest water flux and the highest reverse solute flux (RSF), while MAP and DAP resulted in similar water fluxes with varying RSF. The pH of the FS increased with DAP as the DS due to the reverse diffusion of NH4+ ions from the DS toward the FS, while for MAP and DAP DS, the pH of the FS was not impacted. The OMPs transport behavior (OMPs flux) was evaluated and compared with a simulated OMPs flux obtained via the pore-hindrance transport model to identify the effects of the OMPs structural properties. When MAP was used as DS, the OMPs flux was dominantly influenced by the physicochemical properties (i.e., hydrophobicity and surface charge). Those OMPs with positive charge and more hydrophobic, exhibited higher forward OMP fluxes. With DAP as the DS, the more hydrated FO membrane (caused by increased pH) as well as the enhanced RSF hindered OMPs transport through the FO membrane. With KCl as DS, the structural properties of the OMPs were dominant factors in the OMPs flux, however the higher RSF of the KCl draw solute may likely hamper the OMPs transport through the membrane especially those with higher MW (e.g., atenolol). The pore-hindrance model can be instrumental in understanding the effects of the hydrodynamic properties and the surface propertie...
Kishore Kumar, D, Hsu, M-H, Ivaturi, A, Chen, B, Bennett, N & Upadhyaya, HM 2019, 'Optimizing room temperature binder free TiO 2 paste for high efficiency flexible polymer dye sensitized solar cells', Flexible and Printed Electronics, vol. 4, no. 1, pp. 015007-015007.
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© 2019 IOP Publishing Ltd. Binder free TiO2 paste is prepared using tert-butyl alcohol in dilute acidic conditions at room temperature for flexible polymer dye sensitized solar cells (DSSCs). The present paper reports the detailed studies carried out to elucidate the importance of stirring times during the paste preparation on the final device performance. The maximum conversion efficiency of 4.2% was obtained for flexible DSSCs fabricated on tin doped indium oxide/polyethylene naphthalate substrates using TiO2 paste prepared with an optimum stirring time of 8 h. The effect of optimum stirring times on the device characteristics has been understood in terms of the detailed morphology and surface area measurements.
Kishore Kumar, D, Popuri, SR, Swami, SK, Onuoha, OR, Bos, J-WG, Chen, B, Bennett, N & Upadhyaya, HM 2019, 'Screen printed tin selenide films used as the counter electrodes in dye sensitized solar cells', Solar Energy, vol. 190, pp. 28-33.
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© 2019 International Solar Energy Society In this work, the scalable screen printing process has been adopted to prepare low-cost and earth-abundant tin selenide (SnSe) films to study as the counter electrode in dye-sensitized solar cells (DSSCs). The SnSe powder was synthesized by solid state reaction method and corresponding films were fabricated by screen printing technique. The electrocatalytic activity of SnSe for redox iodide/triiodide (I−/I3−) couple and charge transfer resistance at the CE/electrolyte interface were characterized by cyclic voltammetry and electrochemical impedance spectroscopy. The DSSC with SnSe counter electrode exhibited with power conversion efficiency (PCE) of ~5.76% with open-circuit voltage of 0.63 V and short circuit current density of 12.39 mA/cm2 whereas the DSSC with platinum counter electrode showed PCE of 8.09% with open-circuit voltage of 0.68 V and short circuit current density of 14.77 mA/cm2. Thus, earth abundant and low cost SnSe films fabricated by screen printing technique could be an alternative to costly platinum counter electrode in DSSC.
Ko, L-W, Lin, C-T, Lu, Y-C, Bustince, H, Chang, Y-C, Chang, Y, Ferandez, J, Wang, Y-K, Sanz, JA & Pereira Dimuro, G 2019, 'Multimodal Fuzzy Fusion for Enhancing the Motor-Imagery-Based Brain Computer Interface', IEEE Computational Intelligence Magazine, vol. 14, no. 1, pp. 96-106.
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© 2005-2012 IEEE. Brain-computer interface technologies, such as steady-state visually evoked potential, P300, and motor imagery are methods of communication between the human brain and the external devices. Motor imagery-based brain-computer interfaces are popular because they avoid unnecessary external stimuli. Although feature extraction methods have been illustrated in several machine intelligent systems in motor imagery-based brain-computer interface studies, the performance remains unsatisfactory. There is increasing interest in the use of the fuzzy integrals, the Choquet and Sugeno integrals, that are appropriate for use in applications in which fusion of data must consider possible data interactions. To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain-computer interfaces, to consider possible links between the data. Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system. Ten volunteers performed a motor imagery-based brain-computer interface experiment, and we acquired electroencephalography signals simultaneously. The multimodal fuzzy fusion-based brain-computer interface system enhanced performance compared with traditional brain-computer interface systems. Furthermore, when using the motor imagery-relevant electroencephalography frequency alpha and beta bands for the input features, the system achieved the highest accuracy, up to 78.81% and 78.45% with the Choquet and Sugeno integrals, respectively. Herein, we present a novel concept for enhancing brain-computer interface systems that adopts fuzzy integrals, especially in the fusion for classifying brain-computer interface commands.
Koach, J, Holien, JK, Massudi, H, Carter, DR, Ciampa, OC, Herath, M, Lim, T, Seneviratne, JA, Milazzo, G, Murray, JE, McCarroll, JA, Liu, B, Mayoh, C, Keenan, B, Stevenson, BW, Gorman, MA, Bell, JL, Doughty, L, Hüttelmaier, S, Oberthuer, A, Fischer, M, Gifford, AJ, Liu, T, Zhang, X, Zhu, S, Gustafson, WC, Haber, M, Norris, MD, Fletcher, JI, Perini, G, Parker, MW, Cheung, BB & Marshall, GM 2019, 'Drugging MYCN Oncogenic Signaling through the MYCN-PA2G4 Binding Interface', Cancer Research, vol. 79, no. 21, pp. 5652-5667.
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Abstract MYCN is a major driver for the childhood cancer, neuroblastoma, however, there are no inhibitors of this target. Enhanced MYCN protein stability is a key component of MYCN oncogenesis and is maintained by multiple feedforward expression loops involving MYCN transactivation target genes. Here, we reveal the oncogenic role of a novel MYCN target and binding protein, proliferation-associated 2AG4 (PA2G4). Chromatin immunoprecipitation studies demonstrated that MYCN occupies the PA2G4 gene promoter, stimulating transcription. Direct binding of PA2G4 to MYCN protein blocked proteolysis of MYCN and enhanced colony formation in a MYCN-dependent manner. Using molecular modeling, surface plasmon resonance, and mutagenesis studies, we mapped the MYCN–PA2G4 interaction site to a 14 amino acid MYCN sequence and a surface crevice of PA2G4. Competitive chemical inhibition of the MYCN–PA2G4 protein–protein interface had potent inhibitory effects on neuroblastoma tumorigenesis in vivo. Treated tumors showed reduced levels of both MYCN and PA2G4. Our findings demonstrate a critical role for PA2G4 as a cofactor in MYCN-driven neuroblastoma and highlight competitive inhibition of the PA2G4-MYCN protein binding as a novel therapeutic strategy in the disease. Significance: Competitive chemical inhibition of the PA2G4–MYCN protein interface provides a basis for drug design of small molecules targeting MYC and MYCN-binding partners in malignancies driven by MYC family oncoproteins.
Kocaballi, AB, Berkovsky, S, Quiroz, JC, Laranjo, L, Tong, HL, Rezazadegan, D, Briatore, A & Coiera, E 2019, 'The Personalization of Conversational Agents in Health Care: Systematic Review', Journal of Medical Internet Research, vol. 21, no. 11, pp. e15360-e15360.
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Background The personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents. Objective The goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation. Methods We searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals; (2) involved a conversational agent with an unconstrained natural language interface; (3) tested the system with human subjects; and (4) implemented personalization features. Results The search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common examples of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly. Conclusions ...
Kocaballi, AB, Coiera, E, Tong, HL, White, SJ, Quiroz, JC, Rezazadegan, F, Willcock, S & Laranjo, L 2019, 'A network model of activities in primary care consultations', Journal of the American Medical Informatics Association, vol. 26, no. 10, pp. 1074-1082.
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AbstractObjectiveThe objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods.Materials and MethodsThis is an observational study in Australian general practice involving 31 consultations with 4 primary care physicians. Consultations were audio-recorded, and computer interactions were recorded using screen capture. Physical interactions in consultation rooms were noted by observers. Brief interviews were conducted after consultations. Conversational transcripts were analyzed to identify different activities and their speech content as well as verbal cues signaling activity transitions. An activity transition analysis was then undertaken to generate a network of activities and transitions.ResultsObserved activity classes followed those described in well-known primary care consultation models. Activities were often fragmented across consultations, did not flow necessarily in a defined order, and the flow between activities was nonlinear. Modeling activities as a network revealed that discussing a patient’s present complaint was the most central activity and was highly connected to medical history taking, physical examination, and assessment, forming a highly interrelated bundle. Family history, allergy, and investigation discussions were less connected suggesting less dependency on other activities. Clear verbal signs were often identifiable at transitions between activities.DiscussionPrimary care consultations do not appear to follow a classic linear model of defined inform...
Kocaballi, AB, Laranjo, L & Coiera, E 2019, 'Understanding and Measuring User Experience in Conversational Interfaces', Interacting with Computers, vol. 31, no. 2, pp. 192-207.
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AbstractAlthough various methods have been developed to evaluate conversational interfaces, there has been a lack of methods specifically focusing on evaluating user experience. This paper reviews the understandings of user experience (UX) in conversational interfaces literature and examines the six questionnaires commonly used for evaluating conversational systems in order to assess the potential suitability of these questionnaires to measure different UX dimensions in that context. The method to examine the questionnaires involved developing an assessment framework for main UX dimensions with relevant attributes and coding the items in the questionnaires according to the framework. The results show that (i) the understandings of UX notably differed in literature; (ii) four questionnaires included assessment items, in varying extents, to measure hedonic, aesthetic and pragmatic dimensions of UX; (iii) while the dimension of affect was covered by two questionnaires, playfulness, motivation, and frustration dimensions were covered by one questionnaire only. The largest coverage of UX dimensions has been provided by the Subjective Assessment of Speech System Interfaces (SASSI). We recommend using multiple questionnaires to obtain a more complete measurement of user experience or improve the assessment of a particular UX dimension.RESEARCH HIGHLIGHTSVarying understandings of UX in conversational interfaces literature. A UX assessment framework with UX dimensions and their relevant attributes. Descriptions of the six main questionnaires for evaluating conversational interfaces. A comparison of the six questionnaires based on their coverage of UX dimensions.
Koopialipoor, M, Fallah, A, Armaghani, DJ, Azizi, A & Mohamad, ET 2019, 'Three hybrid intelligent models in estimating flyrock distance resulting from blasting', Engineering with Computers, vol. 35, no. 1, pp. 243-256.
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Koopialipoor, M, Ghaleini, EN, Tootoonchi, H, Jahed Armaghani, D, Haghighi, M & Hedayat, A 2019, 'Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN', Environmental Earth Sciences, vol. 78, no. 5.
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Koopialipoor, M, Jahed Armaghani, D, Haghighi, M & Ghaleini, EN 2019, 'A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels', Bulletin of Engineering Geology and the Environment, vol. 78, no. 2, pp. 981-990.
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Koopialipoor, M, Jahed Armaghani, D, Hedayat, A, Marto, A & Gordan, B 2019, 'Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions', Soft Computing, vol. 23, no. 14, pp. 5913-5929.
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Koopialipoor, M, Nikouei, SS, Marto, A, Fahimifar, A, Jahed Armaghani, D & Mohamad, ET 2019, 'Predicting tunnel boring machine performance through a new model based on the group method of data handling', Bulletin of Engineering Geology and the Environment, vol. 78, no. 5, pp. 3799-3813.
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Koopialipoor, M, Noorbakhsh, A, Noroozi Ghaleini, E, Jahed Armaghani, D & Yagiz, S 2019, 'A new approach for estimation of rock brittleness based on non-destructive tests', Nondestructive Testing and Evaluation, vol. 34, no. 4, pp. 354-375.
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Koopialipoor, M, Tootoonchi, H, Jahed Armaghani, D, Tonnizam Mohamad, E & Hedayat, A 2019, 'Application of deep neural networks in predicting the penetration rate of tunnel boring machines', Bulletin of Engineering Geology and the Environment, vol. 78, no. 8, pp. 6347-6360.
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Korzekwa, K, Puchała, Z, Tomamichel, M & Życzkowski, K 2019, 'Encoding classical information into quantum resources', IEEE Trans. Inf. Theory, vol. 68, no. 7, pp. 4518-4530.
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We introduce and analyse the problem of encoding classical information intodifferent resources of a quantum state. More precisely, we consider a generalclass of communication scenarios characterised by encoding operations thatcommute with a unique resource destroying map and leave free states invariant.Our motivating example is given by encoding information into coherences of aquantum system with respect to a fixed basis (with unitaries diagonal in thatbasis as encodings and the decoherence channel as a resource destroying map),but the generality of the framework allows us to explore applications rangingfrom super-dense coding to thermodynamics. For any state, we find that thenumber of messages that can be encoded into it using such operations in aone-shot scenario is upper-bounded in terms of the information spectrumrelative entropy between the given state and its version with erased resources.Furthermore, if the resource destroying map is the twirling channel over someunitary group, we find matching one-shot lower-bounds as well. In theasymptotic setting where we encode into many copies of the resource state, ourbounds yield an operational interpretation of resource monotones such as therelative entropy of coherence and its corresponding relative entropy variance.
KRÄTZIG, O, FRANZKOWIAK, V & SICK, N 2019, 'MULTI-LEVEL PERSPECTIVE TO FACILITATE SUSTAINABLE TRANSITIONS — A PATHWAY FOR GERMAN OEMS TOWARDS ELECTRIC VEHICLES', International Journal of Innovation Management, vol. 23, no. 08, pp. 1940006-1940006.
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Sustainable transitions within industrial branches are a complex problem since they involve emerging technologies, as well as cultural, market and policy-related changes. Recent studies emphasise the need for analytical approaches that not only do justice to this complexity by reflecting relevant trends and determinants, but also reveal insights that are intuitive enough to be implemented without major effort. Aiming at addressing this trade-off, we pursue a strategic analytical procedure that links external factors from multi-level perspective and internal, company-specific dynamic capabilities. We draw on expert interviews and subsequent qualitative analytical evaluation to obtain insights regarding individual motives, visions and boundary conditions of actors from the German automotive industry. Our contribution is both conceptually and practically important, as it unveils manifestations of significant dynamic capabilities and provides recommendations for change managers and policy makers leading to successful, sustainable transition in the automotive industry.
Krishnamurthi, R, Patan, R & Gandomi, AH 2019, 'Assistive pointer device for limb impaired people: A novel Frontier Point Method for hand movement recognition', Future Generation Computer Systems, vol. 98, pp. 650-659.
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© 2019 Elsevier B.V. In this modern era, the use of computer technology and computing devices play significant role in every day human activities. From the disabled people perspective, there is huge demand to improve Human–Computer Interaction (HCI), to overcome their difficulty in using the standard interactive devices. Basically, HCI provides a way for humans to interact with a computer using a keyboard, a mouse, and other input devices in real-time. This paper proposes a novel assistive pointer device called Frontier Point method (FPM), which is based on a hand movement recognition technique. The proposed hand movement recognition technique primarily focuses on the direction of hand movement for dynamic recognition in real-time using least square fitting and virtual frame techniques. Next based on boundary values, such that if the hand crosses a boundary value of a given quadrant, then a SENDKEY stroke is generated that corresponds to that range. This method is implemented with the help of a depth sensor camera called Kinect. Kinect takes the RGB data and depth data of the human skeleton and generates coordinate information corresponding to specific body joints. Experiments were conducted in which different users were evaluated for their ability to navigate a PowerPoint presentation multiple times. Collectively, an average recognition time of 2.386 s was calculated with an average recognition rate of 97.37%.
Krivtsov, AV, Evans, K, Gadrey, JY, Eschle, BK, Hatton, C, Uckelmann, HJ, Ross, KN, Perner, F, Olsen, SN, Pritchard, T, McDermott, L, Jones, CD, Jing, D, Braytee, A, Chacon, D, Earley, E, McKeever, BM, Claremon, D, Gifford, AJ, Lee, HJ, Teicher, BA, Pimanda, JE, Beck, D, Perry, JA, Smith, MA, McGeehan, GM, Lock, RB & Armstrong, SA 2019, 'A Menin-MLL Inhibitor Induces Specific Chromatin Changes and Eradicates Disease in Models of MLL-Rearranged Leukemia', Cancer Cell, vol. 36, no. 6, pp. 660-673.e11.
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© 2019 Elsevier Inc. Inhibition of the Menin (MEN1) and MLL (MLL1, KMT2A) interaction is a potential therapeutic strategy for MLL-rearranged (MLL-r) leukemia. Structure-based design yielded the potent, highly selective, and orally bioavailable small-molecule inhibitor VTP50469. Cell lines carrying MLL rearrangements were selectively responsive to VTP50469. VTP50469 displaced Menin from protein complexes and inhibited chromatin occupancy of MLL at select genes. Loss of MLL binding led to changes in gene expression, differentiation, and apoptosis. Patient-derived xenograft (PDX) models derived from patients with either MLL-r acute myeloid leukemia or MLL-r acute lymphoblastic leukemia (ALL) showed dramatic reductions of leukemia burden when treated with VTP50469. Multiple mice engrafted with MLL-r ALL remained disease free for more than 1 year after treatment. These data support rapid translation of this approach to clinical trials.
Kuang, B, Fu, A, Yu, S, Yang, G, Su, M & Zhang, Y 2019, 'ESDRA: An Efficient and Secure Distributed Remote Attestation Scheme for IoT Swarms', IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8372-8383.
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© 2014 IEEE. An Internet of Things (IoT) system generally contains thousands of heterogeneous devices which often operate in swarms - large, dynamic, and self-organizing networks. Remote attestation is an important cornerstone for the security of these IoT swarms, as it ensures the software integrity of swarm devices and protects them from attacks. However, current attestation schemes suffer from single point of failure verifier. In this paper, we propose an Efficient and Secure Distributed Remote Attestation (ESDRA) scheme for IoT swarms. We present the first many-to-one attestation scheme for device swarms, which reduces the possibility of single point of failure verifier. Moreover, we utilize distributed attestation to verify the integrity of each node and apply accusation mechanism to report the invaded nodes, which makes ESDRA much easier to feedback the certain compromised nodes and reduces the run-time of attestation. We analyze the security of ESDRA and do some simulation experiments to show its practicality and efficiency. Especially, ESDRA can significantly reduce the attestation time and has a better performance in the energy consumption comparing with list-based attestation schemes.
Kulasinghe, A, Kapeleris, J, Cooper, C, Warkiani, ME, O’Byrne, K & Punyadeera, C 2019, 'Phenotypic Characterization of Circulating Lung Cancer Cells for Clinically Actionable Targets', Cancers, vol. 11, no. 3, pp. 380-380.
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Objectives: In non-small cell lung cancers (NSCLC), tumour biopsy can often be an invasive procedure. The development of a non-invasive methodology to study genetic changes via circulating tumour cells (CTCs) is an appealing concept. Whilst CTCs typically remain as rare cells, improvements in epitope-independent CTC isolation techniques has given rise to a greater capture of CTCs. In this cross sectional study, we demonstrate the capture and characterization of NSCLC CTCs for the clinically actionable markers epidermal growth factor receptor (EGFR) alterations, anaplastic lymphoma kinase (ALK) rearrangements and programmed death ligand-1 (PD-L1) expression. The study identified CTCs/CTC clusters in 26/35 Stage IV NSCLC patients, and subsequently characterized the CTCs for EGFR mutation, ALK status and PD-L1 status. This pilot study demonstrates the potential of a non-invasive fluid biopsy to determine clinically relevant biomarkers in NSCLC.
Kumar, A, Ramachandran, M, Gandomi, AH, Patan, R, Lukasik, S & Soundarapandian, RK 2019, 'A deep neural network based classifier for brain tumor diagnosis', Applied Soft Computing, vol. 82, pp. 105528-105528.
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© 2019 Elsevier B.V. Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset.
Kumar, C, Hejazian, M, From, C, Saha, SC, Sauret, E, Gu, Y & Nguyen, N-T 2019, 'Modeling of mass transfer enhancement in a magnetofluidic micromixer', Physics of Fluids, vol. 31, no. 6, pp. 063603-063603.
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The use of magnetism for various microfluidic functions such as separation, mixing, and pumping has been attracting great interest from the research community as this concept is simple, effective, and of low cost. Magnetic control avoids common problems of active microfluidic manipulation such as heat, surface charge, and high ionic concentration. The majority of past works on micromagnetofluidic devices were experimental, and a comprehensive numerical model to simulate the fundamental transport phenomena in these devices is still lacking. The present study aims to develop a numerical model to simulate transport phenomena in microfluidic devices with ferrofluid and fluorescent dye induced by a nonuniform magnetic field. The numerical results were validated by experimental data from our previous work, indicating a significant increase in mass transfer. The model shows a reasonable agreement with experimental data for the concentration distribution of both magnetic and nonmagnetic species. Magnetoconvective secondary flow enhances the transport of nonmagnetic fluorescent dye. A subsequent parametric analysis investigated the effect of the magnetic field strength and nanoparticle size on the mass transfer process. Mass transport of the fluorescent dye is enhanced with increasing field strength and size of magnetic particles.
Kumar, DK, Suazo-Davila, D, García-Torres, D, Cook, NP, Ivaturi, A, Hsu, M-H, Martí, AA, Cabrera, CR, Chen, B, Bennett, N & Upadhyaya, HM 2019, 'Low-temperature titania-graphene quantum dots paste for flexible dye-sensitised solar cell applications', Electrochimica Acta, vol. 305, pp. 278-284.
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© 2019 Graphene possesses excellent mechanical strength and chemical inertness with high intrinsic carrier mobility and superior flexibility making them exceptional candidates for optoelectronic applications. Graphene quantum dots (GQDs) derived from graphene domains have been widely explored to study their photoluminescence properties which can be tuned by size. GQDs are biocompatible, low cytotoxic, strongly luminescent and disperse well in polar and non-polar solvents showing bright promise for the integration into devices for bioimaging, light emitting and photovoltaic applications. In the present study, graphene quantum dots were synthesized by an electrochemical cyclic voltammetry technique using reduced graphene oxide (rGO). GQDs have been incorporated into binder free TiO 2 paste and studied as a photoelectrode material fabricated on ITO/PEN substrates for flexible dye sensitised solar cells (DSSCs). DSSC based on GQDs-TiO 2 exhibited open circuit output potential difference (V oc ) of 0.73 V, and short circuit current density (J sc ) of 11.54 mA cm −2 with an increment in power conversion efficiency by 5.48%, when compared with those with DSSC build with just a TiO 2 photoanode (open-circuit output potential difference (V oc ) of 0.68 V and short circuit density (J sc ) of 10.67 mA cm −2 ). The results have been understood in terms of increased charge extraction and reduced recombination losses upon GQDs incorporation.
Kumar, DK, Swami, SK, Dutta, V, Chen, B, Bennett, N & Upadhyaya, HM 2019, 'Scalable screen-printing manufacturing process for graphene oxide platinum free alternative counter electrodes in efficient dye sensitized solar cells', FlatChem, vol. 15, pp. 100105-100105.
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The graphene oxide paste (GO) was prepared by mixing α-terpineol and ethyl cellulose, and GO films was prepared by screen printing on fluorine doped Tin oxide (FTO) glass substrates to validate as an alternative counter electrode material to platinum in dye sensitized solar cells (DSSC). The graphene oxide films were characterised by X-Ray Diffraction, Scanning Electron Microscopy, Raman spectroscopy and the catalytic properties of films were being investigated by cyclic voltammetry and electrochemical Impedance measurements. The DSSC fabricated by coupling TiO 2 films soaked in N719 dye with GO as counter electrode exhibited photoconversion efficiency of 5.58% under standard one Sun illumination, whereas platinum based device showed photoconversion efficiency of 7.57%. The present study suggests that graphene oxide counter electrodes can be considered as a promising alternative to platinum, with further optimisation, which clearly has advantages in terms of its abundance and low cost processing towards industrial prospects.
Kumar, P, Beck, D, Galeev, R, Thoms, JAI, Talkhoncheh, MS, de Jong, I, Unnikrishnan, A, Baudet, A, Subramaniam, A, Pimanda, JE & Larsson, J 2019, 'HMGA2 promotes long-term engraftment and myeloerythroid differentiation of human hematopoietic stem and progenitor cells', Blood Advances, vol. 3, no. 4, pp. 681-691.
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Abstract Identification of determinants of fate choices in hematopoietic stem cells (HSCs) is essential to improve the clinical use of HSCs and to enhance our understanding of the biology of normal and malignant hematopoiesis. Here, we show that high-mobility group AT hook 2 (HMGA2), a nonhistone chromosomal-binding protein, is highly and preferentially expressed in HSCs and in the most immature progenitor cell subset of fetal, neonatal, and adult human hematopoiesis. Knockdown of HMGA2 by short hairpin RNA impaired the long-term hematopoietic reconstitution of cord blood (CB)–derived CB CD34+ cells. Conversely, overexpression of HMGA2 in CB CD34+ cells led to overall enhanced reconstitution in serial transplantation assays accompanied by a skewing toward the myeloerythroid lineages. RNA-sequencing analysis showed that enforced HMGA2 expression in CD34+ cells induced gene-expression signatures associated with differentiation toward megakaryocyte-erythroid and myeloid lineages, as well as signatures associated with growth and survival, which at the protein level were coupled with strong activation of AKT. Taken together, our findings demonstrate a key role of HMGA2 in regulation of both proliferation and differentiation of human HSPCs.
Kumar, R, Binetti, L, Nguyen, TH, Alwis, LSM, Agrawal, A, Sun, T & Grattan, KTV 2019, 'Determination of the Aspect-ratio Distribution of Gold Nanorods in a Colloidal Solution using UV-visible absorption spectroscopy', Scientific Reports, vol. 9, no. 1.
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AbstractKnowledge of the distribution of the aspect ratios (ARs) in a chemically-synthesized colloidal solution of Gold Nano Rods (GNRs) is an important measure in determining the quality of synthesis, and consequently the performance of the GNRs generated for various applications. In this work, an algorithm has been developed based on the Bellman Principle of Optimality to readily determine the AR distribution of synthesized GNRs in colloidal solutions. This is achieved by theoretically fitting the longitudinal plasmon resonance of GNRs obtained by UV-visible spectroscopy. The AR distribution obtained from the use of the algorithm developed have shown good agreement with those theoretically generated one as well as with the previously reported results. After bench-marking, the algorithm has been applied to determine the mean and standard deviation of the AR distribution of two GNRs solutions synthesized and examined in this work. The comparison with experimentally derived results from the use of expensive Transmission Electron Microscopic images and Dynamic Light Scattering technique shows that the algorithm developed offers a fast and thus potentially cost-effective solution to determine the quality of the synthesized GNRs specifically needed for many potential applications for the advanced sensor systems.
Kumar, R, Kumar, R, Sharma, N, Vyas, M, Mahajan, S, Satija, S, Singh, SK, Khursheed, R, Mehta, M, Khurana, S & Khurana, N 2019, 'Fisetin: A phytochemical with various pharmacological activities', Plant Archives, vol. 19, pp. 1012-1016.
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Flavonoids are the plant secondary metabolites which work as growth hormone as well as defence mechanism for the plants. These are well known for their antioxidant properties and are part of our daily food. Fisetin is one of the polyphenolic flavonol, present in various fruits and vegetables. Fisetin is reported to have various pharmacological properties. Strawberries have the maximum concentration of fisetin. Despite having various pharmacological properties, low oral bioavailability and high lipophilicity meared its use. In this review we tried to collect the information regarding the various pharmacological properties and its developed formulations to improve its bioavailability.
Kuruneru, STW, Marechal, E, Deligant, M, Khelladi, S, Ravelet, F, Saha, SC, Sauret, E & Gu, Y 2019, 'A Comparative Study of Mixed Resolved–Unresolved CFD-DEM and Unresolved CFD-DEM Methods for the Solution of Particle-Laden Liquid Flows', Archives of Computational Methods in Engineering, vol. 26, no. 4, pp. 1239-1254.
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© 2018, CIMNE, Barcelona, Spain. The exorbitant economic and environmental cost associated with fouling propels the need to develop advanced numerical methods to accurately decipher the underlying phenomena of fouling and multiphase fluid transport in jet-engine fuel systems. Clogging of jet-fuel systems results in the foulants to settle in seconds to form a porous layer which restricts fuel flow. The objective of this research is to numerically examine the transient evolution of particle-laden liquid flow and particle accumulation on an idealized jet-fuel filter. This is achieved by using two numerical approaches: coupled unresolved computational fluid dynamics-discrete element method (CFD-DEM), and coupled mixed resolved–unresolved CFD-DEM method. We assess the efficacy of both numerical methods by comparing the numerical results against experimental data. Results have shown that the particle accumulation and deposition profiles are in good agreement with the experimental results. Moreover, it is found that the particle distribution spread along the length and height of the channel reflects the actual particle spread as observed in the experiments. The unresolved CFD-DEM and mixed resolved–resolved CFD-DEM method could be harnessed to study complex multiphase fluid flow transport in various other applications such as compact heat exchangers and fluidized beds.
Kusuma, MH, Putra, N, Rosidi, A, Ismarwanti, S, Antariksawan, AR, Ardiyati, T, Juarsa, M & Mahlia, TMI 2019, 'Investigation on the Performance of a Wickless-Heat Pipe Using Graphene Nanofluid for Passive Cooling System', Atom Indonesia, vol. 45, no. 3, pp. 173-173.
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To enhance the thermal safety in case of station blackout, a wickless-heat pipe is proposed as an alternative passive cooling system technology to remove decay heat generation in the nuclear spent fuel storage pool. The objectives of this research are to investigate the heat transfer phenomena in vertical straight wickless-heat pipe using Graphene nanofluid working fluid and to study the effect of Graphene nanofluid on the vertical straight wickless-heat pipe thermal performance. The investigation was conducted in 6 meters height and 0.1016 m inside diameter of vertical straight wickless-heat pipe. In this research, the Graphene nanofluid with 1 % of weight concentration was used as working fluid. The effect of working fluid filling ratio, evaporator heat load, and coolant volumetric flow rate on the water jacket were studied. The results showed that the heat transfer phenomena, which were indicated by an overshoot, zigzag, and stable state, were observed. Based on thermal resistance obtained, it was shown that the vertical straight wickless-heat pipe charged with the Graphene nanofluid has a lower thermal resistance compared to one with demineralized water. The thermal resistance of vertical straight wickless-heat pipe using Graphene nanofluid and demineralized water were 0.015 °C/W and 0.016 °C/W, respectively. While the best thermal performance was achieved at a filing ratio of 80 %, higher heat load, and higher coolant volumetric flow rate. It can be concluded that Graphene nanofluid could enhance the thermal performance of vertical straight wickless-heat pipe.
Kusumo, F, Mahlia, TMI, Shamsuddin, AH, Ong, HC, Ahmad, AR, Ismail, Z, Ong, ZC & Silitonga, AS 2019, 'The Effect of Multi-Walled Carbon Nanotubes-Additive in Physicochemical Property of Rice Brand Methyl Ester: Optimization Analysis', Energies, vol. 12, no. 17, pp. 3291-3291.
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Biodiesel as an alternative to diesel fuel produced from vegetable oils or animal fats has attracted more and more attention because it is renewable and environmentally friendly. Compared to conventional diesel fuel, biodiesel has slightly lower performance in engine combustion due to the lower calorific value that leads to lower power generated. This study investigates the effect of multi-walled carbon nanotubes (MWCNTs) as an additive to the rice bran methyl ester (RBME). Artificial neural network (ANN) and response surface methodology (RSM) was used for predicting the calorific value. The interaction effects of parameters such as dosage of MWCNTs, size of MWCNTs and reaction time on the calorific value of RBME were studied. Comparison of RSM and ANN performance was evaluated based on the correlation coefficient (R2), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the average absolute deviation (AAD) showed that the ANN model had better performance (R2 = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM (R2 = 0.9746, RMSE = 0.0170, MAPE = 0.0028, AAD = 0.279). The optimum predicted of RBME calorific value that is generated using the cuckoo search (CS) via lévy flight optimization algorithm is 41.78 (MJ/kg). The optimum value was obtained using 64 ppm of < 7 nm MWCNTs blending for 60 min. The predicted calorific value was validated experimentally as 41.05 MJ/kg. Furthermore, the experimental results have shown that the addition of MWCNTs was significantly increased the calorific value from 36.87 MJ/kg to 41.05 MJ/kg (11.6%). Also, the addition of MWCNTs decreased flashpoint (−18.3%) and acid value (−0.52%). As a conclusion, adding MWCNTs as an additive had improved the physicochemical properties characteristics of RBME. To our best knowledge, no research has yet been performed on the effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester appli...
La, HM, Dinh, TH, Pham, NH, Ha, QP & Pham, AQ 2019, 'Automated robotic monitoring and inspection of steel structures and bridges', Robotica, vol. 37, no. 5, pp. 947-967.
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SummaryThis paper presents visual and 3D structure inspection for steel structures and bridges using a developed climbing robot. The robot can move freely on a steel surface, carry sensors, collect data and then send to the ground station in real-time for monitoring as well as further processing. Steel surface image stitching and 3D map building are conducted to provide a current condition of the structure. Also, a computer vision-based method is implemented to detect surface defects on stitched images. The effectiveness of the climbing robot's inspection is tested in multiple circumstances to ensure strong steel adhesion and successful data collection. The detection method was also successfully evaluated on various test images, where steel cracks could be automatically identified, without the requirement of some heuristic reasoning.
Lalbakhsh, A, Afzal, MU, Esselle, KP & Smith, SL 2019, 'Wideband Near-Field Correction of a Fabry–Perot Resonator Antenna', IEEE Transactions on Antennas and Propagation, vol. 67, no. 3, pp. 1975-1980.
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© 1963-2012 IEEE. A systematic approach to correcting electric near-field phase and magnitude over a wideband for Fabry-Perot resonator antennas (FPRAs) is presented. Unlike all other unit-cell-based near-field correction techniques for FPRAs, which merely focus on phase correction at a single frequency, this method delivers a compact near-field correcting structure (NFCS) with a wide operational bandwidth of 40%. In this novel approach, a time-average Poynting vector in conjunction with a phase gradient analysis is utilized to suggest the initial configuration of the NFCS for wideband performance. A simulation-driven optimization algorithm is then implemented to find the thickness of each correcting region, defined by the gradient analysis, to complete the NFCS design. According to the predicted and measured results, the phase and magnitude distributions of the electric near field have been greatly improved, resulting in a high aperture efficiency of 70%. The antenna under NFCS loading has a peak measured directivity of 21.6 dB, a 3 dB directivity bandwidth of 41% and a 10 dB return loss bandwidth of 46%, which covers the directivity bandwidth. The diameter of the proposed NFCS is 3.8 λ0c, which is around half that of all the other unit-cell-based phase-correcting structures, where λ0c is the free-space wavelength at the central frequency of the NFCS (13.09 GHz).
Lalbakhsh, A, Afzal, MU, Esselle, KP, Smith, SL & Zeb, BA 2019, 'Single-Dielectric Wideband Partially Reflecting Surface With Variable Reflection Components for Realization of a Compact High-Gain Resonant Cavity Antenna', IEEE Transactions on Antennas and Propagation, vol. 67, no. 3, pp. 1916-1921.
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© 1963-2012 IEEE. This communication presents a design methodology for a compact low-cost partially reflecting surface (PRS) for a wideband high-gain resonant cavity antenna (RCA) which requires only a single commercial dielectric slab. The PRS has one nonuniform double-sided printed dielectric, which exhibits a negative transverse-reflection magnitude gradient and, at the same time, a progressive reflection phase gradient over frequency. In addition, a partially shielded cavity is proposed as a method to optimize the directivity bandwidth and the peak directivity of RCAs. A prototype of the PRS was fabricated and tested with a partially shielded cavity, showing good agreement between the predicted and measured results. The measured peak directivity of the antenna is 16.2 dBi at 11.4 GHz with a 3 dB bandwidth of 22%. The measured peak gain and 3 dB gain bandwidth are 15.75 dBi and 21.5%, respectively. The PRS has a radius of 29.25 mm (1.1λ0 ) with a thickness of 1.52 mm ( 0.12λg ), and the overall height of the antenna is 0.6λ0, where λ0 and λg are the free-space and guided wavelengths at the center frequency of 11.4 GHz.
Lammie, C, Hamilton, TJ, van Schaik, A & Rahimi Azghadi, M 2019, 'Efficient FPGA Implementations of Pair and Triplet-Based STDP for Neuromorphic Architectures', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 66, no. 4, pp. 1558-1570.
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© 2018 IEEE. Synaptic plasticity is envisioned to bring about learning and memory in the brain. Various plasticity rules have been proposed, among which spike-timing-dependent plasticity (STDP) has gained the highest interest across various neural disciplines, including neuromorphic engineering. Here, we propose highly efficient digital implementations of pair-based STDP (PSTDP) and triplet-based STDP (TSTDP) on field programmable gate arrays that do not require dedicated floating-point multipliers and hence need minimal hardware resources. The implementations are verified by using them to replicate a set of complex experimental data, including those from pair, triplet, quadruplet, frequency-dependent pairing, as well as Bienenstock-Cooper-Munro experiments. We demonstrate that the proposed TSTDP design has a higher operating frequency that leads to 2.46 × faster weight adaptation (learning) and achieves 11.55 folds improvement in resource usage, compared to a recent implementation of a calcium-based plasticity rule capable of exhibiting similar learning performance. In addition, we show that the proposed PSTDP and TSTDP designs, respectively, consume 2.38 × and 1.78 × less resources than the most efficient PSTDP implementation in the literature. As a direct result of the efficiency and powerful synaptic capabilities of the proposed learning modules, they could be integrated into large-scale digital neuromorphic architectures to enable high-performance STDP learning.
Lamqadem, AA, Saber, H & Pradhan, B 2019, 'Long-Term Monitoring of Transformation from Pastoral to Agricultural Land Use Using Time-Series Landsat Data in the Feija Basin (Southeast Morocco)', Earth Systems and Environment, vol. 3, no. 3, pp. 525-538.
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The expansion of agricultural land at the cost of pastoral land is the common cause of land degradation in the arid areas of developing countries, especially in Morocco. This study aims to assess and monitor the transformation of pastoral land to agricultural land in the arid environment of the Feija Basin (Southeast of Morocco) and to find the key drivers and the issues resulting from this transformation. Spectral mixture analysis was applied to multi-temporal (1975–2017) and multi-sensor (i.e. Multi-spectral Scanner, Thematic Mapper, and Operational Land Imager) Landsat satellite images, from which land use classifications were derived. The remote sensing data in combination with ground reference data (household level), groundwater and climate statistics were used to validate and explain the derived land use change maps. The results of the spatiotemporal changes in agricultural lands show two patterns of changes, a middle expansion from 1975 to 2007, and a rapid expansion from 2008 to 2017. In addition, the overall accuracy demonstrated a high accuracy of 94.4%. In 1975 and 1984, the agricultural lands in Feija covered 0.17 km and 1.32 km , respectively, compared with 20.10 km in 2017. Since the adoption of the Green Morocco Plan in 2008, the number of watermelon farms and wells has increased rapidly in the study area, which induced a piezometric level drawdown. The results show that spectral mixture analysis yields high accuracies for agricultural lands extraction in arid dry lands and accounts for mixed pixels issues. Results of this study can be used by local administrators to prepare an effective environmental management plan of these fragile drylands. The proposed method can be replicated in other regions to analyse land transformation in similar arid conditions. 2 2 2
Lan, C, Peng, H, Hutvagner, G & Li, J 2019, 'Construction of competing endogenous RNA networks from paired RNA-seq data sets by pointwise mutual information', BMC Genomics, vol. 20, no. S9, p. 943.
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Abstract Background A long noncoding RNA (lncRNA) can act as a competing endogenous RNA (ceRNA) to compete with an mRNA for binding to the same miRNA. Such an interplay between the lncRNA, miRNA, and mRNA is called a ceRNA crosstalk. As an miRNA may have multiple lncRNA targets and multiple mRNA targets, connecting all the ceRNA crosstalks mediated by the same miRNA forms a ceRNA network. Methods have been developed to construct ceRNA networks in the literature. However, these methods have limits because they have not explored the expression characteristics of total RNAs. Results We proposed a novel method for constructing ceRNA networks and applied it to a paired RNA-seq data set. The first step of the method takes a competition regulation mechanism to derive candidate ceRNA crosstalks. Second, the method combines a competition rule and pointwise mutual information to compute a competition score for each candidate ceRNA crosstalk. Then, ceRNA crosstalks which have significant competition scores are selected to construct the ceRNA network. The key idea, pointwise mutual information, is ideally suitable for measuring the complex point-to-point relationships embedded in the ceRNA networks. Conclusion Computational experiments and results demonstrate that the ceRNA networks can capture important regulatory mechanism of breast cancer, and have also revealed new insights into the treatment of breast cancer. The proposed method can be directly applied to other RNA-seq data sets for deeper disease understanding.
Lapko, Y, Trianni, A, Nuur, C & Masi, D 2019, 'In Pursuit of Closed‐Loop Supply Chains for Critical Materials: An Exploratory Study in the Green Energy Sector', Journal of Industrial Ecology, vol. 23, no. 1, pp. 182-196.
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SummaryA closed‐loop supply chain (CLSC) is considered not only an important solution for ensuring sustainable exploitation of materials, but also a promising strategy for securing long‐term availability of materials. The latter is especially highlighted in the materials criticality discourse. Critical raw materials (CRMs), being exposed to supply disruptions, create an uncertain operational environment for many industries, particularly for green energy technologies that employ multiple CRMs. However, recycling rates of CRMs are very low and engagement of companies in CLSC for CRM is limited. This study examines factors influencing CLSC for CRM development in photovoltaic panels and wind turbine technologies. The aim is to analyze how the factors manifest themselves in different companies along the supply chain and to identify enabling and bottleneck conditions for implementation of CLSC for CRM. The novelty of the study is twofold: the focus on material rather than product flows, and examination of factors from a multiactor perspective. The evidence obtained suggests that the manufacturing companies and reverse supply‐chain operators engaged in the study take different perspectives (product vs. material) regarding development of CLSC for CRM and thus emphasize different factors. The findings underline the need for interactions between supply‐chain actors, a sound competitive environment for recycling processes, and investment in technologies and infrastructure development if CLSC for CRM is to be developed. The paper provides implications for practitioners and policy makers for implementation of CLSC for CRM, and suggests prospects for further research.
Law, Y, Matysik, A, Chen, X, Swa Thi, S, Ngoc Nguyen, TQ, Qiu, G, Natarajan, G, Williams, RBH, Ni, B-J, Seviour, TW & Wuertz, S 2019, 'High Dissolved Oxygen Selection against Nitrospira Sublineage I in Full-Scale Activated Sludge', Environmental Science & Technology, vol. 53, no. 14, pp. 8157-8166.
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© 2019 American Chemical Society. A single Nitrospira sublineage I OTU was found to perform nitrite oxidation in full-scale domestic wastewater treatment plants (WWTPs) in the tropics. This taxon had an apparent oxygen affinity constant lower than that of the full-scale domestic activated sludge cohabitating ammonium oxidizing bacteria (AOB) (0.09 ± 0.02 g O2 m-3 versus 0.3 ± 0.03 g O2 m-3). Thus, nitrite oxidizing bacteria (NOB) may in fact thrive under conditions of low oxygen supply. Low dissolved oxygen (DO) conditions selected for and high aeration inhibited the NOB in a long-term lab-scale reactor. The relative abundance of Nitrospira sublineage I gradually decreased with increasing DO until it was washed out. Nitritation was sustained even after the DO was lowered subsequently. The morphologies of AOB and NOB microcolonies responded to DO levels in accordance with their oxygen affinities. NOB formed densely packed spherical clusters with a low surface area-to-volume ratio compared to the Nitrosomonas-like AOB clusters, which maintained a porous and nonspherical morphology. In conclusion, the effect of oxygen on AOB/NOB population dynamics depends on which OTU predominates given that oxygen affinities are species-specific, and this should be elucidated when devising operating strategies to achieve mainstream partial nitritation.
Lay, US, Pradhan, B, Yusoff, ZBM, Abdallah, AFB, Aryal, J & Park, H-J 2019, 'Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data', Sensors, vol. 19, no. 16, pp. 3451-3451.
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Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.
Le, AT, Tran, LC, Huang, X & Guo, YJ 2019, 'Analog Least Mean Square Loop With I/Q Imbalance for Self-Interference Cancellation in Full-Duplex Radios', IEEE Transactions on Vehicular Technology, vol. 68, no. 10, pp. 9848-9860.
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© 1967-2012 IEEE. Analog least mean square (ALMS) loop is a promising structure for self-interference (SI) mitigation in full-duplex radios due to its simplicity and adaptive capability. However, being constructed from in-phase/quadrature (I/Q) demodulators and modulators to process complex signals, the ALMS loop may face I/Q imbalance problems. Thus, in this paper, the effects of frequency-independent I/Q imbalance in the ALMS loop are investigated. It is revealed that I/Q imbalance affects the loop gain and the level of SI cancellation. The loop gain can be easily compensated by adjusting the gain at other stages of the ALMS loop. Meanwhile, the degradation on cancellation performance is proved to be insignificant even under severe conditions of I/Q imbalance. In addition, an upper bound of the degradation factor is derived to provide an essential reference for the system design. Simulations are conducted to confirm the theoretical analyses.
Le, AT, Tran, LC, Huang, X, Guo, YJ & Vardaxoglou, JYC 2019, 'Frequency-Domain Characterization and Performance Bounds of ALMS Loop for RF Self-Interference Cancellation', IEEE Transactions on Communications, vol. 67, no. 1, pp. 682-692.
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© 1972-2012 IEEE. Analog least mean square (ALMS) loop is a promising method to cancel self-interference (SI) in in-band full-duplex (IBFD) systems. In this paper, the steady state analyses of the residual SI powers in both analog and digital domains are firstly derived. The eigenvalue decomposition is then utilized to investigate the frequency domain characteristics of the ALMS loop. Our frequency domain analyses prove that the ALMS loop has an effect of amplifying the frequency components of the residual SI at the edges of the signal spectrum in the analog domain. However, the matched filter in the receiver chain will reduce this effect, resulting in a significant improvement of the interference suppression ratio (ISR). It means that the SI will be significantly suppressed in the digital domain before information data detection. This paper also derives the lower bounds of ISRs given by the ALMS loop in both analog and digital domains. These lower bounds are joint effects of the loop gain, tap delay, number of taps, and transmitted signal properties. The discovered relationship among these parameters allows the flexibility in choosing appropriate parameters when designing the IBFD systems under given constraints.
Le, NT & Hoang, DB 2019, 'A Threat Computation Model using a Markov Chain and Common Vulnerability Scoring System and its Application to Cloud Security', Journal of Telecommunications and the Digital Economy, vol. 7, no. 1, pp. 37-56.
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Securing cyber infrastructures has become critical because they are increasingly exposed to attackers while accommodating a huge number of IoT devices and supporting numerous sophisticated emerging applications. Security metrics are essential for assessing the security risks and making effective decisions concerning system security. Many security metrics rely on mathematical models, but are mainly based on empirical data, qualitative methods, or compliance checking, and this renders the outcome far from satisfactory. Computing the probability of an attack, or more precisely a threat that materialises into an attack, forms an essential basis for a quantitative security metric. This paper proposes a novel approach to compute the probability distribution of cloud security threats based on a Markov chain and Common Vulnerability Scoring System. Moreover, the paper introduces the method to estimate the probability of security attacks. The use of the new security threat model and its computation is demonstrated through their application to estimating the probabilities of cloud threats and types of attacks.
Lee, J, Jung, H-S, Zlatanoya, S & Pradhan, B 2019, 'Editorial', International Journal of Urban Sciences, vol. 23, no. 3, pp. 301-302.
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Lee, S, Choi, J, Park, Y-G, Shon, H, Ahn, CH & Kim, S-H 2019, 'Hybrid desalination processes for beneficial use of reverse osmosis brine: Current status and future prospects', Desalination, vol. 454, pp. 104-111.
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© 2018 Elsevier B.V. As water shortage has increasingly become a serious global problem, desalination using seawater reverse osmosis (SWRO) is considered as a sustainable source of potable water sources. However, a major issue on the SWRO desalination plant is the generation of brine that has potential adverse impact due to its high salt concentration. Accordingly, it is necessary to develop technologies that allow environmentally friendly and economically viable management of SWRO brines. This paper gives an overview of recent research works and technologies to treat SWRO brines for its beneficial use. The treatment processes have been classified into two different groups according to their final purpose: 1) technologies for producing fresh water and 2) technologies for recovering energy. Topics in this paper includes membrane distillation (MD), forward osmosis (FO), pressure-retarded osmosis (PRO), reverse electrodialysis (RED) as emerging tools for beneficial use of SWRO brine. In addition, a new approach to simultaneously recover water and energy from SWRO brine is introduced as a case study to provide insight into improving the sustainability of seawater desalination.
LEI Jie, 雷杰, YU Lu-lu, 于, LUO Xiao-hong, 罗 & LI Yun-song, 李 2019, 'Efficient coding method for deep space detection Bayer pattern image', Optics and Precision Engineering, vol. 27, no. 1, pp. 191-200.
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Lei, J, Xie, W, Yang, J, Li, Y & Chang, C-I 2019, 'Spectral–Spatial Feature Extraction for Hyperspectral Anomaly Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 10, pp. 8131-8143.
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Leijia, W 2019, 'Re-examining the Meaning of Sunzi’s Bu zhan er qu ren zhi bing 不戰而屈人之兵 and Its Practicality', Monumenta Serica, vol. 67, no. 2, pp. 293-317.
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© 2019, © Monumenta Serica Institute 2019. This article re-examines the meaning of bu zhan er qu ren zhi bing 不戰而屈人之兵, one of the core notions of Sunzi 孫子, and discusses its practicality. The article questions the popularly pacific and defensive view on this expression and refutes the argument that it is an idealistic concept. By analyzing the context and historical background of Sunzi, this article argues that bu zhan er qu ren zhi bing is a realistic strategy developed to adapt to the increasingly fierce competition during the period of the Chunqiu 春秋 (770–5th c. B.C.E). The term can be applied to both offensive and defensive strategies. Its meaning does not equate to but includes subjugating the enemy or frustrating his strategic goals through famou 伐謀 and fajiao 伐交 rather than engaging in decisive battles. Through studying four historical cases from the Chunqiu, this article analyses how to apply famou and fajiao to achieve bu zhan er qu ren zhi bing in practice.
Leonard, RJ, Pettit, TJ, Irga, P, McArthur, C & Hochuli, DF 2019, 'Acute exposure to urban air pollution impairs olfactory learning and memory in honeybees', Ecotoxicology, vol. 28, no. 9, pp. 1056-1062.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. While the ecological effects of pesticides have been well studied in honeybees, it is unclear to what extent other anthropogenic contaminants such as air pollution may also negatively affect bee cognition and behaviour. To answer this question, we assessed the impacts of acute exposure to four ecologically relevant concentrations of a common urban air pollutant—diesel generated air pollution on honeybee odour learning and memory using a conditioned proboscis extension response assay. The proportion of bees that successfully learnt odours following direct air pollution exposure was significantly lower in bees exposed to low, medium and high air pollutant concentrations, than in bees exposed to current ambient levels. Furthermore, short- and long-term odour memory was significantly impaired in bees exposed to low medium and high air pollutant concentrations than in bees exposed to current ambient levels. These results demonstrate a clear and direct cognitive cost of air pollution. Given learning and memory play significant roles in foraging, we suggest air pollution will have increasing negative impacts on the ecosystem services bees provide and may add to the current threats such as pesticides, mites and disease affecting colony fitness.
León-Castro, E, Espinoza-Audelo, LF, Aviles-Ochoa, E, Merigó, JM & Kacprzyk, J 2019, 'A NEW MEASURE OF VOLATILITY USING INDUCED HEAVY MOVING AVERAGES', Technological and Economic Development of Economy, vol. 25, no. 4, pp. 576-599.
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The volatility is a dispersion technique widely used in statistics and economics. This paper presents a new way to calculate volatility by using different extensions of the ordered weighted average (OWA) operator. This approach is called the induced heavy ordered weighted moving average (IHOWMA) volatility. The main advantage of this operator is that the classical volatility formula only takes into account the standard deviation and the average, while with this formulation it is possible to aggregate information according to the decision maker knowledge, expectations and attitude about the future. Some particular cases are also presented when the aggregation information process is applied only on the standard deviation or on the average. An example in three different exchange rates for 2016 are presented, these are for: USD/MXN, EUR/MXN and EUR/USD
León-Castro, E, Merigó, JM, Avilés-Ochoa, E, Gil-Lafuent, AM & Herrera-Viedma, E 2019, 'MODELLING AND SIMULATION IN BUSINESS, ECONOMICS AND MANAGEMENT', Technological and Economic Development of Economy, vol. 25, no. 4, pp. 571-575.
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Modelling and Simulation in Business, Economics and Management. Technological and Economic Development of Economy, 25(4), pp. 571-575.
Leong, KY, Chew, SP, Gurunathan, BA, Ku Ahmad, KZ & Ong, HC 2019, 'An experimental approach to investigate thermal performance of paraffin wax and 1-hexadecanol based heat sinks for cooling of electronic system', International Communications in Heat and Mass Transfer, vol. 109, pp. 104365-104365.
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© 2019 Elsevier Ltd The efficiency and life span of an electronic device or system depends on its operating temperature. Longer operation period in elevated temperatures leads to system failure. In addition, miniaturization of electronic device and generation of high energy density are the current trend in this field. Therefore, an efficient cooling system is vital for ensuring this system operates in optimum temperature. Integration of heat sinks together with phase change material (PCM) can be adapted to dissipate heat generation by electronic system. This study intends to investigate thermal performance of various configuration of cross-fin heat sinks with and without PCM. Two types of PCMs considered are paraffin wax and 1-hexadecanol. The study implies that addition of fins and PCM capable of augmented thermal performance of the heat sinks. Higher number of cross-fin and amount of PCM led to lower heat sinks base temperature. The base temperature of heat sinks with cross-fin (16 square cavities) fully added with paraffin wax is 46.9 °C compared to 51.6 °C observed for similar heat sinks without paraffin wax. Heat sinks filled with paraffin wax performed better than heat sinks filled with 1-hexadecanol. This translates to lower base heat sinks temperature especially at the mid-region of the test period.
Li, B, Xiong, J, Liu, B, Gui, L, Qiu, M & Shi, Z 2019, 'Cache-Based Popular Services Pushing on High-Speed Train by Using Converged Broadcasting and Cellular Networks', IEEE Transactions on Broadcasting, vol. 65, no. 3, pp. 577-588.
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© 1963-12012 IEEE. This paper presents a cache-based popular services pushing solution on high-speed train (HST) by using converged wireless broadcasting and cellular networks. Pushing and caching popular services on the HST to improve the capacity of the network is a very efficient way; and it can also bring a better user experience. The most popular services are transmitted and cached on the vehicle relay station of the train ahead the departure time in the proposed model. Then, the most popular services are broadcasted and cached on the User Equipment after all the passengers are on the train; the less popular services are delivered to the passengers by P2P mode through the relayed cellular network on the train. Specifically, we firstly use the dynamic programming algorithm to maximize the network capacity in limited pushing time, which can be converted to the 0-1 Knapsack problem. Furthermore, we propose three greedy algorithms to approximate the optimal solution on account of the high time complexity of dynamic programming when the input scale gets bigger. And simulation results show that the proposed popularity-based greedy algorithm performs well. Moreover, as the passengers may get on and off the HST when arriving at an intermediate station, a services rebroadcast algorithm is employed when more intermediate stations are considered. U-shaped distribution is adopted to indicate the number of passengers getting on and off the train. Simulations also show that the proposed rebroadcast algorithm can efficiently improve the capacity of the converged networks.
Li, C, Xie, H-B, Fan, X, Xu, RYD, Van Huffel, S, Sisson, SA & Mengersen, K 2019, 'Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein’s Unbiased Risk Estimator', IEEE Transactions on Image Processing, vol. 28, no. 10, pp. 4899-4911.
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© 1992-2012 IEEE. Singular value thresholding (SVT)- or nuclear norm minimization (NNM)-based nonlocal image denoising methods often rely on the precise estimation of the noise variance. However, most existing methods either assume that the noise variance is known or require an extra step to estimate it. Under the iterative regularization framework, the error in the noise variance estimate propagates and accumulates with each iteration, ultimately degrading the overall denoising performance. In addition, the essence of these methods is still least squares estimation, which can cause a very high mean-squared error (MSE) and is inadequate for handling missing data or outliers. In order to address these deficiencies, we present a hybrid denoising model based on variational Bayesian inference and Stein's unbiased risk estimator (SURE), which consists of two complementary steps. In the first step, the variational Bayesian SVT performs a low-rank approximation of the nonlocal image patch matrix to simultaneously remove the noise and estimate the noise variance. In the second step, we modify the conventional SURE full-rank SVT and its divergence formulas for rank-reduced eigen-triplets to remove the residual artifacts. The proposed hybrid BSSVT method achieves better performance in recovering the true image compared with state-of-the-art methods.
Li, D, Ye, D, Gao, N & Wang, S 2019, 'Service Selection With QoS Correlations in Distributed Service-Based Systems', IEEE Access, vol. 7, pp. 88718-88732.
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© 2013 IEEE. Service selection is an important research problem in distributed service-based systems, which aims to select proper services to meet user requirements. A number of service selection approaches have been proposed in recent years. Most of them, however, overlook quality-of-service (QoS) correlations, which broadly exist in distributed service-based systems. The concept of QoS correlations involves two aspects: 1) QoS correlations among services and 2) QoS correlations of user requirements. The first aspect means that some QoS attributes of service not only depend on the service itself but also have correlations with other services, e.g., buying service 1 and then getting service 2 with half price. The second aspect means the relationships among QoS attributes of user requirements, e.g., a user can accept a service with fast response time and high service cost or the user can also accept a service with slow response time and low service cost (Fig. 1). These correlations significantly affect user selection of services. Currently, only a few existing approaches have considered QoS correlations among services, i.e., the first aspect, but they still overlook QoS correlations of user requirements, i.e., the second aspect, which are also very important in distributed service-based systems. In this paper, a novel service selection approach is proposed, which not only considers QoS correlations of services but also accounts for QoS correlations of user requirements. This approach, to the best of our knowledge, is the first one which considers QoS correlations of user requirements. Also, this approach is decentralized which can avoid the single point of failure. The experimental results demonstrate the effectiveness of the proposed approach.
Li, G, He, J, Peng, S, Jia, W, Wang, C, Niu, J & Yu, S 2019, 'Energy Efficient Data Collection in Large-Scale Internet of Things via Computation Offloading', IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4176-4187.
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© 2014 IEEE. Internet of Things (IoT) can be used to promote many advanced applications by utilizing the sensed data collected from various settings. To reduce the energy consumption of IoT devices, and to extend the lifetime of network, the sensed data are usually compressed before their transmission through compressed sensing theory. By reconstructing the sensed data at the edge of network with more resourceful devices, such as laptops and servers, the intensive computation and energy consumption of the IoT nodes could be effectively offloaded. However, most of the existing data collection schemes are limited in their scalability, because the unified data reconstruction models of them are not suitable for large-scale surveillance scenarios. In our proposed scheme, the whole network is first partitioned into a number of data correlated clusters based on spatial correlation. Then, a data collection tree is built to collect the compressed data in a hybrid mode. Finally, the data reconstruction problem is modelled as a group sparse problem and solved through using an alternating direction method of multiplier-based algorithm. The performance of data communication and reconstruction of the proposed scheme is evaluated through experiments with real data set. The experimental results show that the proposed scheme can indeed lower the amount of data transmission, prolong the network life, and achieve a higher level of accuracy in data collection compared to existing data collection schemes.
Li, G, Zhou, L, Yu, N, Ding, Y, Ying, M & Xie, Y 2019, 'Poq: Projection-based Runtime Assertions for Debugging on a Quantum Computer.', CoRR, vol. abs/1911.12855.
Li, H, Luo, Z, Xiao, M, Gao, L & Gao, J 2019, 'A new multiscale topology optimization method for multiphase composite structures of frequency response with level sets', Computer Methods in Applied Mechanics and Engineering, vol. 356, pp. 116-144.
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© 2019 Elsevier B.V. This paper proposes a new multiscale topology optimization method for the concurrent design of multiphase composite structures under a certain range of excitation frequencies. Distinguished from the existed studies, a general concurrent design formulation for the dynamic composite structures with more than two material phases is developed. The macrostructureand its microstructures with multiple material phases are optimized simultaneously. The integral of the dynamic compliances over an interval of frequencies is formulated as the optimization objective, so as to minimize the frequency response within the concerned excitation range. The effective properties of the multiphase microstructures are evaluated by using the numerical homogenization method, which actually serves as a link to bridge the macro and micro finite element analyses. Furthermore, to describe the boundaries of multiple material phases for the microstructure, a parametric color level set method (PCLSM) is developed by using an efficient interpolation scheme. In this way, L level set functions can represent at most 2L material phases without any overlaps. Moreover, these “color” level sets are updated by directly using the well-established gradient-based algorithm, which can greatly facilitate the proposed method to solve the multi-material optimizations with multiple design constraints. Several 2D and 3D numerical examples are used to demonstrate the effectiveness of the proposed method in the concurrent design of the dynamic composite structures under the excitation frequency ranges.
Li, H, Wang, J, Li, R & Lu, H 2019, 'Novel analysis–forecast system based on multi-objective optimization for air quality index', Journal of Cleaner Production, vol. 208, pp. 1365-1383.
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© 2018 Elsevier Ltd The air quality index (AQI) is an important indicator of air quality. Owing to the randomness and non-stationarity inherent in AQI, it is still a challenging task to establish a reasonable analysis–forecast system for AQI. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to improve both aspects simultaneously, leading to unsatisfactory results. In this study, a novel analysis–forecast system is proposed that consists of complexity analysis, data preprocessing, and optimize–forecast modules and addresses the problems of air quality monitoring. The proposed system performs a complexity analysis of the original series based on sample entropy and data preprocessing using a novel feature selection model that integrates a decomposition technique and an optimization algorithm for removing noise and selecting the optimal input structure, and then forecasts hourly AQI series by utilizing a modified least squares support vector machine optimized by a multi-objective multi-verse optimization algorithm. Experiments based on datasets from eight major cities in China demonstrated that the proposed system can simultaneously obtain high accuracy and strong stability and is thus efficient and reliable for air quality monitoring.
Li, H, Wang, TQ & Huang, X 2019, 'Joint Adaptive AoA and Polarization Estimation Using Hybrid Dual-Polarized Antenna Arrays', IEEE Access, vol. 7, pp. 76353-76366.
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© 2013 IEEE. The propagation of a millimeter wave (mmWave) signal is dominated by its line-of-sight component. Therefore, the knowledge of angle-of-arrival and polarization state of the wave is of great importance for its reception at the receiver. In this paper, we estimate these parameters for an information-bearing signal in mmWave systems using hybrid antenna arrays with dual-polarized dipoles. The estimation is studied in the context of both the interleaved and localized arrays. Two blind adaptive algorithms, namely, the joint differential beam tracking and cross-correlation-to-power ratio polarization tracking, and the differential beam and polarization search, are developed, each tailored for an array. It is shown that the use of dual-polarized dipoles in combination with the developed algorithms effectively lead to polarization diversity which significantly enhances the signal-to-noise ratio at the decoder. The simulation results also show that the antennas with dual dipoles provide improved accuracy and convergence rate for the estimations compared with the conventional arrays.
Li, H, Wang, TQ, Huang, X & Jay Guo, Y 2019, 'Adaptive AoA and Polarization Estimation for Receiving Polarized mmWave Signals', IEEE Wireless Communications Letters, vol. 8, no. 2, pp. 540-543.
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© 2012 IEEE. This letter proposes a novel hybrid dual-polarized antenna array which exploits two orthogonally collocated dipoles to capture the full power of a polarized millimeter wave signal. To maximize the received signal-to-noise ratio (SNR), we study the adaptive angle-of-arrival and polarization state estimation, and develop a differential beam tracking algorithm and a cross-correlation-to-power ratio polarization tracking algorithm for interleaved hybrid dual-polarized arrays. Simulation results verify the superior performance of the proposed algorithms, and confirm the significant improvement of SNR obtained by using the proposed array and algorithms.
Li, H, Wang, TQ, Huang, X, Zhang, JA & Guo, YJ 2019, 'Low-Complexity Multiuser Receiver for Massive Hybrid Array mmWave Communications', IEEE Transactions on Communications, vol. 67, no. 5, pp. 3512-3524.
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© 1972-2012 IEEE. In this paper, we study the low complexity reception of multiuser signals in uplink millimeter wave (mmWave) communications using a partially connected hybrid antenna array. Exploiting the mmWave channel property, we propose a low-complexity user-directed multiuser receiver with three novel schemes for allocating subarrays to users. This receiver only requires the knowledge of angles-of-Arrival (AoAs) for dominating paths and a small amount of equivalent channel information instead of perfect channel state information. For comparison, we also derive a successive interference cancellation-based solution as a performance benchmark. We design two types of reference signals with the channel estimation method to enable efficient and simple estimation for AoA and equivalent baseband channel. Also, we provide analytical results for the performance of the AoA estimation, using the lower bounds of mean square errors in line-of-sight dominated mmWave channels. The simulation results validate that the proposed channel estimation method is effective when employed in combination with a zero-forcing equalizer.
Li, H, Xia, Q, Wen, S, Wang, L & Lv, L 2019, 'Identifying Factors Affecting the Sustainability of Water Environment Treatment Public‐Private Partnership Projects', Advances in Civil Engineering, vol. 2019, no. 1, pp. 1-15.
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Sustainability has recently been acknowledged as a crucial issue in infrastructure projects. Developing a model to evaluate project sustainability according to sustainability indicators plays a major role in promoting the sustainable development of water environment treatment public‐private partnership (PPP) projects. Traditional sustainability assessments are mostly based on the triple bottom line (economic, social, and environmental) and lack a more integrated indicator system. To connect the research gap, this paper identifies 27 factors that affect the sustainability of water environment treatment PPP projects from five dimensions: economy, society, resources and environment, engineering, and project management using exploratory factor analysis. The fitting degree between the model and original data is verified by confirmatory factor analysis. The results showed that the fitting was successful. This paper makes two contributions: first, it provides a comprehensive sustainability evaluation indicator system from five aspects, laying a foundation for the evaluation of project sustainability. Second, this study defines a methodology to evaluate and rank factors, identifies the indicators that show the most significant impact on project sustainability in the five dimensions, which provide a reliable reference for the public and private sector to take appropriate measures to improve the sustainability level of water environment treatment public‐private partnership projects.
Li, J, Zhu, X, Law, S-S & Samali, B 2019, 'Drive-By Blind Modal Identification with Singular Spectrum Analysis', Journal of Aerospace Engineering, vol. 32, no. 4, pp. 04019050-04019050.
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© 2019 American Society of Civil Engineers. Drive-by bridge parameter identification has been an active research area in recent years. An instrumented vehicle passing over a bridge deck captures dynamic information of the bridge structure without bridge closure and on-site instrumentation. The vehicle dynamic response includes components associated with the bridge surface roughness and the vehicle and bridge vibration. It is a challenge to separate these components and extract the bridge modal parameters from the vehicle response. A novel drive-by blind modal identification with singular spectrum analysis is proposed to extract the bridge modal frequencies from the vehicle dynamic response. The single-channel measured vehicular response is decomposed into a multichannel data set using singular spectrum analysis, and the bridge frequencies are then extracted via the blind modal identification. Numerical results showed that the proposed method is effective and robust to extract the bridge frequencies from the vehicle response measurement even with Class B road surface roughness. The effects of the moving speed and the vehicle parameters on the identification were studied. A vehicle-bridge interaction model in the laboratory was studied to further verify the proposed method using one- and two-axle vehicles.
Li, J, Zhu, X, Law, S-S & Samali, B 2019, 'Indirect bridge modal parameters identification with one stationary and one moving sensors and stochastic subspace identification', Journal of Sound and Vibration, vol. 446, pp. 1-21.
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© 2019 Elsevier Ltd A new indirect strategy is proposed to estimate the bridge modal parameters from the dynamic responses of two vehicles using stochastic subspace identification technique. The effect of ambient excitation, such as ongoing traffic, is simulated as white-noise excitation at the bridge supports. The state-space model of the vehicle-bridge interaction system is derived for a single-degree-of-freedom quarter-car model and the bridge deck modeled as a simply-supported Euler-Bernoulli beam. Bridge modal frequencies can be estimated accurately from the vehicle responses. Two instrumented vehicles are required to estimate the bridge mode shapes, with one serving as a fixed reference sensor and the other as a moving sensor. The measured accelerations from the vehicles are divided into segments and each pair of signal segments forms a state-space identification problem. Local mode shape value from each signal segment can be estimated using the reference-based SSI method. A rescaling on the local mode shape values is applied to construct the global mode shapes. Effects of the bridge surface roughness, measurement noise and vehicle properties on the mode shape identification are also numerically studied. A vehicle-bridge interaction model in the laboratory serves for the experimental validation of the proposed strategy. Both numerical and experimental results show that the proposed method can estimate the bridge modal parameters with acceptable accuracy.
Li, JJ, Dunstan, CR, Entezari, A, Li, Q, Steck, R, Saifzadeh, S, Sadeghpour, A, Field, JR, Akey, A, Vielreicher, M, Friedrich, O, Roohani‐Esfahani, S & Zreiqat, H 2019, 'A Novel Bone Substitute with High Bioactivity, Strength, and Porosity for Repairing Large and Load‐Bearing Bone Defects', Advanced Healthcare Materials, vol. 8, no. 13, pp. e1900641-1900641.
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Adv. Healthcare Mater. 2019, 8, 1801298 In the initially published version of this article, the author list and affiliations were incorrect. The correct author list is as follows: Jiao Jiao Li, Colin R. Dunstan, Ali Entezari, Qing Li, Roland Steck, Siamak Saifzadeh, Ameneh Sadeghpour, John R. Field, Austin Akey, David C. Bell, Martin Vielreicher, Oliver Friedrich, Seyed-Iman Roohani-Esfahani, and Hala Zreiqat* The correct affiliation for D.C.B. is as follows: Dr. A. Akey, Prof. D. C. Bell Center for Nanoscale Systems Harvard University Cambridge, MA 02138, USA.
Li, JJ, Dunstan, CR, Entezari, A, Li, Q, Steck, R, Saifzadeh, S, Sadeghpour, A, Field, JR, Akey, A, Vielreicher, M, Friedrich, O, Roohani‐Esfahani, S & Zreiqat, H 2019, 'A Novel Bone Substitute with High Bioactivity, Strength, and Porosity for Repairing Large and Load‐Bearing Bone Defects', Advanced Healthcare Materials, vol. 8, no. 8, pp. e1801298-1801298.
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AbstractAchieving adequate healing in large or load‐bearing bone defects is highly challenging even with surgical intervention. The clinical standard of repairing bone defects using autografts or allografts has many drawbacks. A bioactive ceramic scaffold, strontium‐hardystonite‐gahnite or “Sr‐HT‐Gahnite” (a multi‐component, calcium silicate‐based ceramic) is developed, which when 3D‐printed combines high strength with outstanding bone regeneration ability. In this study, the performance of purely synthetic, 3D‐printed Sr‐HT‐Gahnite scaffolds is assessed in repairing large and load‐bearing bone defects. The scaffolds are implanted into critical‐sized segmental defects in sheep tibia for 3 and 12 months, with bone autografts used for comparison. The scaffolds induce substantial bone formation and defect bridging after 12 months, as indicated by X‐ray, micro‐computed tomography, and histological and biomechanical analyses. Detailed analysis of the bone‐scaffold interface using focused ion beam scanning electron microscopy and multiphoton microscopy shows scaffold degradation and maturation of the newly formed bone. In silico modeling of strain energy distribution in the scaffolds reveal the importance of surgical fixation and mechanical loading on long‐term bone regeneration. The clinical application of 3D‐printed Sr‐HT‐Gahnite scaffolds as a synthetic bone substitute can potentially improve the repair of challenging bone defects and overcome the limitations of bone graft transplantation.
Li, JJ, Dunstan, CR, Entezari, A, Li, Q, Steck, R, Saifzadeh, S, Sadeghpour, A, Field, JR, Akey, A, Vielreicher, M, Friedrich, O, Roohani‐Esfahani, S & Zreiqat, H 2019, 'Bone Regeneration: A Novel Bone Substitute with High Bioactivity, Strength, and Porosity for Repairing Large and Load‐Bearing Bone Defects (Adv. Healthcare Mater. 8/2019)', Advanced Healthcare Materials, vol. 8, no. 8, pp. 1970031-1970031.
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Li, JJ, Hosseini-Beheshti, E, Grau, GE, Zreiqat, H & Little, CB 2019, 'Stem Cell-Derived Extracellular Vesicles for Treating Joint Injury and Osteoarthritis', Nanomaterials, vol. 9, no. 2, pp. 261-261.
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Extracellular vesicles (EVs) are nanoscale particles secreted by almost all cell types to facilitate intercellular communication. Stem cell-derived EVs theoretically have the same biological functions as stem cells, but offer the advantages of small size, low immunogenicity, and removal of issues such as low cell survival and unpredictable long-term behaviour associated with direct cell transplantation. They have been an area of intense interest in regenerative medicine, due to the potential to harness their anti-inflammatory and pro-regenerative effects to induce healing in a wide variety of tissues. However, the potential of using stem cell-derived EVs for treating joint injury and osteoarthritis has not yet been extensively explored. The pathogenesis of osteoarthritis, with or without prior joint injury, is not well understood, and there is a longstanding unmet clinical need to develop new treatments that provide a therapeutic effect in preventing or stopping joint degeneration, rather than merely relieving the symptoms of the disease. This review summarises the current evidence relating to stem cell-derived EVs in joint injury and osteoarthritis, providing a concise discussion of their characteristics, advantages, therapeutic effects, limitations and outlook in this exciting new area.
Li, K & Cao, F 2019, 'Super-resolution using neighbourhood regression with local structure prior', Signal Processing: Image Communication, vol. 72, pp. 58-68.
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Li, K, Cheng, G, Sun, X, Yang, Z & Fan, Y 2019, 'Performance optimization design and analysis of bearingless induction motor with different magnetic slot wedges', Results in Physics, vol. 12, pp. 349-356.
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Li, K, Lu, L, Ni, W, Tovar, E & Guizani, M 2019, 'Secret Key Agreement for Data Dissemination in Vehicular Platoons', IEEE Transactions on Vehicular Technology, vol. 68, no. 9, pp. 9060-9073.
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© 2019 IEEE. In a vehicular platoon, the lead vehicle that is responsible for managing the platoon's moving directions and velocity periodically disseminates messages to the following automated vehicles in a multi-hop vehicular network. However, due to the broadcast nature of wireless channels, this kind of communication is vulnerable to eavesdropping and message modification. Generating secret keys by extracting the shared randomness in a wireless fading channel is a promising way for wireless communication security. We study a security protocol for data dissemination in the platoon, where the vehicles cooperatively generate a shared secret key based on the quantized fading channel randomness. To improve conformity of the generated key, the probability of secret key agreement is formulated, and a novel secret key agreement algorithm is proposed to recursively optimize the channel quantization intervals, maximizing the key agreement probability. Numerical evaluations demonstrate that the key agreement probability achieved by our security protocol given different platoon size, channel quality, and number of quantization intervals. Furthermore, by applying our security protocol, the probability that the encrypted data being cracked by an eavesdropper is less than 5%.
Li, K, Ni, W, Abolhasan, M & Tovar, E 2019, 'Reinforcement Learning for Scheduling Wireless Powered Sensor Communications', IEEE Transactions on Green Communications and Networking, vol. 3, no. 2, pp. 264-274.
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© 2017 IEEE. In a wireless powered sensor network, a base station transfers power to sensors by using wireless power transfer (WPT). Inadequately scheduling WPT and data transmission causes fast battery drainage and data queue overflow of some sensors who could have potentially gained high data reception. In this paper, scheduling WPT and data transmission is formulated as a Markov decision process (MDP) by jointly considering sensors' energy consumption and data queue. In practical scenarios, the prior knowledge about battery level and data queue length in MDP is not available at the base station. We study reinforcement learning at the sensors to find a transmission scheduling strategy, minimizing data packet loss. An optimal scheduling strategy with full-state information is also investigated, assuming that the complete battery level and data queue information are well known by the base station. This presents the lower bound of the data packet loss in wireless powered sensor networks. Numerical results demonstrate that the proposed reinforcement learning scheduling algorithm significantly reduces network packet loss rate by 60%, and increases network goodput by 67%, compared to existing non-MDP greedy approaches. Moreover, comparing the optimal solutions, the performance loss due to the lack of sensors' full-state information is less than 4.6%.
Li, K, Ni, W, Tovar, E & Jamalipour, A 2019, 'On-Board Deep Q-Network for UAV-Assisted Online Power Transfer and Data Collection', IEEE Transactions on Vehicular Technology, vol. 68, no. 12, pp. 12215-12226.
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© 1967-2012 IEEE. Unmanned Aerial Vehicles (UAVs) with Microwave Power Transfer (MPT) capability provide a practical means to deploy a large number of wireless powered sensing devices into areas with no access to persistent power supplies. The UAV can charge the sensing devices remotely and harvest their data. A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e.g., patrolling velocity) for preventing battery drainage and data queue overflow of the devices, while up-To-date knowledge on battery level and data queue of the devices is not available at the UAV. In this paper, an on-board deep Q-network is developed to minimize the overall data packet loss of the sensing devices, by optimally deciding the device to be charged and interrogated for data collection, and the instantaneous patrolling velocity of the UAV. Specifically, we formulate a Markov Decision Process (MDP) with the states of battery level and data queue length of devices, channel conditions, and waypoints given the trajectory of the UAV; and solve it optimally with Q-learning. Furthermore, we propose the on-board deep Q-network that enlarges the state space of the MDP, and a deep reinforcement learning based scheduling algorithm that asymptotically derives the optimal solution online, even when the UAV has only outdated knowledge on the MDP states. Numerical results demonstrate that our deep reinforcement learning algorithm reduces the packet loss by at least 69.2%, as compared to existing non-learning greedy algorithms.
Li, K, Voicu, RC, Kanhere, SS, Ni, W & Tovar, E 2019, 'Energy Efficient Legitimate Wireless Surveillance of UAV Communications', IEEE Transactions on Vehicular Technology, vol. 68, no. 3, pp. 2283-2293.
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© 1967-2012 IEEE. Unmanned aerial vehicles (UAVs) enhance connectivity and accessibility for civilian and military applications. Criminals or terrorists can potentially use UAVs for committing crimes and terrorism, thus endangering public safety. In this paper, we consider that a legitimate UAV is employed to track flight of suspicious UAVs for preventing safety and security threats. To obtain flight information of the suspicious UAVs, the legitimate UAV intentionally jams the suspicious receiver so as to force the suspicious UAV to reduce its data rate, and hence increase the eavesdropping success. An energy-efficient jamming strategy is proposed for the legitimate UAV to maximize the amount of eavesdropped packets. Moreover, a tracking algorithm is developed for the legitimate UAV to track the suspicious flight by comprehensively utilizing eavesdropped packets, angle-of-arrival and received signal strength of the suspicious transmitter's signal. A new simulation framework is implemented to combine the complementary features of optimization toolbox with channel modeling (in MATLAB) and discrete event-driven mobility tracking (in NS3). Moreover, numerical results validate the proposed algorithms in terms of packet eavesdropping rate and tracking accuracy of the suspicious UAVs' trajectory.
Li, K, Wu, D & Gao, W 2019, 'Spectral stochastic isogeometric analysis for linear stability analysis of plate', Computer Methods in Applied Mechanics and Engineering, vol. 352, pp. 1-31.
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Li, K, Wu, D, Gao, W & Song, C 2019, 'Spectral stochastic isogeometric analysis of free vibration', Computer Methods in Applied Mechanics and Engineering, vol. 350, pp. 1-27.
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A novel spectral stochastic isogeometric analysis (SSIGA) is proposed for the free vibration analysis of engineering structures involving uncertainties. The proposed SSIGA framework treats the stochastic free vibration problem as a stochastic generalized eigenvalue problem. The stochastic Young's modulus and material density of the structure are modelled as random fields with Gaussian and non-Gaussian distributions. The basis functions, the non-uniform rational B-spline (NURBS) and T-spline, within Computer Aided Design (CAD) system are adopted within the SSIGA, which can eliminate geometric errors between design model and uncertainty analysis model. The arbitrary polynomial chaos (aPC) expansion is implemented to investigate the stochastic responses (i.e. eigenvalues and eigenvectors) of the structure. A Galerkin-based method is freshly proposed to solve the stochastic generalized eigenvalue problems. The statistical moments, probability density function (PDF) and cumulative distribution function (CDF) of the eigenvalues can be effectively obtained. Two numerical examples with irregular geometries are investigated to illustrate the applicability, accuracy and efficiency of the proposed SSIGA for free vibration analysis of engineering structures.
Li, L, Geng, S, Wu, C, Song, K, Sun, F, Visvanathan, C, Xie, F & Wang, Q 2019, 'Microplastics contamination in different trophic state lakes along the middle and lower reaches of Yangtze River Basin', Environmental Pollution, vol. 254, no. Pt A, pp. 112951-112951.
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© 2019 Elsevier Ltd Microplastics can enter freshwater lakes through many sources. They can act as carriers to adsorb bacteria, virus, or pollutants (e.g., heavy metal and toxic organic compounds) that threaten human health through food chain. Microplastics can exist in surface water and sediments in freshwater lakes after they enter the lakes through discharge points. Wastewater discharge is the main cause of lake eutrophication and is the main emission source of microplastics. The correlation between lake trophic state and microplastic abundance has been rarely reported. This study investigated the microplastic contamination in surface water and sediments of 18 lakes along the middle and lower reaches of the Yangtze River Basin in the period of August–September 2018. The correlation between lake trophic state and microplastic abundance in surface water and sediments was investigated and discussed. The microplastic abundance in surface water was approximately two orders of magnitude lower than that in sediments in all 18 lakes. Hong Lake had the highest microplastic abundance in surface water sample, and Nantaizi Lake had the highest microplastic abundance in sediment sample. The dominant microplastic shape was fiber of 93.81% in surface water sample and 94.77% in sediment sample. Blue-colored microplastics were dominant in nearly all lakes in surface water sample (around 40%–60%) and sediment sample (around 60%–80%), followed by purple- and green-colored ones. The microplastics size <1 mm was dominant in surface water sample (around 40%–60%) and sediment sample (around 50%–80%). The dominant material was polypropylene in surface water sample (around 60%–80%) and sediment sample (around 40%–60%).
Li, L-Q, Ju, N-P, Zhang, S, Deng, X-X & Sheng, D 2019, 'Correction to: Seismic wave propagation characteristic and its effects on the failure of steep jointed anti-dip rock slope', Landslides, vol. 16, no. 1, pp. 125-126.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The published version of this article, unfortunately, contained error. A compass went unconverted in the upper-right corner of Fig. 1. Given in this article is the correct image. The original article has been corrected.
Li, L-Q, Ju, N-P, Zhang, S, Deng, X-X & Sheng, D 2019, 'Seismic wave propagation characteristic and its effects on the failure of steep jointed anti-dip rock slope', Landslides, vol. 16, no. 1, pp. 105-123.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Discontinuities, such as joints and beddings, usually play a significant role in the seismic response and corresponding failure process of slopes, especially for anti-dip rock slide according to field observations. Shaking table tests associated with numerical analyses are carried out in this paper to explore the effect of seismic wave on response of jointed anti-dip rock slopes. Shaking table tests involve anti-dip rock slope models with different rock types and different excitation intensities. Ten accelerometers are installed inside each slope model to monitor the dynamic response and spectrum shifting characteristics. It is found that the area of failure zone in the soft rock anti-dip slope is approximate 1.5 times the size of that in the hard rock anti-dip slope. Meanwhile, the width and ridge number of the fast Fourier-transformation spectrum along the slope surface can reveal the internal damage features within the anti-dip rock slopes, and the multiple failure planes can also be recognized according to the variation of distance between the innermost and outermost ridges in the fast Fourier-transformation spectrum. Moreover, the distinct element method incorporating a damage model is used to interpret the test results and to identify the main influencing factors for seismic instability. It is found that the failure pattern of a jointed anti-dip rock slope is more sensitive to bedding inclination than to joint inclination.
Li, M, Ding, J, Tao, Y, Shi, B & Chen, J-H 2019, 'Polysaccharides for Biomedical Applications', International Journal of Polymer Science, vol. 2019, pp. 1-2.
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Li, M, Sun, Y, Su, S, Tian, Z, Wang, Y & Wang, X 2019, 'DPIF: A Framework for Distinguishing Unintentional Quality Problems From Potential Shilling Attacks', Computers, Materials & Continua, vol. 59, no. 1, pp. 331-344.
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Copyright © 2019 Tech Science Press. Maliciously manufactured user profiles are often generated in batch for shilling attacks. These profiles may bring in a lot of quality problems but not worthy to be repaired. Since repairing data always be expensive, we need to scrutinize the data and pick out the data that really deserves to be repaired. In this paper, we focus on how to distinguish the unintentional data quality problems from the batch generated fake users for shilling attacks. A two-steps framework named DPIF is proposed for the distinguishment. Based on the framework, the metrics of homology and suspicious degree are proposed. The homology can be used to represent both the similarities of text and the data quality problems contained by different profiles. The suspicious degree can be used to identify potential attacks. The experiments on real-life data verified that the proposed framework and the corresponding metrics are effective.
Li, Q, Su, T & Wu, K 2019, 'Accurate DOA Estimation for Large-Scale Uniform Circular Array Using a Single Snapshot', IEEE Communications Letters, vol. 23, no. 2, pp. 302-305.
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© 1997-2012 IEEE. A large-scale antenna array is an enabling technique for millimeter-wave communications. Uniform circular arrays (UCAs) have the spatial invariance property, ensuring the same beamforming performance in the whole angular region. However, the direction-of-arrival (DOA) estimation in UCAs is challenging since the array response of a UCA does not conform to a Vandermonde structure as that of a uniform linear array. This letter proposes an accurate and low-complexity DOA estimation approach by exploiting the good correlation property of the array response of the UCA. The DOA estimates are first obtained from a circular convolution between a single snapshot and the designed coefficient vector. Then, by searching for the best initial phase of the coefficient vector, the DOA estimates can be refined to a configurable accuracy. The simulation results demonstrate that the proposed approach outperforms the state of the art by orders of magnitude in estimation accuracy.
Li, Q, Wang, Q, Wu, D, Chen, X, Yu, Y & Gao, W 2019, 'Geometrically nonlinear dynamic analysis of organic solar cell resting on Winkler-Pasternak elastic foundation under thermal environment', Composites Part B: Engineering, vol. 163, pp. 121-129.
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© 2018 Elsevier Ltd The nonlinear dynamic responses of a nanocomposite organic solar cell (NCOSC) are developed through the classical plate theory. The investigated NCOSC consists of five layers which are including Al, P3HT: PCBM, PEDOT: PSS, IOT and glass. A uniformly distributed external excitation is exerted on the simply supported NCOSC. The impacts of the Winkler-Pasternak elastic foundation, thermal environment and damping on the nonlinear dynamic responses of the NCOSC are investigated. The equations of motion and geometric compatibility of the NCOSC with the consideration of the von Kármán nonlinearity are derived. The governing equation of the dynamic system is formulated by employing the Galerkin and the fourth-order Runge-Kutta methods. Several numerical experiments are thoroughly presented to report the effects of damping ratio, temperature variations, and elastic foundation parameters on the frequency–amplitude curves and nonlinear dynamic response of the NCOSC. The numerical studies indicate that the existence of the Winkler-Pasternak elastic foundation effectively reduces the dynamic response of the NCOSC. In addition, the damping and thermal variation depress the vibration of the NCOSC but with relatively less efficiency compared with the Winkler- Pasternak elastic foundation.
Li, Q, Wu, D, Gao, W, Tin-Loi, F, Liu, Z & Cheng, J 2019, 'Static bending and free vibration of organic solar cell resting on Winkler-Pasternak elastic foundation through the modified strain gradient theory', European Journal of Mechanics - A/Solids, vol. 78, pp. 103852-103852.
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© 2019 Elsevier Masson SAS Organic solar cell (OSC), which is deemed to be the most promising third generation solar energy application, is developing vigorously. Based on the modified strain gradient theory (MSGT) and the refined shear deformation plate theory, static bending and free vibration of the size-dependent OSC are thoroughly investigated in this paper. A Winkler-Pasternak elastic foundation is considered for the OSC. A multiscale suitable plate analysis framework (i.e., both macro- and micro plates can be handled) is developed herein. Three length scale parameters are incorporated in the presented analysis to capture the size-dependency of the OSC. By setting two or three of them into zero, the presented model could degenerate into the modified couple stress theory (MCST) and the classical plate theory (CPT). The derivation of the governing equations and the corresponding boundary conditions are conducted by Hamilton principle. The Navier-type solution is employed for solving the governing equations of the simply supported OSC. The accuracy of the presented method is validated. Extensive numerical experiments have been conducted to investigate the differences between the adopted MSGT, the MCST and the CPT. Moreover, the impacts of the geometrical configuration as well as the elastic foundation parameters on the static bending and free vibration characteristics are illustrated in the numerical studies. This paper also explores the thickness of the active layer effect on the free vibration behaviour in combination with the power conversion efficiency (PCE) of the OSC.
Li, R, Yu, Y, Samali, B & Li, C 2019, 'Parametric Analysis on the Circular CFST Column and RBS Steel Beam Joints', Materials, vol. 12, no. 9, pp. 1535-1535.
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This research analyzes the results of parametric studies of concrete-filled steel tubular (CFST) columns to the reduced beam section (RBS) beam joint with through diaphragm, using ANSYS. Several indices that are able to characterize the cyclic behavior of the composite joints are investigated, including the stiffness degradation, strength deterioration, stress distribution, and energy dissipation capacity. Four main model parameters, including the distance from the diaphragm edge to the cut start, the cut length, the cut depth, and inner diameter of through diaphragm, are analyzed via comparative studies to investigate their impacts on seismic properties of the joint. Finally, the orthogonal experiment is conducted to study the effects of these parameters on the strength and energy dissipation, the results of which are capable of achieving optimal seismic behavior of the joints.
Li, S, Hedley, M, Bengston, K, Humphrey, D, Johnson, M & Ni, W 2019, 'Passive Localization of Standard WiFi Devices', IEEE Systems Journal, vol. 13, no. 4, pp. 3929-3932.
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© 2007-2012 IEEE. Ubiquitous wireless indoor localization can be achieved by leveraging the widespread deployment of WiFi systems. Most existing WiFi-based localization solutions are based on received signal strength (RSS) fingerprinting, which requires a database of the RSS values in the application environment to be built and maintained. The latest 802.11ac WiFi standard offers channels with wide bandwidths, which enables accurate timing-based positioning. This paper presents a passive localization system in which multiple sniffers monitor the WiFi traffic and locate the standard WiFi transmitters based on the time-of-arrival measurements. Multiple implementation issues are addressed, including sniffer clock synchronization and hardware delay calibration. The proposed system is evaluated experimentally using a prototype developed by us. It is shown that the positioning accuracy is significantly improved over existing systems.
Li, W, Huang, L & Ji, J 2019, 'Periodic solution and its stability of a delayed Beddington‐DeAngelis type predator‐prey system with discontinuous control strategy', Mathematical Methods in the Applied Sciences, vol. 42, no. 13, pp. 4498-4515.
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This paper investigates the periodic solution of a delayed Beddington‐DeAngelis (BD) type predator‐prey model with discontinuous control strategy. Firstly, the regularity and visibility analysis of the delayed predator‐prey model is carried out by using the principle of differential inclusion. Secondly, the positiveness and boundeness of the solution is discussed by employing the comparison theorem. Based on the boundary conditions of the model and the Mawhin‐like coincidence theorem, it is shown that the solution of the delayed BD system is asymptotically stable in finite time. Furthermore, it is found that there exists at least one periodic solution of the nonautonomous delayed predator‐prey model by using the principle of topological degree and set value mapping. Specially, when the nonautonomous delayed BD system degenerates into an autonomous system, some criteria are obtained to guarantee the convergence behavior of the harvesting solutions for the corresponding autonomous delayed BD system. Finally, numerical examples are given to demonstrate the applicability and effectiveness of main results. It is worthy to point out that the discontinuous control strategy is superior to the continuous harvesting policies adopted in existing literature.
Li, W, Li, J, Liu, Y, Qu, J, Liu, B, Zhu, M, Li, Y, Huang, Z & Zheng, R 2019, 'Artificial 2D Flux Pinning Centers in MgB2 Induced by Graphitic-Carbon Nitride Coated on Boron for Superconductor Applications', ACS Applied Nano Materials, vol. 2, no. 9, pp. 5399-5408.
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© 2019 American Chemical Society. Systemic investigation was carried out on the microstructure, superconducting properties, and flux pinning mechanism of MgB2 in situ fabricated with magnesium and g-C3N4 coated boron as precursors. The encapsulation of the boron powders with g-C3N4 was achieved by polycondensation of urea on boron powders. The g-C3N4 decomposes during the MgB2 fabrication to induce two-dimensional few-carbon layer, dispersed nanoparticles, and carbon-rich phases in the matrix to enhance the flux pinning force and Hirr of MgB2, which accounts for the in-field critical current density (Jc(H)) increase compared to the pure MgB2. The carbon layers acting as artificial two-dimensional flux pinning centers, have demonstrated high flux pinning efficiency to increase the Jc(H) of MgB2 superconductors.
Li, W, Liu, BM, Liu, D, Liu, RP, Wang, P, Luo, S & Ni, W 2019, 'Unified Fine-Grained Access Control for Personal Health Records in Cloud Computing', IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 3, pp. 1278-1289.
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© 2013 IEEE. Attribute-based encryption has been a promising encryption technology to secure personal health records (PHRs) sharing in cloud computing. PHRs consist of the patient data often collected from various sources including hospitals and general practice centres. Different patients' access policies have a common access sub-policy. In this paper, we propose a novel attribute-based encryption scheme for fine-grained and flexible access control to PHRs data in cloud computing. The scheme generates shared information by the common access sub-policy, which is based on different patients' access policies. Then, the scheme combines the encryption of PHRs from different patients. Therefore, both time consumption of encryption and decryption can be reduced. Medical staff require varying levels of access to PHRs. The proposed scheme can also support multi-privilege access control so that medical staff can access the required level of information while maximizing patient privacy. Through implementation and simulation, we demonstrate that the proposed scheme is efficient in terms of time. Moreover, we prove the security of the proposed scheme based on security of the ciphertext-policy attribute-based encryption scheme.
Li, W, Qiao, M, Qin, L, Zhang, Y, Chang, L & Lin, X 2019, 'Eccentricities on small-world networks.', VLDB J., vol. 28, no. 5, pp. 765-792.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. This paper focuses on the efficiency issue of computing and maintaining the eccentricity distribution on a large and perhaps dynamic small-world network. Eccentricity distribution evaluates the importance of each node in a graph, providing a node ranking for graph analytics; moreover, it is the key to the computation of two fundamental graph measurements, diameter, and radius. Existing eccentricity computation algorithms are not scalable enough to handle real large networks unless approximation is introduced. Such an approximation, however, leads to a prominent relative error on small-world networks whose diameters are notably short. Our solution optimizes existing eccentricity computation algorithms on their bottlenecks—one-node eccentricity computation and the upper/lower bounds update—based on a line of original insights; it also provides the first algorithm on maintaining the eccentricities of a dynamic graph without recomputing the eccentricity distribution upon each edge update. On real large small-world networks, our approach outperforms the state-of-the-art eccentricity computation approach by up to three orders of magnitude and our maintenance algorithm outperforms the recomputation baseline (recompute using our superior eccentricity computation approach) by up to two orders of magnitude, as demonstrated by our extensive evaluation.
Li, W, Tang, X & Yang, Y 2019, 'Design and Implementation of SIW Cavity-Backed Dual-Polarization Antenna Array With Dual High-Order Modes', IEEE Transactions on Antennas and Propagation, vol. 67, no. 7, pp. 4889-4894.
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© 2019 IEEE. In this communication, a novel approach for high-order modes excitation using substrate-integrated waveguide (SIW) is proposed for the implementation of a dual-polarization cavity-backed slot antenna. The antenna consists of a resonant SIW cavity with eight radiating slots and two separated SIW feeding networks. The vertically linear polarization (VLP) and horizontally linear polarization (HLP) are realized by adopting the TE430 and TE340 modes with different signal schemes. The field distributions and the surface currents of the TE430 and TE340 modes are used to analyze and illuminate the radiation mechanism. To further validate the proposed design, a 2 × 2 polarization-diverse SIW cavity-backed antenna array is fabricated and tested. The measured results show that the impedance bandwidths (S11 <-10 dB) for the two linear-polarization (LP) states are 10.73-10.9 GHz and 10.75-10.83 GHz, respectively, while the 3 dB axial ratio bandwidth for the right-hand circular polarization (RHCP) is 10.75-10.83 GHz. The measured peak gains of the two LP modes and CP mode are 13.4, 12.92, and 12.2 dBi, respectively. The proposed approach demonstrates an effective way of high-order modes (TE430 and TE340 modes) generation in a single SIW cavity, which has significant meaning in polarization-diversity applications.
Li, W, Tang, XH & Yang, Y 2019, 'A Ka-Band Circularly Polarized Substrate Integrated Cavity-Backed Antenna Array', IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 9, pp. 1882-1886.
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© 2002-2011 IEEE. A high-gain 4 × 4 substrate integrated waveguide (SIW) circularly polarized (CP) antenna array at a Ka-band with a quadruple-ridge waveguide polarizer is proposed in this letter. The antenna array consists of 16 cavity-backed slot antenna elements, quadruple ridge waveguide polarizers, and SIW T-type power dividers. Dominant resonant mode TE110 is excited in the cavity-backed slot antenna element. The measured impedance bandwidth (S11 < -10 dB) is from 35.3 to 35.55 GHz, and 3 dB axial-ratio bandwidth is from 35.24 to 35.57 GHz. In addition, the maximum measured gain of 18.14 dBi at the boresight is experimentally obtained at 35.42 GHz. The antenna prototype was fabricated by a multilayer printed circuit board technology. To the best of our knowledge, the quadruple-ridge waveguide is used as polarizer to produce CP signals for the first time. Compared with an aperture antenna with a conventional rectangle or a circular waveguide polarizer, this work has high aperture radiation efficiency as well as compact size. This design idea may open a new way for development of millimeter-wave high-efficiency arrays.
Li, X, Khademi, F, Liu, Y, Akbari, M, Wang, C, Bond, PL, Keller, J & Jiang, G 2019, 'Evaluation of data-driven models for predicting the service life of concrete sewer pipes subjected to corrosion', Journal of Environmental Management, vol. 234, pp. 431-439.
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Concrete corrosion is one of the most significant failure mechanisms of sewer pipes, and can reduce the sewer service life significantly. To facilitate the management and maintenance of sewers, it is essential to obtain reliable prediction of the expected service life of sewers, especially if that is based on limited environmental conditions. Recently, a long-term study was performed to identify the controlling factors of concrete sewer corrosion using well-controlled laboratory-scale corrosion chambers to vary levels of H2S concentration, relative humidity, temperature and in-sewer location. Using the results of the long-term study, three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS), as well as the interaction between environmental parameters, were assessed for predicting the corrosion initiation time (ti) and corrosion rate (r). This was performed using the sewer environmental factors as the input under 12 different scenarios after allowing for an initiation corrosion period. ANN and ANFIS models showed better performance than MLR models, with or without considering the interactions between environmental factors. With the limited input data available, it was observed that ti prediction by these models is quite sensitive, however, they are more robust for predicting r as long as the H2S concentration is available. Using the H2S concentration as a single input, all three data driven models can reasonably predict the sewer service life.
Li, X, Liu, Y, Xu, Q, Liu, X, Huang, X, Yang, J, Wang, D, Wang, Q, Liu, Y & Yang, Q 2019, 'Enhanced methane production from waste activated sludge by combining calcium peroxide with ultrasonic: Performance, mechanism, and implication', Bioresource Technology, vol. 279, pp. 108-116.
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© 2019 Elsevier Ltd This study reported a novel and high-efficient pretreatment method for anaerobic digestion, i.e., combining calcium peroxide (CaO2) with ultrasonic (US), by which not only the methane production was remarkably improved but also the removal of refractory organic contaminants was enhanced. Experimental results showed the optimum condition for methane production was achieved at 0.1 g CaO2/g VSS combined with US (1 W/ml, 10 min). Under this condition, the maximal methane yield of 211.90 ± 2.6 L CH4/kg VSS was obtained after 36 d of anaerobic digestion, which was respectively 1.36-fold, 1.19-fold and 1.26-fold of that from the control, solo US (1 W/ml, 10 min) and solo CaO2 (0.1 g/g VSS). Mechanism investigations revealed that CaO2 + US not only improved the disintegration of waste activated sludge (WAS) but also increased the proportion of biodegradable organic matters. Moreover, the total frequency of recalcitrant contaminants contained in WAS decreased significantly when CaO2 + US was applied.
Li, X, Mei, Q, Chen, L, Zhang, H, Dong, B, Dai, X, He, C & Zhou, J 2019, 'Enhancement in adsorption potential of microplastics in sewage sludge for metal pollutants after the wastewater treatment process', Water Research, vol. 157, pp. 228-237.
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© 2019 Elsevier Ltd Microplastics (MPs) as new pollutants of environmental concern have been widely detected in sewage sludge, and may act as significant vectors for metal pollutants due to their adsorption property. Our findings show that Cd, Pb, and Co, but not Ni, contents in sewage sludge are lower than that of corresponding metal irons adsorbed on sludge-based MPs, indicating that the MPs accumulate such metal pollutants as Cd in the sludge samples. In contrast to virgin MPs, sludge-based MPs are one order of magnitude higher adsorption capacity for Cd, which reaches up to 2.523 mg g−1, implying that there is a considerable enhancement in adsorption potential of the MPs for metals after the wastewater treatment process. SEM analysis shows that sludge-based MPs have rougher and more porous surface than virgin MPs, and FTIR spectra reveal that functional groups such as C–O and O–H are found on sludge-based MPs. Further, two-dimensional FTIR correlation spectroscopy indicates that C–O and N–H functional groups play a vital role in the process that sludge-based MPs adsorb Cd, which are not found in virgin MPs. The results imply that increased adsorption potentials of the sludge-based MPs to Cd are attributed to changes in the MP physicochemical properties during wastewater treatment process. In addition, such factors as pH value, and sludge inorganic and organic components also have an effect on the MP adsorption to Cd. Principal component analysis shows that the MPs could be divided into three categories, i.e. polyamide, rubbery MPs (polyethylene and polypropylene) and glassy MPs (polyvinyl chloride and polystyrene). Their adsorption potentials to Cd follow the decreasing order: polyamide > rubbery MPs > glassy MPs. In summary, these findings indicate that MPs may exert an important influence on fate and transport of metal pollutants during sewage sludge treatment process, which deserves to be further concerned.
Li, X, O'Moore, L, Song, Y, Bond, PL, Yuan, Z, Wilkie, S, Hanzic, L & Jiang, G 2019, 'The rapid chemically induced corrosion of concrete sewers at high H2S concentration', Water Research, vol. 162, pp. 95-104.
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Concrete corrosion in sewers is primarily caused by H2S in sewer atmosphere. H2S concentration can vary from several ppm to hundreds of ppm in real sewers. Our understanding of sewer corrosion has increased dramatically in recent years, however, there is limited knowledge of the concrete corrosion at high H2S levels. This study examined the corrosion development in sewers with high H2S concentrations. Fresh concrete coupons, manufactured according to sewer pipe standards, were exposed to corrosive conditions in a pilot-scale gravity sewer system with gaseous H2S at 1100 ± 100 ppm. The corrosion process was continuously monitored by measuring the surface pH, corrosion product composition, corrosion loss and the microbial community. The surface pH of concrete was reduced from 10.5 ± 0.3 to 3.1 ± 0.5 within 20 days and this coincided with a rapid corrosion rate of 3.5 ± 0.3 mm year -1. Microbial community analysis based on 16S rRNA gene sequencing indicated the absence of sulfide-oxidizing microorganisms in the corrosion layer. The chemical analysis of corrosion products supported the reaction of cement with sulfuric acid formed by the chemical oxidation of H2S. The rapid corrosion of concrete in the gravity pipe was confirmed to be caused by the chemical oxidation of hydrogen sulfide at high concentrations. This is in contrast to the conventional knowledge that is focused on microbially induced corrosion. This first-ever systematic investigation shows that chemically induced oxidation of H2S leads to the rapid corrosion of new concrete sewers within a few weeks. These findings contribute novel understanding of in-sewer corrosion processes and hold profound implications for sewer operation and corrosion management.
Li, X, Zhao, S, Hu, W, Zhang, X, Pei, L & Wang, Z 2019, 'Robust superhydrophobic surface with excellent adhesive properties based on benzoxazine/epoxy/mesoporous SiO2', Applied Surface Science, vol. 481, pp. 374-378.
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Li, Y & Li, J 2019, 'Overview of the development of smart base isolation system featuring magnetorheological elastomer', Smart Structures and Systems, vol. 24, no. 1, pp. 37-52.
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Despite its success and wide application, base isolation system has been challenged for its passive nature, i.e., incapable of working with versatile external loadings. This is particularly exaggerated during near-source earthquakes and earthquakes with dominate low-frequency components. To address this issue, many efforts have been explored, including active base isolation system and hybrid base isolation system (with added controllable damping). Active base isolation system requires extra energy input which is not economical and the power supply may not be available during earthquakes. Although with tunable energy dissipation ability, hybrid base isolation systems are not able to alter its fundamental natural frequency to cope with varying external loadings. This paper reports an overview of new adventure with aim to develop adaptive base isolation system with controllable stiffness (thus adaptive natural frequency). With assistance of the feedback control system and the use of smart material technology, the proposed smart base isolation system is able to realize real-time decoupling of external loading and hence provides effective seismic protection against different types of earthquakes.
Li, Y, Chen, W, Chen, J, Chen, X, Liang, J & Du, M 2019, 'Neural network based modeling and control of elbow joint motion under functional electrical stimulation', Neurocomputing, vol. 340, pp. 171-179.
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Li, Y, Huang, C, Ngo, HH, Pang, J, Zha, X, Liu, T & Guo, W 2019, 'In situ reconstruction of long-term extreme flooding magnitudes and frequencies based on geological archives', Science of The Total Environment, vol. 670, pp. 8-17.
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© 2019 Extreme flooding magnitudes and frequencies are essentially related to assessment of risk and reliability in hydrological design. Extreme flooding and its discharge are highly sensitive to regional climate change. Presently, its discharge can be reconstructed by a geological archive or record along the river valley. Two units of typical extreme flooding deposits (EFDs) carrying long-term information preserved in the Holocene loess–palaeosol sequence were found at Xipocun (XPC), which is located in Chengcheng County, China. It is situated in the downstream section of the Beiluohe (hereafter BLH) River. Based on multiple sedimentary proxy indices (grain-size distribution (GSD), magnetic susceptibility (MS), and loss-on-ignition (LOI), etc.), EFDs were interpreted as well-sorted clayey silt in suspension. They were then deposited as a result of riverbank flooding in a stagnant environment during high water level. Through the Optically Stimulated Luminescence (OSL) dating technique and stratigraphic correlations, chronologies of two identified extreme flooding periods were 7600–7400 a B.P. and 3200–3000 a B.P. Two phases of extreme flooding occurrence under climate abnormality scenarios were characterized as having high frequencies of hydrological extremes in river systems. According to simulation and verification using the Slope–Area Method and Hydrologic Engineering Center's River Analysis System (HEC-RAS) model, the extreme flooding discharges at the XPC site were reconstructed between 9625 m 3 /s and 16,635 m 3 /s. A new long-term flooding frequency and peak discharge curve, involved gauged flooding, historical flooding at Zhuangtou station and in situ reconstructed extreme flooding events, was established for the downstream BLH River. The results improve the accuracy of low-frequency flooding risk assessment and provide evidence for predicting the response of fluvial systems to climate instability. Thus, this improves the analysis of the BLH River watershed.
Li, Y, Kong, R, Chen, H, Zhao, Z, Li, L, Li, J, Hu, J, Zhang, G, Pan, S, Wang, Y, Wang, G, Chen, H & Sun, B 2019, 'Overexpression of KLF5 is associated with poor survival and G1/S progression in pancreatic cancer', Aging, vol. 11, no. 14, pp. 5035-5057.
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Li, Y, Lei, L & Li, S 2019, 'Computation tree logic model checking based on multi-valued possibility measures', Information Sciences, vol. 485, pp. 87-113.
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© 2019 Elsevier Inc. Multi-valued model checking has been studied extensively recently, but important uncertain information contained in systems of multi-valued logics has not been considered in previous work and, as a consequence, some serious deficiencies arise. To make up for these deficiencies, this paper considers the possibility information implied in multi-valued systems. Precisely, we investigate computation tree logic model checking based on multi-valued possibility measures. We model multi-valued logic systems by multi-valued Kripke structures (MvKSs) and specify their verification properties by multi-valued computation tree logic (MvCTL) formulae. Based on generalized possibility measures and generalized necessity measures, an MvCTL model checking method is proposed, the pseudocode of the corresponding model checking algorithm is presented, and its time complexity is analyzed in detail. Furthermore, after detailed comparisons with χCTL (introduced in Chechik et al. [10]) and the classical CTL, we show that MvCTL is more general than χCTL, but cannot be reduced to the classical CTL. The conditions on lattice and MvKS under which MvCTL is equivalent to χCTL are given. Finally, some examples and a case study are given to illustrate the MvCTL model-checking method.
Li, Z, Dong, M, Wen, S, Hu, X, Zhou, P & Zeng, Z 2019, 'CLU-CNNs: Object detection for medical images', Neurocomputing, vol. 350, pp. 53-59.
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Medical images have different characteristics from normal images. As an important feature, there usually exists data distribution difference between source domain and target domain for data scarcity and privacy. In this paper, a domain adaptation framework called CLU-CNNs is proposed, which is designed for medical images. CLU-CNNs uses ANCF and BN-IN Net to improve domain adaptation capability without specific domain adaptation training. Based on probability distribution assumptions of networks’ output, ANCF is a new path for domain adaptation. And BN-IN Net is embedded in fully convolutional networks to improve stability. This work has three key contributions: (1) A new object detection domain adaptation method is proposed in this paper without specific domain adaptation training. (2) Designed for medical images, CLU-CNNs performs well on small dataset, and is easy to be expanded. (3) CLU-CNNs obtains high positioning accuracy and fast speed when there is data distribution difference between source domain and target domain. Test on REFUGE CHALLENGE 2018, our way achieves state of the art performance.
Li, Z, Guo, YJ, Chen, S-L & Wang, J 2019, 'A Period-Reconfigurable Leaky-Wave Antenna With Fixed-Frequency and Wide-Angle Beam Scanning', IEEE Transactions on Antennas and Propagation, vol. 67, no. 6, pp. 3720-3732.
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© 1963-2012 IEEE. A novel fixed-frequency beam-scanning leaky-wave antenna (LWA) based on a period-reconfigurable structure is presented. Operating at 5 GHz, the antenna consists of a slotted substrate integrated waveguide and 54 electrically small patches. Each patch element is etched with two dumbbell-shaped slots, and its operating state can be flexibly controlled by the biasing of the p-i-n diode on a parasitic strip. An ideal array model employing isotropic point sources is used for the analysis on the scanning mechanism, based on which a new method for suppressing the higher order space harmonics is developed. Using this method, the monoharmonic radiation range can be dramatically extended, and a wide-angle beam scanning can be achieved by manipulating the period length of the LWA. An FPGA controlling platform is designed for the electronic control of the antenna. The measured results validate that the proposed antenna achieves good performance of wide-angle scanning (125°) with a peak gain of 11.8 dBi at a fixed frequency.
Li, Z, Yao, L, Chang, X, Zhan, K, Sun, J & Zhang, H 2019, 'Zero-shot event detection via event-adaptive concept relevance mining', Pattern Recognition, vol. 88, pp. 595-603.
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Zero-shot complex event detection has been an emerging task in coping with the scarcity of labeled training videos in practice. Aiming to progress beyond the state-of-the-art zero-shot event detection, we propose a new zero-shot event detection approach, which exploits the semantic correlation between an event and concepts. Based on the concept detectors pre-trained from external sources, our method learns the semantic correlation from the concept vocabulary and emphasizes on the most related concepts for the zero-shot event detection. Particularly, a novel Event-Adaptive Concept Integration algorithm is introduced to estimate the effectiveness of semantically related concepts by assigning different weights to them. As opposed to assigning weights by an invariable strategy, we compute the weights of concepts using the area under score curve. The assigned weights are incorporated into the confidence score vector statistically to better characterize the event-concept correlation. Our algorithm is proved to be able to harness the related concepts discriminatively tailored for a target event. Extensive experiments are conducted on the challenging TRECVID event video datasets, which demonstrate the advantage of our approach over the state-of-the-art methods.
Li, Z, Zhang, S, Wang, J, Li, Y, Chen, M, Zhang, Z & Guo, YJ 2019, 'A Method of Generating Radiation Null for Periodic Leaky-Wave Antennas', IEEE Transactions on Antennas and Propagation, vol. 67, no. 6, pp. 4241-4246.
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© 1963-2012 IEEE. A systematic method for generating a radiation null region in the radiation pattern of periodic leaky-wave antennas (LWAs) is proposed using the theory of effective radiation sections (ERSs). In this method, the aperture field is expanded into spatial harmonics using the mode expansion method, and the ERSs of the harmonics are studied. Based on this, the radiation null region is introduced by suppressing the radiation of the ERSs corresponding to the radiating mode. The proposed method is applied to a periodic-strip LWA. The validity of the proposed method is verified by both simulated and experimental results, showing an obvious radiation null in the prescribed angular range. This method has the advantages of easy calculation and implementation, and has little influence on the gain and beamwidth. It is illustrated that only simple modification to the antenna structure is needed to achieve nulling.
Liang, H, Zhang, Q, Fu, C, Liang, F & Sun, Y 2019, 'Surface Modelling of Jun Ware Based on Ordinary Differential Equations', Traitement du Signal, vol. 36, no. 1, pp. 53-58.
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Liang, J, Mondal, AK, Wang, D & Iacopi, F 2019, 'Graphene‐Based Planar Microsupercapacitors: Recent Advances and Future Challenges', Advanced Materials Technologies, vol. 4, no. 1, pp. 1800200-1800200.
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AbstractThe continuous development of integrated electronics such as maintenance‐free biosensors, remote and mobile environmental sensors, wearable personal electronics, nanorobotics etc. and their continued miniaturization has led to an increasing demand for miniaturized energy storage units. Microsupercapacitors with graphene electrodes hold great promise as miniaturized, integrated power sources thanks to their fast charge/discharge rates, superior power performance, and long cycling stability. In addition, planar interdigitated electrodes also have the capability to reduce ion diffusion distances leading to a greatly improved electrochemical performance. Either as standalone power sources or complementing energy harvesting units, it is expected that graphene‐based microsupercapacitors will play a key role as miniaturized power sources in electronic microsystems. This review highlights the recent development, challenges, and perspectives in this area, with an emphasis on the link between material and geometry design of planar graphene‐based electrodes and their electrochemical performance and integrability.
Liang, J, Zhang, Y, Zhong, J-H & Yang, H 2019, 'A novel multi-segment feature fusion based fault classification approach for rotating machinery', Mechanical Systems and Signal Processing, vol. 122, pp. 19-41.
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© 2018 Elsevier Ltd Accurate and efficient rotating machinery fault diagnosis is crucial for industries to guarantee the productivity and reduce the maintenance cost. This paper systematically proposes a new fault diagnosis approach including signal processing techniques and pattern recognition method. In order to reveal more useful details in a fault residing signal, a novel automatic signal segmentation method named Grassmann manifold – angular central Gaussian distribution is proposed to divide a raw signal into several segments, resulting in a significant improvement of diagnosis accuracy. An improved empirical mode decomposition, wavelet transform – ensemble empirical mode decomposition, is also designed which could adequately solve the problems of mode mixing and end effects. Moreover, a morphological method usually used in image processing is investigated and adopted to change the shape of the intrinsic mode functions to further reveal the faulty impulses. In order to reduce the high dimension of the extracted features and improve the computational efficiency and accuracy, a deep belief network is designed to conduct information fusion, and compared with widely adopted kernel principal component analysis. For classification, a pairwise coupling strategy is proposed and combined with sparse Bayesian extreme learning machine. The experiments conducted using the proposed approach demonstrate the effectiveness of the proposed system.
Liang, T, Chen, L, Wu, J, Xu, G & Wu, Z 2019, 'SMS: A Framework for Service Discovery by Incorporating Social Media Information', IEEE Transactions on Services Computing, vol. 12, no. 3, pp. 384-397.
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With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches.
Liang, X, Wu, C, Yang, Y & Li, Z 2019, 'Experimental study on ultra-high performance concrete with high fire resistance under simultaneous effect of elevated temperature and impact loading', Cement and Concrete Composites, vol. 98, pp. 29-38.
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© 2019 Fire is a big risk to buildings and structures, posing a great threat to human lives. In this study, a newly developed ultra-high performance concrete (UHPC) was investigated experimentally. Quasi-static compression tests were conducted after the UHPC was first exposed to a high temperature, i.e. 200, 400, 600, 800 or 1000 °C, and then cooled down to room temperature, while dynamic tests were carried out under combined effect of a high temperature, i.e. 200, 400, 600, or 800 °C, and impact loading. The dynamic tests were done both at high temperatures and after cooling down and comparisons were made between these two scenarios. Based on the tests on this UHPC, mechanical and physical characteristics under the combined effect were studied. Besides, explosive spalling was analysed. It was interesting to find polypropylene (PP) fibre could play a negative role in preventing explosive spalling between 320 and 380 °C.
Liang, X, Wu, C, Yang, Y, Wu, C & Li, Z 2019, 'Coupled effect of temperature and impact loading on tensile strength of ultra-high performance fibre reinforced concrete', Composite Structures, vol. 229, pp. 111432-111432.
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© 2019 Elsevier Ltd This study focused on coupled effect of temperature and impact loading on tensile strength of an ultra-high performance fibre reinforced concrete (UHPFRC), which retains 69% of its original compressive strength after exposure to 1000 °C. The relationship between tensile strength and compressive strength was investigated under the coupled action since temperature may have different effects on them. Static tests and dynamic tests using a self-designed Split Hopkinson Pressure Bar (SHPB) system were conducted at temperatures 20, 200, 400, 600 and 800 °C. Comparison was made between tensile strength and compressive strength of UHPFRC obtained in hot state and cooled-down state. It was found splitting tensile strength fell sharply beyond 400 °C but still retained 41% of its original strength at 800 °C, well above other concretes. Temperature and combined action of elevated temperature and impact loading have different effects on splitting tensile strength and compressive strength.
Liao, H, Liao, C, Blamires, SJ & Tso, I 2019, 'Multifunctionality of an arthropod predator’s body coloration', Functional Ecology, vol. 33, no. 6, pp. 1067-1075.
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AbstractAnimal body colours can be shaped by many factors, including the need to attract mates, avoid predators and lure prey. In some contexts, these needs might compete. A number of studies have recently demonstrated that the silver, white, yellow or red bodies of spiders attract mates, lure prey or startle predators. Nevertheless, when spider bodies display different colours, little is known about the multifunctionality of the colours and whether they interact. The Australasian coin spider, Herrenia multipuncta, displays unconventional body coloration, with orange, black and grey regions across its body.We hypothesized that its coloration serves a multifunctional role, with the dorsal orange bands on its prosoma attracting prey and its orange ventrum deterring predators. We tested our hypothesis with field and laboratory experiments using dummies and real spiders, and modelling the visibility of the various colours to different predators and prey.Our field experiment showed significant prey attraction towards the orange‐grey dorsal pattern during the day and night, while our laboratory experiment showed that the lizard Japalura swinhonis stared at spiders and hesitated before attacking spiders when the orange abdominal region was uncovered. Our various visual models confirmed our experimental results by showing that the orange and grey body parts were always visible when contrasted against their natural backgrounds.Combined, our analyses provide evidence to conclude that the orange body colour of H. multipuncta is multifunctional, serving in both prey attraction and predator avoidance.A
Liao, X, Bai, K, Zhang, Q, Jia, X, Liu, S & Zhan, J 2019, 'Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries', Computing and Informatics, vol. 38, no. 6, pp. 1301-1319.
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Lim, S, Tran, VH, Akther, N, Phuntsho, S & Shon, HK 2019, 'Defect-free outer-selective hollow fiber thin-film composite membranes for forward osmosis applications', Journal of Membrane Science, vol. 586, pp. 281-291.
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© 2019 Elsevier B.V. This study presents the successful fabrication of a novel defect-free outer-selective hollow fiber (OSHF) thin-film composite (TFC) membrane for forward osmosis (FO) applications. Thin and porous FO membrane substrates made of polyether sulfone (PES) with a dense and smooth outer surface were initially fabricated at different air-gap distances. A modified vacuum-assisted interfacial polymerization (VAIP) technique was then successfully utilised for coating polyamide (PA) layer on the hollow fiber (HF) membrane substrate to prepare OSHF TFC membranes. Experimental results showed that the molecular weight cut-off (MWCO) of the surface of the membrane substrate should be less than 88 kDa with smooth surface roughness to obtain a defect-free PA layer via VAIP. The FO test results showed that the newly developed OSHF TFC membranes achieved water flux of 30.2 L m−2 h−1 and a specific reverse solute flux of 0.13 g L−1 using 1 M NaCl and DI water as draw and feed solution, respectively. This is a significant improvement on commercial FO membranes. Moreover, this OSHF TFC FO membrane demonstrated higher fouling resistance and better cleaning efficiency against alginate-silica fouling. This membrane also has a strong potential for scale-up for use in larger applications. It also has strong promise for various FO applications such as osmotic membrane bioreactor (OMBR) and fertilizer-drawn OMBR processes.
Lin, A, Li, J & Ma, Z 2019, 'On Learning and Learned Data Representation by Capsule Networks', IEEE Access, vol. 7, pp. 50808-50822.
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Capsule networks (CapsNet) are recently proposed neural network models containing newly introduced processing layer, which are specialized in entity representation and discovery in images. CapsNet is motivated by a view of parse tree-like information processing mechanism and employs an iterative routing operation dynamically determining connections between layers composed of capsule units, in which the information ascends through different levels of interpretations, from raw sensory observation to semantically meaningful entities represented by active capsules. The CapsNet architecture is plausible and has been proven to be effective in some image data processing tasks, the newly introduced routing operation is mainly required for determining the capsules' activation status during the forward pass. However, its influence on model fitting and the resulted representation is barely understood. In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks. Our investigation yielded the results some of which have been mentioned in the original paper of CapsNet, they are: 1) the routing operation determines the certainty with which a layer of capsules pass information to the layer above and the appropriate level of certainty is related to the model fitness; 2) in a designed experiment using data with a known 2D structure, capsule representations enable a more meaningful 2D manifold embedding than neurons do in a standard convolutional neural network (CNN), and; 3) compared with neurons of the standard CNN, capsules of successive layers are less coupled and more adaptive to new data distribution.
Lin, B-J, Chen, W-H, Hsieh, T-H, Ong, HC, Show, PL & Naqvi, SR 2019, 'Oxidative reaction interaction and synergistic index of emulsified pyrolysis bio-oil/diesel fuels', Renewable Energy, vol. 136, pp. 223-234.
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Lin, C-T, Chiu, C-Y, Singh, AK, King, J-T, Ko, L-W, Lu, Y-C & Wang, Y-K 2019, 'A Wireless Multifunctional SSVEP-Based Brain-Computer Interface Assistive System.', IEEE Trans. Cogn. Dev. Syst., vol. 11, no. 3, pp. 375-383.
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IEEE Several kinds of brain-computer interface (BCI) systems have been proposed to compensate for the lack of medical technology for assisting patients who lose the ability to use motor functions to communicate with the outside world. However, most of the proposed systems are limited by their non-portability, impracticality and inconvenience because of the adoption of wired or invasive electroencephalography (EEG) acquisition devices. Another common limitation is the shortage of functions provided because of the difficulty of integrating multiple functions into one BCI system. In this study, we propose a wireless, non-invasive and multifunctional assistive system which integrates steady state visually evoked potential (SSVEP)-based BCI and a robotic arm to assist patients to feed themselves. Patients are able to control the robotic arm via the BCI to serve themselves food. Three other functions: video entertainment, video calling, and active interaction are also integrated. This is achieved by designing a functional menu and integrating multiple subsystems. A refinement decision-making mechanism is incorporated to ensure the accuracy and applicability of the system. Fifteen participants were recruited to validate the usability and performance of the system. The averaged accuracy and information transfer rate (ITR) achieved is 90.91% and 24.94 bit per min respectively. The feedback from the participants demonstrates that this assistive system is able to significantly improve the quality of daily life.
Lin, C-T, King, J-T, Bharadwaj, P, Chen, C-H, Gupta, A, Ding, W & Prasad, M 2019, 'EOG-Based Eye Movement Classification and Application on HCI Baseball Game', IEEE Access, vol. 7, pp. 96166-96176.
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© 2013 IEEE. Electrooculography (EOG) is considered as the most stable physiological signal in the development of human-computer interface (HCI) for detecting eye-movement variations. EOG signal classification has gained more traction in recent years to overcome physical inconvenience in paralyzed patients. In this paper, a robust classification technique, such as eight directional movements is investigated by introducing a concept of buffer along with a variation of the slope to avoid misclassification effects in EOG signals. Blinking detection becomes complicated when the magnitude of the signals are considered. Hence, a correction technique is introduced to avoid misclassification for oblique eye movements. Meanwhile, a case study has been considered to apply these correction techniques to HCI baseball game to learn eye-movements.
Lin, C-T, Liu, C-H, Wang, P-S, King, J-T & Liao, L-D 2019, 'Design and Verification of a Dry Sensor-Based Multi-Channel Digital Active Circuit for Human Brain Electroencephalography Signal Acquisition Systems', Micromachines, vol. 10, no. 11, pp. 720-720.
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A brain–computer interface (BCI) is a type of interface/communication system that can help users interact with their environments. Electroencephalography (EEG) has become the most common application of BCIs and provides a way for disabled individuals to communicate. While wet sensors are the most commonly used sensors for traditional EEG measurements, they require considerable preparation time, including the time needed to prepare the skin and to use the conductive gel. Additionally, the conductive gel dries over time, leading to degraded performance. Furthermore, requiring patients to wear wet sensors to record EEG signals is considered highly inconvenient. Here, we report a wireless 8-channel digital active-circuit EEG signal acquisition system that uses dry sensors. Active-circuit systems for EEG measurement allow people to engage in daily life while using these systems, and the advantages of these systems can be further improved by utilizing dry sensors. Moreover, the use of dry sensors can help both disabled and healthy people enjoy the convenience of BCIs in daily life. To verify the reliability of the proposed system, we designed three experiments in which we evaluated eye blinking and teeth gritting, measured alpha waves, and recorded event-related potentials (ERPs) to compare our developed system with a standard Neuroscan EEG system.
Lin, J-Y, Wong, S-W, Wu, Y-M, Yang, Y, Zhu, L & He, Y 2019, 'Three-Way Multiple-Mode Cavity Filtering Crossover for Narrowband and Broadband Applications', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 3, pp. 896-905.
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© 1963-2012 IEEE. In this paper, the design of a cavity crossover with three intersecting channels is presented. The three fundamental modes of a cavity resonator, namely, TE011, TE101, and TM110 modes, are adopted to resonate at each of three channels, respectively. Due to the modal orthogonality of these fundamental modes, high isolation among three channels can be achieved. Two kinds of crossovers, for narrowband and broadband applications, are presented. For the narrowband case, the proposed crossover resonates at 2.91 GHz with the fractional bandwidth of 1.4%. For the broadband case, the proposed crossover resonates at 3 GHz with the fractional bandwidth of 24%. The isolations of both designs reach more than 50 dB. For a proof of concept, the broadband example of the cavity crossover structures is fabricated and measured. A good agreement between the simulated and the measured results verifies the accuracy of the proposed design methodology.
Lin, J-Y, Yang, Y, Wong, S-W, Chen, R-S, Li, Y, Zhang, L, He, Y & Zhu, L 2019, 'Cavity Filtering Magic-T and Its Integrations Into Balanced-to-Unbalanced Power Divider and Duplexing Power Divider', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 12, pp. 4995-5004.
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© 1963-2012 IEEE. In this article, a cavity filtering magic-T based on three fundamental modes, namely, TE011, TE101, and TM110, in a single triple-mode resonator (TMR) is presented. Taking advantage of the magic-T concept, two types of cavity-based filtering power dividers (PDs) integrated with balanced and duplexing functions are investigated. For the first design of a balanced-to-unbalanced (B2U) PD, balanced functions are integrated at input ports. Three fundamental modes provide the odd-and even-symmetric field distributions so that in-phase and out-of-phase responses at output ports can be achieved. Meanwhile, the common-mode suppression can be achieved at the balanced ports, and high isolation is achieved at the single-end ports, respectively. For the second design of a duplexing PD, instead of using resistors for output ports isolation, isolated ports are applied for magic-Ts to achieve all ports impedance matched and high isolation between channels of the proposed duplexing PD. To verify the concept, a B2U PD and a duplexing PD are fabricated and tested. Good matching between simulated and measured results shows the accuracy of the proposed design methodologies.
Lin, X, Far, H & Saleh, A 2019, 'Structural Behaviour and Mechanical Properties of Welded Steel I-Girders with Corrugated Webs', International Journal of Steel Structures, vol. 19, no. 4, pp. 1342-1352.
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© 2019, Korean Society of Steel Construction. Steel I girders with corrugated webs are appropriate alternatives for normal flat-web girders in steel structures since they provide lighter and smaller beam features in steel design. Based on the existing literature, the corrugated web beams (CWBs) provide many advantages for structural applications. In this study, a series of numerical analyses have been performed in order to investigate the structural behaviour of steel I girders with corrugated web profile and to compare their mechanical performance with normal welded beams. Theory of Ultimate Limit State design has been adopted in accordance with AS4100 (Steel structures, Standard Australia, Sydney, 1998) along with considering geometric and material non-linearity in the numerical analyses in SAP2000. Comparing the results of the numerical investigation, merits of using corrugated welded beams (CWBs) over normal welded beams (WBs) have become apparent. Moreover, investigations regarding force–displacement relationship and buckling analysis of the webs were carried out and presented to further validate the advantages of using corrugated web beams. CWBs have been used in some parts of Australia without detailed information about their mechanical properties. Thus, based on the outcomes of this study, CWB table for dimensions and cross sectional properties has been developed and proposed for practical applications.
Linares-Mustarós, S, Ferrer-Comalat, JC, Corominas-Coll, D & Merigó, JM 2019, 'The ordered weighted average in the theory of expertons', International Journal of Intelligent Systems, vol. 34, no. 3, pp. 345-365.
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© 2018 Wiley Periodicals, Inc. This work presents a data-fusion mathematical object that incorporates the optimism level of a decision-making agent. The new fusion object is constructed by extending the ordered weighted averaging (OWA) operator in the process of creating an experton. The main advantage of this approach is that it can represent the attitudinal character of the decision maker in the construction of the experton. Therefore, this approach represents a new method for addressing multiperson problems by using optimistic and pessimistic perspectives. The work presents different practical examples based on the absolute hierarchical relationships of the “minimum of the bottom end of the intervals,” “minimum of the top end of the intervals,” and “minimum size of the interval.” The work also considers a wide range of particular cases of the OWA-experton, including the minimum experton, the maximum experton, the average experton, and the olympic experton. In addition, the study presents software for the calculation of OWA-expertons. Finally, the paper ends with an application in business decision-making regarding the calculation of expected benefits.
Ling, X, Tu, J, Wang, J, Shajii, A, Kong, N, Feng, C, Zhang, Y, Yu, M, Xie, T, Bharwani, Z, Aljaeid, BM, Shi, B, Tao, W & Farokhzad, OC 2019, 'Glutathione-Responsive Prodrug Nanoparticles for Effective Drug Delivery and Cancer Therapy', ACS Nano, vol. 13, no. 1, pp. 357-370.
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Liu, B, Chen, L, Zhu, X & Qiu, W 2019, 'Encrypted data indexing for the secure outsourcing of spectral clustering', Knowledge and Information Systems, vol. 60, no. 3, pp. 1307-1328.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. Spectral clustering is one of the most popular clustering methods and is particularly useful for pattern recognition and image analysis. When using spectral clustering for analysis, users are either required to implement their own platforms, which requires strong data analytics and machine learning skills, or allow a third party to access and analyze their data, which may compromise their data privacy or security. Traditionally, this problem is solved by privacy-preserving data mining using randomization perturbation or secure multi-party computation. However, the existing methods suffer from the problems of inaccurate results or high computational requirements on the data owner’s side. To address these problems, in this paper, we propose a new secure outsourcing data mining (SODM) paradigm, which allows data owners to encrypt their data to ensure maximum data security. After the encryption, data owners can outsource their encrypted data to data analytics service providers (i.e., data analytics agent) for knowledge discovery, with a guarantee that neither the data analytics agent nor the other parties can compromise data privacy. To allow data mining to be efficiently carried out on encrypted data, we design a secure KD-tree to index all the encrypted data. Based on the SODM framework, a secure spectral clustering algorithm is proposed. The experiments on real-world datasets demonstrate the effectiveness and the efficiency of the system for the secure outsourcing of data mining.
Liu, B, Ding, M, Zhu, T, Xiang, Y & Zhou, W 2019, 'Adversaries or allies? Privacy and deep learning in big data era', Concurrency and Computation: Practice and Experience, vol. 31, no. 19.
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SummaryDeep learning methods have become the basis of new AI‐based services on the Internet in big data era because of their unprecedented accuracy. Meanwhile, it raises obvious privacy issues. The deep learning–assisted privacy attack can extract sensitive personal information not only from the text but also from unstructured data such as images and videos. In this paper, we proposed a framework to protect image privacy against deep learning tools, along with two new metrics that measure image privacy. Moreover, we propose two different image privacy protection schemes based on the two metrics, utilizing the adversarial example idea. The performance of our solution is validated by simulations on two different datasets. Our research shows that we can protect the image privacy by adding a small amount of noise that has a humanly imperceptible impact on the image quality, especially for images of complex structures and textures.
Liu, F, Liu, Y, Xu, KD, Ban, Y-L, Liu, QH & Guo, YJ 2019, 'Synthesizing Uniform Amplitude Sparse Dipole Arrays With Shaped Patterns by Joint Optimization of Element Positions, Rotations and Phases', IEEE Transactions on Antennas and Propagation, vol. 67, no. 9, pp. 6017-6028.
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Liu, F, Nattestad, A, Naficy, S, Han, R, Casillas, G, Angeloski, A, Sun, X & Huang, Z 2019, 'Fluorescent Carbon‐ and Oxygen‐Doped Hexagonal Boron Nitride Powders as Printing Ink for Anticounterfeit Applications', Advanced Optical Materials, vol. 7, no. 24, pp. 1901380-1901380.
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AbstractIncreasing demands for optical anticounterfeiting technology require the development of versatile luminescent materials with tunable photoluminescence properties. Herein, a number of fluorescent carbon‐ and oxygen‐doped hexagonal boron nitride (denoted as BCNO) phosphors are found to offer a such high‐tech anticounterfeiting solution. These multicolor BCNO powders, developed in a two‐step process with controlled annealing and oxidation, feature rod‐like particle shape, with varied luminescence properties. Studies of the optical properties of BCNO, along with other characterization, provide insight into this underexplored material. Anticounterfeiting applications are demonstrated with printed patterns which are indistinguishable to the naked eye under visible light but become highly discernible under UV irradiation. The fabricated patterns are demonstrated to be both chemically stable in corrosive environments and physically robust in mechanical bending testing. These properties render BCNO as promising and versatile anticounterfeiting material a wide variety of environments.
Liu, G, Quan, W, Cheng, N, Zhang, H & Yu, S 2019, 'Efficient DDoS attacks mitigation for stateful forwarding in Internet of Things', Journal of Network and Computer Applications, vol. 130, pp. 1-13.
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© 2019 Elsevier Ltd Stateful forwarding plane is fully considered as a novel forwarding paradigm, which is proven to be beneficial to delivery efficiency and resilient to certain types of attacks. However, this fresh attempt also introduces “varietal” Denial-of-Service attack due to complicated forwarding state operations, which may cause long-term memory exhaustion of forwarding nodes, especially for resource-limited IoT nodes. This new distributed exhaustion attack is extremely hidden and there is currently no effective defense against it. In this paper, we first establish a game model to analyze the attack benefit between attacker and defender. To further make the defender obtain more utility, it is significative to make the defender manage expired state-entries during stateful forwarding. To this end, we propose an enhanced distributed low-rate attack mitigating (eDLAM) mechanism. Particularly, eDLAM maintains a lightweight malicious request table (MRT), which is very small, to offload burden of practical forwarding state table. When a packet request is matched in MRT, it will be marked and dropped directly without any impact on forwarding state table. Based on this, eDLAM adopts an optimal threshold update method for MRT to achieve a maximum defender utility. We evaluate the eDLAM performance in terms of false negatives rate (FNR) and false positives rate (FPR). Extensive experimental results show that eDLAM can reduce FNR by 10.5% and FPR by 44% on average compared with state-of-the-art mechanisms.
Liu, H, Zhou, X, Ding, W, Zhang, Z, Nghiem, LD, Sun, J & Wang, Q 2019, 'Do Microplastics Affect Biological Wastewater Treatment Performance? Implications from Bacterial Activity Experiments', ACS Sustainable Chemistry & Engineering, vol. 7, no. 24, pp. 20097-20101.
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© 2019 American Chemical Society. Microplastics have been ubiquitously detected in the wastewater treatment plants, while their effects on the activities of wastewater treatment bacteria have never been evaluated. This study investigated the effects of polyester (PES), polyethylene (PE), and polyvinyl chloride (PVC) microplastics (100-1200 μm; 50-10000 particles/L) on the activities of ammonium-oxidizing bacteria (AOB), nitrite-oxidizing bacteria (NOB), denitrifiers, and polyphosphate-accumulating organisms (PAOs). The activities of AOB and NOB without microplastics addition are 6.3 ± 0.3 and 4.0 ± 0.4 mg N/g MLVSS/h (MLVSS: mixed liquor volatile suspended solids), which are similar (p > 0.05) to their activities (5.2 ± 0.7 to 6.8 ± 0.8 and 3.7 ± 0.9 to 5.1 ± 0.8 mg N/g MLVSS/h) with microplastics addition. Similarly, the activities of denitrifiers and PAOs without microplastics addition (14.1 ± 1.1 mg N/g MLVSS/h and 29.2 ± 0.9 mg P/g MLVSS/h) are comparable (p > 0.05) to those with microplastics addition (12.8 ± 1.2 to 15.1 ± 0.5 mg N/g MLVSS/h and 28.0 ± 1.1 to 29.7 ± 2.4 mg P/g MLVSS/h). The results demonstrated that microplastics do not significantly affect the activities of AOB, NOB, denitrifiers, and PAOs, and therefore the effect of microplastics on the wastewater treatment performance should not be overemphasized.
Liu, H, Zhu, X, Lu, M, Sun, Y & Yeo, KS 2019, 'Design of Reconfigurable dB-Linear Variable-Gain Amplifier and Switchable-Order $g_{m}$ -C Filter in 65-nm CMOS Technology', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 12, pp. 5148-5158.
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© 1963-2012 IEEE. A system approach for a power-scalable analog baseband (ABB) design is presented in this article. Using this approach, the energy efficiency of an ABB can be maximized without compromising any other important specifications. To fulfill the feasibility study, a switchable-order gm-C lowpass filter (LPF) along with a voltage-controlled programmable-gain amplifier (VC-PGA) is designed. The selectivity of the LPF can be linearly scaled with power consumption. In addition, the power consumption of VC-PGA has a binary-weighted manner. In contrast to conventional PGAs, the gain step of the designed PGA can be continuously tuned by a control voltage. To prove the concept, the ABB is implemented in 65-nm CMOS technology. The measurements show that the frequency responses of the ABB can be configured as either fifth or seventh order with 16 gain steps. The bandwidth is approximately 50 MHz for all cases, and the gain step can be continuously tuned between 0 and 3 dB. At the high-gain mode, the output third-order intercept point and the input-referred noise of the LPF and PGA are approximate to be 8 dBm and 5 nV/sqrt Hz, respectively. The maximum power consumption of the ABB, excluding the output buffer, is approximately 19.8 mW with a 1.2-V supply voltage. The die area, excluding the pads, is only 0.18 mm2
Liu, J, Tian, Z, Zheng, R & Liu, L 2019, 'A Distance-Based Method for Building an Encrypted Malware Traffic Identification Framework', IEEE Access, vol. 7, pp. 100014-100028.
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Liu, J, Wu, C, Li, C, Dong, W, Su, Y, Li, J, Cui, N, Zeng, F, Dai, L, Meng, Q & Pang, J 2019, 'Blast testing of high performance geopolymer composite walls reinforced with steel wire mesh and aluminium foam', Construction and Building Materials, vol. 197, pp. 533-547.
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© 2018 Elsevier Ltd Two blast tests were conducted to study the blast resistance of high performance geopolymer composite walls reinforced with steel wire mesh (SWM) and aluminium foam (AF). Conventional reinforced concrete (CRC) walls were also tested as control specimens. In total seven walls were tested under different blast loading conditions. The first blast test was conducted on one 2260 mm × 2260 mm × 150 mm SWM reinforced, one 2260 mm × 1000 mm × 150 mm SWM reinforced and one 2260 mm × 1000 mm × 150 mm combined SWM and AF reinforced high performance geopolymer composite walls under 50 kg TNT explosives at a standoff distance of 2.3 m. The second blast test was conducted on one 2260 mm × 2260 mm × 150 mm SWM reinforced and one 2260 mm × 2260 mm × 150 mm combined SWM and AF reinforced high performance geopolymer composite walls under 100 kg TNT explosives on the ground at the same standoff distance. Blast tests were also performed on two 2260 mm × 2260 mm × 150 mm CRC walls under such two designed explosions to compare their behaviours with reinforced high performance geopolymer composite walls. LVDT (linear variable differential transformer) devices were used to record the deflection histories and pressure sensors were used to measure the airblast pressure histories. The testing results indicated that the combined SWM and AF reinforced high performance geopolymer composite walls had a better blast resistance than the CRC walls, and the SWM reinforced high performance geopolymer composite wall was superior to both.
Liu, J, Wu, C, Li, J, Fang, J, Su, Y & Shao, R 2019, 'Ceramic balls protected ultra-high performance concrete structure against projectile impact–A numerical study', International Journal of Impact Engineering, vol. 125, pp. 143-162.
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© 2018 Elsevier Ltd Ceramic materials have excellent mechanical properties such as light weight, great hardness and high compressive strength. In this paper, a numerical study is conducted to investigate the response of ceramic balls protected ultra-high performance concrete (UHPC) targets against the high-velocity rigid projectile impact using the coupled smoothed particle hydrodynamics-finite element (SPH-FE) method in LS-DYNA. Based on the validated numerical models, parametric studies are performed to explore the effect of diameter, spatial arrangement and material type of ceramic balls as well as the impact position on the dynamic performance of UHPC targets, and then perforation and ballistic limits of ceramic balls protected UHPC targets are obtained. Compared with other UHPC slabs at the striking velocities from 500 m/s to 850 m/s, UHPC slabs protected with 6-layer hex-pack arranged ceramic balls with the diameter of 20 mm is most effective in terms of reducing the depth of penetration (DOP). In addition, the utilization of ceramic balls is economical in protective structures since the damaged ceramic balls can be replaced and undamaged ceramic balls are reusable.
Liu, J, Zhou, L & Ying, M 2019, 'Expected Runtime of Quantum Programs.', CoRR, vol. abs/1911.12557.
Liu, L, Amirgholipour, S, Jiang, J, Jia, W, Zeibots, M & He, X 2019, 'Performance-enhancing network pruning for crowd counting', Neurocomputing, vol. 360, pp. 246-253.
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© 2019 The Counting Convolutional Neural Network (CCNN) has been widely used for crowd counting. However, they typically end up with a complicated network model resulting in a challenge for real-time processing. Existing solutions aim to reduce the size of the network model, but unavoidably sacrifice the network accuracy. Different from existing pruning solutions, in this paper, a new pruning strategy is proposed by considering the contributions of various filters to the final result. The filters in the original CCNN model are grouped into positive, negative and irrelevant types. We prune the irrelevant filters of which feature maps contain little information, and the negative filters determined by a mask learned from the training dataset. Our solution improves the results of the counting model without fine-tuning or retraining the pruned model. We demonstrate the advantages of our proposed approach on the problem of crowd counting. Our experimental results on benchmark datasets show that the network model pruned using our approach not only reduces the network size but also improves the counting accuracies by 4% to 17% less MAE than the state of the arts.
Liu, L, Zhang, T, Leighton, B, Zhao, L, Huang, S & Dissanayake, G 2019, 'Robust Global Structure From Motion Pipeline With Parallax on Manifold Bundle Adjustment and Initialization', IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 2164-2171.
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© 2016 IEEE. In this letter, we present a novel global structure from motion (SfM) pipeline that is particularly effective in dealing with low-parallax scenes and camera motion collinear with the features that represent the environment structure. It is therefore particularly suitable in Urban SLAM, in which frequent road-facing motion poses many challenges to conventional SLAM algorithms. Our pipeline includes a recently explored bundle adjustment (BA) method that exploits a feature parameterization using Parallax angle between on-Manifold observation rays (PMBA). It is demonstrated that this BA stage has a consistently stable optimization configuration for features with any parallax and therefore low-parallax features can stay in reconstruction without pre-filtering. To allow practical usage of PMBA, we provide a compatible initialization stage in the SfM to initialize all camera poses simultaneously, exhibiting friendliness to collinear motion. This is achieved by simplifying PMBA into a hybrid graph problem of high connectivity yet small node set size, solved using a robust linear programming technique. Using simulations and a series of publicly available real datasets including 'KITTI' and 'Bundle Adjustment in the Large,' we demonstrate the robustness of the position initialization stage in handling collinear motion and outlier matches, superior convergence performance of the BA stage in the presence of low-parallax features, and effectiveness of our pipeline to handle many sequential or out-of-order urban scenes.
Liu, M, Luo, Y, Nanda, P, Yu, S & Zhang, J 2019, 'Efficient solution to the millionaires' problem based on asymmetric commutative encryption scheme', Computational Intelligence, vol. 35, no. 3, pp. 555-576.
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AbstractSecure multiparty computation is an important scheme in cryptography and can be applied in various real‐life problems. The first secure multiparty computation problem is the millionaires' problem, and its protocol is an important building block. Because of the less efficiency of public key encryption scheme, most existing solutions based on public key cryptography to this problem are inefficient. Thus, a solution based on the symmetric encryption scheme has been proposed. In this paper, we formally analyse the vulnerability of this solution, and propose a new scheme based on the decisional Diffie‐Hellman assumption. Our solution also uses 0‐encoding and 1‐encoding generated by our modified encoding method to reduce the computation cost. We implement the solution based on symmetric encryption scheme and our protocol. Extensive experiments are conducted to evaluate the efficiency of our solution, and the experimental results show that our solution can be much more efficient and be approximately 8000 times faster than the solution based on symmetric encryption scheme for a 32‐bit input and short‐term security. Moreover, our solution is also more efficient than the state‐of‐the‐art solution without precomputation and can also compare well with the state‐of‐the‐art protocol while the bit length of private inputs is large enough.
Liu, M, Nothling, MD, Webley, PA, Fu, Q & Qiao, GG 2019, 'Postcombustion Carbon Capture Using Thin-Film Composite Membranes', Accounts of Chemical Research, vol. 52, no. 7, pp. 1905-1914.
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Climate change due to anthropogenic carbon dioxide emissions (e.g., combustion of fossil fuels) represents one of the most profound environmental disasters of this century. Equipping power plants with carbon capture and storage (CCS) technology has the potential to reduce current worldwide CO2 emissions. However, existing CCS schemes (i.e., amine scrubbing) are highly energy-intensive. The urgent abatement of CO2 emissions relies on the development of new, efficient technologies to capture CO2 from existing power plants. Membrane-based CO2 separation is an attractive technology that meets many of the requirements for energy-efficient industrial carbon capture. Within this domain, thin-film composite (TFC) membranes are particularly attractive, providing high gas permeance in comparison with conventional thicker (∼50 μm) dense membranes. TFC membranes are usually composed of three layers: (1) a bottom porous support layer; (2) a highly permeable intermediate gutter layer; and (3) a thin (<1 μm) species-selective top layer. A key challenge in the development of TFC membranes has been to simultaneously maximize the transmembrane gas permeance of the assembled membrane (by minimizing the gas resistance of each layer) while maintaining high gas-specific selectivity. In this Account, we provide an overview of our recent development of high-performance TFC membrane materials as well as insights into the unique fabrication strategies employed for the selective layer and gutter layer. Optimization of each layer of the membrane assembly individually results in significant improvements in overall membrane performance. First, incorporating nanosized fillers into the selective layer (poly(ethylene glycol)-based polymers) and reducing its thickness (to ca. 50 nm) through continuous assembly of polymers technology yields major improvements in CO2 permeance without loss of selectivity. Second, we focus on optimization of the middle gutter layer of TFC membranes. The de...
Liu, PY, Tee, AE, Milazzo, G, Hannan, KM, Maag, J, Mondal, S, Atmadibrata, B, Bartonicek, N, Peng, H, Ho, N, Mayoh, C, Ciaccio, R, Sun, Y, Henderson, MJ, Gao, J, Everaert, C, Hulme, AJ, Wong, M, Lan, Q, Cheung, BB, Shi, L, Wang, JY, Simon, T, Fischer, M, Zhang, XD, Marshall, GM, Norris, MD, Haber, M, Vandesompele, J, Li, J, Mestdagh, P, Hannan, RD, Dinger, ME, Perini, G & Liu, T 2019, 'The long noncoding RNA lncNB1 promotes tumorigenesis by interacting with ribosomal protein RPL35', Nature Communications, vol. 10, no. 1.
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AbstractThe majority of patients with neuroblastoma due to MYCN oncogene amplification and consequent N-Myc oncoprotein over-expression die of the disease. Here our analyses of RNA sequencing data identify the long noncoding RNA lncNB1 as one of the transcripts most over-expressed in MYCN-amplified, compared with MYCN-non-amplified, human neuroblastoma cells and also the most over-expressed in neuroblastoma compared with all other cancers. lncNB1 binds to the ribosomal protein RPL35 to enhance E2F1 protein synthesis, leading to DEPDC1B gene transcription. The GTPase-activating protein DEPDC1B induces ERK protein phosphorylation and N-Myc protein stabilization. Importantly, lncNB1 knockdown abolishes neuroblastoma cell clonogenic capacity in vitro and leads to neuroblastoma tumor regression in mice, while high levels of lncNB1 and RPL35 in human neuroblastoma tissues predict poor patient prognosis. This study therefore identifies lncNB1 and its binding protein RPL35 as key factors for promoting E2F1 protein synthesis, N-Myc protein stability and N-Myc-driven oncogenesis, and as therapeutic targets.
Liu, R, Zhao, Y, Li, W, Wang, Q, Shen, C, Awe, OW & Hao, X 2019, 'Dynamics of the activated sludge in a newly-defined green bio-sorption reactor (GBR)', Chemical Engineering Journal, vol. 374, pp. 1046-1054.
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© 2019 Elsevier B.V. When upgrading an aging wastewater treatment plant (WWTP), the sludge management line is always out of consideration in terms of cost and easy-operation. This study presented the dynamics of the sludge when upgrading a conventional sequencing batch reactor (SBR) to green bio-sorption reactor (GBR) by embedding alum sludge-based constructed wetland (AlCW). The aluminum (Al(III)) content in the effluent and the resultant impact on organisms were also evaluated. The results showed that the Al(III) residues was at an acceptable level (<0.2 mg/L). The AlCW and its leachate Al(III) did not pose any detrimental impact on the activity of heterotrophic organisms and the nitrifiers whereas the activity of the polyphosphate accumulating organisms was completely suppressed and eliminated out of the reactor. In addition, the Al(III) hydroxides and natural organic matter promoted the flocculation of activated sludge flocs by complexation with the extracellular polymeric substances. As a result, the larger and compact activated sludge led to an increase of the settling velocity and the dewatering efficiency while deteriorating the sludge compressibility (sludge volume index of 150 mL/g). Interestingly, this laboratory-scale GBR was verified to be a promising alternative to upgrade the ageing WWTPs simultaneously with an improvement of the dewatering properties of the activated sludge.
Liu, T, Sun, G, Fang, J, Zhang, J & Li, Q 2019, 'Topographical design of stiffener layout for plates against blast loading using a modified ant colony optimization algorithm', Structural and Multidisciplinary Optimization, vol. 59, no. 2, pp. 335-350.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The stiffened plates are of demonstrable advantages and potential in offering high resistance to such extreme loading scenarios as blast. Since the distribution of the stiffeners has considerable effect on their performance, its design signifies an important topic of research. However, existing research has mainly focused on empirical design, and the configurations were largely experience based, which limits structural explosion-proof capacity. In order to improve the performance of stiffened plates against blast loading, we introduced here two new structural configurations of stiffened plates. In this study, the modified ant colony optimization (MACO) algorithm which introduces the mass constraint factor to the pheromone update function and integrates the idea of crossover and mutation was used to design the subjected to given working conditions. Specifically, material distribution of stiffeners is taken to be the design variables, and minimization of the maximum deflection of the center point of the plate to be the design objective under predetermined mass constraints. Compared with the baseline structure, the optimal designs largely improved the explosion-proof performance through distributing stiffener topology on the plates. The results showed that the optimum designs all present the reinforcement stiffeners to link with the fixed boundaries against the deformation. Moreover, the optimum designs placed more reinforcement materials in the central regions instead of four angles, and with the increase of the mass fraction, the reinforcement placement gradually extends from the center to the edges. The proposed method and new topological configurations are expected to provide some insights into design for novel protective structures.
Liu, T, Zhang, W, Ye, L, Ueland, M, Forbes, SL & Su, SW 2019, 'A novel multi-odour identification by electronic nose using non-parametric modelling-based feature extraction and time-series classification', Sensors and Actuators B: Chemical, vol. 298, pp. 126690-126690.
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© 2019 Elsevier B.V. The electronic nose (e-nose) is an olfaction system that consists of an array of chemical sensors and effective machine learning algorithms for the detection of various target odours. Feature extraction and classification methods are of great importance in improving the performance of the e-nose system. In this paper, a novel odour identification method is presented. Firstly, we use the kernel-based system modelling approach to extract odour features. Its solution is a series of finite impulse responses which containing discriminant information of different odours. In addition, a parameter optimisation method based on normalised mean square error and information entropy is proposed to optimise the kernel function. The entropy is effective in preventing the finite impulse responses from overfitting. Multi-odour classification is achieved based on Gaussian mixture density hidden Markov model (GMM-HMM) considering the characteristic of the extracted features. Also, parameter selection for GMM-HMM is realised according to BIC index and cross-validation. Then, we validate the performance of the proposed feature extraction method in resistance to noise and compare it with other existed features. The modelling-based feature reached the highest performance even without applying any filtering or smoothing techniques. Finally, we compare the proposed combination of feature extraction and classification algorithms with other approaches. The proposed method outperformed other approaches reaching 93.56% in sensitivity and 98.71% in specificity. The results demonstrate that the proposed method is applicable in e-nose-based odour identification.
Liu, W, Shen, X, Du, B, Tsang, IW, Zhang, W & Lin, X 2019, 'Hyperspectral Imagery Classification via Stochastic HHSVMs', IEEE Transactions on Image Processing, vol. 28, no. 2, pp. 577-588.
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© 1992-2012 IEEE. Hyperspectral imagery (HSI) has shown promising results in real-world applications. However, the technological evolution of optical sensors poses two main challenges in HSI classification: 1) the spectral band is usually redundant and noisy and 2) HSI with millions of pixels has become increasingly common in real-world applications. Motivated by the recent success of hybrid huberized support vector machines (HHSVMs), which inherit the benefits of both lasso and ridge regression, this paper first investigates the advantages of HHSVM for HSI applications. Unfortunately, the existing HHSVM solvers suffer from prohibitive computational costs on large-scale data sets. To solve this problem, this paper proposes simple and effective stochastic HHSVM algorithms for HSI classification. In the stochastic settings, we show that with a probability of at least 1-Q, our algorithms find an ϵ-accurate solution using Õ(1/λ2ϵ) iterations. Since the convergence rate of our algorithms does not depend on the size of the training set, our algorithms are suitable for handling large-scale problems. We demonstrate the superiority of our algorithms by conducting experiments on large-scale binary and multiclass classification problems, comparing to the state-of-the-art HHSVM solvers. Finally, we apply our algorithms to real HSI classification and achieve promising results.
Liu, W, Xu, D, Tsang, IW & Zhang, W 2019, 'Metric Learning for Multi-Output Tasks', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 2, pp. 408-422.
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Multi-output learning with the task of simultaneously predicting multiple outputs for an input has increasingly attracted interest from researchers due to its wide application. The k nearest neighbor ([Formula: see text]) algorithm is one of the most popular frameworks for handling multi-output problems. The performance of [Formula: see text] depends crucially on the metric used to compute the distance between different instances. However, our experiment results show that the existing advanced metric learning technique cannot provide an appropriate distance metric for multi-output tasks. This paper systematically studies how to efficiently learn an appropriate distance metric for multi-output problems with provable guarantee. In particular, we present a novel large margin metric learning paradigm for multi-output tasks, which projects both the input and output into the same embedding space and then learns a distance metric to discover output dependency such that instances with very different multiple outputs will be moved far away. Several strategies are then proposed to speed up the training and testing time. Moreover, we study the generalization error bound of our method for three learning tasks, which shows that our method converges to the optimal solutions. Experiments on three multi-output learning tasks (multi-label classification, multi-target regression, and multi-concept retrieval) validate the effectiveness and scalability of the proposed method.
Liu, X, Duan, X, Wei, W, Wang, S & Ni, B-J 2019, 'Photocatalytic conversion of lignocellulosic biomass to valuable products', Green Chemistry, vol. 21, no. 16, pp. 4266-4289.
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This review summarizes the state-of-the-art accomplishments in photocatalytic conversion of lignocellulosic biomass and its derivatives.
Liu, X, Iftikhar, N, Huo, H, Li, R & Nielsen, PS 2019, 'Two approaches for synthesizing scalable residential energy consumption data', Future Generation Computer Systems, vol. 95, pp. 586-600.
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© 2019 Elsevier B.V. Many fields require scalable and detailed energy consumption data for different study purposes. However, due to privacy issues, it is often difficult to obtain sufficiently large datasets. This paper proposes two different methods for synthesizing fine-grained energy consumption data for residential households, namely a regression-based method and a probability-based method. They each use a supervised machine learning method, which trains models with a relatively small real-world dataset and then generates large-scale time series based on the models. This paper describes the two methods in details, including data generation process, optimization techniques, and parallel data generation. This paper evaluates the performance of the two methods, which compare the resulting consumption profiles with real-world data, including patterns, statistics, and parallel data generation in the cluster. The results demonstrate the effectiveness of the proposed methods and their efficiency in generating large-scale datasets.
Liu, X, Xu, Q, Wang, D, Wu, Y, Fu, Q, Li, Y, Yang, Q, Liu, Y, Ni, B-J, Wang, Q, Yang, G, Li, H & Li, X 2019, 'Microwave pretreatment of polyacrylamide flocculated waste activated sludge: Effect on anaerobic digestion and polyacrylamide degradation', Bioresource Technology, vol. 290, pp. 121776-121776.
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© 2019 Elsevier Ltd Deterioration of anaerobic digestion can occur with the presence of polyacrylamide (PAM) in waste activated sludge, but the information on alleviating this deterioration is still limited. In this study, the simultaneous alleviation of negative effect of PAM and improvement of methane production during anaerobic digestion was accomplished by microwave pretreatment. Experimental results showed that with the microwave pretreatment times increased from 0 to 12 min, the biochemical methane potential of PAM-flocculated sludge (12 g PAM/kg total solids) asymptotically increased from 123.1 to 242.5 mL/g volatile solids, hydrolysis rate increased from 0.06 to 0.13 d−1. Mechanism analysis indicated that the microwave pretreatment accelerated the release and hydrolysis of organic substrates from PAM-flocculated sludge, facilitated the breaking of large firm “PAM-sludge” floccules, and benefited the degradation of PAM, which alleviated the PAM inhibitory impacts on digestion and meanwhile provided better contact between the released organic substrates and anaerobic bacteria for methane production.
Liu, X, Xu, Q, Wang, D, Wu, Y, Yang, Q, Liu, Y, Wang, Q, Li, X, Li, H, Zeng, G & Yang, G 2019, 'Unveiling the mechanisms of how cationic polyacrylamide affects short-chain fatty acids accumulation during long-term anaerobic fermentation of waste activated sludge', Water Research, vol. 155, pp. 142-151.
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© 2019 Elsevier Ltd Cationic polyacrylamide, a flocculation powder widely used in wastewater pretreatment and sludge dewatering, was highly accumulated in waste activated sludge. However, its effect on short-chain fatty acids (SCFAs) accumulation from anaerobic fermentation of waste activated sludge has not been investigated. This work therefore aims to deeply unveil how cationic polyacrylamide affects SCFAs production, through both long-term and batch tests using either real waste activated sludge or synthetic wastewaters as fermentation substrates. Experimental results showed that the presence of cationic polyacrylamide not only significantly decreased the accumulation of SCFAs but also affected the composition of individual SCFA. The concentration of SCFAs decreased from 3374.7 to 2391.7 mg COD/L with cationic polyacrylamide level increasing from 0 to 12 g/kg of total suspended solids, whereas the corresponding percentage of acetic acid increased from 45.2% to 55.5%. The mechanism studies revealed that although cationic polyacrylamide could be partially degraded to produce SCFAs during anaerobic fermentation, cationic polyacrylamide and its major degradation metabolite, polyacrylic acid, inhibited all the sludge solubilization, hydrolysis, acidogenesis, acetogenesis and homoacetogenesis processes to some extents. As a result, the accumulation of SCFAs in the cationic polyacrylamide added systems decreased rather than increased. However, the inhibition to acetogenesis and homoacetogenesis was slighter than that to acidogenesis, leading to an increase of acetic acid to total SCFAs. It was further found that cationic polyacrylamide had stronger ability to adhere to protein molecules surface, which inhibited the bioconversion of proteins more severely. Illumina MiSeq sequencing analyses showed that cationic polyacrylamide decreased microbial community diversity, altered community structure and changed activities of key enzymes responsible for SCFAs accumulation.
Liu, X, Xu, Q, Wang, D, Yang, Q, Wu, Y, Li, Y, Fu, Q, Yang, F, Liu, Y, Ni, B-J, Wang, Q & Li, X 2019, 'Thermal-alkaline pretreatment of polyacrylamide flocculated waste activated sludge: Process optimization and effects on anaerobic digestion and polyacrylamide degradation', Bioresource Technology, vol. 281, pp. 158-167.
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© 2019 Elsevier Ltd Deterioration of anaerobic digestion can occur with the presence of polyacrylamide (PAM) in waste activated sludge, and little information on mitigating this deterioration is currently available. In this study, simultaneous mitigation of PAM negative effects and improvement of methane production was accomplished by thermal-alkaline pretreatment. Under the optimized pretreatment conditions (i.e., 75 °C, pH 11.0 for 17.5 h), the biochemical methane potential of PAM-flocculated sludge increased from 100.5 to 210.8 mL/g VS and the hydrolysis rate increased from 0.122 to 0.187 d−1. Mechanism investigations revealed that the pretreatment not only broke the large firm floccules, improved the degradation of PAM, but also facilitated the release of biodegradable organics from sludge, which thereby provided better growth environment and enough nutrients to anaerobic microbes for methane production. The activities of key enzymes responsible for methane production and PAM degradation were greatly improved in pretreated reactor, with the accumulation of acrylamide being avoided.
Liu, X, Xu, Q, Wang, D, Yang, Q, Wu, Y, Yang, J, Liu, Y, Wang, Q, Ni, B-J, Li, X, Li, H & Yang, G 2019, 'Enhanced Short-Chain Fatty Acids from Waste Activated Sludge by Heat–CaO2 Advanced Thermal Hydrolysis Pretreatment: Parameter Optimization, Mechanisms, and Implications', ACS Sustainable Chemistry & Engineering, vol. 7, no. 3, pp. 3544-3555.
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© 2019 American Chemical Society. In the present work,heat-CaO2 advanced thermal hydrolysis pretreatment was applied for enhancing fermentative short-chain fatty acids (SCFAs) production from waste activated sludge (WAS). Various pretreatment conditions including heating temperatures, CaO2 doses, and times were optimized. Simulation and experimental results showed that the optimal pretreatment conditions were a temperature of 67.4 °C, CaO2 of 0.12 g/g VSS, and time of 19 h, under which the maximum SCFAs yield reached to 336.5 mg COD/g VSS after 5 days of fermentation, with the percentage of acetic acid accounted for 70.1%. Mechanism investigations exhibited that CaO2 and heat pretreatment caused positive synergy on sludge solubilization and SCFAs production. Compared with the control, heat pretreatment, and CaO2 addition alone, the heat-CaO2 pretreatment not only facilitated the organic released from WAS but also increased the proportion of biodegradable organic matters, which thereby providing more organics for subsequent SCFA production. It was found that the heat-CaO2 pretreatment improved the activities of both hydrolytic and acid-forming enzymes while it inhibited the coenzymes of methanogens during the fermentation process. In addition, heat-CaO2 pretreatment and subsequent fermentation worked well in removal of refractory organic pollutants and pathogens contained in WAS. Further analysis indicated that the heat-CaO2 pretreatment can be used as an effective method for both valuable carbon source recovery and refractory pollutant removal in the WAS treatment process.
Liu, X-P, Zhang, G-Q, Lu, J & Zhang, J-Q 2019, 'Risk assessment using transfer learning for grassland fires', Agricultural and Forest Meteorology, vol. 269-270, pp. 102-111.
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© 2019 A new direction of risk assessment research in grassland fire management is data-driven prediction, in which data are collected from particular regions. Since some regions have rich datasets that can easily generate knowledge for risk prediction, and some have no data available, this study addresses how we can leverage the knowledge learned from one grassland risk assessment to assist with a current assessment task. In this paper, we first introduce the transfer learning methodology to map and update risk maps in grassland fire management, and we propose a new grassland fire risk analysis method. In this study, two major grassland areas (Xilingol and Hulunbuir) in northern China are selected as the study areas, and five representative indicators (features) are extracted from grassland fuel, fire climate, accessibility, human and social economy. Taking Xilingol as the source domain (where sufficient labelled data are available) and Hulunbuir as the target domain (which contains insufficient data but requires risk assessment/prediction), we then establish the mapping relationship between grassland fire indicators and the degrees of grassland fire risk by using a transfer learning method. Finally, the fire risk in the Hulunbuir grassland is assessed using the transfer learning method. Experiments show that the prediction accuracy reached 87.5% by using the transfer learning method, representing a significant increase over existing methods.
Liu, Y, Jin, W, Zhou, X, Han, S-F, Tu, R, Feng, X, Jensen, PD & Wang, Q 2019, 'Efficient harvesting of Chlorella pyrenoidosa and Scenedesmus obliquus cultivated in urban sewage by magnetic flocculation using nano-Fe3O4 coated with polyethyleneimine', Bioresource Technology, vol. 290, pp. 121771-121771.
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© 2019 Elsevier Ltd In this work, a novel flocculation process by using nano-Fe3O4 coated with polyethyleneimine (Fe3O4@PEI) as magnetic seeds was developed to harvest the microalgae cultivated in urban sewage. Experiment results indicated that the harvest efficiency of Chlorella pyrenoidosa (0.5 g/L) was 98.92 ± 0.41% under the optimal conditions of Fe3O4@PEI dose of 20 mL/L, flocculation time of 20 min, and stirring speed of 800 rpm (3 min), while that of Scenedesmus obliquus (0.4 g/L) was 98.45 ± 0.35% under a Fe3O4@PEI dose of 16 mL/L, flocculation time of 15 min, and stirring speed of 730 rpm (3 min). Moreover, the process did not reduce the lipid content of microalgae and quality of biodiesel. After microalgae harvest, Fe3O4@PEI could be recovered by ultrasonication, re-wrapped with polyethyleneimine and reused to reduce operational cost.
Liu, Y, Ngo, HH, Guo, W, Peng, L, Wang, D & Ni, B 2019, 'The roles of free ammonia (FA) in biological wastewater treatment processes: A review', Environment International, vol. 123, pp. 10-19.
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© 2018 Free ammonia (FA) can pose inhibitory and/or biocidal effects on a variety of microorganisms involved in different biological wastewater treatment process, which is widely presented in wastewater treatment plants (WWTPs) due to the high levels of ammonium in the systems. This review article gives the up-to-date status on several essential roles of FA in biological wastewater treatment processes: the impacts of FA, mechanisms of FA roles, modeling of FA impacts, and implications of FA for wastewater treatment. Specifically, the impacts of FA on both wastewater and sludge treatment lines were firstly summarized, including nitrification, denitrification, anaerobic ammonium oxidation (Anammox), enhanced biological phosphorus removal and anaerobic processes. The involved mechanisms were then analyzed, which indicated FA inhibition can slow specific microbial activities or even reconfigure the microbial community structure, likely due to negative impacts of FA on intracellular pH, specific enzymes and extracellular polymeric substances (EPS), thus causing cell inactivation/lysis. Mathematical models describing the impact of FA on both wastewater and sludge treatment processes were also explored to facilitate process optimization. Finally, the key implications of FA were identified, that is FA can be leveraged to substantially enhance the biodegradability of secondary sludge, which would further improve biological nutrient removal and enhance renewable energy production.
Liu, Y, Yang, X, Jia, Y & Guo, YJ 2019, 'A Low Correlation and Mutual Coupling MIMO Antenna', IEEE Access, vol. 7, pp. 127384-127392.
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© 2013 IEEE. A new two-element multiple-input multiple-output (MIMO) antenna with low correlation and high port isolation is presented. First, two hybrid electromagnetic band gap (EBG) structures with the ability to support and stop surface wave propagation, respectively, are utilized simultaneously for achieving an extremely low envelope correlation coefficient (ECC). Then, based on studying of the ground current of the MIMO antenna with EBG structure, a new defected ground structure (DGS) is used to reduce the mutual coupling by controlling the polarization of the coupling field. The two antenna elements have an edge to edge spacing of 0.13\lambda where \lambda is the free space wavelength at the resonant frequency. Finally, the rectangular slots are introduced to the patch to improve the cross polarization. Experimental results show that the ECC of the MIMO antenna is lower than 0.002. Furthermore, the maximum mutual coupling (MC) reduction of 22dB can be achieved within the working bandwidth. All of above make the MIMO antenna a potential candidate for mobile terminal-based MIMO antenna systems.
Liu, Y, Zhang, LY & Li, J 2019, 'Fast detection of maximal exact matches via fixed sampling of queryK-mers and Bloom filtering of indexK-mers', Bioinformatics, vol. 35, no. 22, pp. 4560-4567.
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AbstractMotivationDetection of maximal exact matches (MEMs) between two long sequences is a fundamental problem in pairwise reference-query genome comparisons. To efficiently compare larger and larger genomes, reducing the number of indexed k-mers as well as the number of query k-mers has been adopted as a mainstream approach which saves the computational resources by avoiding a significant number of unnecessary matches.ResultsUnder this framework, we proposed a new method to detect all MEMs from a pair of genomes. The method first performs a fixed sampling of k-mers on the query sequence, and adds these selected k-mers to a Bloom filter. Then all the k-mers of the reference sequence are tested by the Bloom filter. If a k-mer passes the test, it is inserted into a hash table for indexing. Compared with the existing methods, much less number of query k-mers are generated and much less k-mers are inserted into the index to avoid unnecessary matches, leading to an efficient matching process and memory usage savings. Experiments on large genomes demonstrate that our method is at least 1.8 times faster than the best of the existing algorithms. This performance is mainly attributed to the key novelty of our method that the fixed k-mer sampling must be conducted on the query sequence and the index k-mers are filtered from the reference sequence via a Bloom filter.Availability and implementationhttps://github.com/yuansliu/bfMEMSupplementary informationSupplementary data are available at Bioinformatics online.
Liu, Z, Huang, L, Liang, J & Wu, C 2019, 'A three-dimensional indirect boundary integral equation method for modeling elastic wave scattering in a layered half-space', International Journal of Solids and Structures, vol. 169, pp. 81-94.
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© 2019 A new indirect boundary integral equation method (IBIEM)is proposed in this study to solve three-dimensional (3-D)elastic wave scattering by heterogeneities in a multi-layered half-space, employing Green's function of distributed loads on equivalent circular elements, thus avoiding the element discretization on layer interfaces. The proposed method enables the fictitious loads to be directly distributed on the surfaces of scatterer and the weak singularity to be tackled by analytical integration. Also, the radiation condition in the semi-infinite layered medium can be satisfied accurately, and the memory requirements can also be greatly reduced, especially for a large number of layers or gradient medium. The numerical accuracy was verified through comparisons with existing results and the numerical convergence was also confirmed. The results clearly demonstrate the simplicity and effectiveness of the method, and also reveals the complicated scattering characteristics in a layered half-space that are dominated by the resonant properties of the layered medium.
Liu, Z, Yang, C, Gao, W, Wu, D & Li, G 2019, 'Nonlinear behaviour and stability of functionally graded porous arches with graphene platelets reinforcements', International Journal of Engineering Science, vol. 137, pp. 37-56.
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© 2018 Elsevier Ltd This research presents an analytical approach for nonlinear static responses and stability analysis of functionally graded porous (FGP) arches with graphene platelets (GPLs) reinforcements (i.e., FGP-GPLRC arches). The constitutive material composition of the FGP-GPLRC arch varies along the radial direction of the cross section specifically, so that the mechanical performance of the arch such as buckling strength and weight can be well controlled for various engineering design purposes. The effective Young's modulus of the FGP-GPLRC arch is determined by the volume fraction distribution of materials. Based on the Euler-Bernoulli hypothesis, the structural responses of the arch considering the geometric nonlinearity are derived by using the virtual work method. Two boundary conditions are considered which are including the pinned-pinned and the fixed-fixed supports. The loading condition is defined as uniformly distributed load in the radial direction of the arch. Different buckling modes are discussed by the illustration of the equilibrium paths. By adopting the developed analytical solution, the relationship between the structural response, buckling load, self-weight, porosity level and the percentage of content of the GPLs can be investigated efficiently. The applicability and effectiveness of the proposed analytical approach for the geometric nonlinear analysis of FGP-GPLRC arch structures are demonstrated through numerical examples.
Liu, Z, Zhang, H, Cheng, A, Wu, C & Yang, G 2019, 'Seismic Interaction between a Lined Tunnel and a Hill under Plane SV Waves by IBEM', International Journal of Structural Stability and Dynamics, vol. 19, no. 02, pp. 1950004-1950004.
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This paper investigates the dynamic interaction between a lined tunnel and a hill under plane SV waves using the indirect boundary element method (IBEM), with the displacement and stress characteristics of the system presented in frequency domain. The IBEM has several unique advantages such as reducing calculation dimension, automatically satisfying the infinite radiation condition, etc. The numerical results indicated that the dynamic response of the tunnel–hill system is strongly dependent on incident wave characteristics, geometrical and material properties of the lined tunnel, as well as the topography of the hill. For a dimension ratio between the hill and tunnel of less than 10.0, the lined tunnel has large amplification or deamplification effect on the dynamic response of the hill. Correspondingly, the hill also greatly amplifies the displacement and stress concentration of the tunnel especially in the lower-frequency range, due to the complicated interference effect among the reflected waves and diffracted waves induced by the tunnel and hill. Also demonstrated is that the displacement and stress amplitude spectrums highly depend on the incident frequency and the space location, and there exist multiple peaks and troughs in the spectrum curve with the peaks usually appearing in the low-frequency range. Thus, for the seismic safety assessment of a hill slope or hill tunnel in practice, the dynamic interaction within the tunnel–hill system should be taken into consideration.
Llanos-Herrera, GR & Merigo, JM 2019, 'Overview of brand personality research with bibliometric indicators', Kybernetes, vol. 48, no. 3, pp. 546-569.
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PurposeThe purpose of this paper is to present a global view of the research that has been conducted regarding brand personality by using the Core Collection of the Web of Science (WoS) as a reference. The main bibliometric indicators considered are number of articles, number of citations, main authors, principal journals, institutions, countries and keywords.Design/methodology/approachThrough a bibliometric investigation, this paper performs an analysis of investigations of brand personality that have been conducted to date. In particular, the analysis focuses on the papers that have generated the greatest impact in the scientific community, the journals that have given the most attention to this concept and the authors who have most strongly influenced the academic world in this field. The analysis reveals a series of relationships between the bases of knowledge considered for different authors and journals and the structure of those relationships based on the keywords considered in each contribution.FindingsThis analysis allows to obtain a general and impartial view of brand personality research, and it reveals the most relevant contributions to the academic world in terms of authors, journals, institutions, countries and keywords. The analysis shows that the concept under study seems to still be in an early stage of development and there may well be an important amount of development ahead. Although there have been important contributions to this field, work is still required to consolidate this knowledge.Research limitations/implicationsThe information provided pertains to a re...
Long, G, Li, L, Li, W, Ma, K, Dong, W, Bai, C & Zhou, JL 2019, 'Enhanced mechanical properties and durability of coal gangue reinforced cement-soil mixture for foundation treatments', Journal of Cleaner Production, vol. 231, pp. 468-482.
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© 2019 Elsevier Ltd High-speed railways with high load capacity and long-term performance have been developed by the aid of high-performance construction materials for foundation treatments. The mechanical properties and durability of new cement-soil mixture reinforced by local sourced waste coal gangue aggregate were investigated in this study. Extensive experiments were carried out to analyse the effects of coal gangue on compressive strength, elastic modulus, stress-strain curve and anti-corrosion of cement-soil mixture. The results show that incorporation of coal gangue significantly improve the strength, stiffness and anti-corrosion ability of cement-soil mixture. Strength improvements up to 81.8% was achieved, but the ductile failure model shited to brittle failure with more than 42% coal gangue reinforcements. Except for the declining segment of the stress-strain curve, the ascending segment of the stress-strain curve can be fitted by the existing models. From the microstructural characterization, coal gangue can reduce acid solution permeation compared to the soil. For the cemented soil with coal gangue, the mass-loss rates only reach 4–7% after 140 days acid solution immersion. Therefore, this new clean production of high-performance cement-soil mixture through waste coal gangue reinforcement has great potential for railway foundation treatments.
Lu, J, Yan, Z, Han, J & Zhang, G 2019, 'Data-Driven Decision-Making (D3M): Framework, Methodology, and Directions', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 3, no. 4, pp. 286-296.
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© 2017 IEEE. A decision problem, according to traditional principles, is approached by finding an optimal solution to an analytical programming decision model, which is known as model-driven decision-making. The fidelity of the model determines the quality and reliability of the decision-making; however, the intrinsic complexity of many real-world decision problems leads to significant model mismatch or infeasibility in deriving a model using the first principle. To overcome the challenges that are present in the big data era, both researchers and practitioners emphasize the importance of making decisions that are backed up by data related to decision tasks, a process called data-driven decision-making (D3M). By building on data science, not only can decision models be predicted in the presence of uncertainty or unknown dynamics, but also inherent rules or knowledge can be extracted from data and directly utilized to generate decision solutions. This position paper systematically discusses the basic concepts and prevailing techniques in data-driven decision-making and clusters-related developments in technique into two main categories: programmable data-driven decision-making (P-D3M) and nonprogrammable data-driven decision-making (NP-D3M). This paper establishes a D3M technical framework, main methodologies, and approaches for both categories of D3M, as well as identifies potential methods and procedures for using data to support decision-making. It also provides examples of how D3M is implemented in practice and identifies five further research directions in the D3M area. We believe that this paper will directly support researchers and professionals in their understanding of the fundamentals of D3M and of the developments in technical methods.
Lu, P, Liu, T, Ni, B-J, Guo, J, Yuan, Z & Hu, S 2019, 'Growth kinetics of Candidatus ‘Methanoperedens nitroreducens’ enriched in a laboratory reactor', Science of The Total Environment, vol. 659, pp. 442-450.
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© 2018 Recently it has been shown that Candidatus ‘Methanoperedens nitroreducens’, an anaerobic methanotrophic archaea (ANME), can reduce nitrate to nitrite using electrons derived from anaerobic oxidation of methane. In this study, the growth kinetics of ‘M. nitroreducens’ enriched in a laboratory reactor were studied. In the experimental concentration range (up to 16 mg CH 4 L −1 ), anaerobic oxidation of methane by ‘M. nitroreducens’ was found to comply with first order kinetic model with a rate constant of 0.019 ± 0.006 h −1 and a biomass-specific rate constant of 0.04–0.14 L h −1 g −1 VSS. Meanwhile, the nitrate reduction to nitrite was well described by the Monod-type kinetic model with an affinity constant for nitrate of 2.1 ± 0.4 mg N L −1 , which is slightly higher than, but comparable to, that of most known denitrifying bacteria. This is the first time that the growth kinetics of ‘M. nitroreducens’ have been experimentally studied. The applicability of the kinetic model reported herein to this organism or similar organisms in natural or engineering systems requires further investigation.
Lu, S, Oberst, S, Zhang, G & Luo, Z 2019, 'Bifurcation analysis of dynamic pricing processes with nonlinear external reference effects', Communications in Nonlinear Science and Numerical Simulation, vol. 79, pp. 104929-104929.
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© 2019 Elsevier B.V. Dynamic pricing has been widely implemented to hedge against volatile demand. One challenging problem is the study of optimal price choices under the influence of this volatility. Stochastic demand is a prevalent assumption when it comes to model the volatility on pricing decisions. However, the demand volatility might also be produced by deterministic chaos, which has rarely been studied in this field of research to-date. We propose deterministic dynamic pricing processes that aim to maximise the revenue and to mimic a real pricing decision. Our model includes nonlinear consumer expectations that explain the effects of external information on consumers and discrete optimisations due to a non-smooth demand function that considers asymmetries in the perceptions of gains or losses of consumers and finite price choices of companies. Volatile markets can show up because of non-periodic consumer expectations, period adding bifurcations, codimension-2 points and coexisting solutions. Results highlight that optimal pricing strategies should agree with the dynamics of consumer expectations. Disregarding deterministic dynamics may not only cause revenue losses in practice but might also mislead regulators about the underlying mechanisms that consumers and companies respond to. We introduce for the first time an irregular pricing strategy: a company can make the first return iteration of each sales price non-periodic to follow non-periodic consumer expectations when having finite price choices. These results may justify implementing irregular pricing strategies in the case of practical pricing decisions. Here, the existence of coexisting solutions can assist to identify potential market manipulations within a monopoly market. This not only contributes to a fresh look on volatile markets but also emphasises the importance of initial conditions to pricing decisions and price regulations.
Lu, W & Liu, D 2019, 'A Scalable Sampling-Based Optimal Path Planning Approach via Search Space Reduction', IEEE Access, vol. 7, pp. 153921-153935.
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© 2013 IEEE. Many sampling strategies in Sampling-Based Planning (SBP) often consider goal and obstacle population and may however become less efficient in large and cluttered 3D environments with a goal distanced away. This paper presents a search-space-Reduced optimal SBP approach (RSBP) for a rigid body. This reduced space is found by a sparse search tree, which is enabled by a Metric Function (MF) built on a neural network. The offline-learnt MF estimates the minimum traveling cost between any two nodes in a fixed small workspace with various obstacles. It allows connections of two sparse nodes without path planning, where the connections represent the traveling costs (not paths). It is proven that the asymptotic optimality is preserved in the RSBP (assuming a zero-error MF) and the optimality degeneration is bounded (assuming a bounded-error MF). The computational complexity during planning is shown linear to the Lebesgue measure of the entire search space (assuming the same sampling density across environments). Numerical simulations have shown that in tested large and cluttered environments the RSBP is at least as fast as the bidirectional fast marching tree∗ and informed rapidly exploring random tree∗, with planned paths of similar optimality. The results also have shown the RSBP's improved scalability to large environments and enhanced efficiency in dealing with narrow passages.
Lu, W, Meng, F, Wang, S, Zhang, G, Zhang, X, Ouyang, A & Zhang, X 2019, 'Graph-Based Chinese Word Sense Disambiguation with Multi-Knowledge Integration', Computers, Materials & Continua, vol. 61, no. 1, pp. 197-212.
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© 2019 Tech Science Press. All rights reserved. Word sense disambiguation (WSD) is a fundamental but significant task in natural language processing, which directly affects the performance of upper applications. However, WSD is very challenging due to the problem of knowledge bottleneck, i.e., it is hard to acquire abundant disambiguation knowledge, especially in Chinese. To solve this problem, this paper proposes a graph-based Chinese WSD method with multi-knowledge integration. Particularly, a graph model combining various Chinese and English knowledge resources by word sense mapping is designed. Firstly, the content words in a Chinese ambiguous sentence are extracted and mapped to English words with BabelNet. Then, English word similarity is computed based on English word embeddings and knowledge base. Chinese word similarity is evaluated with Chinese word embedding and HowNet, respectively. The weights of the three kinds of word similarity are optimized with simulated annealing algorithm so as to obtain their overall similarities, which are utilized to construct a disambiguation graph. The graph scoring algorithm evaluates the importance of each word sense node and judge the right senses of the ambiguous words. Extensive experimental results on SemEval dataset show that our proposed WSD method significantly outperforms the baselines.
Lu, Y, Fang, J, Guo, Z & Zhang, JA 2019, 'Distributed transmit beamforming for UAV to base communications', China Communications, vol. 16, no. 1, pp. 15-25.
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Distributed transmit beamforming (DTB) is very efficient for extending the communication distance between a swarm of UAVs and the base, particularly when considering the constraints in weight and battery life for payloads on UAVs. In this paper, we review major function modules and potential solutions in realizing DTB in UAV systems, such as timing and carrier synchronization, phase drift tracking and compensation, and beamforming vector generation and updating. We then focus on beamforming vector generation and updating, and introduce a concatenated training scheme, together with a recursive channel estimation and updating algorithm. We also propose three approaches for tracking the variation of channels and updating the vectors. The effectiveness of these approaches is validated by simulation results.
Lu, Y, Fang, J, Guo, Z & Zhang, JA 2019, 'Performance characterization and receiver design for random temporal multiple access in non-coordinated networks', China Communications, vol. 16, no. 6, pp. 173-184.
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Random access is a well-known multiple access method for uncoordinated communication nodes. Existing work mainly focuses on optimizing iterative access protocols, assuming that packets are corrupted once they are collided, or that feedback is available and can be exploited. In practice, a packet may still be able to be recovered successfully even when collided with other packets. System design and performance analysis under such a situation, particularly when the details of collision are taken into consideration, are less known. In this paper, we provide a framework for analytically evaluating the actual detection performance in a random temporal multiple access system where nodes can only transmit. Explicit expressions are provided for collision probability and signal to interference and noise ratio (SINR) when different numbers of packets are collided. We then discuss and compare two receiver options for the AP, and provide detailed receiver design for the premium one. In particular, we propose a synchronization scheme which can largely reduce the preamble length. We also demonstrate that system performance could be a convex function of preamble length both analytically and via simulation, as well as the forward error correction (FEC) coding rate.
Lu, Z-H, Li, H, Li, W, Zhao, Y-G, Tang, Z & Sun, Z 2019, 'Shear behavior degradation and failure pattern of reinforced concrete beam with chloride-induced stirrup corrosion', Advances in Structural Engineering, vol. 22, no. 14, pp. 2998-3010.
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Reinforcement corrosion exhibits an adverse effect on the shear strength of reinforced concrete structures. In order to investigate the effects of chloride-induced corrosion of reinforcing steel on the shear behavior and failure pattern of reinforced concrete beams, a total of 24 reinforced concrete beams with different concrete strength grades and arrangements of stirrups were fabricated, among which 22 beams were subjected to accelerated corrosion to achieve different degrees of reinforcement corrosion. The failure pattern, crack propagation, load–displacement response, and ultimate strength of these beams were investigated under a standard four-point loading test in this study. Extensive comparative analysis was conducted to investigate the effects of the concrete strength, shear span-to-depth ratio, and stirrup type on the shear behavior of the corroded reinforced concrete beams. The results show that increasing the stirrup yielding strength is more effective in improving the shear strength of corroded reinforced concrete beams than that of concrete compressive strength. In terms of three types of stirrups, the shear strength of the beams with deformed HRB-335 is least sensitive to stirrup corrosion, followed by the beams with smooth HPB-235 and the beams with deformed HRB-400. The effect of the different stirrups on the shear strength depends on the corrosion degree of stirrup and shear span-to-depth ratio of the beam. The predicted results of shear strength of corroded reinforced concrete beams by a proposed analytical model are well consistent with the experimental results.
Lu, Z-H, Lun, P-Y, Li, W, Luo, Z, Li, Y & Liu, P 2019, 'Empirical model of corrosion rate for steel reinforced concrete structures in chloride-laden environments', Advances in Structural Engineering, vol. 22, no. 1, pp. 223-239.
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The corrosion rate of reinforcing steel is an important factor to determine the corrosion propagation of reinforced concrete structures in the chloride-laden environments. Since the corrosion rate of reinforcing steel is affected by several coupled parameters, the efficient prediction of which remains challenging. In this study, a total of 156 experimental data on corrosion rate from the literature were collected and compared. Seven empirical models for predicting the corrosion rate were reviewed and investigated using the collected experimental data. Based on the investigations, a new empirical model is proposed for predicting the corrosion rate in corrosion-affected reinforced concrete structures considering parameters including concrete resistivity, temperature, relative humidity, corrosion duration and concrete chloride content. The comparison between the experimental data and those predicted using the new empirical model demonstrates that the new model gives a good prediction of the corrosion rate. Furthermore, the uncertainty and probability characteristics of these empirical models are also investigated. It is found that the probability distributions of the model errors can be described as lognormal, normal, Weibull or Gumbel distributions. As a result, the new empirical model can provide an efficient prediction of the corrosion rate of reinforcing steel, and the model error analysis results can be utilized for reliability-based service life prediction of reinforced concrete structures under chloride-laden environments.
Luo, F, Jiang, C, Yu, S, Wang, J, Li, Y & Ren, Y 2019, 'Stability of Cloud-Based UAV Systems Supporting Big Data Acquisition and Processing', IEEE Transactions on Cloud Computing, vol. 7, no. 3, pp. 866-877.
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Unmanned Aerial Vehicle (UAV) technology has been widely applied in both military and civilian applications. Recent researches on UAV systems feature in the dramatic augment of the variety and number of equipped sensors, which results in such an issue that multiple UAVs cannot afford to handle the big data generated by a range of sensors in the air. Considering this practical problem, in this paper, we propose a cloud-based UAV system which incorporates the computing capability of the terrestrial cloud into the UAV systems. Relying on proposed cloud-based UAV system, one critical theoretic issue is how to acquire the big data generated by the sensors while guaranteeing a stable operation state of the system. First, we analyze the cloud-based system’s on-demand service ability as well as its impact on UAVs’ control procedure. Second, the UAV cloud control system is modeled as a network control system. Moreover, the stable condition of the UAV cloud control system is derived, which reveals the relationship between the acquisition rate of sensor data and the stability of the cloud-based UAV system. Finally, simulations are conducted to verify the effectiveness of our theoretical analysis.
Luo, M, Yan, C, Zheng, Q, Chang, X, Chen, L & Nie, F 2019, 'Discrete Multi-Graph Clustering', IEEE Transactions on Image Processing, vol. 28, no. 9, pp. 4701-4712.
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© 1992-2012 IEEE. Spectral clustering plays a significant role in applications that rely on multi-view data due to its well-defined mathematical framework and excellent performance on arbitrarily-shaped clusters. Unfortunately, directly optimizing the spectral clustering inevitably results in an NP-hard problem due to the discrete constraints on the clustering labels. Hence, conventional approaches intuitively include a relax-and-discretize strategy to approximate the original solution. However, there are no principles in this strategy that prevent the possibility of information loss between each stage of the process. This uncertainty is aggravated when a procedure of heterogeneous features fusion has to be included in multi-view spectral clustering. In this paper, we avoid an NP-hard optimization problem and develop a general framework for multi-view discrete graph clustering by directly learning a consensus partition across multiple views, instead of using the relax-and-discretize strategy. An effective re-weighting optimization algorithm is exploited to solve the proposed challenging problem. Further, we provide a theoretical analysis of the model's convergence properties and computational complexity for the proposed algorithm. Extensive experiments on several benchmark datasets verify the effectiveness and superiority of the proposed algorithm on clustering and image segmentation tasks.
Luo, Y, Zhang, JA, Huang, X, Ni, W & Pan, J 2019, 'Optimization and Quantization of Multibeam Beamforming Vector for Joint Communication and Radio Sensing', IEEE Transactions on Communications, vol. 67, no. 9, pp. 6468-6482.
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© 1972-2012 IEEE. Joint communication and radio sensing (JCAS) in millimeter-wave (mmWave) systems requires the use of a steerable beam. For analog antenna arrays, a single beam is typically used, which limits the sensing area within the direction of the communication. Multibeam technology can overcome this limitation by separately generating package-level direction-varying sensing subbeams and fixed communication subbeams and then combine them coherently. In this paper, we investigate the optimal combination of the two subbeams and the quantization of the beamforming (BF) vector that generates the combined beam. When either the full channel matrix or only the angle of departure (AoD) of the dominating line-of-sight (LOS) path is known at the transmitter, we derive the closed-form expressions for the optimal combining coefficients that maximize the received communication signal power. For the quantization of the BF vector, we focus on the two-phase-shifter array where two phase shifters are used to represent each BF weight. We propose novel joint quantization methods by combining the codebooks of the two phase shifters. The mean squared quantization error is derived for various quantization methods. Extensive simulation results validate the accuracy of the analytical results and the effectiveness of the proposed multibeam optimization and joint quantization methods.
Luo, Z, Li, W, Tam, VWY, Xiao, J & Shah, SP 2019, 'Current progress on nanotechnology application in recycled aggregate concrete', Journal of Sustainable Cement-Based Materials, vol. 8, no. 2, pp. 79-96.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. As a very promising sustainable construction material, recycled aggregate concrete (RAC) has become a hot research topic and attracted wide attention. Although many investigations have been conducted, it is still a great challenge to product RAC with satisfactory and stable performance for practical engineering application. In recent years, nanotechnology has been introduced to RAC research and displayed distinct advantages over many traditional methods. This article is devoted to reviewing the current research on nanotechnology application in RAC, including nanoengineering and nanoscience. The corresponding results involving microstructure characteristics, mechanical properties, workability, and durability were summarized and discussed. It has been found that nanoscience has promoted better understanding of microstructure and various internal mechanism of RAC, while nanoengineering has helped RAC achieve dense microstructure, improved mechanical properties and durability. However, further efforts are required to realize the potential of nanotechnology and to bring breakthroughs in research and application of RAC.
Luong, NC, Hoang, DT, Gong, S, Niyato, D, Wang, P, Liang, Y-C & Kim, DI 2019, 'Applications of Deep Reinforcement Learning in Communications and Networking: A Survey', IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3133-3174.
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© 1998-2012 IEEE. This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking. Modern networks, e.g., Internet of Things (IoT) and unmanned aerial vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, DRL, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of DRL from fundamental concepts to advanced models. Then, we review DRL approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks, such as 5G and beyond. Furthermore, we present applications of DRL for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying DRL.
Luong, NT, Vo, TT & Hoang, D 2019, 'FAPRP: A Machine Learning Approach to Flooding Attacks Prevention Routing Protocol in Mobile Ad Hoc Networks', Wireless Communications and Mobile Computing, vol. 2019, pp. 1-17.
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Request route flooding attack is one of the main challenges in the security of Mobile Ad Hoc Networks (MANETs) as it is easy to initiate and difficult to prevent. A malicious node can launch an attack simply by sending an excessively high number of route request (RREQ) packets or useless data packets to nonexistent destinations. As a result, the network is rendered useless as all its resources are used up to serve this storm of RREQ packets and hence unable to perform its normal routing duty. Most existing research efforts on detecting such a flooding attack use the number of RREQs originated by a node per unit time as the threshold to classify an attacker. These algorithms work to some extent; however, they suffer high misdetection rate and reduce network performance. This paper proposes a new flooding attacks detection algorithm (FADA) for MANETs based on a machine learning approach. The algorithm relies on the route discovery history information of each node to capture similar characteristics and behaviors of nodes belonging to the same class to decide if a node is malicious. The paper also proposes a new flooding attacks prevention routing protocol (FAPRP) by extending the original AODV protocol and integrating FADA algorithm. The performance of the proposed solution is evaluated in terms of successful attack detection ratio, packet delivery ratio, and routing load both in normal and under RREQ attack scenarios using NS2 simulation. The simulation results show that the proposed FAPRP can detect over 99% of RREQ flooding attacks for all scenarios using route discovery frequency vector of sizes larger than 35 and performs better in terms of packet delivery ratio and routing load compared to existing solutions for RREQ flooding attacks.
Lv, X, Withayachumnankul, W & Fumeaux, C 2019, 'Single-FSS-Layer Absorber With Improved Bandwidth–Thickness Tradeoff Adopting Impedance-Matching Superstrate', IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 5, pp. 916-920.
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Ly, QV, Nghiem, LD, Cho, J, Maqbool, T & Hur, J 2019, 'Organic carbon source-dependent properties of soluble microbial products in sequencing batch reactors and its effects on membrane fouling', Journal of Environmental Management, vol. 244, pp. 40-47.
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This study investigated the influence of three different organic carbon sources including sodium acetate (SOD), glucose (GLU), and starch (STAR), on soluble microbial products (SMP), which presumably have dissimilar uptake rates and metabolic pathways, in sequencing batch reactors (SBR) and their subsequent effects on membrane fouling of ultrafiltration (UF). SMP were mainly characterized by fluorescence excitation emission matrix coupled with parallel factor analysis (EEM-PARAFAC) and size exclusion chromatography (SEC). SMP produced in SOD-fed SBR showed higher abundances of protein-like fluorescent component and large sized aliphatic biopolymer (BP) than GLU- or STAR-fed counterpart did, while the STAR-based operation resulted in more SMP enriched with humic-like fluorescence. The differences in SMP exerted marked effects on UF membrane fouling as indicated by the highest fouling potential with reversibility shown for the SMP from the SOD-fed reactor. Regardless of the carbon source, BP fraction and protein-like component exhibited the greatest extent of reversible fouling, suggesting that size exclusion plays a critical role. However, notable differences in the reversible fouling propensity of relatively smaller size fractions among the three SBRs signified the possible involvement of chemical interactions as a secondary fouling mechanism and its dependency on different carbon sources. Our results provide a new insight into the roles of carbon sources in the characteristics of SMP in biological treatment systems and their effects on the post-treatment using membrane filtration, which is ultimately beneficial to the optimization of biological treatment design and membrane filtration operation.
Lyu, B, Qin, L, Lin, X, Chang, L & Yu, JX 2019, 'Supergraph Search in Graph Databases via Hierarchical Feature-Tree.', IEEE Trans. Knowl. Data Eng., vol. 31, no. 2, pp. 385-400.
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© 1989-2012 IEEE. Supergraph search is a fundamental problem in graph databases that is widely applied in many application scenarios. Given a graph database and a query-graph, supergraph search retrieves all data-graphs contained in the query-graph from the graph database. Most existing solutions for supergraph search follow the pruning-and-verification framework, which prune false answers based on features in the pruning phase and perform subgraph isomorphism testings on the remaining graphs in the verification phase. However, they are not scalable to handle large-sized data-graphs and query-graphs due to three drawbacks. First, they rely on a frequent subgraph mining algorithm to select features which is expensive and cannot generate large features. Second, they require a costly verification phase. Third, they process features in a fixed order without considering their relationships to the query-graph. In this paper, we address the three drawbacks and propose new indexing and query processing algorithms. In indexing, we select features directly from the data-graphs without expensive frequent subgraph mining. The features form a feature-tree that contains all-sized features and both the cost sharing and pruning power of the features are considered. In query processing, we propose a new algorithm, where the order to process features is query-dependent by considering both the cost sharing and the pruning power. We explore two optimization strategies to further improve the algorithm efficiency. The first strategy applies a lightweight graph compression technique and the second strategy optimizes the inclusion of answers. We further introduce how to efficiently maintain the index incrementally when the graph database is updated dynamically. Moreover, we propose an approximation approach to significantly reduce the computational cost for large data-graphs and/or query-graphs while preserving a high result quality. Finally, we conduct extensive performance s...
Lyu, H, Dong, Z, Roobavannan, M, Kandasamy, J & Pande, S 2019, 'Rural unemployment pushes migrants to urban areas in Jiangsu Province, China', Palgrave Communications, vol. 5, no. 1.
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AbstractMigration is often seen as an adaptive human response to adverse socio-environmental conditions, such as water scarcity. A rigorous assessment of the causes of migration, however, requires reliable information on the migration in question and related variables, such as, unemployment, which is often missing. This study explores the causes of one such type of migration, from rural to urban areas, in the Jiangsu province of China. A migration model is developed to fill a gap in the understanding of how rural to urban migration responds to variations in inputs to agricultural production including water availability and labor and how rural population forms expectations of better livelihood in urban areas. Rural to urban migration is estimated at provincial scale for period 1985–2013 and is found to be significantly linked with rural unemployment. Further, migration reacts to a change in rural unemployment after 2–4 years with 1% increase in rural unemployment, on average, leading to migration of 16,000 additional people. This implies that rural population takes a couple of years to internalize a shock in employment opportunities before migrating to cities. The analysis finds neither any evidence of migrants being pulled by better income prospects to urban areas nor being pushed out of rural areas by water scarcity. Corroborated by rural–urban migration in China migration survey data for 2008 and 2009, this means that local governments have 2–4 years of lead time after an unemployment shock, not necessarily linked to water scarcity, in rural areas to prepare for the migration wave in urban areas. This original analysis of migration over a 30-year period and finding its clear link with unemployment, and not with better income in urban areas or poor rainfall, thus provides conclusive evidence in support of policy interventions that focus on generating employment opportunities in rural areas to reduce migration flow to ur...
Lyu, X, Ren, C, Ni, W, Tian, H, Liu, RP & Dutkiewicz, E 2019, 'Optimal Online Data Partitioning for Geo-Distributed Machine Learning in Edge of Wireless Networks', IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2393-2406.
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© 1983-2012 IEEE. To enable machine learning at the edge of wireless networks (such as edge cloud), close to mobile users, is critical for future wireless networks, but challenging since the lower layers in edge cloud are substantially different from existing machine learning configurations in the cloud. In such geo-distributed computing environment, streaming data need to be evenly and cost-efficiently partitioned for different workers to produce an unbiased learning model with reduced parameter synchronization frequency. This paper presents a new online approach to optimally partitioning streaming data under time-varying network conditions. A new measure is proposed to quantify the evenness of data partitioning and restrain the optimization of data admission, partitioning, and processing. Stochastic gradient descent is applied to learn the optimal decisions online and asymptotically maximize the time-average utility of data partitioning. A new protocol is designed to further reduce the measurements of link costs, while preserving the asymptotic optimality, data evenness, and stability of the platform. Simulation results show that the proposed approach is superior to the state of the art in terms of throughput and cost efficiency, while only 24% of the links need to be measured to achieve the asymptotic optimality.
Ma, B, Liu, Z, Zeng, Y, Ma, Z, Zhang, H & Ma, J 2019, 'Cooperative jamming for secrecy of wireless communications', Journal of Information Science and Engineering, vol. 35, no. 5, pp. 1029-1044.
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This paper investigates cooperative jamming for security in wireless networks. No location information of eavesdropper is available and no constraint on the number of eavesdroppers is presupposed. A cooperative jamming strategy is proposed for jamming the eavesdroppers anywhere in the network, even if they are located quite close to the sender or the receiver. The basic ideas behind the strategy are to defeat eavesdroppers by a divide and conquer strategy, and exploit the helpful interference from the sender and the receiver to circumvent the nearby eavesdropper problem. Analysis and simulation results reveal that cooperative jamming can improve the secure performance and can be employed to establish initial connections in wireless networks.
Ma, C, Li, Q, Zheng, P, Zhou, S, Gao, H, Fang, J & Wang, Y 2019, 'Effects of static eccentricity on the no‐load back electromotive force of external rotor permanent magnet brushless DC motor used as in‐wheel motor', IET Electric Power Applications, vol. 13, no. 5, pp. 604-613.
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The no‐load radial magnetic field and no‐load back electromotive force (EMF) of external rotor permanent magnet brushless DC motor (PMBLDCM) are calculated by applying the correction coefficient of magnetic conductance here, taking into account the stator slotting and static eccentricity effects. An external rotor PMBLDCM with 51‐slot/46‐pole, used as in‐wheel motor, is taken as an example, the analytical calculation results of the no‐load back EMF are validated by the finite‐element method and experiment. The influences of static eccentricity ratio on the no‐load radial magnetic field and no‐load back EMF are investigated based on the analytical model. The investigation shows that static eccentricity does not change the harmonic contents of no‐load radial magnetic field, so it does not change the harmonic contents of three‐phase no‐load back EMFs. However, static eccentricity changes the space order of no‐load radial magnetic field, resulting in the different total harmonic distortions of three‐phase no‐load back EMFs; in other words, the asymmetric distortions of three‐phase no‐load back EMFs are generated. The asymmetric distortions of three‐phase no‐load back EMFs are intensified with the increase in static eccentricity ratio.
Ma, C, Tsang, IW, Shen, F & Liu, C 2019, 'Error Correcting Input and Output Hashing', IEEE Transactions on Cybernetics, vol. 49, no. 3, pp. 781-791.
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Most learning-based hashing algorithms leverage sample-to-sample similarities, such as neighborhood structure, to generate binary codes, which achieve promising results for image retrieval. This type of methods are referred to as instance-level encoding. However, it is nontrivial to define a scalar to represent sample-to-sample similarity encoding the semantic labels and the data structure. To address this issue, in this paper, we seek to use a class-level encoding method, which encodes the class-to-class relationship, to take the semantic information of classes into consideration. Based on these two encodings, we propose a novel framework, error correcting input and output (EC-IO) coding, which does class-level and instance-level encoding under a unified mapping space. Our proposed model contains two major components, which are distribution preservation and error correction. With these two components, our model maps the input feature of samples and the output code of classes into a unified space to encode the intrinsic structure of data and semantic information of classes simultaneously. Under this framework, we present our hashing model, EC-IO hashing (EC-IOH), by approximating the mapping space with the Hamming space. Extensive experiments are conducted to evaluate the retrieval performance, and EC-IOH exhibits superior and competitive performances comparing with popular supervised and unsupervised hashing methods.
Ma, J, Fan, F, Zhang, L, Wu, C & Zhi, X 2019, 'Effect of wave reflection on failure modes of single-layer reticulated domes subjected to interior blast loading', Engineering Failure Analysis, vol. 105, pp. 266-275.
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© 2019 Elsevier Ltd The single-layer reticulated dome is common in public structures, which suggests that this kind of building is a potential target of a terrorist attack. Once this structure is severely damaged in a terrorist attack, the people inside could be seriously injured. To avoid this situation, it is of great significance to investigate the failure mechanisms of the single-layer reticulated domes subjected to blast. In addition, shock waves from a blast converge and propagate in the internal space, leading to a nonuniform blast pressure field on the inner surface of the dome. The effects of reflected waves on failure modes of the dome were systematically investigated. A finite element model of a reticulated dome was created using ANSYS/LS-DYNA, where the code for blast loading that accounted for wave reflection was incorporated. Five failure modes were recognized and defined from 1050 simulations. Regularities in the distributions of failure modes were found. The effects of reflected waves on failure modes were analysed quantificationally.
Ma, J, Fan, F, Zhang, L, Wu, C & Zhi, X 2019, 'Experimental and Numerical Investigations of Pressure Field of Curved Shell Structure Subjected to Interior Blast', Shock and Vibration, vol. 2019, no. 1, pp. 1-16.
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A terrorist attack on a long‐span spatial structure would cause horrible results. Therefore, it is important to determine the characteristics of blast pressure fields to protect such structures. In this study, fully confined blast loading tests were conducted using a rigid curved shell model, which had an inner space similar to that of a reticulated dome. Four different scenarios were carried out to record the blast loading on five typical positions. The blast pressure‐time data were compared and analyzed. In addition, a suitable numerical simulation method was proposed for the issues involved in interior blast loading. This numerical model was verified by comparing with the test data. A parametrical analysis of the interior blast simulations was conducted based on this numerical method. The blast loading values at specific positions were obtained with the key parameters varied within a reasonable scope. The blast loading from blast tests and simulations were presented. On this basis, the interior blast loading could conveniently be predicted by using the method and data in this paper, which could be used in the protective design of other reticulated domes.
Ma, L, Pei, Q, Xiang, Y, Yao, L & Yu, S 2019, 'A reliable reputation computation framework for online items in E-commerce', Journal of Network and Computer Applications, vol. 134, pp. 13-25.
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© 2019 Elsevier Ltd Most of online trading platforms allow consumers to give personal ratings to online items. By computing the weighted mean of the ratings, the reputation values of online items can be derived to assist consumers to make purchasing decisions. However, it is never a simple task to derive a reliable reputation value of any given item and existing works fail to achieve this. Thus, in this paper, we propose a reliable reputation computation framework for online items which can be adopted by online trading platforms or run by a third party to provide reputation computation as a service. At first, a fine-grained two-phase detection method is proposed to detect malicious ratings. After filtering out the ratings detected as malicious, the weights of the remaining ratings are determined by computing the degrees to which the users giving these ratings are interested in a target item. Extensive experiments verify that the proposed reliable reputation computation framework is effective to detect different kinds of malicious ratings and determine the interest degrees of users.
Ma, XY, Wang, Y, Dong, K, Wang, XC, Zheng, K, Hao, L & Ngo, HH 2019, 'The treatability of trace organic pollutants in WWTP effluent and associated biotoxicity reduction by advanced treatment processes for effluent quality improvement', Water Research, vol. 159, pp. 423-433.
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© 2019 Elsevier Ltd As increasing attention is paid to surface water protection, there has been demand for improvements of domestic wastewater treatment plant (WWTP) effluent. This has led to the application of many different advanced treatment processes (ATPs). In this study, the treatability of trace organic pollutants in secondary effluent (SE) and associated biotoxicity reduction by four types of ATPs, including coagulation, granular activated carbon (GAC) adsorption, ultraviolet (UV) photolysis and photocatalysis, and ozonation, were investigated at the bench-scale. The ATPs showed different removal capacity for the 48 chemicals, which were classified into seven categories. EDCs, herbicides, bactericides and pharmaceuticals were readily degraded, and insecticides, flame retardants, and UV filters were relatively resistant to removal. During these processes, the efficiency of the ATPs in reducing four biological effects were investigated. Of the four biological effects, the estrogenic activity from SE was not detected using the yeast estrogen screen. In contrast with genotoxicity and photosynthesis inhibition, bacterial cytotoxicity posed by SE was the most difficult biological effect to reduce with these ATPs. GAC adsorption and ozonation were the most robust treatment processes for reducing the three detected biotoxicities. UV photolysis and photocatalysis showed comparable efficiencies for the reduction of genotoxicity and photosynthesis inhibition. However, coagulation only performed well in genotoxicity reduction. The effect-based trigger values for the four bioassays, that were derived from the existing environmental quality standards and from HC5 (hazardous concentration for 5% of aquatic organisms), were all used to select and optimize these ATPs for ecological safety. Conducting ATPs in more appropriate ways could eliminate the negative effects of WWTP effluent on receiving water bodies.
Mahanama, D, De Silva, P, Kim, T, Castel, A & Khan, MSH 2019, 'Evaluating Effect of GGBFS in Alkali–Silica Reaction in Geopolymer Mortar with Accelerated Mortar Bar Test', Journal of Materials in Civil Engineering, vol. 31, no. 8, pp. 04019167-04019167.
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Mahdavi, H, Fatahi, B & Khabbaz, H 2019, 'A comparison of frictional and socketed concrete injected columns in a transition zone', Geosynthetics International, vol. 26, no. 5, pp. 497-514.
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This paper sets out to investigate the options available for the transition from Concrete Injected Columns (CICs) to other ground improvement methods, used away from the bridge abutment. Two possible alternatives, widely spaced CICs socketed into stiff material and shorter, closely spaced, frictional CICs, were numerically simulated using FLAC3D software considering the dissipation of porewater pressure and variation of soil permeability with time. The total length of the CICs and the total volume of concrete used for their construction were the same for both alternatives. A geosynthetic layer was introduced into the load transfer platform, and interface elements were incorporated to simulate CIC-soil interaction. The numerical results were also compared with an established analytical solution and a good agreement was achieved. A comparison was then made between the two scenarios; indeed, the embankment on frictional CICs experienced less settlement on the surface, less loads in the geosynthetic, and the bending moments and shear forces generated in the columns were less than the corresponding values for socketed CICs. This study offers an enhanced understanding of the available options to practising engineers when designing road embankments on soft soil.
Mahdiyar, A, Armaghani, DJ, Marto, A, Nilashi, M & Ismail, S 2019, 'Rock tensile strength prediction using empirical and soft computing approaches', Bulletin of Engineering Geology and the Environment, vol. 78, no. 6, pp. 4519-4531.
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Mahlia, TMI, Ismail, N, Hossain, N, Silitonga, AS & Shamsuddin, AH 2019, 'Palm oil and its wastes as bioenergy sources: a comprehensive review', Environmental Science and Pollution Research, vol. 26, no. 15, pp. 14849-14866.
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Due to global warming and increasing price of fossil fuel, scientists all over the world have been trying to find reliable alternative fuels. One of the most potential candidates is renewable energy from biomass. The race for renewable energy from biomass has long begun and focused on to combat the deteriorating condition of the environment. Palm oil has been in the spotlight as an alternative of bioenergy sources to resolve fossil fuel problem due to its environment-friendly nature. This review will look deep into the origins of palm oil and how it is processed, bioproducts from this biomass, and oil palm biomass-based power plant in Malaysia. Palm oil is usually processed from oil palm fruits and other parts of the oil palm plant are candidates for raw material of bioproduct generation. Oil palm biomass can be turned into three subcategories: bioproduct, biofuels, and biopower. Focusing on biofuel, the biodiesel from palm oil will be explored in detail and its implication in Malaysia as one of the biggest producers of oil palm in the world will also be emphasized comprehensively. The paper presents the detail of a schematic flow diagram of a palm oil mill process of transforming oil palm into crude palm oil and it wastes. This paper will also discuss the current oil palm biomass power plants in Malaysia. Palm oil has been proven itself as a potential alternative to reduce negative environmental impact of global warming.
Mahlia, TMI, Syaheed, H, Abas, AEP, Kusumo, F, Shamsuddin, AH, Ong, HC & Bilad, MR 2019, 'Organic Rankine Cycle (ORC) System Applications for Solar Energy: Recent Technological Advances', Energies, vol. 12, no. 15, pp. 2930-2930.
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Organic Rankine Cycle (ORC) power generation systems may be used to utilize heat source with low pressure and low temperature such as solar energy. Many researchers have focused on different aspects of ORC power generation systems, but none so far has focused on the patent landscape of ORC system applications. As such, the objective of this study is to identify published patents on ORC system applications, particularly for solar energy. Four (4) technologies were identified in ORC application for solar energy: parabolic dish, parabolic trough, solar tower, and linear Fresnel reflector. A methodical search and analysis of the patent landscape in ORC system applications for solar energy published between 2007–2018 was conducted using the Derwent Innovation patent database. From the approximately 51 million patents in the database from various countries and patent agencies, 3859 patents were initially identified to be related to ORC applications for solar energy. After further stringent selection processes, only 1100 patents were included in this review. From these 1100 patents, approximately 12% (130 patents) are associated with parabolic dishes, about 39% (428 patents) are associated with parabolic troughs, approximately 21% (237 patents) are associated with solar towers, and about 28% (305 patents) are associated with linear Fresnel reflectors. Published patents on solar tower technology are currently on an increasing trend, led by China. All of these patents were published in the past 11 years. From this study, further researches on ORC application are still ongoing, but ORC application for solar energy has the potential to advance; allowing the world to ease issues related to over-reliance on fossil fuel.
Mahmoodi, Z, Mohammadnejad, J, Razavi Bazaz, S, Abouei Mehrizi, A, Ghiass, MA, Saidijam, M, Dinarvand, R, Ebrahimi Warkiani, M & Soleimani, M 2019, 'A simple coating method of PDMS microchip with PTFE for synthesis of dexamethasone-encapsulated PLGA nanoparticles', Drug Delivery and Translational Research, vol. 9, no. 3, pp. 707-720.
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© 2019, Controlled Release Society. Dexamethasone is a widely used drug in medical and biological applications. Since the systematic and controllable release of this drug is of significant importance, encapsulation of this anti-inflammatory drug in poly(lactic-co-glycolic acid) (PLGA) nanoparticles can minimize uncontrolled issues. As dexamethasone-encapsulated PLGA nanoparticles are synthesized in the presence of organic solvents, poly(dimethylsiloxane) (PDMS)-based microchannels collapse due to the swelling problem. In present study, PTFE nanoparticles were used for the surface modification of the microchannels to prevent absorption and adhesion of solvents into the microchannels’ wall. The contact angle analysis of microchips after coating showed that the surface of microchannels bear the superhydrophobicity feature (140.30°) and SEM images revealed that PTFE covered the surface of PDMS, favorably. Then, the prepared microchip was tested for the synthesis of dexamethasone-loaded nanoparticles. SEM and atomic force microscopy (AFM) images of the synthesized nanoparticles represented that there was not any evidence of adhesion or absorption of nanoparticles. Furthermore, the monodispersity of nanoparticles was discernible. As AFM results revealed, the average diameters of 47, 63, and 82 nm were achieved for flow ratios of 0.01, 0.05, and 0.1, respectively. To evaluate the drug efficiency, cumulative release and encapsulation efficiency were analyzed which showed much more efficiency than the synthesized nanoparticles in the bulk mode. In addition, MTT test revealed that nanoparticles could be considered as a non-toxic material. Since the synthesis of drug-loaded nanoparticles is ubiquitous in laboratory experiments, the approach presented in this study can render more versatility in this regard.
Mahmoud, AB, Alatrash, M, Fuxman, L, Meero, AA & Yafi, E 2019, 'Total Quality Management Boosters and Blockers in a Humanitarian Setting: An Exploratory Investigation', Sage Open, vol. 9, no. 2, pp. 215824401984191-215824401984191.
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Utilizing qualitative techniques, this research is aimed at investigating total quality management (TQM) implementation practices within a humanitarian setting. The extensive survey instrument of professionals working for the United Nations (UN) organizations operating in the Middle East is used to reveal TQM use within international nongovernmental organizations (INGOs) that provide humanitarian relief. With the goal of helping organizations to address anticipated difficulties in implementing TQM practices that improve performance of humanitarian interventions, this study identifies and examines the boosters and blockers of successful implementation of the TQM practices. The most prominent themes that were identified relate to availability of funding, management commitment to quality, partnerships and communication channels, and knowledge sharing.
Mahmoud, AB, Grigoriou, N, Fuxman, L, Hack-Polay, D, Mahmoud, FB, Yafi, E & Tehseen, S 2019, 'Email is evil!', Journal of Research in Interactive Marketing, vol. 13, no. 2, pp. 227-248.
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PurposeThis study aims to assess consumers’ beliefs in three Middle Eastern Arab countries regarding attitudinal and behavioural responses towards permission-based direct email marketing (hereafter DEM) and the moderating role of gender in the hypothesised path model.Design/methodology/approachStructural equation modelling was used to test the hypothesised path model by using data collected from 829 respondents.FindingsThe findings show that attitude was found to fully mediate the relationship between beliefs and behavioural responses towards permission-based DEM. Gender moderates the relationship between beliefs and attitudes and responses to permission-based DEM. Notably, female respondents were found to react more actively when exposed to permission-based DEM.Research limitations/implicationsFurther qualitative research is needed to learn more about how and why individuals develop behavioural intentions in certain ways towards opt-in DEM. In addition, neuropsychology approaches such as eye-tracking are endorsed for future research to gain more insights and conquer biases associated with self-reporting procedures in countries where such technologies are deemed as legal and ethical to be used with human subjects.Practical implicationsAdvertisers promoting products and services in the Middle Eastern Arab context should take further steps to enhance the quality of information (including cultural sensitiveness) and the perceived entertainment value that could be delivered to c...
Mahmud, K, Hossain, MJ & Ravishankar, J 2019, 'Peak-Load Management in Commercial Systems With Electric Vehicles', IEEE Systems Journal, vol. 13, no. 2, pp. 1872-1882.
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© 2007-2012 IEEE. Electric vehicles (EVs) are getting popular as one of the effective solutions for increased energy efficiency in commercial systems. This paper proposes an improved algorithm for commercial peak-load management using EVs, battery-energy-storage systems, and photovoltaic units. It uses the bidirectional vehicle-to-grid technique to utilize the energy from EVs in a parking lot. The proposed system has been tested in a real power distribution network in realistic load and weather conditions. The financial benefit of the system is also investigated, and it is found that the industrial peak load can be reduced by 50%, and the energy cost can be reduced by up to 27.3%. It also enhances the load factor by 9%. The performance of the proposed control algorithm is compared with that of an artificial-neural-network-based technique and tested in a laboratory prototype. From simulated and experimental results, it is found that the proposed approach provides substantial savings, while reducing the peak demand of the existing grids.
Mai, HT, Tran, TS, Ho-Le, TP, Center, JR, Eisman, JA & Nguyen, TV 2019, 'Two-Thirds of All Fractures Are Not Attributable to Osteoporosis and Advancing Age: Implications for Fracture Prevention', The Journal of Clinical Endocrinology & Metabolism, vol. 104, no. 8, pp. 3514-3520.
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Abstract Context Although bone mineral density (BMD) is strongly associated with fracture and postfracture mortality, the burden of fractures attributable to low BMD has not been investigated. Objectives We sought to estimate the population attributable fraction of fractures and fracture-related mortality that can be attributed to low BMD. Design and Setting This study is a part of an ongoing population-based prospective cohort study, the Dubbo Osteoporosis Epidemiology study. In total, 3700 participants aged ≥50 years participated in the study. Low-trauma fracture was ascertained by X-ray reports, and mortality was ascertained from the Birth, Death and Marriage Registry. Results Overall, 21% of women and 11% of men had osteoporotic BMD. In univariable analysis, 21% and 16% of total fractures in women and men, respectively, were attributable to osteoporosis. Osteoporosis combined with advancing age (>70 years) accounted for 34% and 35% of fractures in women and men, respectively. However, these two factors accounted for ∼60% of hip fractures. About 99% and 66% of postfracture mortality in women and men, respectively, were attributable to advancing age, osteoporosis, and fracture; however, most of the attributable proportion was accounted for by advancing age. Conclusions A substantial health care burden of fracture is ...
Majdi, H, Salehi, R, Pourhassan-Moghaddam, M, Mahmoodi, S, Poursalehi, Z & Vasilescu, S 2019, 'Antibody conjugated green synthesized chitosan-gold nanoparticles for optical biosensing', Colloid and Interface Science Communications, vol. 33, pp. 100207-100207.
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Majeed, K, Ahmed, A, Abu Bakar, MS, Indra Mahlia, TM, Saba, N, Hassan, A, Jawaid, M, Hussain, M, Iqbal, J & Ali, Z 2019, 'Mechanical and Thermal Properties of Montmorillonite-Reinforced Polypropylene/Rice Husk Hybrid Nanocomposites', Polymers, vol. 11, no. 10, pp. 1557-1557.
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In recent years, there has been considerable interest in the use of natural fibers as potential reinforcing fillers in polymer composites despite their hydrophilicity, which limits their widespread commercial application. The present study explored the fabrication of nanocomposites by melt mixing, using an internal mixer followed by a compression molding technique, and incorporating rice husk (RH) as a renewable natural filler, montmorillonite (MMT) nanoclay as water-resistant reinforcing nanoparticles, and polypropylene-grafted maleic anhydride (PP-g-MAH) as a compatibilizing agent. To correlate the effect of MMT delamination and MMT/RH dispersion in the composites, the mechanical and thermal properties of the composites were studied. XRD analysis revealed delamination of MMT platelets due to an increase in their interlayer spacing, and SEM micrographs indicated improved dispersion of the filler(s) from the use of compatibilizers. The mechanical properties were improved by the incorporation of MMT into the PP/RH system and the reinforcing effect was remarkable as a result of the use of compatibilizing agent. Prolonged water exposure of the prepared samples decreased their tensile and flexural properties. Interestingly, the maximum decrease was observed for PP/RH composites and the minimum was for MMT-reinforced and PP-g-MAH-compatibilized PP/RH composites. DSC results revealed an increase in crystallinity with the addition of filler(s), while the melting and crystallization temperatures remained unaltered. TGA revealed that MMT addition and its delamination in the composite systems improved the thermal stability of the developed nanocomposites. Overall, we conclude that MMT nanoclay is an effective water-resistant reinforcing nanoparticle that enhances the durability, mechanical properties, and thermal stability of composites.
Makhdoom, I, Abolhasan, M, Abbas, H & Ni, W 2019, 'Blockchain's adoption in IoT: The challenges, and a way forward', Journal of Network and Computer Applications, vol. 125, pp. 251-279.
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© 2018 Elsevier Ltd The underlying technology of Bitcoin is blockchain, which was initially designed for financial value transfer only. Nonetheless, due to its decentralized architecture, fault tolerance and cryptographic security benefits such as pseudonymous identities, data integrity and authentication, researchers and security analysts around the world are focusing on the blockchain to resolve security and privacy issues of IoT. However, presently, not much work has been done to assess blockchain's viability for IoT and the associated challenges. Hence, to arrive at intelligible conclusions, this paper carries out a systematic study of the peculiarities of the IoT environment including its security and performance requirements and progression in blockchain technologies. We have identified the gaps by mapping the security and performance benefits inferred by the blockchain technologies and some of the blockchain-based IoT applications against the IoT requirements. We also discovered some practical issues involved in the integration of IoT devices with the blockchain. In the end, we propose a way forward to resolve some of the significant challenges to the blockchain's adoption in IoT.
Makhdoom, I, Abolhasan, M, Lipman, J, Liu, RP & Ni, W 2019, 'Anatomy of Threats to the Internet of Things', IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1636-1675.
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© 1998-2012 IEEE. The world is resorting to the Internet of Things (IoT) for ease of control and monitoring of smart devices. The ubiquitous use of IoT ranges from industrial control systems (ICS) to e-Health, e-Commerce, smart cities, supply chain management, smart cars, cyber physical systems (CPS), and a lot more. Such reliance on IoT is resulting in a significant amount of data to be generated, collected, processed, and analyzed. The big data analytics is no doubt beneficial for business development. However, at the same time, numerous threats to the availability and privacy of the user data, message, and device integrity, the vulnerability of IoT devices to malware attacks and the risk of physical compromise of devices pose a significant danger to the sustenance of IoT. This paper thus endeavors to highlight most of the known threats at various layers of the IoT architecture with a focus on the anatomy of malware attacks. We present a detailed attack methodology adopted by some of the most successful malware attacks on IoT, including ICS and CPS. We also deduce an attack strategy of a distributed denial of service attack through IoT botnet followed by requisite security measures. In the end, we propose a composite guideline for the development of an IoT security framework based on industry best practices and also highlight lessons learned, pitfalls and some open research challenges.
Man, X, Luo, Z, Liu, J & Xia, B 2019, 'Hilbert fractal acoustic metamaterials with negative mass density and bulk modulus on subwavelength scale', Materials & Design, vol. 180, pp. 107911-107911.
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© 2019 The Authors Acoustic metamaterials (AMs) are artificially engineered composite materials, structured to have unconventional effective properties for flexibly manipulating the wave propagation, which can produce a broad range of applications such as sound cloaking and tunneling. In nature, bio-inspired fractal organization with multiple length scales has been found in various biological materials, which display enhanced dynamic properties. By introducing Hilbert curve channels, this work will design a class of topological architectures of Hilbert fractal acoustic metamaterials (HFAMs) with negative mass density and bulk modulus on subwavelength scale. In this paper, we will highlight the influences of the self-similar fractal configurations on multipole modes of HFAM. To further demonstrate multipole resonances, the pressure magnifications are assessed in the center region of HFAM with losses. Moreover, based on effective medium theory, we systematically calculate and investigate effective bulk modulus and mass density, as well as density-near-zero of HFAM, to demonstrate the negative properties and the zero-phase-difference effects of HFAMs. Numerical results show that HFAM can enable a number of applications, from sound blocking, quarter bending, sound cloaking to sound tunneling, and may further provide a possibility for the engineering guidances of the exotic properties on subwavelength scale.
Man, X-F, Xia, B-Z, Luo, Z & Liu, J 2019, '3D Hilbert fractal acoustic metamaterials: low-frequency and multi-band sound insulation', Journal of Physics D: Applied Physics, vol. 52, no. 19, pp. 195302-195302.
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© 2019 IOP Publishing Ltd. In this work, we present a class of three-dimensional (3D) labyrinthine acoustic metamaterials with self-similar fractal technique, which can produce multiple frequency-band sound insulation in deep-subwavelength scale. By simultaneously exploiting the multi-frequency bandgaps and the low-frequency characteristics, the Hilbert cubes are explored to design the 3D Hilbert fractal acoustic metamaterials (HFAMs). The multiple-band features of the HFAMs are examined by the finite element method and the effective medium theory, in which the negative bulk modulus and the mass density are responsible for the formation of the multi-bandgaps. These multi-frequency properties are induced by the Fabry-Perot multi-resonance of 3D HFAMs, which possess an ultra-high refractive index. Hence, the multi-band sound insulations of 3D HFAMs with the negative effective property are achieved below 500 Hz. These properties of the designed 3D HFAMs provide an effective way for acoustic metamaterials to achieve multi-band filtering and noise attenuation in the low-frequency regime.
Mandal, R, Roy, PP, Pal, U & Blumenstein, M 2019, 'Bag-of-visual-words for signature-based multi-script document retrieval', Neural Computing and Applications, vol. 31, no. 10, pp. 6223-6247.
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© 2018, The Natural Computing Applications Forum. An end-to-end architecture for multi-script document retrieval using handwritten signatures is proposed in this paper. The user supplies a query signature sample, and the system exclusively returns a set of documents that contain the query signature. In the first stage, a component-wise classification technique separates the potential signature components from all other components. A bag-of-visual-words powered by SIFT descriptors in a patch-based framework is proposed to compute the features and a support vector machine (SVM)-based classifier was used to separate signatures from the documents. In the second stage, features from the foreground (i.e., signature strokes) and the background spatial information (i.e., background loops, reservoirs etc.) were combined to characterize the signature object to match with the query signature. Finally, three distance measures were used to match a query signature with the signature present in target documents for retrieval. The ‘Tobacco’ (The Legacy Tobacco Document Library (LTDL). University of California, San Francisco, 2007. http://legacy.library.ucsf.edu/) document database and an Indian script database containing 560 documents of Devanagari (Hindi) and Bangla scripts were used for the performance evaluation. The proposed system was also tested on noisy documents, and the promising results were obtained. A comparative study shows that the proposed method outperforms the state-of-the-art approaches.
MANNAN, A, SABRI, MFM, KALAM, MA & MASJUKI, HH 2019, 'Tribological Properties of Steel/Steel, Steel/DLC and DLC/DLC Contacts in the Presence of Biodegradable Oil', Journal of the Japan Petroleum Institute, vol. 62, no. 1, pp. 11-18.
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Manzoor, M, Hussain, W, Sohaib, O, Hussain, FK & Alkhalaf, S 2019, 'Methodological investigation for enhancing the usability of university websites.', J. Ambient Intell. Humaniz. Comput., vol. 10, no. 2, pp. 531-549.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. For university websites to be successful and to increase the chance of converting a prospective student into a current student, it is necessary to increase the visibility and accessibility of all related content so that a student can achieve their desired task in the fastest possible time. The criteria for evaluating university websites are very vague and are usually unknown to most developers, which adversely impacts the user-experience of the students visiting such websites. To solve this problem, we devised a usability metric and examined the leading university websites to analyze whether these websites were able to meet the requirements of students. In this research, we applied qualitative and quantitative approaches by considering 300 students and evaluating 86 university websites (26 from Canada, 30 from the United States, and 30 from Europe) based on a six-attribute metric comprising navigation, organization, ease of use (simplicity), design (layout), communication and content. From the evaluation results, we find that the 88% of the students are satisfied with our proposed usability attributes, but that most universities fail to meet basic standards of usability as desired by the students. The findings also show that the usability evaluation score for each usability feature varies from country to country, such as for (1) multiple language support − 23% of the Canadian websites, 63% of the European websites and none of the USA websites has the feature; for (2) Scholarships/Funding/Financial Aid link − 24% of the Canadian websites, 80% of the European and the USA websites has the feature; for (3) admission link − 88% of the Canadian websites, 20% of the European websites and 90% of the USA websites has the feature. In addition, from the evaluative result we find that our proposed approach will not only increase the usability of academic websites but will also provide an easiest way to ...
Mao, M, Lu, J, Han, J & Zhang, G 2019, 'Multiobjective e-commerce recommendations based on hypergraph ranking', Information Sciences, vol. 471, pp. 269-287.
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© 2018 Recommender systems are emerging in e-commerce as important promotion tools to assist customers to discover potentially interesting items. Currently, most of these are single-objective and search for items that fit the overall preference of a particular user. In real applications, such as restaurant recommendations, however, users often have multiple objectives such as group preferences and restaurant ambiance. This paper highlights the need for multi-objective recommendations and provides a solution using hypergraph ranking. A general User–Item–Attribute–Context data model is proposed to summarize different information resources and high-order relationships for the construction of a multipartite hypergraph. This study develops an improved balanced hypergraph ranking method to rank different types of objects in hypergraph data. An overall framework is then proposed as a guideline for the implementation of multi-objective recommender systems. Empirical experiments are conducted with the dataset from a review site Yelp.com, and the outcomes demonstrate that the proposed model performs very well for multi-objective recommendations. The experiments also demonstrate that this framework is still compatible for traditional single-objective recommendations and can improve accuracy significantly. In conclusion, the proposed multi-objective recommendation framework is able to handle complex and changing demands for e-commerce customers.
MARICRUZ, O-L, ERNESTO, L-C, LUIS FERNANDO, E-A, JOSE MARIA, M & ANNA MARÍA, GL 2019, 'Forgotten Effects and Heavy Moving Averages in Exchange Rate Forecasting', ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, vol. 53, no. 4/2019, pp. 79-96.
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Martin Salvador, M, Budka, M & Gabrys, B 2019, 'Automatic Composition and Optimization of Multicomponent Predictive Systems With an Extended Auto-WEKA', IEEE Transactions on Automation Science and Engineering, vol. 16, no. 2, pp. 946-959.
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© 2004-2012 IEEE. Composition and parameterization of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps are a challenging task. Auto-WEKA is a tool to automate the combined algorithm selection and hyperparameter (CASH) optimization problem. In this paper, we extend the CASH problem and Auto-WEKA to support the MCPS, including preprocessing steps for both classification and regression tasks. We define the optimization problem in which the search space consists of suitably parameterized Petri nets forming the sought MCPS solutions. In the experimental analysis, we focus on examining the impact of considerably extending the search space (from approximately 22000 to 812 billion possible combinations of methods and categorical hyperparameters). In a range of extensive experiments, three different optimization strategies are used to automatically compose MCPSs for 21 publicly available data sets. The diversity of the composed MCPSs found is an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. We also present the results on seven data sets from real chemical production processes. Our findings can have a major impact on the development of high-quality predictive models as well as their maintenance and scalability aspects needed in modern applications and deployment scenarios. Note to Practitioners - The extension of Auto-WEKA to compose and optimize multicomponent predictive systems (MCPSs) developed as part of this paper is freely available on GitHub under GPL license, and we encourage practitioners to use it on a broad variety of classification and regression problems. The software can either be used as a blackbox - where search space is made of all possible WEKA filters, predictors, and metapredictors (e.g., ensembles) - or as an optimization tool on a subset of preselected machine ...
Mashat, MEM, Lin, C-T & Zhang, D 2019, 'Effects of Task Complexity on Motor Imagery-Based Brain–Computer Interface', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 10, pp. 2178-2185.
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© 2001-2011 IEEE. The performance of electroencephalogram (EEG)-based brain-computer interfaces (BCIs) still needs improvements for real world applications. An improvement on BCIs could be achieved by enhancing brain signals from the source via subject intention-based modulation. In this work, we aim to investigate the effects of task complexity on performance of motor imagery (MI) based BCIs. In specific, we studied the effects of motor imagery of a complex task versus a simple task on discriminability of brain activation patterns using EEG. The results show an increase of up to 7.25% in BCI classification accuracy for motor imagery of the complex task in comparison to the simple task. Furthermore, spectral power analysis in low frequency bands, alpha and beta, shows a significant decrease in power value for the complex task. However, high frequency gamma band analysis unveils a significant increase for the complex task. These findings may lead to designing better BCIs with high performance.
Mastio, E & Dovey, K 2019, 'Power dynamics in organizational change: an Australian case', International Journal of Sociology and Social Policy, vol. 39, no. 9/10, pp. 796-811.
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PurposeThe purpose of this paper is to contribute to the understanding of the role of abstract forms of power in organizational change by exploring the role of such forms of power in the recent structural transformation of an iconic Australian Intellectual Property law firm. The research literature reflects relatively few studies on the increasing complexity of power dynamics in organizational and institutional arrangements.Design/methodology/approachThe complexity of the investigated phenomena led to the adoption of three qualitative methods in order to access the specific forms of data that were perceived to be relevant to answering the research question (“How did abstract power dynamics influence the nature and outcomes of the firm’s structural transformation?”). Ethnography was used in the attempt to discern, through participation and observation, the assumptions that manifested in action and/or inaction; phenomenology in the exploration through unstructured interviews with 41 staff members and 4 clients of the firm, of their interpretation and “sense-making” of their “lived experience” of “what was going on” in the firm; and narrative enquiry in establishing a narrative of critical events, and their impact on “what was going on” in the firm, including those that had occurred over the years prior to this research initiative.FindingsThe research shows the effects of contradicting forms of abstract power (namely, hegemonic (ideological) power, dominant institutional logic and structural power) as the firm struggled to address challenges to its existence. The impact of these forms of power upon the partners’ apprehension and interpretation of the emerging challenges to the ...
Mastio, E, Chew, E & Dovey, KA 2019, 'The learning organization as a context for value co-creation', The Learning Organization, vol. 27, no. 4, pp. 291-303.
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PurposeThis paper aims to explore the relationship between the concept of the learning organization and that of the co-creation of value.Design/methodology/approachThe paper is conceptual in nature and draws on data from a case study of a small highly innovative Australian company.FindingsThe authors show that, from a value co-creation perspective, the learning organization can be viewed as an open, collaborative, social/economic actor engaged in social/economic activities with other interdependent actors (organizations or stakeholders) in a network or ecosystem of actors to serve its mission/purpose and the well-being of the ecosystem.Research limitations/implicationsAs a conceptual paper, the authors rely primarily on previous research as the basis for the argument. The implications of the findings are that, as value co-creation practices are founded upon the generation and leveraging of specific intangible capital resources, more research located in alternative research paradigms is required.Practical implicationsThere are important implications for organizational leadership in that the practices that underpin value co-creation require the leadership to be able to work constructively with multiple forms of systemic and agentic power.Social implicationsIn increasingly turbulent and hyper-competitive global operational contexts, sustainable value creation is becoming recognized as a collective a...
Mas-Tur, A, Modak, NM, Merigó, JM, Roig-Tierno, N, Geraci, M & Capecchi, V 2019, 'Half a century of Quality & Quantity: a bibliometric review', Quality & Quantity, vol. 53, no. 2, pp. 981-1020.
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© 2018, Springer Nature B.V. The Quality & Quantity was established in 1967 and in 2017 it completed its half century. The journal is interdisciplinary in nature and it mainly discusses methodological application of mathematics and statistics in the social sciences, particularly sociology, economics, and social psychology. It was created with the idea of advancing methodology of the various social studies. This study looks back journey of the journal from 1967 to 2017 aims to develop a bibliometric analysis of all the publications of the journal. Web of Science Core Collection database is used to collect data. The present study discovered the significant contributions of the journal in terms of impact, topics, authors, universities and countries. Utrecht University of Netherlands is the most productive university. Asian Universities are emerging and growing quickly in the recent years. Although USA leads among the countries but Europe leads among the six supranational regions. Finally, the visualization of similarities viewer software is used to present network visualization of the bibliographic coupling, co-citation, citation, co-authorship and co-occurrence of keywords.
Mat Nawi, NI, Bilad, MR, Zolkhiflee, N, Nordin, NAH, Lau, WJ, Narkkun, T, Faungnawakij, K, Arahman, N & Mahlia, TMI 2019, 'Development of A Novel Corrugated Polyvinylidene difluoride Membrane via Improved Imprinting Technique for Membrane Distillation', Polymers, vol. 11, no. 5, pp. 865-865.
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Membrane distillation (MD) is an attractive technology for desalination, mainly because its performance that is almost independent of feed solute concentration as opposed to the reverse osmosis process. However, its widespread application is still limited by the low water flux, low wetting resistance and high scaling vulnerability. This study focuses on addressing those limitations by developing a novel corrugated polyvinylidene difluoride (PVDF) membrane via an improved imprinting technique for MD. Corrugations on the membrane surface are designed to offer an effective surface area and at the same time act as a turbulence promoter to induce hydrodynamic by reducing temperature polarization. Results show that imprinting of spacer could help to induce surface corrugation. Pore defect could be minimized by employing a dual layer membrane. In short term run experiment, the corrugated membrane shows a flux of 23.1 Lm−2h−1 and a salt rejection of >99%, higher than the referenced flat membrane (flux of 18.0 Lm−2h−1 and similar rejection). The flux advantage can be ascribed by the larger effective surface area of the membrane coupled with larger pore size. The flux advantage could be maintained in the long-term operation of 50 h at a value of 8.6 Lm−2h−1. However, the flux performance slightly deteriorates over time mainly due to wetting and scaling. An attempt to overcome this limitation should be a focus of the future study, especially by exploring the role of cross-flow velocity in combination with the corrugated surface in inducing local mixing and enhancing system performance.
Mateos, MK, Trahair, TN, Mayoh, C, Barbaro, PM, Sutton, R, Revesz, T, Barbaric, D, Giles, JE, Alvaro, F, Mechinaud, F, Catchpoole, D, Kotecha, RS, Dalla-Pozza, L, Quinn, MCJ, MacGregor, S, Chenevix-Trench, G & Marshall, GM 2019, 'Risk factors for symptomatic venous thromboembolism during therapy for childhood acute lymphoblastic leukemia', Thrombosis Research, vol. 178, pp. 132-138.
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BACKGROUND:Symptomatic venous thromboembolism (VTE) is an unpredictable and life-threatening toxicity, which occurs early in childhood acute lymphoblastic leukemia (ALL) therapy. Approximately 5% of children will experience VTE which is treated with anticoagulation. Asparaginase and corticosteroids are etiologic factors for VTE, however other clinical factors may modify this risk. PROCEDURE:We sought to i) assess published pre-treatment VTE risk factors ii) identify early clinical factors that were associated with VTE and iii) determine whether single nucleotide polymorphisms (SNPs) associated with VTE in non-cancer patients contributed to VTE in children with ALL. We performed a detailed, retrospective analysis of 1021 ALL patients treated between 1998 and 2013. Individual patient records were reviewed to ascertain VTE incidence and document treatment-related clinical variables. RESULTS:The incidence of VTE was 5.1%. Extremes of weight at diagnosis (<5th or >95th centile) was an independent risk factor in multivariable analysis, when added to published risk factors of age ≥10 years and mediastinal mass. When factors during induction/consolidation were considered separately: bacteremia, elevated serum gamma-glutamyl transferase and bilirubin were associated with VTE occurrence. None of the SNPs associated with VTE in non-cancer populations were significantly associated with VTE in our cohort. CONCLUSION:We found two known risk factors (age ≥ 10 years and mediastinal mass) in a large cohort of children treated for ALL and identified other factors associated with VTE such as weight extremes at diagnosis, bacteremia, and abnormal liver function which warrant further study. These VTE risk factors may form the basis of future thromboprophylaxis trials.
Maynard-Casely, HE, Booth, N, Leung, AE, Stuart, BH & Thomas, PS 2019, 'Potential of neutron powder diffraction for the study of solid triacylglycerols', Food Structure, vol. 22, pp. 100124-100124.
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© 2019 We present a high-resolution neutron powder diffraction study of the triclinic β form of tripalmitin as well as in situ crystallisation experiments, monitored with neutron diffraction, conducted over three different cooling rates. We use the results from the high-resolution study to anticipate if neutron diffraction could be beneficial in differentiating the polymorphism in triacylglycerol systems. We extend on this to present analysis of a diffraction pattern of cocoa butter, to establish the potential for neutron diffraction to study the (hydrogenous) forms of triacylglycerols used in food production.
Mazarov, J, Wolf, P, Schallow, J, Nöhring, F, Deuse, J & Richter, R 2019, 'Industrial Data Science in Wertschöpfungsnetzwerken', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 114, no. 12, pp. 874-877.
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Kurzfassung Industrial Data Science eröffnet produzierenden Unternehmen innovative Möglichkeiten zur Optimierung von Produkten und Prozessen sowie der Initiierung neuer Geschäftsmodelle in Wertschöpfungsnetzwerken. Um Unternehmen zum zielgerichteten Einsatz moderner Analysetechnologien zu befähigen, werden in diesem Beitrag das Konzept eines integrierten, datengetriebenen Referenzbaukastens zur industriellen Datenanalyse sowie dessen Realisierung als kollaborative Service-Plattform vorgestellt und beispielhaft Anwendungsfälle skizziert.
McKinlay, A & Tomamichel, M 2019, 'Decomposition Rules for Quantum Rényi Mutual Information with an Application to Information Exclusion Relations', J. Math. Phys., vol. 61, no. 7, p. 072202.
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We prove decomposition rules for quantum R\'enyi mutual information,generalising the relation $I(A:B) = H(A) - H(A|B)$ to inequalities betweenR\'enyi mutual information and R\'enyi entropy of different orders. The proofuses Beigi's generalisation of Reisz-Thorin interpolation to operator norms,and a variation of the argument employed by Dupuis which was used to show chainrules for conditional R\'enyi entropies. The resulting decomposition rule isthen applied to establish an information exclusion relation for R\'enyi mutualinformation, generalising the original relation by Hall.
Md Rafi, FH, Hossain, MJ, Town, G & Lu, J 2019, 'Smart Voltage-Source Inverters With a Novel Approach to Enhance Neutral-Current Compensation', IEEE Transactions on Industrial Electronics, vol. 66, no. 5, pp. 3518-3529.
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© 1982-2012 IEEE. The presence of a neutral current is quite common in three-phase (3P) four-wire (4W) distribution systems due to an unequal distribution of linear and nonlinear single-phase (1P) loads and small distributed generators. However, a high neutral current can overload the neutral conductor and distribution transformer, which can cause electrical safety concerns and even fire. Among several existing neutral current compensators, the 3P four-leg (4L) voltage-source inverter (VSI) provides better control flexibility and more efficient performance than the passive compensators but requires a higher VSI capacity for the fourth-leg operation. To provide a solution to the aforementioned problem, this paper presents a novel control method to utilize the available capacity of a 3P-4L VSI after active and reactive power regulation to enhance the neutral-current compensation. A smart VSI (SVSI) is designed to operate with a solar photovoltaic unit, regulate the ac side voltage, and minimize the neutral current. Case studies are conducted with actual load data from a commercial building in the PSCAD/EMTDC software environment. The designed system with the proposed control method can provide a significant improvement in the neutral-current compensation, phase balancing, and unbalance factor compared to a fixed-capacity 3P-4L SVSI. Experimental results using a TMS320F28335 digital signal processor microcontroller and modified Semiteach 3P-4L inverter are presented to verify the robustness of the designed controller and the enhancement to the neutral-current compensation using the proposed dynamic capacity-control method.
Meena, NK & Nimbalkar, S 2019, 'Effect of Water Drawdown and Dynamic Loads on Piled Raft: Two-Dimensional Finite Element Approach', Infrastructures, vol. 4, no. 4, pp. 75-75.
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The piled raft foundations are widely used in infrastructure built on soft soil to reduce the settlement and enhance the bearing capacity. However, these foundations pose a potential risk of failure, if dynamic traffic loading and ground conditions are not adequately accounted in the construction phase. The ground conditions are complex because of frequent groundwater fluctuations. The drawdown of the water table profoundly influences the settlement and load sharing capacity of piled raft foundation. Further, the dynamic loading can also pose a potential risk to these foundations. In this paper, the two-dimensional finite element method (FEM) is employed to analyze the impact of water drawdown and dynamic loading on the stability of piled raft. The seismic response of piled raft is also discussed. The stresses and deformations occurring in and around the raft structure are evaluated. The results demonstrate that water drawdown has a significant effect on the stability and seismic response of piled raft. Various foundation improvement methods are assessed, such as the use of geotextile and increasing thickness of the pile cap, which aids of limiting the settlement.
Meilianda, E, Pradhan, B, Syamsidik, Comfort, LK, Alfian, D, Juanda, R, Syahreza, S & Munadi, K 2019, 'Assessment of post-tsunami disaster land use/land cover change and potential impact of future sea-level rise to low-lying coastal areas: A case study of Banda Aceh coast of Indonesia', International Journal of Disaster Risk Reduction, vol. 41, pp. 101292-101292.
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Melhem, MM, Caprani, CC & Stewart, MG 2019, 'Reliability of Super-T PSC girders at serviceability limit state stresses across all span ranges', Structure and Infrastructure Engineering, vol. 15, no. 6, pp. 812-821.
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Reliability assessment gives useful information on the level of performance or adequacy of design rules. There are five standardized Super-T prestressed concrete (PSC) girder sections widely used for bridges in Australia. The Australian standard (AS 5100) requires serviceability limit state criteria of allowable stresses to be met. However, there is not yet an assessment of the performance achieved by these rules. In this study, all potential strand arrangements (more than 50,000) for all Super-T sections, across all feasible span ranges, are assessed. Ten girder-slab bridge decks are analysed; the critical girder in the deck is designed for all possible strand arrangements. Design adequacy is assessed using the annual reliability index. A system reliability analysis is conducted using Ditlevsen bounds to check that none of the four stress limits are exceeded. Generally, it is found that the Super-T girder designs are adequate for most strand arrangements compliant with AS 5100. Further, the reliability varies significantly depending on the selected span, section and strand arrangement. This work informs designers on the reliability performance of Super-T girders designed to AS 5100 and provides background for future revisions of AS 5100.
Melnikov, A, Chiang, YK, Quan, L, Oberst, S, Alù, A, Marburg, S & Powell, D 2019, 'Acoustic meta-atom with experimentally verified maximum Willis coupling', Nature Communications, vol. 10, no. 1, pp. 3148-3148.
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AbstractAcoustic metamaterials are structures with exotic acoustic properties, with promising applications in acoustic beam steering, focusing, impedance matching, absorption and isolation. Recent work has shown that the efficiency of many acoustic metamaterials can be enhanced by controlling an additional parameter known as Willis coupling, which is analogous to bianisotropy in electromagnetic metamaterials. The magnitude of Willis coupling in a passive acoustic meta-atom has been shown theoretically to have an upper limit, however the feasibility of reaching this limit has not been experimentally investigated. Here we introduce a meta-atom with Willis coupling which closely approaches this theoretical limit, that is much simpler and less prone to thermo-viscous losses than previously reported structures. We perform two-dimensional experiments to measure the strong Willis coupling, supported by numerical calculations. Our meta-atom geometry is readily modeled analytically, enabling the strength of Willis coupling and its peak frequency to be easily controlled.
Mendelson, N, Xu, Z-Q, Tran, TT, Kianinia, M, Scott, J, Bradac, C, Aharonovich, I & Toth, M 2019, 'Engineering and Tuning of Quantum Emitters in Few-Layer Hexagonal Boron Nitride', ACS Nano, vol. 13, no. 3, pp. 3132-3140.
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© 2019 American Chemical Society. Quantum technologies require robust and photostable single photon emitters (SPEs). Hexagonal boron nitride (hBN) has recently emerged as a promising candidate to host bright and optically stable SPEs operating at room temperature. However, the emission wavelength of the fluorescent defects in hBN has, to date, been shown to be uncontrolled, with a widespread of zero phonon line (ZPL) energies spanning a broad spectral range (hundreds of nanometers), which hinders the potential development of hBN-based devices and applications. Here we demonstrate chemical vapor deposition growth of large-area, few-layer hBN films that host large quantities of SPEs: -100-200 per 10 × 10 μm 2 . More than 85% of the emitters have a ZPL at (580 ± 10) nm, a distribution that is an order of magnitude narrower than reported previously. Furthermore, we demonstrate tuning of the ZPL wavelength using ionic liquid devices over a spectral range of up to 15 nm-the largest obtained to date from any solid-state SPE. The fabricated devices illustrate the potential of hBN for the development of hybrid quantum nanophotonic and optoelectronic devices based on two-dimensional materials.
Meng, C, Wang, G, Dai, X, Chen, S & Ni, W 2019, 'An Energy-Efficient Transmission Strategy for Cache-Enabled Wireless Networks With Non-Negligible Circuit Power', IEEE Access, vol. 7, pp. 74811-74821.
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In this paper, a cache-enabled wireless network with circuit power consumption is considered, and the strategy of joint transmission and local caching is studied. The optimization problem of energy consumption minimization is formulated, where the optimized variables include the transmission rate and the local caching rate. The constraint conditions contain the maximum data departure curve, the minimum data departure curve, and the range of the local caching rate. The original problem is intractable and is transformed into a two-layer optimization one by variable substitution. Under given caching policy, the sufficient and necessary conditions for the optimal offline transmission strategy are deduced by using the transmission characteristics. Then, an energy-efficient offline transmission strategy is proposed, and its optimality is proved. The simulation results are provided to verify the performance of the proposed strategy. The simulation results reveal the impact of the cache capacity and the file popularity on energy consumption. Under the same conditions, the performance of the proposed strategy is better than that of the other known algorithms. The proposed strategy to energy-efficient communication is helpful for future green communication.
Meng, Q, Wu, C, Su, Y, Li, J, Liu, J & Pang, J 2019, 'A study of steel wire mesh reinforced high performance geopolymer concrete slabs under blast loading', Journal of Cleaner Production, vol. 210, pp. 1150-1163.
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© 2018 Elsevier Ltd In this study, a novel green construction material, high performance alkali-activated geopolymer concrete is introduced. Both numerically and experimentally investigations were conducted on a new type of structural slabs made of steel wire mesh reinforced geopolymer concrete against close-in ground surface explosion. Steel rebar reinforced conventional concrete slabs are also studied to compare the results. The experimental investigation was conducted to study the slab damage mechanism. It is found that the steel wire mesh reinforced geopolymer concrete slab showed less damage and fragmentation under 50 kg Trinitrotoluene (TNT) blast load within 3 m, 5 m and 7 m distances as compared to the C30 concrete slab. Numerical analysis was then conducted to further investigate the slab dynamic responses. Combining the steel wire mesh reinforcement with geopolymer concrete can help increase the blast resistance capacity leading to promising and environmental friendly structural protective design.
Meng, Q, Wu, C, Su, Y, Li, J, Liu, J & Pang, J 2019, 'Experimental and numerical investigation of blast resistant capacity of high performance geopolymer concrete panels', Composites Part B: Engineering, vol. 171, pp. 9-19.
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© 2019 Elsevier Ltd In this study, the mechanical properties of a novel high performance alkali-activated geopolymer concrete under both static and dynamic loads were studied. The ground granulated blast-furnace slag powder (GGBS) and silica fume were used to manufacture this geopolymer concrete. Slabs that cast with this geopolymer concrete and steel wire mesh reinforcement were tested under close-in TNT explosion. The steel rebar reinforced C30 concrete slabs were tested as a control group. It is found that the steel wire mesh reinforced geopolymer concrete slabs achieved a more uniform strain distribution, which means a better structural performance against blast loadings as compared to the conventional C30 concrete slab under the same blast loads. The numerical investigation was then conducted to elaborate the test results.
Merenda, A, Kong, L, Fahim, N, Sadek, A, Mayes, ELH, Hawley, A, Zhu, B, Gray, SR & Dumée, LF 2019, 'Sub-10-nm Mixed Titanium/Tantalum Oxide Nanoporous Films with Visible-Light Photocatalytic Activity for Water Treatment', ACS Applied Nano Materials, vol. 2, no. 4, pp. 1951-1963.
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In the present work, anodic mixed titanium/tantalum oxide nanotubes are prepared for the first time with sub-10-nm surface pore size and tube inner diameter. The morphological changes induced by the introduction of Ta into the Ti metal matrix are investigated, leading to remarkable geometrical variations dependent on the Ta loading. The UV-light activation necessary to trigger electron transfer in TiO2 limits the range of applications, and the shift in light absorption toward the visible range represents a significant challenge. Here, the band gaps of the as-created nanotube thin-film arrays are calculated, and the results, showing the presence of a minimum in the band gap, correlated to the presence of titanium and tantalum suboxides and Ta loading. The potential of the thin films as advanced materials for photocatalytic water treatment is tested against that of pure TiO2, and an enhancement in the visible-light absorption and an almost 3-fold increase in the degradation kinetics under pure visible-light irradiation are demonstrated.
Merenda, A, Rana, A, Guirguis, A, Zhu, DM, Kong, L & Dumée, LF 2019, 'Enhanced Visible Light Sensitization of N-Doped TiO2 Nanotubes Containing Ti-Oxynitride Species Fabricated via Electrochemical Anodization of Titanium Nitride', The Journal of Physical Chemistry C, vol. 123, no. 4, pp. 2189-2201.
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The concentration and chemical state of nitrogen represent critical factors to control the band-gap narrowing and the enhancement of visible light harvesting in nitrogen-doped titanium dioxide. In this study, photocatalytic TiO2-N nanoporous structures were fabricated by the electrochemical anodization of titanium nitride sputtered films. Doping was straightforwardly obtained by oxidizing as-sputtered titanium nitride films containing N-metal bonds varying from 7.3 to 18.5% in the Ti matrix. Severe morphological variations into the as-anodized substrates were registered at different nitrogen concentrations and studied by small-angle X-ray scattering. Titanium nitride films with minimum N content of 6.2 atom % N led to a quasi-nanotubular geometry, whereas an increase in N concentration up to 23.8 atom % determined an inhomogeneous, polydispersed distribution of nanotube apertures. The chemical state of nitrogen in the TiO2 matrix was investigated by X-ray photoelectron spectroscopy depth profile analysis and correlated to the photocatalytic performance. The presence of Ti-N and β-Ti substitutional bonds, as well as Ti-oxynitride species was revealed by the analysis of N 1s X-ray photoelectron spectroscopy high-resolution spectra. The minimum N content of 4.1 atom % in the TiO2-N corresponded to the lowest Ti-oxynitride ratio of 13.5%. The relative variation of N-metal bonds was correlated to the visible light sensitization, and the highest Ti-N/Ti oxynitride ratio of 3.3 was attributed to the lowest band gap of 2.7 eV and associated with a 3-fold increase in the degradation of organic dye. Further increase of N doping led to a dramatic drop of Ti-N/Ti oxynitride ratio, from 3.3 to 0.4, which resulted in a loss of photocatalytic activity. The impact of the chemical state of nitrogen toward efficient doping of TiO2 nanotubes is demonstrated with a direct correlation to N loading and a strategy to optimize these factors based on a simple, rapid synthesis from titani...
Merenda, A, Weber, M, Bechelany, M, Allioux, F-M, Hyde, L, Kong, L & Dumée, LF 2019, 'Fabrication of Pd-TiO2 nanotube photoactive junctions via Atomic Layer Deposition for persistent pesticide pollutants degradation', Applied Surface Science, vol. 483, pp. 219-230.
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The design of nano-structured heterogeneous catalytic junctions with high interface to volume ratio and discrete surface distribution is critical to promote the photoelectron activity in the catalytic degradation of organic pollutants. In this work, photocatalytic palladium‑titanium dioxide nano-junctions were fabricated via Atomic Layer Deposition (ALD) of palladium nanoparticles over the surface of titanium dioxide nanotubes. The Pd catalytic interface and resulting active site density was tailored by varying the nanoparticle growth and coalescence via ALD, leading to Pd-TiO 2 junctions with distinctive morphological aspects and interface properties. The visible light response of the Pd-TiO 2 junctions was attributed to the Surface Plasmon Resonance effect and correlated to the variation of the catalyst morphology tuned by ALD. Uniform, discrete distribution of Pd nanoparticles with diameter lower than 5 nm led to high catalytic interface to deposited volume ratio. The nano-engineered Pd-TiO 2 junctions showed enhanced photocatalytic activity towards the degradation of methylene blue selected as a model contaminant and 2,4 D, with a kinetic constant 4.5 higher than as-annealed anatase TiO 2 nanotubes. The design of well-defined catalytic junctions obtainable by a scalable, accurate deposition technique such as ALD represents a promising route to develop cutting-edge photoactive devices with high performance and minimum noble-metal loading.
Merigó, JM & Yager, RR 2019, 'Aggregation operators with moving averages', Soft Computing, vol. 23, no. 21, pp. 10601-10615.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. A moving average is an average that aggregates a subset of variables from the set and moves across the sample. It is widely used in time-series forecasting. This paper studies the use of moving averages in some representative aggregation operators. The ordered weighted averaging weighted moving averaging (OWAWMA) operator is introduced. It is a new approach based on the use of the moving average in a unified model between the weighted average and the ordered weighted average. Its main advantage is that it provides a parameterized family of moving aggregation operators between the moving minimum and the moving maximum. Moreover, it also includes the weighted moving average and the ordered weighted moving average as particular cases. This approach is further extended by using generalized aggregation operators, obtaining the generalized OWAWMA operator. The construction of interval and fuzzy numbers with these operators obtaining the concept of moving interval number and moving fuzzy number is also studied. The paper ends analyzing the applicability of this new approach in some key statistical concepts such as the variance and the covariance and with a numerical example regarding sales forecasting.
Merigó, JM, Cobo, MJ, Laengle, S, Rivas, D & Herrera-Viedma, E 2019, 'Twenty years of Soft Computing: a bibliometric overview', Soft Computing, vol. 23, no. 5, pp. 1477-1497.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The journal Soft Computing was launched in 1997, and it is dedicated to promote advancements in soft computing theories, which includes fuzzy sets theory, neural networks, evolutionary computation, probabilistic reasoning and hybrid theories. 2017 marks the 20th anniversary of the journal. Motivated by this anniversary, this study presents a bibliometric analysis of the current publications in the journal in order to identify the leading trends ruling the journal. The paper also develops a mapping analysis of the bibliographic material by using the visualization of similarities viewer software. The results show that researchers from all over the world publish regularly in the journal. Soft Computing is growing significantly during the last years, becoming one of the leading journals in the field.
Merigó, JM, Etchebarne, MS & Cancino, CA 2019, 'Evolution of the business and management research in Chile', International Journal of Technology, Policy and Management, vol. 19, no. 2, pp. 108-108.
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Merigó, JM, Miranda, J, Modak, NM, Boustras, G & de la Sotta, C 2019, 'Forty years of Safety Science: A bibliometric overview', Safety Science, vol. 115, pp. 66-88.
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© 2019 Elsevier Ltd Safety Science was established in 1976 as the Journal of Occupational Accidents. Safety Science was established with the vision of promoting multidisciplinary research in the science and technology of human and industrial safety and serving as a guide for the safety of people at work and in other spheres, such as transportation, energy or infrastructure, as well as in every other field of hazardous human activities. To celebrate 40 years of publishing outstanding research, this study intends to develop a bibliometric analysis of the publications of the journal between 1976 and 2016. The purpose is to identify the leading trends of the journal in terms of impact, topics, authors, universities and countries. This study uses the most reliable database, the Web of Science Core Collection. Moreover, the work analyses the mapping of bibliographic couplings, co-citations, citations, co-authorships and co-occurrences of keywords.
Merigó, JM, Mulet-Forteza, C, Valencia, C & Lew, AA 2019, 'Twenty years of Tourism Geographies: a bibliometric overview', Tourism Geographies, vol. 21, no. 5, pp. 881-910.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Tourism Geographies is a prominently ranked journal that emerged from activities of the Tourism Commission of the International Geographical Union. It is indexed in the ‘Tourism, Leisure and Hospitality Management’ and ‘Geography, Planning and Development’ fields in the Scopus database and published its 20th volume in 2018. A bibliometric assessment of the articles and authors who have contributed to Tourism Geographies over its first two decades highlights major trends and dominant issues covered by the journal’s content. Key indicators include the most published and most cited authors and articles, the institutions and countries that those authors are affiliated with, other academic journals that are closely linked to the journal through citations, and the most used keywords in the journal. The Scopus database provides access to these basic bibliometric data, while the VOSviewer software enables graphical analyses and displays of co-citations, co-occurrences of keywords, and bibliographic couplings (shared references) across papers and authors. Overall, Tourism Geographies is closely linked to other leading journals indexed by Scopus in the ‘Tourism’ and ‘Geography’ fields and publishes papers from around the world. Research topics that have been most prominent in the journal include tourism development, tourist destinations, tourist attractions, heritage tourism, tourism perceptions, sustainable tourism, and travel behavior. Among the most viewed individual papers have been those addressing issues related to sustainability, poverty issues (related to tourism in poor areas, volunteering, sustainable tourism, and the environment), and community planning (sustainable tourism planning, tourist routes and movement, and new locations for tourism development).
Merigó, JM, Muller, C, Modak, NM & Laengle, S 2019, 'Research in Production and Operations Management: A University-Based Bibliometric Analysis', Global Journal of Flexible Systems Management, vol. 20, no. 1, pp. 1-29.
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© 2018, Global Institute of Flexible Systems Management. Universities across the world are contributing greatly to production and operations management (POM) research and playing significant roles in social and economic development. This article analyzes the performance of universities in POM research and development between 1990 and 2014. The Web of Science core collection database is used to collect all the necessary data. The results show a wide diversity among the countries of origin of the top universities, with some of them being in Asia, Europe, and North America. These results are quite different from many other management areas where English-speaking countries, especially the USA, tend to be dominant. Hong Kong Polytechnic University is the most productive university, while Michigan State University is the most influential one. Time-based evolution reveals that the USA previously had a more dominant position, while now there is more distribution of top universities around the world. The analysis of selected journals indicates that many journals tend to be more influenced by their respective countries of origin. However, other journals show a more general profile by publishing papers from most of the countries around the world.
Miao, Z, Yu, J, Ji, J & Zhou, J 2019, 'Multi-objective region reaching control for a swarm of robots', Automatica, vol. 103, no. IEEE Transaction Robotics and Automation 14 6 1998, pp. 81-87.
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© 2019 Elsevier Ltd This paper is concerned with the multi-objective region reaching control for a swarm of robots which are formulated by Lagrangian dynamics. Two distributed multi-objective region reaching control protocols are proposed for the networked robotic systems under directed acyclic topology, and a unifying methodology is presented to perform the convergence analysis for the robotic systems with static and moving target regions. The control strategy is developed by using the potential energy function approach, and the specified shapes of the various desired regions are constructed by selecting appropriate objective functions. In this control strategy, a network of a large number of robots evolves into multiple groups, and the robots in each group only require communicating with their neighbors. Thus, the proposed control strategy is effective for multi-objective region reaching control for a swarm of robots in practical applications. Finally, simulation examples are given to show the validity of the theoretical results.
Mihăiţă, AS, Dupont, L, Chery, O, Camargo, M & Cai, C 2019, 'Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling', Journal of Cleaner Production, vol. 221, pp. 398-418.
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© 2019 Elsevier Ltd Air pollution impact assessment is a major objective for various community councils in large cities, which have lately redirected their attention towards using more low-cost sensing units supported by citizen involvement. However, there is a lack of research studies investigating real-time mobile air-quality measurement through smart sensing units and even more of any data-driven modelling techniques that could be deployed to predict air quality accurately from the generated data-sets. This paper addresses these challenges by: a) proposing a comparative and detailed investigation of various air quality monitoring devices (both fixed and mobile), tested through field measurements and citizen sensing in an eco-neighbourhood from Lorraine, France, and by b) proposing a machine learning approach to evaluate the accuracy and potential of such mobile generated data for air quality prediction. The air quality evaluation consists of three experimenting protocols: a) first, we installed fixed passive tubes for monitoring the nitrogen dioxide concentrations placed in strategic locations highly affected by traffic circulation in an eco-neighbourhood, b) second, we monitored the nitrogen dioxide registered by citizens using smart and mobile pollution units carried at breathing level; results revealed that mobile-captured concentrations were 3–5 times higher than the ones registered by passive-static monitoring tubes and c) third, we compared different mobile pollution stations working simultaneously, which revealed noticeable differences in terms of result variability and sensitivity. Finally, we applied a machine learning modelling by using decision trees and neural networks on the mobile-generated data and show that humidity and noise are the most important factors influencing the prediction of nitrogen dioxide concentrations of mobile stations.
Mihăiţă, AS, Ortiz, MB, Camargo, M & Cai, C 2019, 'Predicting Air Quality by Integrating a Mesoscopic Traffic Simulation Model and Simplified Air Pollutant Estimation Models', International Journal of Intelligent Transportation Systems Research, vol. 17, no. 2, pp. 125-141.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Continuous growth in traffic demand has led to a decrease in the air quality in various urban areas. More than ever, local authorities for environmental protection and urban planners are interested in performing detailed investigations using traffic and air pollution simulations for testing various urban scenarios and raising citizen awareness where necessary. This article is focused on the traffic and air pollution in the eco-neighbourhood “Nancy Grand Cœur”, located in a medium-size city from north-eastern France. The main objective of this work is to build an integrated simulation model which would predict and visualize various environmental changes inside the neighbourhood such as: air pollution, traffic flow or meteorological information. Firstly, we conduct a data profiling analysis on the received data sets together with a discussion on the daily and hourly traffic patterns, average nitrogen dioxide concentrations and the regional background concentrations recorded in the eco-neighbourhood for the study period. Secondly, we build the 3D mesoscopic traffic simulation model using real data sets from the local traffic management centre. Thirdly, by using reliable data sets from the local air-quality management centre, we build a regression model to predict the evolution of nitrogen dioxide concentrations, as a function of the simulated traffic flow and meteorological data. We then validate the estimated results through comparisons with real data sets, with the purpose of supporting the traffic engineering decision-making and the smart city sustainability. The last section of the paper is reserved for further regression studies applied to other air pollutants monitored in the eco-neighbourhood, such as sulphur dioxide and particulate matter and a detailed discussion on benefit and challenges to conduct such studies.
Milfont, TL, Amirbagheri, K, Hermanns, E & Merigó, JM 2019, 'Celebrating Half a Century of Environment and Behavior: A Bibliometric Review', Environment and Behavior, vol. 51, no. 5, pp. 469-501.
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Environment and Behavior is a leading international journal that publishes research examining the relationships between human behavior and the built and natural environments since 1969. Motivated by its half-century anniversary, the present article uses the Web of Science Core Collection database to provide a bibliometric overview of the leading trends that have occurred in the journal during the 1969-2018 period. The impact of the journal has increased over the years, Gary W. Evans is the author with most published papers, articles by Paul C. Stern and Thomas Dietz have made a notable scientific impact, the University of Michigan is the institution with the highest number of publications, and there is a growing trend in the number of women and international contributors to the journal. This bibliographic review provides strong evidence of the scientific impact of the journal, and the wider Environment-and-Behavior community should be proud of its story of success.
Miller, HD, Akbarnezhad, A, Mesgari, S & Foster, SJ 2019, 'Performance of oxygen/argon plasma-treated steel fibres in cement mortar', Cement and Concrete Composites, vol. 97, pp. 24-32.
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© 2018 Elsevier Ltd Steel fibres are used widely to control initiation and growth of cracks in concrete. However, the bond between steel fibres and the cement matrix in steel fibre-reinforced concrete (SFRC) is almost always purely physical. A viable approach to reduce cracking in fibre-reinforced concrete is to supplement the physical bond between fibres and cement matrix with a relatively uniform chemical bond along the fibres’ surface. However, despite the promising results reported for other types of fibres, little attention has been paid to chemical surface treatment of steel fibres to improve their ability to bond with concrete. In this study, an oxygen/argon plasma treatment process is investigated as a potential technique to improve the bond between steel fibres and a cementitious matrix. Several variations in treatment parameters are made in order to identify the optimal treatment conditions. The results of X-ray Photoelectron Spectroscopy and surface energy measurements confirm that plasma treatment can significantly increase the fibres’ surface energy and consequently the strength of the fibre-cement bond. The improved bond between steel fibres and concrete is confirmed by the results of fibre pull-out tests, as well as the reduced average crack size observed in restrained drying shrinkage tests. Moreover, the results indicate considerable decrease in the volume of permeable voids in SFRC due to plasma treatment of steel fibres.
Mills, PW, Rundle, RP, Samson, JH, Devitt, SJ, Tilma, T, Dwyer, VM & Everitt, MJ 2019, 'Quantum invariants and the graph isomorphism problem', Physical Review A, vol. 100, no. 5.
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Milne, DN, McCabe, KL & Calvo, RA 2019, 'Improving Moderator Responsiveness in Online Peer Support Through Automated Triage', Journal of Medical Internet Research, vol. 21, no. 4, pp. e11410-e11410.
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© 2019 Journal of Medical Internet Research. All rights reserved. Background: Online peer support forums require oversight to ensure they remain safe and therapeutic. As online communities grow, they place a greater burden on their human moderators, which increases the likelihood that people at risk may be overlooked. This study evaluated the potential for machine learning to assist online peer support by directing moderators' attention where it is most needed. Objective: This study aimed to evaluate the accuracy of an automated triage system and the extent to which it influences moderator behavior. Methods: A machine learning classifier was trained to prioritize forum messages as green, amber, red, or crisis depending on how urgently they require attention from a moderator. This was then launched as a set of widgets injected into a popular online peer support forum hosted by ReachOut.com, an Australian Web-based youth mental health service that aims to intervene early in the onset of mental health problems in young people. The accuracy of the system was evaluated using a holdout test set of manually prioritized messages. The impact on moderator behavior was measured as response ratio and response latency, that is, the proportion of messages that receive at least one reply from a moderator and how long it took for these replies to be made. These measures were compared across 3 periods: before launch, after an informal launch, and after a formal launch accompanied by training. Results: The algorithm achieved 84% f-measure in identifying content that required a moderator response. Between prelaunch and post-training periods, response ratios increased by 0.9, 4.4, and 10.5 percentage points for messages labelled as crisis, red, and green, respectively, but decreased by 5.0 percentage points for amber messages. Logistic regression indicated that the triage system was a significant contributor to response ratios for green, amber, and red messages, but not fo...
Ming, Y, Ding, W, Pelusi, D, Wu, D, Wang, Y-K, Prasad, M & Lin, C-T 2019, 'Subject adaptation network for EEG data analysis', Applied Soft Computing, vol. 84, pp. 105689-105689.
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© 2019 Elsevier B.V. Biosignals tend to display manifest intra- and cross-subject variance, which generates numerous challenges for electroencephalograph (EEG) data analysis. For instance, in the context of classification, the discrepancy between EEG data can make the trained model generalising poorly for new test subjects. In this paper, a subject adaptation network (SAN) inspired by the generative adversarial network (GAN) to mitigate different variances is proposed for analysing EEG data. First the challenges faced by traditional approaches employed for EEG signal processing are emphasised. Then the problem is formulated from mathematical perspective to highlight the key points in resolving such discrepancies. Third, the motivation behind and design principle of the SAN are described in an intuitive manner to reflect its suitability for analysing EEG data. Then after depicting the overall architecture of the SAN, several experiments are used to justify the practicality and efficiency of using the proposed model from different perspectives. For instance, an EEG dataset captured during a stereotypical neurophysiological experiment called the VEP oddball task is utilised to demonstrate the performance improvement achieved by running the SAN.
Ming, Y, Lin, C-T, Bartlett, SD & Zhang, W-W 2019, 'Quantum topology identification with deep neural networks and quantum walks', npj Computational Materials, vol. 5, no. 1.
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AbstractTopologically ordered materials may serve as a platform for new quantum technologies, such as fault-tolerant quantum computers. To fulfil this promise, efficient and general methods are needed to discover and classify new topological phases of matter. We demonstrate that deep neural networks augmented with external memory can use the density profiles formed in quantum walks to efficiently identify properties of a topological phase as well as phase transitions. On a trial topological ordered model, our method’s accuracy of topological phase identification reaches 97.4%, and is shown to be robust to noise on the data. Furthermore, we demonstrate that our trained DNN is able to identify topological phases of a perturbed model, and predict the corresponding shift of topological phase transitions without learning any information about the perturbations in advance. These results demonstrate that our approach is generally applicable and may be used to identify a variety of quantum topological materials.
Mishra, B, Varjani, S, Iragavarapu, GP, Ngo, HH, Guo, W & Vishal, B 2019, 'Microbial Fingerprinting of Potential Biodegrading Organisms', Current Pollution Reports, vol. 5, no. 4, pp. 181-197.
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© 2019, Springer Nature Switzerland AG. The world is witnessing various pollutants in the environment since the last few decades that threaten human life. The biological responses to various pollutants show variations as the living system behaves differently in their sensitivities to the same types of pollutants. The relative response and activity depend upon the duration of exposure to the specific pollutant. It is impossible to stop various activities leading to environmental pollution; however, pollutants can be eliminated from the environment using the microorganisms. Application of biological processes can be executed in order to get rid of toxic pollutants through their biodegradation. The pollutants like hydrocarbons, heavy metals, chlorinated hydrocarbons, nitro-aromatic compounds, non-chlorinated herbicides and pesticides, organophosphates, radionuclides can lead to serious health and environmental problems. The main objective of this paper is to evaluate the effects of pollutants on the living beings and environment, microbial responses to pollution, and distribution of various biodegrading microorganisms in the environment. Profiling of biodegrading microorganisms, microbial biosensor to detect environmental pollution, and strain improvement through genetic manipulation to enhance the biodegradation process have been discussed in detail.
Mishra, N, Bosi, M, Rossi, F, Salviati, G, Boeckl, J & Iacopi, F 2019, 'Growth of graphitic carbon layers around silicon carbide nanowires', Journal of Applied Physics, vol. 126, no. 6, pp. 065304-065304.
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We demonstrate the ability to synthesize graphitic carbon sheets around cubic silicon carbide nanowires via an alloy-mediated catalytic process. The transmission electron microscopy analysis shows multilayer graphitic carbon sheets with a large interatomic layer distance of ∼0.45 nm, suggesting the presence of oxygen in the graphitic system. Oxygen-related peaks observed by energy-dispersive X-ray spectroscopy, Raman spectroscopy, and Fourier-transform infrared spectroscopy further confirm the oxidation of the graphitic carbon layers. A detailed investigation of the Raman spectra reveals a turbostratic stacking of the graphitic carbon layers. The turbostratic nature and the presence of oxidation in the graphitic carbon surrounding the silicon carbide nanowires make them a suitable platform for further functionalization, of particular interest for biosensing, as both graphitic carbon and silicon carbide are biocompatible.
Mistica, M, MacKinlay, A & Piccardi, M 2019, 'Introduction', Proceedings of the Australasian Language Technology Workshop, vol. 17, p. iii.
Mitchell, BG, Cheng, AC, Fasugba, O, Gardner, A, Graves, N, Koerner, J & Collignon, P 2019, 'Chlorhexidine for prevention of catheter-associated urinary tract infections: the totality of evidence – Authors' reply', The Lancet Infectious Diseases, vol. 19, no. 8, pp. 808-809.
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Mitchell, BG, Fasugba, O, Cheng, AC, Gregory, V, Koerner, J, Collignon, P, Gardner, A & Graves, N 2019, 'Chlorhexidine versus saline in reducing the risk of catheter associated urinary tract infection: A cost-effectiveness analysis', International Journal of Nursing Studies, vol. 97, pp. 1-6.
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Mittal, RK, Mittal, S, Saran, S & Rawat, S 2019, 'Pressure–Settlement Characteristics of Strip Footing Resting on Randomly Distributed Fibre-Reinforced Sand Using Constitutive Law', International Journal of Geosynthetics and Ground Engineering, vol. 5, no. 2.
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Mittal, RK, Rawat, S & Bansal, P 2019, 'Multivariable regression model for Fox depth correction factor', Frontiers of Structural and Civil Engineering, vol. 13, no. 1, pp. 103-109.
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Moayedi, H, Tien Bui, D, Gör, M, Pradhan, B & Jaafari, A 2019, 'The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes', ISPRS International Journal of Geo-Information, vol. 8, no. 9, pp. 391-391.
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In this paper, a neuro particle-based optimization of the artificial neural network (ANN) is investigated for slope stability calculation. The results are also compared to another artificial intelligence technique of a conventional ANN and adaptive neuro-fuzzy inference system (ANFIS) training solutions. The database used with 504 training datasets (e.g., a range of 80%) and testing dataset consists of 126 items (e.g., 20% of the whole dataset). Moreover, variables of the ANN method (for example, nodes number for each hidden layer) and the algorithm of PSO-like swarm size and inertia weight are improved by utilizing a total of 28 (i.e., for the PSO-ANN) trial and error approaches. The key properties were fed as input, which were utilized via the analysis of OptumG2 finite element modelling (FEM), containing undrained cohesion stability of the baseline soil (Cu), angle of the original slope (β), and setback distance ratio (b/B) where the target is selected factor of safety. The estimated data for datasets of ANN, ANFIS, and PSO-ANN models were examined based on three determined statistical indexes. Namely, root mean square error (RMSE) and the coefficient of determination (R2). After accomplishing the analysis of sensitivity, considering 72 trials and errors of the neurons number, the optimized architecture of 4 × 6 × 1 was determined to the structure of the ANN model. As an outcome, the employed methods presented excellent efficiency, but based on the ranking method, the PSO-ANN approach might have slightly better efficiency in comparison to the algorithms of ANN and ANFIS. According to statistics, for the proper structure of PSO-ANN, the indexes of R2 and RMSE values of 0.9996, and 0.0123, as well as 0.9994 and 0.0157, were calculated for the training and testing networks. Nevertheless, having the ANN model with six neurons for each hidden layer was formulized for further practical use. This study demonstrates the efficiency of the proposed neu...
Modak, NM, Merigó, JM, Weber, R, Manzor, F & Ortúzar, JDD 2019, 'Fifty years of Transportation Research journals: A bibliometric overview', Transportation Research Part A: Policy and Practice, vol. 120, pp. 188-223.
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© 2018 Elsevier Ltd Transportation Research (TR) was established in 1967 with the vision of promoting multi-disciplinary (economics, engineering, sociology, psychology) research on transport systems. The journal has continuously expanded its wings becoming a world-leading journal, now publishing research work through six parts, A to F, respectively addressing Policy and Practice, Methodological, Emerging Technologies, Transport and Environment, Logistics and Transportation Review, and Traffic Psychology and Behaviour. This study aims to celebrate the first half century of the journal through a bibliometric study of the publications on all six parts between 1967 and 2016. It uses the most reliable database for academic research, the Web of Science Core Collection, to identify the leading trends in all TR journals in terms of impact, topics, authors, universities, and countries. Moreover, it uses the Visualization of Similarities (VOS) viewer software to analyse bibliographic coupling, co-citation, citation, co-authorship, and co-occurrence of keywords.
Mofijur, M, Hasan, MM, Mahlia, TMI, Rahman, SMA, Silitonga, AS & Ong, HC 2019, 'Performance and Emission Parameters of Homogeneous Charge Compression Ignition (HCCI) Engine: A Review', Energies, vol. 12, no. 18, pp. 3557-3557.
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Strict emission regulations and demand for better fuel economy are driving forces for finding advanced engines that will be able to replace the conventional internal combustion engines in the near future. Homogeneous charge compression ignition (HCCI) engines use a different combustion technique; there are no spark plugs or injectors to assist the combustion. Instead, when the mixtures reach chemical activation energy, combustion auto-ignites in multiple spots. The main objective of this review paper is to study the engine performance and emission characteristics of HCCI engines operating in various conditions. Additionally, the impact of different fuels and additives on HCCI engine performance is also evaluated. The study also introduces a potential guideline to improve engine performance and emission characteristics. Compared to conventional compression ignition and spark ignition combustion methods, the HCCI combustion mode is noticeably faster and also provides better thermal efficiency. Although a wide range of fuels including alternative and renewable fuels can be used in the HCCI mode, there are some limitation/challenges, such as combustion limited operating range, phase control, high level of noise, cold start, preparation of homogeneous charge, etc. In conclusion, the HCCI combustion mode can be achieved in existing spark ignition (SI) engines with minor adjustments, and it results in lower oxides of nitrogen (NOx) and soot emissions, with practically a similar performance as that of SI combustion. Further improvements are required to permit extensive use of the HCCI mode in future.
Mofijur, M, Mahlia, T, Silitonga, A, Ong, H, Silakhori, M, Hasan, M, Putra, N & Rahman, SM 2019, 'Phase Change Materials (PCM) for Solar Energy Usages and Storage: An Overview', Energies, vol. 12, no. 16, pp. 3167-3167.
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Solar energy is a renewable energy source that can be utilized for different applications in today’s world. The effective use of solar energy requires a storage medium that can facilitate the storage of excess energy, and then supply this stored energy when it is needed. An effective method of storing thermal energy from solar is through the use of phase change materials (PCMs). PCMs are isothermal in nature, and thus offer higher density energy storage and the ability to operate in a variable range of temperature conditions. This article provides a comprehensive review of the application of PCMs for solar energy use and storage such as for solar power generation, water heating systems, solar cookers, and solar dryers. This paper will benefit the researcher in conducting further research on solar power generation, water heating system, solar cookers, and solar dryers using PCMs for commercial development.
Mofijur, M, Mahlia, TMI, Logeswaran, J, Anwar, M, Silitonga, AS, Rahman, SMA & Shamsuddin, AH 2019, 'Potential of Rice Industry Biomass as a Renewable Energy Source', Energies, vol. 12, no. 21, pp. 4116-4116.
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Fossil fuel depletion, along with its ever-increasing price and detrimental impact on the environment, has urged researchers to look for alternative renewable energy. Of all the options available, biomass presents a very reliable source due to its never-ending supply. As research on various biomasses has grown in recent years, waste from these biomasses has also increased, and it is now time to shift the focus to utilizing these wastes for energy. The current waste management system mainly focuses on open burning and soil incorporation as it is cost-effective; however, these affect the environment. There must be an alternative way, such as to use it for power generation. Rice straw and rice husk are examples of such potential biomass waste. Rice is the main food source for the world, mostly in Asian regions, as most people consume rice daily. This paper reviews factors that impact the implementation of rice-straw-based power plants. Ash content and moisture content are important properties that govern combustion, and these vary with location. Logistical improvements are required to reduce the transport cost of rice husk and rice straw, which is higher than the transportation cost of coal.
Mofradnia, SR, Ashouri, R, Tavakoli, Z, Shahmoradi, F, Rashedi, H, Yazdian, F & Tavakoli, J 2019, 'Effect of zero-valent iron/starch nanoparticle on nitrate removal using MD simulation', International Journal of Biological Macromolecules, vol. 121, pp. 727-733.
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In this study, the efficacy of zero-valent iron nanostructure modified by starch for removal of nitrate was investigated. Effect of zero-valent iron/starch nanoparticle in the presence of Thiobacillus dinitrificans for removal of nitrate was simulated via material studio software. Thermodynamic principles and proper equations were used via molecular dynamic (MD) simulation. The results of software predictions were demonstrated by radial distribution function (RDF), density, potential energy and temperature graphs. According to the graphs, the simultaneous in the presence of zero-valent iron/starch nanoparticle and Thiobacillus dinitrificans increase the removal efficiency of nitrate reached 91% and in the absence of nanoparticle was 44.44%.
Moghadam Banaem, H, Abbasi, M & Tousi, B 2019, 'A new method with minimum number of monitoring points for flicker source tracing by wavelet transform', International Transactions on Electrical Energy Systems, vol. 29, no. 9.
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Moghaddam, F, Sirivivatnanon, V & Vessalas, K 2019, 'The effect of fly ash fineness on heat of hydration, microstructure, flow and compressive strength of blended cement pastes', Case Studies in Construction Materials, vol. 10, pp. e00218-e00218.
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© 2019 In this paper, an experimental study on the effect of fly ash fineness on the heat of hydration, microstructure, flow and compressive strength of blended cement pastes was carried out and evaluated against control cement paste. Fly ashes with different fineness: classified fly ash, run-of-station fly ash and grounded run-of-station fly ash; with a median particle size of 17.4, 11.3 and 5.7 μm, respectively, from the same power station source in Australia were used to partially replace Portland cement at 20% and 40% by weight of cement using a fixed water-to-binder ratio of 0.40. Results of this study showed that the cumulative heat of hydration of blended cement paste decreased as fly ash content in blended cement paste was increased. For a given cement replacement level, blended cement paste containing finer fly ash released more heat of hydration when compared to coarser fly ash. Moreover, increasing the fineness of fly ash resulted in a higher consumption of calcium hydroxide at 7 and 28 days reflecting pozzolanic reactivity and, thus, a denser microstructure than blended pastes containing coarser fly ash as revealed by the X-ray diffraction (XRD), scanning electron microscopy (SEM) and compressive strength results. In addition, the incorporation of fly ash in the blended pastes led to the introduction of an additional hydration peak in the heat evolution curve possibly due to the late activation of fly ash by calcium hydroxide renewing the C 3 A reaction and converting ettringite to monosulfate. The flow of the freshly blended cement pastes was also found to improve slightly with increasing fineness of the fly ash. In addition, the hardened blended cement pastes containing 20% ground run-of-station fly ash showed comparable compressive strength with the control cement pastes at both 7 and 28 days mainly due to the higher fineness of the ground run-of-station fly ash and increased reactivity compared to coarser grade fly ash.
Mohamad, ET, Koopialipoor, M, Murlidhar, BR, Rashiddel, A, Hedayat, A & Jahed Armaghani, D 2019, 'A new hybrid method for predicting ripping production in different weathering zones through in situ tests', Measurement, vol. 147, pp. 106826-106826.
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Mohammadinia, A, Saeidian, B, Pradhan, B & Ghaemi, Z 2019, 'Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches', BMC Infectious Diseases, vol. 19, no. 1.
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AbstractBackgroundRecent reports of the National Ministry of Health and Treatment of Iran (NMHT) show that Gilan has a higher annual incidence rate of leptospirosis than other provinces across the country. Despite several efforts of the government and NMHT to eradicate leptospirosis, it remains a public health problem in this province. Modelling and Prediction of this disease may play an important role in reduction of the prevalence.MethodsThis study aims to model and predict the spatial distribution of leptospirosis utilizing Geographically Weighted Regression (GWR), Generalized Linear Model (GLM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) as capable approaches. Five environmental parameters of precipitation, temperature, humidity, elevation and vegetation are used for modelling and predicting of the disease. Data of 2009 and 2010 are used for training, and 2011 for testing and evaluating the models.ResultsResults indicate that utilized approaches in this study can model and predict leptospirosis with high significance level. To evaluate the efficiency of the approaches, MSE (GWR = 0.050, SVM = 0.137, GLM = 0.118 and ANN = 0.137), MAE (0.012, 0.063, 0.052 and 0.063), MRE (0.011, 0.018, 0.017 and 0.018) and R2(0.85, 0.80, 0.78 and 0.75) are used.ConclusionResults indicate the practical usefulness of approaches for spatial modelling and predicting leptospirosis. The efficiency of models is as follow: GWR > SVM > GLM > ANN. In addition, temperature and humidity are investigated as the most influential parameters. Moreover, the suitable habitat of leptospirosis is mostly within the central rural districts of the province.
Mojaddadi Rizeei, H, Pradhan, B & Saharkhiz, MA 2019, 'Urban object extraction using Dempster Shafer feature-based image analysis from worldview-3 satellite imagery', International Journal of Remote Sensing, vol. 40, no. 3, pp. 1092-1119.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. A detailed and up-to-date land use of the urban environment is essentially required in many applications. Very high-resolution (VHR), Multispectral Scanner System (MSS) Worldview-3 (WV-3) satellite imagery provides detailed information on urban characteristics, which should be professionally mined. In this research, WV-3 was processed by machine learning (ML) methods to extract the most accurate urban features. Fuze-Go panchromatic sharpening in conjunction with atmospheric and topographic correction was initially utilized to increase the image quality and colour contrast. Three image analysis approaches including, current pixel-based image analysis (PBIA), object-based image analysis (OBIA) and new feature-based image analysis (FBIA) were implemented on WV-3 image. The k-nearest neighbour (k-NN), Naive Bayes (NB), support vector machine (SVM) classifiers were represented by PBIA, the Decision Tree (DT) classifier was examined as OBIA and the Dempster–Shafer (DS) fusion classifier was manifested for the first time as FBIA. In order to engage DS as FBIA, four types of Belief Masses, namely, Precision, Recall, Overall Accuracy, and kappa coefficient (ĸ) were implemented and compared to assign the most likelihood urban features. All the applied classifiers were also trained on the first site and then tested on another site to examine the transferability. The accuracy, reliability, and computational time of all classifiers were examined by confusion matrix and McNemar assessment. Results show improvements on the detailed urban extraction obtained using the proposed FBIA with 92.2% overall accuracy in compared with PBIA and OBIA. The FBIA result of urban extraction is more consistent when transferred to another study area and consumes much lesser time than OBIA. Also, the precision mass belief measurement achieved highest efficiency regarding receiver operating characteristic (ROC) curve rate.
Mojiri, A, Zhou, JL, Ohashi, A, Ozaki, N & Kindaichi, T 2019, 'Comprehensive review of polycyclic aromatic hydrocarbons in water sources, their effects and treatments', Science of The Total Environment, vol. 696, pp. 133971-133971.
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© 2019 Elsevier B.V. Polycyclic aromatic hydrocarbons (PAHs) are principally derived from the incomplete combustion of fossil fuels. This study investigated the occurrence of PAHs in aquatic environments around the world, their effects on the environment and humans, and methods for their removal. Polycyclic aromatic hydrocarbons have a great negative impact on the humans and environment, and can even cause cancer in humans. Use of good methods and equipment are essential to monitoring PAHs, and GC/MS and HPLC are usually used for their analysis in aqueous solutions. In aquatic environments, the PAHs concentrations range widely from 0.03 ng/L (seawater; Southeastern Japan Sea, Japan) to 8,310,000 ng/L (Domestic Wastewater Treatment Plant, Siloam, South Africa). Moreover, bioaccumulation of ∑16PAHs in fish has been reported to range from 11.2 ng/L (Cynoscion guatucupa, South Africa) to 4207.5 ng/L (Saurida undosquamis, Egypt). Several biological, physical and chemical and biological techniques have been reported to treat water contaminated by PAHs, but adsorption and combined treatment methods have shown better removal performance, with some methods removing up to 99.99% of PAHs.
Molla, T, Khan, B, Moges, B, Alhelou, HH, Zamani, R & Siano, P 2019, 'Integrated energy optimization of smart home appliances with cost-effective energy management system', CSEE Journal of Power and Energy Systems.
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Moloudi, R, Oh, S, Yang, C, Teo, KL, Lam, AT, Ebrahimi Warkiani, M & Win Naing, M 2019, 'Scaled‐Up Inertial Microfluidics: Retention System for Microcarrier‐Based Suspension Cultures', Biotechnology Journal, vol. 14, no. 5, pp. 1800674-1800674.
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Recently, particle concentration and filtration using inertial microfluidics have drawn attention as an alternative to membrane and centrifugal technologies for industrial applications, where the target particle size varies between 1 µm and 500 µm. Inevitably, the bigger particle size (>50 µm) mandates scaling up the channel cross‐section or hydraulic diameter (DH > 0.5 mm). The Dean‐coupled inertial focusing dynamics in spiral microchannels is studied broadly; however, the impacts of secondary flow on particle migration in a scaled‐up spiral channel is not fully elucidated. The mechanism of particle focusing inside scaled‐up rectangular and trapezoidal spiral channels (i.e., 5–10× bigger than conventional microchannels) with an aim to develop a continuous and clog‐free microfiltration system for bioprocessing is studied in detail. Herein, a unique focusing based on inflection point without the aid of sheath flow is reported. This new focusing mechanism, observed in the scaled‐up channels, out‐performs the conventional focusing scenarios in the previously reported trapezoidal and rectangular channels. Finally, as a proof‐of‐concept, the utility of this device is showcased for the first time as a retention system for a cell–microcarrier (MC) suspension culture.
Moore, SI, Ruppert, MG & Yong, YK 2019, 'An optimization framework for the design of piezoelectric AFM cantilevers', Precision Engineering, vol. 60, pp. 130-142.
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Moore, SI, Ruppert, MG, Harcombe, DM, Fleming, AJ & Yong, YK 2019, 'Design and Analysis of Low-Distortion Demodulators for Modulated Sensors', IEEE/ASME Transactions on Mechatronics, vol. 24, no. 4, pp. 1861-1870.
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Mora, A, Cardenas-Dobson, R, Aguilera, RP, Angulo, A, Donoso, F & Rodriguez, J 2019, 'Computationally Efficient Cascaded Optimal Switching Sequence MPC for Grid-Connected Three-Level NPC Converters', IEEE Transactions on Power Electronics, vol. 34, no. 12, pp. 12464-12475.
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© 1986-2012 IEEE. In this work, a model predictive control (MPC) strategy based on optimal switching sequence (OSS) concepts is proposed for a grid-connected three-level neutral-point clamped converter. The proposed cascaded-OSS-MPC strategy does not require a weighting factor to balance the dc-link capacitor voltages and optimally controls both the grid currents and the capacitor voltages even during disturbances and large step changes in the references. The resulting MPC strategy allows operating the converter with a predefined harmonic spectrum, fixed switching frequency, and fast and robust dynamic response. Besides, an efficient optimization algorithm is also introduced to reduce the computational burden typically observed in this kind of MPC strategies. Experimental and simulation results are provided to demonstrate the effectiveness and high-quality performance of the proposed strategy.
Mostaan, A, Yuan, J, Siwakoti, YP, Esmaeili, S & Blaabjerg, F 2019, 'A Trans-Inverse Coupled-Inductor Semi-SEPIC DC/DC Converter With Full Control Range', IEEE Transactions on Power Electronics, vol. 34, no. 11, pp. 10398-10402.
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© 1986-2012 IEEE. This letter proposes a single switch magnetically coupled dc-dc converter with a high voltage gain. The unique features of the converter are summarized as follows: 1) voltage gain of the converters is raised by lowering its magnetic turn ratio; 2) wide control range (0< D< 1); 3) continuous current from the source that makes it a suitable candidate for renewable energy applications; and 4) there is no dc current saturation in the core due to the presence of capacitor in the primary winding of the inductor. The feasibility of the proposed converter is studied in details supported by circuit analysis and simulation results. Furthermore, the proposed converter is analyzed and compared with other converters with similar features. Finally the superior performance of the circuit is validated experimentally.
Motevalli, A, Naghibi, SA, Hashemi, H, Berndtsson, R, Pradhan, B & Gholami, V 2019, 'Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater', Journal of Cleaner Production, vol. 228, pp. 1248-1263.
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© 2019 Elsevier Ltd Nitrate pollution of groundwater has increased dramatically worldwide due to increase of population and agricultural productivity. The resulting nitrate concentration in groundwater is usually a combination of various types of point and non-point pollutant sources. It is often difficult to distinguish between these sources since groundwater is formed in large and complex catchments with various natural processes and anthropogenic influence that contribute to a certain downstream nitrate concentration. For such conditions, this paper uses a methodology that can be used to inversely determine type and location of main nitrate pollutant source. The methodology builds on two state-of-the-art data mining techniques, boosted regression tree (BRT)and k-nearest neighbor (KNN). These techniques are used to produce a nitrate pollution vulnerability map. The methodology can mitigate effects of subjective judgement on determining importance of different sources and mechanisms for nitrate transport. The investigated mechanisms are hydrogeological, hydrological, anthropogenic, topography, and soil conditioning factors. Thus, the proposed methodology is used to separate between natural processes and anthropogenic effects on nitrate pollution. To calculate the groundwater vulnerability maps, a groundwater nitrate concentration of 40 mg/L (suggested by WHO with a 20% risk margin)was selected as a general threshold for identifying polluted areas that resulted in 96 polluted wells. Non-polluted locations were selected from well data with nitrate concentration less than 15 mg/L (96 non-polluted). The models were trained on 70% polluted and 70% non-polluted site data. The remaining data, 30% polluted and 30% non-polluted sites, were used to validate the simulation results. Results showed that the BRT produced outputs with higher performance than the KNN algorithm. The final ranking results based on the BRT model showed the higher importance of hydraulic ...
Mousavi, M, Holloway, D, Olivier, JC, Alavi, AH & Gandomi, AH 2019, 'A Shannon entropy approach for structural damage identification based on self-powered sensor data', Engineering Structures, vol. 200, pp. 109619-109619.
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© 2019 Elsevier Ltd Piezo-floating-gate (PFG) sensors are a class of self-powered sensors fabricated using piezoelectric transducers and p-channel floating-gate metal-oxide-semiconductor (pMOS) transistors. These sensors are equipped with a series of floating-gates that are triggered when the voltage generated by the piezoelectric transducers exceeds one of the specified thresholds. Upon activation, the floating-gates cumulatively store the duration of the applied strain events. Defining optimal voltage thresholds plays a key role in the efficiency of the PFG sensors for structural damage identification. In this paper, symbolic dynamic analysis (SDA) based on Shannon entropy is used to find the effective voltage thresholds that ensure the maximum detectability of the structural damage-related changes. To this end, a baseline is constructed using the strain data obtained from the undamaged structure. These data are used to set the voltage threshold on every floating gate of the sensor. Then the posterior state of the structure is monitored using thresholds set up on the baseline and a cumulative density function (CDF) of strain events. In order to determine the damage severity, a damage index is defined based on the Euclidean norm of the distance between the CDFs for the damaged and healthy structure. The proposed technique is verified using experimental data for a steel plate subjected to an in-plane tension loading. The results confirm the capability of the proposed method in monitoring structures for damage initiation and/or propagation using the PFG sensors, and the CDFs on which the damage sensitive feature (DSF) is based can provide additional insights into the stress distributions.
Movassaghi, S, Nadia Sharifi, Z, Koosha, M, Abdollahifar, MA, Fathollahipour, S, Tavakoli, J & Abdi, S 2019, 'Effect of Honey/PVA Hydrogel Loaded by Erythromycin on Full-Thickness Skin Wound Healing in Rats; Stereological Study.', Galen Med J, vol. 8, p. e1362.
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BACKGROUND: Skin wounds are a significant public health risk, and treatment of wound remains a challenging clinical problem for medical teams and researchers. MATERIALS AND METHODS: In the present study, we aimed to investigate the healing effects of honey/polyvinyl alcohol (PVA) hydrogel loaded with erythromycin as wound dressing on skin wounds in rats, based on histological studies. In this study, 60 male Wistar rats, with a 1.5 ×1.5 cm2 diameter full-thickness wounds on the backs were divided into four groups: honey/PVA with the erythromycin hydrogel group, honey group, PVA group, and the control group, with no treatment. Skin biopsies were prepared at days 4, 7, and 14 for microscopic analyses. The stereological analysis, including the mean area of the wound, length of vessels, numerical density of fibroblast, macrophage, basal cell and volume of the epidermis, dermis, and fibrous tissue were performed. RESULTS: Wounds area in the honey/PVA hydrogel with the erythromycin group were significantly (P<0.05) smaller than in the other group. The numerical density of fibroblast, macrophage, basal cell and volume of the epidermis in the honey/PVA hydrogel with the erythromycin group were significantly higher than other groups. CONCLUSION: According to our results, honey/PVA hydrogel with erythromycin may promote early wound healing and has a positive influence on fibroblast proliferation and re-epithelialization, and its administration is recommended after further validation of clinical data.
Mukhtar, NM & Lu, DD-C 2019, 'A Bidirectional Two-Switch Flyback Converter With Cross-Coupled LCD Snubbers for Minimizing Circulating Current', IEEE Transactions on Industrial Electronics, vol. 66, no. 8, pp. 5948-5957.
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© 1982-2012 IEEE. This paper proposes a novel isolated bidirectional two-switch flyback converter with two integrated non-dissipative inductor-capacitor-diode (LCD) snubbers. In the proposed topology, the main flyback transformer and the LCD snubbers are cross coupled to minimize circulating current that would occur in the non-cross-coupled case, in addition to recycle leakage energy and protect the power transistors. The same current circulation issue also occurs in the bidirectional flyback converter with conventional resistor-capacitor-diode (RCD) snubbers. The main objective of this paper is to illustrate this issue and propose an alternate circuitry to reduce the current circulation and improve the conversion efficiency. The experimental results of a laboratory prototype are reported to verify the design.
Mulet-Forteza, C, Genovart-Balaguer, J, Mauleon-Mendez, E & Merigó, JM 2019, 'A bibliometric research in the tourism, leisure and hospitality fields', Journal of Business Research, vol. 101, pp. 819-827.
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© 2018 Elsevier Inc. This paper presents a study of the most cited papers, the most productive and influential institutions and countries, and the most influential authors in the tourism, leisure, and hospitality fields. The number of publications in journals focused on these areas has increased exponentially over the past 40 years. This paper examines the fundamental contributions in these areas using a bibliometric approach. This paper also uses the visualization of similarities to graphically map the main topics and keywords. No study has examined all journals indexed in the Web of Science in these fields over a period as wide as the one considered in this study. This study is valuable for several reasons. It can help scholars and researchers to identify the countries and institutions with the most potential to develop and share research, as well as where it would be interesting to carry out their doctoral studies and develop their careers.
Mulet-Forteza, C, Genovart-Balaguer, J, Merigó, JM & Mauleon-Mendez, E 2019, 'Bibliometric structure of IJCHM in its 30 years', International Journal of Contemporary Hospitality Management, vol. 31, no. 12, pp. 4574-4604.
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PurposeThe International Journal of Contemporary Hospitality Management is a leading international journal in the field of hospitality and tourism management. It was started in 1989, and it turns 30 years old this year. To celebrate this anniversary, this paper presents a bibliometric overview of the publication and citation structure of the journal over the past 30 years. The purpose of this paper is to identify the relevant issues in terms of keywords and topics and who is achieving better results in terms of authors, universities and countries.Design/methodology/approachThe Scopus database is used to collect the bibliographical material. A graphical mapping of the bibliographic data is developed by using VOSviewer software. It produces graphical maps with several bibliometric techniques, including co-citation, bibliographic coupling and co-occurrence of keywords.FindingsThe results indicate that English-speaking countries are producing the highest number of articles in the journal, followed by Asian institutions, with the Hong Kong Polytechnic University as the most productive institution.Originality/valueTo the best of the authors’ knowledge, there are no papers that present a general overview of the publication and citation structure of this journal. Its 30th anniversary is a good moment to develop this study.
Naderpour, M, Rizeei, HM, Khakzad, N & Pradhan, B 2019, 'Forest fire induced Natech risk assessment: A survey of geospatial technologies', Reliability Engineering & System Safety, vol. 191, pp. 106558-106558.
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© 2019 Elsevier Ltd Forest fires threaten a large part of the world's forests, communities, and industrial plants, triggering technological accidents (Natechs). Forest fire modelling with respect to contributing spatial parameters is one of the well-known ways not only to predict the fire occurrence in forests, but also to assess the risk of forest-fire-induced Natechs. This study is a review of methods based on geospatial information system (GIS) for modelling forest fires and their potential Natechs that have been implemented all over the world. The present study conducts a systematic literature review of the methods used for forest fire susceptibility, hazard, and risk assessment, while dividing them into four general categories: (a) statistical and data-driven models; (b) machine learning models; (c) multi-criteria decision-making models, and (d) ensemble models. In addition, some forest fire detection techniques using satellite imagery are reviewed. A comparison is also conducted to highlight the research gaps and required future research. The results of the present research assist decision makers to select the most appropriate techniques according to specific forest conditions. Results show that data-driven approaches are the most frequently applied methods while ensemble approaches are more accurate.
Nagy, Z, Kanikevich, M, Koach, J, Mayoh, C, Carter, D, Liu, T, Du, Y, Jiang, C, Haber, M, Norris, M, Cheung, B & Marshall, G 2019, 'Abstract 3659: Alyref is a novel binding partner and co-factor for MYCN-driven oncogenesis in neuroblastoma', Cancer Research, vol. 79, no. 13_Supplement, pp. 3659-3659.
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Abstract MYCN amplification is a poor prognostic indicator in neuroblastoma associated with high-risk disease. Therapies that directly repress the MYCN oncogenic signal in neuroblastoma are limited. We and others have shown that MYCN requires multiple cofactors to increase its protein stability in neuroblastoma cells, so that the very high MYCN levels required to drive tumourigenesis can be achieved. Here, we have identified ALYREF, a nuclear molecular chaperone protein, as a novel regulator of MYCN function in neuroblastoma. High expression of ALYREF predicted poor neuroblastoma patient survival and substantially correlated with MYCN levels in a large dataset (n=649) of human neuroblastoma tumour samples. ALYREF mRNA expression was also significantly increased in ganglia cells from the homozygous TH-MYCN neuroblastoma mouse in comparison to ganglia from wild-type littermates. Using co-immunoprecipitation and mass spectrometry, we identified ALYREF, as a direct binding partner of nuclear MYCN protein. Chromatin immunoprecipitation showed that MYCN bound the ALYREF gene promoter, and knockdown MYCN by MYCN siRNAs decreased ALYREF expression. A set of overexpression and knockdown experiments in MYCN-amplified neuroblastoma cells revealed that MYCN and ALYREF form a positive forward feedback expression loop. Overexpression of ALYREF further increased MYCN expression and protein stability in MYCN-amplified neuroblastoma cells. We found that ALYREF had a critical function in regulating the turnover of MYCN protein through transcriptional repression of the E3 protein ubiquitin ligase, NEDD4. The stabilized N-Myc oncoprotein enhanced ALYREF expression and stimulated cell growth of neuroblastoma. Additionally, we demonstrated that ALYREF plays a significant role in maintaining cell viability and proliferation of MYCN-amplified neuroblastoma cells. Taken together, our findings demonstrate a crucial role for ALYREF ...
Naidu, G, Ryu, S, Thiruvenkatachari, R, Choi, Y, Jeong, S & Vigneswaran, S 2019, 'A critical review on remediation, reuse, and resource recovery from acid mine drainage', Environmental Pollution, vol. 247, pp. 1110-1124.
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© 2019 Elsevier Ltd Acid mine drainage (AMD) is a global environmental issue. Conventionally, a number of active and passive remediation approaches are applied to treat and manage AMD. Case studies on remediation approaches applied in actual mining sites such as lime neutralization, bioremediation, wetlands and permeable reactive barriers provide an outlook on actual long-term implications of AMD remediation. Hence, in spite of available remediation approaches, AMD treatment remains a challenge. The need for sustainable AMD treatment approaches has led to much focus on water reuse and resource recovery. This review underscores (i) characteristics and implication of AMD, (ii) remediation approaches in mining sites, (iii) alternative treatment technologies for water reuse, and (iv) resource recovery. Specifically, the role of membrane processes and alternative treatment technologies to produce water for reuse from AMD is highlighted. Although membrane processes are favorable for water reuse, they cannot achieve resource recovery, specifically selective valuable metal recovery. The approach of integrated membrane and conventional treatment processes are especially promising for attaining both water reuse and recovery of resources such as sulfuric acid, metals and rare earth elements. Overall, this review provides insights in establishing reuse and resource recovery as the holistic approach towards sustainable AMD treatment. Finally, integrated technologies that deserve in depth future exploration is highlighted. Challenges associated with AMD can be sustainability addressed through integrated treatment approaches that attain both water reuse and valuable resource recovery.
Namisango, F & Kang, K 2019, 'Organization-public relationships on social media: The role of relationship strength, cohesion and symmetry', Computers in Human Behavior, vol. 101, pp. 22-29.
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© 2019 Elsevier Ltd This study investigated the role of three relationship properties embedded in organization-public relationships (OPR) built through social media interaction. We hypothesize that relationship strength, cohesion, and symmetry are positively related to three types of OPR, i.e., communal, exchange and symbiotic relationships. To test our hypotheses, we survey 73 nonprofit organizations and apply structural equation modelling to analyze hypothesized relationships. Results indicated that strong and cohesive interaction on social media is positively related to communal and symbiotic OPR, but not exchange OPR. On the other hand, symmetrical interaction is positively related to exchange OPR, but not communal and symbiotic OPR. Exchange OPR allows trading of benefits through social media interaction and this is positively related to symbiotic OPR. Understanding the association between the above relationship properties or patterns and OPR can assist organizations in developing strategic relational practices sufficient for organization-public interaction on social media.
Nanda, P, Puthal, D & Mohanty, SP 2019, 'Editorial to the Special Issue on Recent Advances on Trust, Security and Privacy in Computing and Communications', Concurrency and Computation: Practice and Experience, vol. 31, no. 23.
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Naqshbandi, K, Hoermann, S, Milne, D, Peters, D, Davies, B, Potter, S & Calvo, RA 2019, 'Codesigning technology for a voluntary-sector organization', Human Technology, vol. 15, no. 1, pp. 6-29.
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© 2019 Khushnood Naqshbandi, Simon Hoermann, David Milne, Dorian Peters, Benjamin Davies, Sophie Potter, & Rafael A. Calvo. This paper presents an investigation into the experiences and perceptions of volunteers and community managers of an Australian voluntary-sector organization that supports young help-seeking people. The process focused specifically on the design of a chat tool, a rudimentary version of which was conceptualized and tested during a trial completed prior to this study. The process explored the motivations and experiences of these volunteers using a codesign approach, which led to the development of specific features of the chat tool that were tailored to the nature of their work and organization, as well as the sector-specific ethos. We employed several research methods, which included interviews, focus groups, and participatory design workshops. Thematic analyses were performed on the resultant qualitative data. The methods, motivational themes, and the ensuing design solutions that were implemented are discussed in detail with the aim of encouraging codesign of technology for voluntary-sector organizations.
Nasir, AA, Tuan, HD, Duong, TQ & Debbah, M 2019, 'NOMA Throughput and Energy Efficiency in Energy Harvesting Enabled Networks', IEEE Transactions on Communications, vol. 67, no. 9, pp. 6499-6511.
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© 1972-2012 IEEE. An energy harvesting (EH) enabled network is capable of delivering energy to users, who are located sufficiently close to the base stations. However, wireless energy delivery requires much more transmit power than what the normal information delivery does. It is very challenging to provide the quality of wireless information and power delivery simultaneously. It is of practical interest to employ non-orthogonal multiple access (NOMA) to improve the network throughput, while fulfilling the EH requirements. To realize both the EH and information decoding, this paper considers a transmit time-switching (transmit-TS) protocol. Two important problems of users' max-min throughput optimization and energy efficiency maximization under power constraint and EH thresholds, which are non-convex in beamforming vectors, are addressed by efficient path-following algorithms. In addition, the conventional power splitting (PS)-based EH receiver is also considered. The provided numerical results confirm that the proposed transmit-TS-based algorithms clearly outperform the PS-based algorithms in terms of throughput and energy efficiency.
Nasir, AA, Tuan, HD, Duong, TQ & Poor, HV 2019, 'Improper Gaussian Signaling for Broadcast Interference Networks', IEEE Signal Processing Letters, vol. 26, no. 6, pp. 808-812.
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© 1994-2012 IEEE. For a multi-user multi-cell network, which suffers both intra-cell and inter-cell interference, this letter considers improper Gaussian signaling (IGS) as a means to improve the achievable rate. The problem of interest is designing of improper Gaussian signals' augmented covariance matrices to maximize the users' minimum rate subject to transmit power constraints. This problem is seen as a nonconvex matrix optimization problem, which cannot be solved by conventional techniques, such as weighted minimum mean square error minimization or alternating optimization. A path-following algorithm, which iterates a sequence of improved feasible points, is proposed for its computation. The provided simulation results for three cells serving 18 users show that IGS offers a much better max-min rate compared with that achieved by conventional proper Gaussian signaling. Another problem of maximizing the energy efficiency in IGS is also considered.
Nasir, AA, Tuan, HD, Duong, TQ & Poor, HV 2019, 'UAV-Enabled Communication Using NOMA', IEEE Transactions on Communications, vol. 67, no. 7, pp. 5126-5138.
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© 2019 IEEE. Unmanned aerial vehicles (UAVs) can be deployed as flying base stations (BSs) to leverage the strength of line-of-sight connections and effectively support the coverage and throughput of wireless communication. This paper considers a multiuser communication system, in which a single-antenna UAV-BS serves a large number of ground users by employing non-orthogonal multiple access (NOMA). The max-min rate optimization problem is formulated under total power, total bandwidth, UAV altitude, and antenna beamwidth constraints. The objective of max-min rate optimization is non-convex in all optimization variables, i.e., UAV altitude, transmit antenna beamwidth, power allocation, and bandwidth allocation for multiple users. A path-following algorithm is proposed to solve the formulated problem. Next, orthogonal multiple access (OMA) and dirty paper coding (DPC)-based max-min rate optimization problems are formulated and respective path-following algorithms are developed to solve them. The numerical results show that NOMA outperforms OMA and achieves rates similar to those attained by DPC. In addition, a clear rate gain is observed by jointly optimizing all the parameters rather than optimizing a subset of parameters, which confirms the desirability of their joint optimization.
Nasir, AA, Tuan, HD, Nguyen, HH & Nguyen, NM 2019, 'Physical Layer Security by Exploiting Interference and Heterogeneous Signaling', IEEE Wireless Communications, vol. 26, no. 5, pp. 26-31.
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© 2002-2012 IEEE. Physical layer security (PLS) aims to protect end users who are equipped with low-complexity receivers for which implementing cryptographic algorithms for security purposes is not practical. This article presents a different approach to PLS and suggests natural and simple ways to achieve security in wireless networks. A practical assumption on the availability of channel state information (CSI) is considered for eavesdroppers (EVs) at the transmitter. Moreover, there is no restriction on EVs' placement, and as such EVs could be in better channel conditions when they are closer to the transmitter. The article describes how the interference channels can be exploited to simultaneously reduce the interference for the users' received signals and amplify the interference at the EV's received signal. It is also shown that when considering communication with energy-constrained nodes, the heterogeneous nature of the transmitted signals is an asset and can be exploited to confuse the eavesdropper. This is because information and energy signals are transmitted over different fractions of a time slot, and the EV can be confused since it does not know the time-fraction when information signal or energy signal is transmitted. Finally, the article also suggests how we can achieve secure information transmission under poor scattering environments, such as unmanned-aerial-vehicle-enabled communications.
Nasruddin, Sholahudin, Satrio, P, Mahlia, TMI, Giannetti, N & Saito, K 2019, 'Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm', Sustainable Energy Technologies and Assessments, vol. 35, pp. 48-57.
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© 2019 Elsevier Ltd The optimization of heating, ventilating and air conditioning (HVAC) system operations and other building parameters intended to minimize annual energy consumption and maximize the thermal comfort is presented in this paper. The combination of artificial neural network (ANN) and multi-objective genetic algorithm (MOGA) is applied to optimize the two-chiller system operation in a building. The HVAC system installed in the building integrates radiant cooling system, variable air volume (VAV) chiller system, and dedicated outdoor air system (DOAS). Several parameters including thermostat setting, passive solar design, and chiller operation control are considered as decision variables. Subsequently, the percentage of people dissatisfied (PPD) and annual building energy consumption is chosen as objective functions. Multi-objective optimization is employed to optimize the system with two objective functions. As the result, ANN performed a good correlation between decision variables and the objective function. Moreover, MOGA successfully provides several alternative possible design variables to achieve optimum system in terms of thermal comfort and annual energy consumption. In conclusion, the optimization that considers two objectives shows the best result regarding thermal comfort and energy consumption compared to base case design.
Natgunanathan, I, Mehmood, A, Xiang, Y, Gao, L & Yu, S 2019, 'Location Privacy Protection in Smart Health Care System', IEEE Internet of Things Journal, vol. 6, no. 2, pp. 3055-3069.
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© 2014 IEEE. In a smart health system, patients' location information is periodically sent to hospitals and this information helps hospitals to provide improved health care services. The location information together with time stamp alone can reveal a patient's private information, such as person's life style, places frequently visited by the person, and personal interests. Thus, it is important to protect the location privacy of a patient. In the existing privacy protection mechanisms, trusted third party (TTP) and location perturbation techniques are used. However, in TTP-based mechanism, an adversary who illegally gets access to TTP server will have access to the private location information. On the other hand, in location perturbation technique, utility of the location information is significantly compromised. In this paper, we propose a location privacy protection mechanism in which location privacy is protected while maintaining the utility of the location data. In the proposed mechanism, a main processing unit attached to a patient's body generates the perturbed location by considering the distance between the patient's location and the preidentified patient's sensitive locations. This adaptive generation of perturbed location, removes the necessity to trust other parties while preserving the privacy and utility of the location data. The validity of the proposed mechanism is demonstrated by simulation results.
Nathan, K, Ghosh, S, Siwakoti, Y & Long, T 2019, 'A New DC–DC Converter for Photovoltaic Systems: Coupled-Inductors Combined Cuk-SEPIC Converter', IEEE Transactions on Energy Conversion, vol. 34, no. 1, pp. 191-201.
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© 1986-2012 IEEE. An enhanced DC-DC converter is proposed in this paper, based on the combination of the Cuk and SEPIC converters, which is well-suited for solar photovoltaic (PV) applications. The converter uses only one switch (which is ground-referenced, so simple gate drive circuitry may be used), yet provides dual outputs in the form of a bipolar DC bus. The bipolar output from the DC-DC converter is able to send power to the grid via any inverter with a unipolar or bipolar DC input, and leakage currents can be eliminated if the latter type is used without using lossy DC capacitors in the load current loop. The proposed converter uses integrated magnetics cores to couple the input and output inductors, which significantly reduces the input current ripple and hence greatly improves the power extracted from the solar PV system. The design methodology along with simulation, experimental waveforms, and efficiency measurements of a 4-kW DC-DC converter are presented to prove the concept of the proposed converter. Furthermore, a 1-kW inverter is also developed to demonstrate the converter's grid-connection potential.
Nathan, KS, Ghosh, SS, Tripathi, PR, Siwakoti, YP, Flack, TJ, Li, X & Long, T 2019, 'Benefits of the CI‐CCS converter', The Journal of Engineering, vol. 2019, no. 17, pp. 4527-4531.
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An enhanced DC–DC converter is proposed in this paper, based on the combination of the Cuk converter and single‐ended primary‐inductor converter (SEPIC). This converter uses a single switching node which is common to both Cuk and SEPIC energy transfer stages. The converter uses only one switch, yet provides dual outputs in the form of a bipolar DC bus with a common ground. Since the switch is grounded, a simple, non‐isolated gate driver may be used. The proposed converter uses integrated magnetic cores to couple the input and output inductors, which significantly reduces the input current ripple. The new converter is referred to as the Coupled‐Inductors Combined Cuk‐SEPIC (CI‐CCS) converter.
Nawaz, F, Hussain, O, Hussain, FK, Janjua, NK, Saberi, M & Chang, E 2019, 'Proactive management of SLA violations by capturing relevant external events in a Cloud of Things environment', Future Generation Computer Systems, vol. 95, pp. 26-44.
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© 2018 Elsevier B.V. The cloud of things (CoT) is an emerging paradigm that has merged and combined cloud computing and the Internet of Things (IoT). Such a paradigm has enabled service providers to provide on-demand computing resources from devices spread across different locations for service users to be dynamically connected to them. While this benefits the CoT service providers and users in many ways, it also brings a key challenge of ensuring that the service is delivered according to the promised quality. Failure to ensure this will result in the service provider experiencing penalties of different types and the service user experiencing disruptions. The literature addresses this problem by proactively managing for SLA violations. However, given the geographically dispersed region of a formed CoT service, in this paper we argue that for proactive SLA violation identification, we need specialized techniques that also consider events that are outside the usual control of service providers and users, but will impact the CoT environment and the quality of service. We propose a framework that identifies such external events of interest and ascertains their impact on achieving the service according to the promised quality. We explain the working of our proposed framework in detail and demonstrate its superiority in proactively determining SLA violations as compared to existing approaches.
Nazari, S, Chehreh Chelgani, S, Shafaei, SZ, Shahbazi, B, Matin, SS & Gharabaghi, M 2019, 'Flotation of coarse particles by hydrodynamic cavitation generated in the presence of conventional reagents', Separation and Purification Technology, vol. 220, pp. 61-68.
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Hydrodynamic cavitation (HC) (typically used to generate submicron bubbles) are frequently examined to improve froth flotation efficiency of ultrafine particles (−38 µm); however, the study of their effects on flotation parameters during the process of coarse particles (+100 µm) was not significantly explored. The main aim of this investigation is to discover the impacts of HC on effective flotation variables and flotation recovery of coarse particles (FRCP). Various surfactants (frothers: Methyl isobutyl carbinol (MIBC) and pine oil (PO), and dodecyl amine (DDA)) were used for the HC conditions. For comparison purposes, two series of flotation experiments in the absence and presence of HC were conducted by using coarse pure quartz particles (−425 + 106 µm). Variable importance measurements (VIMs) of random forest were applied to compare and assess impacts of flotation parameters (particle size, flotation conventional bubble (CB) size, impeller speed, and air flow rate) on FRCP in the absence and presence of HC. Outputs of VIMs indicated that the negative effect of particle size on FRCP was decreased and the capability of CB for floating coarse particles was improved in the presence of HC. Moreover, VIM results showed that in the presence of HC, the highest FRCP can be achieved when turbulent is lower. Generally, variations in the airflow rate had negligible impacts on FRCP. Flotation experiments suggested that HC in the presence of the collector can overcome the absence of frothers in a flotation system. These results can be used for enhancement of selective separation via froth flotation.
Nerse, C & Wang, S 2019, 'On the formation of complex modes in non-proportionally damped systems', Journal of Sound and Vibration, vol. 463, pp. 114978-114978.
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In the classical studies of non-proportionally damped systems, the resulting complex modal parameters are obtained by solving the generalized eigenvalue problem. In the present study, we propose a unique method to obtain complex modes for discrete and continuous systems. Based on a wave analogy, the difference between a complex mode and a real normal mode is represented by the summation of patterns that propagate from the boundaries. Owing to the spatial non-proportionality of the damping, these patterns undergo changes at a damping intersection. The governing equation for this phenomenon is expressed by Snell's law. We show that, in a similar manner to the refractive index for the medium in which light waves travel, a damping field index can be conceived for individual damping regions, such that they may be scaled against the damping field index of the undamped region, which is assumed to be unity. However, unlike the refractive index, we show that the damping field index is dependent on the spatial distribution of damping. The procedure for obtaining the complex modes is illustrated based on a plate structure with simply supported boundary conditions. The practical applications of the proposed approach and its limitations are discussed based on numerical examples.
Ngo, HH, Guo, W & Boopathy, R 2019, 'Editorial overview: Green technologies for environmental remediation', Current Opinion in Environmental Science & Health, vol. 12, pp. A1-A3.
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Ngo, TN, Indraratna, B & Rujikiatkamjorn, C 2019, 'Improved performance of ballasted tracks under impact loading by recycled rubber mats', Transportation Geotechnics, vol. 20, pp. 100239-100239.
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© 2019 Elsevier Ltd Ballasted tracks at transition locations such as approaches to bridges and road crossings experience increasing degradation and deformation due to dynamic and high impact forces, a key factor that decreases the stability and longevity of railroads. One solution to minimise ballast degradation at the transition zones is using rubber energy absorbing drainage sheets (READS)manufactured from recycled tyres. When placed beneath the ballast layer, READS distributes the load over wider area and attenuate of the load over a longer duration thus decreasing maximum stress, apart from reducing the energy transferred to the ballast and other substructure components. Subsequently, the track substructure experiences less plastic deformation and degradation. These mats also provide an environmentally friendly and cost-effective alternative. In this study, a series of large-scale drop hammer impact tests was carried out to investigate how effectively the READS could attenuate impact loads and help mitigate ballast deformation and degradation. Soft and stiff subgrade were used to investigate the load-deformation response of ballast (with and without READS), subjected to impact loads from a hammer dropped from various heights (hd = 100–250 mm). Laboratory test results show that the inclusion of READS helps to reduce the dynamic impact load transferred to the ballast layer resulting in significantly less permanent deformation and degradation of ballast, apart from significant attenuation of load magnitude and vibration to the underlying subgrade layers.
Ngoc, TP, Fatahi, B & Khabbaz, H 2019, 'Impacts of Drying-Wetting and Loading-Unloading Cycles on Small Strain Shear Modulus of Unsaturated Soils', International Journal of Geomechanics, vol. 19, no. 8, pp. 04019090-04019090.
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© 2019 American Society of Civil Engineers. The small strain shear modulus (Gmax) is an important parameter in geodynamic problems. To predict the Gmax of unsaturated soils that are normally subjected to complex drying and wetting processes, the effect of hydraulic hysteresis needs to be evaluated. Although several equations have been proposed in recent years, limitations still exist, requiring more research studies in this field. In this study, Gmax was investigated in a multistage test during several drying-wetting cycles and a loading-unloading cycle of net stress. The results revealed four key factors that directly influence the magnitude of Gmax: the void ratio, net stress, matric suction, and degree of saturation. Although variations of the void ratio, net stress, and matric suction cause persistent responses of Gmax (i.e., if all other factors remain unchanged, Gmax would then be reversely proportional to the void ratio and directly proportional to the net stress and matric suction), variations in the degree of saturation result in different responses. A decrease in the degree of saturation may induce a reduction or growth of Gmax because, on the one hand, it reduces the effect of matric suction, whereas on the other hand, it increases the total effect of van der Waals attractions and electric double-layer repulsions. At the same stress state, a reverse trend, induced by an increase in the degree of saturation, will occur with a growth in the effect of matric suction and a reduction in the combined effect of van der Waals attractions and electric double-layer repulsions. An analysis of the results showed that hydraulic hysteresis occurred in all the stress loops, and it directly influenced the response of Gmax. The effect of hydraulic hysteresis can only be captured if the van der Waals attractions and electric double-layer repulsions are considered. A model to estimate Gmax while incorporating the van der Waals attractions and electric double-la...
Nguyen, AQ, Nguyen, LN, Phan, HV, Galway, B, Bustamante, H & Nghiem, LD 2019, 'Effects of operational disturbance and subsequent recovery process on microbial community during a pilot-scale anaerobic co-digestion', International Biodeterioration & Biodegradation, vol. 138, pp. 70-77.
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© 2019 This study investigated changes in microbial community structure and composition in response to operational disturbance and subsequent process recovery by inoculum addition. Amplicon sequencing of 16S rRNA and mcrA marker genes on the Illumina Miseq platform was used for microbial community analysis. The results show that imbalance among core microbial groups caused volatile fatty acid accumulation and subsequent deteriorated biogas production (decreased by 45% of daily volume) and methane content (<49%). Operational disturbance led to the enrichment of hydrolytic and fermentative bacteria (accounted for >57% of the total abundance) and reduction of acetogenic and methanogenic microbes (they accounted for <9% and <3% of the total abundance, respectively). Acetogens and methanogens were replenished by inoculum addition to recover digester performance. Although digester performances were similar in stable (prior to disturbance) and post recovery phases, the microbial community did not return to the original state, suggesting the existence of functional redundancy in the community.
Nguyen, C & Hoang, D 2019, 'S-MANAGE Protocol for Provisioning IoT Applications on Demand', Journal of Telecommunications and the Digital Economy, vol. 7, no. 3, pp. 37-57.
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Internet of Things (IoT)-based services have started making an impact in various domains, such as agriculture, smart farming, smart cities, personal health, and critical infrastructures. Sensor/IoT devices have become one of the indispensable elements in these IoT systems and services. However, their development is restricted by the rigidity of the current network infrastructure, which accommodates heterogeneous physical devices. Software-Defined Networking-Network Functions Virtualization (SDN-NFV) has emerged as a service-enabling solution, supporting network and network function programmability. Provisioning IoT applications on demand is a natural application of programmability. However, these technologies cannot be directly deployed in the sensing/monitoring domain due to the differences in the functionality of SDN network devices and sensor/IoT devices, as well as the limitation of resources in IoT devices. This paper proposes an S-MANAGE protocol that preserves the SDN-NFV paradigm but provides a practical solution in controlling and managing IoT resources for provisioning IoT applications on demand. S-MANAGE is proposed as a new southbound protocol between the software-defined IoT controller and its IoT elements. The paper presents the design of S-MANAGE and demonstrates its use in provisioning IoT services dynamically.
Nguyen, CT, Hoang, DT, Nguyen, DN, Niyato, D, Nguyen, HT & Dutkiewicz, E 2019, 'Proof-of-Stake Consensus Mechanisms for Future Blockchain Networks: Fundamentals, Applications and Opportunities', IEEE Access, vol. 7, pp. 85727-85745.
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© 2013 IEEE. The rapid development of blockchain technology and their numerous emerging applications has received huge attention in recent years. The distributed consensus mechanism is the backbone of a blockchain network. It plays a key role in ensuring the network's security, integrity, and performance. Most current blockchain networks have been deploying the proof-of-work consensus mechanisms, in which the consensus is reached through intensive mining processes. However, this mechanism has several limitations, e.g., energy inefficiency, delay, and vulnerable to security threats. To overcome these problems, a new consensus mechanism has been developed recently, namely proof of stake, which enables to achieve the consensus via proving the stake ownership. This mechanism is expected to become a cutting-edge technology for future blockchain networks. This paper is dedicated to investigating proof-of-stake mechanisms, from fundamental knowledge to advanced proof-of-stake-based protocols along with performance analysis, e.g., energy consumption, delay, and security, as well as their promising applications, particularly in the field of Internet of Vehicles. The formation of stake pools and their effects on the network stake distribution are also analyzed and simulated. The results show that the ratio between the block reward and the total network stake has a significant impact on the decentralization of the network. Technical challenges and potential solutions are also discussed.
Nguyen, D, vanSonnenberg, E, Kang, P & Mueller, PR 2019, 'Urologic and interventional radiology treatment of renal cell carcinomas—similarities and differences', Annals of Translational Medicine, vol. 7, no. S3, pp. S113-S113.
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Nguyen, DD, Jeon, B-H, Jeung, JH, Rene, ER, Banu, JR, Ravindran, B, Vu, CM, Ngo, HH, Guo, W & Chang, SW 2019, 'Thermophilic anaerobic digestion of model organic wastes: Evaluation of biomethane production and multiple kinetic models analysis', Bioresource Technology, vol. 280, pp. 269-276.
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© 2019 Elsevier Ltd The main aim of this work was to test various organic wastes, i.e. from a livestock farm, a cattle slaughterhouse and agricultural waste streams, for its ability to produce methane under thermophilic anaerobic digestion (AD) conditions. The stability of the digestion, potential biomethane production and biomethane production rate for each waste were assessed. The highest methane yield (110.83 mL CH4/g VSadded day) was found in the AD of crushed animal carcasses on day 4. The experimental results were analyzed using four kinetic models and it was observed that the Cone model described the biomethane yield as well as the methane production rate of each substrate. The results from this study showed the good potential of model organic wastes to produce biomethane.
Nguyen, HH, Khabbaz, H & Fatahi, B 2019, 'A numerical comparison of installation sequences of plain concrete rigid inclusions', Computers and Geotechnics, vol. 105, pp. 1-26.
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© 2018 Elsevier Ltd Soil displacement induced when installing controlled modulus columns (CMC) as ground reinforcement could affect the columns installed close by. Realising numerical analyses may provide useful insights, this paper describes a numerical approach to investigate how groups of CMC installed in different sequences could affect columns installed previously. Coupled consolidation analyses in large strain mode and incorporating soil-CMC interaction were carried out using the three-dimensional finite difference software package FLAC3D. The CMCs were modelled using advanced non-linear Hoek-Brown material with a tensile yield criterion while soils with a typical profile were characterised using the modified Cam Clay and the elastic-perfectly plastic material with a Mohr-Coulomb yield criterion. Where possible, the predicted responses of ground surrounding the CMCs were compared to a number of existing analytical methods. Predictions revealed that lateral soil movement and soil heave near existing CMCs induced by installing new CMCs towards the existing CMCs were approximately 15% and 25% greater than corresponding predictions when a reverse installation sequence was adopted. The maximum excess pore water pressures, induced near existing columns due to installing new columns towards the existing ones, were almost twice more than those caused by the reverse sequence of installation. Moreover, the predicted bending moments generated in the existing columns induced by installing new columns towards the existing CMCs were almost 22% greater than the corresponding values when the reverse installation sequence was adopted. This shows the importance of selecting an appropriate installation sequence in the CMC construction process as well as considering the initial stress field and bending moments in the surrounding soil and CMCs, respectively when designing embankments on improved soft soils.
Nguyen, HT, Tuan, HD, Duong, TQ, Poor, HV & Hwang, W-J 2019, 'Collaborative Multicast Beamforming for Content Delivery by Cache-Enabled Ultra Dense Networks', IEEE Transactions on Communications, vol. 67, no. 5, pp. 3396-3406.
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© 1972-2012 IEEE. Caching and multicast have surged as effective tools to alleviate the heavy load from the backhaul links while enabling content-centric delivery in communication networks. The main focus of work in this area has been on the cache placements to manage the network delay and backhaul transmission cost. An important issue of optimizing the cost efficiency in content delivery has not been addressed. This paper tackles this issue by proposing collaborative multicast beamforming in cache-enabled ultra-dense networks. The objective is to maximize the cost efficiency, which is defined as the ratio of the content throughput to the sum of power consumption and backhaul cost, in providing quality-of-service for content delivery. Zero-forcing beamforming and generalized zero-forcing beamforming are employed to force the multi-content interference to zero or mitigate it while amplifying the desired signals for users. These problems of collaborative multicast beamforming design are computationally difficult. Path-following algorithms, which invoke a simple convex quadratic program at each iteration, are developed for their solution. Numerical results are provided to demonstrate the computational efficiency of the proposed algorithms and also give insights into the impact of caching on the cost efficiency.
Nguyen, HTH, Sakakibara, M, Nguyen, MN, Mai, NT & Nguyen, VT 2019, 'Effect of Dissolved Silicon on the Removal of Heavy Metals from Aqueous Solution by Aquatic Macrophyte Eleocharis acicularis', Water, vol. 11, no. 5, pp. 940-940.
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Silicon (Si) has been recently reconsidered as a beneficial element due to its direct roles in stimulating the growth of many plant species and alleviating metal toxicity. This study aimed at validating the potential of an aquatic macrophyte Eleocharis acicularis for simultaneous removal of heavy metals from aqueous solutions under different dissolved Si. The laboratory experiments designed for determining the removal efficiencies of heavy metals were conducted in the absence or presence of Si on a time scale up to 21 days. Eleocharis acicularis was transplanted into the solutions containing 0.5 mg L−1 of indium (In), gallium (Ga), silver (Ag), thallium (Tl), copper (Cu), zinc (Zn), cadmium (Cd), and lead (Pb) with various Si concentrations from 0 to 4.0 mg L−1. The results revealed that the increase of dissolved Si concentrations enhanced removal efficiencies of E. acicularis for Ga, Cu, Zn, Cd, and Pb, while this increase did not show a clear effect for In, Tl, and Ag. Our study presented a notable example of combining E. acicularis with dissolved Si for more efficient removals of Cu, Zn, Cd, Pb, and Ga from aqueous solutions. The findings are applicable to develop phytoremediation or phytomining strategy for contaminated environment.
Nguyen, HV, Nguyen, V-D, Dobre, OA, Nguyen, DN, Dutkiewicz, E & Shin, O-S 2019, 'Joint Power Control and User Association for NOMA-Based Full-Duplex Systems', IEEE Transactions on Communications, vol. 67, no. 11, pp. 8037-8055.
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This paper investigates the coexistence of non-orthogonal multiple access(NOMA) and full-duplex (FD) to improve both spectral efficiency (SE) and userfairness. In such a scenario, NOMA based on the successive interferencecancellation technique is simultaneously applied to both uplink (UL) anddownlink (DL) transmissions in an FD system. We consider the problem of jointlyoptimizing user association (UA) and power control to maximize the overall SE,subject to user-specific quality-of-service and total transmit powerconstraints. To be spectrally-efficient, we introduce the tensor model tooptimize UL users' decoding order and DL users' clustering, which results in amixed-integer non-convex problem. For practically appealing applications, wefirst relax the binary variables and then propose two low-complexity designs.In the first design, the continuous relaxation problem is solved using theinner convex approximation framework. Next, we additionally introduce thepenalty method to further accelerate the performance of the former design. Fora benchmark, we develop an optimal solution based on brute-force search (BFS)over all possible cases of UAs. It is demonstrated in numerical results thatthe proposed algorithms outperform the conventional FD-based schemes and itshalf-duplex counterpart, as well as yield data rates close to those obtained byBFS-based algorithm.
Nguyen, K-D & Liu, D 2019, 'Gibbon-inspired Robust Asymmetric Brachiation along an Upward Slope', International Journal of Control, Automation and Systems, vol. 17, no. 10, pp. 2647-2654.
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© 2019, ICROS, KIEE and Springer. This paper investigates the robust control of an underactuated brachiating robot. The control schemes are motivated by the applications that require robots to move through lattice structures, such as the inspection and maintenance of power transmission lines and towers. Inspired by the pendulum-like movements that enable gibbons' arboreal locomotion, the controllers are designed to synchronize the brachiator with a virtual oscillator. Two controllers are proposed: a model-dependent feedback linearization scheme and a sliding-mode scheme that is independent of the system model. These controllers are tasked to drive a robotic brachiator in two cases with different geometries: symmetric geometry, where its links have equal lengths, and asymmetric geometry, where its links have different lengths. The numerical results illustrate that the proposed schemes are robust to the arbitrary initial conditions of the brachiator, the motor torque limitation at the elbow joint, as well as the geometry of the brachiator. Furthermore, they are able to perform successful fast swing-up and dynamic brachiating along a structural member with an upward slope in a unified control framework for both symmetric and asymmetric geometries.
Nguyen, KT, Nguyen, HM, Truong, CK, Ahmed, MB, Huang, Y & Zhou, JL 2019, 'Chemical and microbiological risk assessment of urban river water quality in Vietnam', Environmental Geochemistry and Health, vol. 41, no. 6, pp. 2559-2575.
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© 2019, Springer Nature B.V. Abstract: The contamination and risk by nutrients (NH4+, NO2−, NO3− and PO43−), COD, BOD5, coliform and potentially toxic elements (PTEs) of As, Cd, Ni, Hg, Cu, Pb, Zn and Cr were investigated in urban river (Nhue River), Vietnam during 2010–2017. The extensive results demonstrated that concentrations of these contaminants showed significant spatial and temporal variations. The Nhue River was seriously polluted by NH4+ (0.025–11.28 mg/L), PO43− (0.17–1.72 mg/L), BOD5 (5.8–179.6 mg/L), COD (1.4–239.8 mg/L) and coliform (1540–326,470 CFU/100 mL); moderately polluted by As (0.2–131.15 μg/L) and Hg (0.11–4.1 μg/L); and slightly polluted by NO2− (0.003–0.33 mg/L) and Cd (2.1–18.2 μg/L). The concentrations of NH4+, PO43−, COD, BOD5 and coliform frequently exceeded both drinking water guidelines and irrigation water standards. Regarding PTEs, As, Cd and Hg concentrations were frequently higher than the regulatory limits. Human health risks of PTEs were evaluated by estimating hazard index (HI) and cancer risk through ingestion and dermal contacts for adults and children. The findings indicated that As was the most important pollutant causing both non-carcinogenic and carcinogenic concerns. The non-carcinogenic risks of As were higher than 1.0 at all sites for both adults (HI = 1.83–7.4) and children (HI = 2.6–10.5), while As posed significant carcinogenic risks for adults (1 × 10−4−4.96 × 10−4). A management strategy for controlling wastewater discharge and protecting human health is urgently needed. Graphical abstract: [Figure not available: see fulltext.]
Nguyen, L, Valls Miro, J & Qiu, X 2019, 'Multilevel B-Splines-Based Learning Approach for Sound Source Localization', IEEE Sensors Journal, vol. 19, no. 10, pp. 3871-3881.
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© 2001-2012 IEEE. In this paper, a new learning approach for sound source localization is presented using ad hoc either synchronous or asynchronous distributed microphone networks based on the time differences of arrival (TDOA) estimation. It is first to propose a new concept in which the coordinates of a sound source location are defined as the functions of TDOAs, computing for each pair of microphone signals in the network. Then, given a set of pre-recorded sound measurements and their corresponding source locations, the multilevel B-splines-based learning model is proposed to be trained by the input of the known TDOAs and the output of the known coordinates of the sound source locations. For a new acoustic source, if its sound signals are recorded, the correspondingly computed TDOAs can be fed into the learned model to predict the location of the new source. Superiorities of the proposed method are to incorporate the acoustic characteristics of a targeted environment and even remaining uncertainty of TDOA estimations into the learning model before conducting its prediction and to be applicable for both synchronous or asynchronous distributed microphone sensor networks. The effectiveness of the proposed algorithm in terms of localization accuracy and computational cost in comparisons with the state-of-the-art methods was extensively validated on both synthetic simulation experiments as well as in three real-life environments.
Nguyen, LD, Tuan, HD, Duong, TQ & Poor, HV 2019, 'Multi-User Regularized Zero-Forcing Beamforming', IEEE Transactions on Signal Processing, vol. 67, no. 11, pp. 2839-2853.
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© 1991-2012 IEEE. Regularized zero-forcing beamforming (RZFB) is an interesting class of linear signal processing problems, which is very attractive for use in large-scale communication networks due its simple visualization as a straightforward extension of the well-accepted zero-forcing beamforming (ZFB). However, unlike ZFB, which is multi-user interference free, RZFB must manage multi-user interference to achieve its high throughput performance. Most existing works focus on the performance analysis of particular RZBF schemes such as the equip-power allocated RZBF under a fixed regularization parameter. This paper is the first work to consider the joint design of power allocation and regularization parameter for RZFB to maximize the worst users' throughput or the quality-of-service awarded energy efficiency under a fixed transmit power constraint. Such designs pose very computationally challenging optimization problems, for which the paper proposes two-stage optimization algorithms of low computational complexity. Their computational and performance efficiencies are substantiated through numerical examples.
Nguyen, LN, Commault, AS, Johir, MAH, Bustamante, H, Aurisch, R, Lowrie, R & Nghiem, LD 2019, 'Application of a novel molecular technique to characterise the effect of settling on microbial community composition of activated sludge', Journal of Environmental Management, vol. 251, pp. 109594-109594.
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Activated sludge (AS) and return activated sludge (RAS) microbial communities from three full-scale municipal wastewater treatment plants (denoted plant A, B and C) were compared to assess the impact of sludge settling (i.e. gravity thickening in the clarifier) and profile microorganisms responsible for nutrient removal and reactor foaming. The results show that all three plants were dominated with microbes in the phyla of Proteobacteria, Bacteroidetes, Verrucomicrobia, Actinobacteria, Chloroflexi, Firmicutes, Nitrospirae, Spirochaetae, Acidobacteria and Saccharibacteria. AS and RAS shared above 80% similarity in the microbial community composition, indicating that sludge thickening does not significantly alter the microbial composition. Autotrophic and heterotrophic nitrifiers were present in the AS. However, the abundance of autotrophic nitrifiers was significantly lower than that of the heterotrophic nitrifiers. Thus, ammonium removal at these plants was achieved mostly by heterotrophic nitrification. Microbes that can cause foaming were at 3.2% abundance, and this result is well corroborated with occasional aerobic biological reactor foaming. By contrast, these microbes were not abundant (<2.1%) at plant A and C, where aerobic biological reactor foaming has not been reported.
Nguyen, LN, Johir, MAH, Commault, A, Bustamante, H, Aurisch, R, Lowrie, R & Nghiem, LD 2019, 'Impacts of mixing on foaming, methane production, stratification and microbial community in full-scale anaerobic co-digestion process', Bioresource Technology, vol. 281, pp. 226-233.
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© 2019 Elsevier Ltd This study investigated the impact of mixing on key factors including foaming, substrate stratification, methane production and microbial community in three full scale anaerobic digesters. Digester foaming was observed at one plant that co-digested sewage sludge and food waste, and was operated without mixing. The lack of mixing led to uneven distribution of total chemical oxygen demand (tCOD) and volatile solid (VS) as well as methane production within the digester. 16S rRNA gene-based community analysis clearly differentiated the microbial community from the top and bottom. By contrast, foaming and substrate stratification were not observed at the other two plants with internal circulation mixing. The abundance of methanogens (Methanomicrobia) at the top was about four times higher than at the bottom, correlating to much higher methane production from the top verified by ex-situ biomethane assay, causing foaming. This result is consistent with foaming potential assessment of digestate samples from the digester.
Nguyen, LN, Labeeuw, L, Commault, AS, Emmerton, B, Ralph, PJ, Johir, MAH, Guo, W, Ngo, HH & Nghiem, LD 2019, 'Validation of a cationic polyacrylamide flocculant for the harvesting fresh and seawater microalgal biomass', Environmental Technology & Innovation, vol. 16, pp. 100466-100466.
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© 2019 Elsevier B.V. A simple, efficient, and fast settling flocculation technique to harvest microalgal biomass was demonstrated using a proprietary cationic polyacrylamide flocculant for a freshwater (Chlorella vulgaris) and a marine (Phaeodactylum tricornutum) microalgal culture at their mid-stationary growth phase. The optimal flocculant doses were 18.9 and 13.7 mg/g of dry algal biomass for C. vulgaris and P. tricornutum, respectively (equivalent to 7 g per m3 of algal culture for both species). The obtained optimal dose was well corroborated with changes in cell surface charge, and culture solution optical density and turbidity. At the optimal dose, charge neutralization of 64 and 86% was observed for C. vulgaris and P. tricornutum algal cells, respectively. Algae recovery was independent of the culture solution pH in the range of pH 6 to 9. Algal biomass recovery was achieved of 100 and 90% for C vulgaris and P. tricornutum respectively, and over 98% medium recovery was achievable by simple decanting.
Nguyen, LN, Nghiem, LD, Pramanik, BK & Oh, S 2019, 'Cometabolic biotransformation and impacts of the anti-inflammatory drug diclofenac on activated sludge microbial communities', Science of The Total Environment, vol. 657, pp. 739-745.
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This study evaluated the removal of diclofenac (DCF) in activated sludge and its long-term exposure effects on the function and structure of the microbial community. Activated sludge could remove <50% of 50 μg/L DCF. The removal decreased significantly to below 15% when DCF concentrations increased to 500 and 5000 μg/L. Quantitative assessment of the fate of DCF showed that its main removal routes were biodegradation (21%) and adsorption (7%), with other abiotic removals being insignificant (<5%). The biodegradation occurred through cometabolic mechanisms. DCF exposure in the range of 50–5000 μg/L did not disrupt the major functions of the activated sludge ecosystem (e.g. biomass yield and heterotrophic activity) over two months of DCF exposure. Consistently, 16S rRNA gene-based community analysis revealed that the overall community diversity (e.g. species richness and diversity) and structure of activated sludge underwent no significant alterations. The analysis did uncover a significant increase in several genera, Nitratireductor, Asticcacaulis, and Pseudacidovorax, which gained competitive advantages under DCF exposure. The enrichment of Nitratireductor, Asticcacaulis, and Pseudacidovorax genus might contribute to DCF biodegradation and emerge as a potential microbial niche for the removal of DCF.
Nguyen, LN, Nguyen, AQ, Johir, MAH, Guo, W, Ngo, HH, Chaves, AV & Nghiem, LD 2019, 'Application of rumen and anaerobic sludge microbes for bio harvesting from lignocellulosic biomass', Chemosphere, vol. 228, pp. 702-708.
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This study investigated the production of biogas, volatile fatty acids (VFAs), and other soluble organic from lignocellulosic biomass by two microbial communities (i.e. rumen fluid and anaerobic sludge). Four types of abundant lignocellulosic biomass (i.e. wheat straw, oaten hay, lurence hay and corn silage) found in Australia were used. The results show that rumen microbes produced four-time higher VFAs level than that of anaerobic sludge reactors, indicating the possible application of rumen microorganism for VFAs generation from lignocellulosic biomass. VFA production in the rumen fluid reactors was probably due to the presence of specific hydrolytic and acidogenic bacteria (e.g. Fibrobacter and Prevotella). VFA production corroborated from the observation of pH drop in the rumen fluid reactors indicated hydrolytic and acidogenic inhibition, suggesting the continuous extraction of VFAs from the reactor. Anaerobic sludge reactors on the other hand, produced more biogas than that of rumen fluid reactors. This observation was consistent with the abundance of methanogens in anaerobic sludge inoculum (3.98% of total microbes) compared to rumen fluid (0.11%). VFA production from lignocellulosic biomass is the building block chemical for bioplastic, biohydrogen and biofuel. The results from this study provide important foundation for the development of engineered systems to generate VFAs from lignocellulosic biomass.
Nguyen, LT, Pham, VN, Chau, PMN, Ho-Pham, LT & Nguyen, TV 2019, 'Association between carotid intima-media thickness and bone mineral density: a cross-sectional study in Vietnamese men and women aged 50 years and older', BMJ Open, vol. 9, no. 9, pp. e028603-e028603.
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ObjectivesThe association between osteoporosis and atherosclerosis remains controversial. We sought to define the relationship between carotid intima-media thickness and bone mineral density (BMD) in individuals of Vietnamese background.Design and settingCross-sectional study in Ho Chi Minh City, Vietnam.ParticipantsThe study involved 1460 individuals (559 men) aged 50 years and older (average age 59 years) who were randomly recruited from the community.Outcome measuresBMD at the femoral neck and lumbar spine was measured by dual-energy X-ray absorptiometry (Hologic, Waltham, Massachusetts, USA). Carotid intima-media thickness (cIMT) was measured using a Philips Ultrasonography (HD7XE). The presence of atherosclerotic plaque was ascertained for each individual. The association between cIMT and BMD was analysed by a multiple linear regression model.ResultsIn unadjusted analysis, cIMT was positively associated with femoral neck BMD in men (p=0.005), but not in women (p=0.242). After adjusting for age, smoking, diabetes and hypertension, the association remained statistically significant in men (partial R2=0.005; p=0.015) but not in women (partial R2=0.008; p=0.369). When the analysis was limited to individuals aged 60 years and older, the association between cIMT and BMD was no longer statistically significant. There was no statistically significant association between cIMT and lumbar spine BMD in either men or women.ConclusionsIn Vietnamese individuals aged 50 years and older, there is a clinically non-significant but statistically significant association betwee...
Nguyen, LTN, Eager, D & Nguyen, H 2019, 'The relationship between compression garments and electrocardiogram signals during exercise and recovery phase', BioMedical Engineering OnLine, vol. 18, no. 1, pp. 27-27.
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© 2019 The Author(s). Background: The direction of the current research was to investigate whether electrocardiogram (ECG) signals have been impacted by using compression garments during exercise and recovery phase. Each subject is non-athletes, conducted two running tests, wearing either non-compression garments (NCGs) or compression garments (CGs) throughout experiments and 2-h of the recovery phase. Experiment 1 (number of participants (n) = 8; 61.4 ± 13.7 kg, 25.1 ± 3.8 years, 165.9 ± 8.3 cm) focused on the exercising phase while Experiment 2 (n = 14; 60.9 ± 12.0 kg, 24.7 ± 4.5 years, 166.0 ± 7.6 cm) concentrated on the recovery phase. Electrocardiogram (ECG) data were collected through wearable biosensors. Results: The results demonstrated a significant difference between compression garments and non-compression garments at the end of the tests and from 90 min onwards during the recovery phase (p < 0.05). Corrected QT (QTc), ST interval and heart rate (HR) indicated the significant difference between NCGs and CGs. Conclusion: Based on the findings, the utilization of compression garments showed a positive influence in non-athletes based on the quicker recovery in HR, ST, and QTc.
Nguyen, M-N, Nguyen, LD, Duong, TQ & Tuan, HD 2019, 'Real-Time Optimal Resource Allocation for Embedded UAV Communication Systems', IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 225-228.
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© 2012 IEEE. We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.
Nguyen, PTK, Tran, HT, Fitzgerald, DA, Tran, TS, Graham, SM & Marais, BJ 2019, 'Characterisation of children hospitalised with pneumonia in central Vietnam: a prospective study', European Respiratory Journal, vol. 54, no. 1, pp. 1802256-1802256.
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Pneumonia is the most common reason for paediatric hospital admission in Vietnam. The potential value of using the World Health Organization (WHO) case management approach in Vietnam has not been documented.We performed a prospective descriptive study of all children (2–59 months) admitted with “pneumonia” (as assessed by the admitting clinician) to the Da Nang Hospital for Women and Children to characterise their disease profiles and assess risk factors for an adverse outcome. The disease profile was classified using WHO pneumonia criteria, with tachypnoea or chest indrawing as defining clinical signs. Adverse outcome was defined as death, intensive care unit admission, tertiary care transfer or hospital stay >10 days.Of 4206 admissions, 1758 (41.8%) were classified as “no pneumonia” using WHO criteria and only 252 (6.0%) met revised criteria for “severe pneumonia”. The inpatient death rate was low (0.4% of admissions) with most deaths (11 out of 16; 68.8%) occurring in the “severe pneumonia” group. An adverse outcome was recorded in 18.7% of all admissions and 60.7% of the “severe pneumonia” group. Children were hospitalised for a median of 7 days at an average cost of 253 USD per admission. Risk factors for adverse outcome included WHO-classified “severe pneumonia”, age <1 year, low birth weight, previous recent admission with an acute respiratory infection and recent tuberculosis exposure. Breastfeeding, day-care attendance and pre-admission antibiotic use were associated with reduced risk.Few hospital admissions met WHO criteria for “severe pneumonia”, suggesting potential unnecessary hospitalisation and use of intravenous antibiotics. Better characterisation of the underlying diagnosis requires careful consideration.
Nguyen, QD, Khan, MSH, Castel, A & Kim, T 2019, 'Durability and Microstructure Properties of Low-Carbon Concrete Incorporating Ferronickel Slag Sand and Fly Ash', Journal of Materials in Civil Engineering, vol. 31, no. 8, pp. 04019152-04019152.
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© 2019 American Society of Civil Engineers. Ferronickel slag (FNS) which is also known as electric arc furnace slag is a byproduct of the production of ferronickel alloy. The production of FNS at Société Le Nickel (SLN) in New Caledonia is about 2 Mt per year with an existing stockpile of 25 Mt, which presents an excellent potential for concrete applications in the Pacific region. The possibility of using FNS from SLN as fine aggregate replacement in concrete is investigated. The low-carbon-concrete mix design includes 50% natural sand replacement by FNS sand and 25% ordinary portland cement substitution by fly ash. Microstructural analysis by scanning electron microscopy - energy dispersive X-ray spectrometer (SEM-EDS) of the interface transition zone (ITZ) of FNS sand shows that the excess in Portlandite weakening the ITZ of natural aggregate is absent in FNS sand ITZ. As a result, the resistance against chemically aggressive ions diffusion, water absorption, sorptivity, bulk and surface resistivity, and volume of permeable voids are significantly improved compared with the reference concretes due to the pozzolanic effect of FNS strengthening the ITZ. The substitution of 50% natural sand by FNS sand allows offsetting the detrimental effect of using fly ash on the concrete resistance against carbonation. All results show that using FNS sand in concrete can improve the concrete performance.
Nguyen, QD, Khan, MSH, Xu, T & Castel, A 2019, 'Mitigating the Risk of Early Age Cracking in Fly Ash Blended Cement-Based Concrete Using Ferronickel Slag Sand', Journal of Advanced Concrete Technology, vol. 17, no. 6, pp. 295-308.
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Copyright © 2019 Japan Concrete Institute. A concrete mix (FNS25) including 50% natural sand replacement by ferronickel slag (FNS) sand and 25% ordinary portland cement (OPC) substitution by fly ash (FA) was considered to mitigate the risk of early-age cracking in fly ash blended cement-based concrete. Experiments were carried out to accurately quantify early-age shrinkage and tensile creep and assess their influence on early-age cracking in reinforced concrete members. The results show the free shrinkage strain is not influenced by either fly ash or FNS significantly, whereas the tensile creep of FNS25 is significantly larger than that of both OPC100 and FA20. Both restrained ring test and simulations on reinforced concrete members confirm that partly replacing conventional sand by FNS sand reduces the risk of early-age cracking. Microstructural analysis of the Interface Transition Zone (ITZ) of FNS sand shows that excess in Portlandite is absent in FNS sand ITZ leading to a higher early-age tensile strength of FNS25 concrete.
Nguyen, TKL, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Nghiem, LD, Liu, Y, Ni, B & Hai, FI 2019, 'Insight into greenhouse gases emissions from the two popular treatment technologies in municipal wastewater treatment processes', Science of The Total Environment, vol. 671, pp. 1302-1313.
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© 2019 Elsevier B.V. Due to the impact of methane, carbon dioxide and nitrous oxide on global warming, the quantity of these greenhouse gases (GHG) emissions from municipal wastewater treatment plants (WWTPs) has attracted more and more attention. Consequently, GHG emissions from the two popular treatment technologies: anaerobic/anoxic/oxic (AAO) process and sequencing batch reactor (SBR) should be properly identified and discussed toward the current situation in developing countries. Direct and indirect carbon dioxide (with and/or without including in Intergovernmental Panel on Climate Change (IPCC) report) are all discussed in this article. This literature study observed that a quantity of total carbon dioxide emissions from SBR (374 g/m3 of wastewater) was double that of AAO whilst 10% of these was direct carbon dioxide. Methane emitted from an SBR was 0.50 g/m3 wastewater while 0.18 g CH4/m3 wastewater was released from an AAO. The level of nitrous oxide from AAO and SBR accounted for 0.97 g/m3 wastewater and 4.20 g/m3 wastewater, respectively. Although these results were collected from different WWTPs and where influent was in various states, GHGs emitted from both biological units and other treatment units in various processes are significant. The results also revealed that aerated zone is the major contributing factor in a wastewater treatment plant to the large amount of GHG emissions.
Nguyen, TN, Yu, Y, Li, J, Gowripalan, N & Sirivivatnanon, V 2019, 'Elastic modulus of ASR-affected concrete: An evaluation using Artificial Neural Network', Computers and Concrete, vol. 24, no. 6, pp. 541-553.
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Alkali-silica reaction (ASR) in concrete can induce degradation in its mechanical properties, leading to compromised serviceability and even loss in load capacity of concrete structures. Compared to other properties, ASR often affects the modulus of elasticity more significantly. Several empirical models have thus been established to estimate elastic modulus reduction based on the ASR expansion only for condition assessment and capacity evaluation of the distressed structures. However, it has been observed from experimental studies in the literature that for any given level of ASR expansion, there are significant variations on the measured modulus of elasticity. In fact, many other factors, such as cement content, reactive aggregate type, exposure condition, additional alkali and concrete strength, have been commonly known in contribution to changes of concrete elastic modulus due to ASR. In this study, an artificial intelligent model using artificial neural network (ANN) is proposed for the first time to provide an innovative approach for evaluation of the elastic modulus of ASR-affected concrete, which is able to take into account contribution of several influence factors. By intelligently fusing multiple information, the proposed ANN model can provide an accurate estimation of the modulus of elasticity, which shows a significant improvement from empirical based models used in current practice. The results also indicate that expansion due to ASR is not the only factor contributing to the stiffness change, and various factors have to be included during the evaluation.
Nguyen, TT & Indraratna, B 2019, 'Micro-CT Scanning to Examine Soil Clogging Behavior of Natural Fiber Drains', Journal of Geotechnical and Geoenvironmental Engineering, vol. 145, no. 9, pp. 04019037-04019037.
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© 2019 American Society of Civil Engineers. The use of jute and coir fibers as natural fiber drains to facilitate drainage and soft soil stabilization has been proposed for decades. However, their uncertain hydraulic behavior has often hampered their wider application in major infrastructure projects. Because these drains have a complex porous structure that can trap soil particles and reduce their discharge capacity, a comprehensive laboratory investigation in which soft soil was used to interact with different fiber drains under varying confining pressure was conducted via a discharge capacity test scheme. Nondestructive micro-computed tomography (CT) scanning followed by a series of image processing techniques was applied to the drains to capture their three-dimensional porous characteristics, which were then used to clarify their hydraulic behavior. The study revealed that there are two major types of components - intra- and interbundle voids - making porosity in a fiber drain, and they can be used to evaluate the drain discharge capacity. The larger the interbundle porosity, the higher the drain discharge capacity. Jute filters not only enlarge the interbundle porosity but also - if they are thick enough - help drains resist undue lateral pressure and clogging. Fiber drains are more sensitive to confinement than polymeric drains, because their discharge capacity decreases considerably at higher confining pressures. This study enables the hydraulic properties of natural fiber drains subjected to soil clogging to be properly understood so that drain designs can be optimized to make them more competitive with conventional polymeric drains.
Nguyen, T-T, Bui, X-T, Dang, B-T, Ngo, H-H, Jahng, D, Fujioka, T, Chen, S-S, Dinh, Q-T, Nguyen, C-N & Nguyen, P-T-V 2019, 'Effect of ciprofloxacin dosages on the performance of sponge membrane bioreactor treating hospital wastewater', Bioresource Technology, vol. 273, pp. 573-580.
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© 2018 Elsevier Ltd This study aimed to evaluate treatment performance and membrane fouling of a lab-scale Sponge-MBR under the added ciprofloxacin (CIP) dosages (20; 50; 100 and 200 µg L−1) treating hospital wastewater. The results showed that Sponge-MBR exhibited effective removal of COD (94–98%) during the operation period despite increment of CIP concentrations from 20 to 200 µg L−1. The applied CIP dosage of 200 µg L−1 caused an inhibition of microorganisms in sponges, i.e. significant reduction of the attached biomass and a decrease in the size of suspended flocs. Moreover, this led to deteriorating the denitrification rate to 3–12% compared to 35% at the other lower CIP dosages. Importantly, Sponge-MBR reinforced the stability of CIP removal at various added CIP dosages (permeate of below 13 µg L−1). Additionally, the fouling rate at CIP dosage of 200 µg L−1 was 30.6 times lower compared to the control condition (no added CIP dosage).
Nguyen, TT, Indraratna, B, Kelly, R, Phan, NM & Haryono, F 2019, 'Mud pumping under railtracks: Mechanisms, assessments and solutions', Australian Geomechanics Journal, vol. 54, no. 4, pp. 59-80.
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Mud pumping under railway tracks has received increasing attention from academic and practical perspectives in recent decades, however, the actual mechanisms and possible solutions are still not understood or well established. Frequent investigations in countries such as Japan, Canada, the USA, China, Australia, the UK, and other European regions where railway systems are the largest and most advanced, indicate that mud pumping still leads to high annual maintenance costs. On this basis, a thorough review is therefore essential, so this paper presents a systematic and comprehensive review of mud pumping in railways. In particular three primary aspects of mud pumping are addressed: (i) the phenomena and mechanisms; (ii) assessments; and (iii) solutions. The review shows the three essential factors that trigger mud pumping, i.e., excess fines, excess water, and cyclic loads. While excess fines can be induced by subgrade fluidisation, ballast breakdown and external sources, the excess water is mainly due to insufficient drainage in the foundations. Given these 3 factors, different contexts where mud pumping can be instigated are summarised such as subgrade fluidisation and infiltration, peat boils from soft roadbeds and upward migration of non-subgrade fines. Unfavourable weather condition, poor sleeper-ballast contact and stress/strain concentration at particular sections such as rail joints, switches, crossings and transition zones can accelerate the inception of mud pumping. In all cases, the generation of excess pore pressure is the driving mechanism. The study also summarises the laboratory and in-situ techniques currently used to assess mud pumping. 4 major groups of mud pumping solutions are highlighted with their advantages and disadvantages: (1) clean, modify and renew problematic layers; (2) enhance drainage condition; (3) geosynthetics; and (4) chemical stabilisations.
Nguyen, TT, Ngo, HH, Guo, W, Wang, XC, Ren, N, Li, G, Ding, J & Liang, H 2019, 'Implementation of a specific urban water management - Sponge City', Science of The Total Environment, vol. 652, pp. 147-162.
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© 2018 Elsevier B.V. Climate change, rapid urbanization and inappropriate urban planning policies in many countries have resulted in urban water-related problems, such as flooding disasters, water pollution and water shortages. To tackle these issues, the specific urban water management strategy known as Sponge City has been implemented in China since 2013. This is a complex method and one involving many challenges. This paper critically assesses the approaches associated with conventional urban water management. The Sponge City concept and its adoption are then scrutinized to comprehensively assess the limitations and opportunities. It emerges that Sponge City has four main principles, these being: urban water resourcing, ecological water management, green infrastructures, and urban permeable pavement. The uncertainties in Sponge City design and planning, and financial insufficiencies are the most serious problems that can risk the failure of the Sponge City concept. While significant barriers exist, the opportunities for implementing a Sponge City are evident. To obtain multi-ecosystem services of Sponge City, it should be implemented at the watershed scales and be flexible, depending on different decision levels or catchment characteristics. It is essential to apply an intelligent decision-making mechanism and consider the need for close cooperation between various agencies with which the central government can work. A suitable sized and harmonious Sponge City, ensuring a good balance between socio-economic development and environmental conservation, is the ideal.
Nguyen, TV, Rivadeneira, F & Civitelli, R 2019, 'New Guidelines for Data Reporting and Statistical Analysis: Helping Authors With Transparency and Rigor in Research', Journal of Bone and Mineral Research, vol. 34, no. 11, pp. 1981-1984.
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Nguyen, XC, Chang, SW, Tran, TCP, Nguyen, TTN, Hoang, TQ, Banu, JR, Al-Muhtaseb, AH, La, DD, Guo, W, Ngo, HH & Nguyen, DD 2019, 'Comparative study about the performance of three types of modified natural treatment systems for rice noodle wastewater', Bioresource Technology, vol. 282, pp. 163-170.
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© 2019 Elsevier Ltd In this study, three semi-pilot scale systems (vertical flow constructed wetland, multi-soil layering, and integrated hybrid systems) for treating real rice noodle wastewater were operated parallelly for the first time in a tropical climate at a loading rate of 50 L/(m2·d) for more than 7 months to determine the optimal conditions and to compare their treatment performance. The results demonstrated that these systems were appropriate for the removal of organics, suspended solids, and total coliform (Tcol). The highest reductions in chemical oxygen demand (CODCr, 73.2%), phosphorus (PO4-P, 54%), and Tcol (4.78 log MPN/100 mL inactivation) were obtained by the integrated hybrid system, while the highest removal efficiencies of ammonium (NH4-N, 60.64%) and suspended solids (80.49%) were achieved in the vertical-flow-constructed wetland and multi-soil layering systems respectively.
Ni, B-J, Huang, Q-S, Wang, C, Ni, T-Y, Sun, J & Wei, W 2019, 'Competitive adsorption of heavy metals in aqueous solution onto biochar derived from anaerobically digested sludge', Chemosphere, vol. 219, pp. 351-357.
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© 2018 Elsevier Ltd Heavy metals often coexist in contaminated wastewater systems and their competitive behavior could affect the adsorption capacity of biochar. Till now, the competitive adsorption of heavy metals by biochar derived from anaerobically digested sludge has never been reported. In this work, biochar from anaerobically digested sludge was synthesized and characterized to explore the competitive behavior of widely co-existed Pb(II) and Cd(II). The mutual effects and inner mechanisms of their adsorption on studied biochar were systematically investigated via single-metal and binary-metals systems. In single-metal system, the biochar exhibited much higher adsorption capacity for Pb(II) compared to that for Cd(II). The maximum adsorption capacities of Pb(II) and Cd(II) based on single-component adsorption isotherm were 0.75 and 0.55 mmoL/g, respectively, which were much higher than those reported biochars from different materials. In binary-metals system, the Cd(II) adsorption on biochar was severely inhibited, while the uptake of Pb(II) was not affected significantly. The results of binary-components adsorption isotherm clearly demonstrated the competitive adsorption between two metals occurred as well as the preference of biochar for Pb(II) compared to Cd(II). FTIR and metal characteristics analysis results revealed that Pb(II) had exactly the same adsorption sites with Cd(II), but Pb(II) has a greater affinity than Cd(II), thereby exhibiting a competitive advantage in the coexisting system.
Ni, B-J, Yan, X, Sun, J, Chen, X, Peng, L, Wei, W, Wang, D, Mao, S, Dai, X & Wang, Q 2019, 'Persulfate and zero valent iron combined conditioning as a sustainable technique for enhancing dewaterability of aerobically digested sludge', Chemosphere, vol. 232, pp. 45-53.
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© 2019 Elsevier Ltd Aerobic digestion followed by dewatering is a widely applied method for sludge stabilization and reduction in decentralized wastewater treatment plants. It is important to enhance the sludge dewaterability of the aerobically digested sludge due to its considerable impact on cost of sludge disposal and management. In this study, an innovative technique is developed for improving the dewaterability of aerobically digested sludge by combined conditioning with persulfate (PS) and zero valent iron (ZVI). The results demonstrated that the dewaterability of aerobically digested sludge could be significantly enhanced with the PS and ZVI dosage in the range of 0–0.5 g/gTS and 0–0.4 g/gTS, respectively. The highest improvement was achieved at 0.05 g ZVI/g TS with 0.1 g PS/g TS, and the capillary suction time was reduced by ∼80%. The extracellular polymeric substances (EPS) characterization revealed that the combined PS-ZVI treatment could largely reduce proteins, polysaccharides and humic acids-like compounds in the tightly bounded EPS of the aerobically digested sludge, leading to bound water releasing from sludge flocs. The recovery of the ZVI particles could reach around 45%–80% after the treatment, further proved the sustainability of the approach. The proposed PS-ZVI conditioning would not have significant impact on the final choice of sludge disposal and the mainstream wastewater treatment. However, plant-scale test are still required for better assessing the proposed technique.
Ni, W, Li, C, Zang, X, Xu, M, Huo, S, Liu, M, Yang, Z & Yan, Y-M 2019, 'Efficient electrocatalytic reduction of CO2 on CuxO decorated graphene oxides: an insight into the role of multivalent Cu in selectivity and durability', Applied Catalysis B: Environmental, vol. 259, pp. 118044-118044.
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Ni, W, Li, Y, Cai, L, Dong, C, Fang, H, Chen, Y, Li, H, Yao, M & Xiao, N 2019, 'SUMOylation is required for PIPK1γ‐driven keratinocyte migration and growth', The FEBS Journal, vol. 286, no. 23, pp. 4709-4720.
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PIPKIγ, a key member of the type I phosphatidylinositol 4‐phosphate kinase (PIPKI) family that regulates the spatial‐temporal generation of PIP2, has been implicated in diverse biological processes including cell survival, cell polarity, and cell migration. An essential role of PIPKIγ in tumor cells and nerve cells has been established in previous studies. However, the function and regulatory mechanism of PIPKIγ remains incompletely understood. Here, we showed that PIPKIγ can specifically associate with the SUMO‐conjugating (E2) enzyme UBC9 and can be SUMOylated both in vivo and in vitro. We further identified that Lys‐591 is the critical SUMO‐acceptor site of PIPKIγ and that SUMO conjugation at this site is required for PIPKIγ‐driven keratinocyte migration and growth. Mechanistically, SUMOylation deficiency significantly disrupts PIPKIγ‐mediated generation of intracellular PIP2, rather than the subcellular translocation and protein stability of PIPKIγ. Our findings reveal that PIPKIγ is a novel SUMOylation target and highlight the essential role of PIPKIγ SUMOylation in human keratinocyte function, providing an important orientation for in‐depth study of wound repair.
Nicolas, C, Valenzuela-Fernandez, L & Merigó, JM 2019, 'Mapping retailing research with bibliometric indicators', Journal of Promotion Management, vol. 25, no. 5, pp. 664-680.
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© 2019, © 2019 Taylor & Francis Group, LLC. Our study aims to give a global perspective regarding scientific research on retailing for the 1990–2014 period. The research shows a knowledge-domain-map that identifies the collaboration networks between authors and the links between journals. This was conducted through a bibliometric study that can be viewed with Visualization of similarities (VOS) viewer software. The results show that the Journal of Retailing and Management Science is the current leader in the field. In addition, Morgan and Hunt’s (1994) article in the Journal of Marketing is the most cited source to date.
Nie, W-B, Xie, G-J, Ding, J, Lu, Y, Liu, B-F, Xing, D-F, Wang, Q, Han, H-J, Yuan, Z & Ren, N-Q 2019, 'High performance nitrogen removal through integrating denitrifying anaerobic methane oxidation and Anammox: from enrichment to application', Environment International, vol. 132, pp. 105107-105107.
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© 2019 The Authors Integrating denitrifying anaerobic methane oxidation (DAMO) with Anammox provides alternative solutions to simultaneously remove nitrogen and mitigate methane emission from wastewater treatment. However, the practical application of DAMO has been greatly limited by slow-growing DAMO microorganisms living on low-solubility gaseous methane. In this work, DAMO and Anammox co-cultures were fast enriched using high concentration of mixed sludges from various environments, and achieved nitrogen removal rate of 76.7 mg NH4+-N L−1 d−1 and 87.9 mg NO3−-N L−1 d−1 on Day 178. Subsequently, nitrogen removal rate significantly decreased but recovered quickly through increasing methane flushing frequency, indicating methane availability could be the limiting factor of DAMO activity. Thus, this work developed a novel Membrane Aerated Membrane Bioreactor (MAMBR), which equipped with gas permeable membrane for efficient methane delivery and ultrafiltration membrane for complete biomass retention. After inoculated with enriched sludge, nitrogen removal rates of MAMBR were significantly enhanced to 126.9 mg NH4+-N L−1 d−1 and 158.8 mg NO3−-N L−1 d−1 by membrane aeration in batch test. Finally, the MAMBR was continuously fed with synthetic wastewater containing ammonium and nitrite to mimic the effluent from partial nitritation. When steady state with nitrogen loading rate of 2500 mg N L−1 d−1 was reached, the MAMBR achieved total nitrogen removal of 2496.7 mg N L−1 d−1, with negligible nitrate in effluent (~6.5 mg NO3−-N L−1). 16S rRNA amplicon sequencing and fluorescence in situ hybridization revealed the microbial community dynamics during enrichment and application. The high performance of nitrogen removal (2.5 kg N m−3 d−1) within 200 days operation and excellent biomass retention capacity (8.67 kg VSS m−3) makes the MAMBR promising for practical application of DAMO and Anammox in wastewater treatment.
Nie, X, Wang, L, Ding, H & Xu, M 2019, 'Strawberry Verticillium Wilt Detection Network Based on Multi-Task Learning and Attention', IEEE Access, vol. 7, pp. 170003-170011.
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© 2013 IEEE. Plant disease detection has an inestimable effect on plant cultivation. Accurate detection of plant disease can control the spread of disease early and prevent unnecessary loss. Strawberry verticillium wilt is a soil-borne, multi-symptomatic disease. To detect strawberry verticillium wilt accurately, we first propose a disease detection network based on Faster R-CNN and multi-task learning to detect strawberry verticillium wilt. Then, the strawberry verticillium wilt detection network (SVWDN), which uses attention mechanisms in the feature extraction of the disease detection network, is proposed. SVWDN detects verticillium wilt according to the symptoms of detected plant components (i.e.,young leaves and petioles). Compared with other existing methods for detecting disease from the whole plant appearance, the SVWDN automatically classifies the petioles and young leaves while determining whether the strawberry has verticillium wilt. To provide a dataset for evaluating and testing our method, we construct a large dataset that contains 3, 531 images with 4 categories (Healthy-leaf, Healthy-petiole, Verticillium-leaf and Verticillium-petiole). Each image also has a label to indicate whether the strawberry is suffering from verticillium wilt. With the proposed strawberry verticillium wilt detection network, we achieved a mAP of 77.54% on object detection of 4 categories and 99.95% accuracy for strawberry verticillium wilt detection.
Nikolay, N, Mendelson, N, Sadzak, N, Böhm, F, Tran, TT, Sontheimer, B, Aharonovich, I & Benson, O 2019, 'Very Large and Reversible Stark-Shift Tuning of Single Emitters in Layered Hexagonal Boron Nitride', Physical Review Applied, vol. 11, no. 4.
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© 2019 American Physical Society. Combining solid-state single-photon emitters (SPEs) with nanophotonic platforms is a key goal in integrated quantum photonics. In order to realize functionality in potentially scalable elements, suitable SPEs have to be bright, stable, and widely tunable at room temperature. In this work, we show that selected SPEs embedded in a few-layer hexagonal boron nitride (h-BN) meet these demands. In order to show the wide tunability of these SPEs we employ an atomic force microscope (AFM) with a conductive tip to apply an electrostatic field to individual h-BN emitters sandwiched between the tip and an indium-tin-oxide-coated glass slide. A very large and reversible Stark shift of (5.5±0.3)nm at a zero-field wavelength of 670 nm is induced by applying just 20 V, which exceeds the typical resonance linewidths of nanodielectric and even nanoplasmonic resonators. Our results help to further understand the physical origin of SPEs in h-BN as well as for practical quantum photonic applications where wide spectral tuning and on/off resonance switching are required.
Nimbalkar, S, Pain, A, Ahmad, SM & Chen, Q 2019, 'Stability Assessment of Earth Retaining Structures under Static and Seismic Conditions', Infrastructures, vol. 4, no. 2, pp. 15-15.
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An accurate estimation of static and seismic earth pressures is extremely important in geotechnical design. The conventional Coulomb’s approach and Mononobe-Okabe’s approach have been widely used in engineering practice. However, the latter approach provides the linear distribution of seismic earth pressure behind a retaining wall in an approximate way. Therefore, the pseudo-dynamic method can be used to compute the distribution of seismic active earth pressure in a more realistic manner. The effect of wall and soil inertia must be considered for the design of a retaining wall under seismic conditions. The method proposed considers the propagation of shear and primary waves through the backfill soil and the retaining wall due to seismic excitation. The crude estimate of finding the approximate seismic acceleration makes the pseudo-static approach often unreliable to adopt in the stability assessment of retaining walls. The predictions of the active earth pressure using Coulomb theory are not consistent with the laboratory results to the development of arching in the backfill soil. A new method is proposed to compute the active earth pressure acting on the backface of a rigid retaining wall undergoing horizontal translation. The predictions of the proposed method are verified against results of laboratory tests as well as the results from other methods proposed in the past.
Nimbalkar, SS, Punetha, P, Basack, S & Mirzababaei, M 2019, 'Piles Subjected to Torsional Cyclic Load: Numerical Analysis', Frontiers in Built Environment, vol. 5.
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Pile foundations supporting large structures (such as high-rise buildings, oil drilling platforms, bridges etc). are often subjected to eccentric lateral load (in addition to the vertical loads) due to the action of wind, waves, high speed traffic, and ship impacts etc. The eccentric lateral load, which is usually cyclic (repetitive) in nature, induces torsion in the pile foundation. This paper presents a numerical model based on boundary element approach to study the performance of a single pile subjected to the torsional cyclic load. The model is initially validated by comparing it with the experimental data available from the literature. Thereafter, the model has been utilized to conduct a parametric study to understand the influence of the torsional cyclic loading parameters on the axial pile capacity. The results indicated that the model is able to capture the degradation in the axial pile capacity due to the torsional cyclic loading with a reasonable accuracy. Moreover, the parametric study showed that the frequency, amplitude and number of cycles play a significant role in the torsional cyclic response of the pile. The present study is essential for the development of design guidelines for pile foundations subjected to torsional cyclic load.
Niu, K, Zhao, X, Li, F, Li, N, Peng, X & Chen, W 2019, 'UTSP: User-Based Two-Step Recommendation With Popularity Normalization Towards Diversity and Novelty', IEEE Access, vol. 7, pp. 145426-145434.
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© 2013 IEEE. Information technologies such as e-commerce and e-news bring overloaded information as well as convenience to users, cooperatives and companies. Recommender system is a significant technology in solving this information overload problem. Due to the outstanding accuracy performance in top-N recommendation tasks, two-step recommendation algorithms are suitable to generate recommendations. However, their recommendation lists are biased towards popular items. In this paper, we propose a user based two-step recommendation algorithm with popularity normalization to improve recommendation diversity and novelty, as well as two evaluation metrics to measure diverse and novel performance. Experimental results demonstrate that our proposed approach significantly improves the diversity and novelty performance while still inheriting the advantage of two-step recommendation approaches on accuracy metrics.
Niu, Q, Xu, Q, Wang, Y, Wang, D, Liu, X, Liu, Y, Wang, Q, Ni, B-J, Yang, Q, Li, X & Li, H 2019, 'Enhanced hydrogen accumulation from waste activated sludge by combining ultrasonic and free nitrous acid pretreatment: Performance, mechanism, and implication', Bioresource Technology, vol. 285, pp. 121363-121363.
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© 2019 Elsevier Ltd This study presents a novel and effective method, i.e., adding nitrite into acidic fermentations after ultrasonic (US) pretreatment to form free nitrous acid (FNA), to further enhance hydrogen yield. Experimental results showed that when 180 mg/L nitrite was added into the US (2 W/mL, 15 min) pretreated waste activated sludge (WAS), the maximal hydrogen yield of 24.81 ± 1.24 mL/g VSS (volatile suspended solids) was obtained under acidic fermentation (1.0 mg/L FNA was initially formed under this condition), which was 2.21-folds (or 1.36-folds) of that from US pretreatment (or FNA treatment) alone. This combination approach caused a positive synergy on sludge disintegration and enhanced the transformation of the released organics from non-biodegradable to biodegradable. Further study showed that the inhibiting effect of this combination method on hydrogen consuming microorganism was severer. Considering its pollution free, this combination strategy is an attractive technology for hydrogen recovery from WAS.
Niu, Y, Cao, R, Wang, H, Li, C, Zhou, M, Guo, Y, Wang, B, Yan, P & Xiang, J 2019, 'Permutation Fuzzy Entropy—An Index for the Analysis of Epileptic Electroencephalogram', Journal of Medical Imaging and Health Informatics, vol. 9, no. 3, pp. 637-645.
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Nizami, MSH, Haque, ANMM, Nguyen, PH & Hossain, MJ 2019, 'On the application of Home Energy Management Systems for power grid support', Energy, vol. 188, pp. 116104-116104.
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© 2019 Elsevier Ltd Home Energy Management Systems (HEMSs) are being implemented for residential energy management in various parts of the world. Conventionally, a HEMS is developed from the consumer's perspective, with the principal aim of cost-saving while maintaining optimal consumers' comfort. In recent years, various Demand Response programs are being incorporated into HEMSs to address the power grid constraints. In this paper, the functionality of grid support through the HEMSs is presented. The developed scheme utilizes an agent-based coordination mechanism in an active distribution network and manages the household appliances to comply with thermal and voltage constraints of the grid. The proposed mechanism is evaluated through simulation of a typical Dutch low-voltage (LV) residential feeder. A hardware prototype has also been developed and tested in the laboratory environment. The proposed methodologies show promising perspectives for local voltage-violation support and direct load control for congestion management of the grid.
Nohani, E, Moharrami, M, Sharafi, S, Khosravi, K, Pradhan, B, Pham, BT, Lee, S & M. Melesse, A 2019, 'Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models', Water, vol. 11, no. 7, pp. 1402-1402.
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Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the ...
Noori, AM, Pradhan, B & Ajaj, QM 2019, 'Dam site suitability assessment at the Greater Zab River in northern Iraq using remote sensing data and GIS', Journal of Hydrology, vol. 574, pp. 964-979.
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Noori, L, Pour, A, Askari, G, Taghipour, N, Pradhan, B, Lee, C-W & Honarmand, M 2019, 'Comparison of Different Algorithms to Map Hydrothermal Alteration Zones Using ASTER Remote Sensing Data for Polymetallic Vein-Type Ore Exploration: Toroud–Chahshirin Magmatic Belt (TCMB), North Iran', Remote Sensing, vol. 11, no. 5, pp. 495-495.
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Polymetallic vein-type ores are important sources of precious metal and a principal type of orebody for various base-metals. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping hydrothermal alteration zones associated with epithermal polymetallic vein-type mineralization in the Toroud–Chahshirin Magmatic Belt (TCMB), North of Iran. The TCMB is the largest known goldfield and base metals province in the central-north of Iran. Propylitic, phyllic, argillic, and advanced argillic alteration and silicification zones are typically associated with Au-Cu, Ag, and/or Pb-Zn mineralization in the TCMB. Specialized image processing techniques, namely Selective Principal Component Analysis (SPCA), Band Ratio Matrix Transformation (BRMT), Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) were implemented and compared to map hydrothermal alteration minerals at the pixel and sub-pixel levels. Subtle differences between altered and non-altered rocks and hydrothermal alteration mineral assemblages were detected and mapped in the study area. The SPCA and BRMT spectral transformation algorithms discriminated the propylitic, phyllic, argillic and advanced argillic alteration and silicification zones as well as lithological units. The SAM and MTMF spectral mapping algorithms detected spectrally dominated mineral groups such as muscovite/montmorillonite/illite, hematite/jarosite, and chlorite/epidote/calcite mineral assemblages, systematically. Comprehensive fieldwork and laboratory analysis, including X-ray diffraction (XRD), petrographic study, and spectroscopy were conducted in the study area for verifying the remote sensing outputs. Results indicate several high potential zones of epithermal polymetallic vein-type mineralization in the northeastern and southwestern parts of the study area, which can be considered for future systematic exploration programs. The appr...
Noushini, A, Castel, A & Gilbert, RI 2019, 'Creep and shrinkage of synthetic fibre-reinforced geopolymer concrete', Magazine of Concrete Research, vol. 71, no. 20, pp. 1070-1082.
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This paper presents the results of an investigation on the use of synthetic polypropylene (PP) and polyolefin (PO) fibres to improve the creep and shrinkage performance of low-calcium fly ash-based geopolymer concrete. Three PP fibres of 18, 19 and 51 mm length and two PO fibres of 48 and 55 mm length with a volume fraction of 0·5% were added to geopolymer concrete. The mechanical properties of the resulting material, such as compressive and splitting tensile strength, modulus of elasticity and modulus of rupture, have been studied. The drying shrinkage and creep of plain and fibre-reinforced geopolymer concrete were examined for a period of 1 year. The results revealed that the inclusion of PP and PO fibres in a volume fraction of 0·5% decreased the drying shrinkage and increased the compressive creep of fly ash-based geopolymer concrete at both early ages and in the long term.
Nur, T, Loganathan, P, Ahmed, MB, Johir, MAH, Nguyen, TV & Vigneswaran, S 2019, 'Removing arsenic from water by coprecipitation with iron: Effect of arsenic and iron concentrations and adsorbent incorporation', Chemosphere, vol. 226, pp. 431-438.
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© 2019 Elsevier Ltd Arsenic (As) contamination of drinking water is a major cause of As toxicity in many parts of the world. A study was conducted to evaluate As removal from water containing 100–700 μg/L of As and As to Fe concentration ratios of 1:5–1:1000 using the coprecipitation process with and without As/Fe adsorption onto granular activated carbon (GAC). Fe concentration required to reduce As concentrations in order to achieve the WHO standard level of 10 μg/L increased exponentially with the increase in initial As concentration. When small amounts of GAC were added to the As/Fe solutions the Fe required to remove these As concentrations reduced drastically. This decline was due to the GAC adsorption of Fe and As, enhancing the removal of these metals through coprecipitation. Predictive regression equations were developed relating the GAC dose requirement to the initial As and Fe concentrations. Zeta potential data revealed that As was adsorbed on the GAC by outer-sphere complexation whereas Fe was adsorbed by inner-sphere complexation reversing the negative charge on GAC to positive values. X-ray diffraction of the GAC samples in the presence of Fe had an additional peak characteristic of ferrihydrite (Fe oxide) compared to that of the GAC sample without Fe. The study showed that incorporating an adsorbent into the coprecipitation process has the advantage of removing As from waters at all concentrations of Fe and As compared to coprecipitation alone which does not remove As to the required levels if Fe concentration is low.
Nurfahmi, Mofijur, M, Ong, HC, Jan, BM, Kusumo, F, Sebayang, AH, Husin, H, Silitonga, AS, Mahlia, TMI & Rahman, SMA 2019, 'Production Process and Optimization of Solid Bioethanol from Empty Fruit Bunches of Palm Oil Using Response Surface Methodology', Processes, vol. 7, no. 10, pp. 715-715.
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This study aimed to observe the potential of solid bioethanol as an alternative fuel with high caloric value. The solid bioethanol was produced from liquid bioethanol, which was obtained from the synthesis of oil palm empty fruit bunches (PEFBs) through the delignification process by using organosolv pretreatment and enzymatic hydrolysis. Enzymatic hydrolysis was conducted using enzyme (60 FPUg−1 of cellulose) at a variety of temperatures (35 °C, 70 °C, and 90 °C) and reaction times (2, 6, 12, 18, and 24 h) in order to obtain a high sugar yield. The highest sugars were yielded at the temperature of 90 °C for 48 h (152.51 mg/L). Furthermore, fermentation was conducted using Saccharomyces cerevisiae. The bioethanol yield after fermentation was 62.29 mg/L. Bioethanol was extracted by distillation process to obtain solid bioethanol. The solid bioethanol was produced by using stearic acid as the additive. In order to get high-quality solid bioethanol, the calorific value was optimized using the response surface methodology (RSM) model. This model provided the factor variables of bioethanol concentration (vol %), stearic acid (g), and bioethanol (mL) with a minus result error. The highest calorific value was obtained with 7 g stearic acid and 5 mL bioethanol (43.17 MJ/kg). Burning time was tested to observe the quality of the solid bioethanol. The highest calorific value resulted in the longest burning time. The solid bioethanol has a potential as solid fuel due to the significantly higher calorific value compared to the liquid bioethanol.
Nuvoli, S, Hernandez, A, Esperança, C, Scateni, R, Cignoni, P & Pietroni, N 2019, 'QuadMixer: layout preserving blending of quadrilateral meshes.', ACM Trans. Graph., vol. 38, no. 6, pp. 180:1-180:1.
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© 2019 Copyright held by the owner/author(s). We propose QuadMixer, a novel interactive technique to compose quad mesh components preserving the majority of the original layouts. Quad Layout is a crucial property for many applications since it conveys important information that would otherwise be destroyed by techniques that aim only at preserving shape. Our technique keeps untouched all the quads in the patches which are not involved in the blending. We first perform robust boolean operations on the corresponding triangle meshes. Then we use this result to identify and build new surface patches for small regions neighboring the intersection curves. These blending patches are carefully quadrangulated respecting boundary constraints and stitched back to the untouched parts of the original models. The resulting mesh preserves the designed edge flow that, by construction, is captured and incorporated to the new quads as much as possible. We present our technique in an interactive tool to show its usability and robustness.
Oberst, S, Lenz, M, Lai, JCS & Evans, TA 2019, 'Termites manipulate moisture content of wood to maximize foraging resources', Biology Letters, vol. 15, no. 7, pp. 20190365-20190365.
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Animals use cues to find their food, in microhabitats within their physiological tolerances. Termites build and modify their microhabitat, to transform hostile environments into benign ones, which raises questions about the relative importance of cues. Termites are desiccation intolerant and foraging termites are attracted to water, so most research has considered moisture to be a cue. However, termites can also transport water to food, and so moisture may play other roles than previously considered. To examine the role of moisture, we compared Coptotermes acinaciformis termite foraging decisions in laboratory experiments when they were offered dry and moist wood, with and without load. Without load, termites preferred moist wood and ate it without any building, whereas they moistened dry wood after wrapping it in a layer of clay. For the ‘With load’ units, termites substituted some of the wood for load-bearing clay walls, and kept the wood drier than on the unloaded units. As drier wood has higher compressive strength and higher rigidity, it allows more of the wood to be consumed. These results suggest that moisture plays a more important role in termite ecology than previously thought. Termites manipulate the moisture content according to the situational context and use it for multiple purposes: increased moisture levels soften the fibre, which facilitates foraging, yet keeping the wood dry provides higher structural stability against buckling which is especially important when foraging on wood under load.
Odriozola-Fernández, I, Berbegal-Mirabent, J & Merigó-Lindahl, JM 2019, 'Open innovation in small and medium enterprises: a bibliometric analysis', Journal of Organizational Change Management, vol. 32, no. 5, pp. 533-557.
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PurposeThe open innovation (OI) paradigm suggests that firms should use inflows and outflows of knowledge in order to accelerate innovation and leverage markets. Literature examining how firms are adopting OI practices is rich; notwithstanding, little research has addressed this topic from the perspective of small- and medium-sized enterprises (SMEs). Given the relevance of SMEs in worldwide economies, the purpose of this paper is to provide a comprehensive overview of research on OI in SMEs.Design/methodology/approachIn total, 112 academic articles were selected from the Web of Science database. Following a bibliometric analysis, the most relevant authors, journals, institutions and countries are presented. Additionally, the main areas these articles cover are summarized.FindingsResults are consistent in that the most prolific authors are affiliated with the universities leading the ranking of institutions. However, it is remarkable that top authors in this field do not possess a large number of publications on OI in SMEs, but combine this research topic with other related ones. At the country level, European countries are on the top together with South Korea.Research limitations/implicationsDespite following a rigorous method, other relevant documents not included in the selected databases might have been ignored.Practical implicationsThis paper outlines the main topics of interest within this area: impact of OI on firm performance and on organizations’ structure, OI as a mechanism to haste...
Ogie, RI & Pradhan, B 2019, 'Natural Hazards and Social Vulnerability of Place: The Strength-Based Approach Applied to Wollongong, Australia', International Journal of Disaster Risk Science, vol. 10, no. 3, pp. 404-420.
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© 2019, The Author(s). Natural hazards pose significant threats to different communities and various places around the world. Failing to identify and support the most vulnerable communities is a recipe for disaster. Many studies have proposed social vulnerability indices for measuring both the sensitivity of a population to natural hazards and its ability to respond and recover from them. Existing techniques, however, have not accounted for the unique strengths that exist within different communities to help minimize disaster loss. This study proposes a more balanced approach referred to as the strength-based social vulnerability index (SSVI). The proposed SSVI technique, which is built on sound sociopsychological theories of how people act during disasters and emergencies, is applied to assess comparatively the social vulnerability of different suburbs in the Wollongong area of New South Wales, Australia. The results highlight suburbs that are highly vulnerable, and demonstrates the usefulness of the technique in improving understanding of hotspots where limited resources should be judiciously allocated to help communities improve preparedness, response, and recovery from natural hazards.
Oh, S-H, Jeong, S, Kim, IS, Shon, HK & Jang, A 2019, 'Removal behaviors and fouling mechanisms of charged antibiotics and nanoparticles on forward osmosis membrane', Journal of Environmental Management, vol. 247, pp. 385-393.
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© 2019 Elsevier Ltd Fouling and rejection mechanisms of both charged antibiotics (ABs) and nanoparticles (NPs) were determined using a negatively-charged polyamide thin film composite forward osmosis (FO) flat sheet membrane. Two types of ABs and NPs were selected as positively and negatively charged foulants at pH 8. The ABs did not cause significant membrane fouling, but the extent of fouling and rejection changed based on the electrostatic attraction or repulsion forces. The addition of opposite charged AB and NP resulted in a decline of the membrane flux by 11.0% but a 6.5% AB average rejection efficiency improvement. On the other hand, mixing of like-charged ABs and NPs generated repulsive forces that improved average rejection efficiency about 5.5% but made no changes in the membrane flux. In addition, NPs and ABs were mixed and tested at various concentrations and pH levels to rectify the behavior of ABs. The aggregate size and removal efficiency were observed to vary with the change in the electron double layer of the mixture. It can help to make the strategy to control the ABs in the FO process and consequently it enables the FO process to produce environmentally safe effluent.
Oltra-Badenes, R, Gil-Gomez, H, Merigo, JM & Palacios-Marques, D 2019, 'Methodology and model-based DSS to managing the reallocation of inventory to orders in LHP situations. Application to the ceramics sector', PLOS ONE, vol. 14, no. 7, pp. e0219433-e0219433.
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© 2019 Oltra-Badenes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Lack of homogeneity in the product (LHP) is a problem when customers require homogeneous units of a single product. In such cases, the optimal allocation of inventory to orders becomes much more complex. Furthermore, in an MTS environment, an optimal initial allocation may become less than ideal over time, due to different circumstances. This problem occurs in the ceramics sector, where the final product varies in tone and calibre. This paper proposes a methodology for the reallocation of inventory to orders in LHP situation (MERIO-LHP) and a model-based decision-support system (DSS) to support the methodology, which enables an optimal reallocation of inventory to order lines to be carried out in real businesses environments in which LHP is inherent. The proposed methodology and modelbased DSS were validated by applying it to a real case at a ceramics company. The analysis of the results indicates that considerable improvements can be obtained with regard to the quantity of orders fulfilled and sales turnover.
Oña Edwin Daniel, García Jaime A., Raffe William, Jardón Alberto & Balaguer Carlos 2019, 'Assessment of Manual Dexterity in VR: Towards a Fully Automated Version of the Box and Blocks Test', Stud Health Technol Inform, vol. 266, pp. 57-62.
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In recent years, the possibility of using serious gaming technology for the automation of clinical procedures for assessment of motor function have captured the interest of the research community. In this paper, a virtual version of the Box and Blocks Test (BBT) for manual dexterity assessment is presented. This game-like system combines the classical BBT mechanics with a play-centric approach to accomplish a fully automated test for assessing hand motor function, making it more accessible and easier to administer. Additionally, some variants of the traditional mechanics are proposed in order to fully exploit the advantages of the chosen technology. This ongoing research aims to provide the clinical practitioners with a customisable, intuitive, and reliable tool for the assessment and rehabilitation of hand motor function.
Ong, HC, Chen, W-H, Farooq, A, Gan, YY, Lee, KT & Ashokkumar, V 2019, 'Catalytic thermochemical conversion of biomass for biofuel production: A comprehensive review', Renewable and Sustainable Energy Reviews, vol. 113, pp. 109266-109266.
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© 2019 Elsevier Ltd The increasing demand for energy and diminishing sources of fossil fuels have called for the discovery of new energy sources. The effective energy conversion process of biomass is able to fulfill energy needs. Among the advanced biomass conversion technologies, thermochemical processes hold considerable potential approaches and needed for optimization. Thus, this study presents a comprehensive review of the research and development on the effects of catalysts on the thermochemical conversion of biomass to determine the progress of catalytic thermochemical conversion processes. The effects of catalysts on torrefaction, pyrolysis, hydrothermal liquefaction, and gasification are highlighted. Aspects related to reaction conditions, reactor types, and products are discussed comprehensively with the reaction mechanisms involved in the catalytic effects. Hydrogenation and hydrodeoxygenation can occur in the presence of zeolite catalysts during fast pyrolysis while producing highly aromatic bio-oil. A heterogeneous catalyst in liquefaction increases the hydrocarbon content and decreases viscosity, acid value, and oxygenated compounds in the bio-oil. Thus, expanding and enhancing knowledge about catalyst utilization in the thermochemical conversion technologies of biomass will play an important role in the generation of renewable and carbon-neutral fuels.
Ong, HC, Milano, J, Silitonga, AS, Hassan, MH, Shamsuddin, AH, Wang, C-T, Indra Mahlia, TM, Siswantoro, J, Kusumo, F & Sutrisno, J 2019, 'Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture: Optimization and characterization', Journal of Cleaner Production, vol. 219, pp. 183-198.
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© 2019 Elsevier Ltd In this study, a novel modeling approach (artificial neural networks (ANN) and ant colony optimization (ACO)) was used to optimize the process variables for alkaline-catalyzed transesterification of CI40CP60 oil mixture (40 wt% of Calophyllum inophyllum oil mixed with 60 wt% of Ceiba pentandra oil) in order to maximize the biodiesel yield. The optimum values of the methanol-to-oil molar ratio, potassium hydroxide catalyst concentration, and reaction time predicted by the ANN-ACO model are 37%, 0.78 wt%, and 153 min, respectively, at a constant reaction temperature and stirring speed of 60 °C and 1000 rpm, respectively. The ANN-ACO model was validated by performing independent experiments to produce the CI40CP60 methyl ester (CICPME) using the optimum transesterification process variables predicted by the ANN-ACO model. There is very good agreement between the average CICPME yield determined from experiments (95.18%) and the maximum CICPME yield predicted by the ANN-ACO model (95.87%) for the same optimum values of process variables, which corresponds to a difference of 0.69%. Even though the ANN-ACO model is only implemented to optimize the transesterification of process variables in this study. It is believed that the model can be used to optimize other biodiesel production processes such as seed oil extraction and acid-catalyzed esterification for various types of biodiesels and biodiesel blends.
Onić, D, Filipović, MD, Bojičić, I, Hurley-Walker, N, Arbutina, B, Pannuti, TG, Maitra, C, Urošević, D, Haberl, F, Maxted, N, Wong, GF, Rowell, G, Bell, ME, Callingham, JR, Dwarakanath, KS, For, B-Q, Hancock, PJ, Hindson, L, Johnston-Hollitt, M, Kapińska, AD, Lenc, E, McKinley, B, Morgan, J, Offringa, AR, Porter, LE, Procopio, P, Staveley-Smith, L, Wayth, RB, Wu, C & Zheng, Q 2019, 'Murchison Widefield Array and XMM-Newton observations of the Galactic supernova remnant G5.9+3.1', A, vol. 625, p. A93.
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In this paper we discuss the radio continuum and X-ray properties of theso-far poorly studied Galactic supernova remnant (SNR) G5.9+3.1. We present theradio spectral energy distribution (SED) of the Galactic SNR G5.9+3.1 obtainedwith the Murchison Widefield Array (MWA). Combining these new observations withthe surveys at other radio continuum frequencies, we discuss the integratedradio continuum spectrum of this particular remnant. We have also analyzed anarchival XMM-Newton observation, which represents the first detection of X-rayemission from this remnant. The SNR SED is very well explained by a simplepower-law relation. The synchrotron radio spectral index of G5.9+3.1, isestimated to be 0.42$\pm$0.03 and the integrated flux density at 1GHz to bearound 2.7Jy. Furthermore, we propose that the identified point radio source,located centrally inside the SNR shell, is most probably a compact remnant ofthe supernova explosion. The shell-like X-ray morphology of G5.9+3.1 asrevealed by XMM-Newton broadly matches the spatial distribution of the radioemission, where the radio-bright eastern and western rims are also readilydetected in the X-ray while the radio-weak northern and southern rims are weakor absent in the X-ray. Extracted MOS1+MOS2+PN spectra from the whole SNR aswell as the north, east, and west rims of the SNR are fit successfully with anoptically thin thermal plasma model in collisional ionization equilibrium witha column density N_H~0.80x$10^{22}$ cm$^{-2}$ and fitted temperatures spanningthe range kT~0.14-0.23keV for all of the regions. The derived electron numberdensities n_e for the whole SNR and the rims are also roughly comparable(ranging from ~$0.20f^{-1/2}$ cm$^{-3}$ to ~$0.40f^{-1/2}$ cm$^{-3}$, where fis the volume filling factor). We also estimate the swept-up mass of the X-rayemitting plasma associated with G5.9+3.1 to be ~$46f^{-1/2}M_{\odot}$.
Ooi, XY, Gao, W, Ong, HC, Lee, HV, Juan, JC, Chen, WH & Lee, KT 2019, 'Overview on catalytic deoxygenation for biofuel synthesis using metal oxide supported catalysts', Renewable and Sustainable Energy Reviews, vol. 112, pp. 834-852.
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Ooi, XY, Oi, LE, Choo, M-Y, Ong, HC, Lee, HV, Show, PL, Lin, Y-C & Juan, JC 2019, 'Efficient deoxygenation of triglycerides to hydrocarbon-biofuel over mesoporous Al2O3-TiO2 catalyst', Fuel Processing Technology, vol. 194, pp. 106120-106120.
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© 2019 Elsevier B.V. The renewable hydrocarbon-like biofuel from biomass is crucial to substitute fossil fuel. A series of mesoporous Al2O3-TiO2 mixed oxide catalysts with different TiO2 content (0.1Ti-0.9Al, 0.2Ti-0.8Al and 0.3Ti-0.7Al) have been synthesized. The physicochemical properties of the catalysts were characterized by XRD, FESEM-EDX, BET, FTIR, NH3-TPD, FTIR-Py, and TGA. The deoxygenation (DO) of triglyceride (i.e. triolein) was carried out in the absence of hydrogen and solvent. The mesoporous Al2O3-TiO2 catalysts showed high catalytic activity performance as compared to that of Al2O3 and TiO2. It was found that 0.2Ti-0.8Al catalyst exhibited the highest conversion (76.86%), and selectivity (27.26%) toward n-C15 + n-C17 at 380 °C after 4 h. The excellence performance of mesoporous Al2O3-TiO2 was attributed to its acidity, mesoporosity and larger surface area. The results reveal that the mesoporous Al2O3-TiO2 catalyst is a promising catalyst for the synthesis of hydrocarbon-like biofuel.
Organ, B, Huang, Y, Zhou, JL, Surawski, NC, Yam, Y-S, Mok, W-C & Hong, G 2019, 'A remote sensing emissions monitoring programme reduces emissions of gasoline and LPG vehicles', Environmental Research, vol. 177, pp. 108614-108614.
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© 2019 Elsevier Inc. Vehicle emissions are a major source of air pollution in Hong Kong affecting human health. A ‘strengthened emissions control of gasoline and liquefied petroleum gas (LPG) vehicles’ programme has been operating in Hong Kong since September 2014 utilising remote sensing (RS) technology. RS has provided measurement data to successfully identify high emitting gasoline and LPG vehicles which then need to be repaired or removed from the on-road vehicle fleet. This paper aims to evaluate the effectiveness of this globally unique RS monitoring programme. A large RS dataset of 2,144,422 records was obtained covering the period from 6th January 2012 to 30th December 2016, of which 1,206,762 records were valid and suitable for further investigation. The results show that there have been significant reductions of emissions factors (EF) for 40.5% HC, 45.3% CO and 29.6% NO for gasoline vehicles. Additionally, EF reductions of 48.4% HC, 41.1% CO and 58.7% NO were achieved for LPG vehicles. For the combined vehicle fleet, the reductions for HC, CO and NO were 55.9%, 50.5% and 60.9% respectively during this survey period. The findings demonstrate that the strengthened emissions control programme utilising RS has been very effective in identifying high emitting vehicles for repair so as to reduce the emissions from gasoline and LPG vehicles under real driving.
Orth, D, Thurgood, C & Hoven, EVD 2019, 'Designing Meaningful Products in the Digital Age', ACM Transactions on Computer-Human Interaction, vol. 26, no. 5, pp. 1-28.
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Devices such as phones, laptops and tablets have become central to the ways in which many people communicate with others, conduct business and spend their leisure time. This type of product uniquely contains both physical and digital components that affect how they are perceived and valued by users. This article investigates the nature of attachment in the context of technological possessions to better understand ways in which designers can create devices that are meaningful and kept for longer. Findings from our study of the self-reported associations and meaningfulness of technological possessions revealed that the digital contents of these possessions were often the primary source of meaning. Technological possessions were frequently perceived as systems of products rather than as singular devices. We identified several design opportunities for materialising the associations ascribed to the digital information contained within technological products to more meaningfully integrate their physical and digital components.
Padhy, RP, Chang, X, Choudhury, SK, Sa, PK & Bakshi, S 2019, 'Multi-stage cascaded deconvolution for depth map and surface normal prediction from single image', Pattern Recognition Letters, vol. 127, pp. 165-173.
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Understanding the 3D perspective of a scene is imperative in improving the precision of intelligent autonomous systems. The difficulty in understanding is compounded when only one image of the scene is available at disposal. In this regard, we propose a fully convolutional deep framework for predicting the depth map and surface normal from a single RGB image in a common architecture. The DenseNet CNN architecture is employed to learn the complex mapping between an input RGB image and its corresponding 3D primitives. We introduce a novel approach of multi-stage cascaded deconvolution, where the output feature maps of one dense block are reused by concatenating with the feature maps of the corresponding deconvolution block. These combined feature maps are progressed along the deep network in a pre-activated manner to construct the final output. The network is trained separately for estimating depth and surface normal while keeping the architecture same. The suggested architecture, compared to the counterparts, uses fewer training samples and model parameters. Exhaustive experiments on benchmark dataset not only reveal the efficacy of the proposed multi-stage scheme over the one-way sequential deconvolution but also outperform the state-of-the-art methods.
Pais-Roldán, P, Edlow, BL, Jiang, Y, Stelzer, J, Zou, M & Yu, X 2019, 'Multimodal assessment of recovery from coma in a rat model of diffuse brainstem tegmentum injury', NeuroImage, vol. 189, pp. 615-630.
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Paler, A, Herr, D & Devitt, SJ 2019, 'Really Small Shoe Boxes: On Realistic Quantum Resource Estimation', Computer, vol. 52, no. 6, pp. 27-37.
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© 2019 IEEE. The reliable resource estimation and benchmarking of quantum algorithms is a critical component of the development cycle of viable quantum applications for quantum computers of all sizes. Determining resource bottlenecks in algorithms, especially when resource intensive error correction protocols are required, is crucial to reduce the cost of implementing viable algorithms on actual quantum hardware.
Pallewattha, M, Indraratna, B, Heitor, A & Rujikiatkamjorn, C 2019, 'Shear strength of a vegetated soil incorporating both root reinforcement and suction', Transportation Geotechnics, vol. 18, pp. 72-82.
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© 2018 Shear strength of the root permeated soil increases due to the mechanical effects of root reinforcement and most of the past studies have been conducted to capture this effect under saturated soil conditions. However, the soil adjacent to the tree roots is usually in an unsaturated condition and this leads to alterations in root-soil interaction mechanisms and associated shear strength of the root permeated soil system. In this paper, the increment in shear strength is studied considering both the effect of suction and root reinforcement patterns. A number of direct shear tests were conducted for different suction levels in root-permeated and unreinforced soil specimens. The results indicate that the shear strength behaviour of the soil-root system is governed by the level of suction and root failure patterns and a new mathematical model incorporating the effect of both parameters is proposed.
Pan, S, Hu, R, Fung, S-F, Long, G, Jiang, J & Zhang, C 2019, 'Learning Graph Embedding with Adversarial Training Methods', IEEE Transactions on Cybernetics, vol. 50, no. 6, pp. 2475-2487.
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Graph embedding aims to transfer a graph into vectors to facilitatesubsequent graph analytics tasks like link prediction and graph clustering.Most approaches on graph embedding focus on preserving the graph structure orminimizing the reconstruction errors for graph data. They have mostlyoverlooked the embedding distribution of the latent codes, which unfortunatelymay lead to inferior representation in many cases. In this paper, we present anovel adversarially regularized framework for graph embedding. By employing thegraph convolutional network as an encoder, our framework embeds the topologicalinformation and node content into a vector representation, from which a graphdecoder is further built to reconstruct the input graph. The adversarialtraining principle is applied to enforce our latent codes to match a priorGaussian or Uniform distribution. Based on this framework, we derive twovariants of adversarial models, the adversarially regularized graph autoencoder(ARGA) and its variational version, adversarially regularized variational graphautoencoder (ARVGA), to learn the graph embedding effectively. We also exploitother potential variations of ARGA and ARVGA to get a deeper understanding onour designs. Experimental results compared among twelve algorithms for linkprediction and twenty algorithms for graph clustering validate our solutions.
Pan, Y, Liu, Y, Peng, L, Ngo, HH, Guo, W, Wei, W, Wang, D & Ni, B-J 2019, 'Substrate Diffusion within Biofilms Significantly Influencing the Electron Competition during Denitrification', Environmental Science & Technology, vol. 53, no. 1, pp. 261-269.
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© 2018 American Chemical Society. A common and long-existing operational issue of wastewater denitrification is the unexpected accumulation of nitrite (NO 2- ) that could suppress the activity of various microorganisms involved in biological wastewater treatment process and nitrous oxide (N 2 O) that could emit as a potent greenhouse gas. Recently, it has been confirmed that the accumulation of these denitrification intermediates in biological wastewater treatment process is greatly influenced by the electron competition between the four denitrification steps. However, little is known about this in biofilm systems. In this work, we applied a mathematical model that links carbon oxidation and nitrogen reduction processes through a pool of electron carriers, to assess electron competition in denitrifying biofilms. Simulations were performed comprehensively at seven combinations of electron acceptor addition scheme (i.e., simultaneous addition of one, two or three among nitrate (NO 3- ), NO 2- , and N 2 O) to compare the effect of electron competition on NO 3- , NO 2- and N 2 O reduction. Overall, the effects of substrate loading, biofilm thickness and effective diffusion coefficients on electron competition are not always intuitive. Model simulations show that electron competition was intensified due to the substrate load limitation (from 120 to 20 mg COD/L) and increasing biofilm thicknesses (from 0.1 to 1.6 mm) in most cases, where electrons were prioritized to nitrite reductase because of the insufficient electron donor availability in the biofilm. In contrast, increasing effective diffusion coefficients did not pose a significant effect on electron competition and only increased electrons distributed to nitrite reductase when both NO 2- and N 2 O are added.
Pan, Y, Liu, Y, Wang, D & Ni, B-J 2019, 'Modeling effects of H2S on electron competition among nitrogen oxide reduction and N2O accumulation during denitrification', Environmental Science: Water Research & Technology, vol. 5, no. 3, pp. 533-542.
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A novel model was developed to describe electron competition during three-step denitrification through linking nitrogen reduction and carbon oxidation with electron carriers.
Pang, T, Zheng, G, Fang, J, Ruan, D & Sun, G 2019, 'Energy absorption mechanism of axially-varying thickness (AVT) multicell thin-walled structures under out-of-plane loading', Engineering Structures, vol. 196, pp. 109130-109130.
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© 2019 Elsevier Ltd Multicell columns have becoming increasingly attractive in crashworthiness applications due to their high efficiency of material utilization. Meanwhile, an urgent need exists to develop new structures to achieve the aim of light weight without sacrificing crashworthiness. A novel multicell column with axially-varying thickness (AVT) is proposed in this study. Quasi-static crushing tests were firstly performed experimentally to investigate crushing behaviors. Subsequently, corresponding numerical simulation models were built, validated, and used to conduct a parametric study. Finally, analytical equations for the mean crushing force for AVT multicell columns were derived and used to assess the crashworthiness of multicell columns according to SFE (super folding element) method. The numerical results agreed well with experimental results in terms of deformation mode and crushing forces, and the theoretical predictions were validated by the experimental results. It was concluded that the thickness gradient of AVT multicell columns could effectively reduce the initial peak crushing force while maintaining energy absorption capacity over a long crushing distance. From this perspective, the AVT multicell columns demonstrated competitive advantages over uniform columns as energy absorbers. Moreover, the analytical prediction could be a powerful tool for designing crashworthy structures.
Parajuli, N, Sreenivasan, N, Bifulco, P, Cesarelli, M, Savino, S, Niola, V, Esposito, D, Hamilton, TJ, Naik, GR, Gunawardana, U & Gargiulo, GD 2019, 'Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation', Sensors, vol. 19, no. 20, pp. 4596-4596.
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Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.
Park, MJ, Lim, S, Gonzales, RR, Phuntsho, S, Han, DS, Abdel-Wahab, A, Adham, S & Shon, HK 2019, 'Thin-film composite hollow fiber membranes incorporated with graphene oxide in polyethersulfone support layers for enhanced osmotic power density', Desalination, vol. 464, pp. 63-75.
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© 2019 Elsevier B.V. This study focused on the development of pressure retarded osmosis (PRO) thin film composite (TFC) membranes for enhanced osmotic power using hollow fiber polyethersulfone (PES) support structure modified by incorporating hydrophilic graphene oxide (GO) nanosheets. The GO loadings in the hollow fiber substrates were varied to improve water flux performances without compromising the mechanical strength. GO embedded (≤0.2 wt%) PES hollow fiber supports revealed noticeable improvements in pure water permeability, improved structural morphologies, as well as the hydrophilicity within the support layer, without deteriorating the mechanical properties. The GO (0.2 wt%)-incorporated TFC-PRO membrane appeared to have an initial PRO flux (without any applied pressure) of 43.74 L m−2 h−1, lower specific reverse salt flux of 0.04 g L−1 and structural parameter (S) of 522 μm, significantly better than the control membrane. The maximum power density of 14.6 W m−2 was achieved at an operating pressure of 16.5 bar under the condition of DI water and 1 M NaCl as feed and draw solutions, respectively. The results obtained in this study indicate that modification of PRO hollow fiber support layer by incorporating nanoparticles such as GO nanosheet can be a useful tool to improve the PRO performance.
Patel, OP, Bharill, N, Tiwari, A, Patel, V, Gupta, O, Cao, J, Li, J & Prasad, M 2019, 'Advanced Quantum Based Neural Network Classifier and Its Application for Objectionable Web Content Filtering', IEEE Access, vol. 7, pp. 98069-98082.
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© 2013 IEEE. In this paper, an Advanced Quantum-based Neural Network Classifier (AQNN) is proposed. The proposed AQNN is used to form an objectionable Web content filtering system (OWF). The aim is to design a neural network with a few numbers of hidden layer neurons with the optimal connection weights and the threshold of neurons. The proposed algorithm uses the concept of quantum computing and genetic concept to evolve connection weights and the threshold of neurons. Quantum computing uses qubit as a probabilistic representation which is the smallest unit of information in the quantum computing concept. In this algorithm, a threshold boundary parameter is also introduced to find the optimal value of the threshold of neurons. The proposed algorithm forms neural network architecture which is used to form an objectionable Web content filtering system which detects objectionable Web request by the user. To judge the performance of the proposed AQNN, a total of 2000 (1000 objectionable + 1000 non-objectionable) Website's contents have been used. The results of AQNN are also compared with QNN-F and well-known classifiers as backpropagation, support vector machine (SVM), multilayer perceptron, decision tree algorithm, and artificial neural network. The results show that the AQNN as classifier performs better than existing classifiers. The performance of the proposed objectionable Web content filtering system (OWF) is also compared with well-known objectionable Web filtering software and existing models. It is found that the proposed OWF performs better than existing solutions in terms of filtering objectionable content.
Patel, OP, Tiwari, A, Chaudhary, R, Nuthalapati, SV, Bharill, N, Prasad, M, Hussain, FK & Hussain, OK 2019, 'Enhanced quantum-based neural network learning and its application to signature verification', Soft Computing, vol. 23, no. 9, pp. 3067-3080.
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© 2017, Springer-Verlag GmbH Germany, part of Springer Nature. In this paper, an enhanced quantum-based neural network learning algorithm (EQNN-S) which constructs a neural network architecture using the quantum computing concept is proposed for signature verification. The quantum computing concept is used to decide the connection weights and threshold of neurons. A boundary threshold parameter is introduced to optimally determine the neuron threshold. This parameter uses min, max function to decide threshold, which assists efficient learning. A manually prepared signature dataset is used to test the performance of the proposed algorithm. To uniquely identify the signature, several novel features are selected such as the number of loops present in the signature, the boundary calculation, the number of vertical and horizontal dense patches, and the angle measurement. A total of 45 features are extracted from each signature. The performance of the proposed algorithm is evaluated by rigorous training and testing with these signatures using partitions of 60–40 and 70–30%, and a tenfold cross-validation. To compare the results derived from the proposed quantum neural network, the same dataset is tested on support vector machine, multilayer perceptron, back propagation neural network, and Naive Bayes. The performance of the proposed algorithm is found better when compared with the above methods, and the results verify the effectiveness of the proposed algorithm.
Pathirage, U, Indraratna, B, Pallewattha, M & Heitor, A 2019, 'A theoretical model for total suction effects by tree roots', Environmental Geotechnics, vol. 6, no. 6, pp. 353-360.
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Strengthening soft and weak soil by way of root reinforcement is a well-known strategy that is adopted worldwide. In Australia, native gum trees remain evergreen throughout the year and have been utilised to stabilise transportation corridors by way of reinforcement provided by the roots and the suction generated within the root domain as a function of evapotranspiration through the canopy. A mature gum tree can induce a missive total suction pressure exceeding 30 MPa through its root water and solute uptake in terms of matric plus osmotic suction. This cumulative effect of matric and osmotic suctions contributes to the overall shear strength of the soil, but the significant osmotic suction is often ignored in classical geotechnical engineering that does not consider the presence of trees. This study is an attempt to demonstrate the important role of osmotic suction, because it is directly proportional to the solute concentration in the soil and the solute uptake mechanisms of the surrounding vegetated ground.
Paull, NJ, Irga, PJ & Torpy, FR 2019, 'Active botanical biofiltration of air pollutants using Australian native plants', Air Quality, Atmosphere & Health, vol. 12, no. 12, pp. 1427-1439.
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© 2019, Springer Nature B.V. Air pollutants are of public concern due to their adverse health effects. Biological air filters have shown great promise for the bioremediation of air pollutants. Different plant species have previously been shown to significantly influence pollutant removal capacities, although the number of species tested to date is small. The aims of this paper were to determine the pollutant removal capacity of different Australian native species for their effect on active biowall particulate matter, volatile organic compounds and carbon dioxide removal, and to compare removal rates with previously tested ornamental species. The single-pass removal efficiency for PM and VOCs of native planted biofilters was determined with a flow-through chamber. CO2 removal was tested by a static chamber pull down study. The results indicated that the native species were not effective for CO2 removal likely due to their high light level requirements in conjunction with substrate respiration. Additionally, the native species had lower PM removal efficiencies compared to ornamental species, with this potentially being due to the ornamental species possessing advantageous leaf traits for increased PM accumulation. Lastly, the native species were found to have similar benzene removal efficiencies to ornamental species. As such, whilst the native species showed a capacity to phytoremediate air pollutants, ornamental species have a comparatively greater capacity to do so and are more appropriate for air filtration purposes in indoor circumstances. However, as Australian native plants have structural and metabolic adaptations that enhance their ability to tolerate harsh environments, they may find use in botanical biofilters in situations where common ornamental plants may be suitable, especially in the outdoor environment.
Peng, J, Liu, D, Parnell, J & Kessissoglou, N 2019, 'Influence of translational vehicle dynamics on heavy vehicle noise emission', Science of The Total Environment, vol. 689, pp. 1358-1369.
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Vehicle dynamics can play a significant role in the noise emission from heavy vehicles. In this work, a heavy vehicle noise emission model is presented to study the influence of translational vehicle dynamics on the sound power level emitted by heavy-duty trucks. Vehicle speed and acceleration are calculated using an analytical approximation that describes the tractive and retarding forces acting on a heavy vehicle on grade. Heavy vehicle noise emission associated with rolling noise is defined with reference to the Nordic traffic noise model that takes into account the number of axles for different articulated trucks. An expression for engine noise emission in terms of vehicle speed, weight, engine power, aerodynamic properties and road grade is derived. The individual and combined effects of engine noise and rolling noise for different vehicle mass combinations are examined. The influence of road grade on vehicle kinematics and noise emission is also investigated.
Peng, J, Parnell, J & Kessissoglou, N 2019, 'A six-category heavy vehicle noise emission model in free-flowing condition', Applied Acoustics, vol. 143, pp. 211-221.
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Annoyance and sleep disturbance caused by transportation noise are frequently associated with heavy vehicles. The ability to accurately predict heavy vehicle noise impact using conventional road traffic noise prediction methods has reduced over the years as the variety of heavy vehicles have increased progressively and the predominant long haul freight vehicle is trending towards larger trucks with a greater number of axles. In this paper, a six-category heavy vehicle source emission model in free-flowing condition has been developed based on the state-wide road setting in New South Wales, Australia. The six-category model allows traffic noise across the road network, carrying a diverse fleet of heavy vehicles, to be predicted with notably higher accuracy and precision in comparison to conventional models that aggregate heavy vehicles into one, or at most, two distinct categories. A comparative analysis is carried out to examine the source emission from various traffic mix scenarios in urban areas and along major freight routes. Current findings also highlight the importance of distinguishing regional characteristics in a harmonised road traffic noise prediction model.
Peng, L, Ngo, HH, Song, S, Xu, Y, Guo, W, Liu, Y, Wei, W, Chen, X, Wang, D & Ni, B-J 2019, 'Heterotrophic denitrifiers growing on soluble microbial products contribute to nitrous oxide production in anammox biofilm: Model evaluation', Journal of Environmental Management, vol. 242, pp. 309-314.
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© 2019 Elsevier Ltd In this work, a model framework was constructed to assess and predict nitrous oxide (N 2 O) production, substrate and microbe interactions in an anammox biofilm bioreactor. The anammox kinetics were extended by including kinetics of autotrophic soluble microbial products (SMP) formation, which consisted of utilization-associated products (UAP) and biomass-associated products (BAP). Heterotrophic bacteria growing on UAP, BAP and decay released substance (SS) were modelled to perform four-step sequential reductions from nitrate to dinitrogen gas. N 2 O was modelled as an intermidiate of heterotrophic denitrification via three pathways with UAP, BAP and SS as the electron donors. The developed model framework was evaluated using long-term operational data from an anammox biofilm reactor and satisfactorily reproduced effluent nitrogen and SMP as well as N 2 O emission factors under different operational conditions. The modeling results revealed that N 2 O was mainly produced with UAP as the electron donor while BAP and SS play minor roles. Heterotrophic denitrifiers growing on UAP would significantly contribute to N 2 O emission from anammox biofilm reactor even though heterotrophs only account for a relatively small fraction of active biomass in the anammox biofilm. Comprehensive simulations were conducted to investigate the effects of N loading rate and biofilm thickness, which indicated that maintaining a low N loading rate and a thick biofilm thickness were essential for high total nitrogen removal efficiency and low N 2 O emission.
Peng, S, Wang, G, Zhou, Y, Wan, C, Wang, C, Yu, S & Niu, J 2019, 'An Immunization Framework for Social Networks Through Big Data Based Influence Modeling', IEEE Transactions on Dependable and Secure Computing, vol. 16, no. 6, pp. 984-995.
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© 2004-2012 IEEE. Social networks are critical in terms of information or malware propagation. However, how to contain the spreading of malware in social networks is still an open and challenging issue. In this paper, we propose a novel defending method through big data based influence modeling. We first establish a social interaction graph based on big data sets of the studied object. Based on the graph, we are able to measure direct influence of individuals by computing each node's strength, which includes the degree of the node and the total number of messages sent by each user to her friends. Then, we design an algorithm to construct influence spreading tree using the breadth first search strategy, and measure indirect influence of individuals by traversing the tree. We identify the top kk influential nodes among all the nodes via the social influence strength, and propose an immunization algorithm to defend social networks against various attacks. The extensive experiments show that influence can spread easily in social networks, and the greater the influence of initial spread node is, the more impact it is on the malware propagation in social networks. The proposed method provides an effective solution to the prevention of malware or malicious messages propagation in social networks.
Peng, Y, Wu, C, Li, J, Liu, J & Liang, X 2019, 'Mesoscale analysis on ultra-high performance steel fibre reinforced concrete slabs under contact explosions', Composite Structures, vol. 228, pp. 111322-111322.
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© 2019 Elsevier Ltd This paper develops a more efficient and applicable three-dimensional mesoscale model to simulate ultra-high performance steel fibre reinforced concrete (UHP-SFRC) slabs under contact explosions. In the proposed mesoscale model, UHP-SFRC consists of two components involving concrete matrix and steel fibres. The straight steel fibres are randomly distributed and orientated in the concrete matrix using the self-coding program. The proposed mesoscale model is firstly validated with a series of static and dynamic tests, and then it is adopted in the numerical simulation of contact explosions. With the verified mesoscale model, parametric studies are conducted to investigate the effects of slab thickness and TNT charge weight on the crater damage of UHP-SFRC slabs under contact explosions. Based on the results of parametric studies, a damage identification multi-classifier is constructed to recognize and predict the damage of UHP-SFRC slabs under contact explosions by using the support vector machine (SVM).
Peng, Y, Zhang, Y, Lin, X, Zhang, W, Qin, L & Zhou, J 2019, 'Hop-constrained s-t Simple Path Enumeration: Towards Bridging Theory and Practice.', Proc. VLDB Endow., vol. 13, no. 4, pp. 463-476.
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Graph is a ubiquitous structure representing entities and their relationships applied in many areas such as social networks, web graphs, and biological networks. One of the fundamental tasks in graph analytics is to investigate the relations between two vertices (e.g., users, items and entities) such as how a vertex A influences another vertex B, or to what extent A and B are similar to each other, based on the graph topology structure. For this purpose, we study the problem of hop-constrained s-t simple path enumeration in this paper, which aims to list all simple paths from a source vertex s to a target vertex t with hop-constraint k. We first propose a polynomial delay algorithm, namely BC-DFS, based on barrier-based pruning technique. Then a join-oriented algorithm, namely JOIN, is designed to further enhance the query response time. On the theoretical side, BC-DFS is a polynomial delay algorithm with O(km) time per output where m is the number of edges in the graph. This time complexity is the same as the best known theoretical result for the polynomial delay algorithms of this problem. On the practical side, our comprehensive experiments on 15 real-life networks demonstrate the superior performance of the BC-DFS algorithm compared to the state-of-the-art techniques. It is also reported that the JOIN algorithm can further significantly enhance the query response time.
Pérez-Arellano, LA, León-Castro, E, Avilés-Ochoa, E & Merigó, JM 2019, 'Prioritized induced probabilistic operator and its application in group decision making', International Journal of Machine Learning and Cybernetics, vol. 10, no. 3, pp. 451-462.
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© 2017, Springer-Verlag GmbH Germany. A new extension of the ordered weighted average (OWA) operator is presented. This new operator includes the characteristics of three other operators: the prioritized, induced and probabilistic. The name is the prioritized induced probabilistic ordered weighted average (PIPOWA) operator. This operator can be used in a group decision-making process for selection of an alternative, taking into account three aspects: (1) not all of the decision-makers are equally important, (2) the probability of success of each alternative, and (3) an induced weighted vector. In the paper, some families of this operator are presented such as the prioritized probabilistic weighted average (PPOWA) operator and the prioritized induced ordered weighted average (PIOWA) operator. Additionally, some of the parameterized family of the aggregation operators, such as the minimum, maximum and total operator, are presented as special cases. The article also generalizes the PIPOWA operator by using quasi-arithmetic means. Finally, an example for selecting an alternative dispute resolution method in a commercial dispute is presented.
Pettit, T, Irga, PJ, Surawski, NC & Torpy, FR 2019, 'An Assessment of the Suitability of Active Green Walls for NO2 Reduction in Green Buildings Using a Closed-Loop Flow Reactor', Atmosphere, vol. 10, no. 12, pp. 801-801.
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Nitrogen dioxide (NO2) is a common urban air pollutant that is associated with several adverse human health effects from both short and long term exposure. Additionally, NO2 is highly reactive and can influence the mixing ratios of nitrogen oxide (NO) and ozone (O3). Active green walls can filter numerous air pollutants whilst using little energy, and are thus a candidate for inclusion in green buildings, however, the remediation of NO2 by active green walls remains untested. This work assessed the capacity of replicate active green walls to filter NO2 at both ambient and elevated concentrations within a closed-loop flow reactor, while the concentrations of NO and O3 were simultaneously monitored. Comparisons of each pollutant’s decay rate were made for green walls containing two plant species (Spathiphyllum wallisii and Syngonium podophyllum) and two lighting conditions (indoor and ultraviolet). Biofilter treatments for both plant species exhibited exponential decay for the biofiltration of all three pollutants at ambient concentrations. Furthermore, both treatments removed elevated concentrations of NO and NO2, (average NO2 clean air delivery rate of 661.32 and 550.8 m3∙h−1∙m−3 of biofilter substrate for the respective plant species), although plant species and lighting conditions influenced the degree of NOx removal. Elevated concentrations of NOx compromised the removal efficiency of O3. Whilst the current work provided evidence that effective filtration of NOx is possible with green wall technology, long-term experiments under in situ conditions are needed to establish practical removal rates and plant health effects from prolonged exposure to air pollution.
Pham, DD, Lee, SK, Shin, C, Kim, NH, Eisman, JA, Center, JR, Nguyen, TV & Leem, CH 2019, 'Koreans Do Not Have Higher Percent Body Fat than Australians: Implication for the Diagnosis of Obesity in Asians', Obesity, vol. 27, no. 11, pp. 1892-1897.
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ObjectiveIt has been assumed that, for a given BMI, Asians have higher percent body fat (PBF) than Caucasians. As a result, it has been suggested that the BMI threshold for diagnosing obesity in Asians be lowered to less than 30 kg/m2. This study sought to compare PBF between Koreans and Australians.MethodsWhole‐body fat mass and PBF were measured in 1,211 Koreans and 1,006 Australians using dual‐energy x‐ray absorptiometry (Lunar Prodigy; GE Healthcare, Madison, Wisconsin). The two groups were then matched for age and BMI by the propensity score method.ResultsFor a given age and BMI, Koreans had lower PBF than Australians, and the difference was statistically significant in women (mean difference: −2.13%; 95% CI: −2.61% to −1.65%) but not in men (difference: −0.54%; 95% CI: −1.22% to 0.14%). Matched‐pair analysis (423 pairs of women and 208 pairs of men) also showed that Korean women had statistically lower PBF than their Australian counterparts (P < 0.001).ConclusionsIn individuals aged 60 years and older, Koreans do not have higher PBF than Australians after adjusting for BMI. These results suggest that there is no evidence for lowering the BMI threshold for the diagnosis of obesity in elderly Koreans.
Pham, T-M & Chu, H-N 2019, 'Multi-Provider and Multi-Domain Resource Orchestration in Network Functions Virtualization', IEEE Access, vol. 7, pp. 86920-86931.
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Phung, VD, Hawryszkiewycz, I, Chandran, D & Ha, BM 2019, 'Promoting Knowledge Sharing Amongst Academics: A Case Study from Vietnam', Journal of Information & Knowledge Management, vol. 18, no. 03, pp. 1950032-1950032.
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This study aims to examine the influences of environmental and personal factors on knowledge-sharing behaviour (KSB) of academics and whether more influence leads to superior innovative work behaviour (IWB) at the tertiary level in Vietnam. A questionnaire survey was conducted as part of the study, including 320 academic staff at Hanoi University, one of the leading public universities in Vietnam. This study applies the structural equation modelling (SEM) to investigate the research model based on social cognitive theory (SCT). The results show that two environmental factors (subjective norms and trust) and two personal factors (knowledge self-efficacy and enjoyment in helping others) significantly influence KSB. The results also indicate that employee willingness to share knowledge enables the organisation to promote innovative work behaviour. The study context was limited to only one Vietnamese university. It appears that the part of a bigger picture of knowledge sharing (KS) in Vietnamese universities is likely to be lost. However, given the previous studies on knowledge sharing in both developed and developing countries, it could be expected that the results of this study can be taken forward by university leaderships, academic staff and researchers in other contexts as well. A clear understanding of the critical factors that influence KSB towards promoting innovative work behaviour may help university leaders to develop suitable and evolving strategies to address the challenges of knowledge sharing. This study contributes to the growing literature on the relationships among environmental and personal factors and KSB towards promoting innovative work behaviour.
Phwan, CK, Chew, KW, Sebayang, AH, Ong, HC, Ling, TC, Malek, MA, Ho, Y-C & Show, PL 2019, 'Effects of acids pre-treatment on the microbial fermentation process for bioethanol production from microalgae', Biotechnology for Biofuels, vol. 12, no. 1.
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© 2019 The Author(s). Background: Microalgae are one of the promising feedstock that consists of high carbohydrate content which can be converted into bioethanol. Pre-treatment is one of the critical steps required to release fermentable sugars to be used in the microbial fermentation process. In this study, the reducing sugar concentration of Chlorella species was investigated by pre-treating the biomass with dilute sulfuric acid and acetic acid at different concentrations 1%, 3%, 5%, 7%, and 9% (v/v). Results: 3,5-Dinitrosalicylic acid (DNS) method, FTIR, and GC-FID were employed to evaluate the reducing sugar concentration, functional groups of alcohol bonds and concentration of bioethanol, respectively. The two-way ANOVA results (p < 0.05) indicated that there was a significant difference in the concentration and type of acids towards bioethanol production. The highest bioethanol yield obtained was 0.28 g ethanol/g microalgae which was found in microalgae sample pre-treated with 5% (v/v) sulfuric acid while 0.23 g ethanol/g microalgal biomass was presented in microalgae sample pre-treated with 5% (v/v) acetic acid. Conclusion: The application of acid pre-treatment on microalgae for bioethanol production will contribute to higher effectiveness and lower energy consumption compared to other pre-treatment methods. The findings from this study are essential for the commercial production of bioethanol from microalgae.
Pileggi, SF 2019, 'Web of Similarity', Journal of Computational Science, vol. 36, pp. 100578-100578.
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© 2016 Elsevier B.V. Despite the achieved maturity and popularity, the current semantic technology has severe limitations in real-world applications as it is unable to represent uncertain knowledge. Probabilistic Semantics partially address this issue. Unfortunately, their quantitative approach fails in many practical applications that require a more abstracted vision and conceptual model of the uncertainties. Indeed, Probabilistic Semantics can only model ecosystems where all the uncertainties are quantified. In this paper, we introduce a qualitative approach for the representation of the uncertainties in the Semantic Web. We propose a human-inspired model that defines the uncertainty as an explicit similarity, providing a flexible range of solutions for approximate semantic reasoning in uncertain ecosystems. The resulting semantic environment, referred to as Web of Similarity (WoS), is an extension of the Web of Data which is able to represent and process analogies among concepts and individuals. As the generic Semantic Web, the Web of Similarity is a global semantic infrastructure that can support specific systems or applications at a global scale. WoS is a step forward to get richer Web Semantics which are closer to the human ones.
Pileggi, SF & Voinov, A 2019, 'PERSWADE-CORE: A Core Ontology for Communicating Socio-Environmental and Sustainability Science', IEEE Access, vol. 7, pp. 127177-127188.
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Pirandola, S, Andersen, UL, Banchi, L, Berta, M, Bunandar, D, Colbeck, R, Englund, D, Gehring, T, Lupo, C, Ottaviani, C, Pereira, J, Razavi, M, Shaari, JS, Tomamichel, M, Usenko, VC, Vallone, G, Villoresi, P & Wallden, P 2019, 'Advances in Quantum Cryptography', Adv. Opt. Photon., vol. 12, no. 4, pp. 1012-1236.
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Quantum cryptography is arguably the fastest growing area in quantuminformation science. Novel theoretical protocols are designed on a regularbasis, security proofs are constantly improving, and experiments are graduallymoving from proof-of-principle lab demonstrations to in-field implementationsand technological prototypes. In this review, we provide both a generalintroduction and a state of the art description of the recent advances in thefield, both theoretically and experimentally. We start by reviewing protocolsof quantum key distribution based on discrete variable systems. Next weconsider aspects of device independence, satellite challenges, and high rateprotocols based on continuous variable systems. We will then discuss theultimate limits of point-to-point private communications and how quantumrepeaters and networks may overcome these restrictions. Finally, we willdiscuss some aspects of quantum cryptography beyond standard quantum keydistribution, including quantum data locking and quantum digital signatures.
Piya, R, Zhu, Y, Soeriyadi, AH, Silva, SM, Reece, PJ & Gooding, JJ 2019, 'Micropatterning of porous silicon Bragg reflectors with poly(ethylene glycol) to fabricate cell microarrays: Towards single cell sensing', Biosensors and Bioelectronics, vol. 127, pp. 229-235.
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The work presented here describes the development of an optical label-free biosensor based on a porous silicon (PSi) Bragg reflector to study heterogeneity in single cells. Photolithographic patterning of a poly(ethylene glycol) (PEG) hydrogel with a photoinitiator was employed on RGD peptide-modified PSi to create micropatterns with cell adhesive and cell repellent areas. Macrophage J774 cells were incubated to form cell microarrays and single cell arrays. Moreover, cells on the microarrays were lysed osmotically with Milli-Q™ water and the infiltration of cell lysate into the porous matrix was monitored by measuring the red shift in the reflectivity. On average, the magnitude of red shift increased with the increase in the number of cells on the micropatterns. The red shift from the spots with single cells varied from spot to spot emphasizing the heterogeneous nature of the individual cells.
Poon, J, Cui, Y, Valls Miro, J & Matsubara, T 2019, 'Learning from demonstration for locally assistive mobility aids', International Journal of Intelligent Robotics and Applications, vol. 3, no. 3, pp. 255-268.
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© 2019, The Author(s). Active assistive systems for mobility aids are largely restricted to environments mapped a-priori, while passive assistance primarily provides collision mitigation and other hand-crafted behaviors in the platform’s immediate space. This paper presents a framework providing active short-term assistance, combining the freedom of location independence with the intelligence of active assistance. Demonstration data consisting of on-board sensor data and driving inputs is gathered from an able-bodied expert maneuvring the mobility aid around a generic interior setting, and used in constructing a probabilistic intention model built with Radial Basis Function Networks. This allows for short-term intention prediction relying only upon immediately available user input and on-board sensor data, to be coupled with real-time path generation based upon the same expert demonstration data via Dynamic Policy Programming, a stochastic optimal control method. Together these two elements provide a combined assistive mobility system, capable of operating in restrictive environments without the need for additional obstacle avoidance protocols. Experimental results in both simulation and on the University of Technology Sydney semi-autonomous wheelchair in settings not seen in training data show promise in assisting users of power mobility aids.
Poongodi, T, Khan, MS, Patan, R, Gandomi, AH & Balusamy, B 2019, 'Robust Defense Scheme Against Selective Drop Attack in Wireless Ad Hoc Networks', IEEE Access, vol. 7, pp. 18409-18419.
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© 2013 IEEE. Performance and security are two critical functions of wireless ad-hoc networks (WANETs). Network security ensures the integrity, availability, and performance of WANETs. It helps to prevent critical service interruptions and increases economic productivity by keeping networks functioning properly. Since there is no centralized network management in WANETs, these networks are susceptible to packet drop attacks. In selective drop attack, the neighboring nodes are not loyal in forwarding the messages to the next node. It is critical to identify the illegitimate node, which overloads the host node and isolating them from the network is also a complicated task. In this paper, we present a resistive to selective drop attack (RSDA) scheme to provide effective security against selective drop attack. A lightweight RSDA protocol is proposed for detecting malicious nodes in the network under a particular drop attack. The RSDA protocol can be integrated with the many existing routing protocols for WANETs such as AODV and DSR. It accomplishes reliability in routing by disabling the link with the highest weight and authenticate the nodes using the elliptic curve digital signature algorithm. In the proposed methodology, the packet drop rate, jitter, and routing overhead at a different pause time are reduced to 9%, 0.11%, and 45%, respectively. The packet drop rate at varying mobility speed in the presence of one gray hole and two gray hole nodes are obtained as 13% and 14% in RSDA scheme.
Pou, J, Perez, MA & Aguilera, RP 2019, 'Modular Multilevel Converters', IEEE Transactions on Industrial Electronics, vol. 66, no. 3, pp. 2204-2206.
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Pramanik, BK, Asif, MB, Kentish, S, Nghiem, LD & Hai, FI 2019, 'Lithium enrichment from a simulated salt lake brine using an integrated nanofiltration-membrane distillation process', Journal of Environmental Chemical Engineering, vol. 7, no. 5, pp. 103395-103395.
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© 2019 Elsevier Ltd. This work aimed to evaluate the enrichment of lithium (Li) from a simulated salt lake brine by using an integrated nanofiltration (NF) and membrane distillation (MD) process. Two types of NF membranes, namely NF90 and NF270, were employed to compare their performances for Li and magnesium (Mg) rejection under various operating conditions. In the presence of a competing ion (i.e., Mg) at different concentration, Li rejection by NF90 and NF270 membrane increased, which could be attributed to ion-shielding effects. On the other hand, Li rejection by the NF membranes slightly reduced by increasing the applied pressure from 4 to 8 bar. Increasing the pH from 3 to 11 did not significantly affect Li rejection efficiency. Under optimum operating conditions, the Mg/Li molar ratio changed from 10 to 0.19 after NF90 treatment, and 10 to 2.1 after NF270 treatment. NF90 and NF270 membranes achieved 23 and 44% Li separation, respectively. The separated Li following NF treatments could be further enriched or concentrated significantly (80%) by using the direct contact-MD system. This study demonstrates that an integrated membrane process could be an efficient method for lithium recovery from salt lake brines.
Pramanik, BK, Hai, FI, Ansari, AJ & Roddick, FA 2019, 'Mining phosphorus from anaerobically treated dairy manure by forward osmosis membrane', Journal of Industrial and Engineering Chemistry, vol. 78, pp. 425-432.
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Prasanna, T, Arasaratnam, M, Boyer, M, McNeil, C, Barnet, MB, Asher, R, Hui, R, Nagrial, A & Kao, S 2019, 'Rate of Cancer Progression as a Predictive Marker of Efficacy of Immunotherapy; an Analysis in Metastatic Non-Small-Cell Lung Cancer', Immunotherapy, vol. 11, no. 8, pp. 657-665.
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Provis, JL, Arbi, K, Bernal, SA, Bondar, D, Buchwald, A, Castel, A, Chithiraputhiran, S, Cyr, M, Dehghan, A, Dombrowski-Daube, K, Dubey, A, Ducman, V, Gluth, GJG, Nanukuttan, S, Peterson, K, Puertas, F, van Riessen, A, Torres-Carrasco, M, Ye, G & Zuo, Y 2019, 'RILEM TC 247-DTA round robin test: mix design and reproducibility of compressive strength of alkali-activated concretes', Materials and Structures, vol. 52, no. 5.
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AbstractThe aim of RILEM TC 247-DTA ‘Durability Testing of Alkali-Activated Materials’ is to identify and validate methodologies for testing the durability of alkali-activated concretes. To underpin the durability testing work of this committee, five alkali-activated concrete mixes were developed based on blast furnace slag, fly ash, and flash-calcined metakaolin. The concretes were designed with different intended performance levels, aiming to assess the capability of test methods to discriminate between concretes on this basis. A total of fifteen laboratories worldwide participated in this round robin test programme, where all concretes were produced with the same mix designs, from single-source aluminosilicate precursors and locally available aggregates. This paper reports the mix designs tested, and the compressive strength results obtained, including critical insight into reasons for the observed variability in strength within and between laboratories.
Pu, Y, Tang, J, Wang, XC, Hu, Y, Huang, J, Zeng, Y, Ngo, HH & Li, Y 2019, 'Hydrogen production from acidogenic food waste fermentation using untreated inoculum: Effect of substrate concentrations', International Journal of Hydrogen Energy, vol. 44, no. 50, pp. 27272-27284.
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© 2019 Hydrogen Energy Publications LLC The effect of substrate concentrations (0, 7.5, 15, 22.5, 30, and 37.5 g-VS/L) on hydrogen production from heat-treated and fresh food waste (FW) using untreated inoculums was investigated in this work. The highest hydrogen yield (75.3 mL/g-VS) was obtained with heat-treated FW at 15 g-VS/L. Lower substrate content could not provide enough organic matter for hydrogen fermentation, while higher substrate concentrations shifted the metabolic pathways from hydrogen fermentation to lactic acid fermentation by enriching the lactic acid bacteria (LAB), which lowered the slurry pH and decreased enzyme activity, resulting in a lower chemical oxygen demand (COD), volatile solid (VS), carbohydrate removal rate, and hydrogen yield. Compared with fresh FW, heat-treated FW is preferred for biohydrogen process with acetate as the main organic product. Additionally, at the optimal concentration (15 g-VS/L) using fresh FW, lactic acid is first accumulated and then degraded to produce hydrogen with butyrate as the main metabolite.
Punetha, P, Samanta, M & Mohanty, P 2019, 'Evaluation of the dynamic response of geosynthetic interfaces', International Journal of Physical Modelling in Geotechnics, vol. 19, no. 3, pp. 141-153.
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An accurate evaluation of interface friction between two geosynthetics under dynamic loading conditions is vital for the seismic design of landfills, flood protection systems and so on. This paper presents the results of an experimental investigation conducted on different geosynthetic–geosynthetic interfaces involving geomembrane and geotextile under static and dynamic loading conditions, using direct shear and shake table tests. The effect of normal stress, sliding velocity, frequency and the number of cycles on the dynamic coefficient of friction for the geosynthetic–geosynthetic interfaces has been investigated using a fixed-block set-up on a shake table. The results show that the dynamic coefficient of friction for geomembrane–geomembrane interfaces decreases with an increase in normal stress, sliding velocity and frequency. The dynamic coefficient of friction for geotextile–geotextile interfaces decreases with an increase in normal stress and frequency, while it increases with an increase in sliding velocity. However, the number of cycles has a negligible effect on the shear behaviour of all the interfaces tested.
Puthal, D, Ranjan, R, Nanda, A, Nanda, P, Jayaraman, PP & Zomaya, AY 2019, 'Secure authentication and load balancing of distributed edge datacenters', Journal of Parallel and Distributed Computing, vol. 124, pp. 60-69.
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© 2018 Edge computing is an emerging research area to incorporate cloud computing into edge network devices. An Edge datacenter, also referred to as EDC, processes data streams and user requests in real-time and is therefore used to decrease the latency and congestion in the network. EDC is usually setup as a distributed system and is accordingly placed between the cloud datacenter and the data source. These EDCs work as an intermediate layer in the fog hierarchy between IoT and Cloud datacenter. EDC's are aided by load balancers, responsible for distributing the workload amongst multiple EDC, in order to optimize resource utilization and response time. The load balancers make sure that the workload is equally divided amongst the available EDCs to avoid over loading of some EDCs while other remain idle as this directly impacts the user response and real-time event detection. Given the fact that EDCs are deployed in remote environments, the need for secure authentication is of major importance. In this paper we propose a novel load balancing technique that enables EDC authentication as well as identification of idle EDCs for better load balancing. The proposed load balancing technique is also compared with existing approaches and proves to be more efficient in locating EDC's with less workload. In addition to the improved efficiency, the proposed scheme also strengthens the security of the network by incorporating destination EDC authentication.
Putra, N, Hakim, II, Erwin, FP, Abdullah, NA, Ariantara, B, Amin, M, Mahlia, TMI & Kusrini, E 2019, 'Development of a novel thermoelectric module based device for thermal stability measurement of phase change materials', Journal of Energy Storage, vol. 22, pp. 331-335.
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© 2019 Elsevier Ltd A recently developed method for the thermal stability measurement of phase-change materials (PCMs) involves thermal cycling using a thermoelectric module as a heating and cooling element. However, the utility of this approach was found to have some limitations, mainly because the thermoelectric polarity is changed according to time rather than the actual sample temperature. A method for thermal cycling test, where the thermoelectric polarity is automatically changed according to the sample temperature was developed in this study. In addition, a new cartridge design in this device requires a small sample volume (1.53 cm 3 ) and can be easily assembled and disassembled. This proposed device was tested on beeswax as a PCM sample. This is very important for savings PCMs material which usually expensive. The results showed that the apparatus had automatically cycled between the melting and cooling temperatures of beeswax. The thermal data showed that beeswax retains consistent melting and freezing temperatures after 1000 cycles, however, its heat of fusion degrades over repeated thermal cycling. This apparatus can be readily applied to study a wide range of PCMs for such as thermal energy storage materials for energy conservation. To our best knowledge, yet no study has been performed on this kind of equipment so far.
Putra, N, Rawi, S, Amin, M, Kusrini, E, Kosasih, EA & Indra Mahlia, TM 2019, 'Preparation of beeswax/multi-walled carbon nanotubes as novel shape-stable nanocomposite phase-change material for thermal energy storage', Journal of Energy Storage, vol. 21, pp. 32-39.
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© 2018 Elsevier Ltd Development of phase-change material (PCM) as thermal energy storage for building envelopes is promising for energy utilization. However, there are two major drawbacks of PCM application, which are low thermal conductivity and high-volume reduction due to phase-change transition. One solution is to develop a shape-stabilized phase-change material (SSPCM) as a composite that is able to prevent leakage during the transition from solid to liquid. Therefore, the objective of this study is to prepare beeswax/multi-walled carbon nanotubes as form-stable nanocomposite phase-change material for thermal energy storage, based on previously unattempted methods. Beeswax was being used as PCM because of its high latent heat and multi-walled carbon nanotubes (MWCNTs) as supporting material with high thermal conductivity. There are three types of MWCNTs applied in this research: pristine MWCNTs, ball-milled MWCNTs and acid-treated MWCNTs. Beeswax/CNT composite samples were prepared with ratios of 5 and 20 wt%. Composite samples were tested from structure modification and thermal performance, including latent heat, sensible heat, melting point, solidifying point, thermal conductivity, and thermal-cycle testing for up to 300 cycles. Experimental results showed that thermal conductivity of novel shape-stable nanocomposite PCM increased by a factor of 2 and there was no significant phase transition in the melting or solidifying temperature. The high heat storage capability and thermal conductivity of nanocomposite PCM enable it to be a potential material for thermal energy storage in practical applications.
Qi, Y, Indraratna, B & Coop, MR 2019, 'Predicted Behavior of Saturated Granular Waste Blended with Rubber Crumbs', International Journal of Geomechanics, vol. 19, no. 8, pp. 04019079-04019079.
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Qi, Y, Indraratna, B, Heitor, A & Vinod, JS 2019, 'Closure to “Effect of Rubber Crumbs on the Cyclic Behavior of Steel Furnace Slag and Coal Wash Mixtures” by Yujie Qi, Buddhima Indraratna, Ana Heitor, and Jayan S. Vinod', Journal of Geotechnical and Geoenvironmental Engineering, vol. 145, no. 1, pp. 07018035-07018035.
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Qiao, C, Lu, L, Yang, L & Kennedy, PJ 2019, 'Identifying Brain Abnormalities with Schizophrenia Based on a Hybrid Feature Selection Technology', Applied Sciences, vol. 9, no. 10, pp. 2148-2148.
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Many medical imaging data, especially the magnetic resonance imaging (MRI) data, usually have a small sample size, but a large number of features. How to reduce effectively the data dimension and locate accurately the biomarkers from such kinds of data are quite crucial for diagnosis and further precision medicine. In this paper, we propose a hybrid feature selection method based on machine learning and traditional statistical approaches and explore the brain abnormalities of schizophrenia by using the functional and structural MRI data. The results show that the abnormal brain regions are mainly distributed in the supramarginal gyrus, cingulate gyrus, frontal gyrus, precuneus and caudate, and the abnormal functional connections are related to the caudate nucleus, insula and rolandic operculum. In addition, some complex network analyses based on graph theory are utilized on the functional connection data, and the results demonstrate that the located abnormal functional connections in brain can distinguish schizophrenia patients from healthy controls. The identified abnormalities in brain with schizophrenia by the proposed hybrid feature selection method show that there do exist some abnormal brain regions and abnormal disruption of the network segregation and network integration for schizophrenia, and these changes may lead to inaccurate and inefficient information processing and synthesis in the brain, which provide further evidence for the cognitive dysmetria of schizophrenia.
Qiao, M, Yu, J, Bian, W, Li, Q & Tao, D 2019, 'Adapting Stochastic Block Models to Power-Law Degree Distributions', IEEE Transactions on Cybernetics, vol. 49, no. 2, pp. 626-637.
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© 2013 IEEE. Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.
Qin, C, Ni, W, Tian, H, Lu, L & Liu, RP 2019, 'Radio over Cloud (RoC): Cloud-Assisted Distributed Beamforming for Multi-Class Traffic', IEEE Transactions on Mobile Computing, vol. 18, no. 6, pp. 1368-1379.
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IEEE Cloud has yet to be applied to computationally intensive radio signal processing, due to closely coupled computing tasks resulting from interference. This paper presents a new cloud-assisted joint beamforming architecture, where computations are decoupled for individual wireless users and pipelined for cloud execution, using Difference of Convex (DC), l1-norm approximations, and dual decompositions. User-specific tasks are constructed and aligned with the cloud to leverage computation reuses and minimize overhead. The time-complexity is dramatically improved to support networks with tens to hundreds of base stations and users, without compromising the sum rate and quality-of-service. Further, the superiority of DC to the state-of-the-art Weighted Minimum Mean Square Error (WMMSE) in terms of convex relaxation is observed and discussed. Corroborated by simulations, the reason is revealed as WMMSE aggressively increases the data rate at interim stages, hence adversely interacting with l1-norm approximation and reducing the feasible solution regions at later stages.
Qin, H & Stewart, MG 2019, 'System fragility analysis of roof cladding and trusses for Australian contemporary housing subjected to wind uplift', Structural Safety, vol. 79, pp. 80-93.
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This paper describes a reliability-based fragility method to evaluate the wind damage to roof cladding and trusses for contemporary houses in non-cyclonic regions of Australia. The fragility assessment considers roof sheeting loss and roof truss failure due to overloading of cladding-to-batten, batten-to-rafter/truss and rafter/truss-to-wall connectors that are typically the ‘weakest links’ of a roof system under wind uplift pressure. The wind fragility herein is expressed by the mean extent of roof sheeting loss and roof truss failures as a function of gust wind speed. Monte Carlo Simulation in conjunction with a finite element approach are employed to carry out the wind fragility assessment, which enables the probabilistic characterization of spatially varying wind uplift pressure, connection resistances, structural response, failure progression of roof connections and internal pressure evolution with increasing roof sheeting loss. The proposed fragility method was illustrated on representative contemporary housing built in Brisbane and Melbourne with complex hip-roof geometries and corrugated metal roof sheeting. It was found that, for the gust wind speed corresponding to a 500-year return period, the mean proportion of roof sheeting loss and roof truss failures is negligible for the representative contemporary house built in Melbourne, whereas considerable roof damage is predicted for those built in Brisbane when windward dominant openings exist.
Qin, P-Y, Song, L-Z & Guo, YJ 2019, 'Beam Steering Conformal Transmitarray Employing Ultra-Thin Triple-Layer Slot Elements', IEEE Transactions on Antennas and Propagation, vol. 67, no. 8, pp. 5390-5398.
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© 1963-2012 IEEE. A novel conformal transmitarray with beam steering ability is presented. First, an ultra-thin transmitarray element consisting of three layers of identical square ring slots is developed. The element has a thickness of 0.508 mm (0.04 wavelength in the free space at 25 GHz), achieving a transmission phase range of 330° with a maximum 3.6 dB loss. The element is then applied to a curved transmitarray conformal to a cylindrical surface fed by a standard gain horn with about a 10-dBi gain. A prototype is fabricated radiating a boresight beam with a peak measured gain of 19.6 dBi and an aperture efficiency of 25.1%. Second, when the transmitting surface of the above array is divided into two parts from the middle with different main beam directions, the combined beam can be radiated to an oblique angle with respect to the boresight direction. Using this method, a mechanical beam scanning conformal transmitarray antenna is designed. Its size is about 2.5 times larger than the fixed-beam one and consists of six transmitting surfaces with main beams directed to different angles. By rotating the feed horn to different positions, the main beam of the array can be switched to ±15°, ±10°, ±5°, and 0°. A prototype is fabricated with a stable gain of about 18.7 dBi at all beam angles.
Qu, Z, Lau, CW, Nguyen, QV, Zhou, Y & Catchpoole, DR 2019, 'Visual Analytics of Genomic and Cancer Data: A Systematic Review', Cancer Informatics, vol. 18, pp. 117693511983554-117693511983554.
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Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation sequencing technologies has allowed the rapid production of massive amounts of genomic data and created a corresponding need for new tools and methods for visualising and interpreting these data. Visualising genomic data requires not only simply plotting of data but should also offer a decision or a choice about what the message should be conveyed in the particular plot; which methodologies should be used to represent the results must provide an easy, clear, and accurate way to the clinicians, experts, or researchers to interact with the data. Genomic data visual analytics is rapidly evolving in parallel with advances in high-throughput technologies such as artificial intelligence (AI) and virtual reality (VR). Personalised medicine requires new genomic visualisation tools, which can efficiently extract knowledge from the genomic data and speed up expert decisions about the best treatment of individual patient’s needs. However, meaningful visual analytics of such large genomic data remains a serious challenge. This article provides a comprehensive systematic review and discussion on the tools, methods, and trends for visual analytics of cancer-related genomic data. We reviewed methods for genomic data visualisation including traditional approaches such as scatter plots, heatmaps, coordinates, and networks, as well as emerging technologies using AI and VR. We also demonstrate the development of genomic data visualisation tools over time and analyse the evolution of visualising genomic data.
Quang Tri, D, Kandasamy, J & Cao Don, N 2019, 'Quantitative Assessment of the Environmental Impacts of Dredging and Dumping Activities at Sea', Applied Sciences, vol. 9, no. 8, pp. 1703-1703.
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The dumping of dredge materials often raises concerns about the release of pollutants to the marine environment. Wind data from the Global Forecast System (GFS) model was used to simulate the wind-wave propagation from offshore in a two-dimensional (2D) model during September and October 2016. The calibration and validation of the 2D model showed a high conformity in both the phases and amplitude between the observed and simulated data. The 2D mud transport simulation results of three scenarios showed that the concentration of suspended material in the third scenario tested (scenario 3) was greater than 0.004 kg/m3 in the low tide, spreading to a 9 km2 area, and in the high tide, the concentration was 0.004 kg/m3 in a 6 km2 area. Finally, the results of 2D particle tracking (PT) showed changes in the seabed due to the concentration of dredged material, and its dump (approximately 180 days) increased from 0.08 m to 0.16 m in 2.85 ha. In scenario 3, the element block moved quite far—approximately 2.9 km—from the dredge position. Therefore, the simulation results were qualified, as the dredging position situated far from the sea is significantly affected by the direction and velocity of wave-wind in the dredging position.
Quiroz, JC, Laranjo, L, Kocaballi, AB, Berkovsky, S, Rezazadegan, D & Coiera, E 2019, 'Challenges of developing a digital scribe to reduce clinical documentation burden', npj Digital Medicine, vol. 2, no. 1, p. 114.
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AbstractClinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms.
Raeisi, S, Kieferová, M & Mosca, M 2019, 'Novel Technique for Robust Optimal Algorithmic Cooling', Physical Review Letters, vol. 122, no. 22, pp. 220501-220501.
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Heat-bath algorithmic cooling provides algorithmic ways to improve the purity of quantum states. These techniques are complex iterative processes that change from each iteration to the next and this poses a significant challenge to implementing these algorithms. Here, we introduce a new technique that on a fundamental level, shows that it is possible to do algorithmic cooling and even reach the cooling limit without any knowledge of the state and using only a single fixed operation, and on a practical level, presents a more feasible and robust alternative for implementing heat-bath algorithmic cooling. We also show that our new technique converges to the asymptotic state of heat-bath algorithmic cooling and that the cooling algorithm can be efficiently implemented; however, the saturation could require exponentially many iterations and remains impractical. This brings heat-bath algorithmic cooling to the realm of feasibility and makes it a viable option for realistic application in quantum technologies.
Rafeie, M, Hosseinzadeh, S, Huang, J, Mihandoust, A, Warkiani, ME & Taylor, RA 2019, 'New insights into the physics of inertial microfluidics in curved microchannels. II. Adding an additive rule to understand complex cross-sections', Biomicrofluidics, vol. 13, no. 3, pp. 034118-034118.
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Curved microchannels allow controllable microparticle focusing, but a full understanding of particle behavior has been limited—even for simple rectangular and trapezoidal shapes. At present, most microfluidic particle separation literature is dedicated to adding “internal” complexity (via sheath flow or obstructions) to relatively simple cross-sectional channel shapes. We propose that, with sufficient understanding of particle behavior, an equally viable pathway for microparticle focusing could utilize complex “external” cross-sectional shapes. By investigating three novel, complex spiral microchannels, we have found that it is possible to passively focus (6, 10, and 13 μm) microparticles in the middle of a convex channel. Also, we found that in concave and jagged channel designs, it is possible to create multiple, tight focusing bands. In addition to these performance benefits, we report an “additive rule” herein, which states that complex channels can be considered as multiple, independent, simple cross-sectional shapes. We show with experimental and numerical analysis that this new additive rule can accurately predict particle behavior in complex cross-sectional shaped channels and that it can help to extract general inertial focusing tendencies for suspended particles in curved channels. Overall, this work provides simple, yet reliable, guidelines for the design of advanced curved microchannel cross sections.
Rafeie, M, Hosseinzadeh, S, Taylor, RA & Warkiani, ME 2019, 'New insights into the physics of inertial microfluidics in curved microchannels. I. Relaxing the fixed inflection point assumption', Biomicrofluidics, vol. 13, no. 3, pp. 034117-034117.
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Inertial microfluidics represents a powerful new tool for accurately positioning cells and microparticles within fluids for a variety of biomedical, clinical, and industrial applications. In spite of enormous advancements in the science and design of these devices, particularly in curved microfluidic channels, contradictory experimental results have confounded researchers and limited progress. Thus, at present, a complete theory which describes the underlying physics is lacking. We propose that this bottleneck is due to one simple mistaken assumption—the locations of inflection points of the Dean velocity profile in curved microchannels are not fixed, but can actually shift with the flow rate. Herein, we propose that the dynamic distance (δ) between the real equilibrium positions and their nearest inflection points can clearly explain several (previously) unexplained phenomena in inertial microfluidic systems. More interestingly, we found that this parameter, δ, is a function of several geometric and operational parameters, all of which are investigated (in detail) here with a series of experiments and simulations of different spiral microchannels. This key piece of understanding is expected to open the door for researchers to develop new and more effective inertial microfluidic designs.
Rahman, SMA, Mahila, TMI, Ahmad, A, Nabi, MN, Jafari, M, Dowell, A, Islam, MA, Marchese, AJ, Tryner, J, Brooks, PR, Bodisco, TA, Stevanovic, S, Rainey, T, Ristovski, ZD & Brown, RJ 2019, 'Effect of Oxygenated Functional Groups in Essential Oils on Diesel Engine Performance, Emissions, and Combustion Characteristics', Energy & Fuels, vol. 33, no. 10, pp. 9828-9834.
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Waste management cost for Australia is increasing every year, and thus, it is important to find alternative ways to use the waste. For example, essential oil has a significant waste stream that can be utilized in vehicles of their producers. However, some of the essential oils contain oxygen which considerably affects engine performance, emission, and combustion characteristics of diesel engines. Thus, this research paper will try to evaluate the essential oils as a replacement of diesel fuel to operate a multicylider diesel engine. For this study, two essential oils are selected which contain different oxygenated functional groups, tea tree oil (5.4% oxygen) and eucalyptus oil (8.4% oxygen), with an aim to evaluate the effect of these functional groups on engine performance and emission parameters. These oils were blended with neat diesel (0% oxygen) to obtain a blend cotaining 2.2% oxygen by weight. The blends produced similar brake power; however, brake-specific fuel consumption (BSFC) increased for eucalyptus oil blends (2.4-3.7%) and tea tree oil blends (3.9-5.3%). Essential oil-diesel blends resulted in less CO and increased NOX emission, produced similar peak pressure, and indicated mean effective pressure. The results then lead to the conclusion that oxygenated essential oils can have a role to reduce dependency of agricultural sector on diesel in the near future.
Rahmati, O, Samadi, M, Shahabi, H, Azareh, A, Rafiei-Sardooi, E, Alilou, H, Melesse, AM, Pradhan, B, Chapi, K & Shirzadi, A 2019, 'SWPT: An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors', Geoscience Frontiers, vol. 10, no. 6, pp. 2167-2175.
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© 2019 China University of Geosciences (Beijing) and Peking University The sub-watershed prioritization is the ranking of different areas of a river basin according to their need to proper planning and management of soil and water resources. Decision makers should optimally allocate the investments to critical sub-watersheds in an economically effective and technically efficient manner. Hence, this study aimed at developing a user-friendly geographic information system (GIS) tool, Sub-Watershed Prioritization Tool (SWPT), using the Python programming language to decrease any possible uncertainty. It used geospatial–statistical techniques for analyzing morphometric and topo-hydrological factors and automatically identifying critical and priority sub-watersheds. In order to assess the capability and reliability of the SWPT tool, it was successfully applied in a watershed in the Golestan Province, Northern Iran. Historical records of flood and landslide events indicated that the SWPT correctly recognized critical sub-watersheds. It provided a cost-effective approach for prioritization of sub-watersheds. Therefore, the SWPT is practically applicable and replicable to other regions where gauge data is not available for each sub-watershed.
Rahmawati, R, Bilad, MR, Laziz, AM, Nordin, NAHM, Jusoh, N, Putra, ZA, Mahlia, TMI & Jaafar, J 2019, 'Finned spacer for efficient membrane fouling control in produced water filtration', Journal of Environmental Management, vol. 249, pp. 109359-109359.
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© 2019 Elsevier Ltd Membrane based technologies are highly reliable for water and wastewater treatment, including for removal of total oil and grease from produced water. However, performances of the pressure driven processes are highly restricted by membrane fouling and the application of traditional air bubbling system is limited by their low shear stress due to poor contacts with the membrane surface. This study develops and assesses a novel finned spacer, placed in between vertical panel, for membrane fouling control in submerged plate-and-frame module system for real produced water filtration. Results show that permeability of the panel is enhanced by 87% from 201 to 381 L/(m2 h bar). The spacer system can be operated in switching mode to accommodate two-sided panel aeration. This leads to panel permeability increment by 22% higher than the conventional vertical system. The mechanisms of finned spacer in encouraging the flow trajectory was proven by visual observation and flow simulation. The fins alter the air bubbles flow trajectory toward the membrane surface to effectively scour-off the foulant. Overall results demonstrate the efficacy of the developed spacer in projecting the air bubble trajectory toward the membrane surface and thus significantly enhances membrane panel productivity.
Rangel, L, Bernabé-Rubio, M, Fernández-Barrera, J, Casares-Arias, J, Millán, J, Alonso, MA & Correas, I 2019, 'Caveolin-1α regulates primary cilium length by controlling RhoA GTPase activity', Scientific Reports, vol. 9, no. 1.
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AbstractThe primary cilium is a single non-motile protrusion of the plasma membrane of most types of mammalian cell. The structure, length and function of the primary cilium must be tightly controlled because their dysfunction is associated with disease. Caveolin 1 (Cav1), which is best known as a component of membrane invaginations called caveolae, is also present in non-caveolar membrane domains whose function is beginning to be understood. We show that silencing of α and β Cav1 isoforms in different cell lines increases ciliary length regardless of the route of primary ciliogenesis. The sole expression of Cav1α, which is distributed at the apical membrane, restores normal cilium size in Cav1 KO MDCK cells. Cells KO for only Cav1α, which also show long cilia, have a disrupted actin cytoskeleton and reduced RhoA GTPase activity at the apical membrane, and a greater accumulation of Rab11 vesicles at the centrosome. Subsequent experiments showed that DIA1 and ROCK help regulate ciliary length. Since MDCK cells lack apical caveolae, our results imply that non-caveolar apical Cav1α is an important regulator of ciliary length, exerting its effect via RhoA and its effectors, ROCK and DIA1.
Rao, P, Zhao, L, Chen, Q & Nimbalkar, S 2019, 'Three-dimensional limit analysis of slopes reinforced with piles in soils exhibiting heterogeneity and anisotropy in cohesion', Soil Dynamics and Earthquake Engineering, vol. 121, pp. 194-199.
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© 2019 Elsevier Ltd Reinforcement of slopes by placing piles is one of the most common and effective techniques. Most of existing studies are limited to homogeneous and isotropic slopes, while in practice, the soil in the slope often exhibits heterogeneous and anisotropic characteristics. To address these issues, an innovative approach is introduced in this Technical Note to evaluate the stability of heterogeneous and anisotropic slopes incorporating the effect of anti-slide piles. Employing a three-dimensional upper-bound limit analysis, safety factor adopting the strength reduction technique is utilized herewith. The effects of soil heterogeneity and anisotropy with reference to cohesion on the optimal pile location and the slope stability in both cohesive-frictional and purely cohesive soils are investigated. The results amply demonstrate that the proposed limit analysis is appropriate for the stability assessment of reinforced slopes in heterogeneous and anisotropic soils. The safety factor increases with increase in heterogeneous factor and decrease in anisotropic factor. The optimal pile location is irrespective of these two factors, which should be carefully considered in engineering design.
Rao, P, Zhao, L, Chen, Q & Nimbalkar, S 2019, 'Three-Dimensional Slope Stability Analysis Incorporating Coupled Effects of Pile Reinforcement and Reservoir Drawdown', International Journal of Geomechanics, vol. 19, no. 4, pp. 06019002-06019002.
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© 2019 American Society of Civil Engineers. In pile-reinforced dams and bank slopes, the antislide effect of piles and drawdown of reservoirs are two aspects that could significantly affect the slope stability. However, existing studies have incorporated these two factors separately, albeit not in tandem. Moreover, stability assessment of these earth structures is usually performed ignoring the three-dimensional (3D) effect. To address these issues, the kinematic approach of limit analysis is adopted in this technical note for evaluating slope stability based on the 3D rotational failure mechanism. In addition, the coupled effects of pile reinforcement and water drawdown are considered. The analysis is performed for four types of drawdown cases. The results demonstrate that the optimal pile location undergoes significant change during the external drawdown process, while the effect of the declining water level on slope stability follows the similar pattern for varying pile locations.
Rao, T, Li, X, Zhang, H & Xu, M 2019, 'Multi-level region-based Convolutional Neural Network for image emotion classification', Neurocomputing, vol. 333, pp. 429-439.
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© 2018 Analyzing emotional information of visual content has attracted growing attention for the tendency of internet users to share their feelings via images and videos online. In this paper, we investigate the problem of affective image analysis, which is very challenging due to its complexity and subjectivity. Previous research reveals that image emotion is related to low-level to high-level visual features from both global and local view, while most of the current approaches only focus on improving emotion recognition performance based on single-level visual features from a global view. Aiming to utilize different levels of visual features from both global and local view, we propose a multi-level region-based Convolutional Neural Network (CNN) framework to discover the sentimental response of local regions. We first employ Feature Pyramid Network (FPN) to extract multi-level deep representations. Then, an emotional region proposal method is used to generate proper local regions and remove excessive non-emotional regions for image emotion classification. Finally, to deal with the subjectivity in emotional labels, we propose a multi-task loss function to take the probabilities of images belonging to different emotion classes into consideration. Extensive experiments show that our method outperforms the state-of-the-art approaches on various commonly used benchmark datasets.
Raoufi, MA, Mashhadian, A, Niazmand, H, Asadnia, M, Razmjou, A & Warkiani, ME 2019, 'Experimental and numerical study of elasto-inertial focusing in straight channels', Biomicrofluidics, vol. 13, no. 3, pp. 034103-034103.
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Elasto-inertial microfluidics has drawn significant attention in recent years due to its enhanced capabilities compared to pure inertial systems in control of small microparticles. Previous investigations have focused mainly on the applications of elasto-inertial sorting, rather than studying its fundamentals. This is because of the complexity of simulation and analysis, due to the presence of viscoelastic force. There have been some investigative efforts on the mechanisms of elasto-inertial focusing in straight channels; however, these studies were limited to simple rectangular channels and neglected the effects of geometry and flow rates on focusing positions. Herein, for the first time, we experimentally and numerically explore the effects of elasticity accompanying channel cross-sectional geometry and sample flow rates on the focusing phenomenon in elasto-inertial systems. The results reveal that increasing the aspect ratio weakens the elastic force more than inertial force, causing a transition from one focusing position to two. In addition, they show that increasing the angle of a channel corner causes the elastic force to push the particles more efficiently toward the center over a larger area of the channel cross section. Following on from this, we proposed a new complex straight channel which demonstrates a tighter focusing band compared to other channel geometries. Finally, we focused Saccharomyces cerevisiae cells (3–5 μm) in the complex channel to showcase its capability in focusing small-size particles. We believe that this research work improves the understanding of focusing mechanisms in viscoelastic solutions and provides useful insights into the design of elasto-inertial microfluidic devices.
Raoufi, MA, Moshizi, SA, Razmjou, A, Wu, S, Ebrahimi Warkiani, M & Asadnia, M 2019, 'Development of a Biomimetic Semicircular Canal With MEMS Sensors to Restore Balance', IEEE Sensors Journal, vol. 19, no. 23, pp. 11675-11686.
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© 2001-2012 IEEE. A third of adults over the age of 50 suffer from chronic impairment of balance, posture, and/or gaze stability due to partial or complete impairment of the sensory cells in the inner ear responsible for these functions. The consequences of impaired balance organ can be dizziness, social withdrawal, and acceleration of the further functional decline. Despite the significant progress in biomedical sensing technologies, current artificial vestibular systems fail to function in practical situations and in very low frequencies. Herein, we introduced a novel biomechanical device that closely mimics the human vestibular system. A microelectromechanical systems (MEMS) flow sensor was first developed to mimic the vestibular haircell sensors. The sensor was then embedded into a three-dimensional (3D) printed semicircular canal and tested at various angular accelerations in the frequency range from 0.5Hz to 1.5Hz. The miniaturized device embedded into a 3D printed model will respond to mechanical deflections and essentially restore the sense of balance in patients with vestibular dysfunctions. The experimental and simulation studies of semicircular canal presented in this work will pave the way for the development of balance sensory system, which could lead to the design of a low-cost and commercially viable medical device with significant health benefits and economic potential.
Rasouli, H & Fatahi, B 2019, 'A novel cushioned piled raft foundation to protect buildings subjected to normal fault rupture', Computers and Geotechnics, vol. 106, pp. 228-248.
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© 2018 Elsevier Ltd Recent earthquake events have shown that besides the earthquake forces, interaction between the fault rupture and structure could cause a lot of damage to the surface and underground structures. Field observations have revealed a need to design structures for fault induced loading in regions with active faults. In this present study, three-dimensional numerical modelling using ABAQUS finite element software is used to study the interactive mechanism of normal fault rupture with a 20-story moment-resisting building frame sitting on a raft, connected piled raft, and cushioned piled raft foundations. The performance of a foundation-structure system is examined by considering geotechnical and structural performance objectives such as structural inter-story drift, raft displacement, and the bending moment and shear forces within the raft and piles. In order to improve the geotechnical and structural performance of foundations and buildings, a new foundation system with cushioned piles below the raft is proposed because of its superior performance with regards to raft rocking and permanent structural inter-story drifts under normal fault rupture. This proposed foundation system also curtailed the bending moments induced in the piles.
Raza, M, Hussain, FK, Hussain, OK, Zhao, M & Rehman, ZU 2019, 'A comparative analysis of machine learning models for quality pillar assessment of SaaS services by multi-class text classification of users’ reviews', Future Generation Computer Systems, vol. 101, pp. 341-371.
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Razavi Bazaz, S, Kashaninejad, N, Azadi, S, Patel, K, Asadnia, M, Jin, D & Ebrahimi Warkiani, M 2019, 'Microfluidics: Rapid Softlithography Using 3D‐Printed Molds (Adv. Mater. Technol. 10/2019)', Advanced Materials Technologies, vol. 4, no. 10, pp. 1970056-1970056.
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Razavi Bazaz, S, Kashaninejad, N, Azadi, S, Patel, K, Asadnia, M, Jin, D & Ebrahimi Warkiani, M 2019, 'Rapid Softlithography Using 3D‐Printed Molds', Advanced Materials Technologies, vol. 4, no. 10, pp. 1900425-1900425.
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AbstractPolydimethylsiloxane (PDMS) is a long‐standing material of significant interest in microfluidics due to its unique features. As such, rapid prototyping of PDMS‐based microchannels is of great interest. The most prevalent and conventional method for fabrication of PDMS‐based microchips relies on softlithography, the main drawback of which is the preparation of a master mold, which is costly and time‐consuming. To prevent the attachment of PDMS to the master mold, silanization is necessary, which can be detrimental for cellular studies. Additionally, using coating the mold with a cell‐compatible surfactant adds extra preprocessing time. Recent advances in 3D printing have shown great promise in expediting microfabrication. Nevertheless, current 3D printing techniques are sub‐optimal for PDMS softlithography. The feasibility of producing master molds suitable for rapid softlithography is demonstrated using a newly developed 3D‐printing resin. Moreover, the utility of this technique is showcased for a number of widely used applications, such as concentration gradient generation, particle separation, cell culture (to show biocompatibility of the process), and fluid mixing. This can open new opportunities for biologists and scientists with minimum knowledge of microfabrication to build functional microfluidic devices for their basic and applied research.
Razzak, I, A. Hameed, I & Xu, G 2019, 'Robust Sparse Representation and Multiclass Support Matrix Machines for the Classification of Motor Imagery EEG Signals', IEEE Journal of Translational Engineering in Health and Medicine, vol. 7, pp. 1-8.
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© 2013 IEEE. Background: EEG signals are extremely complex in comparison to other biomedical signals, thus require an efficient feature selection as well as classification approach. Traditional feature extraction and classification methods require to reshape the data into vectors that results in losing the structural information exist in the original featured matrix. Aim: The aim of this work is to design an efficient approach for robust feature extraction and classification for the classification of EEG signals. Method: In order to extract robust feature matrix and reduce the dimensionality of from original epileptic EEG data, in this paper, we have applied robust joint sparse PCA (RJSPCA), Outliers Robust PCA (ORPCA) and compare their performance with different matrix base feature extraction methods, followed by classification through support matrix machine. The combination of joint sparse PCA with robust support matrix machine showed good generalization performance for classification of EEG data due to their convex optimization. Results: A comprehensive experimental study on the publicly available EEG datasets is carried out to validate the robustness of the proposed approach against outliers. Conclusion: The experiment results, supported by the theoretical analysis and statistical test, show the effectiveness of the proposed framework for solving classification of EEG signals.
Razzak, I, Blumenstein, M & Xu, G 2019, 'Multiclass Support Matrix Machines by Maximizing the Inter-Class Margin for Single Trial EEG Classification', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 6, pp. 1117-1127.
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© 2001-2011 IEEE. Accurate classification of Electroencephalogram (EEG) signals plays an important role in diagnoses of different type of mental activities. One of the most important challenges, associated with classification of EEG signals is how to design an efficient classifier consisting of strong generalization capability. Aiming to improve the classification performance, in this paper, we propose a novel multiclass support matrix machine (M-SMM) from the perspective of maximizing the inter-class margins. The objective function is a combination of binary hinge loss that works on C matrices and spectral elastic net penalty as regularization term. This regularization term is a combination of Frobenius and nuclear norm, which promotes structural sparsity and shares similar sparsity patterns across multiple predictors. It also maximizes the inter-class margin that helps to deal with complex high dimensional noisy data. The extensive experiment results supported by theoretical analysis and statistical tests show the effectiveness of the M-SMM for solving the problem of classifying EEG signals associated with motor imagery in brain-computer interface applications.
Razzak, MI, Imran, M & Xu, G 2019, 'Efficient Brain Tumor Segmentation With Multiscale Two-Pathway-Group Conventional Neural Networks', IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 5, pp. 1911-1919.
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© 2013 IEEE. Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of deep learning such as convolutional neural networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here, we describe a new model two-pathway-group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the two-pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into two-pathway-group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 data sets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive.
Reddy, TK, Arora, V, Behera, L, Wang, Y-K & Lin, C-T 2019, 'Multiclass Fuzzy Time-Delay Common Spatio-Spectral Patterns With Fuzzy Information Theoretic Optimization for EEG-Based Regression Problems in Brain–Computer Interface (BCI)', IEEE Transactions on Fuzzy Systems, vol. 27, no. 10, pp. 1943-1951.
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© 2019 IEEE. Electroencephalogram (EEG) signals are one of the most widely used noninvasive signals in brain-computer interfaces. Large dimensional EEG recordings suffer from poor signal-to-noise ratio. These signals are very much prone to artifacts and noise, so sufficient preprocessing is done on raw EEG signals before using them for classification or regression. Properly selected spatial filters enhance the signal quality and subsequently improve the rate and accuracy of classifiers, but their applicability to solve regression problems is quite an unexplored objective. This paper extends common spatial patterns (CSP) to EEG state space using fuzzy time delay and thereby proposes a novel approach for spatial filtering. The approach also employs a novel fuzzy information theoretic framework for filter selection. Experimental performance on EEG-based reaction time (RT) prediction from a lane-keeping task data from 12 subjects demonstrated that the proposed spatial filters can significantly increase the EEG signal quality. A comparison based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation to true responses is made for all the subjects. In comparison to the baseline fuzzy CSP regression one versus rest, the proposed Fuzzy Time-delay Common Spatio-Spectral filters reduced the RMSE on an average by 9.94%, increased the correlation to true RT on an average by 7.38%, and reduced the MAPE by 7.09%.
Reddy, TK, Arora, V, Kumar, S, Behera, L, Wang, Y-K & Lin, C-T 2019, 'Electroencephalogram Based Reaction Time Prediction With Differential Phase Synchrony Representations Using Co-Operative Multi-Task Deep Neural Networks', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 3, no. 5, pp. 369-379.
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Rehman, J, Sohaib, O, Asif, M & Pradhan, B 2019, 'Applying systems thinking to flood disaster management for a sustainable development', International Journal of Disaster Risk Reduction, vol. 36, pp. 101101-101101.
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© 2019 Elsevier Ltd The rapid urbanization and environmental imbalance have significantly challenged Pakistan's organizational capacity to respond and initiate relief efforts and hence increasing its vulnerability to flood disaster situations. This study considers systems thinking approaches such as, Causal Loop Diagram (CLD) and Driver-Pressures-States-Impacts-Responses (DPSIR) framework to identify key stakeholders to disaster risk reduction and analyze various social, technical, institutional, cultural, infrastructural and environmental factors that contribute to flooding in Pakistan. Based on the information collected through expert interviews with key government officials and analyzing the existing literature and research reports on floods and disaster management, policy recommendations for long-term flood disaster response strategies have been made. The comprehensive set of recommendations towards effective flood management and mitigation would help build resilience from floods by raising community awareness and enhancing institutional capacities at federal, provincial and district government levels in the countries like Pakistan and other developing nations facing catastrophic flood situations.
Ren, C, Lyu, X, Ni, W, Tian, H & Liu, RP 2019, 'Distributed Online Learning of Fog Computing Under Nonuniform Device Cardinality', IEEE Internet of Things Journal, vol. 6, no. 1, pp. 1147-1159.
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© 2014 IEEE. Processing data around the point of capture, fog computing can support computationally demanding Internet-of-Things (IoT) services. Distributed online optimization is important given the size of IoT, but challenging due to time variations of random traffic and nonuniform connectivity (or cardinality) of edge servers and IoT devices. This paper presents a distributed online learning approach to asymptotically minimizing the time-average cost of fog computing in the absence of the a-priori knowledge on traffic randomness, for light-weight, and delay-tolerant application scenarios. Stochastic gradient descent is exploited to decouple the optimizations between time slots. A graph matching problem is then formulated for every time slot by decoupling and unifying the nonuniform cardinalities, and solved in a distributed manner by developing a new linear (1/2)-approximation method. We prove that the optimality loss resulting from the distributed approximate graph matching method can be compensated and diminish by increasing the learning time. Corroborated by simulations, the proposed distributed online learning is asymptotically optimal and superior to the state of the art in terms of throughput and energy efficiency.
Ren, C, Lyu, X, Ni, W, Tian, H & Liu, RP 2019, 'Profitable Cooperative Region for Distributed Online Edge Caching', IEEE Transactions on Communications, vol. 67, no. 7, pp. 4696-4708.
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© 2019 IEEE. Cooperative caching can unify network storage to improve efficiency, but the effective placement and search of contents are challenging especially in distributed edge clouds with neither a-priori knowledge on content requests nor instantaneous global view. This paper establishes a new profitable cooperative region for every content request admitted at an edge server, within which the content, if cached, can be retrieved with guaranteed profit against a direct retrieval from the network backbone. This narrows down the search for the content. The caching density of the content can also be significantly reduced, e.g., to a cached copy per region. The regions are based on a novel distributed framework which allows individual servers to spontaneously admit/dispatch requests and deliver/forward contents, while asymptotically maximizing the time-average profit of caching. The cooperative region for content is erected at individual servers by comparing the upper and lower bounds for the backlogs of unsatisfied requests of the content. Simulations show the substantially improved profit of the proposed approach over existing solutions. The regions can help automate the placement of contents with reduced density and improved efficiency.
Ren, J, Woo, YC, Yao, M, Lim, S, Tijing, LD & Shon, HK 2019, 'Nanoscale zero-valent iron (nZVI) immobilization onto graphene oxide (GO)-incorporated electrospun polyvinylidene fluoride (PVDF) nanofiber membrane for groundwater remediation via gravity-driven membrane filtration', Science of The Total Environment, vol. 688, pp. 787-796.
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Nanoscale zero-valent iron (nZVI), with its high reactivity towards a broad range of contaminants, has been a promising material for groundwater remediation. Membrane-supported nZVI can both avoid nZVI agglomeration for better reactivity and recycle nZVI to lower the risk of secondary pollution. In this study, we successfully fabricated a PVDF-GO membrane via electrospinning technology and employed the functionalized nanofiber membrane to immobilize nZVI particles. The addition of GO into PVDF nanofibers can both increase the hydrophilicity to improve membrane flux and offer -COOH as a binder to immobilize nZVI particles. PVDF-GO-nZVI membranes with different GO loadings (0%, 0.5%, 1%, 3% of PVDF) were tested with two typical nZVI-targeted contaminants (Cd(II) and trichloroethylene (TCE)) via gravity-driven membrane filtration. The results show that membrane with 1% GO had the best nZVI distribution against the aggregation and a better performance in both Cd removal (100%) and TCE removal (82%). The nZVI membrane had a high flux in gravity-driven filtration at 255 LMH for Cd(II) and 265 LMH for TCE respectively. Generally, the developed PVDF-GO-nZVI electrospun nanofiber membrane had an excellent performance in the gravity-driven membrane filtration system for groundwater remediation.
Ren, J, Yao, M, Woo, YC, Tijing, LD, Kim, J-H & Shon, HK 2019, 'Recyclable nanoscale zerovalent iron (nZVI)-immobilized electrospun nanofiber composites with improved mechanical strength for groundwater remediation', Composites Part B: Engineering, vol. 171, pp. 339-346.
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© 2019 Elsevier Ltd Nanoscale zero-valent iron (nZVI), as a promising material, has been widely used in groundwater remediation. Membrane-supported nZVI can both avoid nZVI agglomeration for better reactivity and recycle nZVI/contaminants to lower the risk of secondary pollution. However, membrane mechanical strength is a critical property for long-term operation and the regeneration of nZVI membranes. This study tried to use a high molecular weight dual-crosslinking method to improve the mechanical strength of polymeric electrospun nanofiber membranes. Specifically, high molecular weight polyacrylic acid (PAA, Mw = 450,000) was dual-crosslinked by adding polyvinyl alcohol (PVA) as covalent cross-linker (named as M450k) and Fe(II) or Fe(III) as the ionic cross-linker (named as M450k-II and M450k-III). The results indicated that the M450k had better thermal resistance against membrane shrinkage, thus having larger surface areas and more –COOH groups to immobilize more nZVI particles. Besides, M450k-III had the highest tensile strength at 8.5 MPa, 5 times the figure for the mono-crosslinked low molecular weight membrane (M2k). In terms of nZVI immobilization and filtration performance, the Fe(II)-crosslinked membrane had better nZVI immobilization with the highest removal capacity at 463 mg/g while Fe(III)-crosslinked membrane had overwhelming mechanical strength with decent and stable removal capacity under multiple nZVI regenerations over 15 filtration cycles. Generally, the high molecular weight Fe(III)-crosslinked PAA-PVA electrospun nZVI showed better potential for long-term filtration process.
Ren, Y, Ngo, HH, Guo, W, Ni, B-J & Liu, Y 2019, 'Linking the nitrous oxide production and mitigation with the microbial community in wastewater treatment: A review', Bioresource Technology Reports, vol. 7, pp. 100191-100191.
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© 2019 Elsevier Ltd Nitrous oxide (N2O) is largely produced during wastewater treatment. However, there is a lack of review on linking N2O production and mitigation with microbial communities in wastewater treatment. In this study, various microbial communities contributing to N2O turnovers are reviewed according to their functions in nitrogen cycle, including ammonia oxidizing bacteria and archaea, comammox bacteria, autotrophic denitrifying bacteria, heterotrophic denitrifying bacteria and non-denitrifying N2O reducers. Their metabolic pathways and enzymatic reactions of N2O production are demonstrated, including nitrifier denitrification, nitritation, archaeal N2O production and denitrification pathways. The N2O emission factor of the nitrifier denitrification pathway is generally higher than nitritation pathway, and that of denitrifying bacteria depends on species and electron acceptors. The mitigation strategies are developed according to the dominating microbial communities. Overall, this review illustrates a comprehensive characteristic of N2O production by microbial communities in wastewater treatment, which could contribute to the development of effective N2O mitigation strategies.
Reyhani, A, McKenzie, TG, Fu, Q & Qiao, GG 2019, 'Fenton‐Chemistry‐Mediated Radical Polymerization', Macromolecular Rapid Communications, vol. 40, no. 18, pp. e1900220-1900220.
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AbstractIn this review, the power of a classical chemical reaction, the Fenton reaction for initiating radical polymerizations, is demonstrated. The reaction between the Fenton reagents (i.e., Fe2+ and H2O2) generates highly reactive hydroxyl radicals, which can act as radical initiators for the polymerization of vinyl monomers. Since the Fenton reaction is fast, easy to set up, cheap, and biocompatible, this unique chemistry is widely employed in various polymer synthesis studies via free radical polymerization or reversible addition–fragmentation chain transfer polymerization, and is utilized in a wide range of applications, such as the fabrication of biomaterials, hydrogels, and core‐shell particles. Biologically activated Fenton‐mediated radical polymerization, which can be performed in aerobic environments, are particularly useful for applications in biomedical systems.
Reyhani, A, McKenzie, TG, Fu, Q & Qiao, GG 2019, 'Redox-Initiated Reversible Addition–Fragmentation Chain Transfer (RAFT) Polymerization', Australian Journal of Chemistry, vol. 72, no. 7, pp. 479-479.
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Reversible addition–fragmentation chain transfer (RAFT) polymerization initiated by a radical-forming redox reaction between a reducing and an oxidizing agent (i.e. ‘redox RAFT’) represents a simple, versatile, and highly useful platform for controlled polymer synthesis. Herein, the potency of a wide range of redox initiation systems including enzyme-mediated redox reactions, the Fenton reaction, peroxide-based reactions, and metal-catalyzed redox reactions, and their application in initiating RAFT polymerization, are reviewed. These redox-RAFT polymerization methods have been widely studied for synthesizing a broad range of homo- and co-polymers with tailored molecular weights, compositions, and (macro)molecular structures. It has been demonstrated that redox-RAFT polymerization holds particular promise due to its excellent performance under mild conditions, typically operating at room temperature. Redox-RAFT polymerization is therefore an important and core part of the RAFT methodology handbook and may be of particular importance going forward for the fabrication of polymeric biomaterials under biologically relevant conditions or in biological systems, in which naturally occurring redox reactions are prevalent.
Reyhani, A, Ranji-Burachaloo, H, McKenzie, TG, Fu, Q & Qiao, GG 2019, 'Heterogeneously Catalyzed Fenton-Reversible Addition–Fragmentation Chain Transfer Polymerization in the Presence of Air', Macromolecules, vol. 52, no. 9, pp. 3278-3287.
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© 2019 American Chemical Society. Aqueous Fenton-reversible addition-fragmentation chain transfer (RAFT) polymerization catalyzed by heterogeneous catalysts, that is, Fe(II) metal-organic framework (MOF) particles, coupled with hydrogen peroxide (H2O2) with the reaction mixture exposed to air in open vessels is reported. Reactive hydroxyl radicals are generated via a heterogeneous redox reaction between Fe(II) of the MOF particles and H2O2, which then chemically deoxygenate the reaction mixture in situ, initiating RAFT polymerization. Well-controlled polymers (Ä < 1.1) with experimental molecular weights close to theoretical values at high monomer conversions (ca. 85%) were achieved within 15 min. High 'livingness' of the synthesized polymer chains was demonstrated by chain extension experiments and matrix-assisted laser desorption/ionization time-of-flight analysis. This study contributes to the growing interest in nonenzymatic deoxygenation techniques via heterogeneous catalysis for conducting radical polymerization reactions.
Rezaei, M, Winter, M, Zander-Fox, D, Whitehead, C, Liebelt, J, Warkiani, ME, Hardy, T & Thierry, B 2019, 'A Reappraisal of Circulating Fetal Cell Noninvasive Prenatal Testing', Trends in Biotechnology, vol. 37, no. 6, pp. 632-644.
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© 2018 Elsevier Ltd New tools for higher-resolution fetal genome analysis including microarray and next-generation sequencing have revolutionized prenatal screening. This article provides commentary on this rapidly advancing field and a future perspective emphasizing circulating fetal cell (CFC) utility. Despite the tremendous technological challenges associated with their reliable and cost-effective isolation from maternal blood, CFCs have a strong potential to bridge the gap between the diagnostic sensitivity of invasive procedures and the desirable noninvasive nature of cell-free fetal DNA (cffDNA). Considering the rapid advances in both rare cell isolation and low-input DNA analysis, we argue here that CFC-based noninvasive prenatal testing is poised to be implemented clinically in the near future.
Rialp, A, Merigó, JM, Cancino, CA & Urbano, D 2019, 'Twenty-five years (1992–2016) of the International Business Review: A bibliometric overview', International Business Review, vol. 28, no. 6, pp. 101587-101587.
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© 2019 Elsevier Ltd The International Business Review (IBR) is a leading international academic journal in the field of International Business (IB). Such leadership is reflected in the large number of publications that grow year after year and particularly in the large number of citations received from other journals of high academic prestige. The aim of this study is to conduct a bibliometric overview of the leading trends regarding the journal's publications and citations since its creation in 1992 until 2016. The work identifies the authors, universities, and countries that publish the most in IBR by mainly using the Scopus database though eventually complemented with Web of Science (WoS) Core Collection. It also analyzes the most cited papers and articles of the journal. Besides, the study graphically maps the bibliographic material by using the visualization of similarities (VOS) viewer software. In order to do so, the work uses co-citation analysis, bibliographic coupling, and co-occurrence of author keywords. The results show the prominent European profile of the journal where contributors from European universities and countries are the most productive ones in the journal. Particularly, British and Scandinavian universities obtain the most remarkable results. However, mostly scholars from North America, but also from Oceania and East Asia are increasingly and regularly publishing in the journal. In addition, IBR is very well connected to other leading journals in the field, such as the Journal of International Business Studies (JIBS) and the Journal of World Business (JWB), as well as with other top management journals, thus demonstrating its core position in IB research conducted worldwide.
Rifai, A, Tran, N, Reineck, P, Elbourne, A, Mayes, E, Sarker, A, Dekiwadia, C, Ivanova, EP, Crawford, RJ, Ohshima, T, Gibson, BC, Greentree, AD, Pirogova, E & Fox, K 2019, 'Engineering the Interface: Nanodiamond Coating on 3D-Printed Titanium Promotes Mammalian Cell Growth and Inhibits Staphylococcus aureus Colonization', ACS Applied Materials & Interfaces, vol. 11, no. 27, pp. 24588-24597.
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Rizeei, HM & Pradhan, B 2019, 'Urban Mapping Accuracy Enhancement in High-Rise Built-Up Areas Deployed by 3D-Orthorectification Correction from WorldView-3 and LiDAR Imageries', Remote Sensing, vol. 11, no. 6, pp. 692-692.
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Orthorectification is an important step in generating accurate land use/land cover (LULC) from satellite imagery, particularly in urban areas with high-rise buildings. Such buildings generally appear as oblique shapes on very-high-resolution (VHR) satellite images, which reflect a bigger area of coverage than the real built-up area on LULC mapping. This drawback can cause not only uncertainties in urban mapping and LULC classification, but can also result in inaccurate urban change detection. Overestimating volume or area of high-rise buildings has a negative impact on computing the exact amount of environmental heat and emission. Hence, in this study, we propose a method of orthorectfiying VHR WorldView-3 images by integrating light detection and ranging (LiDAR) data to overcome the aforementioned problems. A 3D rational polynomial coefficient (RPC) model was proposed with respect to high-accuracy ground control points collected from the LiDAR data derived from the digital surface model. Multiple probabilities for generating an orthrorectified image from WV-3 were assessed using 3D RCP model to achieve the optimal combination technique, with low vertical and horizontal errors. Ground control point (GCPs) collection is sensitive to variation in number and data collection pattern. These steps are important in orthorectification because they can cause the morbidity of a standard equation, thereby interrupting the stability of 3D RCP model by reducing the accuracy of the orthorectified image. Hence, we assessed the maximum possible scenarios of resampling and ground control point collection techniques to bridge the gap. Results show that the 3D RCP model accurately orthorectifies the VHR satellite image if 20 to 100 GCPs were collected by convenience pattern. In addition, cubic conventional resampling algorithm improved the precision and smoothness of the orthorectified image. According to the root mean square error, the proposed combination techni...
Rizeei, HM, Pradhan, B & Saharkhiz, MA 2019, 'Allocation of emergency response centres in response to pluvial flooding-prone demand points using integrated multiple layer perceptron and maximum coverage location problem models', International Journal of Disaster Risk Reduction, vol. 38, pp. 101205-101205.
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© 2019 The increases in the frequency and intensity of rainfall events due to global climate change and the development of additional pavement, roads and water storage sites due to population growth have enhanced the probability of pluvial flooding (PF) in urban areas. The estimation of urban pluvial flood vulnerability and prompt emergency responses are crucial steps towards urban planning and risk mitigation. However, uncertainties exist in the optimal allocation of emergency response centres (ERCs). This study assessed the current situation of ERCs in terms of PF-prone demand points. In this study, fire and police stations, hospitals and military camps were defined as ERCs, and residential buildings, where people spend most of their time, were considered demand points. Our study area was Damansara City in Peninsular Malaysia, which is frequently affected by PF. We combined an optimised PF probability model with ideal location allocation methods on a geographic information system platform to construct the proposed model for achieving accurate ERC spatial planning. Firstly, PF-prone urban areas were identified using a recent machine learning multiple layer perceptron (MLP) model. Then, a Taguchi method was used to calibrate the MLP variables, namely, seed, momentum, learning rate, hidden layer attribute and class. Fourteen important PF contributing parameters were weighted on the basis of historical flood events. The predicted PF-prone areas were validated by comparing the predictions with the data from meteorological stations and observed inventory events. In addition, the current locations of ERCs were utilised in the location allocation model to assess the ideal time for providing essential services to elements at risk. Minimum impedance and maximum coverage location problem models were implemented to assess the current allocated location of ERCs and multiple scenarios. The coverage of existing ERCs was calculated, and their suitable and optimal locations wer...
Rizeei, HM, Pradhan, B & Saharkhiz, MA 2019, 'An integrated fluvial and flash pluvial model using 2D high-resolution sub-grid and particle swarm optimization-based random forest approaches in GIS', Complex & Intelligent Systems, vol. 5, no. 3, pp. 283-302.
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Rizeei, HM, Pradhan, B, Saharkhiz, MA & Lee, S 2019, 'Groundwater aquifer potential modeling using an ensemble multi-adoptive boosting logistic regression technique', Journal of Hydrology, vol. 579, pp. 124172-124172.
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© 2019 Machine learning and data-driven models have achieved a favorable reputation in the field of advanced geospatial modeling, particularly for models of groundwater aquifer potential over large areas. Such models built using standalone machine learning techniques retain some uncertainty, including errors associated with the modeling process, sampling approach, and input hyper-parameters. Some of these techniques cannot be applied in data-scarce regions because high bias and variance can lead to oversimplification. Therefore, in the current study, we developed and validated a novel ensemble multi-adaptive boosting logistic regression (MABLR) model for groundwater aquifer potential mapping. This model was validated in a large area of the Gyeongsangbuk-do basin in South Korea and the results were compared to those of different types of machine learning models including multiple-layer perception (MPL), logistic regression (LR), and support vector machine (SVM) models. A forward stepwise LR technique was implemented to assess the importance of contributing morphological factors; we found 15 factors that contributed significantly: topographic wetness index (TWI), topographic roughness index (TRI), stream power index (SPI), topographic position index (TPI), multi-resolution valley bottom flatness (MVBF), slope, aspect, slope length (LS), distance from the river, distance from the fault, profile curvature, plane curvature, altitude, land use/land cover (LULC), and geology. We optimized the MABLR model using a fuzzy logic supervised (FLS) approach with 184 iterations and then validated the results using accuracy assessment metrics including the κ coefficient, root-mean-square error (RMSE), receiver operating characteristics (ROC), and the precision-recall curve (PRC). Our model had superior predictive performance among the models tested, with higher overall goodness-of-fit and validation values according to the κ coefficient (0.819 and 0.781, respectively), ROC (0.917...
Robone, A, Kuruneru, STW, Islam, MS & Saha, SC 2019, 'A macroscopic particle modelling approach for non-isothermal solid-gas and solid-liquid flows through porous media', Applied Thermal Engineering, vol. 162, pp. 114232-114232.
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The complexity of multiphase flows in many engineering systems such as heat exchangers signify the need to develop new and advanced numerical models to analyse the interactions the working fluid and unwanted solid foulants. Fouling is present in a myriad of industrial and domestic processes and it has a negative impact on the economy and the environment. The mechanisms that govern non-isothermal solid-fluid flow through porous metal foam heat exchangers are complex and poorly understood. In this research, a coupled finite volume method (FVM) and macroscopic particle model (MPM) is developed and implemented in ANSYS Fluent to examine the transient evolution of a non-isothermal multiphase solid-fluid flow and the interaction between coupled interactions of solid particles, fluid, and porous media. The maximum particle temperature is dependent on the fluid and solid particle thermo-physical properties in addition to the temperature of the cylindrical ligaments of the porous media. The present results show that the smallest solid particles reach the highest temperatures in the porous heat exchanger and at low inlet velocities, the highest particle temperatures are realized. The results pertaining to maximum particle temperatures are prevalent in many industrial processes and acquiring knowledge of the maximum particle temperature serves as a steppingstone for comprehending complex multiphase solid-fluid flows such as the cohesiveness between the particles and the particle adhesion with the walls. The results of these studies could potentially be used in the future to optimize metal foam heat exchanger designs.
Rojas Sánchez, D, Hoadley, AFA & Khalilpour, KR 2019, 'A multi-objective extended input–output model for a regional economy', Sustainable Production and Consumption, vol. 20, pp. 15-28.
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© 2019 Institution of Chemical Engineers Economies are constrained by a variety of economic, social, and political factors to attempt a reduction in environmental impacts such as global warming. While improvements in technology are commonly the expected general solution, lifestyle changes and modifications on consumption are also necessary to effectively reduce pollution. Such regulations may modify production activities, thus altering economic and social stability. Therefore, analyses need to consider numerous different aspects to locate suitable sectors in the economy in which emission reduction can be accomplished with the least socio-economic impact. Through an extended input–output analysis, this paper gives an insight into the intricate relationship of the sectors of an economic region and their respective greenhouse gas (GHG) emissions. Taking both a producer and a consumer-based perspective, sectors are analysed and categorised through various socio-economic and environmental indicators. The descriptive approach discerns between emission inventories of production activities and embodied emissions of consumption patterns, thus assigning a different responsibility to the carbon footprint of industrial activities. Further, an innovative multi-objective optimisation model is developed with consideration of Gross Domestic Product (GDP), GHG emissions, and employment. This methodology enables mapping an optimised space of scenarios for emission reduction through consumption limitation with a minimal socio-economic loss. The Australian economy was used as a case study. Results show a substantial difference in the allocation of emissions from a producer and a consumer perspective, indicating that many sectors rely on a small number of emissions-intensive sectors for their activities. Through the optimisation, all possible emission reductions are linked to consumption limit scenarios of minimised economic and employment losses. The model is effective in ...
Roodposhti, M, Aryal, J & Pradhan, B 2019, 'A Novel Rule-Based Approach in Mapping Landslide Susceptibility', Sensors, vol. 19, no. 10, pp. 2274-2274.
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Despite recent advances in developing landslide susceptibility mapping (LSM) techniques, resultant maps are often not transparent, and susceptibility rules are barely made explicit. This weakens the proper understanding of conditioning criteria involved in shaping landslide events at the local scale. Further, a high level of subjectivity in re-classifying susceptibility scores into various classes often downgrades the quality of those maps. Here, we apply a novel rule-based system as an alternative approach for LSM. Therein, the initially assembled rules relate landslide-conditioning factors within individual rule-sets. This is implemented without the complication of applying logical or relational operators. To achieve this, first, Shannon entropy was employed to assess the priority order of landslide-conditioning factors and the uncertainty of each rule within the corresponding rule-sets. Next, the rule-level uncertainties were mapped and used to asses the reliability of the susceptibility map at the local scale (i.e., at pixel-level). A set of If-Then rules were applied to convert susceptibility values to susceptibility classes, where less level of subjectivity is guaranteed. In a case study of Northwest Tasmania in Australia, the performance of the proposed method was assessed by receiver operating characteristics’ area under the curve (AUC). Our method demonstrated promising performance with AUC of 0.934. This was a result of a transparent rule-based approach, where priorities and state/value of landslide-conditioning factors for each pixel were identified. In addition, the uncertainty of susceptibility rules can be readily accessed, interpreted, and replicated. The achieved results demonstrate that the proposed rule-based method is beneficial to derive insights into LSM processes.
Roselin, AG, Nanda, P, Nepal, S, He, X & Wright, J 2019, 'Exploiting the Remote Server Access Support of CoAP Protocol', IEEE Internet of Things Journal, vol. 6, no. 6, pp. 9338-9349.
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© 2014 IEEE. The constrained application protocol (CoAP) is a specially designed Web transfer protocol for use with constrained nodes and low-power networks. The widely available CoAP implementations have failed to validate the remote CoAP clients. Each CoAP client generates a random source port number when communicating with the CoAP server. However, we observe that in such implementations it is difficult to distinguish the regular packet and the malicious packet, opening a door for a potential off-path attack. The off-path attack is considered a weak attack on a constrained network and has received a less attention from the research community. However, the consequences resulting from such an attack cannot be ignored in practice. In this article, we exploit the combination of IP spoofing vulnerability and the remote server access support of CoAP is to be launch an off-path attack. The attacker injects a fake request message to change the credentials of the 6LoWPAN smart door keypad lock system. This creates a request spoofing vulnerability in CoAP, and the attacker exploits this vulnerability to gain full access to the system. Through our implementation, we demonstrated the feasibility of the attack scenario on the 6LoWPAN-CoAP network using smart door keypad lock. We proposed a machine learning (ML)-based approach to mitigate such attacks. To the best of our knowledge, we believe that this is the first article to analyze the remote CoAP server access support and request spoofing vulnerability of CoAP to launch an off-path attack and demonstrate how an ML-based approach can be deployed to prevent such attacks.
Rouzbehi, K, Miranian, A, Escaño, J, Rakhshani, E, Shariati, N & Pouresmaeil, E 2019, 'A Data-Driven Based Voltage Control Strategy for DC-DC Converters: Application to DC Microgrid', Electronics, vol. 8, no. 5, pp. 493-493.
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This paper develops a data-driven strategy for identification and voltage control for DC-DC power converters. The proposed strategy does not require a pre-defined standard model of the power converters and only relies on power converter measurement data, including sampled output voltage and the duty ratio to identify a valid dynamic model for them over their operating regime. To derive the power converter model from the measurements, a local model network (LMN) is used, which is able to describe converter dynamics through some locally active linear sub-models, individually responsible for representing a particular operating regime of the power converters. Later, a local linear controller is established considering the identified LMN to generate the control signal (i.e., duty ratio) for the power converters. Simulation results for a stand-alone boost converter as well as a bidirectional converter in a test DC microgrid demonstrate merit and satisfactory performance of the proposed data-driven identification and control strategy. Moreover, comparisons to a conventional proportional-integral (PI) controllers demonstrate the merits of the proposed approach.
Rowlinson, A, Stewart, AJ, Broderick, JW, Swinbank, JD, Wijers, RAMJ, Carbone, D, Cendes, Y, Fender, R, van der Horst, A, Molenaar, G, Scheers, B, Staley, T, Farrell, S, Grießmeier, J-M, Bell, M, Eislöffel, J, Law, CJ, van Leeuwen, J & Zarka, P 2019, 'Identifying transient and variable sources in radio images', Astronomy and Computing, vol. 27, pp. 111-129.
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© 2019 Elsevier B.V. With the arrival of a number of wide-field snapshot image-plane radio transient surveys, there will be a huge influx of images in the coming years making it impossible to manually analyse the datasets. Automated pipelines to process the information stored in the images are being developed, such as the LOFAR Transients Pipeline, outputting light curves and various transient parameters. These pipelines have a number of tuneable parameters that require training to meet the survey requirements. This paper utilises both observed and simulated datasets to demonstrate different machine learning strategies that can be used to train these parameters. We use a simple anomaly detection algorithm and a penalised logistic regression algorithm. The datasets used are from LOFAR observations and we process the data using the LOFAR Transients Pipeline; however the strategies developed are applicable to any light curve datasets at different frequencies and can be adapted to different automated pipelines. These machine learning strategies are publicly available as PYTHON tools that can be downloaded and adapted to different datasets (https://github.com/AntoniaR/TraP_ML_tools).
Ruppert, MG 2019, '2018 IEEE Transactions on Control Systems Technology Outstanding Paper Award', IEEE Transactions on Control Systems Technology, vol. 27, no. 2, pp. 463-463.
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Ruppert, MG, Moore, SI, Zawierta, M, Fleming, AJ, Putrino, G & Yong, YK 2019, 'Multimodal atomic force microscopy with optimized higher eigenmode sensitivity using on-chip piezoelectric actuation and sensing', Nanotechnology, vol. 30, no. 8, pp. 085503-085503.
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Ryu, S, Naidu, G, Hasan Johir, MA, Choi, Y, Jeong, S & Vigneswaran, S 2019, 'Acid mine drainage treatment by integrated submerged membrane distillation–sorption system', Chemosphere, vol. 218, pp. 955-965.
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Acid mine drainage (AMD), an acidic effluent characterized by high concentrations of sulfate and heavy metals, is an environmental and economic concern. The performance of an integrated submerged direct contact membrane distillation (DCMD) - zeolite sorption system for AMD treatment was evaluated. The results showed that modified (heat treated) zeolite achieved 26-30% higher removal of heavy metals compared to natural untreated zeolite. Heavy metal sorption by heat treated zeolite followed the order of Fe > Al > Zn > Cu > Ni and the data fitted well to Langmuir and pseudo second order kinetics model. Slight pH adjustment from 2 to 4 significantly increased Fe and Al removal rate (close to 100%) due to a combination of sorption and partial precipitation. An integrated system of submerged DCMD with zeolite for AMD treatment enabled to achieve 50% water recovery in 30 h. The integrated system provided a favourable condition for zeolite to be used in powder form with full contact time. Likewise, heavy metal removal from AMD by zeolite, specifically Fe and Al, mitigated membrane fouling on the surface of the hollow fiber submerged membrane. The integrated system produced high quality fresh water while concentrating sulfuric acid and valuable heavy metals (Cu, Zn and Ni).
Saberi, M, Hussain, OK & Chang, E 2019, 'Quality Management of Workers in an In-House Crowdsourcing-Based Framework for Deduplication of Organizations’ Databases', IEEE Access, vol. 7, pp. 90715-90730.
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© 2013 IEEE. While organizations in the current era of big data are generating massive volumes of data, they also need to ensure that its quality is maintained for it to be useful in decision-making purposes. The problem of dirty data plagues every organization. One aspect of dirty data is the presence of duplicate data records that negatively impact the organization's operations in many ways. Many existing approaches attempt to address this problem by using traditional data cleansing methods. In this paper, we address this problem by using an in-house crowdsourcing-based framework, namely, DedupCrowd. One of the main obstacles of crowdsourcing-based approaches is to monitor the performance of the crowd, by which the integrity of the whole process is maintained. In this paper, a statistical quality control-based technique is proposed to regulate the performance of the crowd. We apply our proposed framework in the context of a contact center, where the Customer Service Representatives are used as the crowd to assist in the process of deduplicating detection. By using comprehensive working examples, we show how the different modules of the DedupCrowd work not only to monitor the performance of the crowd but also to assist in duplicate detection.
Saberi, Z, Saberi, M, Hussain, O & Chang, E 2019, 'Stackelberg model based game theory approach for assortment and selling price planning for small scale online retailers', Future Generation Computer Systems, vol. 100, pp. 1088-1102.
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© 2019 Assortment Planning (AP) is one of the most significant and challenging decision for online retailers (e-tailers) to make. This decision becomes even more complex when a supplier is considered as a distinctive participant in decision making model. In the bricks and mortar mode of retailing, retailers are more powerful than suppliers in getting the required goods in the required quantity. However, this is not the case for small scale e-tailers. Such e-tailers are faced with situations where large-scale retailers indirectly force the suppliers to refuse supplying to them. In such cases, effective AP decision making approaches are needed for small scale e-tailers to get the required goods to satisfy the customers’ demand. While current advancement in smart cities provide a powerful platform and support for successful operations of online retailing, this needs to be supported by appropriated modeling approaches that assist the e-tailer in getting their required product assortment. In this paper, a game-theoretic model is developed to support the small scale e-tailer in AP decision making. Such that it has two strategies to decide from. The first strategy is that it can offer the product with supreme quality by procuring it from the main powerful supplier and the second strategy is to offer the product from a less popular brand. The first strategy is modeled as a non-cooperative Stackelberg supply chain in which the supplier plays a leader and the e-tailer is a follower and the second strategy is modeled as an assortment planning problem while considering utility degradation of providing alternative brand to the customers. Various analyses are done to find the best strategy in different scenarios before recommending the best strategy to be followed by the e-tailer in given situations.
Saeed, Z, Abbasi, RA, Maqbool, O, Sadaf, A, Razzak, I, Daud, A, Aljohani, NR & Xu, G 2019, 'What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter', Journal of Grid Computing, vol. 17, no. 2, pp. 279-312.
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© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter.
Saeed, Z, Abbasi, RA, Razzak, I, Maqbool, O, Sadaf, A & Xu, G 2019, 'Enhanced Heartbeat Graph for emerging event detection on Twitter using time series networks', Expert Systems with Applications, vol. 136, pp. 115-132.
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© 2019 Elsevier Ltd With increasing popularity of social media, Twitter has become one of the leading platforms to report events in real-time. Detecting events from Twitter stream requires complex techniques. Event-related trending topics consist of a group of words which successfully detect and identify events. Event detection techniques must be scalable and robust, so that they can deal with the huge volume and noise associated with social media. Existing event detection methods mostly rely on burstiness, mainly the frequency of words and their co-occurrences. However, burstiness sometimes dominates other relevant details in the data which could be equally significant. Besides, the topological and temporal relationships in the data are often ignored. In this work, we propose a novel graph-based approach, called the Enhanced Heartbeat Graph (EHG), which detects events efficiently. EHG suppresses dominating topics in the subsequent data stream, after their first detection. Experimental results on three real-world datasets (i.e., Football Association Challenge Cup Final, Super Tuesday, and the US Election 2012) show superior performance of the proposed approach in comparison to the state-of-the-art techniques.
Saeed, Z, Ayaz Abbasi, R, Razzak, MI & Xu, G 2019, 'Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes]', IEEE Computational Intelligence Magazine, vol. 14, no. 3, pp. 29-38.
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© 2019 IEEE. Once an event is detected, WDHG approach suppresses the bursty keywords at subsequent time intervals. This characteristic enables other related information to be more visible and helps in capturing new and emerging events.
Saeidian, B, Mesgari, MS, Pradhan, B & Alamri, AM 2019, 'Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms', Water, vol. 11, no. 12, pp. 2611-2611.
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The present water crisis necessitates a frugal water management strategy. Deficit irrigation can be regarded as an efficient strategy for agricultural water management. Optimal allocation of water to agricultural farms is a computationally complex problem because of many factors, including limitations and constraints related to irrigation, numerous allocation states, and non-linearity and complexity of the objective function. Meta-heuristic algorithms are typically used to solve complex problems. The main objective of this study is to represent water allocation at farm level using temporal cultivation data as an optimisation problem, solve this problem using various meta-heuristic algorithms, and compare the results. The objective of the optimisation is to maximise the total income of all considered lands. The criteria of objective function value, convergence trend, robustness, runtime, and complexity of use and modelling are used to compare the algorithms. Finally, the algorithms are ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). The income resulting from the allocation of water by the imperialist competitive algorithm (ICA) was 1.006, 1.084, and 1.098 times that of particle swarm optimisation (PSO), bees algorithm (BA), and genetic algorithm (GA), respectively. The ICA and PSO were superior to the other algorithms in most evaluations. According to the results of TOPSIS, the algorithms, by order of priority, are ICA PSO, BA, and GA. In addition, the experience showed that using meta-heuristic algorithms, such as ICA, results in higher income (4.747 times) and improved management of water deficit than the commonly used area-based water allocation method.
Sahoo, S, Dey, S, Dhar, A, Debsarkar, A & Pradhan, B 2019, 'On projected hydrological scenarios under the influence of bias-corrected climatic variables and LULC', Ecological Indicators, vol. 106, pp. 105440-105440.
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© 2019 Elsevier Ltd Assessing the impact of climate variability is important for water resources planning and management. In the present study, climate model data were utilized in conjunction with the hydrological model to analyze the effect of climate change on projected streamflow and groundwater recharge values for the Dwarakeswar-Gandherswari basin, India. Regional Climate Model (RCM) data [Representative Concentration Pathway (RCP 2.6, RCP 4.5, RCP 6 and RCP 8.5)] were considered for future climate change scenarios. Five bias correction methods [linear scaling (LS), local intensity scaling (LOCI), power transformation (PWTR), distribution mapping (DM) and variance scaling (VARI)] were applied for RCM based precipitation and temperature data. Projected Land Use and Land Cover (LULC) values were obtained from Dyna-CLUE model. Discharge data (1990–2016) was utilized for model calibration and validation purpose. Total twelve scenarios (4 RCPs per year for the years 2030, 2050 and 2080) were considered. The results showed increasing trend in simulated discharge for the months June to September and reverse trend for the months October to December. The results also showed that groundwater recharge increased for the maximum number of sub-watersheds for the interval 2016–2030 compared to 2016–2050 and 2016–2080 under all RCPs. Uncertainties in streamflow were quantified in terms of exceedance probability and recurrence interval. ALPHA_BF was the most sensitive parameter for the river basin. However, gross increase in groundwater recharge was observed for all the scenarios. These results can be effectively utilized for irrigation planning purpose.
Sajilan, S, Tehseen, S, Yafi, E & Ting, X 2019, 'Impact of Facebook Usage on Firm’s Performances among Malaysian Chinese Retailers', GLOBAL BUSINESS FINANCE REVIEW, vol. 24, no. 4, pp. 45-62.
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Social media including Facebook has been acknowledged to play a vital role in firms achieving superior performance. Malaysia is a multicultural country in which the Malaysian Chinese are considered to be the most successful entrepreneurs. There is, however, a lack of research regarding the influence of Facebook usage on firm performance among Malaysian Chinese retailers. As such, this study had two aims. Firstly, the study investigated the influence of compatibility, cost effectiveness, trust, and interactivity on Facebook usage among Malaysian Chinese retailers. Secondly, the study assessed the impact of these retailers’ Facebook usage on their perceived financial performance, perceived non-financial performance, perceived business growth, and perceived performance relative to competitors under the moderating impact of market turbulence. This study developed a conceptual model based on the Strategic Contingency Theory (SCT) and Diffusion of Innovation (DOI) theory, and used a structured survey instrument to gather data. Using non-probability sampling techniques, 129 Malaysian Chinese retailers from Kuala Lumpur and Selangor were recruited for the study. Data was analysed using PLS-SEM techniques. The results showed that only compatibility, cost effectiveness, and interactivity have a statistically significant positive influence on Facebook usage, which in turn has a statistically significant positive influence on the retailers’ perceived financial performance, perceived non-financial performance, perceived business growth, and perceived performance relative to competitors. Moreover, market turbulence was only found to be a moderator that improves the impact of Facebook usage on perceived financial performance, perceived business growth, and perceived performance relative to competitors, but not perceived non-financial performance. These findings contribute to current literature and provide insights into the role and importance of Facebook usage on firm...
Salah, AA, Dorrell, DG & Guo, Y 2019, 'A Review of the Monitoring and Damping Unbalanced Magnetic Pull in Induction Machines Due to Rotor Eccentricity', IEEE Transactions on Industry Applications, vol. 55, no. 3, pp. 2569-2580.
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Salamai, A, Hussain, OK, Saberi, M, Chang, E & Hussain, FK 2019, 'Highlighting the Importance of Considering the Impacts of Both External and Internal Risk Factors on Operational Parameters to Improve Supply Chain Risk Management', IEEE Access, vol. 7, pp. 49297-49315.
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© 2013 IEEE. Operational risk management in supply chain activities is important for the successful achievement of the desired outcomes. Although it is an active area of research with an aim of improving a firm's success in its operations, a drawback of existing approaches is that they analyze it from only the perspective of events local to the supply chain. In this paper, we argue that it is also important for firms in a supply chain to consider external events as they will directly influence the internal ones and use various real-world examples of the risks in different processes of a supply chain as justification to prove our point. We then consider supply chain risk management not only as an operational research process, as do all the relevant survey papers, but a data science problem to gain deeper real-time insights for information risk management. Then, we suggest directions for future research that will assist supply chain risk managers to undertake better supply chain risk management processes.
Salmasi, F, Pradhan, B & Nourani, B 2019, 'Prediction of the sliding type and critical factor of safety in homogeneous finite slopes', Applied Water Science, vol. 9, no. 7.
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AbstractIn this paper, the effect of soil material parameters including soil specific weight (γ), cohesion (C), angle of internal friction ($$\emptyset$$∅), and geometric parameter of slope including angle with the horizontal (β) for a constant slope height (H) on factor of safety (Fs) was investigated.Fswas considered in two scenarios: (1) slope with dry condition, and (2) with steady-state saturated condition that comprises water level drawdown circumstances. In addition, the type of slip circle was also investigated. For this purpose, theSLOPE/Wsoftware as a subgroup ofGeo-Studiosoftware was implemented. Results showed that decreasing of water table level and omitting the hydrostatic pressure on the slope consequently would result in safety factor decrement. Comparison of the plane and circular failure surfaces showed that plane failure method produced good results for near-vertical slopes only. Determination of slip type showed that for state (30° < β < 45°), the three types of failure circles (toe, slope or midpoint circle) may occur. For state (45° < β < 60°), two modes of failure may occur: midpoint circle and toe circle. For state (β > 60°), the mode of failure circle is only toe circle. Linear and nonlinear regression equations were obtained for estimation of slope safety factor.
Salomon, R, Kaczorowski, D, Valdes-Mora, F, Nordon, RE, Neild, A, Farbehi, N, Bartonicek, N & Gallego-Ortega, D 2019, 'Droplet-based single cell RNAseq tools: a practical guide', Lab on a Chip, vol. 19, no. 10, pp. 1706-1727.
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A step-by-step guide for droplet-based single cell RNAseq experiments, practical considerations and technical notes.
Samadi-Boroujeni, H, Altaee, A, Khabbaz, H & Zhou, J 2019, 'Application of buoyancy-power generator for compressed air energy storage using a fluid–air displacement system', Journal of Energy Storage, vol. 26, pp. 100926-100926.
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© 2019 Elsevier Ltd This study proposes a gravity power generator based on the fluid–air displacement system using Compressed Air Energy Storage from renewable energy sources to increase the solar and wind power system penetration in the power network. A computer model was applied to estimate the performance of the fluid–air displacement system, taking into account the effects of key design and operating parameters. Analysis of the system was performed to calculate the net energy generation as the difference between the energy input and the energy output. Simulation results indicated that the round-trip efficiency of the fluid–air displacement system was between 47% and 60%, assuming 80% compressor efficiency. Results also showed that a system generating the maximum energy density should have a speed of cylinders movement of 0.65 m/s, a cylinder-wall distance of 0.25 × diameter of the cylinder and a gap distance between centers of two tandem cylinders is equal to 1.25. Furthermore, a sensitivity analysis conducted on the main parameters of the system identified that the gap ratio and the buckets moving speed were the highly sensitive parameters to the design and operation of the proposed system. This study also demonstrates the feasibility of using the fluid-displacement system in energy storage from renewable energy technologies.
Samal, PB, Soh, PJ & Zakaria, Z 2019, 'Compact Microstrip-Based Textile Antenna for 802.15.6 WBAN-UWB with Full Ground Plane', International Journal of Antennas and Propagation, vol. 2019, pp. 1-12.
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The paper presents the design and investigation of a flexible all-textile antenna operating in the wireless body area network (WBAN) ultrawideband (UWB) specified by the IEEE 802.15.6 standard. The proposed antenna features an innovative and compact UWB radiator on top of the overall structure with a full ground plane on its reverse side. The radiator, which is based on a microstrip patch combined with multiple miniaturization and broadbanding methods, resulted in a simple topology and a compact size of 39 mm×42 mm×3.34 mm (0.51×0.55×0.043λ). In comparison to the literature, the proposed structure is considered to be the most compact microstrip-based textile UWB antenna to date featuring a full ground plane. The choice of the commercial textiles is also made based on cost efficiency, ease of accessibility, and ease of fabrication using simple tools. Meanwhile, the full ground plane enables the antenna operation in the vicinity of the human body with minimal body coupling and radiation towards it, ensuring operational safety. Besides its operation in the mandatory channels of the WBAN-UWB low and high bands, the proposed antenna also operates and preserves its performance in five other optional channels of the high band when placed on the body and under bend conditions of 30° and 60°. The proposed antenna successfully achieved the specific absorption rate below the regulated limit specified by the Federal Communications Commission.
Samanta, M, Punetha, P, Sarkar, S, Dwivedi, A & Sharma, M 2019, 'Slope stability assessment and design of remedial measures for Tungnath Temple at Uttarakhand, India: a case study', Natural Hazards, vol. 96, no. 1, pp. 225-246.
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The present paper assesses the slope stability of the Tungnath Temple at Rudraprayag District, in the Indian state of Uttarakhand, and suggests the remedial measures. The temple is made of stone masonry and is believed to be over 1000 years old. Recently, signs of distress such as the development and subsequent widening of the cracks were observed on the walls of the temple. The field investigation reveals that the inadequate stability of the site, stagnation of water at the foundation level of the temple and poor drainage of the rainwater from the upper hill are the primary causes of distress for the temple. The factor of safety (FoS) values computed using the limit equilibrium method indicate that the site is marginally stable (FoS—0.8 to 1.0) under static condition and unstable (FoS—0.6 to 0.9) under the pseudo-static condition for a particular section. Thus, suitable control measures have been proposed to ensure the long-term stability of the site. The proposed control measures include the construction of a geosynthetic lined drain at critical locations and geosynthetic lining in the periphery of the temple to prevent the ingress of water. Additionally, the construction of two levels of gabion wall (6 m to 8 m high) at the periphery of the site has been proposed to improve the stability. The paper discusses the possible causes of the cracks, slope stability analysis and subsequently present the design details of the remedial measures for the long-term stability of the temple.
Samaras, E & Johnston, A 2019, 'Off-Lining to Tape Is Not Archiving: Why We Need Real Archiving to Support Media Archaeology and Ensure Our Visual Effects Legacy Thrives', Leonardo, vol. 52, no. 4, pp. 374-380.
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This paper examines digital asset archiving and preservation practice in the visual effects (VFX) industry. The authors briefly summarize media archaeology theory and provide an overview of how VFX studios presently archive project assets and records, based on case study and interview research conducted with expert VFX practitioners from leading international studios. In addition, the authors propose that current practice could be improved by adopting archival science methods, including digital preservation practices. Doing so will support media archaeology studies of digital cultures over time and ensure that the legacy of VFX creative and technical production thrives for future generations.
Sameen, MI & Pradhan, B 2019, 'Landslide Detection Using Residual Networks and the Fusion of Spectral and Topographic Information', IEEE Access, vol. 7, pp. 114363-114373.
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Sameen, MI, Pradhan, B & Lee, S 2019, 'Self-Learning Random Forests Model for Mapping Groundwater Yield in Data-Scarce Areas', Natural Resources Research, vol. 28, no. 3, pp. 757-775.
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© 2018, International Association for Mathematical Geosciences. Globally, groundwater plays a major role in supplying drinking water for urban and rural population and is used for irrigation to grow crops and in many industrial processes. A novel self-learning random forest (SLRF) model is developed and validated for groundwater yield zonation within the Yeondong Province in South Korea. This study was conducted with an inventory data initially divided randomly into 70% for training and 30% for testing and 13 groundwater-conditioning factors. SLRF was optimized using Bayesian optimization method. We also compared our method to other machine learning methods including support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and voting ensemble models. Model validation was accomplished using several methods, including a confusion matrix, receiver operating characteristics, cross-validation, and McNemar’s test. Our proposed self-learning method improves random forest (RF) generalization performance by about 23%, with SLRF success rates of 0.76 and prediction rates of 0.83. In addition, the optimized SLRF performed better [according to a threefold cross-validated AUC (area under curve) of 0.75] than that using randomly initialized parameters (0.57). SLRF outperformed all of the other models for the testing dataset (RF, SVM, ANN, DT, and Voted ANN-RF) when the overall accuracy, prediction rate, and cross-validated AUC metrics were considered. The SLRF also estimated the contribution of individual groundwater conditioning factors and showed that the three most influential factors were geology (1.00), profile curvature (0.97), and TWI (0.95). Overall, SLRF effectively modeled groundwater potential, even within data-scarce regions.
Samiran, NA, Chong, CT, Ng, J-H, Tran, M-V, Ong, HC, Valera-Medina, A, Chong, WWF & Mohd Jaafar, MN 2019, 'Experimental and numerical studies on the premixed syngas swirl flames in a model combustor', International Journal of Hydrogen Energy, vol. 44, no. 44, pp. 24126-24139.
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© 2019 Hydrogen Energy Publications LLC Experimental and numerical investigations were performed to study the combustion characteristics of synthesis gas (syngas) under premixed swirling flame mode. Four different type of syngases, ranging from low to high H2 content were tested and simulated. The global flame structures and post emission results were obtained from experimental work, providing the basis of validation for simulations using flamelet generated manifold (FGM) modelling approach via a commercial computational fluid dynamic software. The FGM method was shown to provide reasonable agreement with experimental result, in particular the post-exhaust emissions and global flame shapes. Subsequently, the FGM method was adopted to model the flame structure and predict the radical species in the reaction zones. Simulation result shows that H2-enriched syngas has lower peak flame temperature with lesser NO species formed in the reaction zone.
Sanders, YR, Low, GH, Scherer, A & Berry, DW 2019, 'Black-Box Quantum State Preparation without Arithmetic', Physical Review Letters, vol. 122, no. 2, p. 020502.
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Black-box quantum state preparation is an important subroutine in many quantum algorithms. The standard approach requires the quantum computer to do arithmetic, which is a key contributor to the complexity. Here we present a new algorithm that avoids arithmetic. We thereby reduce the number of gates by a factor of 286-374 over the best prior work for realistic precision; the improvement factor increases with the precision. As quantum state preparation is a crucial subroutine in many approaches to simulating physics on a quantum computer, our new method brings useful quantum simulation closer to reality.
Sandi, SG, Saco, PM, Saintilan, N, Wen, L, Riccardi, G, Kuczera, G, Willgoose, G & Rodríguez, JF 2019, 'Detecting inundation thresholds for dryland wetland vulnerability', Advances in Water Resources, vol. 128, pp. 168-182.
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Sandu, S, Yang, M, Mahlia, TMI, Wongsapai, W, Ong, HC, Putra, N & Rahman, SMA 2019, 'Energy-Related CO2 Emissions Growth in ASEAN Countries: Trends, Drivers and Policy Implications', Energies, vol. 12, no. 24, pp. 4650-4650.
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The primary objective of this paper is to analyse the growth of energy-related CO2 emissions in ASEAN (Association of Southeast Asian Nations), with specific emphasis on identifying its trends and underlying drivers. This objective is premised on the arguments that: (1) there is a general lack of analysis of energy-related CO2 emissions growth across ASEAN countries; and (2) such an analysis is critical, because it could enable an assessment to be made of the efficacy of existing energy policies for reducing emissions. Decomposition analysis is the main approach adopted in this paper. The findings of this paper suggest that the growth of energy-related CO2 emissions has slowed in some major emitters in the region, due to energy efficiency improvement, and, to a lesser extent, a gradual switch in energy fuel mix towards lower emission sources (gas and renewables). However, this improvement is unlikely to drive a major transformation in the energy sectors of the region to the extent considered adequate for redressing the challenge of rising emissions, as indicated by a steady emissions growth in most ASEAN countries over the entire study period (1971–2016). By implication, this suggests that a significant scale-up of existing policy effort is needed to rectify the situations.
Sang, L, Xu, M, Qian, S & Wu, X 2019, 'Multi-modal multi-view Bayesian semantic embedding for community question answering', Neurocomputing, vol. 334, pp. 44-58.
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© 2018 Semantic embedding has demonstrated its value in latent representation learning of data, and can be effectively adopted for many applications. However, it is difficult to propose a joint learning framework for semantic embedding in Community Question Answer (CQA), because CQA data have multi-view and sparse properties. In this paper, we propose a generic Multi-modal Multi-view Semantic Embedding (MMSE) framework via a Bayesian model for question answering. Compared with existing semantic learning methods, the proposed model mainly has two advantages: (1) To deal with the multi-view property, we utilize the Gaussian topic model to learn semantic embedding from both local view and global view. (2) To deal with the sparse property of question answer pairs in CQA, social structure information is incorporated to enhance the quality of general text content semantic embedding from other answers by using the shared topic distribution to model the relationship between these two modalities (user relationship and text content). We evaluate our model for question answering and expert finding task, and the experimental results on two real-world datasets show the effectiveness of our MMSE model for semantic embedding learning.
Sani, AS, Yuan, D, Jin, J, Gao, L, Yu, S & Dong, ZY 2019, 'Cyber security framework for Internet of Things-based Energy Internet', Future Generation Computer Systems, vol. 93, pp. 849-859.
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© 2018 Elsevier B.V. With the significant improvement in deployment of Internet of Things (IoT) into the smart grid infrastructure, the demand for cyber security is rapidly growing. The Energy Internet (EI) also known as the integrated internet-based smart grid and energy resources inherits all the security vulnerabilities of the existing smart grid. The security structure of the smart grid has become inadequate in meeting the security needs of energy domains in the 21st century. In this paper, we propose a cyber security framework capable of providing adequate security and privacy, and supporting efficient energy management in the EI. The proposed framework uses an identity-based security mechanism (I-ICAAAN), a secure communication protocol and an Intelligent Security System for Energy Management (ISSEM) to certify security and privacy in the EI. Nash Equilibrium solution of game theory is applied for the evaluation of our proposed ISSEM based on security events allocation. The formal verification and theoretical analysis show that our proposed framework provides security and privacy improvement for IoT-based EI.
Saqib, M, Khan, SD, Sharma, N & Blumenstein, M 2019, 'Crowd Counting in Low-Resolution Crowded Scenes Using Region-Based Deep Convolutional Neural Networks', IEEE Access, vol. 7, pp. 35317-35329.
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© 2013 IEEE. Crowd counting and density estimation is an important and challenging problem in the visual analysis of the crowd. Most of the existing approaches use regression on density maps for the crowd count from a single image. However, these methods cannot localize individual pedestrian and therefore cannot estimate the actual distribution of pedestrians in the environment. On the other hand, detection-based methods detect and localize pedestrians in the scene, but the performance of these methods degrades when applied in high-density situations. To overcome the limitations of pedestrian detectors, we proposed a motion-guided filter (MGF) that exploits spatial and temporal information between consecutive frames of the video to recover missed detections. Our framework is based on the deep convolution neural network (DCNN) for crowd counting in the low-to-medium density videos. We employ various state-of-the-art network architectures, namely, Visual Geometry Group (VGG16), Zeiler and Fergus (ZF), and VGGM in the framework of a region-based DCNN for detecting pedestrians. After pedestrian detection, the proposed motion guided filter is employed. We evaluate the performance of our approach on three publicly available datasets. The experimental results demonstrate the effectiveness of our approach, which significantly improves the performance of the state-of-the-art detectors.
Sarker, A, Tran, N, Rifai, A, Brandt, M, Tran, PA, Leary, M, Fox, K & Williams, R 2019, 'Rational design of additively manufactured Ti6Al4V implants to control Staphylococcus aureus biofilm formation', Materialia, vol. 5, pp. 100250-100250.
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Sarker, PC, Islam, MR, Guo, Y, Zhu, J & Lu, HY 2019, 'State-of-the-Art Technologies for Development of High Frequency Transformers with Advanced Magnetic Materials', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2, pp. 1-11.
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© 2002-2011 IEEE. With the development of advanced soft magnetic materials of high-saturation flux density and low specific core loss and semiconductor power devices, the high-frequency transformer (HFT) has received significant attention in recent years for its widespread emerging applications. The optimal design of high-power-density HFTs for high-performance energy conversion systems is, however, a multiphasic problem that needs special considerations on various aspects such as core material selection, minimization of parasitic components, and thermal management. This paper presents a comprehensive review on advancement of soft magnetic materials for high-power-density magnetic devices and advanced technologies for characterizations and optimal design of HFTs. The future research and development trends are also discussed.
SarojiniAmma, BK, Indraratna, B & Vinod, JS 2019, 'A semi-empirical dilatancy model for ballast fouled with plastic fines', Geomechanics and Geoengineering, vol. 14, no. 1, pp. 12-17.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. In the era of high speed trains, it is very important to ensure the stability of rail tracks under adverse conditions including the fouling of ballast. Fouling of ballast from unstable and saturated soft subgrade soil is one of the major reasons for track deterioration. The reported results of a number of large-scale laboratory experiments on the shear behaviour of ballast and fouled ballast are analysed, herein. It was observed that fines have a significant influence on the shear behaviour of ballast. Shear strength increases and dilatancy decreases with the addition of fines. In this paper, a semi-empirical mathematical model has been proposed to capture the dilatancy of ballast fouled with fines during shearing. The empirical constants a, b and c proposed in the model are a function of the fines content Void Contamination Index (VCI). The results of the model have been compared with the laboratory experiments and are found to be in good agreement.
Sarveghadi, M, Gandomi, AH, Bolandi, H & Alavi, AH 2019, 'Development of prediction models for shear strength of SFRCB using a machine learning approach', Neural Computing and Applications, vol. 31, no. 7, pp. 2085-2094.
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© 2015, The Natural Computing Applications Forum. In this study, new design equations were derived for the assessment of shear resistance of steel fiber-reinforced concrete beams (SFRCB) utilizing multi-expression programming (MEP). The superiority of MEP over conventional statistical techniques is due to its ability in modeling of mechanical behavior without a need to pre-define the model structure. The MEP models were developed using a comprehensive database obtained through an extensive literature review. New criteria were checked to verify the validity of the models. A sensitivity analysis was carried out and discussed. The MEP models provide good estimations of the shear strength of SFRCB. The developed models significantly outperform several equations found in the literature.
Sayem, ASM, Simorangkir, RBVB, Esselle, KP & Hashmi, RM 2019, 'Development of Robust Transparent Conformal Antennas Based on Conductive Mesh-Polymer Composite for Unobtrusive Wearable Applications', IEEE Transactions on Antennas and Propagation, vol. 67, no. 12, pp. 7216-7224.
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© 1963-2012 IEEE. In this paper, a detailed investigation of the realization of conformal wearable transparent antennas by integrating conductive mesh with polymer has been presented. The proposed realization method is much simpler and more cost-effective than the existing realization methods of transparent antennas, and the prototype fabricated from the selected composite materials is more flexible and robust in bending operations than other transparent antennas. In this paper, the mechanical, electrical, and optical characteristics of the proposed composite material have been investigated to analyze its suitability for transparent flexible antenna realization. For concept demonstration, a prototype of a dual-band antenna operating at 2.33-2.53 GHz and 4.7-5.6 GHz has been fabricated and tested. These frequencies cover both the instrument, scientific, and measurement (ISM) and the wireless local area network (WLAN) bands. Full ground plane is utilized in the antenna design for on-body operations. The suitability of the antenna for wearable applications has been investigated by measuring its performance under physical deformation and testing its performance on phantom. Next, the RF performance of the antenna has been improved by using two layers of conductor to form the radiating element. Although transparency is slightly compromised, the double-layer element improves the gain and efficiency of the antenna.
Schleppi, J, Gibbons, J, Groetsch, A, Buckman, J, Cowley, A & Bennett, N 2019, 'Manufacture of glass and mirrors from lunar regolith simulant', Journal of Materials Science, vol. 54, no. 5, pp. 3726-3747.
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© 2018, The Author(s). Future planetary surface missions to the Moon or Mars, for example, can be augmented by the use of local materials, in order to reduce launch mass and expand mission capability. Using lunar regolith simulant and heating it within a susceptor-assisted microwave oven, it was possible to manufacture a variety of basaltic glasses. Furthermore, it was possible to shape these glasses by grinding and polishing the surface flat and smooth. Glasses manufactured from different lunar regolith simulants were coated with aluminium or silver, and the reflective properties of the resulting mirrors and uncoated surfaces were measured. It was shown that with a porous and/or smooth surface finish, mirrors could be made that reflect the incident solar light (400 nm–1250 nm) in-between 30% for the worst and 85% for the best samples. The same samples with uncoated surfaces showed to reflect less than 7% of incident solar light in the same wavelength range.
Schmitt, J & Deuse, J 2019, 'Modellbasierte Prüfprozesse', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 114, no. 4, pp. 191-193.
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Kurzfassung Die Anwendung von Data-Mining-Verfahren zur Vorhersage von qualitätsrelevanten Produktmerkmalen gewinnt im Rahmen der industriellen Qualitätssicherung zunehmend an Bedeutung. Um zukünftig modellbasierte Prüfprozesse realisieren zu können, müssen zunächst die Anforderungen an klassische Prüfprozesse auf das modellbasierte Prüfen übertragen und die Veränderungen der Aufgaben des Prüfmittelmanagements beleuchtet werden.
Scott, M, Millar, GJ & Altaee, A 2019, 'Process design of a treatment system to reduce conductivity and ammoniacal nitrogen content of landfill leachate', Journal of Water Process Engineering, vol. 31, pp. 100806-100806.
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© 2019 Elsevier Ltd An innovative combination of computational modelling and laboratory testing was applied to address the challenge of reducing conductivity and ammoniacal nitrogen in landfill leachate. The hypothesis was that accelerated selection of an appropriate treatment process could be achieved by application of new water process engineering software termed AqMB. Several scenarios were investigated incorporating settling ponds, clarifiers, lime softening, ion exchange, pH adjustment and degassing unit operations. Settling ponds reduced the lime demand if a lime softening process was tested, albeit ponds involved greater expense and needed space. Alternately, a clarifier using aluminium chlorohydrate removed suspended solids. Use of a single cation resin bed in series with a strong base anion (SBA) resin column was not able to meet regulatory targets. However, employment of a weak acid cation (WAC) and strong acid cation (SAC) resin combination achieved very low ammoniacal nitrogen levels. To satisfy conductivity limits both a degassing unit and a strong base anion (SBA) resin were also necessary. Bench top testing of actual leachate confirmed that the software predicted the trends in water quality. Final solution conductivity of ca. 250 μS/cm and ammoniacal nitrogen content of <1 mg/l were recorded which were compliant with target values of <1600 μS/cm and <100 mg/l ammoniacal nitrogen. Process economics encompassing power, chemicals, and resin costs were calculated to be A$10.50 per kL leachate.
Seckelmann, T, Barthelmey, A, Kaiser, M & Deuse, J 2019, 'Simulationsgestützte arbeitswissenschaftliche Bewertung von MRK-Arbeitsplätzen', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 114, no. 11, pp. 744-748.
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Kurzfassung Um während der Planung eines MRK-Systems das beste Lösungsszenario auszuwählen, ist eine arbeitswissenschaftliche Bewertung erforderlich. Wirtschaftlichkeit, Ergonomie, Sicherheit sowie die organisatorischen Auswirkungen sind dabei die wichtigsten Bewertungskriterien. Innerhalb des Beitrags werden die Entwicklung eines auf diesen Kriterien basierten Bewertungsverfahrens und dessen Integration in ein Simulationswerkzeug sowie die Validierung anhand eines industriellen Anwendungsfalls dargestellt.
Sedehi, O, Papadimitriou, C & Katafygiotis, LS 2019, 'Probabilistic hierarchical Bayesian framework for time-domain model updating and robust predictions', Mechanical Systems and Signal Processing, vol. 123, pp. 648-673.
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Sedehi, O, Papadimitriou, C, Teymouri, D & Katafygiotis, LS 2019, 'Sequential Bayesian estimation of state and input in dynamical systems using output-only measurements', Mechanical Systems and Signal Processing, vol. 131, pp. 659-688.
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Seifollahi, S, Bagirov, A, Zare Borzeshi, E & Piccardi, M 2019, 'A simulated annealing‐based maximum‐margin clustering algorithm', Computational Intelligence, vol. 35, no. 1, pp. 23-41.
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AbstractMaximum‐margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing‐based algorithm that is able to mitigate the issue of local minima in the maximum‐margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k‐means++ and SVM at each step of the annealing process. More precisely, k‐means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.
Sekaran, K, Khan, MS, Patan, R, Gandomi, AH, Krishna, PV & Kallam, S 2019, 'Improving the Response Time of M-Learning and Cloud Computing Environments Using a Dominant Firefly Approach', IEEE Access, vol. 7, pp. 30203-30212.
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© 2013 IEEE. Mobile learning (m-learning) is a relatively new technology that helps students learn and gain knowledge using the Internet and Cloud computing technologies. Cloud computing is one of the recent advancements in the computing field that makes Internet access easy to end users. Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques. Usually, the Cloud users' requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers. Thus, a Cloud load balancer that uses an efficient load balancing technique is needed in all the cloud servers. We propose a new meta-heuristic algorithm, named the dominant firefly algorithm, which optimizes load balancing of tasks among the multiple virtual machines in the Cloud server, thereby improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of m-learning systems. Our methods and findings used to solve load imbalance issues in Cloud servers, which will enhance the experiences of m-learning users. Specifically, our findings such as Cloud-Structured Query Language (SQL), querying mechanism in mobile devices will ensure users receive their m-learning content without delay; additionally, our method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments.
Selvaraj, A, Patan, R, Gandomi, AH, Deverajan, GG & Pushparaj, M 2019, 'Optimal virtual machine selection for anomaly detection using a swarm intelligence approach', Applied Soft Computing, vol. 84, pp. 105686-105686.
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© 2019 Elsevier B.V. Cloud computing plays a significant role in Healthcare Service (HCS) applications and rapidly improves it. A significant challenge is the selection of Virtual Machine (VM) in order to process a medical request. The optimal selection of VM increases the performance of HCS by minimizing the running time of the medical request and also substantially utilizes cloud resources. This paper presents a new idea for optimizing VM selection using a swarm intelligence approach called Analogous Particle swarm optimization (APSO) which works a cloud computing environment. To compute the running time of a medical request, three parameters are considered: Turnaround Time (TAT), Waiting time (WT), and CPU utilization. In addition, a selected optimal VM is used for predicting kidney disease. Early detection of kidney disease facilitates successful treatment. Here, the neural network is used as an automated technique to diagnose kidney disease. A set of experiments and comparisons were performed to analyze the proposed system (APSO and neural network). The results showed that the APSO model performed well, with an execution time of running all particle is 1 s (50 to 80%). Also, the proposed model improved the system efficiency by 5.6%. The precision of recognizing kidney disease using the neural network was 95.7% which outperfomed five other well-known classifiers.
Sepehrirahnama, S, Ong, ET, Lee, HP & Lim, K-M 2019, 'Fast computation for vibration study of partially submerged structures using low resolution hydrodynamic model', Journal of Fluids and Structures, vol. 91, pp. 102756-102756.
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Sepehrirahnama, S, Xu, D, Ong, ET, Lee, HP & Lim, K-M 2019, 'Fluid–Structure Interaction Effects on Free Vibration of Containerships', Journal of Offshore Mechanics and Arctic Engineering, vol. 141, no. 6.
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The interaction between fluid and structure affects the vibration response of the structure due to the additional hydrodynamic pressure. These effects are accounted for by incorporating the so-called added mass into the vibration equation of the structure. In this paper, a containership was used to study the impact of the added mass on its free vibration response. The natural frequencies of the ship decrease after including the added mass in the vibration analysis. It is shown that the frequency-ascending sequence of the wet mode shapes, for which the added mass is accounted for, may differ from that obtained for the dry state of the ship. Also, the effects of different draft levels on the mode shapes of the ship are reported. These results provide a better insight for designing ships based on their wet-state frequencies and mode shapes, which is the typical operation condition when sailing in the open seas.
Shahid, I, Thalakotuna, D & Heimlich, M 2019, 'A bi-patch loaded microstrip line based 1-D periodic structure with enhanced stop bandwidth and band switching characteristics', Journal of Electromagnetic Waves and Applications, vol. 33, no. 10, pp. 1329-1342.
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A one-dimensional periodic structure comprising of eight unit cells each having two metallic patches sandwiched between microstrip line and ground plane has been investigated. Patches bearing dissimilar dimensions present distinct reactive loads, determined by their respective areas, to generate relatively wider bandgap. Patches can be selectively connected to ground or left floating through a combination of vias and externally controlled FET switches. Dispersion analysis of the structure has been carried out to determine the propagating modes of the line for all four possible states of the unit cell. A top-down, design guide approach has been adopted with the effect of parameters determining performance attributes captured. The proposed structure acts as an all pass filter from DC to 19.5 GHz with all patches floating and exhibits stopband characteristics from 6 to 19.5 GHz with different combinations of the switches offering an overall stop bandwidth greater than 100%. The proposed structure offers tunability from no bandgap to bandgap with added advantages of band switching capability with double the number of unique reconfigurable switch patterns as compared to conventional single patch structures.
Shakor, P, Nejadi, S & Paul, G 2019, 'A Study into the Effect of Different Nozzles Shapes and Fibre-Reinforcement in 3D Printed Mortar', Materials, vol. 12, no. 10, pp. 1708-1708.
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Recently, 3D printing has become one of the most popular additive manufacturing technologies. This technology has been utilised to prototype trial and produced components for various applications, such as fashion, food, automotive, medical, and construction. In recent years, automation also has become increasingly prevalent in the construction field. Extrusion printing is the most successful method to print cementitious materials, but it still faces significant challenges, such as pumpability of materials, buildability, consistency in the materials, flowability, and workability. This paper investigates the properties of 3D printed fibre-reinforced cementitious mortar prisms and members in conjunction with automation to achieve the optimum mechanical strength of printed mortar and to obtain suitable flowability and consistent workability for the mixed cementitious mortar during the printing process. This study also considered the necessary trial tests, which are required to check the mechanical properties and behaviour of the proportions of the cementitious mix. Mechanical strength was measured and shown to increase when the samples were printed using fibre-reinforced mortar by means of a caulking gun, compared with the samples that were printed using the same mix delivered by a progressive cavity pump to a 6 degree-of-freedom robot. The flexural strength of the four-printed layer fibre-reinforced mortar was found to be 3.44 ± 0.11 MPa and 5.78 ± 0.02 MPa for the one-layer. Moreover, the mortar with different types of nozzles by means of caulking is printed and compared. Several experimental tests for the fresh state of the mortar were conducted and are discussed.
Shakor, P, Nejadi, S, Paul, G & Malek, S 2019, 'Review of Emerging Additive Manufacturing Technologies in 3D Printing of Cementitious Materials in the Construction Industry', Frontiers in Built Environment, vol. 4, pp. 1-17.
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Additive manufacturing is a fabrication technology that is rapidly revolutionizing the manufacturing and construction sectors. In this paper, a review of various prototyping technologies for printing cementitious materials and selected 3D printing techniques are presented in detail. Benchmark examples are provided to compare three well-known printing techniques; inkjet printing (binder jetting), selected laser sintering (SLS), and extrusion printing (extrusion based process). A comprehensive search in the literature was conducted to identify various mix designs that could be employed when printing cementitious materials. Aspects of concrete mix design are described, and some new experiments are conducted to analyse the printability of new mixes by the authors. Future research in the area of the rheology of cementitious materials and its relationship with the structural performance of finished concretes are highlighted.
Shakor, P, Nejadi, S, Paul, G, Sanjayan, J & Aslani, F 2019, 'Heat curing as a means of postprocessing influence on 3D printed mortar specimens in powderbased 3D printing', Indian Concrete Journal, vol. 93, no. 9, pp. 65-74.
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Inkjet (Powder-based) three-dimensional printing (3DP) shows significant promise in concrete construction applications. The accuracy, speed, and capacity to build complicated geometries are the most beneficial features of inkjet 3DP. Therefore, inkjet 3DP needs to be carefully studied and evaluated with construction goals in mind and employed in real-world applications, where it is most appropriate. This paper focuses on the important aspect of curing 3DP specimens. It discusses the enhanced mechanical properties of the mortar that are unlocked through a heat-curing process. Experiments were conducted on cubic mortar specimens that were printed and cured in an oven at a range of different temperatures (40, 60, 80, 90, 100°C). The results of the experimental tests showed that 80°C is the optimum heat-curing temperature to achieve the highest compressive strength and flexural strength of the printed mortar specimens. These tests were performed on two different dimensions of the cubic specimens, namely, 20x20x20 mm, 50x50x50 mm and on prism specimens with dimensions of 160x40x40 mm. The inkjet 3DP process and the post-processing curing are discussed. In addition, 3D scanning of the printed specimens was employed and the surface roughness profiles of the 3DP gypsum specimens and cement mortar are recorded 13.76 µm and 22.31 µm, respectively.
Shakor, P, Nejadi, S, Paul, G, Sanjayan, J & Nazari, A 2019, 'Mechanical Properties of Cement-Based Materials and Effect of Elevated Temperature on 3-D Printed Mortar Specimens in Inkjet 3-D Printing', ACI Materials Journal, vol. 116, no. 2, pp. 55-67.
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Copyright © 2019, American Concrete Institute. All rights reserved. Three-dimensional (3-D) printers have the potential to print samples that can be used as a scaffold for a variety of applications in different industries. In this paper, cement-based materials including ordinary portland cement, calcium aluminate cement (passing 150 µm [0.0059 in.] size sieve), and fine sand were investigated as the cement-based materials in inkjet 3-D printing. Prism specimens were printed for the three-point bending test; and cubic specimens were printed for the uniaxial compressive strength test. Prism samples were printed along different directional axes (X, Y, and Z). The tests were conducted at different saturation levels (water-cement ratio [w/c]) as represented by S100C200, S125C250, S150C300, and S170C340. The prism specimens were cured in water for 7 and 28 days while cubic specimens were cured in Ca(OH) 2 and water for 7 and 28 days at the same ambient temperatures. In general, the results changed according to the directional axes of the prisms. However, following water curing, the cubic samples were heated up to 40°C (104°F) in an oven and a higher compressive strength was evident compared to the samples which were only cured in the room-temperature water. The wettability test for both powders has been conducted in the presented study.
Shan, J, Zheng, Y, Shi, B, Davey, K & Qiao, S-Z 2019, 'Regulating Electrocatalysts via Surface and Interface Engineering for Acidic Water Electrooxidation', ACS Energy Letters, vol. 4, no. 11, pp. 2719-2730.
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Shannon, AG 2019, 'Fibonacci functional sequence generating functions', Advanced Studies in Contemporary Mathematics (Kyungshang), vol. 29, no. 3, pp. 285-292.
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The purpose of this paper is to generalize some results of Horadam and to extend some identities due to Carlitz in the context of functional difference equations and their associated generating functions.
Shannon, AG, Zaman, F & Choudhury, T 2019, 'A note on number theoretic approach to credit creation in banking and other financial intermediaries', WSEAS Transactions on Business and Economics, vol. 16, pp. 178-184.
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Credit is the currency of both banking and non-banking financial intermediaries. The creation of credit has always been seen as one of the key determinants of a healthy financial industry. In this paper, our objective is to use a number theoretic approach to the specification of a credit creation multiplier for interactions of banking system with non-bank financial intermediaries to produce a realistic formula. The research is further extended into more general sorting processes so that coefficients of the powers of the liquidity ratios can be selected at any stage of the process. The proposed new model yields a more realistic approach to the credit creation multiplier than traditional models. This leads in the final section to consideration of the estimation of liquidity ratios, which are one actuarial factor (among many) in superannuation calculations. Used correctly in the financial sector, the new model can provide regulators and stakeholders a better view of the credit creation scenario with increased insight into the micro factors of credit creation.
Shao, R, Wu, C, Su, Y, Liu, Z, Liu, J & Xu, S 2019, 'Numerical analysis on impact response of ultra-high strength concrete protected with composite materials against steel ogive-nosed projectile penetration', Composite Structures, vol. 220, pp. 861-874.
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© 2019 Elsevier Ltd In order to investigate the impact behaviours of ultra-high strength concrete (UHSC) target protected with high-toughness lightweight energy absorption composite materials against the projectile penetration thoroughly, a numerical study using LS-DYNA is conducted at impact velocities between 540 m/s and 810 m/s. The major compositions of FE models are the same as those of experimental specimens which include steel wire mesh reinforced concrete (SWMRC) plates, UHMWPE fibre laminates, aluminium foam sheets and the protected UHSC. Numerical results involving depth of penetration (DOP), impact crater (exfoliated) diameter of SWMRC plates, localized damage and ballistic deviation of the projectiles are obtained and then compared with experimental data, where the numerical results show reasonable agreement with the test results. Based on the validated FE models, the projectile penetration process and the energy evolution between the target and the projectile are studied. In addition, a parametric analysis is conducted to investigate the influence of the arrangement order for present composite materials on DOP and impact resistance of reinforced UHSC target, as well as the ballistic deviation and deformation of the projectile. Results of this study indicate that for the current UHSC target, firstly, the ballistic deviation and projectile deformation are two important factors affecting the impact resistance of the target; secondly, the fibre laminates play a major role in the projectile ballistic deviation and the impact kinetic energy of the projectile is mainly absorbed by the concrete matrix, multilayer steel wire meshes and different densities of foam sheets.
Shao, R, Wu, C, Su, Y, Liu, Z, Liu, J, Chen, G & Xu, S 2019, 'Experimental and numerical investigations of penetration resistance of ultra-high strength concrete protected with ceramic balls subjected to projectile impact', Ceramics International, vol. 45, no. 6, pp. 7961-7975.
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© 2019 Elsevier Ltd and Techna Group S.r.l. Ceramic materials characterized by high hardness, high inherent strength, low density and excellent dimensional stability have been extensively applied in the design of high-performance and lightweight protective structures to resist the high-speed projectile impact. In order to study the anti-penetration capability of ceramic balls protected ultra-high strength concrete (CB-UHSC), high-speed projectile impact tests were conducted at striking velocities of 545 m/s, 679 m/s, and 809 m/s to investigate the impact performance of ceramic balls, projectiles, and the protected UHSC. The experimental results indicated the effectiveness and economy of ceramic balls in resisting the high-speed projectile impact. Numerical studies were then conducted to reproduce the projectile penetration process within CB-UHSC targets with the assistance of LS-DYNA. Based on the validated numerical models, impact resistance and ballistic deviation of projectiles, as well as the energy evolution between projectiles and targets, were further investigated to comprehensively understand the impact performance of this newly designed protective structure under projectile penetration.
Sharma, M, Samanta, M & Punetha, P 2019, 'Experimental Investigation and Modeling of Pullout Response of Soil Nails in Cohesionless Medium', International Journal of Geomechanics, vol. 19, no. 3, pp. 04019002-04019002.
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Shashidharan, S, Zhu, F & Yang, Y 2019, 'Coupling of supermodes in dual-core mPOF and its application in temperature and strain sensing', Optik, vol. 195, pp. 163112-163112.
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© 2019 Elsevier GmbH This paper shows a novel dual core microstructured polymer fiber made of poly-methyl-methacrylate (PMMA) with an external diameter of 128 μm. The two solid fiber cores of diameter 4 μm were made of polycarbonate separated by a single air hole of 1 μm diameter at the center of the structure. The cutoff wavelength of each core was designed to be 600 nm. A very minute change in the shape/position of cores or air hole diameter 0.5 microns will result a change in the coupling length between the cores. Mode coupling in dual-core mPOF for y- and x- polarization were examined using effective index of propagating modes. The effect of temperature and strain to the mPOF causes the transmittance nulls to either red shift or blue shift. The numerical calculation from the shifts in transmission nulls shows a sensitivity up to 1.3 nm N/ m2 in the wavelength range of 600-850nm
Shashidharan, S, Zhu, F & Yang, Y 2019, 'Microstructured Multicore Polymer Optical Fiber Temperature-insensitive Stress Sensor', Optik, vol. 186, pp. 458-463.
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© 2018 We describe a novel microstructured multicore polymer optical fiber (m-MPOF) made of PMMA with air-holes running along the entire length of the fiber. In polymer, refractive index decreases with increasing temperature, a negative thermo-optic coefficient which results in the increase of coupling with increasing temperature thus reducing the beat length and causing a blue shift in transmission nulls, but with increase in temperature the spacing between cores will also increase which results in decreases of coupling and an increases the beat length, so a red shift in transmission nulls –a positive thermal expansion coefficient. In most part of the wavelength the thermo-optic effect dominates the thermal expansion effect, but at a particular wavelength both the effects cancel each other and have a zero change in the Neff with a change in temperature. So, as a result, both these effects get nullified at certain temperature making it a temperature insensitive fiber. Numerical calculations show the fiber is temperature insensitive for a range of 40℃ to 60℃ at a wavelength of 700 nm. The fiber is shown to be sensitive to stress at 700 nm with a sensitivity of 1.6 nm/Pa, which makes the said fiber temperature insensitive stress sensor.
Sheikhrahimi, A, Pour, AB, Pradhan, B & Zoheir, B 2019, 'Mapping hydrothermal alteration zones and lineaments associated with orogenic gold mineralization using ASTER data: A case study from the Sanandaj-Sirjan Zone, Iran', Advances in Space Research, vol. 63, no. 10, pp. 3315-3332.
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© 2019 COSPAR The Sanandaj-Sirjan Zone (SSZ) is considered as an important region for gold exploration in the western sector of Iran. Its mountainous topography and unpaved routes make its study challenging for researchers and raise the costs for mining companies strating new exploration plans. Gold mineralization mainly occurs as irregular to lenticular sulfide-bearing quartz veins along shear zones in deformed mafic to intermediate metavolcanic and metasedimentary rocks. In this investigation, ASTER data are used for mapping hydrothermal alteration minerals and to better discriminate geological structural features associated with orogenic gold occurrences in the area. Image transformation techniques such as specialized band ratioing and Principal Component Analysis are used to delineate lithological units and alteration minerals. Supervised classification techniques, namely Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) are applied to detect subtle differences between indicator alteration minerals associated with ground-truth gold locations in the area. The directional filtering technique is applied to help in tracing along the strike the different linear structures. Results demonstrate that the integration of image transformation techniques and supervised classification of ASTER data with fieldwork and geochemical exploration studies has a great efficiency in targeting new prospects of gold mineralization in the SSZ. The approach used in this research provides a fast, cost-efficient means to start a comprehensive geological and geochemical exploration programs in the study area and elsewhere in similar regions.
Shen, S, Zhou, H, Feng, S, Huang, L, Liu, J, Yu, S & Cao, Q 2019, 'HSIRD: A model for characterizing dynamics of malware diffusion in heterogeneous WSNs', Journal of Network and Computer Applications, vol. 146, pp. 102420-102420.
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© 2019 Heterogeneous wireless sensor networks (HWSNs), as blocks of the Internet of Things, are vulnerable to malware diffusion breaking the data confidentiality and service availability, owing to their weak defense mechanism and poor resilience. Thus, constructing a malware diffusion model and revealing the rules of malware diffusion in HWSNs are urgently needed. In this context, we propose a Heterogeneous Susceptible-Infectious-Removed-Dead (HSIRD) model based on epidemiology, in order to not only characterize the dead state where a heterogeneous sensor node (HSN) may lose its functionality owing to physical damage or malware attacks but also represent the HSN communication connectivity, which is one of the heterogeneities that exist universally in HWSNs. We then analyze the dynamics of the fractions of HSNs belonging to different degrees in different states and obtain the corresponding differential equations. Using these equations, we prove the existence of equilibrium points of the HSIRD model. Subsequently, we attain the basic reproduction number governing the stability of the equilibrium points. We further prove the stability of the equilibrium points of the model and attain the conditions indicating whether malware in HWSNs will diffuse or die out. Finally, we validate the effectiveness of the model via simulation. The results provide a theoretical foundation for suppressing malware diffusion in malware-infected HWSNs.
Shen, W, Wu, Y, Yuan, J, Duan, L, Zhang, J & Jia, Y 2019, 'Robust Distracter-Resistive Tracker via Learning a Multi-Component Discriminative Dictionary', IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 7, pp. 2012-2028.
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IEEE Discriminative dictionary learning (DDL) provides an appealing paradigm for appearance modeling in visual tracking. However, most existing DDL based trackers cannot handle drastic appearance changes, especially for scenarios with background cluster and/or similar object interference. One reason is that they often suffer from the loss of subtle visual information which is critical to distinguish an object from distracters. In this paper, we explore the use of deep features extracted from the Convolutional Neural Networks (CNNs) to improve the object representation and propose a robust distracter-resistive tracker via learning a multi-component discriminative dictionary. The proposed method exploits both the intra-class and the interclass visual information to learn shared atoms and the classspecific atoms. By imposing several constraints into the objective function, the learned dictionary is reconstructive, compressive and discriminative, thus can better distinguish an object from the background. In addition, our convolutional features (deep features extracted from CNNs) have structural information for object localization and balance the discriminative power and semantic information of the object. Tracking is carried out within a Bayesian inference framework where a joint decision measure is used to construct the observation model. To alleviate the drift problem, the reliable tracking results obtained online are accumulated to update the dictionary. Both the qualitative and quantitative results on the CVPR2013 benchmark, the VOT2015 dataset and the SPOT dataset demonstrate that our tracker achieves better performance over the state-of-the-art approaches.
Shen, W, Zhang, C & Yu, S 2019, 'An Energy-Efficient Scheme for Constructing Underwater Sensor Barrier with Minimum Mobile Sensors', AD HOC & SENSOR WIRELESS NETWORKS, vol. 43, no. 1-2, pp. 57-84.
Shi, J, Chu, L & Braun, R 2019, 'A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method', International Journal of Molecular Sciences, vol. 20, no. 9, pp. 2355-2355.
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Due to the inevitable presence of random defects, unpredictable grain boundaries in macroscopic samples, stress concentration at clamping points, and unknown load distribution in the investigation of graphene sheets, uncertainties are crucial and challenging issues that require more exploration. The application of the Kriging surrogate model in vibration analysis of graphene sheets is proposed in this study. The Latin hypercube sampling method effectively propagates the uncertainties in geometrical and material properties of the finite element model. The accuracy and convergence of the Kriging surrogate model are confirmed by a comparison with the reported references. The uncertainty analysis for both Zigzag and Armchair graphene sheets are compared and discussed.
Shi, X, Zhu, J, Li, L & Lu, DD-C 2019, 'Model-Predictive-Based Duty Cycle Control With Simplified Calculation and Mutual Influence Elimination for AC/DC Converter', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 7, no. 1, pp. 504-514.
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© 2013 IEEE. The single-vector-based model-predictive-based direct power control (MPDPC) is commonly used for control of three-phase full-bridge ac/dc converters, but it could only select the best switching vector to be implemented. Due to the limited number of voltage vectors in a two-level three-phase converter, the sampling frequency needs to be high to achieve an acceptable performance. Also, it bears variable switching frequency that causes spread spectrum nature of harmonics. In this paper, a three-vector-based simplified model predictive duty cycle control (SMPDCC) is proposed. The adjacent two nonzero vectors are selected by evaluating the effects of each vector pairs based on the revised cost function. The duration calculation is simplified compared with the conventional predictive duty cycle control (CPDCC) by allocating a control period in reciprocal proportion with the corresponding cost function value of the selected vectors. Besides, the negative duration issue of CPDCC could be completely avoided and the mutual influence elimination ability could be realized. A comparative study with MPDPC and CPDCC has been conducted to verify the superiority of the proposed scheme by the simulation and experimental results. It shows that the SMPDCC has the advantages of lower power ripple, fixed switching frequency, lower total harmonic distortion, and mutual influence elimination ability.
Shi, X, Zhu, J, Li, L, Lu, DD-C, Zhang, J & Yang, H 2019, 'Predictive Duty Cycle Control With Reversible Vector Selection for Three-Phase AC/DC Converters', IEEE Transactions on Power Electronics, vol. 34, no. 5, pp. 4868-4882.
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© 2018 IEEE. The conventional predictive duty cycle control (CPDCC) of three-phase full-bridge ac/dc converters selects adjacent nonzero vector pair based on the grid-voltage vector location, then the duration for each vector is calculated. Though the vector selection method is quite simple, it has a significant disadvantage that the values of calculated durations could be frequently less than zero due to nonoptimal vector selection, which results in high current harmonics and power notches. It could be improved with improved predictive duty cycle control (IPDCC) by reselecting the nonzero vector pair when negative duration exists; however, the whole vector selection and calculation procedure are repeated. By theoretical verification that the power variation rates of reversible vector pair are symmetrical with respect to that of zero vector, this paper proposes the reversible predictive duty cycle control (RPDCC) simply by replacing the original vector with its opposite vector and the recalculation of vector duration is eliminated compared with IPDCC. Thus, the calculation effort is almost not increased compared with CPDCC while system performance is significantly improved. The proposed control is theoretically derived and verified with the simulation and experimental results showing that RPDCC has better steady and dynamic performance than CPDCC and IPDCC methods.
Shi, Y, Lei, J, Yin, Y, Cao, K, Li, Y & Chang, C-I 2019, 'Discriminative Feature Learning With Distance Constrained Stacked Sparse Autoencoder for Hyperspectral Target Detection', IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 9, pp. 1462-1466.
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Shi, Y, Tuan, HD, Savkin, AV, Duong, TQ & Poor, HV 2019, 'Model Predictive Control for Smart Grids With Multiple Electric-Vehicle Charging Stations', IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 2127-2136.
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© 2017 IEEE Next-generation power grids will likely enable concurrent service for residences and plug-in electric vehicles (PEVs). While the residence power demand profile is known and thus can be considered inelastic, the PEVs' power demand is only known after random PEV arrivals. PEV charging scheduling aims at minimizing the potential impact of the massive integration of PEVs into power grids to save service costs to customers while power control aims at minimizing the cost of power generation subject to operating constraints and meeting demand. This paper develops a model predictive control-based approach to address joint PEV charging scheduling and power control to minimize both PEV charging cost and energy generation cost in meeting both residence and PEV power demands. Unlike in related works, no assumptions are made about the probability distribution of PEVs' arrivals, knowledge of PEVs' future demand, or unlimited charging capacity of PEVs. The proposed approach is shown to achieve a globally optimal solution. Numerical results for IEEE benchmark power grids serving Tesla model S PEVs show the merit of this approach.
Shiri, F, Yu, X, Porikli, F, Hartley, R & Koniusz, P 2019, 'Identity-Preserving Face Recovery from Stylized Portraits', International Journal of Computer Vision, vol. 127, no. 6-7, pp. 863-883.
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Shit, RC, Sharma, S, Puthal, D, James, P, Pradhan, B, Moorsel, AV, Zomaya, AY & Ranjan, R 2019, 'Ubiquitous Localization (UbiLoc): A Survey and Taxonomy on Device Free Localization for Smart World', IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3532-3564.
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© 1998-2012 IEEE. The 'Smart World' envisioned by technology will be achieved by the penetration of intelligence into ubiquitous things, including physical objects, cyber-entities, social-elements or individuals, and human thinking. The development of Smart World is enabled by diverse applications of wireless sensor networks (WSNs) into those components identified as things. Such a smart-world will have features controlled significantly by the location information. Control and Policy information of Smart World services, often addressed as location-based services (LBSs), are governed by location data. Localization thus becomes the key enabling technology for Smart World facilities. It is generally classified as active and passive techniques in nature. Active localization is a widely adopted localization scheme where the target is detected and tracked carries a tag or attached device. The other category, Passive methods, defines targets to be localized as free of carrying a tag or device, hence also referred to as device-free localization (DFL) or sensor-less localization. The passive approach is a well suited for the development of diverse smart world applications with ubiquitous localization. DFL schemes fall into a wide range of application scenarios within the Smart World ecosystem. A few notable examples are occupancy detection, identity definition, positioning, gesture detection, activity monitoring, pedestrian and vehicle-traffic flow surveillance, security safeguarding, ambient intelligence-based systems, emergency rescue operations, smart work-spaces and patient or elderly monitoring. In this paper, the revolution of DFL technologies have been reviewed and classified comprehensively. Further, the emergence of the Smart World paradigm is analyzed in the context of DFL principles. Moreover, the inherent challenges within the application domains have been extensively discussed and improvement strategies for multi-target localization and counting approach are ...
Shon, HK, Jegatheesan, V & Chiemchaisri, C 2019, 'Challenges in environmental science and engineering 2018', Process Safety and Environmental Protection, vol. 131, pp. 329-330.
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Shon, HS, Yi, Y, Kim, KO, Cha, E-J & Kim, K-A 2019, 'Classification of stomach cancer gene expression data using CNN algorithm of deep learning', Journal of Biomedical Translational Research, vol. 20, no. 1, pp. 15-20.
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Shrestha, J, Ghadiri, M, Shanmugavel, M, Razavi Bazaz, S, Vasilescu, S, Ding, L & Ebrahimi Warkiani, M 2019, 'A rapidly prototyped lung-on-a-chip model using 3D-printed molds', Organs-on-a-Chip, vol. 1, pp. 100001-100001.
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Shukla, N, Tiwari, MK & Beydoun, G 2019, 'Next generation smart manufacturing and service systems using big data analytics.', Comput. Ind. Eng., vol. 128, pp. 905-910.
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© 2018 Elsevier Ltd This special issue explores advancements in the next generation manufacturing and service systems by examining the novel methods, practical challenges and opportunities in the use of big data analytics. The selected articles analyse a range of scenarios where big data analytics and its applications were used for improving decision making in manufacturing and services sector such as online data analytics, sourcing decisions with considerations for big data analytics, barriers in the adoption of big data analytics, maintenance planning, and multi-sensor data for fault pattern extraction. The paper summarises the discussions on the use of big data analytics in manufacturing and service sectors.
Si, H, Shi, J-G, Tang, D, Wen, S, Miao, W & Duan, K 2019, 'Application of the Theory of Planned Behavior in Environmental Science: A Comprehensive Bibliometric Analysis', International Journal of Environmental Research and Public Health, vol. 16, no. 15, pp. 2788-2788.
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Since the theory of planned behavior (TPB) was proposed by Ajzen in 1985, it has attracted extensive interest and been widely applied worldwide. Although an increasing number of studies have employed the TPB in the domain of environmental science, there have been no attempts to retrospectively analyze existing articles. The current study aimed to holistically understand the application status of the TPB in environmental science from a knowledge domain visualization perspective. A total of 531 journal articles were obtained through the Scopus database to perform a bibliometric analysis and content analysis. The results showed that waste management, green consumption, climate and environment, saving and conservation, and sustainable transportation are the primary research topics; the United States (U.S.), Mainland China, the United Kingdom (UK), and Malaysia are the most productive countries/regions. Moreover, the cross-disciplinary situations, main source journals, and key articles were revealed. Furthermore, the extended factors, integrated theories, major methods, specific groups, and control variables of environmental science research using the TPB were elaborated and integrated into a comprehensive application framework. Constructive criticisms were ultimately discussed. The findings contribute in several ways to help relevant researchers learn about the application of TPB to environmental science and provide new insights and holistic references for further research on environment-related behavior.
Si, L, Eisman, JA, Winzenberg, T, Sanders, KM, Center, JR, Nguyen, TV & Palmer, AJ 2019, 'Microsimulation model for the health economic evaluation of osteoporosis interventions: study protocol', BMJ Open, vol. 9, no. 2, pp. e028365-e028365.
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IntroductionOsteoporosis is a systemic skeletal disease that is characterised by reduced bone strength and increased fracture risk. Osteoporosis-related fractures impose enormous disease and economic burden to the society. Although many treatments and health interventions are proven effective to prevent fractures, health economic evaluation adds evidence to their economic merits. Computer simulation modelling is a useful approach to extrapolate clinical and economic outcomes from clinical trials and it is increasingly used in health economic evaluation. Many osteoporosis health economic models have been developed in the past decades; however, they are limited to academic use and there are no publicly accessible health economic models of osteoporosis.Methods and analysisWe will develop the Australian osteoporosis health economic model based on our previously published microsimulation model of osteoporosis in the Chinese population. The development of the model will follow the recommendations for the conduct of economic evaluations in osteoporosis by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases and the US branch of the International Osteoporosis Foundation. The model will be a state-transition semi-Markov model with memory. Clinical parameters in the model will be mainly obtained from the Dubbo Osteoporosis Epidemiology Study and the health economic parameters will be collected from the Australian arm of the International Costs and Utilities Related to Osteoporotic Fractures Study. Model transparency and validates will be tested using the recommendations from Good Research Practices in Modelling Task Forces. The model will be used in economic evaluations of osteoporosis interventions including pharmaceutical treatments and primary care interventions. A user-friendly graphical user ...
Sick, N, Preschitschek, N, Leker, J & Bröring, S 2019, 'A new framework to assess industry convergence in high technology environments', Technovation, vol. 84-85, pp. 48-58.
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© 2018 Elsevier Ltd The process of convergence, from science and technology convergence to that of markets as well as entire industries can be witnessed in a range of different high technology environments such as IT and NanoBiotech. Although this phenomenon has been subject of analysis in an increasing number of studies, the notion of industry convergence – the final step of a full convergence process - still lacks a common definition. The missing conceptual definition of what industry convergence really is and how it can be assessed impedes both analyses and monitoring - let alone its anticipation. To address the missing conceptual definition of the final step in convergence, this paper seeks to develop a framework based on novel indicators that enable identifying and monitoring trends of industry convergence in high technology environments. Building on indicators in the domain of collaboration, a framework, which distinguishes different stages and types of industry convergence is developed. Subsequently, the newly developed framework is empirically illustrated in the area of stationary energy storage based on publicly available data. To this end, the full text database Nexis is used to conduct a search in news reports on collaborations in the domain of stationary energy storage. The study contributes to the growing body of convergence literature by providing a novel framework allowing the identification of not only industry convergence as the final step of the convergence process but also the classification of its type. Practical implications include an orientation for companies in converging environments on when and how to close the resulting technology and market competence gaps.
Siddiqi, MWU, Fedeli, P, Tu, C, Frangi, A & Lee, JE-Y 2019, 'Numerical analysis of anchor loss and thermoelastic damping in piezoelectric AlN-on-Si Lamb wave resonators', Journal of Micromechanics and Microengineering, vol. 29, no. 10, pp. 105013-105013.
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Sielicki, PW 2019, 'Introduction of the Special Issue', International Journal of Protective Structures, vol. 10, no. 3, pp. 269-269.
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Sielicki, PW, Ślosarczyk, A & Szulc, D 2019, 'Concrete slab fragmentation after bullet impact: An experimental study', International Journal of Protective Structures, vol. 10, no. 3, pp. 380-389.
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The response of nonreinforced concrete to high-speed flying fragments depends on many factors such as composition, geometry and boundary conditions of the obstacle. In this study, the authors verified experimentally a series of concrete slabs with various aggregate types and amounts of steel reinforcement. The material properties were previously developed and verified during the laboratory experiments with standard cubic specimens. Moreover, the main goal of this research was the verification of high-strength concrete plates using different kinds of aggregate, that is, traditional gravel, granite, basalt and amphibolite subjected to the military bullet impact. It is known that the aggregate filled about 70% of total concrete volume, and therefore, the properties of aggregate, like density, strength and surface will be the main factors influencing the concrete strength and further concrete resistance to projectiles. The research presented shows that the type and shape of aggregate determined the conditions of concrete failure. The outcomes show that the obtained compressive strength varied between 60 and 80 MPa. In the second stage of the test, randomly distributed reinforcement was added to selected concrete mixtures. Hooked steel fibres with an aspect ratio of 50 in the amounts of 50 and 100 kg per cubic metre were applied. The influence of steel fibres on the concrete properties was verified in compressive and splitting strength tests. On the basis of preliminary laboratory tests, the final slab specimens, with the dimensions of 0.5 × 0.5 m and 0.05 m thickness, were produced. The final verification was performed in the real field conditions using the most popular military bullet, whose kinetic energy was more than 2000 J. The measurements of the initial and final velocities of the bullets and concrete fragments were obtained using high-speed camera measurement equipment. The final failure of the slabs was presented as the primary outcome.
Silitonga, A, Mahlia, T, Shamsuddin, A, Ong, H, Milano, J, Kusumo, F, Sebayang, A, Dharma, S, Ibrahim, H, Husin, H, Mofijur, M & Rahman, S 2019, 'Optimization of Cerbera manghas Biodiesel Production Using Artificial Neural Networks Integrated with Ant Colony Optimization', Energies, vol. 12, no. 20, pp. 3811-3811.
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Optimizing the process parameters of biodiesel production is the key to maximizing biodiesel yields. In this study, artificial neural network models integrated with ant colony optimization were developed to optimize the parameters of the two-step Cerbera manghas biodiesel production process: (1) esterification and (2) transesterification. The parameters of esterification and transesterification processes were optimized to minimize the acid value and maximize the C. manghas biodiesel yield, respectively. There was excellent agreement between the average experimental values and those predicted by the artificial neural network models, indicating their reliability. These models will be useful to predict the optimum process parameters, reducing the trial and error of conventional experimentation. The kinetic study was conducted to understand the mechanism of the transesterification process and, lastly, the model could measure the physicochemical properties of the C. manghas biodiesel.
Silitonga, AS, Mahlia, TMI, Kusumo, F, Dharma, S, Sebayang, AH, Sembiring, RW & Shamsuddin, AH 2019, 'Intensification of Reutealis trisperma biodiesel production using infrared radiation: Simulation, optimisation and validation', Renewable Energy, vol. 133, pp. 520-527.
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© 2018 Elsevier Ltd Biodiesel production using intensification of methyl ester is becoming very important due to its considerably lower energy requirement and shorter reaction time in obtaining feedstock oil. The present study investigated utilisation of Reutealis trisperma oil to produce biodiesel. A Box-Behnken experimental design was used to optimise the transesterification process. The process variables were explored and the optimum methanol to oil molar ratio, catalyst concentration, reaction temperature, and reaction time were 8:1, 1.2 wt%, 64 °C and 68 min respectively and the corresponding methyl ester yield was 98.39%. The experiment was conducted in triplicate to validate the quadratic model. Results showed average methyl ester yield was 97.78%, which is close to the predicted value, indicating reliability of the model. Results also indicated that using infrared radiation method has many advantageous, such as less energy consumption as a result of deeper penetration of reactant mass which can improve mass transfer between the immiscible reactants in order to improve quality of biodiesel. The physicochemical properties of Reutealis trisperma methyl ester produced under optimum transesterification process variables were also measured and the properties fulfilled the fuel specifications as per ASTM D6751 and EN 14214 standards.
Silverman, BG, Bharathy, G & Weyer, N 2019, 'What is a good pattern of life model? Guidance for simulations.', Simul., vol. 95, no. 8, pp. 693-706.
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We have been modeling an ever-increasing scale of applications with agents that simulate the pattern of life (PoL) and real-world human behaviors in diverse regions of the world. The goal is to support sociocultural training and analysis. To measure progress, we propose the definition of a measure of goodness for such simulated agents, and review the issues and challenges associated with first-generation (1G) agents. Then we present a second generation (2G) agent hybrid approach that seeks to improve realism in terms of emergent daily activities, social awareness, and micro-decision making in simulations. We offer a PoL case study with a mix of 1G and 2G approaches that was able to replace the pucksters and avatar operators needed in large-scale immersion exercises. We conclude by observing that a 1G PoL simulation might still be best where large-scale, pre-scripted training scenarios will suffice, while the 2G approach will be important for analysis or if it is vital to learn about adaptive opponents or unexpected or emergent effects of actions. Lessons are shared about ways to blend 1G and 2G approaches to get the best of each.
Silwal, S, Taghizadeh, S, Karimi-Ghartemani, M, Hossain, MJ & Davari, M 2019, 'An Enhanced Control System for Single-Phase Inverters Interfaced With Weak and Distorted Grids', IEEE Transactions on Power Electronics, vol. 34, no. 12, pp. 12538-12551.
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© 2019 IEEE. This paper presents an enhanced current controller for improving the performance of a class of single-phase grid-connected inverters operating in weak and distorted grid conditions. An inverter designed to operate at normal (strong or stiff and clean) grid conditions may not perform satisfactorily during weak and distorted grid conditions. One major reason is the interfering dynamics of the synchronization or phase-locked loop (PLL). This paper proposes an enhanced control structure for a popular class of single-phase inverters to address this problem. The proposed idea is to include the PLL state variables into the main inverter controller thereby minimizing the undesirable interactions of the PLL with the other components. A method for optimally designing the controller gains is also proposed. Compared to the conventional one, the proposed controller is shown to have a more robust performance over a substantially wider range of weak and distorted grid conditions. Extensive simulation and experimental results are presented to validate the proposed controls.
Sin-Chan, P, Mumal, I, Suwal, T, Ho, B, Fan, X, Singh, I, Du, Y, Lu, M, Patel, N, Torchia, J, Popovski, D, Fouladi, M, Guilhamon, P, Hansford, JR, Leary, S, Hoffman, LM, Mulcahy Levy, JM, Lassaletta, A, Solano-Paez, P, Rivas, E, Reddy, A, Gillespie, GY, Gupta, N, Van Meter, TE, Nakamura, H, Wong, T-T, Ra, Y-S, Kim, S-K, Massimi, L, Grundy, RG, Fangusaro, J, Johnston, D, Chan, J, Lafay-Cousin, L, Hwang, EI, Wang, Y, Catchpoole, D, Michaud, J, Ellezam, B, Ramanujachar, R, Lindsay, H, Taylor, MD, Hawkins, CE, Bouffet, E, Jabado, N, Singh, SK, Kleinman, CL, Barsyte-Lovejoy, D, Li, X-N, Dirks, PB, Lin, CY, Mack, SC, Rich, JN & Huang, A 2019, 'A C19MC-LIN28A-MYCN Oncogenic Circuit Driven by Hijacked Super-enhancers Is a Distinct Therapeutic Vulnerability in ETMRs: A Lethal Brain Tumor', Cancer Cell, vol. 36, no. 1, pp. 51-67.e7.
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© 2019 Elsevier Inc. Embryonal tumors with multilayered rosettes (ETMRs) are highly lethal infant brain cancers with characteristic amplification of Chr19q13.41 miRNA cluster (C19MC) and enrichment of pluripotency factor LIN28A. Here we investigated C19MC oncogenic mechanisms and discovered a C19MC-LIN28A-MYCN circuit fueled by multiple complex regulatory loops including an MYCN core transcriptional network and super-enhancers resulting from long-range MYCN DNA interactions and C19MC gene fusions. Our data show that this powerful oncogenic circuit, which entraps an early neural lineage network, is potently abrogated by bromodomain inhibitor JQ1, leading to ETMR cell death. Sin-Chan et al. uncover a C19MC-LIN28A-MYCN super-enhancer-dependent oncogenic circuit in embryonal tumors with multilayered rosettes (ETMRs). The circuit entraps an early neural lineage network to sustain embryonic epigenetic programming and is vulnerable to bromodomain inhibition, which promotes ETMR cell death.
Singh, AM & Ha, QP 2019, 'Fast Terminal Sliding Control Application for Second-order Underactuated Systems', International Journal of Control, Automation and Systems, vol. 17, no. 8, pp. 1884-1898.
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© 2019, ICROS, KIEE and Springer. In this paper, we propose a robust and finite-time control method, based on the terminal sliding mode (TSM), for a class of two-degree-of-freedom (2-DOF) underactuated electromechanical systems subject to bounded uncertainties and disturbances. First, the proposed Fast Terminal Sliding Mode (FTSM) method is presented. Then for the underactuated system control, hierarchical sliding surfaces are defined, consisting of two layers. In the first layer, separate FTSM sliding functions are selected for each state of the system. In the second layer, the system sliding manifold is a linear combination of the first layer sliding surfaces. A control law is derived and stability conditions of the nonlinear system are obtained by using the Lyapunov theory. To verify the effectiveness of our proposed method, the developed control technique is applied to control both the swinging load and the cart position of an underactuated gantry crane. Extensive simulation and real-time experiments demonstrate enhanced performance of the system and robustness against parametric variations in comparison to conventional TSM and sliding mode control.
Singh, H, Satija, S, Kaur, H, Khurana, N, Sharma, N, Vyas, M, Singh, TG, Mahajan, S & Mehta, M 2019, 'Novel drug delivery approaches for guggul', Plant Archives, vol. 19, pp. 983-993.
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Guggul, an oleo gum resin released from Commiphora weightii, known for its immense applicability as hypolipidemic, anti-inflammatory, antioxidant, thyroid stimulatory agent, Platelet aggregation, fibrinolytic agent and the cytotoxic agent. Guggulsterones i.e. E & Z guggulsterones are the major constituents responsible for its pharmacological use. Traditionally, it's been used as antimalarial, antidysenteric, anticholesterolemic, antihypertensive, anti-rheumatic and indicated for many clinical conditions like dysmenorrhea, dyspepsia, impotence, leprosy, leucoderma, anemia etc. Nowadays, Guggul is available as the marketed formulation for curing numerous clinical conditions and is accessible in combination with various other ingredients. Though conventional dosage form shows the dominance as patient compliance and easy availability, yet it has found to pose the problems like dose fluctuation, peak-valley effect, non-adjustment of the administered drug, invasiveness etc. Guggul lacks its desired effect due to its low bioavailability and less water solubility. This makes it a partial or a deficient therapy for remedy of many signs and symptoms. Novel drug delivery system (NDDS), a new approach in the pharma sector has excluded many of drawbacks exhibited by conventional dosage forms. Some of the novel dosage forms of guggul has been formed like nanoparticles, nanovesicles, gugglusomes and proniosomal gel. But still, the novel formulations for guggul has its less outspread in the market. Guggul can be executed as a profitable drug using NDDS. There is a need to highlight the unidentified and unexplained facts about guggul so as to make it more efficacious and effective in terms of bioavailability and aqueous insolubility.
Singh, M, Hossain, A & Wei, DB 2019, 'A Hybrid Model for Studying the Size Effects on Flow Stress in Micro-Forming with the Consideration of Grain Hardening', Key Engineering Materials, vol. 794, pp. 97-104.
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Size effects extremely exist in the metal micro-forming process. When a deformation process scales down to micro scale, the appearances of geometry size and single grain size start to play a major role in deformation. Generally, the size effects are unavoidable in the experimental work and cannot be neglect in the optimization of micro-forming processes. In this paper, size effect on flow stress is investigated in the form of the coupled effect of workpiece geometry (sample thickness) and grain size, (T/D) by the micro tensile test of pure copper foil. Following the previous approaches, a new hybrid material model is projected to describe the hardening behavior of grains in polycrystalline material. Tensile tests performed on the copper foil with constant thickness and width, while to get dissimilar grain sizes, the foil annealed for different times. The ratio of thickness to grain size (T/D) is limited to larger than 1 (T/D˃1). A hybrid material model is proposed and established based on grain heterogeneity and sample thickness. The hybrid material model builds a relationship between the surface layer and sheet interior. The hybrid material model developed by the strain gradient theory in which the dislocation cell structure, cell densities (interior and wall) engaged to define the polycrystalline aggregate and calculated the dislocations in a grain (grain interior and grain wall). The results show that flow stress varies with the different values of T/D, but with an increase of the share of the grains flow stress start to decreases. After applying the hybrid material model of flow stress, the micro-tensile test of copper foil is simulated by finite element method. The simulation outcomes well matched with experimental results.
Singh, RK, Xu, Y, Wang, R, Hamilton, TJ, Denham, SL & van Schaik, A 2019, 'CAR-Lite: A Multi-Rate Cochlear Model on FPGA for Spike-Based Sound Encoding', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 66, no. 5, pp. 1805-1817.
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© 2018 IEEE. Filters in cochlear models use different coefficients to break sound into a 2-D time-frequency representation. On digital hardware with a single sampling rate, the number of bits required to represent these coefficients requires substantial computational resources such as memory storage. In this paper, we present a cochlear model operating at multiple sampling rates. As a result, fewer bits are required to represent filter coefficients on hardware as opposed to all the filters operating at a single sampling rate; with a 108-filter cochlear implementation, up to nine times fewer coefficients are needed. We present an implementation of this model in Matlab and on an Altera Cyclone V field-programmable gate array. We also demonstrate the capability of our model to encode sound at various intensity levels and with real-world signals.
Sirivivatnanon, V 2019, 'Sixty-Year Service Life of Port Kembla Saltwater Concrete Swimming Pool', ACI Materials Journal, vol. 116, no. 5, pp. 31-36.
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© 2019 American Concrete Institute. All rights reserved. The durability performance of Port Kembla Olympic Pool, built in 1937, has been investigated. Nearly all structural components were reinforced concrete and were exposed to marine environments with some components ‘permanently submerged’ while others were in an ‘atmospheric zone’ and ‘tidal or splash zone.’ After more than 60 years in service, most structural components were found to be in excellent condition. This paper discusses the site investigation that examined strength, carbonation, chloride penetration, and cover depths. The results revealed the quality of the concrete to be uniform in the pool but variable in other structural members. There was little carbonation but extensive chloride penetration, depending on the exposure condition. The average compressive strength of the 60-year-old concrete in the pool and its surrounding structures was 5700 and 4280 psi (40 and 30 MPa), respectively. The covers were between 2.0 and 2.5 in. (50 and 65 mm). Despite the extent of chloride penetration into the cover concrete, limited corrosion was observed. The concrete has proven to give a service life of over 60 years, which confirms the importance of achieving adequate strength and, perhaps more importantly, cover.
Sirunyan, AM, Tumasyan, A, Adam, W, Ambrogi, F, Asilar, E, Bergauer, T, Brandstetter, J, Dragicevic, M, Erö, J, Escalante Del Valle, A, Flechl, M, Frühwirth, R, Ghete, VM, Hrubec, J, Jeitler, M, Krammer, N, Krätschmer, I, Liko, D, Madlener, T, Mikulec, I, Rad, N, Rohringer, H, Schieck, J, Schöfbeck, R, Spanring, M, Spitzbart, D, Taurok, A, Waltenberger, W, Wittmann, J, Wulz, C-E, Zarucki, M, Chekhovsky, V, Mossolov, V, Suarez Gonzalez, J, De Wolf, EA, Di Croce, D, Janssen, X, Lauwers, J, Pieters, M, Van Haevermaet, H, Van Mechelen, P, Van Remortel, N, Abu Zeid, S, Blekman, F, D'Hondt, J, De Bruyn, I, De Clercq, J, Deroover, K, Flouris, G, Lontkovskyi, D, Lowette, S, Marchesini, I, Moortgat, S, Moreels, L, Python, Q, Skovpen, K, Tavernier, S, Van Doninck, W, Van Mulders, P, Van Parijs, I, Beghin, D, Bilin, B, Brun, H, Clerbaux, B, De Lentdecker, G, Delannoy, H, Dorney, B, Fasanella, G, Favart, L, Goldouzian, R, Grebenyuk, A, Kalsi, AK, Lenzi, T, Luetic, J, Postiau, N, Starling, E, Thomas, L, Vander Velde, C, Vanlaer, P, Vannerom, D, Wang, Q, Cornelis, T, Dobur, D, Fagot, A, Gul, M, Khvastunov, I, Poyraz, D, Roskas, C, Trocino, D, Tytgat, M, Verbeke, W, Vermassen, B, Vit, M, Zaganidis, N, Bakhshiansohi, H, Bondu, O, Brochet, S, Bruno, G, Caputo, C, David, P, Delaere, C, Delcourt, M, Francois, B, Giammanco, A, Krintiras, G, Lemaitre, V, Magitteri, A, Mertens, A, Musich, M, Piotrzkowski, K, Saggio, A, Vidal Marono, M, Wertz, S, Zobec, J, Alves, FL, Alves, GA, Correa Martins Junior, M, Correia Silva, G, Hensel, C, Moraes, A, Pol, ME, Rebello Teles, P, Belchior Batista Das Chagas, E, Carvalho, W, Chinellato, J, Coelho, E, Da Costa, EM, Da Silveira, GG, De Jesus Damiao, D, De Oliveira Martins, C, Fonseca De Souza, S, Malbouisson, H, Matos Figueiredo, D, Melo De Almeida, M, Mora Herrera, C, Mundim, L, Nogima, H, Prado Da Silva, WL, Sanchez Rosas, LJ, Santoro, A, Sznajder, A, Thiel, M, Tonelli Manganote, EJ, Torres Da Silva De Araujo, F, Vilela Pereira, A, Ahuja, S, Bernardes, CA, Calligaris, L, Fernandez Perez Tomei, TR, Gregores, EM, Mercadante, PG, Novaes, SF, Padula, SS, Aleksandrov, A, Hadjiiska, R, Iaydjiev, P, Marinov, A, Misheva, M, Rodozov, M, Shopova, M, Sultanov, G, Dimitrov, A, Litov, L, Pavlov, B, Petkov, P, Fang, W, Gao, X, Yuan, L, Ahmad, M, Bian, JG, Chen, GM, Chen, HS, Chen, M, Chen, Y & et al. 2019, 'Evidence for light-by-light scattering and searches for axion-like particles in ultraperipheral PbPb collisions at sNN=5.02TeV', Physics Letters B, vol. 797, pp. 134826-134826.
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Sirunyan, AM, Tumasyan, A, Adam, W, Ambrogi, F, Asilar, E, Bergauer, T, Brandstetter, J, Dragicevic, M, Erö, J, Escalante Del Valle, A, Flechl, M, Frühwirth, R, Ghete, VM, Hrubec, J, Jeitler, M, Krammer, N, Krätschmer, I, Liko, D, Madlener, T, Mikulec, I, Rad, N, Rohringer, H, Schieck, J, Schöfbeck, R, Spanring, M, Spitzbart, D, Taurok, A, Waltenberger, W, Wittmann, J, Wulz, C-E, Zarucki, M, Chekhovsky, V, Mossolov, V, Suarez Gonzalez, J, De Wolf, EA, Di Croce, D, Janssen, X, Lauwers, J, Pieters, M, Van Haevermaet, H, Van Mechelen, P, Van Remortel, N, Abu Zeid, S, Blekman, F, D'Hondt, J, De Bruyn, I, De Clercq, J, Deroover, K, Flouris, G, Lontkovskyi, D, Lowette, S, Marchesini, I, Moortgat, S, Moreels, L, Python, Q, Skovpen, K, Tavernier, S, Van Doninck, W, Van Mulders, P, Van Parijs, I, Beghin, D, Bilin, B, Brun, H, Clerbaux, B, De Lentdecker, G, Delannoy, H, Dorney, B, Fasanella, G, Favart, L, Goldouzian, R, Grebenyuk, A, Kalsi, AK, Lenzi, T, Luetic, J, Postiau, N, Starling, E, Thomas, L, Vander Velde, C, Vanlaer, P, Vannerom, D, Wang, Q, Cornelis, T, Dobur, D, Fagot, A, Gul, M, Khvastunov, I, Poyraz, D, Roskas, C, Trocino, D, Tytgat, M, Verbeke, W, Vermassen, B, Vit, M, Zaganidis, N, Bakhshiansohi, H, Bondu, O, Brochet, S, Bruno, G, Caputo, C, David, P, Delaere, C, Delcourt, M, Giammanco, A, Krintiras, G, Lemaitre, V, Magitteri, A, Mertens, A, Musich, M, Piotrzkowski, K, Saggio, A, Vidal Marono, M, Wertz, S, Zobec, J, Alves, FL, Alves, GA, Correa Martins Junior, M, Correia Silva, G, Hensel, C, Moraes, A, Pol, ME, Rebello Teles, P, Belchior Batista Das Chagas, E, Carvalho, W, Chinellato, J, Coelho, E, Da Costa, EM, Da Silveira, GG, De Jesus Damiao, D, De Oliveira Martins, C, Fonseca De Souza, S, Malbouisson, H, Matos Figueiredo, D, Melo De Almeida, M, Mora Herrera, C, Mundim, L, Nogima, H, Prado Da Silva, WL, Sanchez Rosas, LJ, Santoro, A, Sznajder, A, Thiel, M, Tonelli Manganote, EJ, Torres Da Silva De Araujo, F, Vilela Pereira, A, Ahuja, S, Bernardes, CA, Calligaris, L, Fernandez Perez Tomei, TR, Gregores, EM, Mercadante, PG, Novaes, SF, Padula, SS, Aleksandrov, A, Hadjiiska, R, Iaydjiev, P, Marinov, A, Misheva, M, Rodozov, M, Shopova, M, Sultanov, G, Dimitrov, A, Litov, L, Pavlov, B, Petkov, P, Fang, W, Gao, X, Yuan, L, Ahmad, M, Bian, JG, Chen, GM, Chen, HS, Chen, M, Chen, Y, Jiang, CH & et al. 2019, 'Search for an L − L gauge boson using Z → 4μ events in proton-proton collisions at s=13 TeV', Physics Letters B, vol. 792, pp. 345-368.
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Siwakoti, YP, Mahajan, A, Rogers, DJ & Blaabjerg, F 2019, 'A Novel Seven-Level Active Neutral-Point-Clamped Converter With Reduced Active Switching Devices and DC-Link Voltage', IEEE Transactions on Power Electronics, vol. 34, no. 11, pp. 10492-10508.
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© 1986-2012 IEEE. This paper presents a novel seven-level inverter topology for medium-voltage high-power applications. It consists of eight active switches and two inner flying capacitor (FC) units forming a similar structure as in a conventional active neutral-point-clamped (ANPC) inverter. This unique arrangement reduces the number of active and passive components. A simple modulation technique reduces cost and complexity in the control system design without compromising reactive power capability. In addition, compared to major conventional seven-level inverter topologies, such as the neutral point clamped, FC, cascaded H-bridge, and ANPC topologies, the new topology reduces the dc-link voltage requirement by 50%. This recued dc-link voltage makes the new topology appealing for various industrial applications. Experimental results from a 2.2-kVA prototype are presented to support the theoretical analysis presented in this paper. The prototype demonstrates a conversion efficiency of around 97.2% ± 1% for a wide load range.
Siwakoti, YP, Mostaan, A, Abdelhakim, A, Davari, P, Soltani, MN, Khan, MNH, Li, L & Blaabjerg, F 2019, 'High-Voltage Gain Quasi-SEPIC DC–DC Converter', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 7, no. 2, pp. 1243-1257.
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© 2013 IEEE. This paper proposes a modified coupled-inductor SEPIC dc-dc converter for high-voltage-gain (2< G< 10) applications. It utilizes the same components as the conventional SEPIC converter with an additional diode. The voltage stress on the switch is minimal, which helps the designer to select a low-voltage and low R{mathrm {DS}-mathrm{scriptscriptstyle ON}} MOSFET, resulting in a reduction of cost, conduction, and turn ON losses of the switch. Compared to equivalent topologies with similar voltage-gain expression, the proposed topology uses lower component count to achieve the same or even higher voltage gain. This helps to design a very compact and lightweight converter with higher power density and reliability. Operating performance, steady-state analysis and mathematical derivations of the proposed dc-dc converter have been demonstrated in this paper. Moreover, extension of the circuit for higher gain (G>10) application is also introduced and discussed. Finally, the main features of the proposed converter have been verified through simulation and experimental results of a 400-W laboratory prototype. The efficiency is almost flat over a wide range of load with the highest measured efficiency of 96.2%, and the full-load efficiency is 95.2% at a voltage gain of 10.
Skilodimou, HD, Bathrellos, GD, Chousianitis, K, Youssef, AM & Pradhan, B 2019, 'Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study', Environmental Earth Sciences, vol. 78, no. 2.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Multi-hazard assessment modeling comprises an essential tool in any plan that aims to mitigate the impact of future natural disasters. For a particular area they can be generated by combining assessment maps for different types of natural hazards. In the present study, the analytical hierarchy process (AHP) supported by a Geographical Information System (GIS) was utilized to initially produce assessment maps on hazards from landslides, floods and earthquakes and subsequently to combine them into a single multi-hazard map. Evaluation of the reliability of the proposed model predictions was performed through uncertainty analysis of the variables that we used for producing the final model. The drainage basin of Peneus (Pinios) River (Western Peloponnesus, Greece), an area that is prone to landslides, floods and seismic events, was selected for the implementation of the aforementioned approach. Our findings revealed that the high hazard zones are mainly distributed in the western and north-eastern part of the region under investigation. The calculated multi-hazard map, which corresponds to the potential urban development suitability map of the study area, was classified into five classes, namely of very low, low, moderate, high and very high suitability. The most suitable areas for urban development are distributed mostly in the eastern part, in agreement with the low and very low hazard level for the three considered natural hazards. In addition, by performing uncertainty analysis we showed that the spatial distribution of the suitability zones does not change significantly. Ultimately, the final map was verified using the actual inventory of landslides and floods that affected the study area. In this context, we showed that 80% of the landslide occurrences and all the recorded flood events fall within the boundaries of the moderate, low and very low suitability zones. Consequently, the predictive capaci...
Smit, R & Kingston, P 2019, 'Measuring On-Road Vehicle Emissions with Multiple Instruments Including Remote Sensing', Atmosphere, vol. 10, no. 9, pp. 516-516.
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The objective of this paper is to use remote sensing to measure on-road emissions and to examine the impact and usefulness of additional measurement devices at three sites. Supplementing remote sensing device (RSD) equipment with additional equipment increased the capture rate by almost 10%. Post-processing of raw data is essential to obtain useful and accurate information. A method is presented to identify vehicles with excessive emission levels (high emitters). First, an anomaly detection method is applied, followed by identification of cold start operating conditions using infrared vehicle profiles. Using this method, 0.6% of the vehicles in the full (enhanced) RSD data were identified as high emitters, of which 35% are likely in cold start mode where emissions typically stabilize to low hot running emission levels within a few minutes. Analysis of NOx RSD data confirms that poor real-world NOx performance of Euro 4/5 light-duty diesel vehicles observed around the world is also evident in Australian measurements. This research suggests that the continued dieselisation in Australia, in particular under the current Euro 5 emission standards and the more stringent NO2 air quality criteria expected in 2020 and 2025, could potentially result in local air quality issues near busy roads.
Smit, R, Kingston, P, Neale, DW, Brown, MK, Verran, B & Nolan, T 2019, 'Monitoring on-road air quality and measuring vehicle emissions with remote sensing in an urban area', Atmospheric Environment, vol. 218, pp. 116978-116978.
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Simultaneous day-time measurement (8 a.m.–5 p.m.) of on-road air quality and emissions (remote sensing) on an urban road with traffic volumes varying between approximately 400-800 vehicles per hour and an average speed of about 40 km/h has been used to compare multiple measurement techniques and assess on-road air quality. It was found that observed daytime concentration levels of CO, NO , NO , O , PM and PM are below current WHO health based guideline values, varying from a few percent (CO) to above 60% (O and PM ) of the guideline values. NO to NO ratios were about a factor of two higher than measured in a previous tunnel study, which indicates that about half of measured NO concentrations are due to urban background levels. Statistical analysis suggest that on-road NO and ozone concentrations are largely driven by atmospheric chemistry processes, and not significantly affected by variation in local traffic volume and fleet mix. A significant positive weighted correlation (r = 0.71–0.74) between remote sensing and air concentration monitoring is observed for CO in calm conditions. Speciated VOC measurements on the road show large discrepancies with current COPERT (Australia) vehicle emission factors, confirming the results from an earlier tunnel study. The weight of evidence suggests that the current (EU based) VOC speciation in COPERT Australia is likely not appropriate for the Australian on-road fleet. x 2 3 10 2.5 3 2.5 2 x 2 2
Smith, AJ, Best, G, Yu, J & Hollinger, GA 2019, 'Real-time distributed non-myopic task selection for heterogeneous robotic teams', Autonomous Robots, vol. 43, no. 3, pp. 789-811.
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Smith, CM, Catchpoole, D & Hutvagner, G 2019, 'Non-Coding RNAs in Pediatric Solid Tumors', Frontiers in Genetics, vol. 10.
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© Copyright © 2019 Smith, Catchpoole and Hutvagner. Pediatric solid tumors are a diverse group of extracranial solid tumors representing approximately 40% of childhood cancers. Pediatric solid tumors are believed to arise as a result of disruptions in the developmental process of precursor cells which lead them to accumulate cancerous phenotypes. In contrast to many adult tumors, pediatric tumors typically feature a low number of genetic mutations in protein-coding genes which could explain the emergence of these phenotypes. It is likely that oncogenesis occurs after a failure at many different levels of regulation. Non-coding RNAs (ncRNAs) comprise a group of functional RNA molecules that lack protein coding potential but are essential in the regulation and maintenance of many epigenetic and post-translational mechanisms. Indeed, research has accumulated a large body of evidence implicating many ncRNAs in the regulation of well-established oncogenic networks. In this review we cover a range of extracranial solid tumors which represent some of the rarer and enigmatic childhood cancers known. We focus on two major classes of ncRNAs, microRNAs and long non-coding RNAs, which are likely to play a key role in the development of these cancers and emphasize their functional contributions and molecular interactions during tumor formation.
Smith, MR, Chai, R, Nguyen, HT, Marcora, SM & Coutts, AJ 2019, 'Comparing the Effects of Three Cognitive Tasks on Indicators of Mental Fatigue', The Journal of Psychology, vol. 153, no. 8, pp. 759-783.
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© 2019, © 2019 Taylor & Francis Group, LLC. This investigation assessed the impact of three cognitively demanding tasks on cognitive performance, subjective, and physiological indicators of mental fatigue. Following familiarization, participants completed four testing sessions, separated by 48 h. During each session, participants watched a 45-min emotionally neutral documentary (control) or completed one of the following computer tasks: Psychomotor Vigilance Task (PVT); AX-Continuous Performance Test (AX-CPT); or Stroop Task. Mental fatigue was assessed before and at regular periods for 60 min following the 45-min treatments. Cognitive performance was assessed using 3-min PVT, and task performance. Subjective assessments were conducted using the Brunel Mood Scale, and visual analog scales (VAS). Physiological indicators of mental fatigue included electroencephalography (EEG), and heart rate variability (HRV). Subjective ratings of mental fatigue increased from pre to 0-min post in all-treatments, but not the documentary (p < 0.05). Subjective fatigue (VAS) remained higher (p < 0.05) than pretreatment values for 20-, 50-, and 60-min following the PVT, Stroop, and AX-CPT respectively. The cognitively demanding tasks had unclear effects on 3-min PVT, EEG, and HRV assessments. Tasks requiring response inhibition appear to induce fatigue for longer durations than a simple vigilance task. Simple VAS appear to be the most practical method for assessing mental fatigue.
Sohaib, O, Kang, K & Miliszewska, I 2019, 'Uncertainty Avoidance and Consumer Cognitive Innovativeness in E-Commerce.', J. Glob. Inf. Manag., vol. 27, no. 2, pp. 59-77.
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Copyright © 2019, IGI Global. This article describes how despite the extensive academic interest in e-commerce, an investigation of consumer cognitive innovativeness towards new product purchase intention has been neglected. Based on the stimulus–organism–response (S–O–R) model, this study investigates the consumer cognitive innovativeness and the moderating role of the individual consumer-level uncertainty avoidance cultural value towards new product purchase intention in business-to-consumer (B2C) e-commerce. Structural equation modelling, such as partial least squares (PLS) path modelling was used to test the model, using a sample of 255 participants in Australia who have had prior online shopping experience. The findings show that the online store web atmosphere influences consumers’ cognitive innovativeness to purchase new products in countries with diverse degrees of uncertainty avoidance such as Australia. The results provide some guidance for a B2C website design based on how individual’s uncertainty avoidance and cognitive innovativeness can aid the online consumer purchasing decision-making process.
Sohaib, O, Kang, K & Nurunnabi, M 2019, 'Gender-Based iTrust in E-Commerce: The Moderating Role of Cognitive Innovativeness', Sustainability, vol. 11, no. 1, pp. 175-175.
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Despite the extensive academic interest in e-commerce, cognitive innovativeness in e-commerce context has been neglected. This study focuses on the moderating role of consumer cognitive innovativeness on the influencing factors of interpersonal trust (iTrust) towards online purchase intention of new product in business-to-consumer (B2C) e-commerce. Data were collected in Australia from consumers who has had prior online shopping experience. Variance-based structural equation modeling such as partial least squares (PLS-SEM) is used to test the research model. The results show men and women have different perceptions of what is important to be provided by an online store to make a positive shopping experience. We highlighted that in-addition to the e-commerce web design aspects; the individual cognitive innovativeness can influence females more to purchase online. Practitioners should adjust their online business strategies, considering consumer cognitive innovativeness to enhance their e-commerce desirable outcomes. This means online business should not treat their consumers as a uniform group with a ‘one-design-fits-all’ web design strategy but need to consider the individual needs of their male and female consumers.
Sohaib, O, Naderpour, M, Hussain, W & Martínez-López, L 2019, 'Cloud computing model selection for e-commerce enterprises using a new 2-tuple fuzzy linguistic decision-making method.', Comput. Ind. Eng., vol. 132, pp. 47-58.
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© 2019 Elsevier Ltd Cloud computing is truly transforming the way e-commerce firms do business. While there has been a sharp increase in the use of cloud computing in e-commerce, the benefits of cloud service models have yet to be explored, particularly for small-to-medium-sized businesses. A strong e-commerce offering depends on a reliable and secure online store, therefore it is important for decision makers to adopt the optimal cloud computing service model such as software-as-a-service (SaaS), platform-as-a-service (PaaS), or infrastructure-as-a-service (IaaS), which is a multi-criteria decision-making problem (MCDM). To address this MCDM problem, we propose a novel 2-tuple fuzzy linguistic multi-criteria group decision-making method based on the technique for order preference by similarity to ideal solution (TOPSIS) and rely upon a technology-organization-environment (TOE) framework to determine a set of appropriate criteria. The proposed methodology is applied to a small-to-medium-sized company to facilitate assessing the factors associated with cloud-based e-commerce and making the decision. The result analysis indicates that SaaS is the best choice for small and medium-sized e-commerce businesses considering criteria such as complexity, reliability, security and privacy, organization readiness and firm size, while the selection of PaaS or IaaS can be reinforced considering their compatibility and scalability.
Sohaib, O, Solanki, H, Dhaliwa, N, Hussain, W & Asif, M 2019, 'Integrating design thinking into extreme programming.', J. Ambient Intell. Humaniz. Comput., vol. 10, no. 6, pp. 2485-2492.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The increased demand for information systems drives businesses to rethink their customer needs to a greater extent and undertake innovation to compete in the marketplace. The design thinking (DT) is a human-centered methodology leads to creativity and innovation. The agile applications development such as extreme programming (XP) as a rapid application development approach tends to focus on perfecting functionality requirement and technical implementation. However, it causes significant challenges to building software/applications to meet the needs of end-user. This study integrates DT practices into XP methodology to improve the quality of software product for the end-users and enable businesses to achieve creativity and innovation. The proposed integrated DT@XP framework presents the various DT practices (empathy, define, persona, DT user stories) are adapted into XP exploration phase, prototyping and usability evaluation into XP planning phase. Our work demonstrates the applicability of DT concepts to analyze customer/user involvement in XP projects.
Song, J-H, Shon, HK, Wang, P, Jang, A & Kim, IS 2019, 'Tuning the nanostructure of nitrogen-doped graphene laminates for forward osmosis desalination', Nanoscale, vol. 11, no. 45, pp. 22025-22032.
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Studies have concentrated on the physicochemical properties of graphene-based membranes that can replace polymeric membranes for use in forward osmosis (FO) systems.
Song, R, Clemon, L & Telenko, C 2019, 'Uncertainty and Variability of Energy and Material Use by Fused Deposition Modeling Printers in Makerspaces', Journal of Industrial Ecology, vol. 23, no. 3, pp. 699-708.
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© 2018, Yale University. Desktop-grade fused deposition modeling (FDM) printers are popular because of compact sizes and affordable prices. If we are moving toward a future where desktop FDM printers are in every school and office, like conventional printers, then these machines will consume a large amount of energy and material. However, it is very difficult to evaluate the environmental impacts of FDM printers since there are so many different brands and types of printers using different raw materials under different scenarios. This study uses data from two different printing sites to evaluate the scenario and parameter uncertainty and variability in energy and material balances for FDM printers. Data from the two makerspaces provide insight into the material and energy consumption data using polylactic acid and acrylonitrile butadiene styrene (ABS) with four types of printers. The use of actual performance data allowed for the additional study of scrap ratio. Regressions provide insight into predictive factors for energy and material consumption. Monte Carlo simulations show the range of energy life cycle inventory values for the desktop-grade FDM printers. From the regressions, Type A Pro was the most energy-intensive machine. For material waste, an open-access makerspace using ABS was associated with higher scrap ratio. Regression analysis indicates that the rate of material usage is not a strong predictor of waste rates. The amount of waste generated across both sites indicates that more ubiquitous access to FDM printing may create a significant addition to the waste stream.
Song, Y, Wightman, E, Tian, Y, Jack, K, Li, X, Zhong, H, Bond, PL, Yuan, Z & Jiang, G 2019, 'Corrosion of reinforcing steel in concrete sewers', Science of The Total Environment, vol. 649, pp. 739-748.
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Hydrogen sulfide is a controlling factor for concrete corrosion in sewers, although its impact on sewer rebar corrosion has not been investigated to date. This study determined the corrosion mechanism of rebar in sewers by elucidating the roles of chloride ions, apart from the effects of hydrogen sulfide and biogenic sulfuric acid. The nature and distribution of rusts at the steel/concrete interface were delineated using the advanced mineral analytical techniques, including mineral liberation analysis and micro X-ray diffraction which is the first-ever use in such studies. The corrosion products were found to be mainly iron oxides or oxy-hydroxides. H2S and biogenic sulfuric acid did not directly participate in the product formation of steel partly covered by concrete or directly exposed to sewer atmosphere. Instead, chloride ions played an important role in initiating steel corrosion in sewers, supported by a thin chloride-enriched layer at the steel/rust interface. Away from the chloride-enriched layer, iron oxides accumulated on both sides of the mill-scale to form a corrosion layer and corrosion-filled paste respectively. The corrosion layer around rebar circumference was non-uniform and the rust thickness with respect to polar coordinates followed a Gaussian model. These findings support predictions of sewer service lifetime and developments of corrosion prevention strategies.
Song, Z, Zhang, X, Ngo, HH, Guo, W, Song, P, Zhang, Y, Wen, H & Guo, J 2019, 'Zeolite powder based polyurethane sponges as biocarriers in moving bed biofilm reactor for improving nitrogen removal of municipal wastewater', Science of The Total Environment, vol. 651, no. Pt 1, pp. 1078-1086.
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This study aims to enhance nitrogen removal efficiency of a moving bed biofilm reactor (MBBR) by developing a new MBBR with zeolite powder-based polyurethane sponges as biocarriers (Z-MBBR). Results indicated the total nitrogen (TN) removal efficiency and simultaneous nitrification and denitrification (SND) performance in Z-MBBR were nearly 10% higher than those in the conventional MBBR with sponges as biocarriers (S-MBBR). About 84.2 ± 4.8% of TN was removed in Z-MBBR compared to 75.1 ± 6.8% in S-MBBR. Correspondingly, the SND performance in Z-MBBR and S-MBBR was 90.7 ± 4.1% and 81.7 ± 6.5%, respectively. The amount of biofilm attached to new biocarriers (0.470 ± 0.131 g/g carrier) was 1.3 times more than that of sponge carriers (0.355 ± 0.099 g/g carrier). Based on the microelectrode measurements and microbial community analysis, more denitrifying bacteria existed in the Z-MBBR system, and this can improve the SND performance. Consequently, this new Z-MBBR can be a promising option for a hybrid treatment system to better nitrogen removal from wastewater.
Song, Z, Zhang, X, Ngo, HH, Guo, W, Wen, H & Li, C 2019, 'Occurrence, fate and health risk assessment of 10 common antibiotics in two drinking water plants with different treatment processes', Science of The Total Environment, vol. 674, pp. 316-326.
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© 2019 Elsevier B.V. The occurrence of antibiotics in drinking water has become a serious problem worldwide as they are a potential and real threat to human health. In this study, the variability of 10 typical antibiotics in two drinking water plants was investigated in two seasons (n = 12). The total concentrations of target antibiotics in raw water were significantly higher in winter than in summer, which may be attributed to the more frequent occurrence of colds and respiratory diseases as well as less rainfall in winter. The efficiency in removing the antibiotics varied from −46.5% to 45.1% in water plant A (WP-A) using a conventional process and 40.3% to 70.3% in water plant B (WP-B) with an advanced treatment process. Results indicated that the antibiotics in WP-A were mainly removed via the coagulation process. However in WP-B, the ultraviolet + chlorination process played a key role in antibiotics removal, followed by the pre-ozone + coagulation process. According to the human health risk assessment, it was suggested that the risk of drinking water was significantly higher than that of skin contact. However, the risk of carcinogenesis and non-carcinogenesis caused by antibiotics was at an acceptable level.
Sood, K, Karmakar, KK, Varadharajan, V, Tupakula, U & Yu, S 2019, 'Analysis of Policy-Based Security Management System in Software-Defined Networks', IEEE Communications Letters, vol. 23, no. 4, pp. 612-615.
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© 1997-2012 IEEE. In software-defined networks, policy-based security management or architecture (PbSA) is an ideal way to dynamically control the network. We observe that on the one hand, this enables security capabilities intelligently and enhance fine-grained control over end user behavior. But, on the other hand, dynamic variations in network, rapid increases in security attacks, geographical distribution of nodes, complex heterogeneous networks, and so on have serious effects on the performance of PbSAs. These affect the flow specific quality of service requirements with further degradation of the performance of the security context. Hence, in this letter, PbSA's performance is evaluated. The key factors including a number of rules, rule-table size, position of rules, flow arrival rate, and CPU utilization are examined, and found to have considerable impact on the performance of PbSAs.
Soon, L, Ng, PQ, Chellian, J, Madheswaran, T, Panneerselvam, J, Gupta, G, Nammi, S, Hansbro, NG, Hsu, A, Dureja, H, Mehta, M, Satija, S, Hansbro, PM, Dua, K, Collet, T & Chellappan, DK 2019, 'Therapeutic potential of Artemisia vulgaris: An insight into underlying immunological mechanisms', Journal of Environmental Pathology, Toxicology and Oncology, vol. 38, no. 3, pp. 205-216.
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© 2019 by Begell House, Inc. Artemisia vulgaris is a traditional Chinese herb believed to have a wide range of healing properties; it is traditionally used to treat numerous health ailments. The plant is commonly called mugwort or riverside wormwood. The plant is edible, and in addition to its medicinal properties, it is also used as a culinary herb in Asian cooking in the form of a vegetable or in soup. The plant has garnered the attention of researchers in the past few decades, and several research studies have investigated its biological effects, including antioxidant, anti-inflammatory, anticancer, hypolipidemic, and antimicrobial properties. In this review, various studies on these biological effects are discussed along with the tests conducted, compounds involved, and proposed mechanisms of action. This review will be of interest to the researchers working in the field of herbal medicine, pharmacology, medical sciences, and immunology.
Soudagar, MEM, Nik-Ghazali, N-N, Kalam, MA, Badruddin, IA, Banapurmath, NR, Yunus Khan, TM, Bashir, MN, Akram, N, Farade, R & Afzal, A 2019, 'The effects of graphene oxide nanoparticle additive stably dispersed in dairy scum oil biodiesel-diesel fuel blend on CI engine: performance, emission and combustion characteristics', Fuel, vol. 257, pp. 116015-116015.
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In the present investigation, the effects of graphene oxide nanoparticles on performance and emissions of a CI engine fueled with dairy scum oil biodiesel was studied. Nanofuel blend was prepared by dispersing graphene oxide in varying quantities in dairy scum oil methyl ester (DSOME)-diesel blend. Sodium dodecyl sulfate (SDS) was used as a surfactant for a steady dispersion of graphene oxide nanoparticles in the fuel blends. The dispersion and homogeneity were characterized by ultraviolet–visible spectrometry. An ideal graphene-to-surfactant ratio was defined, highest absolute value UV-absorbency was seen for a mass fraction of 1:4. The concentration of surfactant above or below this ratio resulted in reduction in the stability of dispersion. Graphene oxide nanoparticles were amalgamated with dairy scum oil biodiesel at proportions of 20, 40 and 60 parts per million using ultrasonication technique. Experiments were performed at a constant speed and varying the brake power and load condtions. The results were notable enhancements in the performance and emissions characteristics, the brake thermal efficiency improved by 11.56%, a reduction in brake specific fuel consumption by 8.34%, unburnt hydrocarbon by 21.68%, smoke by 24.88%, carbon monoxide by 38.662% for the nanofuel blend DSOME2040 and oxides of nitrogen emission by 5.62% for fuel DSOME(B20). Similarly, the addition of graphene nanoparticles in DSOME fuel blends resulted in significant reduction in the combustion duration, ignition delay period, improvement in the peak pressure and heat release rate at maximum load condition. Finally, it is concluded that nano-graphene oxide nanoparticles can be introduced as a suitable substitute fuel additive for dairy scum oil biodiesel blends to enhance the overall engine performance and emissions characteristics.
Srivastava, K, Kumar, A, Chaudhary, P, Kanaujia, BK, Dwari, S, Verma, AK, Esselle, KP & Mittra, R 2019, 'Wideband and high‐gain circularly polarised microstrip antenna design using sandwiched metasurfaces and partially reflecting surface', IET Microwaves, Antennas & Propagation, vol. 13, no. 3, pp. 305-312.
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In this study, the authors propose a new type of metasurface, namely a sandwiched anisotropic metasurface, for converting a linearly polarised elliptical patch antenna into a wideband circularly polarised antenna. A partially reflecting surface (PRS) as a superstrate is further used to enhance the gain of the antenna. The design rules of the antenna are also presented. The combined metasurface and the PRS‐based patch antenna is designed on a low‐cost substrate FR‐4. The realised 3 dB axial ratio bandwidth (ARBW) of the antenna is 1.01 GHz (3.55–4.56 GHz), its impedance matching bandwidth is 1.33 GHz (3.08–4.41 GHz), and its peak gain varies from 7 to 7.84 dB within the band. By placing the PRS superstrate above the antenna, gain further improves to 9.32 dBi without degrading the performance of the antenna. Measured results are presented to validate the antenna performance and results are compared against a large number of similarly available antenna.
Stender, M, Oberst, S & Hoffmann, N 2019, 'Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction', Vibration, vol. 2, no. 1, pp. 25-46.
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Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for (a) describing the dynamics and (b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and nonlinearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and how to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of nonlinearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.
Stender, M, Oberst, S, Tiedemann, M & Hoffmann, N 2019, 'Complex machine dynamics: systematic recurrence quantification analysis of disk brake vibration data', Nonlinear Dynamics, vol. 97, no. 4, pp. 2483-2497.
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Complex machine dynamics, as caused by friction-induced vibrations and related to brake squeal, have gained significant attention in research and industry for decades. Today, remedies heavily rely on experimental testing due to the low prediction quality of numerical models. However, there is considerable lack of in-depth studies in characterizing self-excited oscillations encoded in scalar measurements. We complement previous works on phase-space reconstruction and recurrence plots analysis to a larger data base by applying a novel systematic approach using a large
data base. This framework considers appropriate delay embedding, time series partitioning into squealing and non-squealing parts and comparison to operational parameters of the brake system. By means of recurrence plot analysis, we illustrate that friction-excited vibrations are multi-scale in nature. Results confirm the existence of low-dimensional attractors in squealing regimes with increasing values of determinism and periodicity with rising vibration levels. It is shown that the squeal propensity can be directly linked to recurrence quantification measures. Using determinism and the clustering coefficient as metrics, we show for the first time that is possible to predict instabilities in regions of non-squealing conditions.
Stewart, C, Kianinia, M, Previdi, R, Tran, TT, Aharonovich, I & Bradac, C 2019, 'Quantum emission from localized defects in zinc sulfide', Optics Letters, vol. 44, no. 19, pp. 4873-4873.
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© 2019 Optical Society of America Single-photon sources in solid-state systems are widely explored as fundamental constituents of numerous quantum-based technologies. We report the observation of single-photon emitters in zinc sulfide and present their photo-physical properties via established spectroscopy techniques. The emitter behaves like a three-level system with an intermediate metastable state. It emits at ∼640 nm, and its emission is linearly polarized, with a lifetime of (2.2 ± 0.8) ns. The existence of single-photon sources in zinc sulfide is appealing due to the well-established manufacturing techniques of the material, its versatile technological uses, as well as the availability of many zinc isotopes with potential for designing ad hoc emitter–host pairs with tailored properties.
Stewart, MG 2019, 'Reliability-based load factors for airblast and structural reliability of reinforced concrete columns for protective structures', Structure and Infrastructure Engineering, vol. 15, no. 5, pp. 634-646.
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Reliability-based design allows the decision-maker to select the level of reliability for a specific explosive blast loading scenario. Important to this is an understanding of airblast and resistance uncertainty. Reliability-based load factors are calculated and are dependent on the variability of model error, explosive mass and range. Reliability-based design load factors (RBDFs) are estimated for explosive ordnance, terrorism, weaponeering and other scenarios. The effect of RBDFs on structural reliabilities for reinforced concrete (RC) columns is then calculated where resistance and loading are random variables, and compared to target values. It was found, for realistic combinations of range and explosive mass variabilities, that RC columns designed to existing standards have a significant margin of safety with a probability of failure of 1 × 10 −3 to 1 × 10 −5 . However, if there is large airblast variability, then the application of RBDFs is necessary to ensure that safety levels are acceptable according to international standards.
Stewart, MG & Netherton, MD 2019, 'A probabilistic risk-acceptance model for assessing blast and fragmentation safety hazards', Reliability Engineering & System Safety, vol. 191, pp. 106492-106492.
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There are many circumstances where decision-makers consider risks associated with explosions – from either natural or deliberate events – where the goal is clarity with respect to the actual safety and hazard risks posed to society and its people, systems and infrastructure. The paper describes how probabilistic safety and hazard modelling of blast and fragmentation can better inform a Quantitative Explosive Risk assessment (QERA). A QERA may be used to define an explosive safety distance based on the risk of explosive hazards being less than a societal acceptable risk. The concepts are illustrated with scenarios at a generic explosives ordnance (EO) site. In one scenario we demonstrate that current, deterministically based, regulations in Australia and internationally may be overly conservative. In other words, a deterministic based regulation may show that a building is located in an unsafe area, whereas a QERA can show, for the same building, that fatality risks are less than those deemed acceptable by society. Another example demonstrates the significant effect that uncertainty modelling, particularly that associated with post-detonation blast-loads, has on fatality risks.
Stewart, MG, Dorrough, B & Netherton, MD 2019, 'Field testing and probabilistic assessment of ballistic penetration of steel plates for small calibre military ammunition', International Journal of Protective Structures, vol. 10, no. 4, pp. 421-438.
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The penetration of projectiles into semi-infinite targets helps in the understanding and modelling of terminal ballistics. The article describes field test results of 5.56×45 mm F1 Ball and 7.62×51 mm M80 Ball ammunition. The targets were 25-mm-thick mild and high strength steel plates of Grade 250 MPa and 350 MPa, respectively. The tests recorded penetration depth, muzzle and impact velocities, and bullet mass. Despite its smaller calibre, the 5.56 mm × 45 mm F1 Ball ammunition recorded deeper penetrations than the larger calibre 7.62 mm × 51 mm M80 Ball ammunition. This is due to the 5.56 mm ammunition comprising a hardened steel penetrator and lead core, whereas the 7.62 mm ammunition comprised only a lead core. Multiple shots were fired for each type of munition. The coefficient of variation of steel penetration is approximately 0.10 and 0.03 for 5.56 mm and 7.62 mm rounds, respectively. The article also presents predictive models of steel penetration depth and compares these to the field test results.
Stuart, BH, Maynard-Casely, HE, Booth, N, Leung, AE & Thomas, PS 2019, 'Neutron diffraction of deuterated tripalmitin and the influence of shear on its crystallisation', Chemistry and Physics of Lipids, vol. 221, pp. 108-113.
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© 2019 This neutron diffraction study of deuterated tripalmitin has provided further insight into a forensic observation of the crystallisation of lipids under high-shear conditions. To achieve this, an experimental set up was designed to enable simultaneous rheological data from a Couette cell to be recorded with neutron powder diffraction, enabling the influence of shear on the polymorph transformation on cooling to be monitored in real time. Tripalmitin was observed to directly transform from a liquid phase to a β polymorph under the influence of shear. Although the liquid to β transition was not observed to be influenced by shear rate, the degree of crystallinity, qualitatively denoted by an increase in the sharpness of the diffraction peaks, was observed at higher shear rates. Evidence is also presented that the rate of cooling influences the ordering in the β-polymorph produced in zero shear conditions.
Stuart, BH, Thomas, PS, Barrett, M & Head, K 2019, 'Modelling clay materials used in artworks: an infrared spectroscopic investigation', Heritage Science, vol. 7, no. 1.
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Abstract Modelling clays are utilised by artists for their malleable properties. One of the challenges in managing collections containing such materials is the variety of commercial compositions available and, therefore, the variation in the requirements for storage and maintenance of such artefacts. The Art Gallery of New South Wales in Australia is responsible for the care of a range of artworks that contain modelling materials, some of which show detrimental property changes and there is concern for the longevity of such works. The aim of the current research is to determine the compositions of the modelling materials utilised in works produced by different artists in the gallery’s collection. Infrared spectroscopy was used to identify the main constituents of samples collected from the works of four different artists and a variety of material types were determined. Oil-based, air-hardening and polymer clays of varying composition were identified in the survey of artworks. Signs of deterioration in particular artworks were able to be characterised using spectroscopy, with the mechanisms identified including loss and oxidation of the oil component. Where a polymer clay was chosen by one artist, the distortion of the artwork was due to flow of the material over time and demonstrates the need for an understanding of the long term properties of the materials being used. The study has highlighted the need for conservators to have a detailed understanding of modelling materials to ensure the longevity of artworks containing this class of materials.
Styrkarsdottir, U, Stefansson, OA, Gunnarsdottir, K, Thorleifsson, G, Lund, SH, Stefansdottir, L, Juliusson, K, Agustsdottir, AB, Zink, F, Halldorsson, GH, Ivarsdottir, EV, Benonisdottir, S, Jonsson, H, Gylfason, A, Norland, K, Trajanoska, K, Boer, CG, Southam, L, Leung, JCS, Tang, NLS, Kwok, TCY, Lee, JSW, Ho, SC, Byrjalsen, I, Center, JR, Lee, SH, Koh, J-M, Lohmander, LS, Ho-Pham, LT, Nguyen, TV, Eisman, JA, Woo, J, Leung, P-C, Loughlin, J, Zeggini, E, Christiansen, C, Rivadeneira, F, van Meurs, J, Uitterlinden, AG, Mogensen, B, Jonsson, H, Ingvarsson, T, Sigurdsson, G, Benediktsson, R, Sulem, P, Jonsdottir, I, Masson, G, Holm, H, Norddahl, GL, Thorsteinsdottir, U, Gudbjartsson, DF & Stefansson, K 2019, 'GWAS of bone size yields twelve loci that also affect height, BMD, osteoarthritis or fractures', Nature Communications, vol. 10, no. 1.
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AbstractBone area is one measure of bone size that is easily derived from dual-energy X-ray absorptiometry (DXA) scans. In a GWA study of DXA bone area of the hip and lumbar spine (N ≥ 28,954), we find thirteen independent association signals at twelve loci that replicate in samples of European and East Asian descent (N = 13,608 – 21,277). Eight DXA area loci associate with osteoarthritis, including rs143384 in GDF5 and a missense variant in COL11A1 (rs3753841). The strongest DXA area association is with rs11614913[T] in the microRNA MIR196A2 gene that associates with lumbar spine area (P = 2.3 × 10−42, β = −0.090) and confers risk of hip fracture (P = 1.0 × 10−8, OR = 1.11). We demonstrate that the risk allele is less efficient in repressing miR-196a-5p target genes. We also show that the DXA area measure contributes to the risk of hip fracture independent of bone density.
Su, D, Zhang, QH, Ngo, HH, Dzakpasu, M, Guo, WS & Wang, XC 2019, 'Development of a water cycle management approach to Sponge City construction in Xi'an, China', Science of The Total Environment, vol. 685, pp. 490-496.
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In recent years, climate change, population growth, and inefficient use of water have exacerbated the water resources scarcity problems around the world. Hence, this paper establishes a new approach of Sponge City construction (SCC) based on water cycle management (WCM) for the sustainable exploitation of groundwater, recycled wastewater and rainwater in the Xi'an Siyuan University. The University is located in an isolated area that is far away from the city center so that no centralized water supply system could be utilized. To mitigate water scarcity problems in the University, 39% of the annual rainfall is harvested and stored from impervious surfaces and grasslands by using the Curve Number (CN) method. This stored water is reused for non-potable purposes: 40% for toilet flushing and 60% as miscellaneous water. According to findings, the available rainwater of500-700 m3/d accounts for 16-23% of the non-potable water from April to December. Moreover, the utilization rate of water resources increases from 204% to 227%. With the minimum volume of large-scale rainwater harvesting cistern of 52,760 m3, the environment could be adequately watered while improving the expansion and development conditions on the campus. Furthermore, water scarcity problems could be mitigated through optimization of the water resources utilization system. This study demonstrates that this new approach of SCC based on WCM could alleviate water resources scarcity problems in Xi'an Siyuan University effectively. It is hoped that this study will provide a model and example of the new approach for future applications.
Su, X, Qu, X, Zou, Z, Zhou, P, Wei, W, Wen, S & Hu, M 2019, 'k-Reciprocal Harmonious Attention Network for Video-Based Person Re-Identification', IEEE Access, vol. 7, pp. 22457-22470.
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Video-based person re-identification aims to retrieve video sequences of the same person in the multi-camera system. In this paper, we propose a k -reciprocal harmonious attention network (KHAN) to jointly learn discriminative spatiotemporal features and the similarity metrics. In KHAN, the harmonious attention module adaptively calibrates response at each spatial position and each channel by explicitly inspecting position-wise and channel-wise interactions over feature maps. Besides, the k -reciprocal attention module attends key features from all frame-level features with a discriminative feature selection algorithm; thus, useful temporal information within contextualized key features can be assimilated to produce more robust clip-level representation. Compared with commonly used local-context based approaches, the proposed KHAN captures long dependency of different spatial regions and visual patterns while incorporating informative context at each time-step in a non-parametric manner. The extensive experiments on three public benchmark datasets show that the performance of our proposed approach outperforms the state-of-the-art methods.
Suarez-Rodriguez, C, He, Y & Dutkiewicz, E 2019, 'Theoretical Analysis of REM-Based Handover Algorithm for Heterogeneous Networks.', IEEE Access, vol. 7, pp. 96719-96731.
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© 2013 IEEE. Handover has been a widely studied topic since the beginning of the mobile communications era, but with the advent of another generation, it is worth seeing it with fresh eyes. Data traffic is expected to keep growing as new use cases will coexist under the same umbrella, e.g., vehicle-to-vehicle or massive-machine-type communications. Heterogeneous networks will give way to multi-tiered networks, and mobility management will become challenging once again. Under the current approach, based uniquely on measurements, the number of handovers will soar, so will the signaling. We propose a handover algorithm that employs multidimensional radio-cognitive databases, namely radio environment maps, to predict the best network connection according to the user's trajectory. Radio environment maps have been extensively used in spectrum-sharing scenarios, and recently, some advances in other areas have been supported by them, such as coverage deployment or interference management. We also present a geometric model that translates the 3GPP specifications into geometry and introduce a new framework that can give useful insights into our proposed technique's performance. We validate our framework through Monte Carlo simulations, and the results show that a drastic reduction of at least 10% in the ping-pong handovers can be achieved, thus reducing the signaling needed.
Subasinghage, K, Gunawardane, K, Kularatna, N & Lie, TT 2019, 'Extending the Supercapacitor-Assisted Low-Dropout Regulator (SCALDO) Technique to a Split-Rail DC–DC Converter Application', IEEE Access, vol. 7, pp. 124034-124047.
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Sullivan, C, Thomas, P & Stuart, B 2019, 'An atomic force microscopy investigation of plastic wrapping materials of forensic relevance buried in soil environments', Australian Journal of Forensic Sciences, vol. 51, no. 5, pp. 596-605.
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© 2018 Australian Academy of Forensic Sciences Plastics are one means of disposal of items or remains associated with criminal activity. The surface characteristics of plastic wrapping materials of forensic interest in soil environments have been investigated to determine the environmental factors that have the greatest influence on the degradation process of such polymers. Polyethylene bags and poly(vinyl chloride) sheeting were buried in model environments encompassing different soil types, moisture content, pH and temperature. Atomic force microscopy was used to monitor the changes to the polymer surface at a nanometre level. Over a two-year burial period, the degradation of polyethylene was found to be enhanced by an increased moisture content and an elevated soil pH. The plasticizer content of poly(vinyl chloride) was affected by burial and was observed to leach from the plastic in all environments continually over the burial period. A moist environment was shown to have a more pronounced effect on the removal of plasticizer. A measurement of the surface roughness of plastics using atomic force microscopy has been shown to be sensitive to the burial environment and demonstrates the potential of this technique to measure relatively subtle changes to burial items exposed to different environments.
Sun, F, Zhu, H, Zhu, X, Yang, Y & Gomez-Garcia, R 2019, 'Design of On-Chip Millimeter-Wave Bandpass Filters Using Multilayer Patterned-Ground Element in 0.13-$\mu$ m (Bi)-CMOS Technology', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 12, pp. 5159-5170.
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© 1963-2012 IEEE. A novel design methodology for transmission-zero (TZ) generation in on-chip millimeter-wave (mm-wave) bandpass filters (BPFs) based on the original concept of multilayer patterned-ground (MPG) element is presented in this article. Unlike most of prior-art techniques available in the technical literature, this method has two distinct features. First, it is inherently suitable for miniaturized BPF design since the MPG element can be implemented through the layers below the top-metal layer and, thus, without occupying any additional die/chip area. Second, it provides a simple but effective way to produce a TZ at the upper stopband without adversely affecting other BPF performance metrics. To fully understand the operational insight of the engineered approach, a simplified LC-equivalent behavioral circuit model for the MPG element is developed. Using this model, three second-order BPFs based on different circuit configurations are codesigned to further demonstrate the experimental feasibility of the technique. All the filter prototypes are fabricated in a standard 0.13-μ m bipolar complementary metal-oxide-semiconductor [(Bi)-CMOS] technology. The obtained on-wafer measurements show that all fabricated BPF chips have the capability to suppress the second-order harmonic by more than 30 dB, which indicates the effectiveness of the proposed integrated BPF design approach with the MPG element.
Sun, F, Zhu, H, Zhu, X, Yang, Y, Sun, Y & Zhang, X 2019, 'Design of Millimeter-Wave Bandpass Filters With Broad Bandwidth in Si-Based Technology', IEEE Transactions on Electron Devices, vol. 66, no. 3, pp. 1174-1181.
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© 2019 IEEE. In this paper, a novel design approach is proposed for on-chip bandpass filter (BPF) design with improved passband flatness and stopband suppression. The proposed approach simply uses a combination of meander-line structures with metal-insulator-metal (MIM) capacitors. To demonstrate the insight of this approach, a simplified equivalent LC-circuit model is used for theoretical analysis. Using the analyzed results as a guideline along with a full-wave electromagnetic (EM) simulator, two BPFs are designed and implemented in a standard 0.13-μm (Bi)-CMOS technology. The measured results show that good agreements between EM simulated and measured results are achieved. For the first BPF, the return loss is better than 10 dB from 13.5 to 32 GHz, which indicates a fractional bandwidth (FBW) of more than 78%. In addition, the minimum insertion loss of 2.3 dB is achieved within the frequency range from 17 to 27 GHz and the in-band magnitude ripple is less than 0.1 dB. The chip size of this design, excluding the pads, is 0.148 mm 2 . To demonstrate a miniaturized design, a second design example is given. The return loss is better than 10 dB from 17.3 to 35.9 GHz, which indicates an FBW of more than 70%. In addition, the minimum insertion loss of 2.6 dB is achieved within the frequency range from 21.4 to 27.7 GHz and the in-band magnitude ripple is less than 0.1 dB. The chip size of the second design, excluding the pads, is only 0.066 mm 2 .
Sun, G, Zhang, J, Li, S, Fang, J, Wang, E & Li, Q 2019, 'Dynamic response of sandwich panel with hierarchical honeycomb cores subject to blast loading', Thin-Walled Structures, vol. 142, pp. 499-515.
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© 2019 Elsevier Ltd This paper introduces a novel hierarchical core structure to sandwich panel for bearing the blast loading, in which each vertex of a regular hexagonal cell was replaced with a smaller hexagonal unit. The finite element (FE) models of such hierarchical honeycomb sandwich panels were established and validated with the experiments under different impulse loads. The hierarchical honeycomb cores were compared with the regular honeycomb counterpart in terms of the peak deflection on the back facesheet, compression and specific energy absorption (SEA) of the core. The results showed that the maximum deflection at the back facesheet of the hierarchical honeycomb sandwich panels were smaller than the regular honeycomb counterpart for a higher level of blast load (specifically, the dimensionless impulse higher than 0.06). It was found that the structural hierarchical parameter γ (i.e. the ratio of the newly-introduced smaller hexagonal edge length (L1) to the regular hexagon edge length (L0)), had limited influence on the maximum deflection of back facesheet of the sandwich panel, but had a significant effect on the SEA of the cores.
Sun, H-H, Ding, C, Zhu, H, Jones, B & Guo, YJ 2019, 'Suppression of Cross-Band Scattering in Multiband Antenna Arrays', IEEE Transactions on Antennas and Propagation, vol. 67, no. 4, pp. 2379-2389.
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© 1963-2012 IEEE. This paper presents a novel method of suppressing cross-band scattering in dual-band dual-polarized antenna arrays. The method involves introducing chokes into low-band (LB) elements to suppress high-band (HB) scattering currents. The experimental results show that by inserting LB-pass HB-stop chokes into LB radiators, suppression of induced HB currents on the LB elements is achieved. This greatly reduces the pattern distortion of the HB array caused by the presence of LB elements. The array considered is configured as two columns of HB antennas operating from 1.71 to 2.28 GHz interleaved with a single column of LB antennas operating from 0.82 to 1.0 GHz. The realized array with choked LB element has stable and symmetrical radiation in both HB and LB.
Sun, J, Dai, X, Wang, Q, van Loosdrecht, MCM & Ni, B-J 2019, 'Microplastics in wastewater treatment plants: Detection, occurrence and removal', Water Research, vol. 152, pp. 21-37.
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© 2018 Elsevier Ltd Microplastics have aroused increasing concern as they pose threats to aquatic species as well as human beings. They do not only contribute to accumulation of plastics in the environment, but due to absorption they can also contribute to spreading of micropollutants in the environment. Studies indicated that wastewater treatment plants (WWTPs) play an important role in releasing microplastics to the environment. Therefore, effective detection of the microplastics and understanding their occurrence and fate in WWTPs are of great importance towards microplastics control. In this review, the up-to-date status on the detection, occurrence and removal of microplastics in WWTPs are comprehensively reviewed. Specifically, the different techniques used for collecting microplastics from both wastewater and sewage sludge, and their pretreatment and characterization methods are reviewed and analyzed. The key aspects regarding microplastics occurrence in WWTPs, such as concentrations, total discharges, materials, shapes and sizes are summarized and compared. Microplastics removal in different treatment stages and their retention in sewage sludge are explored. The development of potential microplastics-targeted treatment technologies is also presented. Although previous researches in microplastics have undoubtedly improved our level of understanding, it is clear that much remains to be learned about microplastics in WWTPs, as many unanswered questions and thereby concerns still remain; some of these important future research areas are outlined. The key challenges appear to be to harmonize detection methods as well as microplastics mitigation from wastewater and sewage sludge.
Sun, L, Dong, H, Hussain, OK, Hussain, FK & Liu, AX 2019, 'A framework of cloud service selection with criteria interactions', Future Generation Computer Systems, vol. 94, pp. 749-764.
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© 2018 Elsevier B.V. Existing cloud service selection techniques assume that service evaluation criteria are independent. In reality, there are different types of interactions between criteria. These interactions influence the performance of a service selection system in different ways. In addition, a lack of measurement indices to validate the performance of service selection methods has hindered the development of decision making techniques in the service selection area. This paper addresses these critical issues of modeling the interactions between cloud service selection criteria, and designing indices to validate service selection methods. In this paper, we propose a Cloud Service Selection with Criteria Interactions framework (CSSCI) that applies a fuzzy measure and Choquet integral to measure and aggregate non-linear relations between criteria. We employ a non-linear constraint optimization model to estimate the Shapley importance and criteria interaction indices. In addition, we design a priority-based CSSCI (PCSSCI) to solve service selection problems in the situation where there is a lack of historical information to determine criteria relations and weights. Furthermore, we discuss an approximate solution for CSSCI to reduce its computing complexity. Finally, we design three indices to validate the cloud service selection methods. The experimental results preliminarily prove the technical advantage of the proposed models in contrast to several existing models.
Sun, Q, Indraratna, B & Heitor, A 2019, 'Behaviour of a capping layer reinforced with recycled tyres', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 172, no. 3, pp. 127-137.
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In this paper, a sustainable approach for reducing lateral displacement in a track by increasing the confining pressure in the track substructure is demonstrated by placing a cellular rubber (tyre) membrane infilled with crushed ballast, as an alternative to a traditional capping layer of compacted granulates. Plate-load tests on a single tyre filled with gravel and subjected to a vertical load were carried out to investigate the interaction between tyre and gravel. A track model with tyre reinforcement was created to evaluate the performance of a tyre-reinforced capping layer under cyclic loading, and a numerical model was developed to determine the benefit that tyres would provide to railway substructure, especially when spent ballast is recycled as capping layer materials.
Sun, Q, Indraratna, B & Ngo, NT 2019, 'Effect of increase in load and frequency on the resilience of railway ballast', Géotechnique, vol. 69, no. 9, pp. 833-840.
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This paper presents the results of a series of large-scale cyclic triaxial tests conducted on ballast subjected to increased load and frequency of loading. For a given loading, the laboratory test data demonstrate that the resilient modulus of ballast is influenced by the frequency of loading. Both strain hardening and strain softening can be observed in response to increasing magnitude of load and frequency. A correlation between the resilient modulus and bulk stress is introduced to describe both the strain-hardening and strain-softening behaviour of ballast under different frequencies. A good corroboration between the cyclic stress ratio and the accumulated permanent strain and the resilient strain is demonstrated.
Sun, X, Gui, G, Li, Y, Liu, RP & An, Y 2019, 'ResInNet: A Novel Deep Neural Network With Feature Reuse for Internet of Things', IEEE Internet of Things Journal, vol. 6, no. 1, pp. 679-691.
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© 2014 IEEE. Deep neural networks (DNNs) have widely used in various Internet-of-Things (IoT) applications. Pursuing superior performance is always a hot spot in the field of DNN modeling. Recently, feature reuse provides an effective means of achieving favorable nonlinear approximation performance in deep learning. Existing implementations utilizes a multilayer perception (MLP) to act as a functional unit for feature reuse. However, determining connection weight and bias of MLP is a rather intractable problem, since the conventional back-propagation learning approach encounters the limitations of slow convergence and local optimum. To address this issue, this paper develops a novel DNN considering a well-behaved alternative called reservoir computing, i.e., reservoir in network (ResInNet). In this structure, the built-in reservoir has two notable functions. First, it behaves as a bridge between any two restricted Boltzmann machines in the feature learning part of ResInNet, performing a feature abstraction once again. Such reservoir-based feature translation provides excellent starting points for the following nonlinear regression. Second, it serves as a nonlinear approximation, trained by a simple linear regression using the most representative (learned) features. Experimental results over various benchmark datasets show that ResInNet can achieve the superior nonlinear approximation performance in comparison to the baseline models, and produce the excellent dynamic characteristics and memory capacity. Meanwhile, the merits of our approach is further demonstrated in the network traffic prediction related to real-world IoT application.
Sun, X, Jin, Z, Wang, S, Yang, Z, Li, K, Fan, Y & Chen, L 2019, 'Performance Improvement of Torque and Suspension Force for a Novel Five-Phase BFSPM Machine for Flywheel Energy Storage Systems', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2, pp. 1-5.
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Sun, Y & Nimbalkar, S 2019, 'Stress-fractional soil model with reduced elastic region', Soils and Foundations, vol. 59, no. 6, pp. 2007-2023.
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Sun, Y, Nimbalkar, S & Chen, C 2019, 'Particle breakage of granular materials during sample preparation', Journal of Rock Mechanics and Geotechnical Engineering, vol. 11, no. 2, pp. 417-422.
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© 2019 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences Particle breakage is commonly observed in granular materials when subjected to external loads. It was found that particle breakage would occur during both sample preparation and loading stages. However, main attention was usually paid to the particle breakage behaviour of samples during loading stage. This study attempts to explore the breakage behaviour of granular materials during sample preparation. Triaxial samples of rockfill aggregates are prepared by layered compaction method to achieve different relative densities. Extents of particle breakage based on the gradings before and after test are presented and analysed. It is found that particle breakage during sample preparation cannot be ignored. Gradings after test are observed to shift away from the initial grading. Aggregates with larger size that appear to break are more than the smaller-sized ones. Irrespective of the initial gradings, an increase in the extent of particle breakage with the increasing relative density is observed during sample preparation.
Sun, Y, Tian, Z, Wang, Y, Li, M, Su, S, Wang, X & Fan, D 2019, 'Lightweight Anonymous Geometric Routing for Internet of Things', IEEE Access, vol. 7, pp. 29754-29762.
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Sun, Y, Wang, C, Guo, G, Fu, Q, Xiong, Z, Li, D & Liu, Y 2019, 'Facile synthesis of highly efficient photocatalysts based on organic small molecular co-catalyst', Applied Surface Science, vol. 469, pp. 553-563.
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© 2018 Elsevier B.V. A new metal-free organic molecular as co-catalyst was developed, which could drastically enhance the photocatalytic performance of photocatalyst (eg. g-C 3 N 4 , MoS 2 and TiO 2 ). The enhancing photocatalytic activity was evaluated for photocatalytic degradation of organic dyes. It showed ultra-high photocatalytic rate (4.17 mg/g·min) and ultra-short time (about 6.0 min) for photocatalytic degradation of organic dyes (25 mg/ml). The enhanced photocatalytic performance was attributed to suppress recombination of photogenerated charges and provide a new photoredox reaction pathway under molecular co-catalyst assistance. The study represents a facile method to develop ultra-effectively photocatalyst for applications in production of green and renew-able energy carrier, H 2 from water, reduction of CO 2 , synthesis of fine chemicals and remediation of environmental pollutants.
Surindra, MD, Caesarendra, W, Prasetyo, T, Mahlia, TMI & Taufik 2019, 'Comparison of the Utilization of 110 °C and 120 °C Heat Sources in a Geothermal Energy System Using Organic Rankine Cycle (ORC) with R245fa, R123, and Mixed-Ratio Fluids as Working Fluids', Processes, vol. 7, no. 2, pp. 113-113.
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Binary cycle experiment as one of the Organic Rankine Cycle (ORC) technologies has been known to provide an improved alternate scenario to utilize waste energy with low temperatures. As such, a binary geothermal power plant simulator was developed to demonstrate the geothermal energy potential in Dieng, Indonesia. To better understand the geothermal potential, the laboratory experiment to study the ORC heat source mechanism that can be set to operate at fixed temperatures of 110 °C and 120 °C is conducted. For further performance analysis, R245fa, R123, and mixed ratio working fluids with mass flow rate varied from 0.1 kg/s to 0.2 kg/s were introduced as key parameters in the study. Data from the simulator were measured and analyzed under steady-state condition with a 20 min interval per given mass flow rate. Results indicate that the ORC system has better thermodynamic performance when operating the heat source at 120 °C than those obtained from 110 °C. Moreover, the R123 fluid produces the highest ORC efficiency with values between 9.4% and 13.5%.
Sutton, GJ, Zeng, J, Liu, RP, Ni, W, Nguyen, DN, Jayawickrama, BA, Huang, X, Abolhasan, M, Zhang, Z, Dutkiewicz, E & Lv, T 2019, 'Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives', IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2488-2524.
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© 1998-2012 IEEE. Future 5th generation networks are expected to enable three key services-enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements.
Sutton, SK, Cheung, BB, Massudi, H, Tan, O, Koach, J, Mayoh, C, Carter, DR & Marshall, GM 2019, 'Heterozygous loss of keratinocyte TRIM16 expression increases melanocytic cell lesions and lymph node metastasis', Journal of Cancer Research and Clinical Oncology, vol. 145, no. 9, pp. 2241-2250.
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© 2019, The Author(s). Purpose: The tripartite motif (TRIM)16 acts as a tumour suppressor in both squamous cell carcinoma (SCC) and melanoma. TRIM16 is known to be secreted by keratinocytes, but no studies have been reported yet to assess the relationship between TRIM16 keratinocyte expression and melanoma development. Methods: To study the role of TRIM16 in skin cancer development, we developed a keratinocyte TRIM16-specific knockout mouse model, and used the classical two-stage skin carcinogenesis challenge method, to assess the loss of keratinocyte TRIM16 on both papilloma, SCC and melanoma development in the skin after topical carcinogen treatment. Results: Heterozygous, but not homozygous, TRIM16 knockout mice exhibited an accelerated development of skin papillomas and melanomas, larger melanoma lesions and an increased potential for lymph node metastasis. Conclusion: This study provides the first evidence that keratinocyte loss of the putative melanoma tumour suppressor protein, TRIM16, enhances melanomagenesis. Our data also suggest that TRIM16 expression in keratinocytes is involved in cross talk between keratinocytes and melanocytes, and has a role in melanoma tumorigenesis.
Taghikhah, F, Voinov, A & Shukla, N 2019, 'Extending the supply chain to address sustainability', Journal of Cleaner Production, vol. 229, pp. 652-666.
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© 2019 Elsevier Ltd In today's growing economy, overconsumption and overproduction have accelerated environmental deterioration worldwide. Consumers, through unsustainable consumption patterns, and producers, through production based on traditional resource depleting practices, have contributed significantly to the socio-environmental problems. Consumers and producers are linked by supply chains, and as sustainability became seen as a way to reverse socio-environmental degradation, it has also started to be introduced in research on supply chains. We look at the evolution of research on sustainable supply chains and show that it is still largely focused on the processes and networks that take place between the producer and the consumer, hardly taking into account consumer behavior and its influence on the performance of the producer and the supply chain itself. We conclude that we cannot be talking about sustainability, without extending the supply chains to account for consumers' behavior and their influence on the overall system performance. A conceptual framework is proposed to explain how supply chains can become sustainable and improve their economic and socio-environmental performance by motivating consumer behavior toward green consumption patterns, which, in turn, motivate producers and suppliers to change their operations.
Taghizadeh, S, Hossain, MJ, Lu, J & Karimi-Ghartemani, M 2019, 'An Enhanced DC-Bus Voltage-Control Loop for Single-Phase Grid-Connected DC/AC Converters', IEEE Transactions on Power Electronics, vol. 34, no. 6, pp. 5819-5829.
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© 1986-2012 IEEE. This paper presents a method to enhance the dc-bus voltage-control loop of a single-phase grid-connected dc/ac converter, which improves its responses in terms of oscillation on its dc-bus voltage as well as its output ac current. Conventionally, the double-frequency (2-f) ripple is reduced by using a large electrolyte capacitor, which increases the cost and size of the system. A state-of-the-art approach is to use a notch filter (NF) to block the 2-f ripple in the voltage-control loop. This can significantly reduce the capacitor size. The existing presentations of this method, however, do not integrate the internal dynamics of the NF into consideration. This paper proposes a new way of implementing the NF, which allows integration of its internal variables into the control loop. The resulted system exhibits enhanced transient responses at both the dc-bus voltage and the output ac current. The proposed method is analyzed in detail and its effectiveness is verified through simulations and experimental results.
Taghizadeh, S, Karimi-Ghartemani, M, Hossain, MJ & Lu, J 2019, 'A Fast and Robust DC-Bus Voltage Control Method for Single-Phase Voltage-Source DC/AC Converters', IEEE Transactions on Power Electronics, vol. 34, no. 9, pp. 9202-9212.
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© 1986-2012 IEEE. This paper presents a fast and robust dc-bus voltage control method for single-phase grid-connected dc/ac converters. The proposed technique precisely estimates the double-frequency (2-f) ripple of a dc-bus voltage and removes it from the voltage-control loop without adding any additional dynamics or oscillations. Conventionally, the 2-f ripple is managed by using large capacitors, which increase the cost and bulkiness of a converter. As a state-of-the-art approach, a notch filter (NF) or a dc-voltage estimator is used to effectively block the 2-f ripple from the voltage-control loop, which can significantly reduce the capacitor size. However, such an approach introduces new dynamics in the control loop, causes additional oscillations on the bus voltage, and increases the settling time of its response. This limits the degrees of freedom of the design to improve the overall system damping. The proposed method in this paper has no adverse impact on the original bus-voltage dynamic response. As a result, the bus-voltage control can be designed with higher speed and robustness and the whole system can operate with a reduced transient at both the bus voltage and the output ac current. The proposed approach is thoroughly analyzed and its effectiveness is validated through simulations and experimental results.
Tan, SX, Lim, S, Ong, HC & Pang, YL 2019, 'State of the art review on development of ultrasound-assisted catalytic transesterification process for biodiesel production', Fuel, vol. 235, no. Energy Convers Manage 128 2016, pp. 886-907.
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© 2018 Elsevier Ltd Excessive utilization of petroleum diesel has led to severe environmental pollution. Biodiesel, which is greener and renewable, can be a potential alternative fuel. Biodiesel is produced through transesterification reaction between vegetable oil, animal fat or even waste cooking oil (WCO) and alcohol in the presence of catalyst. Under process intensification, ultrasonic irradiation is employed in the transesterification reaction to enhance the agitation between immiscible reactants. Besides providing intensive mixing, it also offers uniform heating due to the localized temperature increase and formation of micro jets from the transient collapse of cavitation bubbles, thus reducing the energy consumption. The focus of this paper is to review the recent research progress on the ultrasound-assisted catalytic transesterification of non-edible vegetable oils using homogeneous and heterogeneous catalysts. The primary factors that affect the operation and efficiency of ultrasound-assisted transesterification such as alcohol to oil molar ratio, catalyst loading, reaction time, reaction temperature, energy consumption, phase separation time, ultrasonic pulse mode and biodiesel conversion or yield have been reviewed. The highlights of this review paper are the provisions on the mechanism of ultrasonic reactive extraction (RE) in the biodiesel production, kinetic study and the existing pilot reactors on the ultrasound-assisted transesterification which are still rarely reviewed in the current literature. Lastly, the challenges and feasibility for future development in the process intensification of biodiesel production are also addressed.
Tan, SX, Lim, S, Ong, HC, Pang, YL, Fitranto, K, Goh, BHH & Chong, CT 2019, 'Two-step catalytic reactive extraction and transesterification process via ultrasonic irradiation for biodiesel production from solid Jatropha oil seeds', Chemical Engineering and Processing - Process Intensification, vol. 146, pp. 107687-107687.
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Tan, SX, Ong, HC, Lim, S, Pang, YL & Milano, J 2019, 'Process intensification of biodiesel synthesis via ultrasound‐assisted in situ esterification of Jatropha oil seeds', Journal of Chemical Technology & Biotechnology, vol. 94, no. 5, pp. 1362-1373.
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AbstractBACKGROUNDNon‐edible oil such as Jatropha oil has high free fatty acids (FFAs) content. Therefore, acid esterification is a suitable route to reduce its FFA content to an acceptable limit (2 FFA%) before being subjected to further transesterification. In the present study, Jatropha seeds were utilized as the feedstock directly instead of Jatropha oil during ultrasound‐assisted in situ esterification. The objective of this work is to evaluate the feasibility of in situ esterification of Jatropha oil seeds using sulphuric acid (H2SO4) as catalyst with the aid of ultrasound.RESULTSThe reaction parameters (particle size, n‐hexane to methanol volume ratio, H2SO4 amount, reaction time and ultrasonic amplitude) were optimized and evaluated in term of extraction and esterification efficiencies as well as fatty acid methyl ester (FAME) yield. The highest extraction efficiency of 83.96%, esterification efficiency of 71.10% and FAME yield of 38.58% were achieved at particle size of 1–2 mm, n‐hexane to methanol volume ratio of 3:1, 5 vol% of H2SO4 and ultrasonic amplitude of 60% with reaction time of 150 min.CONCLUSIONSynthesis of biodiesel via ultrasound‐assisted in situ esterification of Jatropha oil seeds was successful with considerable yield, which could provide improvement in terms of process intensification and more value added by‐products. © 2018 Society of Chemical Industry
Tan, X, Li, W, Zhao, M & Tam, VWY 2019, 'Numerical Discrete-Element Method Investigation on Failure Process of Recycled Aggregate Concrete', Journal of Materials in Civil Engineering, vol. 31, no. 1, pp. 04018353-04018353.
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© 2018 American Society of Civil Engineers. This study numerically investigates the failure processes of recycled aggregate concrete (RAC) and natural aggregate concrete (NAC). A two-dimensional simulation based on a discrete-element method (DEM) is conducted with a universal distinct-element code (UDEC) program. RAC is modeled by a combination of rigid Voronoi blocks cemented to each other using contacts for interfaces. The determination procedure of contact microparameters is analyzed, and a series of microscopic contact parameters in different components of modeled recycled aggregate concrete (MRAC) is calibrated using nanoindentation results. The complete stress-strain curves, fracture process, and failure pattern of numerical model are verified by experimental results, proving its accuracy and validation. The initiation, propagation, and coalescence of microcracks and subsequent nonlinear deformation behaviors of cement mortar, modeled natural aggregate, and recycled aggregate concrete are captured through DEM numerical simulations and compared with digital image correlation (DIC) results. It is found that both the new interfacial transition zone and the old interfacial transition zone are the weak links in RAC, where most microcracks initiate and propagate into the cement mortar region. The failure behaviors of MRAC revealed by both experimental and numerical results can effectively provide insights into the failure mechanism and enhancement of RAC.
Tang, J, Pu, Y, Wang, XC, Hu, Y, Huang, J, Ngo, HH, Pan, S, Li, Y & Zhu, N 2019, 'Effect of additional food waste slurry generated by mesophilic acidogenic fermentation on nutrient removal and sludge properties during wastewater treatment', Bioresource Technology, vol. 294, pp. 122218-122218.
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Fermentation slurry from food waste (FSFW) generated by acidogenic fermentation at mesophilic temperature was utilized to improve the nutrients removal from wastewater. Organic acids (such as lactate and volatile fatty acids) in the FSFW behaved as readily biodegradable carbon sources, while the particulate and macromolecular organics acted as slowly biodegradable carbon sources during denitrification processes. The FSFW dosage significantly influenced the nitrogen removal performance, and a C/N ratio (in terms of chemical oxygen demand to nitrogen ratio) of 8 could achieve complete denitrification in the batch tests. In a sequencing batch reactor (SBR) using FSFW for long-term wastewater treatment, extracellular polymeric substances (EPS) gradually accumulated, sludge particle size significantly increased, and microbial communities were selectively enriched, which contributed to promoting the nitrogen (>80%) and phosphate (90.1%) removal efficiencies. Overall, the FSFW produced by acidogenic fermentation under mesophilic temperature served as an excellent intermediary between FW valorization and wastewater treatment.
Tang, J, Wang, XC, Hu, Y, Pu, Y, Huang, J, Ngo, HH, Zeng, Y & Li, Y 2019, 'Nutrients removal performance and sludge properties using anaerobic fermentation slurry from food waste as an external carbon source for wastewater treatment', Bioresource Technology, vol. 271, pp. 125-135.
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Enhancement of nitrogen and phosphate removal using thermophilic fermentation slurry from food waste (FSFW) as external carbon source was investigated. Based on the batch tests, the soluble and particulate fractions of the FSFW acted as easily and slowly biodegradable carbon sources, respectively, and the fermented slurry showed the combined nutrients removal properties of soluble and solid organics. During the long-term operation of a sequencing batch reactor (SBR) with FSFW for wastewater treatment, the sludge particle size increased obviously, the bacterial metabolic capacity improved significantly, and some functional microorganisms were enriched selectively, which significantly promoted the nitrogen removal efficiency (approximately 90%) by enhancing the anoxic denitrification and simultaneous nitrification and denitrification (SND) processes. Moreover, high phosphate removal efficiency (above 98%) was achieved through the aerobic and anoxic phosphate accumulation processes. Thus, using the FSFW as supplementary carbon source is a suitable solution for both food waste disposal and wastewater treatment.
Tang, L, Wang, Y, Ding, X, Yin, H, Xiong, R & Huang, S 2019, 'Topological local-metric framework for mobile robots navigation: a long term perspective', Autonomous Robots, vol. 43, no. 1, pp. 197-211.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Long term mapping and localization are the primary components for mobile robots in real world application deployment, of which the crucial challenge is the robustness and stability. In this paper, we introduce a topological local-metric framework (TLF), aiming at dealing with environmental changes, erroneous measurements and achieving constant complexity. TLF organizes the sensor data collected by the robot in a topological graph, of which the geometry is only encoded in the edge, i.e. the relative poses between adjacent nodes, relaxing the global consistency to local consistency. Therefore the TLF is more robust to unavoidable erroneous measurements from sensor information matching since the error is constrained in the local. Based on TLF, as there is no global coordinate, we further propose the localization and navigation algorithms by switching across multiple local metric coordinates. Besides, a lifelong memorizing mechanism is presented to memorize the environmental changes in the TLF with constant complexity, as no global optimization is required. In experiments, the framework and algorithms are evaluated on 21-session data collected by stereo cameras, which are sensitive to illumination, and compared with the state-of-art global consistent framework. The results demonstrate that TLF can achieve similar localization accuracy with that from global consistent framework, but brings higher robustness with lower cost. The localization performance can also be improved from sessions because of the memorizing mechanism. Finally, equipped with TLF, the robot navigates itself in a 1 km session autonomously.
Tang, Y, Liu, H, Ren, H, Cheng, Q, Cui, Y & Zhang, J 2019, 'Development KCl/CaO as a catalyst for biodiesel production by tri‐component coupling transesterification', Environmental Progress & Sustainable Energy, vol. 38, no. 2, pp. 647-653.
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In this research, the optimized experimental conditions to obtain CaO supported KCl (KCl/CaO) as an efficient heterogeneous basic catalyst to produce no‐glycerol biodiesel using tri‐component (canola oil, dimethyl carbonate [DMC], and methanol) coupling transesterification was established. The solid base catalyst was prepared by wet impregnation of CaO in KCl solution and calcination subsequently, which greatly improved the reaction efficiency with high biodiesel yield of 96.4% at only 2 h. The KCl/CaO was characterized by techniques, such as BET surface area, XRD, CO2‐TPD, and SEM. Then, it was observed that the KCl introduced not only differed from CaO particles in the surface area and number of basic sites but also changed its base strength significantly. To obtain an improvement in conversion of canola oil, the influence on the catalytic performance of several kinetic parameters, such as mass ratio of catalyst to oil, reaction time, and molar ratio of methanol/oil/DMC were evaluated separately.Novelty or Significance: In this work, it was found that the catalytic performance of CaO‐based catalyst to coupling transesterification for no‐glycerol biodiesel production can be greatly improved by modifying CaO with KCl. As a result, the time to obtain as high as 96.4% yield of biodiesel over KCl/CaO can be greatly shortened from 5 to 2 h compared with common CaO. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 647–653, 2019
Tang, Y, Liu, H, Zhou, L, Ren, H, Li, H, Zhang, J, Chen, G & Qu, C 2019, 'Enhanced Fenton-like oxidation of hydroxypropyl guar gum catalyzed by EDTA-metal complexes in a wide pH range', Water Science and Technology, vol. 79, no. 9, pp. 1667-1674.
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Abstract A series of EDTA-metal complexes was prepared for the Fenton oxidation catalysts and Fe(II)L exhibits high catalytic performance for degradation of hydroxypropyl guar gum in a wide pH range 7.0–13.0. The viscosity of hydroxypropyl guar gum can be reduced with the 10.0% H2O2 and 5.0% Fe(II)L. The viscosity average molecular weight of hydroxypropyl guar gum was decreased from almost 2 million to 3,199. Most important of all, the chemical oxygen demand (COD) value can be decreased to 104 mg/L from 8,080 mg/L with enough H2O2, and Fe(II)L also shows great catalytic ability in the degradation of various polymers by H2O2. The proposed mechanism of the activation of H2O2 by the complex was studied.
Tang, Z, Hu, Y, Tam, VWY & Li, W 2019, 'Uniaxial compressive behaviors of fly ash/slag-based geopolymeric concrete with recycled aggregates', Cement and Concrete Composites, vol. 104, pp. 103375-103375.
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© 2019 Elsevier Ltd The uniaxial compressive stress-strain behaviors of fly ash/ground granulated blast furnace slag (GGBFS) geopolymeric concrete containing recycled aggregate were investigated in this study. Geopolymeric concretes with the variations of three recycled aggregate replacement ratios (i.e., 0%, 50% and 100%) and four contents of slag (i.e., 0%, 10%, 20% and 30% of the mass of total binder) were tested under uniaxial compression. Special attention was devoted to the failure behaviors and patterns, stress-strain characteristics (such as the peak stress, the elastic modulus, the peak strain, and the ultimate strain) and energy absorption capacity. The results showed that the peak stress, elastic modulus and energy absorption (toughness) decreased with the increase of the replacement ratio of recycled aggregate, while these mechanical properties increased when the content of slag increased. The reverse trend was observed with respect to the ductility. Moreover, the inclusion of slag could alleviate the nagative effects of the recycled aggregate replacement on the stress-strain characteristics of geopolymeric concrete. Additionally, a stress-strain model was developed in this study by modifying the parameters of the existing stress-strain model with the best prediction. This new proposed model can satisfactorily describe the stress-strain behaviors for both geopolymeric natural aggregate concrete and geopolymeric recycled aggregate concrete.
Tang, Z, Li, W, Hu, Y, Zhou, JL & Tam, VWY 2019, 'Review on designs and properties of multifunctional alkali-activated materials (AAMs)', Construction and Building Materials, vol. 200, pp. 474-489.
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© 2018 Elsevier Ltd The stream of research on alkali-activated materials (AAMs) has expanded rapidly during the last decades owing to the potential as a viable alternative to cement-based materials. In addition to the load-carrying function, AAMs have been integrated with other functions to develop advanced construction materials, namely multifunctional AAMs. Multifunctional AAMs are intelligent systems not only serve a basic structural function but also exhibit other functional properties or have the abilities to react upon external stimuli or disturbances. Materials of this kind have tremendous potential to enhance the mechanical performance and durability of structure, improve the reliability and longevity of infrastructure, as well as reduce life-cycle service and maintenance cost. These multifunctional properties are mainly achieved through materials composition design, incorporation of functional elements, or microstructure modification. This paper presents an overview on designs and properties of multifunctional AAMs covering the smart functions, mechanical functions, and electrical functions, and with special attention to their definition, principles, and current progress. Furthermore, the challenges in the research of multifunctional AAMs have been discussed, as well as the future directions to increase the innovation and engineering application of these materials and structures.
Tang, Z, Li, W, Ke, G, Zhou, JL & Tam, VWY 2019, 'Sulfate attack resistance of sustainable concrete incorporating various industrial solid wastes', Journal of Cleaner Production, vol. 218, pp. 810-822.
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© 2019 Elsevier Ltd Industrial solid wastes are inducing severe environmental problems, but the problem can be overcame by reusing them as construction materials. The sulfate resistances of sustainable concrete incorporating various solid waste materials, including waste glass powder (WGP), coal gangue powder (CGP) and fly ash (FA) were investigated in this study. Concrete mixes with different water to binder (w/b) ratios and containing various solid waste materials as partial replacement of Portland cement by ratios of 10%, 20%, and 30% were prepared. These mixes were immersed in the 5% Na 2 SO 4 solution for a total period of 22 months. The sulfate attack resistances were evaluated extensively based on visual appearance, mass change, compressive strength, splitting tensile strength, ultrasonic pulse velocity, mineralogy, and microstructure. The results indicate that regardless of the type and content of solid waste materials, the replacement of cement by solid waste materials exhibit a positive impact on the sulfate attack resistance. Under the same substitution level, WGP appear to be the most effective in offsetting the destructive effect of sulfate attack, followed by CGP and FA. Therefore, sustainable concrete incorporating solid waste materials can not only promote the recycling of solid waste, but also provide high sulfate attack resistance.
Tang, Z, Shan, B, Li, WG, Peng, Q & Xiao, Y 2019, 'Structural behavior of glubam I-joists', Construction and Building Materials, vol. 224, pp. 292-305.
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© 2019 Elsevier Ltd Glubam, a new type of engineered bamboo composites, is a promising structural material characterized by outstanding mechanical performance and environmental friendliness. In this study, glubam I-joists, with the spans ranging from 2.4 m to 7.5 m, were prepared and tested to evaluate their structural behavior. The glubam I-joists were lengthened by finger joints, and two kinds of connections were used to connect the web and flanges. Four-point bending tests were conducted to examine the failure modes, load-deflection relationships and load carrying capacity of glubam I-joists. Test results indicated that the dominant failure modes of glubam I-joists included shear failure at the finger joint in the web, bending failure at the finger joint in the bottom flange and lateral buckling. Correspondingly, the load carrying capacity of glubam I-joists was governed by the bending strength, shear capacity and critical bending moment. Glubam I-joists have relatively higher mechanical performance compared with other engineered bamboo or timber I-joists with similar dimension, and the bending capacity of the glubam I-joist with continuous web-to-flange connection meets the requirement specified in Chinese code. Based on the experimental findings and existing methods, theoretical methods were proposed for predicting the stiffness and load carrying capacity of glubam I-joist and were also validated by the test results.
Tao, M, Li, Z, Cao, W, Li, X & Wu, C 2019, 'Stress redistribution of dynamic loading incident with arbitrary waveform through a circular cavity', International Journal for Numerical and Analytical Methods in Geomechanics, vol. 43, no. 6, pp. 1279-1299.
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SummaryIn actual geotechnical and civil engineering, dynamic stress concentrations around cavities generated by wave sources widely exist. In this study, based on the complex variable theory and Fourier transform method, the expression of the dynamic stress concentration factor (DSCF) around a circular cavity in infinite homogeneous media subjected to transient waves with arbitrary waveform is obtained. The relationships between both steady‐state and transient DSCF and their waveform parameters are investigated quantitatively. The results indicate that a relatively large tensile stress is generated with low Poisson's ratio under steady‐state incidence. Under the condition of transient incidence, the position of the wave peak has a minor effect on the DSCF in the case of small wavenumber, but it has a significant effect in the opposite case. It is found that when the wavenumber is high, such as 0.5, the stress response lags behind the stress wave. In addition, the closer the wave peak to the center of the waveform, the greater the potential damage of the transient incidence.
Tavakoli, J, Gascooke, J, Xie, N, Tang, BZ & Tang, Y 2019, 'Enlightening Freeze–Thaw Process of Physically Cross-Linked Poly(vinyl alcohol) Hydrogels by Aggregation-Induced Emission Fluorogens', ACS Applied Polymer Materials, vol. 1, no. 6, pp. 1390-1398.
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Tavakoli, J, Laisak, E, Gao, M & Tang, Y 2019, 'AIEgen quantitatively monitoring the release of Ca2+ during swelling and degradation process in alginate hydrogels', Materials Science and Engineering: C, vol. 104, pp. 109951-109951.
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© 2019 Elsevier B.V. Alginate-based hydrogels are extensively used for different biomedical applications. While the swelling and degradation of alginate-based hydrogels affect their structure-property relationship, many studies employed gravimetric analysis to characterize the swelling-degradation process. Accurate or not, this traditional method is difficult to be consistently performed with minimized errors, especially at the late stage of the process. For the first time, this study introduced a reliable, accurate and cost-effective method to minimize the human-sourced errors during repetitive measurement of swelling and degradation of alginate-based hydrogels based on Ca2+ specified aggregation-induced emission fluorogen technology. This study provides an approach for characterization of different properties of alginate-based tissue engineered scaffolds. The established relation between the changes in released Ca2+ into the swelling environment and its relative intensity identified the potential application of the proposed method for prediction of swelling and degradation behaviour in alginate-based hydrogels.
Tavakoli, J, Zhang, H-P, Tang, BZ & Tang, Y 2019, 'Aggregation-induced emission lights up the swelling process: a new technique for swelling characterisation of hydrogels', Materials Chemistry Frontiers, vol. 3, no. 4, pp. 664-667.
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The characterization of the swelling properties in hydrogels suffers uncertainty due to the limitations that occur during weight change measurement.
Tehrani, K, Zhang, Y, Scheuermann, A & Williams, DJ 2019, 'Comparison of air entry values from soil water retention and volumetric shrinkage characteristic curves', Japanese Geotechnical Society Special Publication, vol. 7, no. 2, pp. 340-343.
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Tehseen, S, Yafi, E, Sajilan, S, Rehman, HU & Butt, SM 2019, 'Does ICT based network competence mediate strategic competency's impact on SMEs' competitve advantage? An empirical evidence from Malaysian manufacturing SMEs', Journal of Legal, Ethical and Regulatory Issues, vol. 23, no. 2.
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Information and communication technologies (ICTs) have been considered as a strong mechanism for improving business growth and achieving competitive advantage. ICT based network competence is critical for developing and maintaining long-term relationships with customers, suppliers and other relevant parties. Assuming the mediating role of ICT based network competence, the main objective of this paper was to analyse the mediating effects of ICT based network competence in the relationship between strategic competency and firms' competitive advantage in the context of Malaysian manufacturing SMEs. Survey strategy was used to collect data via standard structured questionnaire from 170 Malaysian entrepreneurs of manufacturing SMEs from Selangor and Kuala Lumpur. PLS-SEM technique was used for analysis of data. The study found a positive significant impact of strategic competency on competitive advantage, and also revealed a strong mediating influence of ICT based network competence on the relationship between strategic competency and competitive advantage.
Tehseen, S, Yafi, E, Sajilan, S, Ur Rehman, H & Butt, SM 2019, 'Does ICT based network competence mediate strategic competency’s impact on smes’ competitve advantage? An empirical evidence from malaysian manufacturing smes', International Journal of Entrepreneurship, vol. 23, no. 2.
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Information and communication technologies (ICTs) have been considered as a strong mechanism for improving business growth and achieving competitive advantage. ICT based network competence is critical for developing and maintaining long-term relationships with customers, suppliers and other relevant parties. Assuming the mediating role of ICT based network competence, the main objective of this paper was to analyse the mediating effects of ICT based network competence in the relationship between strategic competency and firms’ competitive advantage in the context of Malaysian manufacturing SMEs. Survey strategy was used to collect data via standard structured questionnaire from 170 Malaysian entrepreneurs of manufacturing SMEs from Selangor and Kuala Lumpur. PLS-SEM technique was used for analysis of data. The study found a positive significant impact of strategic competency on competitive advantage, and also revealed a strong mediating influence of ICT based network competence on the relationship between strategic competency and competitive advantage.
Tejani, GG, Pholdee, N, Bureerat, S, Prayogo, D & Gandomi, AH 2019, 'Structural optimization using multi-objective modified adaptive symbiotic organisms search', Expert Systems with Applications, vol. 125, pp. 425-441.
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© 2019 Elsevier Ltd Multiple objective structural optimization is a challenging problem in which suitable optimization methods are needed to find optimal solutions. Therefore, to answer such problems effectively, a multi-objective modified adaptive symbiotic organisms search (MOMASOS) with two modified phases is planned along with a normal line method as an archiving technique for designing of structures. The proposed algorithm consists of two separate improved phases including adaptive mutualism and modified parasitism phases. The probabilistic nature of mutualism phase of MOSOS lets design variables to have higher exploration and higher exploitation simultaneously. As search advances, a stability between the global search and a local search has a significant effect on the solutions. Therefore, an adaptive mutualism phase is added to the offer MOASOS. Also, the parasitism phase of MOSOS offers over exploration which is a major issue of this phase. The over exploration results in higher computational cost since the majority of the new solutions gets rejected due to inferior objective functional values. In consideration of this issue, the parasitism phase is upgraded to a modified parasitism phase to increase the possibility of getting improved solutions. In addition, the proposed changes are comparatively simple and do not need an extra parameter setting for MOSOS. For the truss problems, mass minimization and maximization of nodal deflection are considered as objective functions, elemental stresses are considered as behavior constraints and (discrete) elemental sections are considered as side constraints. Five truss optimization problems validate the applicability of the considered meta-heuristics to solve complex engineering. Also, four constrained benchmark engineering design problems are solved to demonstrate the effectiveness of MOMASOS. The results confirmed that the proposed adaptive mutualism phase and modified parasitism phase with a normal lin...
Teng, J, Kou, J, Zhang, S & Sheng, D 2019, 'Evaluating the Influence of Specimen Preparation on Saturated Hydraulic Conductivity Using Nuclear Magnetic Resonance Technology', Vadose Zone Journal, vol. 18, no. 1, pp. 1-7.
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Core IdeasSpecimen preparation methods have a significant influence on hydraulic conductivity.The difference caused by different methods can be large as one order of magnitude.Soil pore structure should be considered in predicting hydraulic conductivity.A pore‐information‐based model is presented to predict hydraulic conductivity.The new model is more accurate than traditional particle information based models.A series of laboratory tests were performed to investigate the influences of specimen preparation on pore size distribution of soil and saturated hydraulic conductivity (Ks). Nuclear magnetic resonance technology was used to measure the pore size distribution of the saturated samples of silty soil, which were prepared by three different kinds of methods: Proctor compaction, static compaction, and the consolidation method. The Ks of the samples was measured by the falling head permeability test. The results show that the difference in Ks caused by different specimen preparations can be large as one order of magnitude, as the measured Ks varied from 3.09 × 10−3 to 3.36 × 10−4 cm s−1. The consolidated specimen tended to have the greatest Ks value, followed by those prepared by Proctor compaction and static compaction. The observed difference highlights the importance of pore structure in determining
Teng, J, Shan, F, He, Z, Zhang, S, Zhao, G & Sheng, D 2019, 'Experimental study of ice accumulation in unsaturated clean sand', Géotechnique, vol. 69, no. 3, pp. 251-259.
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A series of laboratory experiments is carried out to replicate moisture accumulation in an unsaturated coarse-grained soil underneath an impervious cover. The results show that significant moisture accumulation occurs in relatively dry specimens when the temperature at the cover drops below the freezing point. The tested soil is a coarse-grained sand and is not expected to generate much moisture accumulation according to existing frost susceptibility criteria in the literature. The primary mechanism of moisture migration in the soil is observed to be vapour diffusion, and the primary mechanism of moisture accumulation is ice formation by way of vapour–ice desublimation. It is also observed that two peak values exist along the total water content profile of the 13·5 cm long specimen. For a non-freezing condition, water content gradually decreases from the warmer end to the colder end without any peak value, and the amount of moisture accumulation is less than occurs under freezing conditions. The test results are considered to be useful for understanding vapour diffusion in unsaturated freezing soils, and for validating theoretical and numerical models.
Teng, J, Zhang, X, Zhang, S, Zhao, C & Sheng, D 2019, 'An analytical model for evaporation from unsaturated soil', Computers and Geotechnics, vol. 108, pp. 107-116.
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© 2018 Evaporation from unsaturated soil is characterized by vapor transfer in the upper part and liquid water transfer in the lower part of the soil. This study develops an analytical model for identifying the vaporization plane where the evaporation occurs, and the model consists of three partial differential equations that respectively govern the vapor flow, liquid water flow and heat transfer. These equations are solved simultaneously for the transient water content profile, evaporation rate, transient temperature profile and location of the vaporization plane. A series of experiments are used to validate the proposed analytical model, which indicates that this model can reasonably well predict the temporal water content profile and evaporation rate during the evaporation process. The result shows that the evaporation rate during falling rate stage is proportional to the inverse of the square root of elapsed time, and the proportionality is affected by the vapor diffusion coefficient, heat diffusion coefficient, and critical water content. The depth of vaporization plane is found to be independent of soil hydraulic properties, but only dependent on the heat diffusion coefficient of the soil. It is also revealed that heat diffusion coefficient has a pronounced influence on the evaporation process, which has not been observed in previous studies. A larger thermal diffusion coefficient leads to a faster advancing and a deeper vaporization plane, as well as a faster decreasing evaporation rate. The analytical model provides a useful tool for investigating the mechanism of the evaporation process.
Teoh, YH, How, HG, Masjuki, HH, Nguyen, H-T, Kalam, MA & Alabdulkarem, A 2019, 'Investigation on particulate emissions and combustion characteristics of a common-rail diesel engine fueled with Moringa oleifera biodiesel-diesel blends', Renewable Energy, vol. 136, pp. 521-534.
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Terechovs, AKE, Ansari, AJ, McDonald, JA, Khan, SJ, Hai, FI, Knott, NA, Zhou, J & Nghiem, LD 2019, 'Occurrence and bioconcentration of micropollutants in Silver Perch (Bidyanus bidyanus) in a reclaimed water reservoir', Science of The Total Environment, vol. 650, no. Pt 1, pp. 585-593.
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© 2018 This study examined the occurrence of 49 micropollutants in reclaimed water and Silver Perch (Bidyanus bidyanus) living in a reclaimed water reservoir. The numbers of micropollutants detected in reclaimed water, Silver Perch liver, and Silver Perch flesh were 20, 23, and 19, respectively. Concentrations of all micropollutants in reclaimed water, except benzotriazole, were well below the Australian Guideline for Recycled Water (AGRW) values for potable purposes. The concentration of benzotriazole in reclaimed water was 675 ± 130 ng/L while the AGRW value for this compound was 7 ng/L. Not all micropollutants detected in the water phase were identified in the Silver Perch flesh and liver tissues. Likewise, not all micropollutants detected in the Silver Perch flesh and liver were identified in the reclaimed water. In general, micropollutant concentrations in the liver were higher than in the flesh. Perfluorooctane sulfonate (PFOS) was detected at a trace level in reclaimed water well below the AGRW guideline value for potable purposes, but showed a high and medium bioconcentration factor in Silver Perch liver and flesh, respectively. In addition, the risk quotient for PFOS was medium and high when considering its concentration in Silver Perch liver and flesh, respectively. Results reported here highlight the need to evaluate multiple parameters for a comprehensive risk assessment. The results also single out PFOS as a notable contaminant of concern for further investigation.
Thabit, MS, Hawari, AH, Ammar, MH, Zaidi, S, Zaragoza, G & Altaee, A 2019, 'Evaluation of forward osmosis as a pretreatment process for multi stage flash seawater desalination', Desalination, vol. 461, pp. 22-29.
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© 2019 The present study evaluates the feasibility of applying forward osmosis (FO) process for the pretreatment of feed solution to a Multi Stage Flash (MSF) desalination plant. For the first time, real brine reject and real seawater were used as the draw solution and the feed solution, respectively in the FO process. The FO pretreatment is expected to dilute the brine reject and reduce the concentration of divalent ions, which are responsible for scale formation on the surface of heat exchanger in the MSF evaporator unit. The FO experiments were performed at different draw solution temperatures ranging between 25 and 40 °C, different draw and feed solutions flowrates and different membrane orientations. A maximum average membrane flux of 22.3 L/m2·h was reported at a draw solution temperature of 40 °C and 0.8 and 2.0 LPM flow rate of draw and feed solutions, respectively. The experimental results also revealed the process sensitivity to the feed solution temperature. It was found that the average membrane flux in the FO process operating at 0.8 and 2 LPM draw and feed solution flow rates, respectively was 16.9 L/m2·h at 25 °C brine temperature but increased to 22.3 L/m2·h at 40 °C brine temperature. These membrane fluxes resulted in 3% and 8.5% dilution of the draw solution at 25 °C and 40 °C temperatures, respectively. The average membrane flux in the FO mode was equal to that in the PRO mode at low flow rates but it was lower than that in the PRO mode at high flow rates of the feed and draw solutions. The outcomes of the study are very promising with regard to membrane flux and dilution of draw solution.
Thanh, HT, Li, J & Zhang, YX 2019, 'Numerical modelling of the flow of self-consolidating engineered cementitious composites using smoothed particle hydrodynamics', Construction and Building Materials, vol. 211, pp. 109-119.
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Thomas, D, Shankaran, R, Orgun, M, Hitchens, M & Ni, W 2019, 'Energy-Efficient Military Surveillance: Coverage Meets Connectivity', IEEE Sensors Journal, vol. 19, no. 10, pp. 3902-3911.
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© 2001-2012 IEEE. Sensor networks are increasingly being used in the development and application of military surveillance systems. Small size battery-powered sensor devices are deployed in unattended and hostile environments, such as battlefields to detect any physical intrusion. Energy efficiency, coverage, and connectivity are the three major quality-of-service requirements of such mission critical applications. Energy-efficient communication is required to prolong the network lifetime. Better coverage is required to detect the physical intrusion attempts of all kinds. Similarly, connectivity is necessary to provide mission critical messages to the base station in a timely manner. A scheme that satisfies energy efficiency, coverage, and connectivity requirements is an N-P complete problem. This paper proposes an energy-efficient node scheduling algorithm called EC2 that addresses the problems of energy efficiency, coverage, and connectivity in military surveillance applications using the fuzzy graphs.
Thomas, P, Aldrige, L & Smallwood, A 2019, 'Water in opal – what can it tell us?', InColor Magazine, no. 41, pp. 62-69.
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Opal is a hydrous silica composed of predominantly silicon dioxide and water. The chemical composition of opal is normally described by the general formula SiO2.nH2O. The formula indicates that opal contains water and the value of ‘n’ is variable so the water content is variable and is known to range widely. Such a simple formula hides much of the important characteristics of how water is contained in opal and the variability in the water content and states of water is intricately involved in the formation of opal and may influence properties of the opal as a gemstone. The understanding of the states of water in opal is therefore of importance. The way in which the water is contained provides clues to the mechanisms of formation of opal. The water contained may also be used as a probe to help elucidate the complex microstructure beyond the sphere array structure in which precious opal, in particular, is described. This article will outline the types of water present in opal that displays play-of colour (POC) and how these types have been determined using chemical and physical laboratory characterisation techniques.
Thoms, JAI, Beck, D & Pimanda, JE 2019, 'Transcriptional networks in acute myeloid leukemia', Genes, Chromosomes and Cancer, vol. 58, no. 12, pp. 859-874.
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AbstractAcute myeloid leukemia (AML) is a complex disease characterized by a diverse range of recurrent molecular aberrations that occur in many different combinations. Components of transcriptional networks are a common target of these aberrations, leading to network‐wide changes and deployment of novel or developmentally inappropriate transcriptional programs. Genome‐wide techniques are beginning to reveal the full complexity of normal hematopoietic stem cell transcriptional networks and the extent to which they are deregulated in AML, and new understandings of the mechanisms by which AML cells maintain self‐renewal and block differentiation are starting to emerge. The hope is that increased understanding of the network architecture in AML will lead to identification of key oncogenic dependencies that are downstream of multiple network aberrations, and that this knowledge will be translated into new therapies that target these dependencies. Here, we review the current state of knowledge of network perturbation in AML with a focus on major mechanisms of transcription factor dysregulation, including mutation, translocation, and transcriptional dysregulation, and discuss how these perturbations propagate across transcriptional networks. We will also review emerging mechanisms of network disruption, and briefly discuss how increased knowledge of network disruption is already being used to develop new therapies.
Thomson, S, Lu, W, Zreiqat, H, Li, JJ, Tetsworth, K & Al Muderis, M 2019, 'Proximal Bone Remodeling in Lower Limb Amputees Reconstructed With an Osseointegrated Prosthesis', Journal of Orthopaedic Research, vol. 37, no. 12, pp. 2524-2530.
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ABSTRACTMobility outcomes and changes in bone mineral density (BMD) of the spine and femoral necks in response to unilateral osseointegrated implants was investigated over a 3‐year period. A total of 48 unilateral amputees who received an osseointegrated implant, comprising 33 trans‐femoral amputees (TFA) and 15 trans‐tibial amputees (TTA), underwent dual‐energy X‐ray absorptiometry (DXA) scans of the lumbar spine (L2–L4) and femoral necks at baseline, 1‐, and 3‐years follow‐ups. Mobility outcomes, including the Six‐Minute‐Walk Test (6MWT) and Timed‐Up‐and‐Go (TUG), were measured before surgery, at 1 year, and more than 2 years following the osseointegration procedure. We observed a significant increase (p < 0.05) in Z‐score values in the femoral neck of the amputated side in TFA patients without a femoral neck lag screw at the 1‐ and 3‐year follow‐ups, as well as in TFA patients with a lag screw present at 3‐year follow‐up. The BMD at 1‐year follow‐up was found to be positively correlated with pre‐surgery 6MWT values in patients who were mobile using a traditional socket prosthesis before receiving an osseointegrated implant. These results suggest that osseointegrated implants induce a physiological response in the femoral neck of recipients and appear to be evidence of restored biomechanical loading in the proximal femur. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2524–2530, 2019
Thöns, S & Stewart, MG 2019, 'On decision optimality of terrorism risk mitigation measures for iconic bridges', Reliability Engineering & System Safety, vol. 188, pp. 574-583.
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This paper describes the assessment of the cost efficiency of risk mitigation strategies for terrorist attacks with Improvised Explosive Devices (IEDs) for an iconic bridge structure. The assessment is performed with a decision theoretical framework building upon very recent advances in the COST Action TU1402 on Quantifying the Value of Structural Heath Monitoring. The decision scenario is formulated for a decision maker constituting an authority responsible for the societal safety of the infrastructure and consequently the direct risks for the infrastructure owner and the indirect risk due to fatalities and importance of the infrastructure are considered. The mitigation strategies are classified within the decision theoretical context as prior analyses for the assessment of protection strategies and as control strategies requiring a pre-posterior decision analysis. The identification of efficient risk mitigation strategies is based (1) on the risk and expected cost based optimization of actions and information and their combination before implementation, (2) on quantifying and ensuring the significance in risk and expected cost reduction and (3) on quantifying and ensuring a high probability of cost efficiency. These criteria, i.e. the optimality, significance and efficiency ensure the performance of the strategies at the decision point in time before implementation. It is found that the strategies are relying on the identification of the threat level and that control strategies are in favor as their significance and probability of efficiency are higher and their costs are adjustable. However, for high threat levels, both the bridge protection strategies and control strategies are cost efficient.
Thürer, M, Maschek, T, Fredendall, L, Gianiodis, P, Stevenson, M & Deuse, J 2019, 'On the integration of manufacturing strategy: deconstructing Hoshin Kanri', Management Research Review, vol. 42, no. 3, pp. 412-426.
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PurposeThe purpose of this paper is to show that Hoshin Kanri has the potential to integrate the operations strategy literature into a coherent structure. Hoshin Kanri’s planning process is typically described as a top-down cascading of goals, starting with the senior management’s goals and moving to the lowest organizational level. The authors argue that this misrepresents a firm’s actual cognitive processes in practice because it implies reasoning from the effects to the cause, and assumes a direct causal relationship between what the customer wants and what is realizable by the system.Design/methodology/approachThis study is conceptual, based on abductive reasoning and the literature.FindingsThe actual strategic thought process executed in an organization consists of three iterative processes: (i) a translation process that derives the desired customer attributes from customer/stakeholder data, (ii) a process of causal inference that predicts realizable customer attributes from a possible system design and (iii) an integrative process of strategic choices whereby (i) and (ii) are aligned. Each element relies on different cognitive processes (logical relation, causal relation and choice).Research limitations/implicationsBy aligning the thought and planning processes, the competing concepts of manufacturing strategy are integrated into a coherent structure.Practical implicationsDifferent techniques have to be applied for each of the three elements. As each element relies on different cogniti...
Tian, Z, Li, Y, Zheng, J & Wang, S 2019, 'A state-of-the-art on self-sensing concrete: Materials, fabrication and properties', Composites Part B: Engineering, vol. 177, pp. 107437-107437.
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© 2019 Elsevier Ltd Self-sensing concrete combines electrically conductive filler material and conventional building material together, and is able to realise a sensing function that by measuring the change of electrical properties of the composite under external loading, the stress, deformation and damage could be monitored. It has the advantages of high sensitivity, long service period, excellent compatibility, durability and mechanical strength, and low maintenance cost, and can be potentially applied in structure health monitoring, weight in motion, traffic detection, parking management and many other fields. This paper overviews the details of the composition and role of each component, the fabrication method and the mechanism of the self-sensing concrete. The future prospects are discussed at the end of the paper.
Tien Bui, D, Khosravi, K, Shahabi, H, Daggupati, P, Adamowski, J, Melesse, AM, Thai Pham, B, Pourghasemi, H, Mahmoudi, M, Bahrami, S, Pradhan, B, Shirzadi, A, Chapi, K & Lee, S 2019, 'Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model', Remote Sensing, vol. 11, no. 13, pp. 1589-1589.
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Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and ...
Tien Bui, D, Shahabi, H, Omidvar, E, Shirzadi, A, Geertsema, M, Clague, J, Khosravi, K, Pradhan, B, Pham, B, Chapi, K, Barati, Z, Bin Ahmad, B, Rahmani, H, Gróf, G & Lee, S 2019, 'Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm', Remote Sensing, vol. 11, no. 8, pp. 931-931.
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We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates 20 landslide conditioning factors, which we ranked using the least-square support vector machine (LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were used for training and 30% (19) for validation processes. Model validation was performed using sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver operatic characteristic (AUC) curve. We also used soft computing benchmark models, including SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model validation and comparison. The selected conditioning factors were significant in landslide occurrence but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860) outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides.
Tien Bui, D, Shirzadi, A, Chapi, K, Shahabi, H, Pradhan, B, Pham, B, Singh, V, Chen, W, Khosravi, K, Bin Ahmad, B & Lee, S 2019, 'A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping', Water, vol. 11, no. 10, pp. 2013-2013.
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This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely “AB–ADTree”, for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including single ADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB–ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources.
Tien Bui, D, Shirzadi, A, Shahabi, H, Chapi, K, Omidavr, E, Pham, BT, Talebpour Asl, D, Khaledian, H, Pradhan, B, Panahi, M, Bin Ahmad, B, Rahmani, H, Gróf, G & Lee, S 2019, 'A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)', Sensors, vol. 19, no. 11, pp. 2444-2444.
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In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).
To, VHP, Nguyen, TV, Bustamante, H & Vigneswaran, S 2019, 'Deleterious effects of soluble extracellular polymeric substances on polyacrylamide demand for conditioning of anaerobically digested sludge', Journal of Environmental Chemical Engineering, vol. 7, no. 2, pp. 102941-102941.
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© 2019 Elsevier Ltd. All rights reserved. High polyacrylamide (polymer) demand for conditioning of sludge, especially anaerobically digested sludge (ADS), is a major issue for the water industry. Currently, this problem is being investigated and the reasons for doing so are varied. It has been demonstrated that excess amounts of soluble extracellular polymeric substances (EPS) can lead to high polymer demand for conditioning. This study developed a simple and unique yet effective method for quantifying the contribution of soluble EPS to conditioning polymer demand. It did this by measuring absorbance at 191.5 nm wavelength of the supernatant derived from conditioned ADS. Experimental results confirmed that approximately 87 wt% of soluble EPS interacted with polyacrylamides during the conditioning process. Furthermore, they revealed that a specified amount of soluble EPS could not be removed by polymer flocculation despite high polymer dosage. This study concluded that about 86 wt% of the polyacrylamide used for conditioning was consumed solely by soluble EPS. These results confirm the important role of reducing this EPS fraction in ADS in order to curtail significant chemical costs for sludge conditioning and dewatering.
Tofigh, F, Amiri, M, Shariati, N, Lipman, J & Abolhasan, M 2019, 'Low-Frequency Metamaterial Absorber Using Space-Filling Curve', Journal of Electronic Materials, vol. 48, no. 10, pp. 6451-6459.
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© 2019, The Minerals, Metals & Materials Society. The extensive use of metamaterials and metamaterial absorbers increases the demand for compact structures in various frequencies. Designing electrically small absorbers for lower frequencies, especially sub-gigahertz applications, is one of the open issues in this field. In this paper, a space filling curve is used to design an absorber operating on low frequencies. The unit cell design is based on a Sierpinski curve with the size of 25×25×1.6mm3 and air-gap of 10 mm. The structure shows 99.9% absorption at 900 MHz on the third step. The system also shows multiple resonances due to its structure. The proposed structure is fabricated and tested and shows a good agreement with simulation results.
Tong, C-X, Zhang, K-F, Zhang, S & Sheng, D 2019, 'A stochastic particle breakage model for granular soils subjected to one-dimensional compression with emphasis on the evolution of coordination number', Computers and Geotechnics, vol. 112, pp. 72-80.
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© 2019 Elsevier Ltd Prediction of the evolution of particle size distribution (PSD) is of great importance for studying particle breakage. This paper presents a stochastic approach, namely a Markov chain model, for predicting the evolution of PSD of granular materials during one-dimensional compression tests. The model requires the survival probability of each size group particles in an assembly, named as the survival probability matrix. The Weibull distribution is used to capture the particle size and particle strength effects of single particles. The evolution of the coordination number is investigated via 3D discrete element simulations. The proposed analytical form of survival probability matrix with consideration of the coupling effect of particle-scale factors (i.e., particle size, particle strength) and evolution of the coordination number during one-dimensional compression shows that the largest particles in an assembly do not always have the maximum breakage probability (or the minimum survival probability). This also confirms the dominant role of the coordination number on the balance of evolution of PSD within granular soils. The proposed model is validated against experimental data from one-dimensional compression tests on different granular materials. The limitations as well as possible future developments of the model are discussed.
Torres-Robles, A, Wiecek, E, Cutler, R, Drake, B, Benrimoj, SI, Fernandez-Llimos, F & Garcia-Cardenas, V 2019, 'Using Dispensing Data to Evaluate Adherence Implementation Rates in Community Pharmacy', Frontiers in Pharmacology, vol. 10, no. FEB, p. 130.
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Copyright © 2019 Torres-Robles, Wiecek, Cutler, Drake, Benrimoj, Fernandez-Llimos and Garcia-Cardenas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Background: Medication non-adherence remains a significant problem for the health care system with clinical, humanistic and economic impact. Dispensing data is a valuable and commonly utilized measure due accessibility in electronic health data. The purpose of this study was to analyze the changes on adherence implementation rates before and after a community pharmacist intervention integrated in usual real life practice, incorporating big data analysis techniques to evaluate Proportion of Days Covered (PDC) from pharmacy dispensing data. Methods: Retrospective observational study. A de-identified database of dispensing data from 20,335 patients (n = 11,257 on rosuvastatin, n = 6,797 on irbesartan, and n = 2,281 on desvenlafaxine) was analyzed. Included patients received a pharmacist-led medication adherence intervention and had dispensing records before and after the intervention. As a measure of adherence implementation, PDC was utilized. Analysis of the database was performed using SQL and Python. Results: Three months after the pharmacist intervention there was an increase on average PDC from 50.2% (SD: 30.1) to 66.9% (SD: 29.9) for rosuvastatin, from 50.8% (SD: 30.3) to 68% (SD: 29.3) for irbesartan and from 47.3% (SD: 28.4) to 66.3% (SD: 27.3) for desvenlafaxine. These rates declined over 12 months to 62.1% (SD: 32.0) for rosuvastatin, to 62.4% (SD: 32.5) for irbesartan and to 58.1% (SD: 31.1) for desvenla...
Tran, HN, Nguyen, DT, Le, GT, Tomul, F, Lima, EC, Woo, SH, Sarmah, AK, Nguyen, HQ, Nguyen, PT, Nguyen, DD, Nguyen, TV, Vigneswaran, S, Vo, D-VN & Chao, H-P 2019, 'Adsorption mechanism of hexavalent chromium onto layered double hydroxides-based adsorbents: A systematic in-depth review', Journal of Hazardous Materials, vol. 373, pp. 258-270.
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© 2019 Elsevier B.V. An attempt has been made in this review to provide some insights into the possible adsorption mechanisms of hexavalent chromium onto layered double hydroxides-based adsorbents by critically examining the past and present literature. Layered double hydroxides (LDH) nanomaterials are typical dual-electronic adsorbents because they exhibit positively charged external surfaces and abundant interlayer anions. A high positive zeta potential value indicates that LDH has a high affinity to Cr(VI) anions in solution through electrostatic attraction. The host interlayer anions (i.e., Cl−, NO3−, SO42−, and CO32−) provide a high anion exchange capacity (53–520 meq/100 g) which is expected to have an excellent exchangeable capacity to Cr(VI) oxyanions in water. Regarding the adsorption-coupled reduction mechanism, when Cr(VI) anions make contact with the electron-donor groups in the LDH, they are partly reduced to Cr(III) cations. The reduced Cr(III) cations are then adsorbed by LDH via numerous interactions, such as isomorphic substitution and complexation. Nonetheless, the adsorption-coupled reduction mechanism is greatly dependent on: (1) the nature of divalent and trivalent salts utilized in LDH preparation, and the types of interlayer anions (i.e., guest intercalated organic anions), and (3) the adsorption experiment conditions. The low Brunauer–Emmett–Teller specific surface area of LDH (1.80–179 m2/g) suggests that pore filling played an insignificant role in Cr(VI) adsorption. The Langmuir maximum adsorption capacity of LDH (Qomax) toward Cr(VI) was significantly affected by the natures of used inorganic salts and synthetic methods of LDH. The Qomax values range from 16.3 mg/g to 726 mg/g. Almost all adsorption processes of Cr(VI) by LDH-based adsorbent occur spontaneously (ΔG° <0) and endothermically (ΔH° >0) and increase the randomness (ΔS° >0) in the system. Thus, LDH has much potential as a promising material that can effectively rem...
Tran, HN, Nguyen, HC, Woo, SH, Nguyen, TV, Vigneswaran, S, Hosseini-Bandegharaei, A, Rinklebe, J, Kumar Sarmah, A, Ivanets, A, Dotto, GL, Bui, TT, Juang, R-S & Chao, H-P 2019, 'Removal of various contaminants from water by renewable lignocellulose-derived biosorbents: a comprehensive and critical review', Critical Reviews in Environmental Science and Technology, vol. 49, no. 23, pp. 2155-2219.
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© 2019, © 2019 Taylor & Francis Group, LLC. Contaminants in water bodies cause potential health risks for humans and great environmental threats. Therefore, the development and exploration of low-cost, promising adsorbents to remove contaminants from water resources as a sustainable option is one focus of the scientific community. Here, we conducted a critical review regarding the application of pristine and modified/treated biosorbents derived from leaves for the removal of various contaminants. These include potentially toxic cationic and oxyanionic metal ions, radioactive metal ions, rare earth elements, organic cationic and anionic dyes, phosphate, ammonium, and fluoride from water media. Similar to lignocellulose-based biosorbents, leaf-based biosorbents exhibit a low specific surface area and total pore volume but have abundant surface functional groups, high concentrations of light metals, and a high net surface charge density. The maximum adsorption capacity of biosorbents strongly depends on the operation conditions, experiment types, and adsorbate nature. The absorption mechanism of contaminants onto biosorbents is complex; therefore, typical experiments used to identify the primary mechanism of the adsorption of contaminants onto biosorbents were thoroughly discussed. It was concluded that byproduct leaves are renewable, biodegradable, and promising biosorbents which have the potential to be used as a low-cost green alternative to commercial activated carbon for effective removal of various contaminants from the water environment in the real-scale plants.
Tran, HV, Tran‐Le, PT & Nguyen, TV 2019, 'Treatment of vocal cord paralysis by autologous fat injection: Our experience with 41 patients', Clinical Otolaryngology, vol. 44, no. 1, pp. 76-80.
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Tran, NH, Hoang, L, Nghiem, LD, Nguyen, NMH, Ngo, HH, Guo, W, Trinh, QT, Mai, NH, Chen, H, Nguyen, DD, Ta, TT & Gin, KY-H 2019, 'Occurrence and risk assessment of multiple classes of antibiotics in urban canals and lakes in Hanoi, Vietnam', Science of The Total Environment, vol. 692, pp. 157-174.
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Tran, TT, Bradac, C, Solntsev, AS, Toth, M & Aharonovich, I 2019, 'Suppression of spectral diffusion by anti-Stokes excitation of quantum emitters in hexagonal boron nitride', Applied Physics Letters, vol. 115, no. 7, pp. 071102-071102.
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Solid-state quantum emitters are garnering a lot of attention due to their role in scalable quantum photonics. A notable majority of these emitters, however, exhibit spectral diffusion due to local, fluctuating electromagnetic fields. In this work, we demonstrate efficient anti-Stokes (AS) excitation of quantum emitters in hexagonal boron nitride (hBN) and show that the process results in the suppression of a specific mechanism responsible for spectral diffusion of the emitters. We also demonstrate an all-optical gating scheme that exploits Stokes and anti-Stokes excitation to manipulate spectral diffusion so as to switch and lock the emission energy of the photon source. In this scheme, reversible spectral jumps are deliberately enabled by pumping the emitter with high energy (Stokes) excitation; AS excitation is then used to lock the system into a fixed state characterized by a fixed emission energy. Our results provide important insights into the photophysical properties of quantum emitters in hBN and introduce a strategy for controlling the emission wavelength of quantum emitters.
Tran, TT, Regan, B, Ekimov, EA, Mu, Z, Zhou, Y, Gao, W-B, Narang, P, Solntsev, AS, Toth, M, Aharonovich, I & Bradac, C 2019, 'Anti-Stokes excitation of solid-state quantum emitters for nanoscale thermometry', Science Advances, vol. 5, no. 5.
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We demonstrate anti-Stokes excitation of single color centers in diamond for high-sensitivity, nanoscale temperature measurements.
Tran, VH, Lim, S, Han, DS, Pathak, N, Akther, N, Phuntsho, S, Park, H & Shon, HK 2019, 'Efficient fouling control using outer-selective hollow fiber thin-film composite membranes for osmotic membrane bioreactor applications', Bioresource Technology, vol. 282, pp. 9-17.
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© 2019 Elsevier Ltd This paper investigates the efficiency of fouling mitigation methods using a novel outer selective hollow fiber thin-film composite forward osmosis (OSHF TFC FO) membrane for osmosis membrane bioreactor (OMBR) system treating municipal wastewater. Two home-made membrane modules having similar transport properties were used. Two operation regimes with three different fouling mitigation strategies were utilized to test the easiness of membrane for fouling cleaning. These two membrane modules demonstrated high performance with high initial water flux of 14.4 LMH and 14.1 LMH and slow increase rate of mixed liquor's salinity in the bioreactor using 30 g/L NaCl as draw solution. OMBR system showed high removals of total organic carbon and NH4 + -N (>98%). High fouling cleaning efficiency was achieved using OSHF TFC FO membrane with different fouling control methods. These results showed that this membrane is suitable for OMBR applications due to its high performance and its simplicity for fouling mitigation.
Tri, DQ, Mai Linh, NT, Thai, TH & Kandasamy, J 2019, 'Application of 1D–2D coupled modeling in water quality assessment: A case study in Ca Mau Peninsula, Vietnam', Physics and Chemistry of the Earth, Parts A/B/C, vol. 113, pp. 83-99.
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© 2018 Elsevier Ltd A few 1D and 2D models are used to simulate and calculate the water level and water quality regarding four state variable (DO, NH4, NO3, and BOD) observations in the main rivers and coastal estuaries on the Ca Mau peninsula. This study calibrates and validates 1D and 2D models during the dry and flood seasons for 2014 and 2015, as well as assesses water quality in coastal estuaries during the dry and flood season of 2016 by using a 2D model. The calibration and validation results of the hydrodynamic 1D and 2D models show that there is a high degree of conformity regarding the phase and amplitude of water level at observing stations with mean absolute error (MAE) ranges from 0.05 m to 0.37 m. The RMSE–observation standard deviation ratio (RSR) vary from 0.12 to 0.64, and the percent bias (PBIAS) is from −8.9% to 3.2%. Calibration and validation of water quality parameter (DO, NH4, NO3, and BOD5 concentration) results have a high correlation coefficient during both the dry and flood season of 2014 and 2015. The standards, originating from the National Technical Regulation on Surface Water quality and on Coastal Water Quality, are used to evaluate pollutant concentrations in estuaries in the study area during the dry and flood seasons of 2016. The water quality parameters contain DO (4.6 mg/l–7.9 mg/l) and BOD (4.6 mg/l–10.7 mg/l) concentrations over the National Technical Regulation on Surface and Coastal Water Quality and the A1 limit with DO (>4 mg/l) and BOD5 (4 mg/l) on surface water quality for domestic water use in the dry and flood seasons. The calculated results will help managers make better plans for aquaculture and aquatic conservation zones in coastal estuaries in the future.
Trianni, A, Cagno, E & Accordini, D 2019, 'A review of Energy Efficiency Measures Within Electric Motors Systems', Energy Procedia, vol. 158, pp. 3346-3351.
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© 2019 The Authors. Published by Elsevier Ltd.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-review under responsibility of the scientific committee of ICAE2018 The 10th International Conference on Applied Energy. Electric motor systems (EMS) play the lion's share in industrial power consumption. Many opportunities for energy efficiency - most of which apparently cost-effective - can be found, but often decision-makers do not take them as the detail for a specific decision can be too high. In many cases, information regarding the characteristics of such energy efficiency measures (EEMs) is quite vague. For this reason, in the present study we offer a thorough overview of EEMs for EMS, basing on an extensive review of scientific and industrial literature, aimed at offering specific detail over single EEMs and thus support to industrial decision-makers. EEMs are presented according to four main groups, as follows: hardware, motor system drives, management of motors in the plant, and power quality. The new categorization could be helpful to support research for the development of a novel framework to represent the main factors the affect the adoption of EMS for EMS.
Trianni, A, Cagno, E & Accordini, D 2019, 'Energy efficiency measures in electric motors systems: A novel classification highlighting specific implications in their adoption', Applied Energy, vol. 252, pp. 113481-113481.
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© 2019 Elsevier Ltd Electric motor systems (EMS) cover a remarkable share of industrial power consumption. Despite the wide set of apparently cost-effective opportunities to improve energy efficiency in this cross-cutting technology, often decision-makers do not take them, as the detail for a specific decision can be too high, resulting in an implementation rate quite low. In particular, little knowledge of the features that should be considered when deciding to undertake an action in this area represents a serious hurdle. In many cases, information regarding the characteristics of such energy efficiency measures (EEMs) is quite vague. For this reason, in the present study, we present a thorough overview of EEMs for EMS, basing on an extensive review of scientific and industrial literature. By highlighting their characteristics and productivity benefits, most of which impacting on the adoption decision-making process, we re-categorise EEMs for EMS, offering specific detail over single EEMs and thus support to industrial decision-makers. EEMs are presented according to four main groups, as follows: hardware, motor system drives, management of motors in the plant, and power quality. The novel classification is helpful to support research for the development of a new framework to represent the main factors that affect the adoption of EEMs for EMS. Further, it may help the identification and quantification of productivity benefits for those EEMs. Finally, it could result in a valuable tool offering different perspectives in the decision-making of industrial managers and technology suppliers, as well as industrial policy-makers.
Trianni, A, Cagno, E, Bertolotti, M, Thollander, P & Andersson, E 2019, 'Energy management: A practice-based assessment model', Applied Energy, vol. 235, pp. 1614-1636.
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© 2018 Elsevier Ltd Industrial energy efficiency is crucial for energy cost saving and sustainable competitiveness, but its potential is not exploited due to several barriers. Previous literature has pointed out that, among the most effective means, energy management in industrial companies could bring a valuable contribution. Therefore, it is crucial to assess and evaluate the energy management status in an organisation so to undertake the most appropriate improvement actions. So far, literature has neither described the fundamental characteristics of energy management practices, nor specifically developed an assessment model to support industrial decision-makers. Stemming from those research gaps, the present work presents and discusses an innovative energy management assessment model based on a novel characterization of energy management practices. We validated and applied the model through case studies among large Italian and Swedish manufacturing companies, both proving the model to be able to thoroughly describe the energy management status and benchmarking the adoption level of energy management practices with respect to specific baselines. The model highlights both strengths and critical areas in an industrial company's energy management, thus offering a valuable support to drive further improvement activities. The work concludes with interesting suggestions for industrial decision-makers and policy-makers, sketching also some further research avenues.
Trianni, A, Cagno, E, Neri, A & Howard, M 2019, 'Measuring industrial sustainability performance: Empirical evidence from Italian and German manufacturing small and medium enterprises', Journal of Cleaner Production, vol. 229, pp. 1355-1376.
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© 2019 Elsevier Ltd Measuring industrial sustainability performance in manufacturing firms is still a major challenge for both policy and industrial decision makers, with many firms, particularly small and medium enterprises, struggling to properly engage with them. Hence, to understand the level of adoption of industrial sustainability indicators and the issues preventing their effective measurement, and stimulate further research in this area, a multiple case analysis of 26 small and medium manufacturing enterprises across Germany and Italy operating in the chemical and metalworking sectors was conducted. The findings show that only 18 indicators are in place on average. Furthermore, too many firms still focus almost exclusively on the economic pillar of sustainability, while social and environmental pillars are addressed almost exclusively for compliance with legislation. Moreover, the research suggests that contextual factors may influence the firms’ perspective on sustainability and the way it is managed, as well as the certifications held by firms, influencing, in turn, the number and types of indicators considered. An exploratory investigation allowed identification of several important open issues, leading to future research avenues, and in particular towards the development of a novel model to gauge sustainability in industrial activities, as well as adoption of policy-making measures for further emphasis on environmental and social pillars when promoting the adoption of sustainability indicators.
Trycz, A, Regan, B, Kianinia, M, Bray, K, Toth, M & Aharonovich, I 2019, 'Bottom up engineering of single crystal diamond membranes with germanium vacancy color centers', Optical Materials Express, vol. 9, no. 12, pp. 4708-4708.
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© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement. Color centers in diamond have garnered significant attention for applications in integrated quantum photonics. The availability of thin (~ hundred of nanometers) diamond membranes is paramount to achieve this goal. In this paper, we describe in detail a robust, reproducible and cost effective fabrication method that enables engineering high quality thin diamond membranes with uniform distribution of germanium vacancies employing microwave plasma chemical vapor deposition. We use a combination of different germanium precursors for homogeneous doping of the membranes to increase the probability of germanium incorporation into the diamond lattice. Our fabrication methodology can be further extended to implementation of other color centers in thin diamond membranes and be used for engineering quantum photonic devices.
Tu, J, Zhang, P, Ji, Z, Henneicke, H, Li, J, Kim, S, Swarbrick, MM, Wu, Y, Little, CB, Seibel, MJ & Zhou, H 2019, 'Disruption of glucocorticoid signalling in osteoblasts attenuates age-related surgically induced osteoarthritis', Osteoarthritis and Cartilage, vol. 27, no. 10, pp. 1518-1525.
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Tu, R, Jin, W, Han, S-F, Zhou, X, Wang, T, Gao, S-H, Wang, Q, Chen, C, Xie, G-J & Wang, Q 2019, 'Rapid enrichment and ammonia oxidation performance of ammonia-oxidizing archaea from an urban polluted river of China', Environmental Pollution, vol. 255, no. Pt 2, pp. 113258-113258.
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© 2019 Elsevier Ltd In this study, the optimum growth conditions of AOA in a polluted river were investigated. AOB also play an important role in ammonia oxidation in the river water.
Tuan, HD, Nasir, AA, Nguyen, HH, Duong, TQ & Poor, HV 2019, 'Non-Orthogonal Multiple Access With Improper Gaussian Signaling', IEEE Journal of Selected Topics in Signal Processing, vol. 13, no. 3, pp. 496-507.
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© 2007-2012 IEEE. Improper Gaussian signaling (IGS) helps to improve the throughput of a wireless communication network by taking advantage of the additional degrees of freedom in signal processing at the transmitter. This paper exploits IGS in a general multiuser multi-cell network, which is subject to both intra-cell and inter-cell interference. With IGS under orthogonal multiple access (OMA) or non-orthogonal multiple access (NOMA), designs of transmit beamforming to maximize the users' minimum throughput subject to transmit power constraints are addressed. Such designs are mathematically formulated as nonconvex optimization problems of structured matrix variables, which cannot be solved by popular techniques such as weighted minimum mean square error or convex relaxation. By exploiting the lowest computational complexity of 2× 2 linear matrix inequalities, lower concave approximations are developed for throughput functions, which are the main ingredients for devising efficient algorithms for finding solution of these difficult optimization problems. Numerical results obtained under practical scenarios reveal that there is an almost two-fold gain in the throughput by employing IGS instead of the conventional proper Gaussian signaling under both OMA and NOMA; and NOMA-IGS offers better throughput compared to that achieved by OMA-IGS.
Tuan, HD, Nasir, AA, Nguyen, M-N & Masood, M 2019, 'Han–Kobayashi Signaling in MIMO Broadcasting', IEEE Communications Letters, vol. 23, no. 5, pp. 855-858.
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IEEE This letter applies Han-Kobayashi (H-K) superposition signaling to a network of a multi-antenna transmitter serving two multi-antenna users. By making the rate of the common message for both users contribute to user individual rate, it shows that H-K superposition signaling clearly outperforms both stateof- the-art orthogonal multiaccess (OMA) and nonorthogonal multiaccess schemes (NOMA) in terms of the worst user rate. More importantly, unlike NOMA, H-K superposition signaling does not require the user channels to be differentiated for efficient implementation.
Tuan, LA, Ha, Q & Van Trieu, P 2019, 'Observer-Based Nonlinear Robust Control of Floating Container Cranes Subject to Output Hysteresis', Journal of Dynamic Systems, Measurement, and Control, vol. 141, no. 11, pp. 111002-111002.
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A container crane mounted on a pontoon is utilized to transfer containers to smaller ships when a large container ship cannot reach the shallow water port. The shipboard container is considered as an underactuated system having complicated kinematic constraints and hysteretic nonlinearities, with only two actuators to conduct simultaneous tasks: tracking the trolley to destination, lifting the container to the desired cable length, and suppressing the axial container oscillations and container swing. Parameter variations, wave-induced motions of the ship, wind disturbance, and nonlinearities remain challenges for control of floating container cranes. To deal with these problems, this study presents the design of two nonlinear robust controllers, taking into account the influence of the output hysteresis, and using velocity feedback from a state observer. Control performance of the proposed controllers is verified in both simulation and experiments. Together with consistently stabilizing outputs, the proposed control approach well rejects hysteresis and disturbance.
Turner, BD, Sloan, SW & Currell, GR 2019, 'Novel remediation of per- and polyfluoroalkyl substances (PFASs) from contaminated groundwater using Cannabis Sativa L. (hemp) protein powder', Chemosphere, vol. 229, pp. 22-31.
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Ubando, AT, Chen, W-H & Ong, HC 2019, 'Iron oxide reduction by graphite and torrefied biomass analyzed by TG-FTIR for mitigating CO2 emissions', Energy, vol. 180, pp. 968-977.
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Uddin, MB, Su, SW, Chen, W & Chow, CM 2019, 'Dynamic changes in electroencephalogram spectral power with varying apnea duration in older adults', Journal of Sleep Research, vol. 28, no. 6.
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AbstractSleep apnea elicits brain and physiological changes and its duration varies across the night. This study investigates the changes in the relative powers in electroencephalogram (EEG) frequency bands before and at apnea termination and as a function of apnea duration. The analysis was performed on 30 sleep records (375 apnea events) of older adults diagnosed with sleep apnea. Power spectral analysis centered on two 10‐s EEG epochs, before apnea termination (BAT) and after apnea termination (AAT), for each apnea event. The relative power changes in EEG frequency bands were compared with changes in apnea duration, defined as Short (between 10 and 20 s), Moderate (between 20 and 30 s) and Long (between 30 and 40 s). A significant reduction in EEG relative powers for lower frequency bands of alpha and sigma were observed for the Long compared to the Moderate and Short apnea duration groups at BAT, and reduction in relative theta, alpha and sigma powers for the Long compared to the Moderate and Short groups at AAT. The proportion of apnea events showed a significantly decreased trend with increased apnea duration for non‐rapid eye movement sleep but not rapid eye movement sleep. The proportion of central apnea events decreased with increased apnea duration, but not obstructive episodes. The findings suggest EEG arousal occurred both before and at apnea termination and these transient arousals were associated with a reduction in relative EEG powers of the low‐frequency bands: theta, alpha and sigma. The clinical implication is that these transient EEG arousals, without awakenings, are protective of sleep. Further studies with large datasets and different age groups are recommended.
Usman, B, Sharma, N, Satija, S, Mehta, M, Vyas, M, Khatik, GL, Khurana, N, Hansbro, PM, Williams, K & Dua, K 2019, 'Recent Developments in Alpha-Glucosidase Inhibitors for Management of Type-2 Diabetes: An Update', Current Pharmaceutical Design, vol. 25, no. 23, pp. 2510-2525.
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The incidence of diabetes has increased globally in recent years and figures of diabetic patients were estimated to rise up to 642 million by 2040. The disorder is accompanied with various complications if not managed at the early stages, and interlinked high mortality rate and morbidity with time. Different classes of drugs are available for the management of type 2 diabetes but were having certain limitations of their safety. Alphaglucosidase is a family of enzyme originated from the pancreas which plays a role in the anabolism of 80-90% of carbohydrate consumed into glucose. This glucose is absorbed into the blood and results in frank postprandial hyperglycemia and worsens the conditions of diabetic patients which precipitate complications. Inhibition of these enzymes helps to prevent postprandial hyperglycemia and the formation of glycated end products. Alphaglucosidase inhibitors are reported to be more important in adequate control of type 2, but marketed drugs have various side effects, such as poor patient compliance and also expensive. This proves the needs for other class of drugs with better efficacy, safety, patient compliance and economic. In this review, we have emphasized the recent advances in the field of new alpha-glucosidase inhibitors with improved safety and pharmacological profile.
Usman, M, He, X, Lam, K-M, Xu, M, Bokhari, SMM, Chen, J & Jan, MA 2019, 'Error Concealment for Cloud–Based and Scalable Video Coding of HD Videos', IEEE Transactions on Cloud Computing, vol. 7, no. 4, pp. 975-987.
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IEEE The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bit-rates. Packet-loss is very common during the transmission of these video streams over the Internet and becomes another challenge. One solution to address this challenge is to retransmit lost video packets, but this will create end-to-end delay. Therefore, it would be good if the problem of packet-loss can be dealt with at the user's side. In this paper, we present a novel system that encodes and stores the videos using the Amazon cloud computing platform, and recover lost video frames on user side using a new Error Concealment (EC) technique. To efficiently utilize the computation power of a user's mobile device, the EC is performed based on a multiple-thread and parallel process. The simulation results clearly show that, on average, our proposed EC technique outperforms the traditional Block Matching Algorithm (BMA) and the Frame Copy (FC) techniques.
Vahedian, A, Shrestha, R & Crews, K 2019, 'Experimental and analytical investigation on CFRP strengthened glulam laminated timber beams: Full-scale experiments', Composites Part B: Engineering, vol. 164, pp. 377-389.
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Vakhshouri, B & Nejadi, S 2019, 'Empirical models and design codes in prediction of modulus of elasticity of concrete', Frontiers of Structural and Civil Engineering, vol. 13, no. 1, pp. 38-48.
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© 2018, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature. Modulus of Elasticity (MOE) is a key parameter in reinforced concrete design. It represents the stress-strain relationship in the elastic range and is used in the prediction of concrete structures. Out of range estimation of MOE in the existing codes of practice strongly affect the design and performance of the concrete structures. This study includes: (a) evaluation and comparison of the existing analytical models to estimating the MOE in normal strength concrete, and (b) proposing and verifying a new model. In addition, a wide range of experimental databases and empirical models to estimate the MOE from compressive strength and density of concrete are evaluated to verification of the proposed model. The results show underestimation of MOE of conventional concrete in majority of the existing models. Also, considering the consistency between density and mechanical properties of concrete, the predicted MOE in the models including density effect, are more compatible with the experimental results.
Vakhshouri, B, Rasiah, SR & Nejadi, S 2019, 'Analytical study of the drying shrinkage in light-weight concrete containing EPS beads', Advances in Cement Research, vol. 31, no. 7, pp. 308-318.
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This study investigates the drying shrinkage of light-weight concrete containing expanded polystyrene beads. Nine shrinkage specimens in three groups were subjected to three different conditions of drying and wetting periods for 450 d. The effect of the weight loss of specimens on the shrinkage is studied. A proposed new relationship to predict the shrinkage behaviour of the specimens is verified by the existing models. A bilinear relationship between the rate of weight loss and shrinkage increment is also developed. Results show that a longer curing period strongly affects the shrinkage behaviour over the first few months. The rate of weight loss, rate of shrinkage with time and the ratio of the rate of weight loss to the initial weight of specimens are also investigated.
Valenzuela Fernandez, LM, Nicolas, C, Merigó, JM & Arroyo-Cañada, F-J 2019, 'Industrial marketing research: a bibliometric analysis (1990-2015)', Journal of Business & Industrial Marketing, vol. 34, no. 3, pp. 550-560.
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PurposeThe purpose of this paper is to determine the most influential countries and universities that have contributed to science in the field of industrial marketing research during the period from 1990 to 2015.Design/methodology/approachBibliometric methodology is adopted, focusing on the most productive and influential countries and universities within this discipline, for the scientific community analyzing journals listed in the Web of Science (WoS) database from 1990 to 2015 and is supplemented by using VOS viewer to graph the existing bibliometric networks for each and every variable.FindingsEvidence that the USA and UK remain leaders in the investigation of industrial marketing research. Finland stands at the third place, leaving Australia and Germany behind. In reference to the universities, Michigan State University ranks as the leader.Research limitations/implicationsThe process of data classification originates from WoS. Moreover, to provide a comprehensive analytical scenario, other factors could have potentially been considered such as the editor’s commitment to leading journals, to partnerships and conferences, as well as other databases.Originality/valueThis paper takes into account alternative variables that have not been previously considered in previous studies, such as universities and countries in which the transcendental contributions to this field have taken place, providing a closer look, which gives rise to further discussions and studies with more detail to the histor...
Valenzuela-Fernandez, L, Merigó, JM, Lichtenthal, JD & Nicolas, C 2019, 'A Bibliometric Analysis of the First 25 Years of the Journal of Business-to-Business Marketing', Journal of Business-to-Business Marketing, vol. 26, no. 1, pp. 75-94.
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© 2019, © 2019 Taylor & Francis Group, LLC. Purpose: As part of the recognition of the 25th anniversary of the Journal of Business-to-Business Marketing (JBBM), this paper presents an overview of the JBBM through a bibliometric analysis (BA) of its content from 1992 to 2016. The analysis focuses on the most cited articles and authors, h-index, publications per year, among others that typically are conducted for BAs. Design/Methodology/Approach: This paper begins with an introduction to the JBBM, showing its characteristics, its history as well as the editorial development and subsequent journal positioning. This information is followed by an analysis based on bibliometric methodology (BM) which considers the h-index, total citations (TCs), total papers (TPs), TC/TP ratio and other similar measures. To display this information, investigation was done to determine the most cited journals, articles, authors, universities and countries, ergo with the greatest incidence within JBBM. Analyzed are 329 articles, reviews and notes taken from the Scopus database for the periods between 1992 and 2016 for the JBBM. Findings: At the time of this work, the completion of the 25th anniversary of this journal, there is a rising trend in the number of JBBM publications per year. The researchers from the United States were most frequent contributors to the journal, while researchers from Germany, Australia, Norway and the United Kingdom were well represented. Multiple coauthors were more frequent while topics across the general model of business-to-business (B-to-B) marketing were typically found. Special issues on all three university-level education, technology in the classroom as well as Internet in effect B-to-B tactical marketing. Practical Implications: After observing the different perspectives of the journal’s production, we gain another objective view on the evolution of the JBBM in prior 25 years. This approach is useful for the readers of this journal in order to obtai...
Valenzuela-Fernández, LM, Merigó, JM, Nicolas, C & Kleinaltenkamp, M 2019, 'Leaders in industrial marketing research: 25 years of analysis', Journal of Business & Industrial Marketing, vol. 35, no. 3, pp. 586-601.
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PurposeThis paper aims to present a bibliometric overview of the leading trends of the journals in industrial marketing during for 25 years. Thus, the purpose is to carry out an analysis about contributions that industrial marketing or business to business (B2B) marketing discipline has done for scientific investigation, presenting a ranking of the 30 most influential journals and their global evolution by five-year periods from 1992 to 2016. Moreover, this study presents the amount of citations, who quotes who from the top 15 ranking and self-citations.Design/methodology/approachThis study analyzes 3,587 documents classified as articles, letters, notes and reviews from Clarivate Analytics’ Web of Science for the period 1992- 2016, by bibliometric indicators such as H-index, total citations (TC), total papers (TP), TC/TP. Furthermore, this paper develops a graphical visualization of the bibliographic material by using the visualization of similarities viewer software for constructing and visualizing bibliometric networks in leading journals, publications and keywords with bibliographic coupling and co-citation analysis.FindingsIndustrial Marketing Management is the leader of the ranking, representing 34 per cent of the total manuscripts considered in this study. The most influential journals were classified by periods of five years and the top five for the period 2012-2016 were in ascending order: Industrial Marketing Management, Journal of Business & Industrial Marketing, Journal of Business-to-Business Marketing, Journal of Business Research
Vallaster, C, Kraus, S, Merigó Lindahl, JM & Nielsen, A 2019, 'Ethics and entrepreneurship: A bibliometric study and literature review', Journal of Business Research, vol. 99, pp. 226-237.
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© 2019 The entrepreneurship literature pays increasing attention to the ethical aspects of the field. However, only a fragmented understanding is known about how the context influences the ethical judgment of entrepreneurs. We argue that individual socio-cultural background, organizational and societal context shape entrepreneurial ethical judgment. In our article, we contribute to contemporary literature by carving out the intersections between Ethics and Entrepreneurship. We do this by employing a two-step research approach: 1) We use bibliometric techniques to analyze 719 contributions in Business and Economics research and present a comprehensive contextual picture of ethics in entrepreneurship research by a analyzing the 30 most relevant foundation articles. 2) A subsequent content analysis of the 50 most relevant academic contributions was carried with an enlarged database out to augment these findings, detailing ethics and entrepreneurship research on an individual, organizational and societal level of analyses. By comparing the two analyses, this paper concludes by outlining possible avenues for future research.
Van, HT, Nguyen, LH, Nguyen, VD, Nguyen, XH, Nguyen, TH, Nguyen, TV, Vigneswaran, S, Rinklebe, J & Tran, HN 2019, 'Characteristics and mechanisms of cadmium adsorption onto biogenic aragonite shells-derived biosorbent: Batch and column studies', Journal of Environmental Management, vol. 241, pp. 535-548.
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© 2018 Elsevier Ltd Calcium carbonate (CaCO3)-enriched biomaterial derived from freshwater mussel shells (FMS) was used as a non-porous biosorbent to explore the characteristics and mechanisms of cadmium adsorption in aqueous solution. The adsorption mechanism was proposed by comparing the FMS properties before and after adsorption alongside various adsorption studies. The FMS biosorbent was characterized using nitrogen adsorption/desorption isotherm, X-ray diffraction, scanning electron microscopy with energy dispersive spectroscopy, Fourier-transform infrared spectroscopy, and point of zero charge. The results of batch experiments indicated that FMS possessed an excellent affinity to Cd(II) ions within solutions pH higher than 4.0. An increase in ionic strength resulted in a significant decrease in the amount of Cd(II) adsorbed onto FMS. Kinetic study demonstrated that the adsorption process quickly reached equilibrium at approximately 60 min. The FMS biosorbent exhibited the Langmuir maximum adsorption capacity as follows: 18.2 mg/g at 10 °C < 26.0 mg/g at 30 °C < 28.6 mg/g at 50 °C. The Cd(II) adsorption process was irreversible, spontaneous (−ΔG°), endothermic (+ΔH°), and more random (+ΔS°). Selective order (mmol/g) of metal cations followed as Pb2+ > Cd2+ > Cu2+ > Cr3+ > Zn2+. For column experiments, the highest Thomas adsorption capacity (7.86 mg/g) was achieved at a flow rate (9 mL/min), initial Cd(II) concentration (10 mg/L), and bed height (5 cm). The Cd(II) removal by FMS was regarded as non-activated chemisorption that occurred very rapidly (even at a low temperature) with a low magnitude of activation energy. Primary adsorption mechanism was surface precipitation. Cadmium precipitated in the primary (Cd,Ca)CO3 form with a calcite-type structure on the FMS surface. A crust of rhombohedral crystals on the substrate was observed by SEM. Freshwater mussel shells have the potential as a renewable adsorbent to remove cadmium from water.
Verma, R & Merigó, JM 2019, 'On generalized similarity measures for Pythagorean fuzzy sets and their applications to multiple attribute decision‐making', International Journal of Intelligent Systems, vol. 34, no. 10, pp. 2556-2583.
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© 2019 Wiley Periodicals, Inc. In this paper, we develop a new and flexible method for Pythagorean fuzzy decision-making using some trigonometric similarity measures. We first introduce two new generalized similarity measures between Pythagorean fuzzy sets based on cosine and cotangent functions and prove their validity. These similarity measures include some well-known Pythagorean fuzzy similarity measures as their particular and limiting cases. The measures are demonstrated to satisfy some very elegant properties which prepare the ground for applications in different areas. Further, the work defines a generalized hybrid trigonometric Pythagorean fuzzy similarity measure and discuss its properties with particular cases. Then, based on the generalized hybrid trigonometric Pythagorean fuzzy similarity measure, a method for dealing with multiple attribute decision-making problems under Pythagorean fuzzy environment is developed. Finally, a numerical example is given to demonstrate the flexibility and effectiveness of the developed approach in solving real-life problems.
Verma, R & Merigó, JM 2019, 'Variance measures with ordered weighted aggregation operators', International Journal of Intelligent Systems, vol. 34, no. 6, pp. 1184-1205.
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© 2019 Wiley Periodicals, Inc. The variance is a statistical measure widely used in many real-life application areas. This article makes an extensive investigation on variance measure in the case when the uncertainty is not of a probabilistic nature. It generalizes the notion of variance as well as the theory of ordered weighted aggregation operators. First, we extend the idea of representative value/expected value of a decision maker and develop some new deviation measures based on ordered weighted geometric (OWG) average and ordered weighted harmonic average (OWHA) operators. These measures are developed with the consideration that decision maker can represent his/her attitudinal expected value by using any one of the ordered weighted aggregation (OWA) operators. Further, this study proposes some deviation measures by using the generalized-OWA (GOWA) and Quasi-OWA as an expected value of decision maker and discusses their particular cases. Second, a number of generalized deviation measures are introduced by taking the generalized arithmetic mean and quasi-arithmetic means for aggregation of the individual dispersion. This approach provides an ability to the user for considering the deviation under different realistic-scenario. These measures lead to many interesting particular and limiting cases and enrich the use of ordered weighted aggregation operators in the variance.
Viera, C, Garcia, LF, Lacava, M, Fang, J, Wang, X, Kasumovic, MM & Blamires, SJ 2019, 'Author Correction: Silk physico-chemical variability and mechanical robustness facilitates intercontinental invasibility of a spider', Scientific Reports, vol. 9, no. 1.
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An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Viera, C, Garcia, LF, Lacava, M, Fang, J, Wang, X, Kasumovic, MM & Blamires, SJ 2019, 'Silk physico-chemical variability and mechanical robustness facilitates intercontinental invasibility of a spider', Scientific Reports, vol. 9, no. 1.
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AbstractThere are substantive problems associated with invasive species, including threats to endemic organisms and biodiversity. Understanding the mechanisms driving invasions is thus critical. Variable extended phenotypes may enable animals to invade into novel environments. We explored here the proposition that silk variability is a facilitator of invasive success for the highly invasive Australian house spider,Badumna longinqua. We compared the physico-chemical and mechanical properties and underlying gene expressions of its major ampullate (MA) silk between a native Sydney population and an invasive counterpart from Montevideo, Uruguay. We found that while differential gene expressions might explain the differences in silk amino acid compositions and protein nanostructures, we did not find any significant differences in silk mechanical properties across the populations. Our results accordingly suggest thatB.longinqua’s silk remains functionally robust despite underlying physico-chemical and genetic variability as the spider expands its range across continents. They also imply that a combination of silk physico-chemical plasticity combined with mechanical robustness might contribute more broadly to spider invasibilities.
Vijayan, MK, Chitambar, E & Hsieh, M-H 2019, 'Simple Bounds for One-shot Pure-State Distillation in General Resource Theories', Phys. Rev. A, vol. 102, no. 5, p. 052403.
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We present bounds for distilling many copies of a pure state from anarbitrary initial state in a general quantum resource theory. Our bounds applyto operations that are able to generate no more than a $\delta$ amount ofresource, where $\delta \geq 0$ is a given parameter. To maximize applicabilityof our upper bound, we assume little structure on the set of free states underconsideration besides a weak form of superadditivity of the function$G_{min}(\rho)$, which measures the overlap between $\rho$ and the set of freestates. Our bounds are given in terms of this function and the robustness ofresource. Known results in coherence and entanglement theory are reproduced inthis more general framework.
Vijayan, MK, Lund, AP & Rohde, PP 2019, 'A robust W-state encoding for linear quantum optics', Quantum, vol. 4, p. 303.
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Error-detection and correction are necessary prerequisites for any scalablequantum computing architecture. Given the inevitability of unwanted physicalnoise in quantum systems and the propensity for errors to spread ascomputations proceed, computational outcomes can become substantiallycorrupted. This observation applies regardless of the choice of physicalimplementation. In the context of photonic quantum information processing,there has recently been much interest in passive linear optics quantumcomputing, which includes boson-sampling, as this model eliminates thehighly-challenging requirements for feed-forward via fast, active control. Thatis, these systems are passive by definition. In usual scenarios, errordetection and correction techniques are inherently active, making themincompatible with this model, arousing suspicion that physical error processesmay be an insurmountable obstacle. Here we explore a photonic error-detectiontechnique, based on W-state encoding of photonic qubits, which is entirelypassive, based on post-selection, and compatible with these near-term photonicarchitectures of interest. We show that this W-state redundant encodingtechniques enables the suppression of dephasing noise on photonic qubits viasimple fan-out style operations, implemented by optical Fourier transformnetworks, which can be readily realised today. The protocol effectively mapsdephasing noise into heralding failures, with zero failure probability in theideal no-noise limit. We present our scheme in the context of a single photonicqubit passing through a noisy communication or quantum memory channel, whichhas not been generalised to the more general context of full quantumcomputation.
Vinod, M & Khabbaz, H 2019, 'Comparison of rectangular and circular bored twin tunnels in weak ground', Underground Space, vol. 4, no. 4, pp. 328-339.
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© 2019 Tongji University and Tongji University Press The recent innovation of a rectangular tunnel boring machine (TBM), and its use in the Hongzhuan Road tunnel underpass by the China Railway Engineering Group (CREG), has revitalized shallow depth soft soil tunneling. This paper presents the findings of a numerical study using PLAXIS to determine the surface settlements and moments produced in tunnel linings for circular and rectangular twin tunnels. The effects of the relative positions of twin tunnels, critical distances, volume losses, depths of burial, and tunnel sizes for both circular and rectangular tunnels are the key parameters of this investigation. The results indicate that rectangular tunnels are suitable for shallow depths in weak ground as they have lesser settlement compared with circular tunnels. This is crucial for tunneling beneath important structures such as railway lines and existing roads. However, the maximum bending moment produced in the rectangular tunnel lining is higher than that for circular tunnels. The use of rectangular TBMs is an unconventional method in modern day tunneling; however, the analysis in this project recommends that tunnel industry engineers consider this method for shallow depth weak ground tunneling.
Vo, HNP, Ngo, HH, Guo, W, Liu, Y, Chang, SW, Nguyen, DD, Nguyen, PD, Bui, XT & Ren, J 2019, 'Identification of the pollutants’ removal and mechanism by microalgae in saline wastewater', Bioresource Technology, vol. 275, pp. 44-52.
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© 2018 Elsevier Ltd This study investigated the growth dynamics of a freshwater and marine microalgae with supported biochemical performance in saline wastewater, the pollutants assimilation by a developed method, and the mechanism of salinity's effect to pollutants assimilation. Maximal biomass yield was 400–500 mg/L at 0.1–1% salinity while the TOC, NO3−-N, PO43−-P were eliminated 39.5–92.1%, 23–97.4% and 7–30.6%, respectively. The biomass yield and pollutants removal efficiencies reduced significantly when salinity rose from 0.1 to 5%. The freshwater Chlorella vulgaris performed its best with a focus on TOC removal at 0.1% salinity. The marine Chlorella sp. was prominent for removing NO3−-N at 0.1–1% salinity. Through the developed method, the freshwater C. vulgaris competed to the marine microalgae referring to pollutants assimilation up to 5% salinity. This study unveiled the mechanism of salinity's effect with evidence of salt layer formation and salt accumulation in microalgae.
Vo, HNP, Ngo, HH, Guo, W, Nguyen, TMH, Liu, Y, Liu, Y, Nguyen, DD & Chang, SW 2019, 'A critical review on designs and applications of microalgae-based photobioreactors for pollutants treatment', Science of The Total Environment, vol. 651, no. Pt 1, pp. 1549-1568.
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© 2018 Elsevier B.V. The development of the photobioreactors (PBs) is recently noticeable as cutting-edge technology while the correlation of PBs' engineered elements such as modellings, configurations, biomass yields, operating conditions and pollutants removal efficiency still remains complex and unclear. A systematic understanding of PBs is therefore essential. This critical review study is to: (1) describe the modelling approaches and differentiate the outcomes; (2) review and update the novel technical issues of PBs' types; (3) study microalgae growth and control determined by PBs types with comparison made; (4) progress and compare the efficiencies of contaminants removal given by PBs' types and (5) identify the future perspectives of PBs. It is found that Monod model's shortcoming in internal substrate utilization is well fixed by modified Droop model. The corroborated data also remarks an array of PBs' types consisting of flat plate, column, tubular, soft-frame and hybrid configuration in which soft-frame and hybrid are the latest versions with higher flexibility, performance and smaller foot-print. Flat plate PBs is observed with biomass yield being 5 to 20 times higher than other PBs types while soft-frame and membrane PBs can also remove pharmaceutical and personal care products (PPCPs) up to 100%. Looking at an opportunity for PBs in sustainable development, the flat plate PBs are applicable in PB-based architectures and infrastructures indicating an encouraging revenue-raising potential.
Vo, NNY, He, X, Liu, S & Xu, G 2019, 'Deep learning for decision making and the optimization of socially responsible investments and portfolio', Decision Support Systems, vol. 124, pp. 113097-113097.
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© 2019 Elsevier B.V. A socially responsible investment portfolio takes into consideration the environmental, social and governance aspects of companies. It has become an emerging topic for both financial investors and researchers recently. Traditional investment and portfolio theories, which are used for the optimization of financial investment portfolios, are inadequate for decision-making and the construction of an optimized socially responsible investment portfolio. In response to this problem, we introduced a Deep Responsible Investment Portfolio (DRIP) model that contains a Multivariate Bidirectional Long Short-Term Memory neural network, to predict stock returns for the construction of a socially responsible investment portfolio. The deep reinforcement learning technique was adapted to retrain neural networks and rebalance the portfolio periodically. Our empirical data revealed that the DRIP framework could achieve competitive financial performance and better social impact compared to traditional portfolio models, sustainable indexes and funds.
Vo, TT, Luong, NT & Hoang, D 2019, 'MLAMAN: a novel multi-level authentication model and protocol for preventing wormhole attack in mobile ad hoc network', Wireless Networks, vol. 25, no. 7, pp. 4115-4132.
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Voinov, A, Morales, J & Hogenkamp, H 2019, 'Analyzing the social impacts of scooters with geo-spatial methods', Journal of Environmental Management, vol. 242, pp. 529-538.
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Scooters, or gasoline powered two-wheelers, are becoming increasingly popular in the Netherlands. They provide fast, independent and affordable transportation, especially in urban congested areas. Unfortunately, they also have considerable adverse impacts on the environment and human health. The three most prominent impacts are associated with air pollution, noise pollution and traffic accidents. While the total contribution of emissions by scooters is relatively small compared to total traffic related emissions, they have a disproportionally large impact on their direct environment, especially when sharing roads with bicycles as in the Netherlands, where they are characterized as super-polluters. A scoping GIS based assessment, using theoretical and available secondary data, could identify routes with highest likelihood of scooter presence to estimate exhaust and noise impacts and related traffic accidents. Estimated are provided for the total population, and the number of childcare facilities within the impact areas. For future projections four different scenarios are analyzed. For the case study of the town of Enschede in the Netherlands the present noise/exhaust environmental impact of scooters is affecting at least 30% of the population and in the future this number can increase to 38%-53%.
Volkova, L, Roxburgh, SH, Surawski, NC, Meyer, CPM & Weston, CJ 2019, 'Improving reporting of national greenhouse gas emissions from forest fires for emission reduction benefits: An example from Australia', Environmental Science & Policy, vol. 94, pp. 49-62.
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© 2018 Elsevier Ltd Forest fires are a significant contributor to global greenhouse gas (GHG) emissions. Accurate reporting of GHG emissions from forest fires requires development of detailed methodologies and country specific data for estimating emissions. In recent years, Australia has updated its national methodology for reporting GHG emissions from fires on temperate forested lands, using a Tier 2 approach of the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories. This involved refinement of the equation for estimating GHG emissions from fires provided in the Guidance, and the revision of country specific data which was derived from a comprehensive literature review. The refinements were key to transparent reporting and evaluation of the climatic impacts of mitigation actions such as forest fire management. In this paper we describe the steps required to develop a Tier 2 method in reporting fire emissions using this Australian example, the lessons learnt, and the steps required to reduce uncertainties in estimates. This paper may assist other countries seeking to estimate and report GHG emissions from forest fires by moving from the default Tier 1 method to Tier 2 using country-specific information.
Volpin, F, Chekli, L, Phuntsho, S, Ghaffour, N, Vrouwenvelder, JS & Shon, HK 2019, 'Optimisation of a forward osmosis and membrane distillation hybrid system for the treatment of source-separated urine', Separation and Purification Technology, vol. 212, pp. 368-375.
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© 2018 Elsevier B.V. The high concentration of nitrogen, phosphorous and potassium in human urine makes it a suitable raw material for fertiliser production. However, urine is often diluted with a significant amount of flushing water which increases the costs for the downstream nutrients recovery process. Re-using the water and the nutrients in the urine is paramount for enhancing the sustainability of our waste management system. In this work, a combination of forward osmosis (FO) and membrane distillation (MD) was used to extract distilled water from human urine. FO was chosen as MD pre-treatment to increase the overall nitrogen rejection and to prevent wetting of the MD membrane. The goal of this investigation was to tune the FO and MD operating parameters to reduce the nitrogen transport to the MD permeate. Urine pH, draw solution (DS) salt concentration and operating pressure were varied as a means to enhance the FO performances. On the other hand, feed temperature, nitrogen concentration and membrane characteristics were investigated to optimise the MD process. With 2.5 M NaCl as DS commercial FO membranes achieved a water flux between 31.5 and 28.7 L m−2 h−1 and a minimum nitrogen flux of 1.4 g L−1. An additional 33% reduction in the nitrogen transport was observed by applying minimal hydraulic pressure on the DS. However, this was also found to significantly reduce the net transmembrane water flux. Acidification of the feed was also beneficial for both FO and MD nitrogen rejection. Finally, we demonstrated that, by tuning the MD membrane porosity and thickness, higher MD permeate quality could be achieved. To conclude, the hybrid FO-MD process is expected to be an effective solution for the production of clean water and concentrated fertiliser from human urine. This double barrier separation process could be suitable for both water reclamation in space application and resource recovery in urban application.
Volpin, F, Heo, H, Hasan Johir, MA, Cho, J, Phuntsho, S & Shon, HK 2019, 'Techno-economic feasibility of recovering phosphorus, nitrogen and water from dilute human urine via forward osmosis', Water Research, vol. 150, pp. 47-55.
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© 2018 Elsevier Ltd Due to high phosphorus (P) and nitrogen (N) content, human urine has often proven to suitable raw material for fertiliser production. However, most of the urine diverting toilets or male urinals dilute the urine 2 to 10 times. This decreases the efficiency in the precipitation of P and stripping of N. In this work, a commercial fertiliser blend was used as forward osmosis (FO) draw solution (DS) to concentrate real diluted urine. During the concentration, the urea in the urine is recovered as it diffuses to the fertiliser. Additionally, the combination of concentrate PO43-, reverse Mg2+ flux from the DS and the Mg2+ presents in the flushing water, was able to recover the PO43- as struvite. With 50% concentrated urine, 93% P recovery was achieved without the addition of an external Mg2+. Concurrently, 50% of the N was recovered in the diluted fertiliser DS. An economic analysis was performed to understand the feasibility of this process. It was found that the revenue from the produced fertilisers could potentially offset the operational and capital costs of the system. Additionally, if the reduction in the downstream nutrients load is accounted for, the total revenue of the process would be over 5.3 times of the associated costs.
Volpin, F, Yu, H, Cho, J, Lee, C, Phuntsho, S, Ghaffour, N, Vrouwenvelder, JS & Shon, HK 2019, 'Human urine as a forward osmosis draw solution for the application of microalgae dewatering', Journal of Hazardous Materials, vol. 378, pp. 120724-120724.
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© 2019 Elsevier B.V. Human urine is a unique solution that has the right composition to constitute both a severe environmental threat and a rich source of nitrogen and phosphorous. In fact, between 4–9% of urine mass consists of ions, such as K+, Cl−, Na+ or NH4+. Because of its high ionic strength, urine osmotic pressure can reach values of up to 2000 kPa. With this in mind, this work aimed to study the effectiveness of real urine as a novel draw solution for forward osmosis. Water flux, reverse nitrogen flux and membrane fouling were investigated using fresh or hydrolysed urine. Water flux as high as 16.7 ± 1.1 L m−2 h−1 was recorded using real hydrolysed urine. Additionally, no support layer membrane fouling was noticed in over 20 h of experimentation. Urine was also employed to dewater a Chlorella vulgaris culture. A fourfold increase in algal concentration was achieved while having an average flux of 14.1 L m−2 h−1. During the algae dewatering, a flux decrease of about 19% was noticed; this was mainly due to a thin layer of algal deposition on the active side of the membrane. Overall, human urine was found to be an effective draw solution for forward osmosis.
Vosoughi, N, Abbasi, M, Abbasi, E & Sabahi, M 2019, 'A Zeta‐based switched‐capacitor DC‐DC converter topology', International Journal of Circuit Theory and Applications, vol. 47, no. 8, pp. 1302-1322.
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SummaryThis article proposes a new Zeta‐based switched‐capacitor (SC) dc‐dc converter, which has many advantages such as increased voltage gain, decreased duty‐cycle, lower voltage stress on components such as its capacitors and input switch, and increased output power over traditional dc‐dc converter structures. In traditional converters such as Zeta converter, there is only one coupling capacitor, which works as a medium for transferring the power between input and the output. However, in the proposed Zeta‐based converter, there are multiple coupling capacitors, which are used based on dc‐dc SC converter principles. By using these switched coupling capacitors, the mentioned advantages are obtained for the proposed structure, which in turn make this converter more applicable for industrial applications. The analysis has been validated by comprehensive and precise comparisons and experimental results.
Vu, MN, Tran, NH, Tuan, HD, Nguyen, TV & Nguyen, DHN 2019, 'Optimal Signaling Schemes and Capacities of Non-Coherent Correlated MISO Channels Under Per-Antenna Power Constraints', IEEE Transactions on Communications, vol. 67, no. 1, pp. 190-204.
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© 1972-2012 IEEE. This paper investigates the optimal signaling schemes and capacities of non-coherent correlated multiple-input single-output (MISO) channels in fast Rayleigh fading. We consider both channels under per-antenna power constraints as well as channels under joint per-antenna and sum power constraints. For per-antenna power constraint channels, we first establish the convex and compact properties of the feasible sets, and demonstrate the existence of optimal input distribution and the uniqueness of optimal effective magnitude input distribution. By exploiting the solutions of a quadratic optimization problem, we show that the Kuhn-Tucker condition on the optimal inputs can be simplified to a single dimension. As a result, we can apply the Identity Theorem to show the discrete and finite nature of the optimal effective magnitude distribution, with a mass point located at the origin. By using this distribution, we then construct a finite and discrete optimal input vector distribution. The use of this input allows us to determine the capacity gain of MISO over SISO via the phase solutions of a constrained quadratic optimization problem on a sphere, which can be obtained using a proposed penalized optimization algorithm. We also extend the results to MISO channels subject to the joint per-antenna and sum power constraints. Under this consideration, it is shown that not all per-antenna constraints are active. While the finiteness and discreteness of the optimal effective magnitude and the optimal input vector distributions still hold, the optimal phases and the optimal power allocation among the transmit antennas need to be determined simultaneously via a quadratic optimization problem under inequality constraints. These solutions can finally be used to obtain the MISO capacity gain.
Vu, MT, Price, WE, He, T, Zhang, X & Nghiem, LD 2019, 'Seawater-driven forward osmosis for pre-concentrating nutrients in digested sludge centrate', Journal of Environmental Management, vol. 247, pp. 135-139.
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© 2019 Seawater-driven forward osmosis to enrich nutrients from sludge centrate and reduce membrane fouling is demonstrated. Due to enrichment and pH increase in the feed solution, without appropriate control measure, nutrient precipitation can occur directly on the membrane surface causing severe membrane fouling and reducing nutrient enrichment efficiency. Indeed without agitating the feed, there was less precipitation on the membrane surface, compared to with agitation. In addition, increase in the membrane area over permeate volume ratio significantly reduced the filtration time and nutrient precipitation. A novel technique to maintain the draw solution (DS) at an acidic condition was developed to improve nutrient enrichment and reduce membrane fouling. By using this technique and a high membrane surface to permeate volume ratio, nutrient enrichment similar to the theoretical efficiency was successfully demonstrated. Our technique reduced the filtration time to achieve 70% water recovery by over 90% (compared to unbuffered seawater as the DS, small membrane area, and feed agitation), as a result of significantly less membrane fouling. The amount of phosphorus precipitate on the membrane surface decreased by more than 10 times. The enrichment of ammonia and phosphorus as a function of water recovery was similar to the theoretical calculation, indicating negligible nutrient loss due to precipitation.
Vu, T, Gowripalan, N, De Silva, P, Kidd, P & Sirivivatnanon, V 2019, 'Influence of curing and retarder on early-age properties of powder geopolymer concrete', Concrete in Australia, vol. 45, no. 2, pp. 41-46.
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Dry powder geopolymer is a new approach to geopolymer production for field applications. However, this new production technique has a limitation of rapid setting time. Until now, most studies on retarders for geopolymers were performed for a two-part mix. This paper aims to determine the influence of a sodium based retarder on setting time of dry powder geopolymer from a blend of fly ash and slag. With 2% of retarder, initial setting time of dry powder geopolymer (fly ash 50%, slag 32%) was 80-110 minutes. For dry powder geopolymer using 4% retarder (fly ash 30%, slag 50%) initial setting time was 53-75 minutes. Considering heat evolution, workability and compressive strength, the optimum retarder dosage was about 2 - 4% (by weight of dry powder). For precast elements, sealed and heat curing was found to be an effective curing regimeThe effect of different curing conditions and the addition of a retarder on flow characteristics, setting time and strength development of a powder form of geopolymer is reported.
Vu, TT, Nguyen, DN, Hoang, DT, Dutkiewicz, E & Nguyen, TV 2019, 'Optimal Energy Efficiency with Delay Constraints for Multi-layer Cooperative Fog Computing Networks'.
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We develop a joint offloading and resource allocation framework for amulti-layer cooperative fog computing network, aiming to minimize the totalenergy consumption of multiple mobile devices subject to their service delayrequirements. The resulting optimization involves both binary (offloadingdecisions) and real variables (resource allocations), making it an NP-hard andcomputationally intractable problem. To tackle it, we first propose an improvedbranch-and-bound algorithm (IBBA) that is implemented in a centralized manner.However, due to the large size of the cooperative fog computing network, thecomputational complexity of the proposed IBBA is relatively high. To speed upthe optimal solution searching as well as to enable its distributedimplementation, we then leverage the unique structure of the underlying problemand the parallel processing at fog nodes. To that end, we propose a distributedframework, namely feasibility finding Benders decomposition (FFBD), thatdecomposes the original problem into a master problem for the offloadingdecision and subproblems for resource allocation. The master problem (MP) isthen equipped with powerful cutting-planes to exploit the fact of resourcelimitation at fog nodes. The subproblems (SP) for resource allocation can findtheir closed-form solutions using our fast solution detection method. These(simpler) subproblems can then be solved in parallel at fog nodes. Thenumerical results show that the FFBD always returns the optimal solution of theproblem with significantly less computation time (e.g., compared with thecentralized IBBA approach). The FFBD with the fast solution detection method,namely FFBD-F, can reduce up to $60\%$ and $90\%$ of computation time,respectively, compared with those of the conventional FFBD, namely FFBD-S, andIBBA.
Wadhwa, R, Aggarwal, T, Malyla, V, Kumar, N, Gupta, G, Chellappan, DK, Dureja, H, Mehta, M, Satija, S, Gulati, M, Maurya, PK, Collet, T, Hansbro, PM & Dua, K 2019, 'Identification of biomarkers and genetic approaches toward chronic obstructive pulmonary disease', Journal of Cellular Physiology, vol. 234, no. 10, pp. 16703-16723.
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AbstractChronic obstructive pulmonary disease accounts as the leading cause of mortality worldwide prominently affected by genetic and environmental factors. The disease is characterized by persistent coughing, breathlessness airways inflammation followed by a decrease in forced expiratory volume1 and exacerbations, which affect the quality of life. Determination of genetic, epigenetic, and oxidant biomarkers to evaluate the progression of disease has proved complicated and challenging. Approaches including exome sequencing, genome‐wide association studies, linkage studies, and inheritance and segregation studies played a crucial role in the identification of genes, their pathways and variation in genes. This review highlights multiple approaches for biomarker and gene identification, which can be used for differential diagnosis along with the genome editing tools to study genes associated with the development of disease and models their function. Further, we have discussed the approaches to rectify the abnormal gene functioning of respiratory tissues and various novel gene editing techniques like Zinc finger nucleases (ZFN), transcription activator‐like effector nucleases (TALEN), and clustered regulatory interspaced short palindromic repeats/CRISPR‐associated protein 9 (CRISPR/Cas9).
Wadhwa, R, Pandey, P, Gupta, G, Aggarwal, T, Kumar, N, Mehta, M, Satija, S, Gulati, M, Madan, JR, Dureja, H, Balusamy, SR, Perumalsamy, H, Maurya, PK, Collet, T, Tambuwala, MM, Hansbro, PM, Chellappan, DK & Dua, K 2019, 'Emerging Complexity and the Need for Advanced Drug Delivery in Targeting Candida Species', Current Topics in Medicinal Chemistry, vol. 19, no. 28, pp. 2593-2609.
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Background:Candida species are the important etiologic agents for candidiasis, the most prevalent cause of opportunistic fungal infections. Candida invasion results in mucosal to systemic infections through immune dysfunction and helps in further invasion and proliferation at several sites in the host. The host defence system utilizes a wide array of the cells, proteins and chemical signals that are distributed in blood and tissues which further constitute the innate and adaptive immune system. The lack of antifungal agents and their limited therapeutic effects have led to high mortality and morbidity related to such infections.Methods:The necessary information collated on this review has been gathered from various literature published from 1995 to 2019.Results:This article sheds light on novel drug delivery approaches to target the immunological axis for several Candida species (C. albicans, C. glabrata, C. parapsilosis, C. tropicalis, C. krusei, C. rugose, C. hemulonii, etc.).Conclusion:It is clear that the novel drug delivery approaches include vaccines, adoptive transfer of primed immune cells, recombinant cytokines, therapeutic antibodies, and nanoparticles, which have immunomodulatory effects. Such advancements in targeting various underpinning mechanisms using the concept of novel drug delivery will provide a new dimension to the fungal infection clinic particularly due to Candida species with improved patient compliance and lesser side effects. This advancement in knowledge can also be extended to target various other similar microbial species and infections.
Wahid-Ul-Ashraf, A, Budka, M & Musial, K 2019, 'How to predict social relationships — Physics-inspired approach to link prediction', Physica A: Statistical Mechanics and its Applications, vol. 523, pp. 1110-1129.
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© 2019 Elsevier B.V. Link prediction in social networks has a long history in complex network research area. The formation of links in networks has been approached by scientists from different backgrounds, ranging from physics to computer science. To predict the formation of new links, we consider measures which originate from network science and use them in the place of mass and distance within the formalism of Newton's Gravitational Law. The attraction force calculated in this way is treated as a proxy for the likelihood of link formation. In particular, we use three different measures of vertex centrality as mass, and 13 dissimilarity measures including shortest path and inverse Katz score in place of distance, leading to over 50 combinations that we evaluate empirically. Combining these through gravitational law allows us to couple popularity with similarity, two important characteristics for link prediction in social networks. Performance of our predictors is evaluated using Area Under the Precision–Recall Curve (AUC)for seven different real-world network datasets. The experiments demonstrate that this approach tends to outperform the setting in which vertex similarity measures like Katz are used on their own. Our approach also gives us the opportunity to combine network's global and local properties for predicting future or missing links. Our study shows that the use of the physical law which combines node importance with measures quantifying how distant the nodes are, is a promising research direction in social link prediction.
Wan Mohd, WR, Abdullah, L, Yusoff, B, Taib, CMIC & Merigo, JM 2019, 'An Integrated MCDM Model based on Pythagorean Fuzzy Sets for Green Supplier Development Program', Malaysian Journal of Mathematical Sciences, vol. 13, pp. 23-37.
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Green supplier development is becoming vital for many industrial firms for effective green supply chain management. Most of the suppliers are willing to invest in many green supplier programs that developed in their firms’ performance. The evaluation and selection of an adequate green supplier development program is too complex and challenging as it has multiple criteria and alternatives to be chosen. These criteria involve both qualitative and quantitative information. To select the best alternative of the green supplier development program, it is necessary to settle these problems using multi-criteria decision-making (MCDM) method. This paper proposes the integration of Pythagorean fuzzy AHP and Pythagorean fuzzy VIKOR approach to resolve the green supplier development program selection. The main goal of this study is to present a useful and reliable method to identify the most important criteria and alternatives using Pythagorean fuzzy AHP and Pythagorean fuzzy VIKOR. The first innovation is finding the weight for each criteria using Pythagorean fuzzy AHP. In order to do so, the crisp value evaluated by the decision makers (DMs) are presented in the pair-wise comparison matrix and converted to Pythagorean fuzzy number. The VIKOR is used to rank the alternatives of the green supplier development programs and suggest which program is the best program. Then, the obtained results are compared with the existing VIKOR method in the same case study. The results found the supplier training is the best alternative to select in the green supplier development programs. It is noted that the integration of Pythagorean fuzzy AHP and Pythagorean fuzzy VIKOR is a holistic approach to the MCDM problem.
Wang, B, Lau, Y-S, Huang, Y, Organ, B, Lee, S-C & Ho, K-F 2019, 'Investigation of factors affecting the gaseous and particulate matter emissions from diesel vehicles', Air Quality, Atmosphere & Health, vol. 12, no. 9, pp. 1113-1126.
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© 2019, Springer Nature B.V. This study presents a detailed investigation of diesel vehicle emissions utilizing chassis dynamometer testing. The recruited vehicle fleet consists of 15 in-use diesel vehicles, spanning a wide range of emission standards, engine sizes, weight, model year, etc. The real-time emission concentrations of nitrogen oxides (NOx), total hydrocarbons (THC), carbon monoxide (CO) and carbon dioxide (CO2), and the mass of particulate matter (PM) collected on filters are measured and used to calculate the vehicle emission factors (EFs) under various driving conditions. Results show that in general EFs of NOx, CO, THC, and PM of the recruited fleet span a wide range of values (NOx 0.80 ± 0.34 to 60.28 ± 2.94 g kg−1; THC 0.10 ± 0.04 to 5.28 ± 1.28 g kg−1; CO below detection limits to 24.01 ± 8.48 g kg−1; PM below detection limits to 2.47 ± 1.22 g kg−1). Further data analysis shows that the implementation of a higher emission standard has a significant effect on reducing the emission of pollutants, except for NOx. Driving conditions are also important factors affecting the EFs. Besides, statistical analysis shows a significant correlation between EFs of NOx with the testing weight and the maximum engine power of the vehicle. Further investigation is recommended to explore the effect of maintenance of the vehicles to the vehicular emission.
Wang, B, Ni, B-J, Yuan, Z & Guo, J 2019, 'Cometabolic biodegradation of cephalexin by enriched nitrifying sludge: Process characteristics, gene expression and product biotoxicity', Science of The Total Environment, vol. 672, pp. 275-282.
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© 2019 Elsevier B.V. The nitrifying systems have been reported to be able to biodegrade micropollutants, yet it is still unclear about the cometabolism of ammonia-oxidizing bacteria (AOB) towards micropollutants, in particular their enzyme and transcriptional responses under exposure of micropollutants. This study investigated cometabolic biodegradation of a selected antibiotic, cephalexin (CFX), by an enriched nitrifying culture through a series of batch experiments, together with the assessments of enzymatic activity, key gene expression, and biotoxicity of the degradation products. More than 99% CFX with an initial concentration of 50 μg/L could be removed with the presence of ammonium, while <44% of CFX removal was observed in the absence of ammonium, suggesting the cometabolic degradation of CFX by ammonia-oxidizing bacteria (AOB). After the addition of 50 μg/L CFX, the ammonia oxidizing rate (AOR) decreased from 36.6 to 11.0 mg N/(L·h·g VSS), followed by a slight recovery when CFX concentration decreased to below 8 μg/L. Ammonia monooxygenase (AMO) activity showed a similar trend with that of AOR. The quantitative reverse transcription PCR assay indicated that the expression level of amoA gene was significantly upregulated (up to 3-fold, p < 0.05) due to the addition of CFX, while decreased to the normal level once CFX was degraded, suggesting a mechanism of AOB to neutralize the toxicity of CFX by metabolizing ammonia more effectively. Meanwhile, the biotoxicity test showed the degradation products of CFX did not exhibit any antibacterial impacts in terms of cell viability, compared to the parent compounds. Our finding shed a light on AMO-mediated cometabolic biodegradation of antibiotics in nitrifying cultures.
Wang, B, Ni, B-J, Yuan, Z & Guo, J 2019, 'Insight into the nitrification kinetics and microbial response of an enriched nitrifying sludge in the biodegradation of sulfadiazine', Environmental Pollution, vol. 255, no. Pt 1, pp. 113160-113160.
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© 2019 Elsevier Ltd SDZ could be cometabolically degraded by enriched nitrifying culture, and the expression level of amoA gene was down-regulated during the process, but didn't decrease proportionally with AOR.
Wang, B, Xing, D, Li, JJ, Zhu, Y, Dong, S & Zhao, B 2019, 'Lateral or medial approach for valgus knee in total knee arthroplasty - which one is better? A systematic review', Journal of International Medical Research, vol. 47, no. 11, pp. 5400-5413.
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Objective To identify whether the medial or lateral approach is superior for patients with valgus knees undergoing primary total knee arthroplasty (TKA). Methods Studies evaluating the 2 approaches were sourced from the PUBMED, EMBASE, Web of Science, and OVID databases. The quality of included studies was assessed using a modified quality evaluation method, and differences between approaches were systematically reviewed. Results Seventeen observational studies were included. The studies were published between 1991 and 2016, and included 5 retrospective studies and 12 prospective studies. Sixteen evaluation methods for the study outcomes were identified. Twelve and eight complication types were identified by studies reporting the lateral and medial approaches for valgus knee, respectively. Several studies showed that pain scores and knee function were superior using a lateral approach. Conclusion The lateral approach (combined with a tibial tubercle osteotomy or proximal quadriceps snip) was more useful and safer than the medial approach in the treatment of severe uncorrectable valgus knee deformity in patients undergoing TKA. Most of the available evidence supports the use of a lateral approach provided that the surgeon is familiar with the pathological anatomy of the valgus knee.
Wang, D, He, D, Liu, X, Xu, Q, Yang, Q, Li, X, Liu, Y, Wang, Q, Ni, B-J & Li, H 2019, 'The underlying mechanism of calcium peroxide pretreatment enhancing methane production from anaerobic digestion of waste activated sludge', Water Research, vol. 164, pp. 114934-114934.
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© 2019 Elsevier Ltd Recent investigations verified that calcium peroxide (CaO2) could be used to pretreat waste activated sludge to promote methane yield from anaerobic digestion. However, the underlying mechanism of how CaO2 pretreatment promotes methane production is unclear. This work therefore aims to provide insights into such systems. Experimental results showed that with an increase of CaO2 dosage from 0 to 0.14 g/g VSS (volatile suspended solids) the methane yield increased linearly from 146.3 to 215.9 mL/g VSS. Further increases of CaO2 resulted in decreases in methane yield. CaO2 pretreatment promoted the disintegration of sludge and the degradation of sludge recalcitrant organics (especially humus and lignocellulose), thereby providing more substrates for subsequent methane production. Ultraviolet absorption spectroscopy indicated that CaO2 enhanced the cleavage of unsaturated conjugated bonds and reduced the aromaticity of humus and lignocellulose. Fourier transform infrared spectroscopy showed that CaO2 changed the structures and functional groups of humus and lignocellulose, making them transform to be biodegradable. GC/MS analyses exhibited that the degradation products of humus and lignocellulose included several types of small molecular organics such as ester-like, acid-like, and alcohol-like substances. Further investigation demonstrated that substantial methane could be produced from these degradation products. It was also found that the presence of recalcitrant organics was detrimental to anaerobes relevant to anaerobic digestion, and the degradation of such recalcitrant organics mitigated their inhibitions to the anaerobes. Model-based analysis suggested that CaO2 pretreatment increased the maximum methane yield and methane production rate, which were consistent with the analysis above.
Wang, D, Tawk, M, Indraratna, B, Heitor, A & Rujikiatkamjorn, C 2019, 'A mixture of coal wash and fly ash as a pavement substructure material', Transportation Geotechnics, vol. 21, pp. 100265-100265.
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Wang, D, Wang, Y, Liu, X, Xu, Q, Yang, Q, Li, X, Zhang, Y, Liu, Y, Wang, Q, Ni, B-J & Li, H 2019, 'Heat pretreatment assists free ammonia to enhance hydrogen production from waste activated sludge', Bioresource Technology, vol. 283, pp. 316-325.
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© 2019 Elsevier Ltd Controlling free ammonia in an anaerobic fermenter at pertinent levels is reported recently to be an economically attractive and practically feasible approach to enhance hydrogen yield from waste activated sludge (WAS). This paper reports a new technology for WAS dark fermentation, i.e., using heat pretreatment (70 °C for 60 min) to assist free ammonia for further improving hydrogen yield. The experimental results showed that the accumulative hydrogen production from combined reactors was promoted from 12.3 to 19.2 mL/g VSS (volatile suspended solids), the maximum of which was 1.8, 2.7, and 7.1 times of that from sole free ammonia (131.9 mg NH3-N/L), sole heat, and blank reactors, respectively. Mechanism explorations showed that the combination strategy significantly enhanced WAS disintegration, providing more substrates for hydrogen production. Moreover, the combination suppressed activities of all microbes associated with anaerobic fermentation, but its inhibition to hydrogen consumers was much severer than that to other microbes.
Wang, D, Wu, C, Huang, W & Zhang, Y 2019, 'Vibration investigation on fluid-structure interaction of AP1000 shield building subjected to multi earthquake excitations', Annals of Nuclear Energy, vol. 126, pp. 312-329.
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© 2018 Elsevier Ltd Fluid-structure interaction (FSI) between water and water tank of AP1000 nuclear power plant has always been a hot topic because the gravity water tank plays a key role in protecting structural safety in an emergency such as an earthquake. The main target of this study is to investigate the FSI effect on structural dynamic responses of AP1000 shield building with filled and empty water tanks and to explore the most reasonable height of water level for reducing seismic response under inputs of multi three-direction earthquake excitations. For this purpose, method of nonlinear FSI algorithm of finite element is employed based on ANSYS platform. The numerical procedure is validated by comparison with theoretical calculation and existing experimental results of fluid free vibration and structural seismic responses in the situation of El Centro wave. Based on the validated numerical model, a series of numerical simulations on seven partially-filled models in six natural and one artificial earthquake are carried out and corresponding results, such as peak acceleration & displacement, floor response spectrum and structural base shear, are studied comparatively in details. Discussions of this study show that the partially-filled shield building appears significant FSI effect, which generates great influence on structural dynamic characteristics and responses. Reasonable design of the water level can contribute to reducing structural responses and improving seismic safety.
Wang, D, Wu, C, Zhang, Y & Li, S 2019, 'Study on vertical vibration control of long-span steel footbridge with tuned mass dampers under pedestrian excitation', Journal of Constructional Steel Research, vol. 154, pp. 84-98.
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© 2018 Elsevier Ltd This paper aims to study crowd-induced vibration control of long-span steel footbridges with different dynamic characteristics by combining methods of site measurement and numerical simulation. Four kinds of footbridge models with sensitive vertical natural frequencies of easily generating human-bridge resonance under pedestrian loading are designed based on an actual steel footbridge. The numerical models are firstly validated by comparative investigation between the site measurement and the simulation. Detailed study on dynamic responses of the four footbridges with and without controlling systems of tuned mass damper (TMD) and multiple tuned mass damper (MTMD) is then conducted under 13 cases of crowd random excitations, rhythmic running and jumping excitations. Results show that the numerical simulation agrees well with the site measurement data. TMD system is found to be highly efficient in reducing vibration responses only when the excitation frequency is basically consistent with the structural natural frequency, which obviously limits the application of TMD system in footbridges as wider excitation frequency bandwidth is caused by human activities. MTMD system are demonstrated to be with higher vibration absorption robustness appearing predominant and stable capacity of reducing structural vibrations under all the crowd random and rhythmic excitations for the four footbridges.
Wang, D, Wu, C, Zhang, Y, Ding, Z & Chen, W 2019, 'Elastic-plastic behavior of AP1000 nuclear island structure under mainshock-aftershock sequences', Annals of Nuclear Energy, vol. 123, pp. 1-17.
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© 2018 Elsevier Ltd This paper studies dynamic responses of AP1000 nuclear island structure in strong earthquake sequences. A numerical model to simulate nuclear structural behaviors in earthquake is validated by comparison with data from a previous study on a nonlinear dynamic analysis of a reinforced concrete shield building. The validated numerical model is then used to carry out a series of parametric analyses with 112 computational cases so as to determine influence of strong aftershocks on structural elastic-plastic behavior considering input of three-dimensional ground motions. The results indicate that the influence of aftershocks on structural horizontal/vertical dynamic responses is very small in design basis earthquake sequences. However, the influence must be considered seriously in beyond-design basis earthquake sequences as values of RMVs (Ratio of Mean Value) deviating IPRs (Input Peak Ratio) obviously, which means structural dynamic responses are greatly changed in strong aftershocks. Damage aggravating effect induced by strong aftershocks can cause severe damage of structural members and it is found the greater the magnitude of aftershocks, the severer the aggravation effect. Although earthquake input energy is mostly dissipated by damping energy, plastic damage energy plays considerable role in strong aftershocks as it shares beyond 8 percent of the total input energy, which is 10 times more compared to design basis earthquake sequences.
Wang, D, Zhang, D, Xu, Q, Liu, Y, Wang, Q, Ni, B-J, Yang, Q, Li, X & Yang, F 2019, 'Calcium peroxide promotes hydrogen production from dark fermentation of waste activated sludge', Chemical Engineering Journal, vol. 355, pp. 22-32.
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© 2018 Elsevier B.V. Calcium peroxide (CaO2), one of versatile and harmless inorganic peroxy compounds, has been recently used as a highly effective oxidant to degrade pollutants in sludge and a method to achieve sludge resource utilization. However, its impact on hydrogen production has never been studied before. This investigation therefore aims to fill this knowledge gap. Results indicated that with CaO2 increased from 0.05 to 0.25 g/g VSS (volatile suspended solids), the maximum hydrogen yield increased from 0.77 to 10.55 mL/g VSS. The mechanism studies revealed that CaO2 accelerated the breakage and death of sludge cells. Excitation emission matrix (EEM) analyses further indicated that CaO2 increased the biodegradability of the released substances, which could provide more biodegradable organics for the following reactions. Although CaO2 caused inhibitions to some extents to all the tested microbes, its inhibition to hydrogen consumers was much severer than that to hydrolytic microbes and hydrogen producers. The enzyme analyses also demonstrated that the suppression of calcium peroxide to the activities of enzymes related to hydrogen consumption process was much severer than that to the activities of the activities of enzymes related to hydrogen production process. Further investigations exhibited that alkali, [rad]O2− and [rad]OH radicals, were the major intermediate products of CaO2 decomposition. All of them were verified to contribute the increased hydrogen production, and their contributions were in the order of alkali > [rad]O2− > [rad]OH. This is the first work demonstrating that CaO2 can enhance hydrogen production from WAS. The findings reported in our paper not only expand the application field of CaO2 but also deepen the understanding of the CaO2-involved sludge fermentation process.
Wang, D, Zhang, Y, Wu, C, Xue, G & Huang, W 2019, 'Seismic performance of base-isolated AP1000 shield building with consideration of fluid-structure interaction', Nuclear Engineering and Design, vol. 353, pp. 110241-110241.
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© 2019 Elsevier B.V. Seismically-induced FSI effect on dynamic responses of AP1000 shield building with base isolation is focused in this study using numerical simualtion. The numerical model is firstly validated by comparison with existing experimental results, which is capable of simulating dynamic behaviors of the water partially-filled shield building with seismic isolation using high damping rubber (HDR) bearings. The influences of FSI on seismic performance of the base-isolated and base-fixed AP1000 shield building with various water levels and on the corresponding isolation effectiveness are comparatively explored in details. Results show that base isolation can reduce the fundamental frequency of the shield building and make it close to water sloshing frequency. It is necessary to ensure an reasonable isolation design to keep the fundamental frequency away from the sloshing frequency and then to avoid the water resonance. Seismic isolation can offer a substantial benefit for the earthquake-resistant design of the shield building even filled with different levels of water, because the structural primary resonance is effectively transformed to the sub-resonance by application of base isolation. Dynamic responses of base-isolated models are influenced significantly by FSI and an optimal water level ratio of 0.8 is suggested to achieve excellent seismic performance for such structures.
Wang, G, Fu, L, Walker, A, Chen, X, Lovejoy, DB, Hao, M, Lee, A, Chung, R, Rizos, H, Irvine, M, Zheng, M, Liu, X, Lu, Y & Shi, B 2019, 'Label-Free Fluorescent Poly(amidoamine) Dendrimer for Traceable and Controlled Drug Delivery', Biomacromolecules, vol. 20, no. 5, pp. 2148-2158.
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Wang, G, Ji, J & Zhou, J 2019, 'Practical stochastic synchronisation of coupled harmonic oscillators subjected to heterogeneous noises and its applications to electrical systems', IET Control Theory & Applications, vol. 13, no. 1, pp. 96-105.
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This study focuses on the practical stochastic synchronisation of coupled harmonic oscillators subjected to heterogeneous noises, where the dissipative and restorative couplings are no longer required in the connected network topologies. By employing the variational approach in combination with Lyapunov‐like analysis, some simple yet generic practical synchronisation criteria are established in the sense of probability distribution and of mean square for coupled harmonic oscillator with directed network topology. Three main issues on stochastic synchronisation, including practical distribution synchronisation, stochastic distribution synchronisation, and practical mean square synchronisation, as well as their differences and relationships are fully addressed. The developed practical synchronisation criteria are then applied to a representative model of electrical systems which are composed of LC oscillators with linear time‐invariant (LTI) resistors and inductors. Finally, numerical simulations are provided to show the effectiveness of the developed methods.
Wang, G, Zhang, G, Choi, K-S & Lu, J 2019, 'Deep Additive Least Squares Support Vector Machines for Classification With Model Transfer', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 7, pp. 1527-1540.
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© 2013 IEEE. The additive kernel least squares support vector machine (AK-LS-SVM) has been well used in classification tasks due to its inherent advantages. For example, additive kernels work extremely well for some specific tasks, such as computer vision classification, medical research, and some specialized scenarios. Moreover, the analytical solution using AK-LS-SVM can formulate leave-one-out cross-validation error estimates in a closed form for parameter tuning, which drastically reduces the computational cost and guarantee the generalization performance especially on small and medium datasets. However, AK-LS-SVM still faces two main challenges: 1) improving the classification performance of AK-LS-SVM and 2) saving time when performing a grid search for model selection. Inspired by the stacked generalization principle and the transfer learning mechanism, a layer-by-layer combination of AK-LS-SVM classifiers embedded with transfer learning is proposed in this paper. This new classifier is called deep transfer additive kernel least square support vector machine (DTA-LS-SVM) which overcomes these two challenges. Also, considering that imbalanced datasets are involved in many real-world scenarios, especially for medical data analysis, the deep-transfer element is extended to compensate for this imbalance, thus leading to the development of another new classifier iDTA-LS-SVM. In the hierarchical structure of both DTA-LS-SVM and iDTA-LS-SVM, each layer has an AK-LS-SVM and the predictions from the previous layer act as an additional input feature for the current layer. Importantly, transfer learning is also embedded to guarantee generalization consistency between the adjacent layers. Moreover, both iDTA-LS-SVM and DTA-LS-SVM can ensure the minimal leave-one-out error by using the proposed fast leave-one-out cross validation strategy on the training set in each layer. We compared the proposed classifiers DTA-LS-SVM and iDTA-LS-SVM with the traditional LS-...
Wang, G-G, Gandomi, AH, Alavi, AH & Gong, D 2019, 'A comprehensive review of krill herd algorithm: variants, hybrids and applications', Artificial Intelligence Review, vol. 51, no. 1, pp. 119-148.
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© 2017, Springer Science+Business Media Dordrecht. Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.
Wang, H & Wen, S 2019, 'Existence and Exponential Stability of Solutions for Quaternion-Valued Delayed Hopfield Neural Networks by $\xi$ -Norms', IEEE Access, vol. 7, pp. 184509-184517.
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© 2013 IEEE. Recently, with the development of quaternion applications, quaternion-valued neural networks (QVNNs) have been presented and studied by more and more scholars. In this paper, the existence, uniqueness and exponential stability criteria of solutions for the quaternion-valued delayed Hopfield neural networks (QVDHNNs) are mainly investigated by means of the definitions of ξ-norms. In order to construct a ξ-norm, QVDHNNs system are decomposed into four real-number systems according to Hamilton rules. Then, taking advantage of ξ-norms, inequality technique and Cauchy's test for convergence, time-invariant delays and time-varying delays are considered successively to derive ξ-exponential type sufficient conditions. Based on these, several corollaries about the existence, uniqueness and exponential stability of solutions are obtained. Finally, two numerical examples with time-invariant delays and time-varying delays are given respectively. Their simulated images illustrate the effectiveness of the main theoretical results.
Wang, H, Li, Y, Zhang, G & Wang, J 2019, 'Effect of temperature on rheological properties of lithium-based magnetorheological grease', Smart Materials and Structures, vol. 28, no. 3, pp. 035002-035002.
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© 2019 IOP Publishing Ltd. This paper investigates the impact of temperature on the rheological properties of magnetorheological (MR) grease containing carbonyl iron suspended in lithium-based grease Lithium-based MR grease with 70% weight fraction of carbonyl iron is firstly prepared by mechanical mixing. The apparent viscosity and shear stress as a function of shear rate under different temperatures and magnetic field strengths are measured and discussed. It is found that the influence of temperature on apparent viscosity reduces with the increase of magnetic field strength. The dynamic properties of MR grease are obtained under oscillatory shear test. The influences of strain amplitude, driving frequency and magnetic field on the dynamic properties of MR grease at different temperature are discussed. The results demonstrate that the enhancement of temperature leads to the increase of storage modulus and the reduction of the loss factor. Microstructural variation of grease matrix at different temperature is proposed as an explanation of the rheological changes of MR grease.
Wang, J, Song, J, Zhao, L, Huang, S & Xiong, R 2019, 'A submap joining algorithm for 3D reconstruction using an RGB-D camera based on point and plane features', Robotics and Autonomous Systems, vol. 118, pp. 93-111.
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© 2019 Elsevier B.V. In standard point-based methods, the depth measurements of the point features suffer from noise, which will lead to incorrect global structure of the environment. This paper presents a submap joining based SLAM with an RGB-D camera by introducing planes as well as points as features.This work is consisted of two steps: submap building and submap joining. Several adjacent keyframes, with the corresponding small patches, visual feature points, and planes observed from these keyframes, are used to build a submap. We fuse the submaps into a global map in a sequential fashion, such that, the global structure is recovered gradually through plane feature associations and optimization. We also show that the proposed algorithm can handle plane association problem incrementally in submap level, as the plane covariance can be obtained in each submap. The use of submap significantly reduces the computational cost during the optimization process, while keeping all information about planes. The proposed method is validated using both publicly available RGB-D benchmarks and datasets collected by authors. The algorithm can produce accurate trajectories and high quality 3D models on these challenging datasets, which are difficult for existing RGB-D SLAM or SFM algorithms.
Wang, J, Tavakoli, J & Tang, Y 2019, 'Bacterial cellulose production, properties and applications with different culture methods – A review', Carbohydrate Polymers, vol. 219, pp. 63-76.
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© 2019 Elsevier Ltd Bacterial cellulose (BC) is an organic compound produced by certain types of bacteria. In natural habitats, the majority of bacteria synthesize extracellular polysaccharides, such as cellulose, which form protective envelopes around the cells. Many methods are currently being investigated to enhance cellulose growth. The various celluloses produced by different bacteria possess different morphologies, structures, properties, and applications. However, the literature lacks a comprehensive review of the different methods of BC production, which are critical to BC properties and their final applications. The aims of this review are to provide an overview of the production of BC from different culture methods, to analyze the characteristics of particular BC productions, to indicate existing problems associated with different methods, and to choose suitable culture approaches for BC applications in different fields. The main goals for future studies have also been discussed here.
Wang, J, Zhang, N & Lu, H 2019, 'A novel system based on neural networks with linear combination framework for wind speed forecasting', Energy Conversion and Management, vol. 181, pp. 425-442.
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The absence of accurate and stable prediction of wind speed remains a major obstacle to the rational planning, scheduling, and maintenance of wind power generation. Currently, an extensive body of methods that aim to enhance the accuracy of wind speed prediction have been proposed. However, the majority of previous studies have tended to emphasize the structural improvement of individual forecasting models without considering the validity of data preprocessing. This can result in poor forecasting accuracy due to their failure to fully capture the effective information of the wind speed data. A new approach is proposed in this paper that successfully combines a data preprocessing technique with a linear combination method. Further, a new neural network framework is employed to determine the required combination weights to ensure improved prediction performance, thereby overcoming the drawback of the low accuracy of individual prediction models. Six wind speed datasets from Penglai are regarded as expository cases to analyze the forecasting validity and stability of the developed model. It can be concluded from the experiments that the combined forecasting system outperforms the individual models and the traditional linear combination models with higher accuracy and stronger stability.
Wang, K, Lin, X, Qin, L, Zhang, W & Zhang, Y 2019, 'Vertex Priority Based Butterfly Counting for Large-scale Bipartite Networks.', Proc. VLDB Endow., vol. 12, no. 10, pp. 1139-1152.
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Bipartite networks are of great importance in many real-world applications. In bipartite networks, butterfly (i.e., a complete 2 x 2 biclique) is the smallest non-trivial cohesive structure and plays a key role. In this paper, we study the problem of efficient counting the number of butterflies in bipartite networks. The most advanced techniques are based on enumerating wedges which is the dominant cost of counting butterflies. Nevertheless, the existing algorithms cannot efficiently handle large-scale bipartite networks. This becomes a bottleneck in large-scale applications. In this paper, instead of the existing layer-priority-based techniques, we propose a vertex-priority-based paradigm BFC-VP to enumerate much fewer wedges; this leads to a significant improvement of the time complexity of the state-of-the-art algorithms. In addition, we present cache-aware strategies to further improve the time efficiency while theoretically retaining the time complexity of BFC-VP. Moreover, we also show that our proposed techniques can work efficiently in external and parallel contexts. Our extensive empirical studies demonstrate that the proposed techniques can speed up the state-of-the-art techniques by up to two orders of magnitude for the real datasets.
Wang, M, Xu, C, Chen, X, Hao, H, Zhong, L & Yu, S 2019, 'Differential Privacy Oriented Distributed Online Learning for Mobile Social Video Prefetching', IEEE Transactions on Multimedia, vol. 21, no. 3, pp. 636-651.
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© 2019 IEEE The ever fast growing mobile social video traffic has motivated the urgent requirement of alleviating backbone pressures while ensuring the user-quality experience. Mobile video prefetching previously caches the future accessed videos at the edge, which has become a promising solution for traffic offloading and delay reduction. However, providing high performance prefetching still remains problematic in the presence of high dynamic mobile users' viewing behaviors and consecutive generated video content. Besides, given the fact that making prefetching decision requires viewing history that is sensitive, the increasing privacy issues should also be considered. In this paper, we propose a differential privacy oriented distributed online learning method for mobile social video prefetching (DPDL-SVP). Through a large-scale data analysis based on one of the most popular online social network sites, WeiBo.cn, we reveal that users' viewing behaviors have strong a relation with video preference, content popularity, and social interactions. We then formulate the prefetching problem as an online convex optimization based on these three factors. Furthermore, the problem is divided into two subproblems, and we implement a distributed algorithm separately to solve them with differential privacy. The performance bound of the proposed online algorithms is also theoretically proved. We conduct a series simulation based on real viewing traces to evaluate the performance of DPDL-SVP. Evaluation results show how our proposed algorithms achieve superior performance in terms of the prediction accuracy, delay reduction, and scalability.
Wang, N, Gao, C, Ding, C, Jia, H-Z, Sui, G-R & Gao, X-M 2019, 'A Thermal Management System to Reuse Thermal Waste Released by High-Power Light-Emitting Diodes', IEEE Transactions on Electron Devices, vol. 66, no. 11, pp. 4790-4797.
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© 1963-2012 IEEE. In this article, a comprehensive and efficient thermal management system is proposed to harvest and reuse the thermal waste of high-power light-emitting diodes (HP-LEDs) for the first time. Besides a conventional cooling system, including a thermoelectric (TE) cooler (TEC), a heatsink, and a fan, the proposed thermal management system also employs a TE generator (TEG), a temperature sensor, a voltage boost converter, and a microcontroller for thermal waste recycling. In this system, some of the thermal waste released by the HP-LED is harvested by the TEG and converted into electrical energy. With the help of a voltage boost converter, the harvested electrical power is used to power a temperature sensor for monitoring the surface temperature of the HP-LED. The entire system is regulated by the microcontroller. The system is elaborately established, tested, and the results are discussed. The experimental results show that the proposed system has an output electrical power of approximately 696.5μW , which is used to power a temperature sensor as a demonstration. The sensor works well, and the discrepancy of the surface temperature of the HP-LED measured by the sensor and by a thermometer is less than 5.38%, which validates the proposed thermal management system.
Wang, Q, Gong, Y, Liu, S, Wang, D, Liu, R, Zhou, X, Nghiem, LD & Zhao, Y 2019, 'Free Ammonia Pretreatment To Improve Bio-hydrogen Production from Anaerobic Dark Fermentation of Microalgae', ACS Sustainable Chemistry & Engineering, vol. 7, no. 1, pp. 1642-1647.
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© 2018 American Chemical Society. Microalgae are third generation feedstocks for bio-hydrogen production to achieve a low carbon economy. Nevertheless, the bio-hydrogen production from microalgae is generally low. In this study, an innovative free ammonia (FA, i.e., NH 3 ) pretreatment technology was first demonstrated to improve bio-hydrogen production from the secondary effluent cultivated microalgae during the anaerobic dark fermentation experiments. Scanning electron microscopy revealed that FA pretreatment disrupted microalgae surface morphology. The soluble chemical oxygen demand (SCOD) release increased from 0.01 g SCOD/g VS microalgae (VS = volatile solids) to 0.05-0.07 g SCOD/g VS microalgae after FA pretreatment of 240-530 mg NH 3 -N/L for 1 day, indicating the enhanced microalgae solubilization. Dark fermentation bio-hydrogen potential experiments showed that bio-hydrogen production from microalgae was substantially improved following FA pretreatment of 240-530 mg NH 3 -N/L. The bio-hydrogen production potential and maximum bio-hydrogen production rate increased from 18.2 L H 2 /kg VS microalgae and 2.5 L H 2 /kg VS microalgae/d to 19.9-22.1 L H 2 /kg VS microalgae and 3.1-3.8 L H 2 /kg VS microalgae/d, respectively, after FA pretreatment of 240-530 mg NH 3 -N/L was implemented on the microalgae for 1 day. This FA technology follows a circular economic model because the required FA is from the FA rich dark fermentation liquid, which is a wastewater treatment waste.
Wang, Q, Liu, J & Ying, M 2019, 'Equivalence Checking of Quantum Finite-State Machines.', CoRR, vol. abs/1901.02173, pp. 1-21.
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In this paper, we introduce the model of quantum Mealy machines and study the equivalence checking and minimisation problems of them. Two efficient algorithms are developed for checking equivalence of two states in the same machine and for checking equivalence of two machines. As an application, they are used in equivalence checking of quantum circuits. Moreover, the minimisation problem is proved to be in PSPACE.
Wang, Q, Sun, J, Liu, S, Gao, L, Zhou, X, Wang, D, Song, K & Nghiem, LD 2019, 'Free ammonia pretreatment improves anaerobic methane generation from algae', Water Research, vol. 162, pp. 269-275.
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© 2019 Elsevier Ltd Anaerobic methane generation from algae is hindered by the slow and poor algae biodegradability. A novel free ammonia (NH3 i.e. FA) pretreatment technology was proposed in this work to enhance anaerobic methane generation from algae cultivated using a real secondary effluent. The algae solubilisation was 0.05–0.06 g SCOD/g TCOD (SCOD: soluble chemical oxygen demand; TCOD: total chemical oxygen demand) following FA pretreatment of 240–530 mg NH3–N/L for 24 h, whereas the solubilisation was only 0.01 g SCOD/g TCOD for the untreated algae. This indicates that FA pretreatment at 240–530 mg NH3–N/L could substantially enhance algae solubilisation. Biochemical methane potential tests revealed that FA pretreatment on algae at 240–530 mg NH3–N/L is able to significantly enhance anaerobic methane generation. The hydrolysis rate (k) and biochemical methane potential (P0) of algae increased from 0.21 d−1 and 132 L CH4/kg TCOD to 0.33–0.50 d−1 and 140–154 L CH4/kg TCOD, respectively, after the algae was pretreated by FA at 240–530 mg NH3–N/L. Further analysis indicated that FA pretreatment improved k of both quickly and slowly biodegradable substrates, and also increased P0 of the slowly biodegradable substrate although it negatively affected P0 of the quickly biodegradable substrate. This FA technology is a closed-loop technology.
Wang, Q, Wu, D, Tin-Loi, F & Gao, W 2019, 'Machine learning aided stochastic structural free vibration analysis for functionally graded bar-type structures', Thin-Walled Structures, vol. 144, pp. 106315-106315.
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© 2019 Elsevier Ltd This paper presents a machine learning aided stochastic free vibration analysis for functionally graded (FG) bar-type structures through finite element method (FEM). The considered system uncertainties including the constituent material properties, the dimensions of structural members, and the degree of the gradation of the FGM are incorporated. A novel kernel-based machine learning technique, namely the extended support vector regression (X-SVR), is presented to estimate the governing relationship between the uncertain system parameters and the structural natural frequencies. Subsequently, by applying the Monte-Carlo Simulation (MCS) through the established regression model, various types of statistical characteristics (i.e., mean, standard deviation, probability density function or PDF, and cumulative distribution function or CDF) of structural natural frequencies can be effectively established. Four numerical examples including test functions and practically stimulated engineering structures are thoroughly investigated herein to demonstrate the accuracy, applicability, and computational efficiency of the proposed approach.
Wang, R, Hao, Q, Feng, J, Wang, G-C, Ding, H, Chen, D & Ni, B 2019, 'Enhanced separation of photogenerated charge carriers and catalytic properties of ZnO-MnO2 composites by microwave and photothermal effect', Journal of Alloys and Compounds, vol. 786, pp. 418-427.
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© 2019 Elsevier B.V. To improve the solar energy utilization and photodegradation efficiency of ZnO and α-MnO2, ZnO/MnO2 composite materials were prepared by a facile method. The materials are characterized by XRD, Raman spectra, X-ray photoelectron spectroscopy, scanning electronic microscopy, transmission electron microscopy, and UV–vis diffuse reflection spectroscopy. The photocatalytic activity and microwave-assisted photocatalytic activity of the composite are much higher than that of ZnO or α-MnO2. The main active species in the reaction progress were confirmed by electron paramagnetic resonance spectra and trapping experiments. According to the DFT calculation result and photothermal images, the enhanced catalytic activity is attributed to the photothermal and microwave-assisted effect. The addition of α-MnO2 improves the absorption of light and microwave by the composite, which can further heat up the catalysts. As a result, the separation of photogenerated charge carriers is accelerated. Finally, a mechanism for the enhanced catalytic performance of the composite materials was proposed.
Wang, S, Cao, Y, Huang, T & Wen, S 2019, 'Passivity and passification of memristive neural networks with leakage term and time-varying delays', Applied Mathematics and Computation, vol. 361, pp. 294-310.
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This paper investigates passivity and passification for memristive neural networks (MNNs) with both leakage and time-varying delays. MNNs are converted into traditional neural networks (NNs) by nonsmooth analysis, then sufficient conditions are derived to guarantee the passivity based on Lyapunov method. A novel Lyapunov–Krasovskii functional (LKF) is constructed without requiring all the symmetric matrices to be positive definite. The relaxed passivity criteria with less conservativeness or complexity are obtained in the form of linear matrix inequalities (LMIs), which can be verified easily by the LMI toolbox. Then, the passification controller is designed with the relaxed criteria to ensure that MNNs with both leakage and time-varying delays are passive. Finally, two pertinent examples are presented to show the effectiveness of the theoretical results.
Wang, S, Liu, C, Wang, Y, Lei, G & Zhu, J 2019, '6σ Robust Multidisciplinary Design Optimization Method for Permanent Magnet Motors with Soft Magnetic Composite Cores', Diangong Jishu Xuebao/Transactions of China Electrotechnical Society, vol. 34, no. 4, pp. 637-645.
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Soft magnetic composite (SMC) is a new kind of magnetic material, which has been widely used in the design of permanent magnet machines due to its unique electromagnetic characteristic. The cores made by SMC are isotropic magnetically and mechanically with lower eddy current loss, and can be manufactured by molded technology. Therefore, this material is promising for the design of motors with complex structure, such as transverse flux machine and claw pole motor. To improve the application of the motors made by SMC, two main research topics need to be investigated. The first one is the multidisciplinary design optimization, which mainly includes the electromagnetic analysis and thermal analysis. The second one is the robust design optimization, which mainly investigates the manufacturing precision/tolerances in the engineering manufacturing process and their effects on motor's performance. The main aim of this work is to present a Six Sigma (6σ) robust design optimization method for SMC motors under the framework of multidisciplinary design optimization. From the discussion, it can be found that the proposed method can improve the motor's performance while keeping the requirements in term of temperature rise conditions. Compared with traditional deterministic design approach, the new method can improve the reliability of the designed motor significantly, which will benefit the batch production of SMC motors in industry.
Wang, S, Mao, G & Zhang, JA 2019, 'Joint Time-of-Arrival Estimation for Coherent UWB Ranging in Multipath Environment With Multi-User Interference', IEEE Transactions on Signal Processing, vol. 67, no. 14, pp. 3743-3755.
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© 1991-2012 IEEE. Time-of-Arrival (ToA) estimation becomes extremely challenging for ultra-wideband ranging systems in dense multipath environment with multiuser interference. In this paper, we propose a high accuracy joint ToA estimation (JToAE) algorithm, which can provide dominantly better performance than existing techniques. Based on the requirement of time synchronization among base stations, our proposed JToAE algorithm jointly exploits spatial information of each base station and the ToA of each multipath component of each received signal in ToA estimation. Our scheme is insensitive to the selection of the threshold, and does not require any additional information such as channel, noise power, preamble, or synchronization between transmitters and receivers. We also propose how to effectively distinguish the ToA of the first path of the desired user from the interfering signals in multi-user case without generating error propagation. The proposed JToAE is verified by extensive Monto Carlo simulation that is based on IEEE 802.15.4a channel models, and the simulation results indicate that even in low signal-to-noise ratio and multi-user case, our proposed technique can achieve significantly higher ranging accuracy compared to those in the literature in recent decades.
Wang, TQ, Li, H & Huang, X 2019, 'Analysis and Mitigation of Clipping Noise in Layered ACO-OFDM Based Visible Light Communication Systems', IEEE Transactions on Communications, vol. 67, no. 1, pp. 564-577.
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© 1972-2012 IEEE. Due to the limited dynamic range of the off-the-shelf electrical and optical components, deliberate digital clipping (DDC) is widely applied to optical orthogonal frequency division multiplexing (OFDM) based visible light communication systems. In this paper, we present a theoretical characterization of the layered asymmetrically clipped optical OFDM (ACO-OFDM) signals subject to peak clipping. We decouple a clipped L-layer ACO-OFDM symbol to L single-layer ACO-OFDM symbols, each corresponding to a layer, and show that these symbols are subject to symmetrical peak clippings at random levels. Using Bussgang's theorem, the resulting attenuation factors and variances of the additive noise associated with each layer are derived. It is shown that the clipping noise caused by the DDC mainly falls onto the first layer, and its impact is gradually reduced in the subsequent layers. In order to combat the clipping noise, a novel receiver based on decision aided reconstruction is proposed. Simulation results show that the proposed receiver can effectively mitigate the clipping noise, leading to significant improvement of bit error rates over the conventional receiver.
Wang, W, Hoang, DT, Hu, P, Xiong, Z, Niyato, D, Wang, P, Wen, Y & Kim, DI 2019, 'A Survey on Consensus Mechanisms and Mining Strategy Management in Blockchain Networks', IEEE Access, vol. 7, pp. 22328-22370.
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© 2013 IEEE. The past decade has witnessed the rapid evolution in blockchain technologies, which has attracted tremendous interests from both the research communities and industries. The blockchain network was originated from the Internet financial sector as a decentralized, immutable ledger system for transactional data ordering. Nowadays, it is envisioned as a powerful backbone/framework for decentralized data processing and data-driven self-organization in flat, open-access networks. In particular, the plausible characteristics of decentralization, immutability, and self-organization are primarily owing to the unique decentralized consensus mechanisms introduced by blockchain networks. This survey is motivated by the lack of a comprehensive literature review on the development of decentralized consensus mechanisms in blockchain networks. In this paper, we provide a systematic vision of the organization of blockchain networks. By emphasizing the unique characteristics of decentralized consensus in blockchain networks, our in-depth review of the state-of-the-art consensus protocols is focused on both the perspective of distributed consensus system design and the perspective of incentive mechanism design. From a game-theoretic point of view, we also provide a thorough review of the strategy adopted for self-organization by the individual nodes in the blockchain backbone networks. Consequently, we provide a comprehensive survey of the emerging applications of blockchain networks in a broad area of telecommunication. We highlight our special interest in how the consensus mechanisms impact these applications. Finally, we discuss several open issues in the protocol design for blockchain consensus and the related potential research directions.
Wang, W, Wu, C & Liu, Z 2019, 'Compressive behavior of hybrid double-skin tubular columns with ultra-high performance fiber-reinforced concrete (UHPFRC)', Engineering Structures, vol. 180, pp. 419-441.
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© 2018 Elsevier Ltd This study presents the results of an experimental program on the compressive behavior of hybrid fiber-reinforced polymer (FRP)-concrete-steel double-skin tubular columns (DSTCs) with ultra-high performance fiber-reinforced concrete (UHPFRC). In total 40 specimens, including 32 hybrid DSTCs and eight FRP confined solid concrete (FCSC) specimens, were prepared and tested under axial compression. In addition to hybrid UHPFRC DSTCs, hybrid DSTCs with ultra-high performance concrete without steel fiber addition (UHPC), high-strength concrete (HSC), and normal-strength concrete (NSC) were also tested. The investigated parameters included the FRP tube thickness, steel tube thickness, void ratio, steel fiber addition, concrete type, UHPFRC-filling inside the steel tube, and the column type. The test results indicate that the hybrid UHPFRC DSTCs can exhibit highly ductile behavior when a thick FRP tube is used. However, due to the ultra-high strength and the dense microstructure of UHPFRC, the hybrid UHPFRC DSTCs are likely to exhibit more brittle behavior than the hybrid DSTCs with NSC and HSC. Even though a high confinement level is provided, sudden stress reduction or stress fluctuations can be observed for the UHPFRC in hybrid DSTCs. The influences of FRP tube thickness, void ratio, steel fiber addition, and UHPFRC-filling inside the steel tube on the compressive behavior of the hybrid UHPFRC DSTCs are significant, while the influence of steel tube thickness is insignificant. Moreover, when compared to the FCSC specimens, the presence of an inner void is beneficial for the compressive behavior of UHPFRC in the hybrid DSTCs, especially when a thick FRP tube is used. Furthermore, the performance of existing stress-strain model to predict the compressive behavior of UHPFRC in the hybrid DSTCs is investigated.
Wang, W, Wu, C, Li, J, Liu, Z & Lv, Y 2019, 'Behavior of ultra-high performance fiber-reinforced concrete (UHPFRC) filled steel tubular members under lateral impact loading', International Journal of Impact Engineering, vol. 132, pp. 103314-103314.
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© 2019 This study investigates the behavior of ultra-high performance fiber-reinforced concrete (UHPFRC) filled steel tubular (UHPFRCFST) members under lateral impact loading. A total of five specimens were prepared and tested under lateral impact loading. All specimens were 168 mm in diameter and 2000 mm in length. In addition to UHPFRCFST members, normal strength concrete (NSC) filled steel tubular (NSCFST) members were also tested for comparison purpose. Other investigated parameters in this study include the impact energy and the presence of an inner void. The test results show that as compared to the NSCFST members, the UHPFRCFST members exhibit higher lateral impact resistance with higher peak and plateau impact forces, smaller deflection, and less local indentation. With the increase of impact energy, the peak impact force, the impact duration, and the deflection of the UHPFRCFST members are increased, while the plateau impact force is almost kept constant. Moreover, the presence of an inner void does not deteriorate the lateral impact resistance of the UHPFRCFST members. Finite element (FE) model was then developed and validated by the test results in this study. Afterwards, full-range analysis was performed to investigate the damage evolution, sectional bending moment distribution, and the interactions between the steel tube and the concrete during the impact process. Finally, detailed parametric analyses were carried out to investigate the influences of different parameters on the lateral impact behavior of UHPFRCFST members.
Wang, W, Wu, C, Li, J, Liu, Z & Zhi, X 2019, 'Lateral impact behavior of double-skin steel tubular (DST) members with ultra-high performance fiber-reinforced concrete (UHPFRC)', Thin-Walled Structures, vol. 144, pp. 106351-106351.
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© 2019 Elsevier Ltd This study investigates the lateral impact behavior of double-skin steel tubular (DST) members with ultra-high performance fiber-reinforced concrete (UHPFRC). A total of six specimens were prepared and tested under lateral impact loading. In addition to UHPFRC filled DST members, normal strength concrete (NSC) filled DST member was also tested for comparison. Other investigated parameters in this study include the impact energy, the outer steel tube thickness, the inner steel tube thickness, and the presence of axial force. The test results demonstrate that the UHPFRC filled DST members exhibit significantly higher lateral impact resistance capacity than the NSC filled DST member. The impact energy and the outer steel tube thickness significantly affect the lateral impact behavior of UHPFRC filled DST members, while the influence of inner steel tube thickness is insignificant. With the applied axial force in this study, the influence of axial force is also insignificant. Afterwards, numerical model was developed and validated by the present test results. Based on the validated numerical model, the mid-span bending moment distributions and the stress wave propagations were investigated. Finally, parametric analyses were carried out to investigate the influences of different parameters on the lateral impact behavior of UHPFRC filled DST members.
Wang, W, Zhang, G & Lu, J 2019, 'Hierarchy Visualization for Group Recommender Systems', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 6, pp. 1152-1163.
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© 2013 IEEE. Most recommender systems (RSs), especially group RSs, focus on methods and accuracy but lack explanations, hence users find them difficult to trust. We present a hierarchy visualization method for group recommender (HVGR) systems to provide visual presentation and intuitive explanation. We first use a hierarchy graph to organize all the entities using nodes (e.g., neighbor nodes and recommendation nodes) and illustrate the overall recommender process using edges. Second, a pie chart is attached to every entity node in which each slice represents a single member, which makes it easy to track the influence of each member on a specific entity. HVGR can be extended to adapt different pseudouser modeling methods by resizing group member nodes and pseudouser nodes. It can also be easily extended to individual RSs through the use of a single member group. An implementation has been developed and feasibility is tested using a real data set.
Wang, X, Liu, Y, Lu, J, Xiong, F & Zhang, G 2019, 'TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators', Future Generation Computer Systems, vol. 94, pp. 224-236.
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Wang, X, Qin, L, Lin, X, Zhang, Y & Chang, L 2019, 'Leveraging set relations in exact and dynamic set similarity join.', VLDB J., vol. 28, no. 2, pp. 267-292.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Set similarity join, which finds all the similar set pairs from two collections of sets, is a fundamental problem with a wide range of applications. Existing works study both exact set similarity join and approximate similarity join problems. In this paper, we focus on the exact set similarity join problem. The existing solutions for exact set similarity join follow a filtering-verification framework, which generates a list of candidate pairs through scanning indexes in the filtering phase and reports those similar pairs in the verification phase. Though much research has been conducted on this problem, set relations have not been well studied on improving the algorithm efficiency through computational cost sharing. Therefore, in this paper, we explore the set relations in different levels to reduce the overall computational cost. First, it has been shown that most of the computational time is spent on the filtering phase, which can be quadratic to the number of sets in the worst case for the existing solutions. Thus, we explore index-level set relations to reduce the filtering cost while keeping the same filtering power. We achieve this by grouping related sets into blocks in the index and skipping useless index probes in joins. Second, we explore answer-level set relations to further improve the algorithm based on the intuition that if two sets are similar, their answers may have a large overlap. We derive an algorithm which incrementally generates the answer of one set from an already computed answer of another similar set rather than compute the answer from scratch to reduce the computational cost. In addition, considering that in real applications, the data are usually updated dynamically, we extend our techniques and design efficient algorithms to incrementally update the join result when any element in the sets is updated. Finally, we conduct extensive performance studies using 21 rea...
Wang, X, Song, B, Ni, W, Liu, RP, Guo, YJ, Niu, X & Zheng, K 2019, 'Group-Based Susceptible-Infectious-Susceptible Model in Large-Scale Directed Networks', Security and Communication Networks, vol. 2019, pp. 1-9.
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Epidemic models trade the modeling accuracy for complexity reduction. This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy. A group-based continuous-time Markov SIS model is developed. The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model. By adopting the mean-field approximation on the groups of nodes and links, the model complexity is significantly reduced as compared with previous topological epidemic models. An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix. The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale. Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model.
Wang, X, Xu, X, Sheng, QZ, Wang, Z & Yao, L 2019, 'Novel Artificial Bee Colony Algorithms for QoS-Aware Service Selection', IEEE Transactions on Services Computing, vol. 12, no. 2, pp. 247-261.
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Wang, X, Yu, G, Zha, X, Ni, W, Liu, RP, Guo, YJ, Zheng, K & Niu, X 2019, 'Capacity of blockchain based Internet-of-Things: Testbed and analysis', Internet of Things, vol. 8, pp. 100109-100109.
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Wang, X, Zha, X, Ni, W, Liu, RP, Guo, YJ, Niu, X & Zheng, K 2019, 'Game Theoretic Suppression of Forged Messages in Online Social Networks', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-11.
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Wang, X, Zha, X, Ni, W, Liu, RP, Guo, YJ, Niu, X & Zheng, K 2019, 'Survey on blockchain for Internet of Things', Computer Communications, vol. 136, pp. 10-29.
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© 2019 Elsevier B.V. The Internet of Things (IoT) is poised to transform human life and unleash enormous economic benefit. However, inadequate data security and trust of current IoT are seriously limiting its adoption. Blockchain, a distributed and tamper-resistant ledger, maintains consistent records of data at different locations, and has the potential to address the data security concern in IoT networks. While providing data security to the IoT, Blockchain also encounters a number of critical challenges inherent in the IoT, such as a huge number of IoT devices, non-homogeneous network structure, limited computing power, low communication bandwidth, and error-prone radio links. This paper presents a comprehensive survey on existing Blockchain technologies with an emphasis on the IoT applications. The Blockchain technologies which can potentially address the critical challenges arising from the IoT and hence suit the IoT applications are identified with potential adaptations and enhancements elaborated on the Blockchain consensus protocols and data structures. Future research directions are collated for effective integration of Blockchain into the IoT networks.
Wang, Y, Chou, J, Sun, Y, Wen, S, Vasilescu, S & Zhang, H 2019, 'Supramolecular-based nanofibers', Materials Science and Engineering: C, vol. 101, pp. 650-659.
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© 2019 Elsevier B.V. Supramolecular-based nanofibers, which successfully combine the unique properties of supramolecular interactions with the advantages of nanofibrous structure, are widely used in a variety of biomedical applications such as controlled drug delivery. Compared with traditional polymer nanofibers, supramolecular-based nanofibers can overcome the bottleneck of sensitivity because of the non-covalent binding modes, and therefore match the requirements of rapid and reversible response to the external stimuli. In addition, supramolecular-based nanofibers can achieve extra controllable and dynamic responsive (e.g. pH, temperature) functions in different environments. In this review, we retrospected and summarized the recent development of supramolecular-based nanofibers, focusing particularly on electrospun supramolecular nanofibers, while also touching on the advances of directly self-assembled supramolecular nanofibers without the use of electrospinning. Furthermore, we discussed the potential biomedical applications of supramolecular nanofibers. Finally, this review was concluded by elaborating upon individual reflection on the current situation, forecasting the future trend of this promising material.
Wang, Y, Fan, S, Liao, F, Zheng, X, Huang, Z, Wang, Y & Han, X 2019, 'In situ formation and superior lithium storage properties of tentacle-like ZnO@NC@CNTs composites', Nanoscale Advances, vol. 1, no. 3, pp. 1200-1206.
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A novel structure of double carbon coated tentacle-like ZnO composite has been synthesized, which delivers remarkable Li+ storage properties.
Wang, Y, Feng, C, Chen, L, Yin, H, Guo, C & Chu, Y 2019, 'User identity linkage across social networks via linked heterogeneous network embedding', World Wide Web, vol. 22, no. 6, pp. 2611-2632.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature User identity linkage has important implications in many cross-network applications, such as user profile modeling, recommendation and link prediction across social networks. To discover accurate cross-network user correspondences, it is a critical prerequisite to find effective user representations. While structural and content information describe users from different perspectives, there is a correlation between the two aspects of information. For example, a user who follows a celebrity tends to post content about the celebrity as well. Therefore, the projections of structural and content information of a user should be as close to each other as possible, which inspires us to fuse the two aspects of information in a unified space. However, owing to the information heterogeneity, most existing methods extract features from content and structural information respectively, instead of describing them in a unified way. In this paper, we propose a Linked Heterogeneous Network Embedding model (LHNE) to learn the comprehensive representations of users by collectively leveraging structural and content information in a unified framework. We first model the topics of user interests from content information to filter out noise. Next, cross-network structural and content information are embedded into a unified space by jointly capturing the friend-based and interest-based user co-occurrence in intra-network and inter-network, respectively. Meanwhile, LHNE learns user transfer and topic transfer for enhancing information exchange across networks. Empirical results show LHNE outperforms the state-of-the-art methods on both real social network and synthetic datasets and can work well even with little or no structural information.
Wang, Y, Sun, Y, Su, S, Tian, Z, Li, M, Qiu, J & Wang, X 2019, 'Location Privacy in Device-Dependent Location-Based Services: Challenges and Solution', Computers, Materials & Continua, vol. 59, no. 3, pp. 983-993.
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Copyright c 2019 Tech Science Press With the evolution of location-based services (LBS), a new type of LBS has already gain a lot of attention and implementation, we name this kind of LBS as the Device-Dependent LBS (DLBS). In DLBS, the service provider (SP) will not only send the information according to the user’s location, more significant, he also provides a service device which will be carried by the user. DLBS has been successfully practised in some of the large cities around the world, for example, the shared bicycle in Beijing and London. In this paper, we, for the first time, blow the whistle of the new location privacy challenges caused by DLBS, since the service device is enabled to perform the localization without the permission of the user. To conquer these threats, we design a service architecture along with a credit system between DLBS provider and the user. The credit system tie together the DLBS device usability with the curious behaviour upon user’s location privacy, DLBS provider has to sacrifice their revenue in order to gain extra location information of their device. We make the simulation of our proposed scheme and the result convince its effectiveness.
Wang, Y, Wang, D, Chen, F, Yang, Q, Ni, B-J, Wang, Q, Sun, J, Li, X & Liu, Y 2019, 'Nitrate addition improves hydrogen production from acidic fermentation of waste activated sludge', Chemosphere, vol. 235, pp. 814-824.
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© 2019 Elsevier Ltd In this work, a low-cost alternative method (i.e., adding nitrate into WAS) to significantly enhance hydrogen production was reported. Experimental results showed that with an increase of nitrate addition from 0 to 362 mg/L, the maximal hydrogen production from acidic (pH 5.5) fermentation of WAS obviously increased from 12.6 ± 0.5 to 19.3 ± 0.9 mL per gram volatile suspended solids (VSS). The mechanism investigations illustrated more substrates were provided for subsequent hydrogen production. Although the nitrate added inhibited all the biological processes, its inhibition to the hydrogen consumption processes was much severer than that to the hydrogen production processes. The enzyme analyses on the long-term semi-continuous fermenters showed that the nitrate addition slightly inhibited the relative activities of protease, butyrate kinase, acetate kinase, CoA-transferase, and [FeFe] hydrogenase but largely suppressed the relative activities of coenzyme F420, carbon monoxide dehydrogenase, and adenylyl sulfate reductase.
Wang, Y, Yin, Q, Dong, D, Qi, B, Petersen, IR, Hou, Z, Yonezawa, H & Xiang, G-Y 2019, 'Quantum gate identification: Error analysis, numerical results and optical experiment', Automatica, vol. 101, pp. 269-279.
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Wang, Z, Chen, C, Li, H-X, Dong, D & Tarn, T-J 2019, 'Incremental Reinforcement Learning With Prioritized Sweeping for Dynamic Environments', IEEE/ASME Transactions on Mechatronics, vol. 24, no. 2, pp. 621-632.
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Wang, Z, Chen, X-M, Ni, B-J, Tang, Y-N & Zhao, H-P 2019, 'Model-based assessment of chromate reduction and nitrate effect in a methane-based membrane biofilm reactor', Water Research X, vol. 5, pp. 100037-100037.
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© 2019 Zhejiang University Chromate contamination can pose a high risk to both the environment and public health. Previous studies have shown that CH4-based membrane biofilm reactor (MBfR) is a promising method for chromate removal. In this study, we developed a multispecies biofilm model to study chromate reduction and its interaction with nitrate reduction in a CH4-based MBfR. The model-simulated results were consistent with the experimental data reported in the literature. The model showed that the presence of nitrate in the influent promoted the growth of heterotrophs, while suppressing methanotrophs and chromate reducers. Moreover, it indicated that a biofilm thickness of 150 μm and an influent dissolved oxygen concentration of 0.5 mg O2/L could improve the reactor performance by increasing the chromate removal efficiency under the simulated conditions.
Wang, Z, Xu, M, Ye, N, Wang, R & Huang, H 2019, 'RF-Focus', Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, no. 1, pp. 1-30.
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Capturing RFID tags in the region of interest (ROI) is challenging. Many issues, such as multipath interference, frequency-dependent hardware characteristics and phase periodicity, make RF phase difficult to accurately indicate the tag-to-antenna distance for RFID tag localization. In this paper, we propose a comprehensive solution, called RF-Focus, which fuses RFID and computer vision (CV) techniques to recognize and locate moving RFID-tagged objects within ROI. Firstly, we build a multipath propagation model and propose a dual-antenna solution to minimize the impact of multipath interference on RF phase. Secondly, by extending the multipath model, we estimate phase shifts due to hardware characteristics at different operating frequencies. Thirdly, to minimize the tag position uncertainty due to RF phase periodicity, we leverage CV to extract image regions of being likely to contain ROI RFID-tagged objects, and then associate them with the processed RF phase after the removal of the phase shifts due to multipath interference and hardware characteristics for recognition and localization. Our experiments demonstrate the effectiveness of multipath modelling and hardware-related phase shift estimation. When five RFID-tagged objects are moving in the ROI, RF-Focus achieves the average recognition accuracy of 91.67% and localization accuracy of 94.26% given a false positive rate of 10%.
Watson, PH, Hewitt, RE, Catchpoole, DR & Grizzle, WE 2019, 'Biobank: What's in a Name?', Biopreservation and Biobanking, vol. 17, no. 3, pp. 204-208.
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Wei, F, Yang, Z-J, Qin, P-Y, Guo, YJ, Li, B & Shi, X-W 2019, 'A Balanced-to-Balanced In-Phase Filtering Power Divider With High Selectivity and Isolation', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 2, pp. 683-694.
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© 1963-2012 IEEE. In this paper, a balanced-to-balanced in-phase filtering power divider (FPD) is proposed, which can realize a two-way equal power division with high selectivity and isolation. The design of the proposed FPD is primarily based on microstrip/slotline transition structures and slotline T-junction. A U-type microstrip feed line integrated with a stepped-impedance slotline resonator is adopted at the input and output ports, which makes the differential-mode (DM) responses independent of the common-mode (CM) ones. Meanwhile, superior DM transmission and CM suppression are achieved intrinsically, thereby simplifying the design procedure significantly. By employing slotline resonators loaded with resistors, the isolation between the two output ports can be improved greatly. In addition, a DM passband with a sharp filtering performance is realized by introducing the microstrip stub-loaded resonators (SLRs). By changing the electrical length of the open stub of the SLR, the fractional bandwidth is controllable. In order to verify the feasibility of the proposed design method, two prototype circuits of the proposed FPDs with different bandwidths are fabricated and measured. Good agreement between the simulation and measurement results is observed.
Wei, J, Li, J & Wu, C 2019, 'An experimental and numerical study of reinforced conventional concrete and ultra-high performance concrete columns under lateral impact loads', Engineering Structures, vol. 201, pp. 109822-109822.
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© 2019 Elsevier Ltd This paper presents an experimental and numerical study on the dynamic behaviour of axially-loaded reinforced conventional concrete (RC) and ultra-high performance concrete (UHPC) columns against low-velocity impact loading. The test specimens were divided into two groups with square and circular cross-section shapes, and each group includes both RC and UHPC columns. The impact scenario was modelled with a drop weight falling freely on the column mid-span. Brittle failure with shear plug formation was observed in RC columns while UHPC columns remained a flexure response with minimal damage under severe impact loads. To further interpret the experimental data, detailed finite element (FE) models were developed for RC and UHPC columns. A Continuous Surface Cap Model (CSCM) which accounts for the triaxial material strength, post peak softening and strain rate effect was adopted for UHPC material. After validating the material and structural model based on the testing data, extensive numerical simulations were performed to predict the UHPC column residual loading capacity after lateral impacts. Impact mass-velocity (M-V) diagrams were derived for the UHPC column damage assessment, and analytical formulae which could be easily applied to generate M-V diagrams were derived based on parametric studies.
Wei, K, Cheng, T, Lu, DD-C, Siwakoti, YP & Zhang, C 2019, 'Multi-Variable Thermal Modeling of Power Devices Considering Mutual Coupling', Applied Sciences, vol. 9, no. 16, pp. 3240-3240.
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In relation to power converter design, power density is increasing while the form factor is decreasing. This trend generally reduces the rate of the cooling process, which increases the mutual thermal coupling among the surrounding power components. Most of the traditional models usually ignore the mutual effects or just focus on the conduction coupling. To deal with these factors, the thermal modeling for a boost converter system has been built to compare the junction temperatures (Tj) and the increments under different working conditions in order to consider the conduction coupling. A multi-variable thermal resistances model is proposed in this paper to incorporate the convection thermal coupling into the mutual thermal effects. The coupling resistances, MOSFET to the diode ( R cp- MD ⇀ ), and the diode to MOSFET ( R cp- DM ⇀ ) have been calculated and the relationships between coupling resistances and their impact factors (separation distances and working currents) have been discussed. New case temperatures (Tc) obtained by calculation and additional measurements at other separation distances serve to verify the efficacy of the proposed model. This model enhances the current thermal models and provides an effective method to calculate the thermal coupling resistances which can be used to estimate the Tj. As the coupling resistances are distance dependent, the model also helps to optimize and fine-tune the placements of components in high-power density converters.
Wei, K, Lu, DD-C, Zhang, C, Siwakoti, YP, Soon, JL & Yao, Q 2019, 'Modeling and Analysis of Thermal Resistances and Thermal Coupling Between Power Devices', IEEE Transactions on Electron Devices, vol. 66, no. 10, pp. 4302-4308.
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© 1963-2012 IEEE. The recent trend in the design of the high-power density power converter generally reduces the rate of the device cooling process. As a result, increased thermal coupling among devices exists. Based on measurements, a thermal coupling resistances network (TCRN) model is proposed in this article. Considering different spacings and current values at a fixed value of case temperature ( T _text c ), the relationships between the case-to-ambient thermal resistance ( R _text ca ) of individual power devices and their thermal coupling resistance ( R _text cp ) to the adjacent device are established. The close correspondence of T _text c from the calculation of the different spacing and experimental results obtained from a thermal coupling measurement platform confirms the established TCRN model and the relationships. Traditional thermal models do not consider the changes of R _text ca and also ignore the effect of thermal coupling among the adjacent devices. Compared with these models, the proposed thermal resistances modeling approach provides a better understanding of the thermal behavior of power devices.
Wei, W, Huang, Q-S, Sun, J, Dai, X & Ni, B-J 2019, 'Revealing the Mechanisms of Polyethylene Microplastics Affecting Anaerobic Digestion of Waste Activated Sludge', Environmental Science & Technology, vol. 53, no. 16, pp. 9604-9613.
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Copyright © 2019 American Chemical Society. Polyethylene (PE) microplastics retained in sewage sludge inevitably enter the anaerobic digestion system. To date, no information has been reported on the mechanisms of PE microplastics affecting anaerobic digestion of waste activated sludge (WAS). This study evaluated the mechanisms using batch and continuous tests. Short exposure to PE microplastics at lower levels (i.e., 10, 30, and 60 particles/g-TS) did not significantly affect the methane production, but higher levels of PE microplastics (i.e., 100 and 200 particles/g TS) significantly (P = 0.006 and 0.0003) decreased methane production by 12.4-27.5%, with a lower methane potential and hydrolysis coefficient. In continuous test over 130 days, feeding WAS with 200 particles PE microplastics/g TS decreased vs destruction by up to 27.3% (P = 2.18 × 10-18) and resulted in a 9.1% (P = 0.002) increase in the volume of digested sludge for disposal. Correspondingly, the microbial community was shifted in the direction against anaerobic digestion. A mechanisms study revealed that the negative effect of PE microplastics was likely attributed to the induction of reactive oxygen species (ROS) rather than the released acetyl tri-n-butyl citrate. The generation of ROS caused a 7.6-15.4% reduction of cell viability, thereby restraining sludge hydrolysis, acidification, and methanogenesis.
Wei, W, Huang, Q-S, Sun, J, Wang, J-Y, Wu, S-L & Ni, B-J 2019, 'Polyvinyl Chloride Microplastics Affect Methane Production from the Anaerobic Digestion of Waste Activated Sludge through Leaching Toxic Bisphenol-A', Environmental Science & Technology, vol. 53, no. 5, pp. 2509-2517.
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© 2019 American Chemical Society. The retention of polyvinyl chloride (PVC) microplastics in sewage sludge during wastewater treatment raises concerns. However, the effects of PVC microplastics on methane production from anaerobic digestion of waste activated sludge (WAS) have never been documented. In this work, the effects of PVC microplastics (1 mm, 10-60 particles/g TS) on anaerobic methane production from WAS were investigated. The presence of 10 particles/g TS of PVC microplastics significantly (P = 0.041) increased methane production by 5.9 ± 0.1%, but higher levels of PVC microplastics (i.e., 20, 40, and 60 particles/g TS) inhibited methane production to 90.6 ± 0.3%, 80.5 ± 0.1%, and 75.8 ± 0.2% of the control, respectively. Model-based analysis indicated that PVC microplastics at >20 particles/g TS decreased both methane potential (B0) and hydrolysis coefficient (k) of WAS. The mechanistic studies showed that bisphenol A (BPA) leaching from PVC microplastics was the primary reason for the decreased methane production, causing significant (P = 0.037, 0.01, 0.004) inhibitory effects on the hydrolysis-acidification process. The long-term effects of PVC microplastics revealed that the microbial community was shifted in the direction against hydrolysis-acidification and methanation. In conclusion, PVC microplastic caused negative effects on WAS anaerobic digestion through leaching the toxic BPA.
Wei, W, Zhang, Y-T, Huang, Q-S & Ni, B-J 2019, 'Polyethylene terephthalate microplastics affect hydrogen production from alkaline anaerobic fermentation of waste activated sludge through altering viability and activity of anaerobic microorganisms', Water Research, vol. 163, pp. 114881-114881.
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© 2019 Alkaline (especially pH 10) anaerobic fermentation of waste activated sludge (WAS) has been reported to be an effective approach for hydrogen production through inhibiting the homoacetogenesis and methanogenesis. However, the potential effect of the widespread microplastics in sludge on the performance of hydrogen production has never been reported. To fill this knowledge gap, the dominant polyethylene terephthalate (PET) microplastics in WAS were selected as the model microplastics to evaluate their influences on hydrogen production during alkaline anaerobic fermentation of WAS as well as the key mechanisms involved. Experimental results demonstrated that hydrogen production from WAS decreased in the presence of PET microplastics (i.e., 10, 30 and 60 particles/g-TS) compared to the control, with the hydrogen yield at 60 particles/g-TS being only 70.7 ± 0.9% of the control. Although the hydrogen consumption (i.e., homoacetogenesis and methanogenesis) was restrained under alkaline (pH 10) condition, PET microplastics inhibited hydrolysis, acidogenesis and acetogenesis in alkaline WAS anaerobic fermentation, leading to the inhibitory effect on hydrogen production. This was further confirmed by the microbial analysis, which clearly showed PET microplastics caused the shift of the microbial community toward the direction against hydrolysis-acidification. Mechanism studies revealed that PET microplastics carried on their negative influence mainly through leaching the toxic di-n-butyl phthalate (DBP). The reactive oxygen species (ROS) and live/dead staining tests indicated that the increased ROS was induced by PET microplastics, causing more cells dead, which further resulted in the decreased production of hydrogen.
Wen, D, Qin, L, Zhang, Y, Chang, L & Lin, X 2019, 'Efficient structural graph clustering: an index-based approach.', VLDB J., vol. 28, no. 3, pp. 377-399.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Graph clustering is a fundamental problem widely applied in many applications. The structural graph clustering (SCAN) method obtains not only clusters but also hubs and outliers. However, the clustering results heavily depend on two parameters, ϵ and μ, while the optimal parameter setting depends on different graph properties and various user requirements. In addition, all existing SCAN solutions need to scan at least the whole graph, even if only a small number of vertices belong to clusters. In this paper, we propose an index-based method for SCAN. Based on our index, we cluster the graph for any ϵ and μ in O(∑ C∈C| EC|) time, where C is the result set of all clusters and | EC| is the number of edges in a specific cluster C. In other words, the time spent on computing structural clustering depends only on the result size, not on the size of the original graph. Our index’s space complexity is O(m), where m is the number of edges in the graph. To handle dynamic graph updates, we propose algorithms and several optimization techniques for maintaining our index. We also design an index for I/O efficient query processing. We conduct extensive experiments to evaluate the performance of all our proposed algorithms on 10 real-world networks, with the largest one containing more than 1 billion edges. The experimental results demonstrate that our approaches significantly outperform existing solutions.
Wen, D, Qin, L, Zhang, Y, Lin, X & Yu, JX 2019, 'I/O Efficient Core Graph Decomposition: Application to Degeneracy Ordering.', IEEE Trans. Knowl. Data Eng., vol. 31, no. 1, pp. 75-90.
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© 1989-2012 IEEE. Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are too big to reside in memory. In this paper, we study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. We propose a semi-external algorithm and an optimized algorithm for I/O efficient core decomposition. To handle dynamic graph updates, we firstly show that our algorithm can be naturally extended to handle edge deletion. Then, we propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost. In addition, based on our core decomposition algorithms, we further propose an I/O efficient semi-external algorithm for degeneracy ordering, which is an important graph problem that is highly related to core decomposition. We also consider how to maintain the degeneracy order. We conduct extensive experiments on 12 real large graphs. Our optimal core decomposition algorithm significantly outperforms the existing I/O efficient algorithm in terms of both processing time and memory consumption. They are very scalable to handle web-scale graphs. As an example, we are the first to handle a web graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory. We also show that our proposed algorithms for degeneracy order computation and maintenance can handle big graphs efficiently with small memory overhead.
Wen, S, Hu, R, Yang, Y, Huang, T, Zeng, Z & Song, Y-D 2019, 'Memristor-Based Echo State Network With Online Least Mean Square', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 9, pp. 1787-1796.
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In this paper, we propose a novel computational architecture of memristor-based echo state network (MESN) with the online least mean square (LMS) algorithm. Newman and Watts small-world network is adopted for the topological structure of MESN network with memristive neural synapses. In the MESN network, the state matrix of the reservoir layer, which is obtained by raising the dimension of input data, is utilized as an input of the LMS algorithm to train the output weight matrix on chip. After certain iterations, the resistance value of memristor is adjusted to a constant. Thus, the final weight output matrix is obtained. To verify the effectiveness of the proposed MESN network, car evaluation and short-term power load forecasting are employed with the effect evaluation of the node number and the connectivity degree of the reservoir layer. The research provides a novel way to design neuromorphic computing systems.
Wen, S, Liu, W, Yang, Y, Huang, T & Zeng, Z 2019, 'Generating Realistic Videos From Keyframes With Concatenated GANs', IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 8, pp. 2337-2348.
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Given two video frames X0 and Xn+1, we aim to generate a series of intermediate frames Y1, Y2, . . ., Yn, such that the resulting video consisting of frames X0, Y1 − Yn, andXn+1 appears realistic to a human watcher. Such video generation has numerous important applications, including video compression, movie production, slow-motion filming, video surveillance, and forensic analysis. Yet, video generation is highly challenging due to the vast search space of possible frames. Previous methods, mostly based on video prediction and/or video interpolation, tend to generate poor-quality videos with severe motion blur. This paper proposes a novel, end-to-end approach to video generation using generative adversarial networks (GANs). In particular, our design involves two concatenated GANs, one capturing motions and the other generating frame details. The loss function is also carefully engineered to include adversarial loss, gradient difference (for motion learning), and normalized product correlation loss (for frame details). Experiments using three video datasets, namely, Google Robotic Push, KTH human actions, and UCF101, demonstrate that the proposed solution generates high-quality, realistic, and sharp videos, whereas all previous solutions output noisy and blurry results.
Wen, S, Wei, H, Yang, Y, Guo, Z, Zeng, Z, Huang, T & Chen, Y 2019, 'Memristive LSTM Network for Sentiment Analysis', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 3, pp. 1-11.
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This paper presents a complete solution for the hardware design of a memristor-based long short-term memory (MLSTM) network. Throughout the design process, we fully consider the external and internal structures of the long short-term memory (LSTM), both of which are efficiently implemented by memristor crossbars. In the specific design of the internal structure, the parameter sharing mechanism is used between the LSTM cells to minimize the hardware design scale. In particular, we designed a circuit that requires only one memristor crossbar for each unit in the LSTM cell. The activation function, including sigmoid and tanh (hyperbolic tangent function), involved in each unit is approximated by a piecewise function, which is designed with the corresponding hardware. To verify the effectiveness of the system we designed, we test it on IMDB and SemEval datasets. Considering the huge impact of the dimensions of the input data on the scale of the hardware design, we use word2vector instead of one-hot encoding for the input data encoding. With the parameter sharing mechanism, the transformed vectors are input in different periods, so only 65 memristive crossbars are needed in the entire system to complete the sentiment analysis of the input text. The experimental results verify the effectiveness of our proposed MLSTM system.
Wen, S, Xiao, S, Yang, Y, Yan, Z, Zeng, Z & Huang, T 2019, 'Adjusting Learning Rate of Memristor-Based Multilayer Neural Networks via Fuzzy Method', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 38, no. 6, pp. 1084-1094.
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© 1982-2012 IEEE. Back propagation (BP) based on stochastic gradient descent is the prevailing method to train multilayer neural networks (MNNs) with hidden layers. However, the existence of the physical separation between memory arrays and arithmetic module makes it inefficient and ineffective to implement BP in conventional digital hardware. Although CMOS may alleviate some problems of the hardware implementation of MNNs, synapses based on CMOS cost too much power and areas in very large scale integrated circuits. As a novel device, memristor shows promises to overcome this shortcoming due to its ability to closely integrate processing and memory. This paper proposes a novel circuit for implementing a synapse based on a memristor and two MOSFET tansistors (p-type and n-type). Compared with a CMOS-only circuit, the proposed one reduced the area consumption by 92%-98%. In addition, we develop a fuzzy method for the adjustment of the learning rates of MNNs, which increases the learning accuracy by 2%-3% compared with a constant learning rate. Meanwhile, the fuzzy adjustment method is robust and insensitive to parameter changes due to the approximate reasoning. Furthermore, the proposed methods can be extended to memristor-based multilayer convolutional neural network for complex tasks. The novel architecture behaves in a human-liking thinking process.
Why, ESK, Ong, HC, Lee, HV, Gan, YY, Chen, W-H & Chong, CT 2019, 'Renewable aviation fuel by advanced hydroprocessing of biomass: Challenges and perspective', Energy Conversion and Management, vol. 199, pp. 112015-112015.
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Wickham, R, Xie, S, Galway, B, Bustamante, H & Nghiem, LD 2019, 'Pilot-scale operation experience of anaerobic Co-digestion for possible full scale implementation', International Biodeterioration & Biodegradation, vol. 142, pp. 137-142.
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© 2019 Anaerobic co-digestion of sewage sludge with four beverage wastes (namely beer, soft drink, fruit juice, and wine)was evaluated using a dedicated pilot research plant. Temporal variation in the sludge's organic content highlighted the importance of using a mono-digestion control reactor for a systematic comparison with co-digestion. Results indicate that chemical oxygen demand (COD)is a better parameter compared to volatile solids (VS)for determining the organic loading rate during co-digestion with beverage waste that contain solubilised organic carbon. In this study, all beverage wastes investigated here (with the exception of wine)were suitable for co-digestion. H2S content in biogas decreased during co-digestion, possibly due to the dilution effect by the additional biogas generated from sulphur-lean organic rich waste. Results from this study show that the organic content in most beverage waste can be readily and completely converted to biogas. At 10% substrate addition (v/v)beer, soft drink and juice addition did not observably affect total COD and VS in the digestate, whilst increasing biomethane production relative to the control by 39, 41 and 64% respectively. Furthermore, the interchanging of co-substrates did not result in any observable impact on digestion performance. Further investigation is recommended to ascertain the low performance of wine waste co-digestion with sewage sludge.
Wicks, M, Millar, GJ & Altaee, A 2019, 'Process simulation of ion exchange desalination treatment of coal seam gas associated water', Journal of Water Process Engineering, vol. 27, pp. 89-98.
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© 2018 Elsevier Ltd The aim of this investigation was to develop an ion exchange process for the remediation of coal seam gas (CSG) associated water to make it suitable for beneficial reuse. The hypothesis was that computational modelling could accelerate the selection of appropriate ion exchange desalination strategies. Hence, we applied AqMB water process engineering software to predict which combination of weak acid cation (WAC), strong acid cation (SAC), weak base anion (WBA) and strong base anion (SBA) resins were most appropriate. Simulation results revealed that both SAC/WBA and SAC/SBA resin combinations were unable to meet water beneficial reuse standards for conductivity (< 950 μS/cm) due to the presence of bicarbonate species (4973 and 1918 μS/cm, respectively). Thus, a degasser unit was necessary to remove the large concentrations (ca. 1328 mg/L) of dissolved carbon dioxide formed due to decomposition of bicarbonate/carbonate species under acidic conditions in the cation resin stages. pH adjustment of effluent from the preferred SBA resin with acid not only did not meet solution conductivity guidelines but also raised the concentration of chloride or sulphate ions to levels, which may be detrimental for crop growth. Addition of a WAC resin allowed production of high quality water (either SAC/SBA/WAC or WAC/SAC/SBA combinations). To comply with sodium adsorption ratio requirements for irrigating soil it was suggested to apply micronized gypsum to the treated water. Economic evaluation suggested the treated water cost was A$1003 (WAC/SAC/SBA) to A$1276 (SAC/SBA/WAC) per ML treated which was comparable to estimated costs for a reverse osmosis desalination system.
Wijayaratna, KP, Cunningham, ML, Regan, MA, Jian, S, Chand, S & Dixit, VV 2019, 'Mobile phone conversation distraction: Understanding differences in impact between simulator and naturalistic driving studies', Accident Analysis & Prevention, vol. 129, pp. 108-118.
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A current issue within the driver distraction community centres around different findings regarding the impact of mobile phone conversation on driving found in driving simulators versus instrumented vehicles employed in real-world naturalistic driving studies (NDSs). This paper compares and contrasts the two types of studies and aims to provide reasons for the differences in findings that have been documented. A comprehensive review of literature and consultations with human factors experts highlighted that simulator studies tend to show degradation in driving performance, suggestive of increased crash risk as a result of mobile phone conversation. Whilst NDSs, at times, present data suggesting that mobile phone conversation distraction actually reduces crash risk. This study identifies that these differences may be attributed to behavioural hypotheses associated with driver self-regulation, arousal from cognitive loading, task displacement and gaze concentration - all of which need to be explicitly tested in future driving studies. Metric estimation and application was also revealed to be polarising results and the subsequent assessment of the crash risk. A common metric applied in this domain is the 'Odds Ratio', particularly prevalent in NDSs. This study presents a detailed investigation into the assumptions and application of the Odds Ratio which revealed the potential for over- and under-estimation of the metric depending on the core data and sampling assumptions. Furthermore, this research presents a comparative analysis of select driving simulator studies and an NDS considering only driving behaviour data as a means to consistently compare the findings of both methodologies. The findings from this investigation implores the need for greater consistency in the application of analysis methods and metrics across both simulator and NDSs. Improvements can yield a more robust platform to systematically compare and interpret data across both approaches, ultimatel...
Winter, M, Cai, Z, Winkler, K, Georgiou, K, Inglis, D, Lavranos, T, Rezaei, M, Warkiani, M & Thierry, B 2019, 'Circulating tumour cell RNA characterisation from colorectal cancer patient blood after inertial microfluidic enrichment', MethodsX, vol. 6, pp. 1512-1520.
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© 2019 The Authors The detection and molecular analysis of circulating tumour cells (CTCs) potentially provides a significant insight to the characterisation of disease, stage of progression and therapeutic options for cancer patients. Following on from the protocol by Warkiani et al. 2016, which describes a method of enriching CTCs from cancer patient blood with inertial microfluidics, we describe a method to measure the CTC RNA expression in the enriched fraction using droplet digital PCR and compare transcript detection with and without RNA pre-amplification. • Inertial microfluidics combined with droplet digital PCR is advantageous as it allows for CTC enrichment and subsequent RNA analysis from patient blood. This allows for patient tumour analysis with increased sensitivity and precision compared to quantitative Real Time PCR and enables the direct quantification of nucleic acids without the need for tumour biopsy. • This method demonstrates an efficient approach providing important insights into the analysis of colorectal cancer patients’ CTCs using a specific gene subset or biomarkers, an approach that may be tailored to tumour type or expanded to larger panels.
Wocker, M, Wolf, P, Lindworsky, A & Deuse, J 2019, 'Opportunistische Instandhaltungsplanung in Flexiblen Fertigungssystemen', ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 114, no. 5, pp. 250-254.
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Due to the complexity of flexible manufacturing systems (FMS), the economic effects of maintenance measures on plants during operation are often not quantifiable and unplannable yield losses occur. In order to consider the boundary conditions of FFS when planning maintenance measures, the opportunistic maintenance is extended by the consideration of the undirected material flow as well as parallel resources.
Wolf, P, Deuse, J & Richter, R 2019, 'Einfluss und Ursachen von Variabilität in der kunden-auftragsspezifischen Produktion', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 114, no. 11, pp. 730-733.
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Kurzfassung Zur Wahrung ihrer Wettbewerbsfähigkeit ist von Unternehmen mit kundenauftragsspezifischer Produktion ein hohes Maß an Flexibilität zum Umgang mit wertschöpfender und nicht wertschöpfender Variabilität erforderlich. Der vorliegende Beitrag definiert 14 Variabilitätskriterien inklusive mathematischer Beschreibungen dieser, die im Folgenden den Input einer auf die Bedürfnisse der kundenauftragsspezifischen Produktion angepassten variabilitätsberücksichtigenden Maschinenbelegungsplanung darstellen.
Woolfrey, J, Lu, W & Liu, D 2019, 'A Control Method for Joint Torque Minimization of Redundant Manipulators Handling Large External Forces', Journal of Intelligent & Robotic Systems, vol. 96, no. 1, pp. 3-16.
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© 2019, The Author(s). In this paper, a control method is developed for minimizing joint torque on a redundant manipulator where an external force acts on the end-effector. Using null space control, the redundant task is designed to minimize the torque required to oppose the external force, and reduce the dynamic torque. Furthermore, the joint motion can be weighted to factor in physical constraints such as joint limits, collision avoidance, etc. Conventional methods for joint torque minimization only consider the internal dynamics of the manipulator. If external forces acting on the end-effector are inadvertently implemented in to these control methods this could lead to joint configurations that amplify the resulting joint torque. The proposed control method is verified through two different case studies. The first case study involves simulation of high-pressure blasting. The second is a simulation of a manipulator lifting and moving a heavy object. The results show that the proposed control method reduces overall joint torque compared to conventional methods. Furthermore, the joint torque is minimized such that there is potential for a manipulator to execute certain tasks beyond its nominal payload capacity.
Woolfrey, J, Lu, W, Vidal-Calleja, T & Liu, D 2019, 'Clarifying clairvoyance: Analysis of forecasting models for near-sinusoidal periodic motion as applied to AUVs in shallow bathymetry', Ocean Engineering, vol. 190, pp. 106385-106385.
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© 2019 Elsevier Ltd This paper shows that Gaussian Process Regression (GPR) with a periodic kernel has better mean prediction accuracy and uncertainty bounds than time series or Fourier series when forecasting motion data of underwater vehicles subject to wave excitation. Many robotic systems, such as autonomous underwater vehicles (AUVs), are required to operate in environments with disturbances and relative motion that make task performance difficult. This motion often exhibits periodic, near-sinusoidal behaviour. By predicting this motion, control strategies can be developed to improve accuracy. Moreover, factoring in uncertainty can aid the robustness of these predictive control methods. Time series and Fourier series have been applied to several predictive control problems in a variety of fields. However, there are contradictory results in performance based on parameters, assessment criteria, and application. This paper seeks to clarify these discrepancies using AUV motion as a case study. GPR is also introduced as a third candidate for prediction based on previous applications to time series forecasting in other fields of science. In addition to assessing mean prediction accuracy, the ability of each model to adequately bound prediction error is also considered as a key performance indicator.
Wöstmann, R, Reckelkamm, T, Deuse, J, Kimberger, J, Temme, F, Schlunder, P & Klinkenberg, R 2019, 'Data-driven process optimization in the beverage industry', Fabriksoftware, vol. 24, no. 3, pp. 21-24.
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Increased pressure on prices and competition is presenting the beverage industry with major challenges in terms of rationalization. Existing Lean and Six Sigma approaches are reaching their limits in biochemical processes with complex multivariate influences. The paper presents an approach for data-driven process optimization in the beverage industry based on machine learning.
Wu, C, Fang, J & Li, Q 2019, 'Multi-material topology optimization for thermal buckling criteria', Computer Methods in Applied Mechanics and Engineering, vol. 346, pp. 1136-1155.
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© 2018 Elsevier B.V. The structures in thermal environment often suffer from severe thermal expansion, potentially leading to buckling failure. This study aims to address this issue by proposing multi-material topology optimization for thermomechanical buckling problems. The density-based model with the rational approximation of material properties (RAMP) is adopted here for parameterization of multiple materials. The sensitivities of thermomechanical compliance and buckling are derived through the adjoint technique. The globally convergent version of the method of moving asymptotes (GCMMA) is employed to solve the non-monotonic topology optimization problem. In this study, two numerical examples are presented to illustrate the effectiveness of the proposed method, in which the total volume of multi-materials is minimized subject to thermoelastic compliance and buckling constraints. The examples exhibit significant difference in the final topologies for mechanical buckling and thermomechanical buckling optimization. The study demonstrates the importance of thermomechanical buckling criteria for the design of structures operating in a temperature-varying environment.
Wu, C, Gao, Y, Fang, J, Lund, E & Li, Q 2019, 'Simultaneous Discrete Topology Optimization of Ply Orientation and Thickness for Carbon Fiber Reinforced Plastic-Laminated Structures', Journal of Mechanical Design, vol. 141, no. 4.
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This study developed a discrete topology optimization procedure for the simultaneous design of ply orientation and thickness for carbon fiber reinforced plastic (CFRP)-laminated structures. A gradient-based discrete material and thickness optimization (DMTO) algorithm was developed by using casting-based explicit parameterization to suppress the intermediate void across the thickness of the laminate. A benchmark problem was first studied to compare the DMTO approach with the sequential three-phase design method using the free size, ply thickness, and stacking sequence of the laminates. Following this, the DMTO approach was applied to a practical design problem featuring a CFRP-laminated engine hood by minimizing overall compliance subject to volume-related and functional constraints under multiple load cases. To verify the optimized design, a prototype of the CFRP engine hood was created for experimental tests. The results showed that the simultaneous discrete topology optimization of ply orientation and thickness was an effective approach for the design of CFRP-laminated structures.
Wu, D, Lin, C-T & Huang, J 2019, 'Active learning for regression using greedy sampling', Information Sciences, vol. 474, pp. 90-105.
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Wu, D, Liu, A, Huang, Y, Huang, Y, Pi, Y & Gao, W 2019, 'Time dependent uncertain free vibration analysis of composite CFST structure with spatially dependent creep effects', Applied Mathematical Modelling, vol. 75, pp. 589-606.
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© 2019 Elsevier Inc. In this study, the time dependent free vibration analysis of composite concrete-filled steel tubular (CFST) arches with various uncertainties is thoroughly investigated within a non-stochastic framework. From the practical inspiration, both uncertain material properties and mercurial creep effect associated with such composite materials are simultaneously incorporated. Unlike traditional non-probabilistic schemes, both spatially independent (i.e., conventional interval models) and dependent (i.e., interval fields) interval system parameters can be comprised within a unified uncertain free vibration analysis framework for CFST arches. For the purpose of achieving a robust framework of the time-dependent uncertain free vibration analysis, a new computational approach, which has been developed within the scheme of the finite element method (FEM), has been proposed for determining the extreme bounds of the natural frequencies of practically motivated CFST arches. Consequently, by successfully solving two eigenvalue problems, the upper and lower bounds of the natural frequencies of such composite structures with various uncertainties can be rigorously secured. The unique advantage of the proposed approach is that it can be effectively integrated within commercial FEM software with preserved sharp bounds on natural frequencies for any interval field discretisation. The competence of the proposed computational analysis framework has been thoroughly demonstrated through investigations on both 2D and3D engineering structures.
Wu, D, Wang, Q, Liu, A, Yu, Y, Zhang, Z & Gao, W 2019, 'Robust free vibration analysis of functionally graded structures with interval uncertainties', Composites Part B: Engineering, vol. 159, pp. 132-145.
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© 2018 Elsevier Ltd In this paper, a robust interval free vibration analysis for 3D functionally graded frame type engineering structure is presented through the finite element method (FEM). Uncertain material properties, which are including the Young's modulus and material density, of the functionally graded material are considered. Unlike the conventional uncertainty quantification through stochastic approach, the uncertain system inputs are modelled by the interval approach. Instead of straining on the precise statistical information of the uncertain parameters, only upper and lower bounds of the uncertain system inputs are required for valid structural safety assessment. By implementing the mathematical programming approach combined with the intrinsic characteristics of the non-deficient engineering structures, the upper and lower bounds of the natural frequencies of 3D functionally graded frame structure can be explicitly formulated by two independent eigen-problems. The sharpness and physical feasibility of the interval natural frequencies of the functionally graded structure can be well preserved. To demonstrate the competence of the proposed method, two numerical examples have been thoroughly investigated. In addition, diverse numerical investigations have been conducted to explore the impacts of uncertain material properties and the power-law index of the functionally graded materials on the overall structural performance.
Wu, K, Ni, W, Su, T, Liu, RP & Guo, YJ 2019, 'Efficient Angle-of-Arrival Estimation of Lens Antenna Arrays for Wireless Information and Power Transfer', IEEE Journal on Selected Areas in Communications, vol. 37, no. 1, pp. 116-130.
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© 1983-2012 IEEE. Antenna design and angle-of-arrival (AoA) estimation are critical to the efficiency of wireless information and power transfer. The AoA estimation is challenging for energy-efficient lens antenna arrays (LAAs), due to discrete sets of fixed discrete Fourier transform (DFT) beams. This paper presents a novel fast and accurate approach for the AoA estimation of LAAs. The key idea is that we prove the two differential outputs of three adjacent lens beams, referred to as 'DFT beam differences (DBDs),' that are the strongest at the two sides of an AoA. They are easy to identify and robust to noises, and their powers are proved to provide an accurate estimate of the AoA. Another important aspect is a new beam synthesis technique which produces different beam widths based on DFT beams and practical 1-bit phase shifts in real time. As a result, the angular region containing the AoA can exponentially narrow down, and the two strongest DBDs can be quickly identified. The proposed approach can operate in coupling with successive interference cancellation to estimate the AoAs of multiple paths. Simulations show that the proposed approach is able to outperform the state of the art by orders of magnitude in terms of accuracy. The power transfer efficiency can be dramatically improved.
Wu, K, Ni, W, Su, T, Liu, RP & Guo, YJ 2019, 'Expeditious Estimation of Angle-of-Arrival for Hybrid Butler Matrix Arrays', IEEE Transactions on Wireless Communications, vol. 18, no. 4, pp. 2170-2185.
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© 2002-2012 IEEE. Arrays of Butler matrices provide a promising front-end design for massive MIMO transceivers with low cost and low complexity. However, this advanced design does not necessarily translate to effective applications, unless the angle-of-arrival (AoA) of signals avails to the Butler matrices. This paper presents an efficient approach to the unprecedented AoA estimation for the arrays of Butler matrices. Specifically, we design a new beam synthesis method to recursively narrow down and increasingly focus on the angular region of interest, and hence achieving robust estimation of the phase offset between Butler matrices. With the phase offset canceled in the received signals, we are able to identify the set of critical Butler beams with the dominating effect on the AoA estimation, and estimate the AoA accordingly with minimum signaling. The mean squared error of the proposed estimation is analyzed in the presence of non-negligible noises, with closed-form lower bounds derived. Validated by simulations, the proposed algorithm is able to indistinguishably approach the lower bounds, and significantly outperforms the state-of-the-art developed for discrete antenna arrays by orders of magnitude in terms of accuracy, especially in low signal-to-noise regimes.
Wu, K, Ni, W, Su, T, Liu, RP & Guo, YJ 2019, 'Exploiting Spatial-Wideband Effect for Fast AoA Estimation at Lens Antenna Array', IEEE Journal of Selected Topics in Signal Processing, vol. 13, no. 5, pp. 902-917.
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© 2007-2012 IEEE. Energy-efficient, highly integrated lens antenna arrays (LAAs) have found widespread applications in wideband millimeter wave or terahertz communications, localization and tracking, and wireless power transfer. Accurate estimation of angle-of-arrival (AoA) is key to those applications, but has been hindered by a spatial-wideband effect in wideband systems. This paper proposes to exploit (rather than circumventing) the spatial-wideband effect to develop a fast and accurate AoA estimation approach for LAAs. Specifically, we unveil new spatial-frequency patterns based on the spatial-wideband effect, and establish one-to-one mappings between the patterns and the strongest discrete Fourier transform (DFT) beam containing the AoA. With the strongest DFT beam identified, we propose a method to estimate the AoA uniquely and accurately, using only a few training symbols. This is achieved by deriving a new one-to-one mapping between the AoA and the set of DFT beams judiciously selected based on the strongest. In the case that an impinging path is uniformly distributed in [0,2π], simulations show that the proposed algorithm is able to reduce the mean squared error of the AoA estimation by as much as 82.1% while reducing the number of required symbols by 93.2$, as compared to existing techniques. The algorithm can also increase the spectral efficiency by 89% when the average SNR is 20 dB at each antenna of the receiver.
Wu, K, Ni, W, Su, T, Liu, RP & Guo, YJ 2019, 'Recent Breakthroughs on Angle-of-Arrival Estimation for Millimeter-Wave High-Speed Railway Communication', IEEE Communications Magazine, vol. 57, no. 9, pp. 57-63.
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© 2019 IEEE. With significantly improved efficiency, largescale hybrid antenna arrays with tens to hundreds of antennas have great potential to support millimeter-wave (mmWave) communication for high-speed railway (HSR) applications. The significant beamforming gains rely on fast and accurate estimation of the angle-of-arrival (AoA), but this can be impeded by the high train speed, the cost/energy oriented design of arrays, and the severe attenuation of mmWave signals. This article reviews these challenges, and discusses the limitations of existing AoA estimation techniques under hybrid antenna array settings. The article further reveals a few recent theoretical breakthroughs that can potentially enable fast and reliable estimation, even based on severely attenuated signals. Under a speed setting of 500 km/h, a performance study is carried out to confirm the significant improvements of estimation accuracy and subsequent beamforming gains as the results of the breakthroughs.
Wu, L, Liu, M, Huo, S, Zang, X, Xu, M, Ni, W, Yang, Z & Yan, Y-M 2019, 'Mold-casting prepared free-standing activated carbon electrodes for capacitive deionization', Carbon, vol. 149, pp. 627-636.
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Wu, L, Sun, Q, Wang, X, Wang, J, Yu, S, Zou, Y, Liu, B & Zhu, Z 2019, 'An Efficient Privacy-Preserving Mutual Authentication Scheme for Secure V2V Communication in Vehicular Ad Hoc Network', IEEE Access, vol. 7, pp. 55050-55063.
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© 2019 IEEE. Recent years have witnessed that the new mobility Intelligent Transportation System is booming, especially the development of Vehicular Ad Hoc Networks (VANETs). It brings convenience and a good experience for drivers. Unfortunately, VANETs are suffering from potential security and privacy issues due to the inherent openness of VANETs. In the past few years, to address security and privacy-preserving problems, many identity-based privacy-preserving authentication schemes have been proposed by researchers. However, we found that these schemes fail to meet the requirements of user privacy protection and are vulnerable to attacks or have high computational complexity. Hence, we focus on enhancing privacy-preserving via authentication and achieving better performance. In this paper, first, we describe the vulnerabilities of the previous scheme. Furthermore, to enhance privacy protection and achieve better performance, we propose an efficient privacy-preserving mutual authentication protocol for secure V2V communication in VANETs. Through security analysis and comparison, we formally demonstrate that our scheme can accomplish security goals under dynamic topographical scenario compared with the previous scheme. Finally, the efficiency of the scheme is showed by performance evaluation. The results of our proposed scheme are computationally efficient compared with the previously proposed privacy-preserving authentication scheme.
Wu, P, Li, H, Merigo, JM & Zhou, L 2019, 'Integer Programming Modeling on Group Decision Making With Incomplete Hesitant Fuzzy Linguistic Preference Relations', IEEE Access, vol. 7, pp. 136867-136881.
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Wu, P, Wu, C, Liu, Z & Hao, H 2019, 'Investigation of shear performance of UHPC by direct shear tests', Engineering Structures, vol. 183, pp. 780-790.
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© 2019 Elsevier Ltd Ultra-high performance concrete (UHPC) has been applied widely in modern structure construction. The outstanding mechanical properties of UHPC not only enable strong yet slim structural designs but also highlight its potential in protective structural constructions against extreme loads. In this study, the shear transfer behaviors of UHPC are investigated by push-off tests on Z-shaped specimens, investigating the influences of the microsteel fiber volume ratio and stirrup reinforcement ratio on the shear strength, shear slip, and shear crack width of UHPC. The test results indicate that using a microsteel fiber can enhance the shear strength of UHPC specimens. Within a reasonable range of the steel fiber volume ratio (optimum volume ratio ranges from 0% to 2.5% for microsteel fiber), the shear strength and shear slip of UHPC increase significantly, and the shear crack width reduces with an increasing steel fiber volume ratio. Additionally, the ductility, shear strength, and shear slip of UHPC increase significantly, and the shear crack width reduces with increasing stirrup ratio. Finally, the simplified empirical equations for the ultimate shear strengths of UHPC specimens are deduced, and indicate good agreement with the experimental results.
Wu, S, Rizoiu, M-A & Xie, L 2019, 'Estimating Attention Flow in Online Video Networks', Proceedings of the ACM on Human-Computer Interaction, vol. 3, no. CSCW.
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Online videos have shown tremendous increase in Internet traffic. Most videohosting sites implement recommender systems, which connect the videos into adirected network and conceptually act as a source of pathways for users tonavigate. At present, little is known about how human attention is allocatedover such large-scale networks, and about the impacts of the recommendersystems. In this paper, we first construct the Vevo network -- a YouTube videonetwork with 60,740 music videos interconnected by the recommendation links,and we collect their associated viewing dynamics. This results in a total of310 million views every day over a period of 9 weeks. Next, we presentlarge-scale measurements that connect the structure of the recommendationnetwork and the video attention dynamics. We use the bow-tie structure tocharacterize the Vevo network and we find that its core component (23.1% of thevideos), which occupies most of the attention (82.6% of the views), is made outof videos that are mainly recommended among themselves. This is indicative ofthe links between video recommendation and the inequality of attentionallocation. Finally, we address the task of estimating the attention flow inthe video recommendation network. We propose a model that accounts for thenetwork effects for predicting video popularity, and we show it consistentlyoutperforms the baselines. This model also identifies a group of artistsgaining attention because of the recommendation network. Altogether, ourobservations and our models provide a new set of tools to better understand theimpacts of recommender systems on collective social attention.
Wu, W, Li, B, Chen, L, Zhang, C & Yu, PS 2019, 'Improved Consistent Weighted Sampling Revisited', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 12, pp. 2332-2345.
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IEEE Min-Hash is a popular technique for efficiently estimating the Jaccard similarity of binary sets. Consistent Weighted Sampling (CWS) generalizes the Min-Hash scheme to sketch weighted sets and has drawn increasing interest from the community. Due to its constant-time complexity independent of the values of the weights, Improved CWS (ICWS) is considered as the state-of-the-art CWS algorithm. In this paper, we revisit ICWS and analyze its underlying mechanism to show that there actually exists dependence between the two components of the hash-code produced by ICWS, which violates the condition of independence. To remedy the problem, we propose an Improved ICWS (I2CWS) algorithm which not only shares the same theoretical computational complexity as ICWS but also abides by the required conditions of the CWS scheme. The experimental results on a number of synthetic data sets and real-world text data sets demonstrate that our I2CWS algorithm can estimate the Jaccard similarity more accurately, and also competes with or outperforms the compared methods, including ICWS, in classification and top-K retrieval, after relieving the underlying dependence.
Wu, Y, Liu, T, Ling, SH, Szymanski, J, Zhang, W & Su, SW 2019, 'Air Quality Monitoring for Vulnerable Groups in Residential Environments Using a Multiple Hazard Gas Detector', Sensors, vol. 19, no. 2, pp. 362-362.
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This paper presents a smart “e-nose” device to monitor indoor hazardous air. Indoor hazardous odor is a threat for seniors, infants, children, pregnant women, disabled residents, and patients. To overcome the limitations of using existing non-intelligent, slow-responding, deficient gas sensors, we propose a novel artificial-intelligent-based multiple hazard gas detector (MHGD) system that is mounted on a motor vehicle-based robot which can be remotely controlled. First, we optimized the sensor array for the classification of three hazardous gases, including cigarette smoke, inflammable ethanol, and off-flavor from spoiled food, using an e-nose with a mixing chamber. The mixing chamber can prevent the impact of environmental changes. We compared the classification results of all combinations of sensors, and selected the one with the highest accuracy (98.88%) as the optimal sensor array for the MHGD. The optimal sensor array was then mounted on the MHGD to detect and classify the target gases without a mixing chamber but in a controlled environment. Finally, we tested the MHGD under these conditions, and achieved an acceptable accuracy (70.00%).
Wu, Y, Song, K, Sun, X, Ngo, H, Guo, W, Nghiem, LD & Wang, Q 2019, 'Mechanisms of free nitrous acid and freezing co-pretreatment enhancing short-chain fatty acids production from waste activated sludge anaerobic fermentation', Chemosphere, vol. 230, pp. 536-543.
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© 2019 Elsevier Ltd Free nitrous acid (FNA) or freezing has been recently utilized as an efficient pretreatment method to increase short-chain fatty acids (SCFAs) yield from waste activated sludge (WAS) anaerobic fermentation (AF). But until now, the performances and mechanisms of the co-pretreatment for SCFAs production are unknown. This research aimed to investigate the AF mechanisms through studying its influence on sludge solubilization and related bioprocesses. WAS was pretreated for 48 h with FNA (1.07 mg N/L), freezing (−5 °C) and combination of FNA and freezing (0.53–2.13 mg N/L FNA at −5 °C), respectively, then conducted batch AF. Experimental results indicated that the optimal total SCFAs yield of 391.19 ± 5.54 mg COD/g VSS was achieved after 1.07 mg N/L FNA + freezing pretreatment at 9 days of AF, which was 2.2, 1.6 and 1.3-fold of the blank, freezing and FNA pretreated samples, respectively. The mechanisms analysis showed that co-pretreatment showed synergetic effects on sludge disintegration and solubilization, which could release more soluble substrates for SCFAs production. The co-pretreatment resulted in slight inhibition to hydrolysis and negligible inhibition to acidogenesis but severe inhibition to methanogenesis, maybe due to less endurance of methanogens.
Wu, Y, Wang, D, Liu, X, Xu, Q, Chen, Y, Yang, Q, Li, H & Ni, B 2019, 'Effect of poly aluminum chloride on dark fermentative hydrogen accumulation from waste activated sludge', Water Research, vol. 153, pp. 217-228.
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© 2019 Elsevier Ltd Poly aluminum chloride (PAC), an inorganic coagulant being accumulated in waste activated sludge (WAS) at substantial levels, are generally thought to inhibit WAS anaerobic fermentation. However, its effect on dark fermentative hydrogen accumulation has not been documented. This work therefore aimed to explore its effect on hydrogen accumulation and to elucidate the mechanism of how PAC affects hydrogen accumulation. Experimental results showed that with an increase of PAC addition from 0 to 20 mg Al per gram of total suspended solids (TSS), the maximal hydrogen yield from alkaline fermentation (pH 9.5) increased from 20.9 mL to 27.4 mL per gram volatile suspended solids (VSS) under the standard condition. Further increase of PAC to 30 mg Al/g TSS didn't cause a significant increase of hydrogen yield (p > 0.05). The mechanism explorations revealed that although PAC reduced the total short-chain fatty acid (SCFA) production, this reduction was mainly enforced to propionic acid fermentation type, which did not contribute hydrogen production. PAC suppressed all the microbial processes relevant to anaerobic fermentation to some extents, but its inhibition to hydrogen consumption was much severer than that to hydrogen production. Illumina Miseq sequencing analysis revealed that PAC did not affect the populations of SCFA and hydrogen producers, but the two hydrogen consumers, Acetoanaerobium and Desulfobulbus, were almost washed out by PAC. Among the three types of Al species present in the anaerobic fermentation systems, Ala (monomeric species) significantly affected the maximal hydrogen production potential while Alb (medium polymer species) and Alc (species of sol or gel) posed impacts on hydrogen production rate and the lag time.
Wu, Y, Wong, S, Lin, J, Yang, Y, Zhang, L, Choi, W, Zhu, L & He, Y 2019, 'Design of triple‐band and triplex slot antenna using triple‐mode cavity resonator', IET Microwaves, Antennas & Propagation, vol. 13, no. 13, pp. 2303-2309.
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A class of triple‐band and triplex cavity‐backed slot antenna is proposed by using three fundamental modes in a single metal cavity: TE011, TE101, and TM110 modes, simultaneously. These three resonant modes can be excited by changing the position of the feeding slot without any extra components configured inside the cavity. By opening a single radiation slot, a single band triple‐mode cavity‐backed slot antenna is achieved. By opening three slots at three side walls of the cavity, a triple‐band cavity‐backed slot antenna is realised with three different radiation directions at three different operation frequencies. Moreover, a triplex triple‐band antenna can be formed by combining a single slot antenna and three input ports in the proposed rectangular cavity. Finally, a triple‐band multi‐directional cavity‐backed slot antenna prototype and a triplex triple‐band cavity‐backed single slot antenna prototype are fabricated and tested. The tested results are in good agreement with the simulated results, which indicates the feasibility of the proposed design methodology.
Wu, Y, Wu, Z, Chu, H, Li, J, Ngo, HH, Guo, W, Zhang, N & Zhang, H 2019, 'Comparison study on the performance of two different gas-permeable membranes used in a membrane-aerated biofilm reactor', Science of The Total Environment, vol. 658, pp. 1219-1227.
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Wu, Z, Ambrožová, N, Eftekhari, E, Aravindakshan, N, Wang, W, Wang, Q, Zhang, S, Kočí, K & Li, Q 2019, 'Photocatalytic H2 generation from aqueous ammonia solution using TiO2 nanowires-intercalated reduced graphene oxide composite membrane under low power UV light', Emergent Materials, vol. 2, no. 3, pp. 303-311.
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We report for the first time the nitrogen doping of reduced graphene oxide (rGO) and TiO2 nanowires (NWs) when TiO2 NWs intercalated rGO membranes were immersed in ammonia aqueous solution under 8 W 254 nm UV irradiation. Such nitrogen-doped rGO/TiO2 NWs photocatalytic membrane produced H2 at a rate of 208 μmol h−1 g−1 under 8 W 254 nm UV irradiation, which is more than 14 times higher than the yield of the TiO2-P25 and 30-fold higher than TiO2 NWs alone under the same condition. Our study demonstrates a new synthesis route for doping nitrogen in rGO and TiO2, as well as the preliminary feasibility of hydrogen extraction from ammonia-containing wastewater with such a low-cost recyclable photocatalyst. In addition, the study illustrates the complexity of photocatalysis of ammonia aqueous solution, which involves multiple reactions in concurrence.
Wu, Z, Pan, S, Chen, F, Long, G, Zhang, C & Yu, PS 2019, 'A Comprehensive Survey on Graph Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 4-24.
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Deep learning has revolutionized many machine learning tasks in recent years,ranging from image classification and video processing to speech recognitionand natural language understanding. The data in these tasks are typicallyrepresented in the Euclidean space. However, there is an increasing number ofapplications where data are generated from non-Euclidean domains and arerepresented as graphs with complex relationships and interdependency betweenobjects. The complexity of graph data has imposed significant challenges onexisting machine learning algorithms. Recently, many studies on extending deeplearning approaches for graph data have emerged. In this survey, we provide acomprehensive overview of graph neural networks (GNNs) in data mining andmachine learning fields. We propose a new taxonomy to divide thestate-of-the-art graph neural networks into four categories, namely recurrentgraph neural networks, convolutional graph neural networks, graph autoencoders,and spatial-temporal graph neural networks. We further discuss the applicationsof graph neural networks across various domains and summarize the open sourcecodes, benchmark data sets, and model evaluation of graph neural networks.Finally, we propose potential research directions in this rapidly growingfield.
Xia, J, Cao, L, Zhang, G & Liao, J 2019, 'Head Pose Estimation in the Wild Assisted by Facial Landmarks Based on Convolutional Neural Networks', IEEE Access, vol. 7, pp. 48470-48483.
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Convolutional neural networks (CNNs) exhibit excellent performance on the head pose estimation problem under controllable conditions, but their generalization ability in the wild needs to be improved. To address this issue, we propose an approach involving the introduction of facial landmark information into the task simplifier and landmark heatmap generator constructed before the feed-forward neural network, which can use this information to normalize the face shape into a canonical shape and generate a landmark heatmap based on the transformed facial landmarks to assist in feature extraction, for enhancing generalization ability in the wild. Our method was trained on 300W-LP and tested on AFLW2000-3D. The result shows that for the same feed-forward neural network when our method is used to introduce facial landmark information into a CNN, accuracy improves from 88.5% to 99.0% and mean average error decreases from 5.94° to 1.46° on AFLW2000-3D. Furthermore, we evaluate our method on several datasets used for pose estimation and compare the result with AFLW2000-3D, finding that the features extracted by a CNN could not reflect the head pose efficiently, which limits the performance of the CNN on the head pose estimation problem in wild. By introducing facial landmarks, the CNN could extract features that reflect head pose more efficiently, thereby significantly improving the accuracy of head pose estimation in the wild.
Xia, Q, Xu, Z, Liang, W, Yu, S, Guo, S & Zomaya, AY 2019, 'Efficient Data Placement and Replication for QoS-Aware Approximate Query Evaluation of Big Data Analytics', IEEE Transactions on Parallel and Distributed Systems, vol. 30, no. 12, pp. 2677-2691.
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© 1990-2012 IEEE. Enterprise users at different geographic locations generate large-volume data that is stored at different geographic datacenters. These users may also perform big data analytics on the stored data to identify valuable information in order to make strategic decisions. However, it is well known that performing big data analytics on data in geographical-located datacenters usually is time-consuming and costly. In some delay-sensitive applications, the query result may become useless if answering a query takes too long time. Instead, sometimes users may only be interested in timely approximate rather than exact query results. When such approximate query evaluation is the case, applications must sacrifice timeliness to get more accurate evaluation results or tolerate evaluation result with a guaranteed error bound obtained from analyzing the samples of the data to meet their stringent timeline. In this paper, we study quality-of-service (QoS)-aware data replication and placement for approximate query evaluation of big data analytics in a distributed cloud, where the original (source) data of a query is distributed at different geo-distributed datacenters. We focus on the problems of placing data samples of the source data at some strategic datacenters to meet stringent query delay requirements of users, by exploring a non-trivial trade-off between the cost of query evaluation and the error bound of the evaluation result. We first propose an approximation algorithm with a provable approximation ratio for a single approximate query. We then develop an efficient heuristic algorithm for evaluating a set of approximate queries with the aim to minimize the evaluation cost while meeting the delay requirements of these queries. We finally demonstrate the effectiveness and efficiency of the proposed algorithms through both experimental simulations and implementations in a real test-bed, real datasets are employed. Experimental results show that the ...
Xiao, C, Zeng, J, Ni, W, Liu, RP, Su, X & Wang, J 2019, 'Delay Guarantee and Effective Capacity of Downlink NOMA Fading Channels', IEEE Journal of Selected Topics in Signal Processing, vol. 13, no. 3, pp. 508-523.
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© 2007-2012 IEEE. Nonorthogonal multiple access (NOMA) is promising for increasing connectivity and capacity. But there has been little consideration on the quality of service of NOMA; let alone that in generic fading channels. This paper establishes closed-form upper bounds for the delay violation probability of downlink Nakagami-m and Rician NOMA channels, by exploiting stochastic network calculus (SNC). The key challenge addressed is to derive the Mellin transforms of the service processes in the NOMA fading channels. The transforms are proved to be stable, and incorporated into the SNC to provide the closed-form upper bounds of the delay violation probability. The paper also applies the Mellin transforms to develop the closed-form expressions for the effective capacity of the NOMA fading channels, which measures the channel capacity under statistical delay guarantees. By further applying the min-max and max-min rules, two new power allocation algorithms are proposed to optimize the closed-form expressions, which can provide the NOMA users fairness in terms of delay violation probability and effective capacity. Simulation results substantiate the derived upper bounds of the delay violation probabilities, and the effective capacity. The proposed power allocation algorithms are also numerically validated.
Xiao, C, Zeng, J, Ni, W, Su, X, Liu, RP, Lv, T & Wang, J 2019, 'Downlink MIMO-NOMA for Ultra-Reliable Low-Latency Communications', IEEE Journal on Selected Areas in Communications, vol. 37, no. 4, pp. 780-794.
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© 2019 IEEE. With the emergence of the mission-critical Internet of Things applications, ultra-reliable low-latency communications are attracting a lot of attentions. Non-orthogonal multiple access (NOMA) with multiple-input multiple-output (MIMO) is one of the promising candidates to enhance connectivity, reliability, and latency performance of the emerging applications. In this paper, we derive a closed-form upper bound for the delay target violation probability in the downlink MIMO-NOMA, by applying stochastic network calculus to the Mellin transforms of service processes. A key contribution is that we prove that the infinite-length Mellin transforms resulting from the non-negligible interferences of NOMA are Cauchy convergent and can be asymptotically approached by a finite truncated binomial series in the closed form. By exploiting the asymptotically accurate truncated binomial series, another important contribution is that we identify the critical condition for the optimal power allocation of MIMO-NOMA to achieve consistent latency and reliability between the receivers. The condition is employed to minimize the total transmit power, given a latency and reliability requirement of the receivers. It is also used to prove that the minimal total transmit power needs to change linearly with the path losses, to maintain latency and reliability at the receivers. This enables the power allocation for mobile MIMO-NOMA receivers to be effectively tracked. The extensive simulations corroborate the accuracy and effectiveness of the proposed model and the identified critical condition.
Xiao, N, Li, H, Yu, W, Gu, C, Fang, H, Peng, Y, Mao, H, Fang, Y, Ni, W & Yao, M 2019, 'SUMO‐specific protease 2 (SENP2) suppresses keratinocyte migration by targeting NDR1 for de‐SUMOylation', The FASEB Journal, vol. 33, no. 1, pp. 163-174.
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ABSTRACTA key member of the sentrin/small ubiquitin‐like modifier (SUMO)‐specific protease (SENP) family, SENP2 has been shown to implicate embryonic development, fatty acid metabolism, atherosclerosis, and neurodegenerative diseases. However, other biologic functions of SENP2 and its specific targets are incompletely understood. Here, we uncovered a novel role of SENP2 in negative regulation of keratinocyte migration, a process crucial to wound epithelialization. Defects in this function are often associated with the clinical phenotypes of chronic nonhealing wounds. Mechanistically, SENP2 as a specific de‐SUMOylase targets NDR1 (nuclear Dbf2‐related 1), also called STK38 (serine‐threonine kinase 38), for de‐SUMOylation and SUMO conjugation of NDR1 on Lys‐465 attenuates its inhibition of p38/ERK1/2 activation by decreasing the association of NDR1 with MEK kinase 1/2. Significantly, low‐level laser (LLL) irradiation increases NDR1 SUMOylation and subsequent p38/ERK1/2 activation via down‐regulation of SENP2, leading to faster keratinocyte migration. Our findings fill the gaps that linger in the basic mechanisms underlying LLL therapy.—Xiao, N., Li, H., Yu, W., Gu, C., Fang, H., Peng, Y., Mao, H., Fang, Y., Ni, W., Yao, M.SUMO‐specific protease 2 (SENP2) suppresses keratinocyte migration by targeting NDR1 for de‐SUMOylation. FASEB J. 33, 163–174 (2019). www.fasebj.org
Xiao, Z, Li, Z, Guo, H, Liu, Y, Wang, Y, Yin, H, Li, X, Song, J, Nghiem, LD & He, T 2019, 'Scaling mitigation in membrane distillation: From superhydrophobic to slippery', Desalination, vol. 466, pp. 36-43.
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© 2019 Elsevier B.V. Scaling is a major obstacle to commercial application of membrane distillation (MD) for desalination. Contemporary understanding of scaling formation onto hydrophobic membrane was built on thermodynamic assumption of a non-slip condition. This research provides an alternative theory and a novel insight from a hydrodynamic view of slip boundary. We purposely selected three polyvinylidene difluoride (PVDF) membranes with different surface characteristics - namely a tailor made superhydrophobic micro-pillared (CF4-MP-PVDF), a micro-pillared (MP-PVDF) and a commercial (C-PVDF) membranes, for direct contact membrane distillation (DCMD) using a supersaturated CaSO4 feed. MD flux analysis showed that CF4-MP-PVDF was highly scaling resistant whereas the other two membranes were not. Nucleation energy barrier, wetting state factor and slip length were used to explain for the observed difference in scaling behavior. Results showed that hydrodynamic properties, such as the wetting state and slip length, play a critical role in determining the anti-scaling behavior of a hydrophobic membrane rather than the contact angle nor the thermodynamic nucleation energy barrier. New findings from this study serve as a new guideline for the fabrication of antiscaling membranes by creating a slippery surface.
Xie, K, Fu, Q, Qiao, GG & Webley, PA 2019, 'Recent progress on fabrication methods of polymeric thin film gas separation membranes for CO2 capture', Journal of Membrane Science, vol. 572, pp. 38-60.
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© 2018 Elsevier B.V. Membrane technology has been recognized as an attractive and environment-friendly technology for carbon capture due to its low expense (capital and operating), ease of operation as well as low energy consumption. Traditionally, the membrane materials are cast into dense membranes with a thickness of 50–150 µm and their gas separation properties are evaluated by the trade-off between permeability and selectivity. However, permeance (gas flux), rather than permeability, is more emphasized recently because the increase of the real gas flux through a membrane without the loss of selectivity has been recognized to be more important in industrial scenarios. The permeance is inversely proportional to the membrane thickness, and thus the thin film membranes with sub-micro scale selective layers as part of a composite membrane has drawn particular interests. In thin film membranes, the membrane fabrication technique as well as the membrane configuration design are more important than the membrane materials. However, the recent progress on membrane fabrication techniques, especially the bottom-up approaches for composite membranes, have not been fully reviewed and compared. This review focuses on the recent progress in fabrication techniques and approaches of the thin film (sub-micron) polymeric membranes for CO2 capture, the state-of-art performance of those membranes will be summarized, and future direction of thin film composite membrane will be discussed.
Xie, W, Jia, X, Li, Y & Lei, J 2019, 'Hyperspectral Image Super-Resolution Using Deep Feature Matrix Factorization', IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 8, pp. 6055-6067.
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Xie, W, Jiang, T, Li, Y, Jia, X & Lei, J 2019, 'Structure Tensor and Guided Filtering-Based Algorithm for Hyperspectral Anomaly Detection', IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 7, pp. 4218-4230.
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Xie, W, Lei, J, Liu, B, Li, Y & Jia, X 2019, 'Spectral constraint adversarial autoencoders approach to feature representation in hyperspectral anomaly detection', Neural Networks, vol. 119, pp. 222-234.
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Xie, W, Shi, Y, Li, Y, Jia, X & Lei, J 2019, 'High-quality spectral-spatial reconstruction using saliency detection and deep feature enhancement', Pattern Recognition, vol. 88, pp. 139-152.
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Xie, X, Zou, L, Wen, S, Zeng, Z & Huang, T 2019, 'A Flux-Controlled Logarithmic Memristor Model and Emulator', Circuits, Systems, and Signal Processing, vol. 38, no. 4, pp. 1452-1465.
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The HP TiO2 model, as it is well known, is the most widely used physical model of memristor. However, deriving a mathematical model that fully characterizes the HP TiO2 memristor is a challenging task. As a result, simplified models such as the nonlinear quadratic model and the cubic memristor model are utilized in theoretic quantitative analysis of memristor circuits. These models result in unsatisfactory performance for many applications. To mitigate this problem, this paper proposes a new nonlinear logarithmic model to characterize memristor. Additionally, a memristor emulator circuit is developed. Finally, the relationships among the HP TiO2 memristor, the logarithmic model, and the emulator are thoroughly discussed.
Xu, B, He, N, He, B, Li, D & Wu, S 2019, 'Experiment study on pipeline bending deformation monitoring based on distributed optical fiber sensor', Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, vol. 40, no. 8, pp. 20-30.
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Scientific and reasonable pipeline safety monitoring technology is of great significance to pipeline engineering operation. This paper carries out the experiment study on pipeline bending deformation monitoring based on distributed optical fiber sensing technology. Aiming at the deficiency of existing distributed fiber deformation calculation method, a calculation method of pipeline bending deformation monitoring using distributed optical fiber sensor is proposed, and the calculation program of pipeline bending deformation using distributed optical fiber sensor based on MATLAB is written. The study results show that the proposed pipeline bending deformation monitoring method based on distributed optical fiber sensing technology has high overall measurement accuracy. Within the bending deformation range of 180 mm, the absolute error is less than 4 mm and the average relative error is within 2%. When the bending deformation is getting larger, the absolute error increases, however the average relative error is below 3.2%. The pipeline force analysis based on distributed optical fiber sensing technology was carried out preliminarily. The results show that the simulated pipeline shear force pattern is in good agreement with the theoretical pattern and the actual situation. The proposed pipeline bending deformation monitoring method based on distributed optical fiber sensing technology possesses high measurement accuracy and small error, which can meet the requirements of pipeline bending deformation monitoring and has good application prospect. The method is an ideal deformation monitoring technology and can also be extended to the application of other safety analysis such as pipeline force analysis and etc.
Xu, C, Gordan, B, Koopialipoor, M, Armaghani, DJ, Tahir, MM & Zhang, X 2019, 'Improving Performance of Retaining Walls Under Dynamic Conditions Developing an Optimized ANN Based on Ant Colony Optimization Technique', IEEE Access, vol. 7, pp. 94692-94700.
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Xu, C, Han, Z, Wang, Q, Zhao, G & Yu, S 2019, 'Modelling the impact of interference on the energy efficiency of WLANs', Concurrency and Computation: Practice and Experience, vol. 31, no. 17, pp. e5217-e5217.
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SummaryThe high‐bandwidth demands from variety of applications drive a dense wireless local access network (WLAN), which results in a complicated wireless network scene with serious co‐channel interference and energy waste. In this paper, to reveal the interactions between interference and energy efficiency, we propose an interference‐energy efficiency (IFEE) model to quantify the interference impact on the energy efficiency of 802.11 access point (AP) devices. Firstly, we introduce the channel separation and the difference of received signal strength indication (D‐RSSI) as two indicators to extend the classical signal to interference plus noise ratio (SINR) notion and rate adaptive mechanism. Then, these two parameters are integrated into the energy consumption model to establish the IFEE model. Lastly, we conduct extensive measurements with five typical WiFi interference scene in real network to validate the effectiveness of our model. The comparisons between simulation results and real data demonstrate that the proposed IFEE model can quantify the interference and energy efficiency with high accuracy, which can be used for wireless network optimization and protocol design.
Xu, C, Xiong, Z, Zhao, G & Yu, S 2019, 'An Energy-Efficient Region Source Routing Protocol for Lifetime Maximization in WSN', IEEE Access, vol. 7, pp. 135277-135289.
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As the sensor layer of Internet of Things (IOT), enormous amount of sensor nodes are densely deployed in a hostile environment to monitor and sense the changes in the physical space. Since sensor nodes are driven with limited power batteries, it is very difficult and expensive for wireless sensor networks (WSNs) to extend network lifetime. In order to achieve reliable data transmission in WSNs, energy efficient routing protocol is a crucial issue in extending the network lifetime of a network. However, traditional routing protocols usually propagate throughout the whole network to discover a reliable route or employ some cluster heads to undertake data transmission for other nodes, which both require large amount energy consumption. In this paper, to maximize the network lifetime of the WSN, we propose a novel energy efficient region source routing protocol (referred to ER-SR). In ER-SR, a distributed energy region algorithm is proposed to select the nodes with high residual energy in the network as source routing node dynamically. Then, the source routing nodes calculate the optimal source routing path for each common node, which enables partial nodes to participate in the routing process and balances the energy consumption of sensor nodes. Furthermore, to minimize the energy consumption of data transmission, we propose an effective distance-based ant colony optimization algorithm to search the global optimal transmission path for each node. Simulation results demonstrate that ER-SR exhibits higher energy efficiency, and has moderate performance improvements on network lifetime, packet delivery ratio, and delivery delay, compared with other routing protocols in WSNs.
Xu, D, Shi, Y, Tsang, IW, Ong, Y-S, Gong, C & Shen, X 2019, 'Survey on Multi-Output Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 7, pp. 1-21.
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The aim of multi-output learning is to simultaneously predict multiple outputs given an input. It is an important learning problem for decision-making since making decisions in the real world often involves multiple complex factors and criteria. In recent times, an increasing number of research studies have focused on ways to predict multiple outputs at once. Such efforts have transpired in different forms according to the particular multi-output learning problem under study. Classic cases of multi-output learning include multi-label learning, multi-dimensional learning, multi-target regression, and others. From our survey of the topic, we were struck by a lack in studies that generalize the different forms of multi-output learning into a common framework. This article fills that gap with a comprehensive review and analysis of the multi-output learning paradigm. In particular, we characterize the four Vs of multi-output learning, i.e., volume, velocity, variety, and veracity, and the ways in which the four Vs both benefit and bring challenges to multi-output learning by taking inspiration from big data. We analyze the life cycle of output labeling, present the main mathematical definitions of multi-output learning, and examine the field's key challenges and corresponding solutions as found in the literature. Several model evaluation metrics and popular data repositories are also discussed. Last but not least, we highlight some emerging challenges with multi-output learning from the perspective of the four Vs as potential research directions worthy of further studies.
Xu, D, Tsang, IW, Chew, EK, Siclari, C & Kaul, V 2019, 'A Data-Analytics Approach for Enterprise Resilience', IEEE Intelligent Systems, vol. 34, no. 3, pp. 6-18.
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IEEE Enterprise resilience plays an important role to prevent business services from disruptions caused by human-induced disasters such as failed change implementations and software bugs. Traditional expert-centric approach has difficulty to maintain continued critical business functions because the disasters can often only be handled after their occurrence. This paper introduces a data-analytics approach, which leverages system monitoring data for the enterprise resilience. With the power of data mining and machine learning techniques, we build an intelligent business analytics system to detect the potential disruptions proactively, and to assist the operational team for enterprise resilience enhancement. We demonstrate the effectiveness of our approach on a real enterprise system monitoring dataset in simulation.
Xu, H & Ji, J 2019, 'Analytical-numerical studies on the stability and bifurcations of periodic motion in the vibro-impact systems with clearances', International Journal of Non-Linear Mechanics, vol. 109, pp. 155-165.
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© 2018 This paper investigates the stability and bifurcations of periodic solutions in three-degree-of-freedom vibro-impact systems based on the explicit critical criteria and the discontinuous mapping method. Firstly, a six-dimensional Poincaré map is established by taking the impact surface as the Poincaré section. The explicit criteria including eigenvalue assignment and transversality condition are applied to determine the bifurcation point of co-dimension one pitchfork bifurcation. The stability and direction of the bifurcation solution are then studied by using center manifold reduction theory and normal form approach. Secondly, the bifurcation points of co-dimension-two Hopf–Hopf interaction bifurcation and pitchfork–Neimark–Sacker bifurcation are determined by applying the explicit critical criteria, and the local dynamic behaviors are examined in the neighborhood of these co-dimension-two bifurcation points. Finally, a six-dimensional Poincaré map formed by choosing the constant phase angle as the Poincaré section is used to investigate the existence and stability of grazing bifurcation based on the piecewise compound normal form map. The causes of the discontinuous jump and the coexistence of attractors near the grazing periodic motion are explained for the three-degree-of-freedom vibro-impact system with a moving constraint.
Xu, H, Duan, Y, Yuan, X, Wu, H, Song, Y, Xu, J & Sun, H 2019, 'Residual left ventricular hypertrophy with adverse clinical outcomes in patients with severe aortic stenosis and asymmetric septal hypertrophy after aortic valve replacement', European Journal of Cardio-Thoracic Surgery, vol. 56, no. 2, pp. 343-350.
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Abstract OBJECTIVES The aim of this study is to describe the temporal pattern of left atrial (LA) and left ventricular (LV) reverse remodelling and to evaluate the impact of residual LV hypertrophy on the prognosis of patients with severe aortic stenosis and asymmetric septal hypertrophy undergoing aortic valve replacement (AVR). METHODS We retrospectively reviewed 59 consecutive patients who underwent AVR for severe aortic stenosis and asymmetric septal hypertrophy. They were divided into the normal LV mass group and the residual LV hypertrophy group according to the LV mass index (LVMI) 2 years after AVR. Thirty patients were eligible for analysis of the time-dependent changes in LA and LV reverse remodelling. RESULTS The interventricular septal thickness and LVMI gradually decreased and reached their lowest points 2 years after operation, whereas the LA dimension rapidly decreased in the early postoperative period and plateaued at 3 months. The multivariable analysis revealed a higher preoperative LVMI [odds ratio 6.36 (1.678–24.11); P = 0.007] as an independent predictor of residual hypertrophy 2 years after operation. The Cox proportional hazards model showed that a higher postoperative peak velocity [hazard ratio 6.715 (1.405–32.104); P = 0.017] was an independent predictor of long-term non-fatal cardiovascular hospitalization. Patients with residual hypertrophy 2 years after AVR had a higher rate of non-fatal cardiovascular hospitalization (P = 0.014). CON...
Xu, H, Duan, Y, Yuan, X, Wu, H, Sun, H & Ji, H 2019, 'Intravenous Iron Versus Placebo in the Management of Postoperative Functional Iron Deficiency Anemia in Patients Undergoing Cardiac Valvular Surgery: A Prospective, Single-Blinded, Randomized Controlled Trial', Journal of Cardiothoracic and Vascular Anesthesia, vol. 33, no. 11, pp. 2941-2948.
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Xu, J, Cao, Z, Wang, Y, Zhang, Y, Gao, X, Ahmed, MB, Zhang, J, Yang, Y, Zhou, JL & Lowry, GV 2019, 'Distributing sulfidized nanoscale zerovalent iron onto phosphorus-functionalized biochar for enhanced removal of antibiotic florfenicol', Chemical Engineering Journal, vol. 359, pp. 713-722.
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© 2018 Aggregation of nZVI and sulfur-modified nZVI (S-nZVI) can lower its reactivity with contaminants in water. To overcome this limitation, we synthesized biochar-supported nZVI and S-nZVI using a phosphate pretreatment of the biochar (pBC) to uniformly distribute the nZVI and S-nZVI onto the biochar support. The participation of phosphorus groups in the synthesis, and the good distribution of S-nZVI on the pBC were confirmed by FTIR, SEM, XRD, and XPS. Pretreatment of the biochar led to smaller well-dispersed S-nZVI compared to S-nZVI supported on untreated biochar. This increased the surface area of the S-nZVI and the reaction rate with the antibiotic florfenicol (FF). The removal rate of FF by pBC-S-nZVI was 4.3 times higher than that by unsupported S-nZVI. Even though FF strongly adsorbed to the pBC support, FF was fully degraded based on the mass balance results. Surface area normalized reaction rate constants (kSA) for FF removal by S-nZVI, BC-S-nZVI, and pBC-S-nZVI were similar, suggesting that the enhanced reactivity is due to the greater dispersion of S-nZVI on the treated biochar. These results provide a simple pretreatment method for dispersing nZVI or S-nZVI onto biochar supports.
Xu, J-X, Li, H-Y, Zhang, XY, Yang, Y, Xue, Q & Dutkiewicz, E 2019, 'Compact Dual-Channel Balanced Filter and Balun Filter Based on Quad-Mode Dielectric Resonator', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 2, pp. 494-504.
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© 1963-2012 IEEE. In this paper, we propose a method for designing a dual-channel balanced filter and a dual-channel balun filter based on the quad-mode dielectric resonator (DR) for the first time. By sharing one common quad-mode DR, two balanced filters or two balun filters are integrated as one single-cavity configuration, featuring compact size and high integration. A cylindrical DR with two short ends is investigated to construct the quad-mode DR. By properly arranging input and output feeding probes, two modes of the DR are only excited by the feeding probes of one channel and the other two modes are excited by that of the other channel. Accordingly, signals cannot be transmitted between the two channels, resulting in high isolation. Moreover, the required out-of-phase characteristics of the balanced and balun filters can be obtained by the inherent electromagnetic field properties of the DR without adding additional circuits, featuring a simple structure. For demonstration, a dual-channel balanced filter and a dual-channel balun filter are designed and fabricated, showing excellent balanced or balun filter performance of each channel as well as high isolation between the two channels. As compared to the other reported DR balanced and balun filters, the proposed designs exhibit a significant size reduction, which are attractive in wireless systems.
Xu, J-X, Yang, L, Yang, Y & Zhang, XY 2019, 'High-$Q$ -Factor Tunable Bandpass Filter With Constant Absolute Bandwidth and Wide Tuning Range Based on Coaxial Resonators', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 10, pp. 4186-4195.
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© 1963-2012 IEEE. This paper presents a method for designing low-loss varactor-based tunable bandpass filters (BPFs) with constant absolute bandwidth (CABW) and wide tuning range based on coaxial resonators. The whole structure consists of a metallic cavity and a piece of printed circuit board (PCB). Varactors and their biasing components are mounted on the PCB, which are connected to the coaxial resonators to enable frequency tuning. The coupling control between coaxial resonators and the nonsynchronous tuning of the resonators are utilized to realize the CABW over a wide tuning range. Moreover, transmission zeros are generated near the passband, resulting in high skirt selectivity. For demonstration, a second-order tunable coaxial BPF with a wide tuning range from 698 to 960 MHz (31.5%) is designed and fabricated. Due to the use of high- Q -factor coaxial resonators, the proposed tunable filter realizes low insertion losses ranging from 0.82 to 2.03 dB under a narrowband specification. The 1-dB bandwidth is measured as 35.3 ± 2.7 MHz, showing excellent CABW responses. Moreover, a third-order tunable coaxial BPF is also designed and implemented with enhanced skirt selectivity and out-of-band rejection. Comparison with other reported tunable BPFs is given, where the proposed designs show excellent performance of wide tuning range, CABW, high selectivity, and high tuning speed.
Xu, M, Chen, H, Zhao, Y, Ni, W, Liu, M, Xue, Y, Huo, S, Wu, L, Yang, Z & Yan, Y 2019, 'Ultrathin‐Carbon‐Layer‐Protected PtCu Nanoparticles Encapsulated in Carbon Capsules: A Structure Engineering of the Anode Electrocatalyst for Direct Formic Acid Fuel Cells', Particle & Particle Systems Characterization, vol. 36, no. 7.
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AbstractStructure engineering is an effective strategy to enhance the performance of electrocatalysts for the formic acid oxidation reaction. However, it remains a challenge to prepare a highly active electrocatalyst based on a distinct understanding of its structure‐dependent performance. The design and synthesis of ultrathin‐carbon‐layer‐protected PtCu nanoparticles (NPs) encapsulated in a N‐doped carbon capsule (PtCu@NCC) is reported. This system is fabricated by using Zn‐based metal–organic frameworks as the carbon support source and metal‐containing tannic acid as the protecting shell template. It displays 9.8‐ and 9.6‐fold enhancements in mass activity and specific activity compared to commercial Pt/C. Moreover, a constructed direct formic acid fuel cell using PtCu@NCC as the anodic electrocatalyst delivers a maximum power density of 121 mW cm−2. Significantly, PtCu@NCC exhibits superior structural stability and catalytic durability in both half‐cell and full‐cell tests. A mechanism study reveals that the enhanced activity is partially attributed to facilitated electro‐oxidation kinetics of formic acid in the unique structure of PtCu@NCC, while the excellent durability stems from the “protecting effect” of the in‐situ‐formed ultrathin carbon layer on the surface of the PtCu NPs. This work opens a new avenue for the development of high‐performance electrocatalysts for fuel‐cell applications by offering essential insights into the structure–performance relationship of the materials.
Xu, M, Zhao, Y, Chen, H, Ni, W, Liu, M, Huo, S, Wu, L, Zang, X, Yang, Z & Yan, Y 2019, 'Role of Ultrathin Carbon Shell in Enhancing the Performance of PtZn Intermetallic Nanoparticles as an Anode Electrocatalyst for Direct Formic Acid Fuel Cells', ChemElectroChem, vol. 6, no. 8, pp. 2316-2323.
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AbstractIntroducing a carbon shell to Pt‐based intermetallic nanoparticles (iNPs) is an effective approach to prepare structurally ordered electrocatalysts for fuel cell applications. However, it remains a huge challenge to synthesize small‐sized Pt‐based iNPs with a concisely controlled carbon shell based on fully understanding the role of the carbon shell in the electrocatalytic performance. Herein, we report the preparation of ultrathin carbon shell (UCS)‐coated, small‐sized PtZn iNPs, which are obtained by using the simple heat treatment of polypyrrole‐coated PtZn/C (PPy−PtZn/C), which can produce a carbon shell in situ and simultaneously transfer a disordered PtZn alloy into ordered intermetallic PtZn. Interestingly, the thickness of the carbon shell can be well‐controlled by tuning the polymerization time of pyrrole (Py). The obtained PtZn iNPs (an average diameter of ca. 4 nm) coated with UCS (ca. 0.5 nm thickness) shows the best electrocatalytic performance toward the formic acid oxidation reaction. Moreover, the UCS−PtZn iNPs offers remarkable long‐term stability as an anode electrocatalyst in a constructed direct formic acid fuel cell. The results demonstrate that the UCS formed in situ only acts as a physicochemical protecting shell with high permeability for the reactants, but it does not block the catalytic reaction sites of iNPs.
Xu, Q, Liu, X, Wang, D, Liu, Y, Wang, Q, Ni, B-J, Li, X, Yang, Q & Li, H 2019, 'Enhanced short-chain fatty acids production from waste activated sludge by sophorolipid: Performance, mechanism, and implication', Bioresource Technology, vol. 284, pp. 456-465.
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© 2019 Elsevier Ltd It was found in this study that the presence of sophorolipid (SL) enhanced the production of short-chain fatty acid (SCFA) from anaerobic fermentation of waste activated sludge (WAS). Experimental results showed that with an increase of SL addition from 0 to 0.1 g/g TSS, the maximal SCFA yield increased from 50.5 ± 4.9 to 246.2 ± 7.5 mg COD/g VSS. The presence of SL reduced the surface tension between hydrophobic organics and fermentation liquid, which thereby accelerated the disintegration of WAS and improved the biodegradability of the released organics. SL promoted the carbon/nitrogen ratio of the fermentation system, enhancing the conversion of proteins in WAS. Moreover, SL suppressed severely the activities of methanogens, probably due to the drop of pH caused by SL addition. Amplicon sequencing analyses revealed that SL increased the abundance of hydrolytic microbes such as Bacteroides sp. and Macellibacteroides sp., and SCFA producers (e.g., Acinetobacter sp.).
Xu, Q, Liu, X, Yang, G, Wang, D, Wang, Q, Liu, Y, Li, X & Yang, Q 2019, 'Free nitrous acid-based nitrifying sludge treatment in a two-sludge system obtains high polyhydroxyalkanoates accumulation and satisfied biological nutrients removal', Bioresource Technology, vol. 284, pp. 16-24.
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© 2019 Elsevier Ltd A novel strategy to achieve substantial polyhydroxyalkanoates (PHA) accumulation in waste activated sludge (WAS) was developed, which was conducted in a two-sludge system consisted of an anaerobic/anoxic/oxic reactor (AAO-SBR) and a nitrifying reactor (N-SBR), where the nitrifying-sludge was treated by free nitrous acid (FNA). Initially, 0.98 ± 0.09 and 1.46 ± 0.10 mmol-c/g VSS of PHA were respectively determined in the control-SBR and AAO-SBR. When 1/16 of nitrifying sludge was daily treated with 1.49 mg N/L FNA for 24 h, ∼46.5% of nitrite was accumulated in the N-SBR, ∼2.43 ± 0.12 mmol-c/g VSS of PHA was accumulated in WAS in AAO-SBR without deteriorating nutrient removal. However, nutrient removal of control-SBR was completely collapsed after implementing the same FNA treatment. Further investigations revealed that the activity and abundance of nitrite oxidizing bacteria (NOB) was decreased significantly after FNA treatment. Finally, sludge with high PHA level to generate more methane was confirmed.
Xu, R & Fatahi, B 2019, 'Impact of In Situ Soil Shear-Wave Velocity Profile on the Seismic Design of Tall Buildings on End-Bearing Piles', Journal of Performance of Constructed Facilities, vol. 33, no. 5, pp. 04019053-04019053.
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© 2019 American Society of Civil Engineers. In this study, a numerical investigation into how the shear wave velocity profile affects the seismic performance of tall buildings and foundations was carried out using FLAC3D software. The in situ soil profile and equivalent average soil profile, which reflect the actual soil shear wave velocity profile and the corresponding uniform time-averaged soil shear wave profile based on the actual profile, respectively, were studied. Overconsolidated soil near the ground surface was considered in the in situ soil profile. A 20-story building supported by an end-bearing pile foundation was designed and simulated. A fully coupled nonlinear dynamic analysis was carried out in the time domain to evaluate the seismic response of the structure and foundation system under strong earthquakes. The variations of the interface parameters with depth around the piles were considered according to the variations in the stiffness of surrounding soil with depth in the numerical model when the in situ soil profile was used. The predicted shear forces, maximum lateral deformation, and maximum interstory drifts of the building are presented and discussed, as are the maximum shear forces, maximum bending moments, and the maximum lateral deformation of the piles. The results indicate that the use of an actual shear wave velocity profile instead of an equivalent average profile gives design engineers the opportunity to optimize their design and achieve cost-effective solutions.
Xu, R & Fatahi, B 2019, 'Novel application of geosynthetics to reduce residual drifts of mid-rise buildings after earthquakes', Soil Dynamics and Earthquake Engineering, vol. 116, pp. 331-344.
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© 2018 Elsevier Ltd Geosynthetics have been used in variety of geotechnical engineering projects, such as ground improvement, erosion control, slope stabilisation and foundation strength improvement and they have been proved to be cost and time effective in many cases. In this study, a geosynthetic reinforced composite soil (GRCS) foundation system is proposed for seismic protection of mid-rise buildings supported by a shallow foundation potentially suffering from residual structural drift or permanent foundation settlement. To evaluate the proposed GRCS, a conventional reinforced concrete moment resisting building sitting on this composite ground under the earthquake excitations of 1978 Tabas, 1994 Northridge and 1995 Kobe was numerically simulated using FLAC3D software. The effect of soil-structure interaction (SSI) was captured using direct method of analysis adopting a three-dimensional numerical model. By adopting direct calculation method, the soil deposit, the geosynthetic reinforcement, the foundation and the structure were simulated simultaneously. Inelastic behaviour of the structure was considered, while hysteretic damping algorithm was adopted representing the variation of the shear modulus and corresponding damping ratio of the soil with cyclic shear strain capturing the energy dissipation characteristics of the soil. Both material and geometry nonlinearities were taken into account at the interface between the foundation and ground surface. The results are then presented in terms of mobilised tensile force in geosynthetic layers, the response spectra at bedrock and ground surface level, the shear force developed in the superstructure, the maximum foundation rocking angle, the maximum lateral deflection, the maximum inter-storey drift, and most importantly the residual inter-storey drift and permanent foundation settlement. The results showed that the proposed GRCS could offer design engineers a rational and cost-effective alternative solution to con...
Xu, S, Wu, C, Liu, Z & Shao, R 2019, 'Experimental investigation on the cyclic behaviors of ultra-high-performance steel fiber reinforced concrete filled thin-walled steel tubular columns', Thin-Walled Structures, vol. 140, pp. 1-20.
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© 2019 This paper presents an experimental investigation on the cyclic behaviors of ultra-high performance steel fiber reinforced concrete filled thin-walled steel tubular columns under combined axial compression and cyclic lateral displacement loading. The failure modes, hysteretic behaviors, envelop diagrams, ductile performance, stiffness degradation and energy dissipation capacity were analyzed in detail. Notably, the cyclic behaviors of referenced high strength concrete and normal strength concrete filled thin-walled steel tubular columns were also studied to get a better illustration of the cyclic behaviors of ultra-high-performance steel fiber reinforced concrete filled thin-walled steel tubular columns. Furthermore, the effects of steel tube thickness, axial compression ratio, volume ratio of steel fiber and slenderness on the cyclic behaviors of ultra-high-performance steel fiber reinforced concrete filled thin-walled steel tubular columns were also investigated in detail. The test results indicate that the high strength concrete filled thin-walled steel tubular columns represent a poor cyclic behavior. However, replacing high strength concrete with ultra-high performance steel fiber reinforced concrete to infill thin-walled steel tubes can get an excellent cyclic behavior. Moreover, the cyclic behavior of ultra-high performance steel fiber reinforced concrete filled thin-walled steel tubular columns is also much better than that of normal strength concrete filled thin-walled steel tubular columns.
Xu, W, Hu, D, Lei, G & Zhu, J 2019, 'System-level efficiency optimization of a linear induction motor drive system', CES Transactions on Electrical Machines and Systems, vol. 3, no. 3, pp. 285-291.
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Xu, X, Tsang, IW & Liu, C 2019, 'Improving Generalization via Attribute Selection on Out-of-the-box Data', Neural Computation, vol. 32, no. 2, pp. 485-514.
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Zero-shot learning (ZSL) aims to recognize unseen objects (test classes)given some other seen objects (training classes), by sharing information ofattributes between different objects. Attributes are artificially annotated forobjects and treated equally in recent ZSL tasks. However, some inferiorattributes with poor predictability or poor discriminability may have negativeimpacts on the ZSL system performance. This paper first derives ageneralization error bound for ZSL tasks. Our theoretical analysis verifiesthat selecting the subset of key attributes can improve the generalizationperformance of the original ZSL model, which utilizes all the attributes.Unfortunately, previous attribute selection methods are conducted based on theseen data, and their selected attributes have poor generalization capability tothe unseen data, which is unavailable in the training stage of ZSL tasks.Inspired by learning from pseudo relevance feedback, this paper introduces theout-of-the-box data, which is pseudo data generated by an attribute-guidedgenerative model, to mimic the unseen data. After that, we present an iterativeattribute selection (IAS) strategy which iteratively selects key attributesbased on the out-of-the-box data. Since the distribution of the generatedout-of-the-box data is similar to the test data, the key attributes selected byIAS can be effectively generalized to test data. Extensive experimentsdemonstrate that IAS can significantly improve existing attribute-based ZSLmethods and achieve state-of-the-art performance.
Xu, X, Zhou, Y, Han, R, Song, K, Zhou, X, Wang, G & Wang, Q 2019, 'Eutrophication triggers the shift of nutrient absorption pathway of submerged macrophytes: Implications for the phytoremediation of eutrophic waters', Journal of Environmental Management, vol. 239, pp. 376-384.
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© 2019 Elsevier Ltd Ecologically restoring eutrophic water bodies by using submerged macrophytes is an economical, effective and sustainable technology worldwide. However, current understanding on the nutrient absorption pathway of submerged macrophytes in freshwater ecosystems, especially under different trophic states, is still limited. In this study, two strategically designed systems were established to form isolated units for preventing nutrient exchange amongst Potamogeton crispus, water column and sediments. Results showed that, in oligotrophic state, P. crispus mainly relied on their roots to absorb nutrients from sediments for maintaining stable growth, with the maximum average height, fresh weight and relative growth rate of 12.85 cm, 4.86 g ind −1 and 0.062, respectively. However, the eutrophic conditions (TN of 4 mg L −1 and TP of 0.3 mg L −1 ) triggered the shift of the nutrient absorption pathway from the roots to the shoots to some extent, that is, the shoots of P. crispus gradually became a remarkable pathway to directly absorb nutrients from the water column. Approximately 49.85% and 18.35% of total nitrogen (TN) and total phosphorus (TP) from overlying water were allocated to the shoots of P. crispus, but had no effects on the growth, photosynthesis and ecological stoichiometric differences (p > 0.05). Sediments acting as a nitrogen (N) source supported nearly 11.56% of TN for shoot uptake and simultaneously received around 13.33% of TP subsidy from the overlying water. The no longer sole dependence of submerged macrophytes on their root system to absorb N and phosphorus nutrients indicated that the ability of shoots to absorb nutrients increased with the gradual increase in nutrients in water column. These findings imply that the large specific surface area of shoots is beneficial for restoring eutrophic waters.
Xu, X, Zhou, Z, Liu, Y, Wen, S, Guo, Z, Gao, L & Wang, F 2019, 'Optimising passivation shell thickness of single upconversion nanoparticles using a time-resolved spectrometer', APL Photonics, vol. 4, no. 2, pp. 026104-026104.
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© 2019 Author(s). Lanthanide-doped upconversion nanoparticles (UCNPs) are the most efficient multi-photon probe that can be used for deep tissue bio-imaging, fluorescence microscopy, and single molecule sensing applications. Passivating UCNPs with inert shell has been demonstrated to be an effective method to significantly enhance their brightness. However, this method also increases the overall size of the nanoparticles, which limited their cellular applications. Current reports to optimise the thickness of the shell are based on the spectrum measurement of ensembles of UCNPs, which are less quantitative. The characterisation of single UCNPs would be desirable, but is limited by the sensitivity of conventional spectrometers. We developed an optical filter-based spectrometer coupled to a laser scanning microscopy system and achieved a high degree of sensitivity - seven times more than the traditional amount. Through highly controlled syntheses of a range Yb 3+ and Tm 3+ doped UCNPs with different shell thickness, quantitative characterization of the emission intensity and lifetime on single UCNPs were comprehensively studied using a home-made optical system. We found that the optimal shell thickness was 6.3 nm. We further demonstrated that the system was sensitive enough to measure the time-resolved spectrum from a single UCNP, which is significantly useful for a comprehensive study of the energy transfer process of UCNPs.
Xu, Y, Gao, Y, Wu, C, Fang, J & Li, Q 2019, 'Robust topology optimization for multiple fiber-reinforced plastic (FRP) composites under loading uncertainties', Structural and Multidisciplinary Optimization, vol. 59, no. 3, pp. 695-711.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. This study proposes a non-deterministic robust topology optimization of ply orientation for multiple fiber-reinforced plastic (FRP) materials, such as carbon fiber–reinforced plastic (CFRP) and glass fiber–reinforced plastic (GFRP) composites, under loading uncertainties with both random magnitude and random direction. The robust topology optimization is considered here to minimize the fluctuation of structural performance induced by load uncertainty, in which a joint cost function is formulated to address both the mean and standard deviation of compliance. The sensitivities of the cost function are derived with respect to the design variables in a non-deterministic context. The discrete material optimization (DMO) technique is extended here to accommodate robust topology optimization for FRP composites. To improve the computational efficiency, the DMO approach is revised to reduce the number of design variables by decoupling the selection of FRP materials and fiber orientations. In this study, four material design examples are presented to demonstrate the effectiveness of the proposed methods. The robust topology optimization results exhibit that the composite structures with the proper ply orientations are of more stable performance when the load fluctuates.
Xu, Z, Liu, Y, Li, M & Li, Y 2019, 'Linearly Polarized Shaped Power Pattern Synthesis With Dynamic Range Ratio Control for Arbitrary Antenna Arrays', IEEE Access, vol. 7, pp. 53621-53628.
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© 2013 IEEE. This paper extends the semidefinite relaxation (SDR) method to be capable of synthesizing linearly polarized shaped patterns with accurate control of sidelobe level (SLL), cross-polarization level (XPL), and dynamic range ratio (DRR) of the excitation distribution for arbitrary antenna arrays. In addition, by using the vectorial active element patterns, mutual coupling and platform effect can be also incorporated into the proposed vectorial shaped pattern synthesis. Three examples for synthesizing linearly polarized patterns with different pattern shape requirements and different antenna array geometries have been conducted to check the effectiveness and robustness of the proposed method. Compared to the original vectorial shaped pattern synthesis without DRR control, the proposed method with the DRR control can significantly reduce the obtained DRR which is very useful in many antenna array applications.
Xue, C, Li, W, Li, J & Wang, K 2019, 'Numerical investigation on interface crack initiation and propagation behaviour of self-healing cementitious materials', Cement and Concrete Research, vol. 122, pp. 1-16.
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© 2019 Elsevier Ltd Based on the extended finite element method (XFEM) and cohesive surface (CS) technique, the interface cracks between healing agent and cementitious materials in the self-healing mortar beam under three-point bending are numerically investigated in this study. After obtaining original crack feature using XFEM, a parametric study was conducted to comprehensively discuss effects of the elastic ratio between self-healing agent and cementitious materials, bonding strength and fracture toughness of the self-healing agent-cementitious material interface on crack initiation and propagation. The results reveal that crack initiation seriously degrades stiffness of cementitious materials. Flexible healing agent increases the probability of new crack initiation and healed crack propagation, while stiffer healing agent induces obvious stress concentration around the interface, increasing fracture chance of interfacial zone. The numerical model and methodology developed in this study are useful to investigate the self-healing behaviours and develop high efficient self-healing cementitious materials.
Xue, C, Li, W, Li, J, Tam, VWY & Ye, G 2019, 'A review study on encapsulation‐based self‐healing for cementitious materials', Structural Concrete, vol. 20, no. 1, pp. 198-212.
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Encapsulation‐based self‐healing technology is an effective method for healing the crack‐deteriorated cementitious material. Encapsulation‐based self‐healing initiates by crack occurrence and progresses by chemical reaction of released self‐healing agents in the cracks, which are contained in capsules. In this paper, a review has been conducted on various healing agents, encapsulation techniques, as well as experimental approaches, basing on existing substantial studies. Recently, there is no consistent agreement on the effective criteria for evaluating encapsulation‐based self‐healing and mature solution for increasing the survival ratio of capsules during mixing. However, the polyurethane‐based healing agents filled in glass or ceramic tubes are popularly applied for self‐healing cementitious materials. Besides, the polymer capsules present promising attractions for engineering application. Mechanical strength and durability are the most widely used self‐healing efficiency assessment indexes. On the other hand, nondestructive technique and numerical modeling have also extensively adopted to visualize and evaluate the self‐healing behavior of cementitious materials. However, there are still some challenges, which require further investigations, such as behavior of crack propagation, kinetics of healing agent in discrete crack surfaces, effect of inserted capsules on the mechanical properties of self‐healed cementitious materials.
Xue, J, Fan, Y, Su, B & Fuentes, S 2019, 'Assessment of canopy vigor information from kiwifruit plants based on a digital surface model from unmanned aerial vehicle imagery', International Journal of Agricultural and Biological Engineering, vol. 12, no. 1, pp. 165-171.
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Xue, J-X, Sun, C-Y, Cheng, J-J, Xu, M-L, Li, Y-F & Yu, S 2019, 'Wheat ear growth modeling based on a polygon', Frontiers of Information Technology & Electronic Engineering, vol. 20, no. 9, pp. 1175-1184.
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© 2019, Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature. Visual inspection of wheat growth has been a useful tool for understanding and implementing agricultural techniques and a way to accurately predict the growth status of wheat yields for economists and policy decision makers. In this paper, we present a polygonal approach for modeling the growth process of wheat ears. The grain, lemma, and palea of wheat ears are represented as editable polygonal models, which can be re-polygonized to detect collision during the growth process. We then rotate and move the colliding grain to resolve the collision problem. A linear interpolation and a spherical interpolation are developed to simulate the growth of wheat grain, performed in the process of heading and growth of wheat grain. Experimental results show that the method has a good modeling effect and can realize the modeling of wheat ears at different growth stages.
Xue, S, Lu, J & Zhang, G 2019, 'Cross-domain network representations', Pattern Recognition, vol. 94, pp. 135-148.
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Xue, S, Zhang, X, Ngo, HH, Guo, W, Wen, H, Li, C, Zhang, Y & Ma, C 2019, 'Food waste based biochars for ammonia nitrogen removal from aqueous solutions', Bioresource Technology, vol. 292, pp. 121927-121927.
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© 2019 Elsevier Ltd Biochar derived from waste has been increasingly considered as a potential green adsorbent due to its significant ability and affordable production costs. This study prepared and evaluated 7 types of food waste-based biochars (FWBBs) (including meat and bone, starchy staples, leafy stemmed vegetables, nut husks, fruit pericarp, bean dreg and tea leaves). The impacts of raw materials, pyrolysis temperatures (300, 400, 500, 600 and 700 °C), and residence time (2 h and 4 h) on the removal of ammonia nitrogen at different ammonia nitrogen concentrations (5, 10, 20, 50, 100, 150 mg/L) were investigated. The batch equilibrium and kinetic experiments confirmed that a FWBB dosage of 3 g/L at 25 °C could remove up to 92.67% ammonia nitrogen. The Langmuir isotherm model had the best fit to equilibrium experimental data with a maximum adsorption capacity of 7.174 mg/g at 25 °C. The pseudo-second order kinetic model well describes the ammonia nitrogen adsorption.
Xuefan Gu, Zhang, H, Liu, H, Tang, Y & Zhang, Z 2019, 'Addition Reaction of Benzaldehydes and Chloroform Catalyzed by Modified Calcium Oxide', Russian Journal of Physical Chemistry A, vol. 93, no. 10, pp. 2009-2015.
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Yamasaki, H, Vijayan, MK & Hsieh, M-H 2019, 'Hierarchy of quantum operations in manipulating coherence and entanglement', Quantum, vol. 5, p. 480.
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Quantum resource theory under different classes of quantum operationsadvances multiperspective understandings of inherent quantum-mechanicalproperties, such as quantum coherence and quantum entanglement. We establishhierarchies of different operations for manipulating coherence and entanglementin distributed settings, where at least one of the two spatially separatedparties are restricted from generating coherence. In these settings, weintroduce new classes of operations and also characterize those maximal, i.e.,the resource-non-generating operations, progressing beyond existing studies onincoherent versions of local operations and classical communication and thoseof separable operations. The maximal operations admit asemidefinite-programming formulation useful for numerical algorithms, whereasthe existing operations not. To establish the hierarchies, we prove a sequenceof inclusion relations among the operations by clarifying tasks whereseparation of the operations appears. We also demonstrate an asymptoticallynon-surviving separation of the operations in the hierarchy in terms ofperformance of the task of assisted coherence distillation, where a separationin a one-shot scenario vanishes in the asymptotic limit. Our results serve asfundamental analytical and numerical tools to investigate interplay betweencoherence and entanglement under different operations in the resource theory.
Yan, H, Yu, X, Zhang, Y, Zhang, S, Zhao, X & Zhang, L 2019, 'Single Image Depth Estimation With Normal Guided Scale Invariant Deep Convolutional Fields', IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 1, pp. 80-92.
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Yan, Z, Liu, W, Wen, S & Yang, Y 2019, 'Multi-Label Image Classification by Feature Attention Network', IEEE Access, vol. 7, pp. 98005-98013.
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Learning the correlation among labels is a standing-problem in the multi-label image recognition task. The label correlation is the key to solve the multi-label classification but it is too abstract to model. Most solutions try to learn image label dependencies to improve multi-label classification performance. However, they have ignored two more realistic problems: object scale inconsistent and label tail (category imbalance). These two problems will impact the bad influence on the classification model. To tackle these two problems and learn the label correlations, we propose feature attention network (FAN) which contains feature refinement network and correlation learning network. FAN builds top-down feature fusion mechanism to refine more important features and learn the correlations among convolutional features from FAN to indirect learn the label dependencies. Following our proposed solution, we achieve performed classification accuracy on MSCOCO 2014 and VOC 2007 dataset.
Yang, B, Wen, D, Qin, L, Zhang, Y, Wang, X & Lin, X 2019, 'Fully Dynamic Depth-First Search in Directed Graphs.', Proc. VLDB Endow., vol. 13, no. 2, pp. 142-154.
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Depth-first search (DFS) is a fundamental and important algorithm in graph analysis. It is the basis of many graph algorithms such as computing strongly connected components, testing planarity, and detecting biconnected components. The result of a DFS is normally shown as a DFS-Tree. Given the frequent updates in many real-world graphs (e.g., social networks and communication networks), we study the problem of DFS-Tree maintenance in dynamic directed graphs. \other{In the literature, most works focus on the DFS-Tree maintenance problem in undirected graphs and directed acyclic graphs.} However, their methods cannot easily be applied in the case of general directed graphs. Motivated by this, we propose a framework and corresponding algorithms for both edge insertion and deletion in general directed graphs. We further give several optimizations to speed up the algorithms. We conduct extensive experiments on 12 real-world datasets to show the efficiency of our proposed algorithms.
Yang, C-T, Xu, Y, Pourhassan-Moghaddam, M, Tran, DP, Wu, L, Zhou, X & Thierry, B 2019, 'Surface Plasmon Enhanced Light Scattering Biosensing: Size Dependence on the Gold Nanoparticle Tag', Sensors, vol. 19, no. 2, pp. 323-323.
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Surface plasmon enhanced light scattering (SP-LS) is a powerful new sensing SPR modality that yields excellent sensitivity in sandwich immunoassay using spherical gold nanoparticle (AuNP) tags. Towards further improving the performance of SP-LS, we systematically investigated the AuNP size effect. Simulation results indicated an AuNP size-dependent scattered power, and predicted the optimized AuNPs sizes (i.e., 100 and 130 nm) that afford extremely high signal enhancement in SP-LS. The maximum scattered power from a 130 nm AuNP is about 1700-fold higher than that obtained from a 17 nm AuNP. Experimentally, a bio-conjugation protocol was developed by coating the AuNPs with mixture of low and high molecular weight PEG molecules. Optimal IgG antibody bioconjugation conditions were identified using physicochemical characterization and a model dot-blot assay. Aggregation prevented the use of the larger AuNPs in SP-LS experiments. As predicted by simulation, AuNPs with diameters of 50 and 64 nm yielded significantly higher SP-LS signal enhancement in comparison to the smaller particles. Finally, we demonstrated the feasibility of a two-step SP-LS protocol based on a gold enhancement step, aimed at enlarging 36 nm AuNPs tags. This study provides a blue-print for the further development of SP-LS biosensing and its translation in the bioanalytical field.
Yang, D, Du, L, Liu, H & Ni, W 2019, 'Novel Polarimetric Contrast Enhancement Method Based on Minimal Clutter to Signal Ratio Subspace', IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 11, pp. 8570-8583.
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Yang, D, Zou, Y, Zhang, J & Li, G 2019, 'C-RPNs: Promoting object detection in real world via a cascade structure of Region Proposal Networks', Neurocomputing, vol. 367, pp. 20-30.
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© 2019 Elsevier B.V. Recently, significant progresses have been made in object detection on common benchmarks (i.e., Pascal VOC). However, object detection in real world is still challenging due to the serious data imbalance. Images in real world are dominated by easy samples like the wide range of background and some easily recognizable objects, for example. Although two-stage detectors like Faster R-CNN achieved big successes in object detection due to the strategy of extracting region proposals by Region Proposal Network, they show their poor adaption in real-world object detection as a result of without considering mining hard samples during extracting region proposals. To address this issue, we propose a Cascade framework of Region Proposal Networks, referred to as C-RPNs, which adopts multiple stages to mine hard samples while extracting region proposals and learn stronger classifiers. Meanwhile, a feature chain and a score chain are proposed to help learning more discriminative representations for proposals. Moreover, a loss function of cascade stages is designed to train cascade classifiers through backpropagation. Our proposed method has been evaluated on Pascal VOC and several challenging datasets like BSBDV 2017, CityPersons, etc. Our method achieves competitive results compared with the current state-of-the-arts and attains all-sided improvements in error analysis, validating its efficacy for detection in real world.
Yang, G, Jiang, Y, Nimbalkar, S, Sun, Y & Li, N 2019, 'Influence of Particle Size Distribution on the Critical State of Rockfill', Advances in Civil Engineering, vol. 2019, no. 1, pp. 1-7.
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In order to study the effect of particle size distribution on the critical state of rockfill, a series of large‐scale triaxial tests on rockfill with different maximum particle sizes were performed. It was observed that the intercept and gradient of the critical state line in the e − p′ plane decreased as the grading broadened with the increase in particle size while the gradient of the critical state line in the p′ − q plane increased as the particle size increased. A power law function is found to appropriately describe the relationship between the critical state parameters and maximum particle size of rockfill.
Yang, G, Yan, X, Nimbalkar, S & Xu, J 2019, 'Effect of Particle Shape and Confining Pressure on Breakage and Deformation of Artificial Rockfill', International Journal of Geosynthetics and Ground Engineering, vol. 5, no. 2.
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© 2019, Springer Nature Switzerland AG. The rockfill exhibits a substantial amount of particle breakage when subjected to higher range of stresses. The deformations of rockfill under such excessive stresses often lead to failure and cannot be ignored. The degree of particle breakage is related to the type of the material as well as the particle shape. Based on this, artificially simulated rockfill materials with three different aggregate shapes (prism, cube, and cylinder) were prepared by cement paste-casting method. Through a series of medium-sized triaxial shear tests, the effects of confining pressure and particle shape on the fracture characteristics of the artificial rockfill and its secant modulus were investigated. The useful relationships between particle sphericity and roundness with deformation modulus and particle breakage rate were proposed.
Yang, J, Zhang, Y, Zhang, W & Lin, X 2019, 'Cost optimization based on influence and user preference', Knowledge and Information Systems, vol. 61, no. 2, pp. 695-732.
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Yang, L, Zhi, Y, Wei, T, Yu, S & Ma, J 2019, 'Inference attack in Android Activity based on program fingerprint', Journal of Network and Computer Applications, vol. 127, pp. 92-106.
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© 2018 Private breach has always been an important threat to mobile security. Recent studies show that an attacker can infer users’ private information through side channels, such as the use of runtime memory and network usage. For side-channel attacks, malicious applications generally run parallel in the background with a foreground application and stealthily collect side-channel information. In this paper, we analyze the relationship between memory changes and Activity transition, then use side-channel information to label an Activity and build an Activity signature database. We show how to use the runtime memory exposure to infer the Activity transition of the current application and use other side channels to infer its Activity interface. We demonstrate the effectiveness of the attacks with 5 popular applications that contain user sensitive information, and successfully inferred most of the Activity transition and Activity interface process. Moreover, we propose a protection scheme which can effectively resist side-channel attacks.
Yang, R, Huang, J, Griffiths, DV, Li, J & Sheng, D 2019, 'Importance of soil property sampling location in slope stability assessment', Canadian Geotechnical Journal, vol. 56, no. 3, pp. 335-346.
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Site investigations provide characterization of soil properties, but inevitable uncertainty remains at locations that have not been examined. Only a limited scope of site investigation can be conducted due to budget and time constraints, hence there are always risks associated with design based on limited investigation information. An efficient geotechnical site investigation should involve choosing the optimal number and location of borehole sites to gain adequate information for a given cost. Using a slope as an example, this paper proposes a framework to find the best sampling location that gives the most information while minimizing the probability of making the wrong decisions. The results suggest that the slope crest appears to be the optimal location to conduct geotechnical site exploration for slope stability assessment.
Yang, S, Gao, B, Jang, A, Shon, HK & Yue, Q 2019, 'Municipal wastewater treatment by forward osmosis using seawater concentrate as draw solution', Chemosphere, vol. 237, pp. 124485-124485.
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Forward osmosis (FO) has been used in the wastewater treatment due to its advantages including low energy consumption and low membrane fouling. In this study, real municipal wastewater was concentrated by FO process using seawater concentrate as draw solution (DS). The influences of operating conditions such as temperature, flow velocity and sewage pre-filtration on water flux were investigated. Chemical oxygen demand, total nitrogen, ammonia nitrogen and total phosphorus could not be enriched by 4 times while sewage was reduced to 1/4 volume. Excitation and emission matrix fluorescence spectrum showed that a fraction of dissolved organic compounds in sewage transported across membrane into DS. Membrane fouling was evaluated by scanning electronic microscope analysis that a dense cake layer was formed on the membrane surface after sewage filtration. However, water flux of the fouled membrane was highly recovered after 1 h of physical cleaning.
Yang, S, Gao, B, Zhao, P, Wang, C, Shen, X, Yue, Q & Shon, HK 2019, 'The application of forward osmosis for simulated surface water treatment by using trisodium citrate as draw solute', Environmental Science and Pollution Research, vol. 26, no. 9, pp. 8585-8593.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. In this study, trisodium citrate was used as draw solute in forward osmosis (FO) due to its biodegradability and easy reuse after FO dilution. The effect of operating conditions on FO performance was investigated. The study focused on the long-term flux performance and membrane fouling when surface water was used as feed solution. A water flux of 9.8 LMH was observed using 0.5 M trisodium citrate as draw solution in PRO mode. In the long-term FO process, trisodium citrate showed a slight decrease in total flux loss (13.06%) after 20 h of operation. The membrane fouling was significantly reduced after a two-step physical cleaning. A considerable flux recovery (> 95%) of the fouled membrane was finally obtained. Therefore, this study proves the superiority of trisodium citrate as draw solution and paves a new way in applying FO directly for surface water reclamation.
Yang, T, Ding, C & Guo, YJ 2019, 'A Highly Birefringent and Nonlinear AsSe<inline-formula> <tex-math notation='LaTeX'>$_2$</tex-math> </inline-formula>–As<inline-formula> <tex-math notation='LaTeX'>$_2$</tex-math> </inline-formula>S<inline-formula> <tex-math notation='LaTeX'>$_5$</tex-math> </inline-formula> Photonic Crystal Fiber With Two Zero-Dispersion Wavelengths', IEEE Photonics Journal, vol. 11, no. 1, pp. 1-7.
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© 2018 IEEE. A hybrid AsSe2-As2S5 photonic crystal fiber (PCF) with a solid elliptical core is proposed and studied theoretically by the full-vector finite element method. The core and cladding of the PCF are made of AsSe2 and As2S5 glasses, respectively. Simulation results demonstrates that, at the operating wavelength of 1.55 μm, the proposed PCF not only exhibits a very high birefringence of 0.091 but also has large nonlinear coefficients of 147.8 and 78.2 W-1m-1 for the X- A nd Y-polarized (X and Y-pol) modes, respectively. Moreover, it is able to achieve two zero-dispersion wavelengths for both the X-pol (1.52 and 2.19 μm) and Y-pol (1.43 and 2.12 μm) modes. The proposed hybrid PCF exhibits excellent polarization maintaining and the nonlinearity performance, thus, suitable to be used in supercontinuum spectrum generation and polarization maintaining nonlinear signal processing.
Yang, T, Ding, C, Ziolkowski, RW & Guo, YJ 2019, 'A Terahertz (THz) Single-Polarization-Single-Mode (SPSM) Photonic Crystal Fiber (PCF)', Materials, vol. 12, no. 15, pp. 2442-2442.
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This paper presents a novel approach to attain a single-polarization-single-mode (SPSM) photonic crystal fiber (PCF) in the terahertz (THz) regime. An initial circular hole PCF design is modified by introducing asymmetry in the first ring of six air holes in the cladding, i.e., epsilon-near-zero (ENZ) material is introduced into only four of those air holes and the other two remain air-filled but have different diameters. The resulting fundamental X-polarized (XP) and Y-polarized (YP) modes have distinctly different electric field distributions. The asymmetry is arranged so that the YP mode has a much larger amount of the field distributed in the ENZ material than the XP mode. Since the ENZ material is very lossy, the YP mode suffers a much higher loss than the XP mode. Consequently, after a short propagation distance, the loss difference (LD) between the XP and YP modes will be large enough that only the XP mode still realistically exists in the PCF. To further enhance the outcome, gain material is introduced into the core area to increase the LDs between the wanted XP mode and any unwanted higher order (HO) modes, as well as to compensate for the XP mode loss without affecting the LD between the XP and YP modes. The optimized PCF exhibits LDs between the desired XP mode and all other modes greater than 8.0 dB/cm across a wide frequency range of 0.312 THz. Consequently, the reported PCF only needs a length of 2.5 cm to attain an SPSM result, with the unwanted modes being more than 20 dB smaller than the wanted mode over the entire operational band.
Yang, W, Wang, J, Lu, H, Niu, T & Du, P 2019, 'Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China', Journal of Cleaner Production, vol. 222, pp. 942-959.
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© 2019 Elsevier Ltd Wind energy, acknowledged as a promising form of renewable energy and the fastest-growing clean method for electricity generation, has attracted considerable attention from many scientists and researchers in recent decades. However, wind energy forecasting is still a challenging task owing to its inherent features of non-linearity and randomness. Therefore, this study develops a hybrid wind energy forecasting and analysis system including a deterministic forecasting module and an uncertainty analysis module to mitigate the challenges in existing studies. In particular, these challenges are as follows: (1) It is difficult to guarantee that the data characteristics underlying the time series are effectively extracted; (2) in the modeling of each subseries, i.e., when the original data is decomposed into some time series, forecasting accuracy and stability are not simultaneously considered, and thus, they are not properly modeled; and (3) the best function to perform a deterministic forecasting and uncertainty analysis based on the forecaster of each subseries is unknown. The developed hybrid system consists of three steps: First, data preprocessing is conducted to capture and mine the main feature of the wind energy time series and weaken the noises’ negative effects; second, multi-objective optimization is proposed to achieve the forecasting of each subseries with improvements in accuracy and stability; finally, search for the best function, which obtains the deterministic forecasting and uncertainty analysis results using an optimized extreme learning machine based on different modeling objectives, is conducted. Experimental simulations are performed using data from three sites in a real wind farm, which indicate that the developed system has a better performance in engineering applications than that of other methods. Furthermore, this system could not only be used as an effective tool for wind energy deterministic forecasting and uncertainty ...
Yang, Y, Liu, Y, Ma, X, Li, M, Xu, K-D & Guo, YJ 2019, 'Synthesizing Unequally Spaced Pattern-Reconfigurable Linear Arrays With Minimum Interspacing Control', IEEE Access, vol. 7, pp. 58893-58900.
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© 2013 IEEE. Previously, the alternating convex optimization (ACO) was used to reduce the number of elements in the single-pattern linear array. This work extends the ACO method to synthesize the unequally spaced sparse linear arrays with reconfigurable multiple patterns. In this extended ACO, the minimum interspacing constraint can be easily incorporated in the sparse array synthesis by performing a set of constrained alternating convex optimizations. Three examples for synthesizing sparse linear array with different multiple-pattern requirements are conducted to validate the effectiveness, robustness, and advantages of the proposed method. The synthesis results show that the proposed method can effectively reduce the number of elements in the reconfigurable multiple-pattern linear arrays with good control of the sidelobe levels and minimum interspacing. The comparisons with other methods are also given in the examples.
Yang, Y, Wu, C, Liu, Z, Liang, X & Xu, S 2019, 'Experimental investigation on the dynamic behaviors of UHPFRC after exposure to high temperature', Construction and Building Materials, vol. 227, pp. 116679-116679.
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© 2019 Elsevier Ltd Split Hopkinson pressure bar (SHPB) tests were conducted to experimentally study the dynamic behaviors of ultra-high-performance fiber-reinforced concrete (UHPFRC) after being first exposed to elevated temperatures, followed by cooling. The dynamic stress–strain relationships were measured as key parameters to study the effects of high temperature on the dynamic behaviors of fire-damaged UHPFRC. In addition, dynamic increase factor (DIF) values for the dynamic compressive strength were generated. It was found that the strength of UHPFRC increased with the increase in strain rates with high temperatures. A significant difference in the dynamic compressive strength was found under two different temperature scenarios, i.e., elevated temperatures and cooling. Scanning electron microscopy (SEM) analysis was conducted to understand the macroscopic failure phenomenon, element composition and concrete hydration process. The results provide a basis for assessing the impact resistance and anti-collapse resistance of fire-damaged UHPFRC structures.
Yang, Y, Zhang, W, Zhang, Y, Lin, X & Wang, L 2019, 'Selectivity Estimation on Set Containment Search', Data Science and Engineering, vol. 4, no. 3, pp. 254-268.
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Abstract In this paper, we study the problem of selectivity estimation on set containment search. Given a query record Q and a record dataset $${\mathcal {S}}$$ S , we aim to accurately and efficiently estimate the selectivity of set containment search of query Q over $${\mathcal {S}}$$ S . We first extend existing distinct value estimating techniques to solve this problem and develop an inverted list and G-KMV sketch-based approach IL-GKMV. We analyze that the performance of IL-GKMV degrades with the increase in vocabulary size. Motivated by limitations of existing techniques and the inherent challenges of the problem, we resort to developing effective and efficient sampling approaches and propose an ordered trie structure-based sampling approach named OT-Sampling. OT-Sampling partitions records based on element frequency and occurrence patterns and is significantly more accurate compared with simple random sampling method and IL-GKMV. To further enhance the performance, a divide-and-conquer-based sampling approach, DC-Sampling, is presented with an inclusion/exclusion prefix to explore the pruning opportunities. Meanwhile, we consider weighted set containment selectivity estimation and devise str...
Yang, Z, Huang, Y, Liu, A, Fu, J & Wu, D 2019, 'Nonlinear in-plane buckling of fixed shallow functionally graded graphene reinforced composite arches subjected to mechanical and thermal loading', Applied Mathematical Modelling, vol. 70, pp. 315-327.
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© 2019 Elsevier Inc. The nonlinear in-plane buckling analysis for fixed shallow functionally graded (FG) graphene reinforced composite arches which are subjected to uniform radial load and temperature field is presented in this paper. The arch is composed of multiple graphene platelet reinforced composite (GPLRC) layers with gradient changes of concentration of graphene platelets (GPLs) in each layer. The principle of virtual work, combined with the effective materials properties estimated by the Halpin-Tsai micromechanics model for GPLRC layer, is used to derive the nonlinear buckling equilibrium equations of the FG-GPLRC arch, and then the analytical solutions for the limit point and bifurcation buckling loads are obtained. Comprehensive parametric studies are conducted to explore the effects of various distribution patterns and geometries of GPL, temperature field and arch geometry on the nonlinear equilibrium path and buckling behavior of the composite arch. The influence of temperature on the geometric parameters which are defined as switches between limit point buckling, bifurcation buckling and no buckling are also discussed. It is found that a higher temperature field can increase the buckling loads of the FG-GPLRC arch but reduce the value of the minimum geometric parameters that switching the buckling modes. The results also show that even a small amount of GPLs filler content can increase the buckling loads of the FG-GPLRC arch considerably, and distributing more GPLs near the surface layers is the best pattern to enhance the buckling performances of FG-GPLRC arches.
Yao, H, Yuan, X, Zhang, P, Wang, J, Jiang, C & Guizani, M 2019, 'Machine Learning Aided Load Balance Routing Scheme Considering Queue Utilization', IEEE Transactions on Vehicular Technology, vol. 68, no. 8, pp. 7987-7999.
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Yao, J, Wang, J, Tsang, IW, Zhang, Y, Sun, J, Zhang, C & Zhang, R 2019, 'Deep Learning From Noisy Image Labels With Quality Embedding', IEEE Transactions on Image Processing, vol. 28, no. 4, pp. 1909-1922.
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© 1992-2012 IEEE. There is an emerging trend to leverage noisy image datasets in many visual recognition tasks. However, the label noise among datasets severely degenerates the performance of deep learning approaches. Recently, one mainstream is to introduce the latent label to handle label noise, which has shown promising improvement in the network designs. Nevertheless, the mismatch between latent labels and noisy labels still affects the predictions in such methods. To address this issue, we propose a probabilistic model, which explicitly introduces an extra variable to represent the trustworthiness of noisy labels, termed as the quality variable. Our key idea is to identify the mismatch between the latent and noisy labels by embedding the quality variables into different subspaces, which effectively minimizes the influence of label noise. At the same time, reliable labels are still able to be applied for training. To instantiate the model, we further propose a contrastive-additive noise network (CAN), which consists of two important layers: 1) the contrastive layer that estimates the quality variable in the embedding space to reduce the influence of noisy labels and 2) the additive layer that aggregates the prior prediction and noisy labels as the posterior to train the classifier. Moreover, to tackle the challenges in optimization, we deduce an SGD algorithm with the reparameterization tricks, which makes our method scalable to big data. We validate the proposed method on a range of noisy image datasets. Comprehensive results have demonstrated that CAN outperforms the state-of-the-art deep learning approaches.
Yao, L, Wang, X, Sheng, QZ, Dustdar, S & Zhang, S 2019, 'Recommendations on the Internet of Things: Requirements, Challenges, and Directions', IEEE Internet Computing, vol. 23, no. 3, pp. 46-54.
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© 1997-2012 IEEE. The Internet of Things (IoT) is accelerating the growth of data available on the Internet, which makes the traditional search paradigms incapable of digging the information that people need from massive and deep resources. Furthermore, given the dynamic nature of organizations, social structures, and devices involved in IoT environments, intelligent and automated approaches become critical to support decision makers with the knowledge derived from the vast amount of information available through IoT networks. Indeed, IoT is more desirable of an effective and efficient paradigm of proactive discovering rather than postactive searching. This paper discusses some of the important requirements and key challenges to enable effective and efficient thing-of-interest recommendation and provides an array of new perspectives on IoT recommendation.
Yao, M, Ren, J, Akther, N, Woo, YC, Tijing, LD, Kim, S-H & Shon, HK 2019, 'Improving membrane distillation performance: Morphology optimization of hollow fiber membranes with selected non-solvent in dope solution', Chemosphere, vol. 230, pp. 117-126.
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© 2019 Elsevier Ltd This study aimed at improving membrane distillation (MD) performance by mixing various non-solvents (NSs) in polymer dope solutions. The effect of each NS on the inner structure and surface morphology of hollow fiber (HF) membrane was investigated. Membrane morphology is manipulated by controlling liquid-liquid (L-L) and solid-liquid (S-L) demixing time, which is a function of the viscosity and water affinity of dope solutions. Consequently, the addition of NSs altered membrane morphology by affecting the diffusion rate during NS induced phase separation (NIPS) process. The performance results showed that the dope solution composed of 11/71.2/17.8 wt% polyvinylidene fluoride (PVDF)/triethyl phosphate (TEP)/toluene produced the most promising HF membrane for MD. The optimal membrane demonstrated a unique bicontinuous structure with increased porosity and mean pore size. The addition of toluene as NS in dope solutions enhanced crystallization process, which increased the Young's modulus of membrane but slightly decreased its maximum tensile strength at break. The optimal PVDF HF membrane demonstrated a steady flux of 18.9 LMH at 60 °C/20 °C of feed/permeate temperatures and a salt rejection of 99.99% when tested for 72 h. The results suggest that incorporation of toluene as a NS into PVDF dope solutions can increase permeation performance in MD by enhancing the morphology of HF membranes.
Yao, M, Woo, YC, Ren, J, Tijing, LD, Choi, J-S, Kim, S-H & Shon, HK 2019, 'Volatile fatty acids and biogas recovery using thermophilic anaerobic membrane distillation bioreactor for wastewater reclamation', Journal of Environmental Management, vol. 231, pp. 833-842.
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© 2018 Elsevier Ltd The effects of bioreactor temperatures and salinities of an anaerobic membrane distillation bioreactor (anMDBR) on the permeation performance and their potential recovery of bioresources were fully examined in this study. To the best of our knowledge, this is the first study of a lab-scale anMDBR process utilizing sub-merged hollow fiber membranes. The hybrid system utilizing both membrane distillation (MD) and anaerobic bioreactors achieved 99.99% inorganic salt rejection regardless the operation temperatures and high initial flux from (2–4 L m−2 h−1) at 45–65 °C. However, after 7-day operation, the flux dropped by 16–50% proportional to the bioreactor temperatures. It was found that the effects of bioreactor temperatures had strong impacts on both the permeation performance and fouling behavior while salinity had insignificant effect. A compact non-porous fouling layer was observed on the membrane surface from the bioreactor operated at 65 °C while only a few depositions was found on the membrane from 45 °C bioreactor. In the present study, the optimal anMDBR temperature was found to be 45 °C, showing a balanced biogas production and membrane permeation performance including less fouling formation. At this bioreactor temperature (45 °C), the biogas yield was 0.14 L/g CODremoval, while maintaining a methane recovery of 42% in the biogas, similar recovery to those at bioreactor temperatures of 55 and 65 °C. The potential recovery of volatile fatty acids made anMDBR a more economically efficient system, in addition to its lower operation cost and smaller footprint compared with most other technologies for on-site wastewater treatment.
Yao, X, Wu, Q, Zhang, P & Bao, F 2019, 'Adaptive rational fractal interpolation function for image super-resolution via local fractal analysis', Image and Vision Computing, vol. 82, pp. 39-49.
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© 2019 Elsevier B.V. Image super-resolution aims to generate high-resolution image based on the given low-resolution image and to recover the details of the image. The common approaches include reconstruction-based methods and interpolation-based methods. However, these existing methods show difficulty in processing the regions of an image with complicated texture. To tackle such problems, fractal geometry is applied on image super-resolution, which demonstrates its advantages when describing the complicated details in an image. The common fractal-based method regards the whole image as a single fractal set. That is, it does not distinguish the complexity difference of texture across all regions of an image regardless of smooth regions or texture rich regions. Due to such strong presumption, it causes artificial errors while recovering smooth area and texture blurring at the regions with rich texture. In this paper, the proposed method produces rational fractal interpolation model with various setting at different regions to adapt to the local texture complexity. In order to facilitate such mechanism, the proposed method is able to segment the image region according to its complexity which is determined by its local fractal dimension. Thus, the image super-resolution process is cast to an optimization problem where local fractal dimension in each region is further optimized until the optimization convergence is reached. During the optimization (i.e. super-resolution), the overall image complexity (determined by local fractal dimension) is maintained. Compared with state-of-the-art method, the proposed method shows promising performance according to qualitative evaluation and quantitative evaluation.
Yao, Y, Shen, F, Zhang, J, Liu, L, Tang, Z & Shao, L 2019, 'Extracting Multiple Visual Senses for Web Learning', IEEE Transactions on Multimedia, vol. 21, no. 1, pp. 184-196.
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© 1999-2012 IEEE. Labeled image datasets have played a critical role in high-level image understanding. However, the process of manual labeling is both time consuming and labor intensive. To reduce the dependence on manually labeled data, there have been increasing research efforts on learning visual classifiers by directly exploiting web images. One issue that limits their performance is the problem of polysemy. Existing unsupervised approaches attempt to reduce the influence of visual polysemy by filtering out irrelevant images, but do not directly address polysemy. To this end, in this paper, we present a multimodal framework that solves the problem of polysemy by allowing sense-specific diversity in search results. Specifically, we first discover a list of possible semantic senses from untagged corpora to retrieve sense-specific images. Then, we merge visual similar semantic senses and prune noise by using the retrieved images. Finally, we train one visual classifier for each selected semantic sense and use the learned sense-specific classifiers to distinguish multiple visual senses. Extensive experiments on classifying images into sense-specific categories and reranking search results demonstrate the superiority of our proposed approach.
Yao, Y, Shen, F, Zhang, J, Liu, L, Tang, Z & Shao, L 2019, 'Extracting Privileged Information for Enhancing Classifier Learning', IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 436-450.
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© 1992-2012 IEEE. The accuracy of data-driven learning approaches is often unsatisfactory when the training data is inadequate either in quantity or quality. Manually labeled privileged information (PI), e.g., attributes, tags or properties, is usually incorporated to improve classifier learning. However, the process of manually labeling is time-consuming and labor-intensive. Moreover, due to the limitations of personal knowledge, manually labeled PI may not be rich enough. To address these issues, we propose to enhance classifier learning by exploring PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data and obtain much richer PI. In detail, we treat each selected PI as a subcategory and learn one classifier for each subcategory independently. The classifiers for all subcategories are integrated together to form a more powerful category classifier. Particularly, we propose a novel instance-level multi-instance learning model to simultaneously select a subset of training images from each subcategory and learn the optimal SVM classifiers based on the selected images. Extensive experiments on four benchmark data sets demonstrate the superiority of our proposed approach.
Yap, HC, Pang, YL, Lim, S, Abdullah, AZ, Ong, HC & Wu, C-H 2019, 'A comprehensive review on state-of-the-art photo-, sono-, and sonophotocatalytic treatments to degrade emerging contaminants', International Journal of Environmental Science and Technology, vol. 16, no. 1, pp. 601-628.
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© 2018, Islamic Azad University (IAU). Emerging contaminants (ECs) are commonly originated from personal care products, cosmetics, pharmaceuticals, pesticides, dioxins, polycyclic aromatic hydrocarbons (PAHs), and alkylphenolic compounds. Due to the huge development of these industries, these ECs have been constantly detected in wastewater, groundwater, and surface water in hazardous quantity. The discharge of these ECs into the environment causes considerable non-esthetic pollution and could be a great threat to the entire ecosystem. The common wastewater treatment plants (WWTPs) which consist of biological, physical, and chemical treatments such as activated sludge, filtration, adsorption, and coagulation are found to be ineffective for desired removal of ECs. In turn, various emerging advanced oxidation processes (AOPs) such as ultrasonic and ultraviolet irradiation with or without the presence of catalyst have raised great attention due to their great potential in remediation of ECs. This paper presents a critical review on types, recent occurrence, sources, environmental impacts, and emerging treatment methods applicable to treat ECs. The current research and applications of ultrasonic, ultraviolet, and combination of both irradiations to treat ECs in wastewater are particularly reviewed. The effect of key parameters on photo-, sono- and, sonophotocatalytic degradation of ECs are commendably accessed such as ultrasonic power, ultrasonic frequency, light intensity, ultraviolet wavelength, solution pH, oxidizing agents, chemical additives, catalyst dosage, and modification of catalyst. The possible reaction mechanisms of ECs degradation process and kinetic model study are also elucidated in detail. Lastly, future research directions and conclusions are proposed to strengthen the understanding on their fate in water. All this information is vital to predict the negative impacts of ECs on the receiving environment effectively.
Yazdani, D, Nguyen, TT & Branke, J 2019, 'Robust Optimization Over Time by Learning Problem Space Characteristics', IEEE Transactions on Evolutionary Computation, vol. 23, no. 1, pp. 143-155.
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Robust optimization over time is a new way to tackle dynamic optimization problems where the goal is to find solutions that remain acceptable over an extended period of time. The state-of-the-art methods in this domain try to identify robust solutions based on their future predicted fitness values. However, predicting future fitness values is difficult and error prone. In this paper, we propose a new framework based on a multipopulation method in which subpopulations are responsible for tracking peaks and also gathering characteristic information about them. When the quality of the current robust solution falls below the acceptance threshold, the algorithm chooses the next robust solution based on the collected information. We propose four different strategies to select the next solution. The experimental results on benchmark problems show that our newly proposed methods perform significantly better than existing algorithms.
Yazdani, D, Omidvar, MN, Deplano, I, Lersteau, C, Makki, A, Wang, J & Nguyen, TT 2019, 'Real-time seat allocation for minimizing boarding/alighting time and improving quality of service and safety for passengers', Transportation Research Part C: Emerging Technologies, vol. 103, pp. 158-173.
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Rail is considered as one of the most important ways of transferring passengers. High passenger loads has implications on train punctuality. One of the important parameters affecting punctuality is the average boarding/alighting time. Organizing boarding/alighting flows not only reduces the risk of extended dwell time, but also minimizes the risk of injuries and improves the overall service quality. In this paper, we investigate the possibility of minimizing the boarding/alighting time by maintaining a uniform load on carriages through systematic distribution of passengers with flexible tickets, such as season or anytime tickets where no seat information are provided at the time of reservation. To achieve this, the proposed algorithm takes other information such as passenger final destination, uniform load of luggage areas, as well as group travelers into account. Moreover, a discrete event simulation is designed for measuring the performance of the proposed method. The performance of the proposed method is compared with three algorithms on different test scenarios. The results show the superiority of the proposed method in terms of minimizing boarding/alighting time as well as increasing the success rate of assigning group of seats to group of passengers.
Yazdani, M, Babagolzadeh, M, Kazemitash, N & Saberi, M 2019, 'Reliability estimation using an integrated support vector regression – variable neighborhood search model', Journal of Industrial Information Integration, vol. 15, pp. 103-110.
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© 2019 Elsevier Inc. As failure and reliability predictions play a significant role in production systems they have caught the attention of researchers. In this study, Support Vector Regression (SVR), which is known as a powerful neural network method, is developed as a way of forecasting reliability. Generally, SVR is applied in many research environments, and the results illustrate that SVR is a successful method in solving non-linear regression problems. However, SVR parameters tuning is a vital task for performing an accurate reliability estimation. We propose variable neighborhood search (VNS) for continuous space, including some simple but efficient shaking and local search as its main operators, to tune the SVR parameters and create a novel SVR-VNS hybrid system to improve the reliability of estimation accuracy. The proposed method is validated with a benchmark from the former literature and compared with conventional techniques, namely RBF (Gaussian), AR (autoregressive), MLP (logistic), MLP (Gaussian), and SVMG (SVM with genetic algorithm). The experimental results indicate that the proposed model has a superior performance for prediction reliability than other techniques.
Ye, D, He, Q, Wang, Y & Yang, Y 2019, 'An Agent-Based Integrated Self-Evolving Service Composition Approach in Networked Environments', IEEE Transactions on Services Computing, vol. 12, no. 6, pp. 880-895.
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© 2008-2012 IEEE. Service composition is an important research problem in service computing systems, which combines simple and individual services into composite services to fulfill users' complex requirements. Service composition usually consists of four stages, i.e., service discovery, candidate selection, service negotiation and task execution. In self-organising systems, there is the fifth stage of service composition: self-evolution. Most of existing works study only some of the five stages. However, these five stages should be systematically studied so as to develop an integrated and efficient service composition approach. Against this background, this paper proposes an agent-based integrated self-evolving service composition approach. This approach systematically takes the five stages of service composition into consideration. It is also decentralised and self-evolvable. Experimental results demonstrate that the proposed approach can achieve almost the same success rates while uses much less communication overhead and time consumption in comparison with three existing representative approaches.
Ye, J, Yang, X, Xu, M, Chan, PK-S & Ma, C 2019, 'Novel N-Substituted oseltamivir derivatives as potent influenza neuraminidase inhibitors: Design, synthesis, biological evaluation, ADME prediction and molecular docking studies', European Journal of Medicinal Chemistry, vol. 182, pp. 111635-111635.
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Ye, K & Ji, J 2019, 'Current, wave, wind and interaction induced dynamic response of a 5 MW spar-type offshore direct-drive wind turbine', Engineering Structures, vol. 178, pp. 395-409.
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© 2018 Elsevier Ltd This paper studies the dynamic response of a spar-type direct-drive wind turbine subjected to external and internal excitations. A free-free end model is developed for the wind turbine structure with a spar-type floating platform under deep sea condition. Firstly, the spar supported platform with tower structure is modelled as a rigid body while the nacelle is considered as a point mass attached on the top of the tower. Then the dynamic interaction between the drive-train system and the tower is considered by incorporating the modelling of a direct-drive drive-train system. The hydrodynamic and aerodynamic excitations applied include current, wave, and wind excitations as well as buoyant forces. The misalignments of the wind, wave and current are also considered to examine the induced response. With the help of the time history and FFT spectrum, the effects of both hydrodynamic and aerodynamic excitations along with the dynamic interaction between the drive-train system and tower structure on the dynamic behaviour of the spar-type floating platform are investigated under different sea conditions.
Ye, L, Guo, Y, Dong, L, Yu, H, Nguyen, H & Su, SW 2019, 'A fast-converge, real-time auto-calibration algorithm for triaxial accelerometer', Measurement Science and Technology, vol. 30, no. 6, pp. 065010-065010.
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Ye, X, Wang, Q, Wang, S, Sloan, S & Sheng, D 2019, 'Performance of a compaction-grouted soil nail in laboratory tests', Acta Geotechnica, vol. 14, no. 4, pp. 1049-1063.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. This study proposed a new soil nail known as the compaction-grouted soil nail, and a physical model was established to investigate its pull-out behaviour with different grouting pressures. The study on scale effect of the physical model was performed subsequently via numerical modelling. Additionally, interface shear tests were performed using the same boundary conditions as the physical model test. The strength parameters obtained were used to estimate the pull-out resistance of a conventional soil nail. The merits of these two soil nail types were compared based on their pull-out resistances. The physical model test results showed that the pull-out resistance of the compaction-grouted soil nail increases with increasing grouting pressure. In addition, the pull-out resistance exhibits hardening behaviour without a yield point, indicating that the compaction-grouted soil nail enables soils to remain stable against a relatively large deformation before ultimate failure. Furthermore, a higher grouting pressure results in a higher rate of increase for pull-out resistance versus pull-out displacement, which improves the performance of the compaction-grouted soil nail in the stabilization of large deformation problems. A comparison of the two types of soil nails suggests that the new compaction-grouted soil nail is more sensitive to grouting pressure than the conventional soil nail in terms of pull-out resistance improvement. In other words, the performance (pull-out resistance) of the compaction-grouted soil nail can be markedly improved by increasing the grouting pressure without inducing any accidental or undesired cracking or soil displacement.
Ye, X, Wang, S, Wang, Q, Sloan, SW & Sheng, D 2019, 'The influence of the degree of saturation on compaction-grouted soil nails in sand', Acta Geotechnica, vol. 14, no. 4, pp. 1101-1111.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. A series of large-scale model tests was conducted on compaction-grouted soil nails to study the influence of the degree of saturation on the soil response to compaction grouting and pull-out. The experimental results show that the initial degree of saturation of the soil strongly influences the grout injectability, thus the formed diameter of grout bulb. Subsequently, the diameter of the grout bulb alters the pull-out force, with larger grout bulbs generating higher pull-out forces and exhibiting greater hardening behaviour. Interestingly, the initial pull-out forces are the same for the same grouting pressure, regardless of the initial degree of saturation and the subsequently grout bulb. In addition, some of the main factors influencing the pressure grouting and pull-out of the soil nail, as the initial degree of saturation varies, are as follows. First, the variations in the soil pressure and density with the initial degree of saturation are similar to that of the volume of grout injected, and the compression of the soil induced by pressure grouting exhibits a similar evolution with the initial degree of saturation at different locations. Second, the initial degree of saturation of the soil sample plays a dominant role in the change in suction during pressure grouting and pull-out of soil nail. Third, the horizontal soil pressure derived from the pull-out of soil nail propagates closely in the soil sample of lower initial degree of saturation. The vertical soil pressure induced by the vertical soil dilation and squeezing effect varies in accidence with the initial degree of saturation and the grout bulb.
Ye, X, Wang, S, Xiao, X, Sloan, S & Sheng, D 2019, 'Numerical Study for Compaction-Grouted Soil Nails with Multiple Grout Bulbs', International Journal of Geomechanics, vol. 19, no. 2, pp. 04018193-04018193.
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© 2018 American Society of Civil Engineers. A finite-element model was adopted to numerically simulate compaction-grouted soil nailswithmultiple grout bulbs. The numerical model was first verified by the corresponding experimental results. Then a series of numerical simulations were carried out to investigate the pull-out behavior of compaction-grouted soil nails with multiple grout bulbs. Numerical results show that the pull-out force increases with the increasing diameter of the grout bulb and the spacing between the grout bulbs. Furthermore, the pull-out displacement at failure of the soil nail decreases for the bigger grout bulb. Soil nails with larger back-end and smaller front-end grout bulbs experience the higher peak pull-out force and larger pull-out displacement at failure. Two types of failure surfaces were found for the soil nails with a double-grouted bulb, and those with a curved failure surface gave the largest pull-out displacement at failure. It indicates that the grouting point placed at the end of the nail rod is more preferable in field application. An equal spacing and grout bulb diameter can help to maximize the performance of a compaction-grouted soil nail with multiple grout bulbs.
Ye, Y, Jiao, J, Kang, D, Jiang, W, Kang, J, Ngo, HH, Guo, W & Liu, Y 2019, 'The adsorption of phosphate using a magnesia–pullulan composite: kinetics, equilibrium, and column tests', Environmental Science and Pollution Research, vol. 26, no. 13, pp. 13299-13310.
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A magnesia-pullulan (MgOP) composite has been developed to remove phosphate from a synthetic solution. In the present study, the removal of phosphate by MgOP was evaluated in both a batch and dynamic system. The batch experiments investigated the initial pH effect on the phosphate removal efficiency from pH 3 to 12 and the effect of co-existing anions. In addition, the adsorption isotherms, thermodynamics, and kinetics were also investigated. The results from the batch experiments indicate that MgOP has encouraging performance for the adsorption of phosphate, while the initial pH value (3-12) had a negligible influence on the phosphate removal efficiency. Analysis of the adsorption thermodynamics demonstrated that the phosphate removal process was endothermic and spontaneous. Investigations into the dynamics of the phosphate removal process were carried out using a fixed bed of MgOP, and the resulting breakthrough curves were used to describe the column phosphate adsorption process at various bed masses, volumetric flow rates, influent phosphate concentrations, reaction temperatures, and inlet pH values. The results suggest that the adsorption of phosphate on MgOP was improved using an increased bed mass, while the reaction temperature did not significantly affect the performance of the MgOP bed during the phosphate removal process. Furthermore, higher influent phosphate concentrations were beneficial towards increasing the column adsorption capacity for phosphate. Several mathematic models, including the Adams-Bohart, Wolboska, Yoon-Nelson, and Thomas models, were employed to fit the fixed-bed data. In addition, the effluent concentration of magnesium ions was measured and the regeneration of MgOP investigated.
Ye, Y, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Nghiem, LD, Zhang, X & Wang, J 2019, 'Effect of organic loading rate on the recovery of nutrients and energy in a dual-chamber microbial fuel cell', Bioresource Technology, vol. 281, pp. 367-373.
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This study aimed to assess the impacts of organic loading rate (OLR) (435-870 mgCOD/L·d) on nutrients recovery via a double-chamber microbial fuel cell (MFC) for treating domestic wastewater. Electricity generation was also explored at different OLRs, including power density and coulombic efficiency. Experimental results suggested the MFC could successfully treat municipal wastewater with over 90% of organics being removed at a wider range of OLR from 435 to 725 mgCOD/L·d. Besides, the maximum power density achieved in the MFC was 253.84 mW/m2 at the OLR of 435 mgCOD/L·d. Higher OLR may disrupt the recovery of PO43--P and NH4+-N via the MFC. The same pattern was observed for the coulombic efficiency of the MFC and its highest value was 25.01% at the OLR of 435 mgCOD/L·d. It can be concluded that nutrients and electrical power can be simultaneously recovered from municipal wastewater via the dual-chamber MFC.
Ye, Y, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Ni, B-J & Zhang, X 2019, 'Microbial fuel cell for nutrient recovery and electricity generation from municipal wastewater under different ammonium concentrations', Bioresource Technology, vol. 292, pp. 121992-121992.
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© 2019 Elsevier Ltd In the present study, a dual-compartment microbial fuel cell (MFC) was constructed and continuously operated under different influent concentrations of ammonium-nitrogen (5–40 mg/L). The impacts of ammonium on organics removal, energy output and nutrient recovery were investigated. Experimental results demonstrated that this MFC reactor achieved a CDO removal efficiency of greater than 85%. Moreover, excess ammonium concentration in the feed solution compromises the generation of electricity. Simultaneously, the recovery rate of phosphate achieved in the MFC was insignificantly influenced at the wider influent ammonium concentration. In contrast, a high concentration of ammonium may not be beneficial for its recovery.
Ye, Y, Ngo, HH, Guo, W, Liu, Y, Chang, SW, Nguyen, DD, Ren, J, Liu, Y & Zhang, X 2019, 'Feasibility study on a double chamber microbial fuel cell for nutrient recovery from municipal wastewater', Chemical Engineering Journal, vol. 358, pp. 236-242.
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Yeganeh, N & Fatahi, B 2019, 'Effects of choice of soil constitutive model on seismic performance of moment-resisting frames experiencing foundation rocking subjected to near-field earthquakes', Soil Dynamics and Earthquake Engineering, vol. 121, pp. 442-459.
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© 2019 Elsevier Ltd The current study investigated the extent to which the choice of the soil constitutive models can impact the predicted seismic performance of a 20-story reinforced concrete moment-resisting building with a mat foundation considering the Seismic Soil-Structure Interaction (SSSI). Since the soil, in general, is the weakest material, involved in the commonplace geotechnical engineering projects, a soil constitutive model would be able to rule the dynamic response of the system. In this research, the hardening plasticity-based soil constitutive model, named “hyperbolic hardening with hysteretic damping” in conjunction with the two simple, conventional soil models, namely, the isotropic elastic with hysteretic damping model, and elastic-perfectly plastic Mohr-Coulomb with hysteretic damping model, were invoked in the three-dimensional coupled soil-structure numerical simulations using FLAC3D software. The direct method of analysis was used for analyzing the soil-foundation-structure system in one single step without a need to separately analyze each part of the domain. The cherry-picked earthquake excitations, viz, the 1999 Chi-Chi (Taiwan), and 2011 Kohriyama (Japan), were scaled by means of the widely-used response spectrum matching method as per the design response spectrum of a strong rock. The plastic moment concept was employed so as to assign the elastic-perfectly plastic model to the superstructure and its foundation. Additionally, the strain-compatible shear modulus and damping dependency on the cyclic shear strain were considered via the programmed hysteretic damping algorithm. The numerical predictions included the response spectra at the seismic bedrock and ground surface, base shear forces, shear force distributions along the building height, maximum and permanent foundation displacements, and foundation rocking, plus the flooring lateral deflections and inter-story drifts. The life safety limits for the transient and residual total in...
Yetemen, O, Saco, PM & Istanbulluoglu, E 2019, 'Ecohydrology Controls the Geomorphic Response to Climate Change', Geophysical Research Letters, vol. 46, no. 15, pp. 8852-8861.
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AbstractErosion rate data worldwide show complex and contrasting dependencies to climate. Laboratory and numerical model experiments on abiotic landscapes suggest a positive response: Wetter (drier) shift in climate leads to an increase (decrease) in erosion rates with longer relaxation times under a drier climate. We performed eco‐geomorphic landscape evolution model simulations driven by abrupt climate shift in a semiarid climate. With dynamic vegetation, the erosional response to climate shift was opposite to bare soil, variability of erosion rate lessened, and landscape relaxation time scales became insensitive to climate change direction. The spatial geomorphic response to a wetter climate was depositional in vegetated, incisional in barren landscapes, and got reversed with drier climate. A relationship between net erosion rate and mean landscape slope emerged, exhibiting a hysteresis loop. Our study offers insights to the interpretation of observed acceleration of erosion rates and increase mountain relief during Quaternary climate change.
Yin, K, Laranjo, L, Tong, HL, Lau, AYS, Kocaballi, AB, Martin, P, Vagholkar, S & Coiera, E 2019, 'Context-Aware Systems for Chronic Disease Patients: Scoping Review', Journal of Medical Internet Research, vol. 21, no. 6, pp. e10896-e10896.
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Yin, R, Li, K, Zhang, G & Lu, J 2019, 'A deeper graph neural network for recommender systems', Knowledge-Based Systems, vol. 185, pp. 105020-105020.
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© 2019 Elsevier B.V. Interaction data in recommender systems are usually represented by a bipartite user–item graph whose edges represent interaction behavior between users and items. The data sparsity problem, which is common in recommender systems, is the result of insufficient interaction data in the link prediction on graphs. The data sparsity problem can be alleviated by extracting more interaction behavior from the bipartite graph, however, stacking multiple layers will lead to over-smoothing, in which case, all nodes will converge to the same value. To address this issue, we propose a deeper graph neural network in this paper that can predict links on a bipartite user–item graph using information propagation. An attention mechanism is introduced to our method to address the problem that variable size inputs for each node on a bipartite graph. Our experimental results demonstrate that our proposed method outperforms five baselines, suggesting that the interactions extracted help to alleviate the data sparsity problem and improve recommendation accuracy.
Yin, S, Ji, J & Wen, G 2019, 'Complex near-grazing dynamics in impact oscillators', International Journal of Mechanical Sciences, vol. 156, pp. 106-122.
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© 2019 Elsevier Ltd Impact oscillators frequently appear in various physical and engineering systems with non-smooth characteristics and can exhibit different dynamic behavior from the smooth nonlinear systems, including grazing bifurcation in which an impact with zero velocity occurs. This paper investigates the near-grazing dynamics of the multi-degree-of-freedom impact oscillators in the small neighborhood of degenerate grazing points, with a focus on the stability and potential bifurcations of near-grazing period-one impact motions. The high order zero time discontinuity mapping method is applied to perform the prospective analyses of stability and bifurcations. Particularly, this paper shows that the peculiar Neimark-Sacker bifurcations regaining the stability of near-grazing period-one impact motion can be induced by two different ways, either through the interaction between the singular and regular real eigenvalues or via a grazing bifurcation directly. A two degree-of-freedom impact oscillator is taken as an example to present detailed numerical results for the verification of proposed analysis.
Yin, S, Ji, J, Deng, S & Wen, G 2019, 'Degenerate grazing bifurcations in a three-degree-of-freedom impact oscillator', Nonlinear Dynamics, vol. 97, no. 1, pp. 525-539.
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© 2019, Springer Nature B.V. This paper presents the analysis of the degenerate grazing bifurcation in a three-degree-of-freedom impact oscillator by studying the bifurcations of near-grazing period-one impact motion near the degenerate grazing point. Actually, this paper extends the higher-order zero time discontinuity mapping to perform the perturbation analysis of characteristic equation of period-one impact motion and obtains feasible eigenvalue approximation to study the potential bifurcations. The shooting method is applied to verify the validity of the derived approximation and corresponding computation results. In addition to the known bifurcation scenarios of saddle-node and period-doubling, novel Neimark–Sacker bifurcation and related co-dimension two bifurcation points of near-grazing period-one impact motion are also found to arise near the degenerate grazing point in a three-degree-of-freedom impact oscillator. For the in-depth understanding of near-grazing dynamics, the obtained results are compared with the reported results in the single- and two-degree-of-freedom impact oscillators.
Yin, S, Ji, J, Wen, G & Wu, X 2019, 'Use of degeneration to stabilize near grazing periodic motion in impact oscillators', Communications in Nonlinear Science and Numerical Simulation, vol. 66, pp. 20-30.
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© 2018 In controlling the discontinuous grazing bifurcations in impact oscillators, a discrete-in-time linear feedback control strategy in the existing literature was used to change the conditions at the grazing point based on the grazing stability criterion. Though this strategy is effective for its linear inequality constraint of the control parameter domain, a smooth and predictable bifurcating response cannot be obtained for the controlled system, but the grazing induced chaos or period-adding phenomena. To improve this control strategy and stabilize the elementary near-grazing impact periodic motion in impact oscillators, one feasible control criterion is established in this paper by performing the perturbation analysis of the eigenvalues of the Jacobian matrix. It is found that the degeneration of both eigenvalues and grazing bifurcation can stabilize the elementary near-grazing impact periodic motion and eliminate the discontinuous jump phenomenon at grazing.
Ying, H, Wu, J, Xu, G, Liu, Y, Liang, T, Zhang, X & Xiong, H 2019, 'Time-aware metric embedding with asymmetric projection for successive POI recommendation', World Wide Web, vol. 22, no. 5, pp. 2209-2224.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Successive Point-of-Interest (POI) recommendation aims to recommend next POIs for a given user based on this user’s current location. Indeed, with the rapid growth of Location-based Social Networks (LBSNs), successive POI recommendation has become an important and challenging task, since it can help to meet users’ dynamic interests based on their recent check-in behaviors. While some efforts have been made for this task, most of them do not capture the following properties: 1) The transition between consecutive POIs in user check-in sequences presents asymmetric property, however existing approaches usually assume the forward and backward transition probabilities between a POI pair are symmetric. 2) Users usually prefer different successive POIs at different time, but most existing studies do not consider this dynamic factor. To this end, in this paper, we propose a time-aware metric embedding approach with asymmetric projection (referred to as MEAP-T) for successive POI recommendation, which takes the above two properties into consideration. In addition, we exploit three latent Euclidean spaces to project the POI-POI, POI-user, and POI-time relationships. Finally, the experimental results on two real-world datasets show MEAP-T outperforms the state-of-the-art methods in terms of both precision and recall.
Yip, HL, Fattah, IMR, Yuen, ACY, Yang, W, Medwell, PR, Kook, S, Yeoh, GH & Chan, QN 2019, 'Flame–Wall Interaction Effects on Diesel Post-injection Combustion and Soot Formation Processes', Energy & Fuels, vol. 33, no. 8, pp. 7759-7769.
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Copyright © 2019 American Chemical Society. The aim of this study is to investigate the impact of walls on soot processes of a post-injection strategy at different dwell times. The experiments were performed in an optically accessible constant-volume combustion chamber simulating compression ignition engine conditions with moderate exhaust gas recirculation. The experiments with various injection strategies were performed under ambient conditions with gas density, pressure, and temperature of 20.8 kg/m3, 6 MPa, and 1000 K, respectively, and 15 vol % O2 concentration. The main and post injections had a quantity ratio of 8:2 (main/post) totaling 10 mg, and a flat wall was placed 35 mm axially from the injector. The dwell time between the main and post injections was also varied to induce different levels of interaction between the injections. High-speed flame natural luminosity imaging and two-color pyrometry techniques were applied to observe flame characteristics and to obtain soot temperature and KL factor information, respectively. By comparing the wall jet and free jet cases with no direct jet interaction, it was found that the wall affected the post jet flame structure similarly to a single jet or the main jet. However, the post jet with a greater extent of interaction with the main jet induced by shorter dwell time can achieve better mixing for the wall jet case. Increased interaction between the main and post jets also appeared to induce a soot oxidation phase, which was otherwise not observed when the injections were more temporally separated.
Youssef, AM, Abu Abdullah, MM, Pradhan, B & Gaber, AFD 2019, 'Agriculture Sprawl Assessment Using Multi-Temporal Remote Sensing Images and Its Environmental Impact; Al-Jouf, KSA', Sustainability, vol. 11, no. 15, pp. 4177-4177.
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In this paper, multispectral and multi-temporal satellite data were used to assess the spatial and temporal evolution of the agriculture activities in the Al-Jouf region, Kingdom of Saudi Arabia (KSA). In the current study, an attempt was made to map the agriculture sprawl from 1987 to 2017 using temporal Landsat images in a geographic information system (GIS) environment for better decision-making and sustainable agriculture expansion. Our findings indicated that the agriculture activities developed through two crucial stages: high and low rise stages. Low rise stages occurred during three sub-stages from April 1987 to April 1988, from September 1993 to August 1998, and from April 2008 to May 2015, with overall change rates of 37.9, 44.4, and 30.5 km2/year, respectively. High rise stages occurred during three sub-stages from April 1988 to February 1993, from September 2000 to March 2006, and from April 2016 to August 2017, with overall change rates of 132.4, 159.1, and 119.5 km2/year, respectively. Different environmental problems due to uncontrolled agriculture activities were observed in the area, including substantial depletion of the groundwater table. Another environmental impact observed was the appearance of sinkholes that occurred suddenly with no warning signs. These environmental impacts will increase in the future if no regulated restrictions are implemented by decision-makers.
Youssry, A, Chapman, RJ, Peruzzo, A, Ferrie, C & Tomamichel, M 2019, 'Modeling and Control of a Reconfigurable Photonic Circuit using Deep Learning', Quantum Science and Technology 5 (2), 025001 (2020), vol. 5, no. 2, pp. 025001-025001.
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The complexity of experimental quantum information processing devices isincreasing rapidly, requiring new approaches to control them. In this paper, weaddress the problems of practically modeling and controlling an integratedoptical waveguide array chip, a technology expected to have many applicationsin telecommunications and optical quantum information processing. This photoniccircuit can be electrically reconfigured, but only the output optical signalcan be monitored. As a result, the conventional control methods cannot benaively applied. Characterizing such a chip is challenging for three reasons.First, there are uncertainties associated with the Hamiltonian describing thechip. Second, we expect distortions of the control voltages caused by thechip's electrical response, which cannot be directly observed. Finally, thereare imperfections in the measurements caused by losses from coupling the chipexternally to optical fibers. We developed a deep neural network approach tosolve these problems. The architecture is designed specifically to overcome theaforementioned challenges using a Gated Recurrent Unit (GRU)-based network asthe central component. The Hamiltonian is estimated as a blackbox, while therules of quantum mechanics such as state evolution is embedded in the structureas a whitebox. The resulting overall graybox model of the chip shows goodperformance both quantitatively in terms of the mean square error andqualitatively in terms of the predicted waveforms. We use this neural networkto solve a classical and a quantum control problem. In the classicalapplication we find a control sequence to approximately realize atime-dependent output power distribution. For the quantum application we obtainthe control voltages to realize a target set of quantum gates. The proposedmethod is generic and can be applied to other systems that can only be probedindirectly.
Yu, C, Wang, H, Wu, Z-X, Sun, W-J & Fatahi, B 2019, 'Analytical Solution for Pollutant Diffusion in Soils with Time-Dependent Dispersion Coefficient', International Journal of Geomechanics, vol. 19, no. 10, pp. 04019109-04019109.
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Yu, D, Fu, B, Xu, G & Qin, A 2019, 'Constrained nonnegative matrix factorization-based semi-supervised multilabel learning', International Journal of Machine Learning and Cybernetics, vol. 10, no. 5, pp. 1093-1100.
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Yu, E, Sun, J, Li, J, Chang, X, Han, X-H & Hauptmann, AG 2019, 'Adaptive Semi-Supervised Feature Selection for Cross-Modal Retrieval', IEEE Transactions on Multimedia, vol. 21, no. 5, pp. 1276-1288.
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In order to exploit the abundant potential information of the unlabeled data and contribute to analyzing the correlation among heterogeneous data, we propose the semi-supervised model named adaptive semi-supervised feature selection for cross-modal retrieval. First, we utilize the semantic regression to strengthen the neighboring relationship between the data with the same semantic. And the correlation between heterogeneous data can be optimized via keeping the pairwise closeness when learning the common latent space. Second, we adopt the graph-based constraint to predict accurate labels for unlabeled data, and it can also keep the geometric structure consistency between the label space and the feature space of heterogeneous data in the common latent space. Finally, an efficient joint optimization algorithm is proposed to update the mapping matrices and the label matrix for unlabeled data simultaneously and iteratively. It makes samples from different classes to be far apart, while the samples from same class lie as close as possible. Meanwhile, the l 2,1 -norm constraint is used for feature selection and outlier reduction when the mapping matrices are learned. In addition, we propose learning different mapping matrices corresponding to different sub-tasks to emphasize the semantic and structural information of query data. Experiment results on three datasets demonstrate that our method performs better than the state-of-the-art methods.
Yu, H, Lu, W, Liu, D, Han, Y & Wu, Q 2019, 'Speeding up Gaussian Belief Space Planning for Underwater Robots Through a Covariance Upper Bound', IEEE Access, vol. 7, pp. 121961-121974.
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Yu, L, Zeng, S, Merigó, JM & Zhang, C 2019, 'A new distance measure based on the weighted induced method and its application to Pythagorean fuzzy multiple attribute group decision making', International Journal of Intelligent Systems, vol. 34, no. 7, pp. 1440-1454.
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© 2019 Wiley Periodicals, Inc. This paper investigates a novel induced ordered weighted averaging (IOWA) distance operator and its application in Pythagorean fuzzy (PF) multiattribute group decision making (MAGDM). First, a new induced aggregated distance operator named the weighted IOWA distance (WIOWAD) operator is developed, which differs from the existing methods in that it considers the dual roles of the order-inducing variables at the same time. In other words, in addition to inducing the order of the arguments, the order-inducing variables of the WIOWAD operator also plays an important role in moderating the associated weight vector. Some useful properties and different families of the WIOWAD are also discussed. Then, an extension of the WIOWAD within the PF situation is presented, thus obtaining the PFWIOWAD operator. Furthermore, a MAGDM method based on the PFWIOWAD is introduced. Finally, the practicality and effectiveness of proposed approach are illustrated in a research and development project selection problem.
Yu, X, Fernando, B, Hartley, R & Porikli, F 2019, 'Semantic Face Hallucination: Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Yu, Y, Chen, X, Gao, W, Wu, D & Castel, A 2019, 'Impact of atmospheric marine environment on cementitious materials', Corrosion Science, vol. 148, pp. 366-378.
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© 2018 Elsevier Ltd This paper presents a novel and comprehensive numerical method to study the impact of the atmospheric marine environment on cementitious materials. The transportations of moisture, aqueous and gaseous substances are modelled by a multi-species transportation model. A hybrid thermodynamic modelling method is developed to consider the chemical interactions, including carbonation, chloride binding, and other simultaneous chemical reactions. The proposed method is implemented to model the reported experiments, and the detailed degradation mechanisms are revealed. Furthermore, the significances of modelling the carbonation-induced water release and the mutual influences between carbonation and chloride aerosol attack are demonstrated.
Yu, Y, Chen, X, Gao, W, Wu, D & Castel, A 2019, 'Modelling non-isothermal chloride ingress in unsaturated cement-based materials', Construction and Building Materials, vol. 217, pp. 441-455.
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© 2019 Elsevier Ltd The realistic environments for engineering practices are diverse, where the temperature, relative humidity, and surface ionic concentrations often experience seasonal variations. The accurate evaluation of the multi-species transportation is crucial in terms of durability assessment of cement-based materials. In this work, an improved moisture transportation model is developed, which takes the influences of the changes in relative humidity, temperature and microstructure into account. The moisture transportation model is coupled with the Poisson-Nernst-Planck model to study the chloride ingress problems. To model the chloride binding effect, both the pre-designed chloride binding isotherm and the thermodynamic modelling method are implemented. In the thermodynamic method, the physical and chemical binding of chloride are modelled in details, and coupled with other simultaneous reactions so as to model the variations of the microstructure. The proposed method is validated against a variety of reported experiments and a long-term field study. The Numerical modelling demonstrates the effectiveness of the improved moisture transportation model and the significance of using the thermodynamic modelling method for long-term durability assessments.
Yu, Y, Dackermann, U, Li, J & Niederleithinger, E 2019, 'Wavelet packet energy–based damage identification of wood utility poles using support vector machine multi-classifier and evidence theory', Structural Health Monitoring, vol. 18, no. 1, pp. 123-142.
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This article presents a novel assessment framework to identify the health condition of wood utility poles. The innovative approach is based on the integration of data mining and machine learning methods and combines advanced signal processing, multi-sensor data fusion and decision ensembles to classify different damage condition types of wood poles. In the proposed framework, wavelet packet analysis is employed to transform captured multi-channel stress wave signals into energy information, which is consequently compressed by principal component analysis to extract a feature vector. Furthermore, support vector machine multi-classifier, optimized by genetic algorithm, is designed to identify the pole condition type. Finally, evidence theory is applied to fuse different assessment results from different sensors for a final decision. For validation of the proposed approach, the wood pole specimens with three common damage condition types are tested using a novel multi-sensor narrow-band frequency-excitation non-destructive testing system in the laboratory. The final experimental analysis results confirm that the proposed approach is capable of making full use of multi-sensor information and providing an effective and accurate identification on types of conditions in wood poles.
Yu, Y, Li, J, Li, Y, Li, S, Li, H & Wang, W 2019, 'Comparative Investigation of Phenomenological Modeling for Hysteresis Responses of Magnetorheological Elastomer Devices', International Journal of Molecular Sciences, vol. 20, no. 13, pp. 3216-3216.
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Magnetorheological elastomer (MRE) is a type of magnetic soft material consisting of ferromagnetic particles embedded in a polymeric matrix. MRE-based devices have characteristics of adjustable stiffness and damping properties, and highly nonlinear and hysteretic force–displacement responses that are dependent on external excitations and applied magnetic fields. To effectively implement the devices in mitigating the hazard vibrations of structures, numerically traceable and computationally efficient models should be firstly developed to accurately present the unique behaviors of MREs, including the typical Payne effect and strain stiffening of rubbers etc. In this study, the up-to-date phenomenological models for describing hysteresis response of MRE devices are experimentally investigated. A prototype of MRE isolator is dynamically tested using a shaking table in the laboratory, and the tests are conducted based on displacement control using harmonic inputs with various loading frequencies, amplitudes and applied current levels. Then, the test results are used to identify the parameters of different phenomenological models for model performance evaluation. The procedure of model identification can be considered as solving a global minimization optimization problem, in which the fitness function is the root mean square error between the experimental data and the model prediction. The genetic algorithm (GA) is employed to solve the optimization problem for optimal model parameters due to its advantages of easy coding and fast convergence. Finally, several evaluation indices are adopted to compare the performances of different models, and the result shows that the improved LuGre friction model outperforms other models and has optimal accuracy in predicting the hysteresis response of the MRE device.
Yu, Y, Li, Y, Li, J & Gu, X 2019, 'Characterizing nonlinear oscillation behavior of an MRF variable rotational stiffness device', Smart Structures and Systems, vol. 24, no. 3, pp. 303-317.
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Magneto-rheological fluid (MRF) rotatory dampers are normally used for controlling the constant rotation of machines and engines. In this research, such a device is proposed to act as variable stiffness device to alleviate the rotational oscillation existing in the many engineering applications, such as motor. Under such thought, the main purpose of this work is to characterize the nonlinear torque-angular displacement/angular velocity responses of an MRF based variable stiffness device in oscillatory motion. A rotational hysteresis model, consisting of a rotatory spring, a rotatory viscous damping element and an error function-based hysteresis element, is proposed, which is capable of describing the unique dynamical characteristics of this smart device. To estimate the optimal model parameters, a modified whale optimization algorithm (MWOA) is employed on the captured experimental data of torque, angular displacement and angular velocity under various excitation conditions. In MWOA, a nonlinear algorithm parameter updating mechanism is adopted to replace the traditional linear one, enhancing the global search ability initially and the local search ability at the later stage of the algorithm evolution. Additionally, the immune operation is introduced in the whale individual selection, improving the identification accuracy of solution. Finally, the dynamic testing results are used to validate the performance of the proposed model and the effectiveness of the proposed optimization algorithm.
Yu, Y, Samali, B, Zhang, C & Askari, M 2019, 'Hysteresis modeling for cyclic behavior of concrete-steel composite joints using modified CSO', Steel and Composite Structures, vol. 33, no. 2, pp. 277-298.
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Concrete filled steel tubular (CFST) column joints with composite beams have been widely used as lateral loading resisting elements in civil infrastructure. To better utilize these innovative joints for the application of structural seismic design and analysis, it is of great importance to investigate the dynamic behavior of the joint under cyclic loading. With this aim in mind, a novel phenomenal model has been put forward in this paper, in which a Bouc-Wen hysteresis component is employed to portray the strength and stiffness deterioration phenomenon caused by increment of loading cycle. Then, a modified chicken swarm optimization algorithm was used to estimate the optimal model parameters via solving a global minimum optimization problem. Finally, the experimental data tested from five specimens subjected to cyclic loadings were used to validate the performance of the proposed model. The results effectively demonstrate that the proposed model is an easy and more realistic tool that can be used for the pre-design of CFST column joints with reduced beam section (RBS) composite beams.
Yu, Y, Subhani, M, Dackermann, U & Li, J 2019, 'Novel Hybrid Method Based on Advanced Signal Processing and Soft Computing Techniques for Condition Assessment of Timber Utility Poles', Journal of Aerospace Engineering, vol. 32, no. 4, pp. 04019032-04019032.
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© 2019 American Society of Civil Engineers. Recently, a variety of nondestructive evaluation (NDE) approaches have been developed for health assessment and residual capacity estimation of timber structures. Among these methods, guided wave (GW)-based techniques are highly regarded as effective tools for potential use in real situations. Nevertheless, because it is hard to comprehensively grasp the behavior of wave propagation in a wood structure, existing NDE-based techniques mainly depend on an oversimplified hypothesis, which can result in inaccurate or even misleading results in practice. Understanding the complex behavior of GW propagation in wood structures and extracting appropriate information from captured GW signals is a key for successful assessments of in situ conditions of timber structures. This paper analyzes the existing feature extraction and damage detection algorithms, and proposes a novel approach based on an integration of wavelet packet transform (WPT) and ensemble empirical mode decomposition (EEMD) for extracting damage-sensitive patterns, and then a soft computing method like support vector machine (SVM) for pole condition identification. In the proposed method, GW signals measured from a multisensing system with pole health condition as the baseline are divided into a series of subfrequency bands based on WPT. Then EEMD is adopted to extract the intrinsic mode functions (IMFs) that possess the features extracted at corresponding subfrequency bands. Hence, the IMF component was segregated from the original signals of tested poles, and the IMF Shannon entropy was employed to build up the feature vector to effectively demonstrate the health condition. To decrease the size of the feature vector and avoid multiple collinearity among obtained patterns, principal component analysis was employed and entropy information in the feature vector was replaced with main principal components, which will be employed as input variables of the dev...
Yu, Y, Wang, C, Gu, X & Li, J 2019, 'A novel deep learning-based method for damage identification of smart building structures', Structural Health Monitoring, vol. 18, no. 1, pp. 143-163.
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In the past few years, intelligent structural damage identification algorithms based on machine learning techniques have been developed and obtained considerable attentions worldwide, due to the advantages of reliable analysis and high efficiency. However, the performances of existing machine learning–based damage identification methods are heavily dependent on the selected signatures from raw signals. This will cause the fact that the damage identification method, which is the optimal solution for a specific application, may fail to provide the similar performance on other cases. Besides, the feature extraction is a time-consuming task, which may affect the real-time performance in practical applications. To address these problems, this article proposes a novel method based on deep convolutional neural networks to identify and localise damages of building structures equipped with smart control devices. The proposed deep convolutional neural network is capable of automatically extracting high-level features from raw signals or low-level features and optimally selecting the combination of extracted features via a multi-layer fusion to satisfy any damage identification objective. To evaluate the performance of the proposed deep convolutional neural network method, a five-level benchmark building equipped with adaptive smart isolators subjected to the seismic loading is investigated. The result shows that the proposed method has outstanding generalisation capacity and higher identification accuracy than other commonly used machine learning methods. Accordingly, it is deemed as an ideal and effective method for damage identification of smart structures.
Yu, Y, Wu, D, Wang, Q, Chen, X & Gao, W 2019, 'Machine learning aided durability and safety analyses on cementitious composites and structures', International Journal of Mechanical Sciences, vol. 160, pp. 165-181.
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© 2019 The adverse impacts of material deterioration on structural durability and human safety have become increasingly recognised. This paper is concerned with novel metamodeling on the degradation of cementitious composites and structures under environmental attacks. The material deterioration represents a chemophysical process, consisting of the reactive transportations of multiple species. Various coupling effects and associated uncertainties, both material and environmental, may be involved, leading to a complex stochastic system that can only be solved by Monte Carlo simulation. The computational intensiveness calls for advanced methods for uncertainty quantifications. In this paper, an eXtended support vector regression (X-SVR) method is developed to achieve the high-fidelity and efficient stochastic chemophysical modelling. The advanced performance of the proposed method is explored by modelling a laboratory test and a real-life engineering structure.
Yu, Z, Hu, Y, Dzakpasu, M, Wang, XC & Ngo, HH 2019, 'Dynamic membrane bioreactor performance enhancement by powdered activated carbon addition: Evaluation of sludge morphological, aggregative and microbial properties', Journal of Environmental Sciences, vol. 75, pp. 73-83.
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© 2018 The effects of powdered activated carbon (PAC) addition on sludge morphological, aggregative and microbial properties in a dynamic membrane bioreactor (DMBR) were investigated to explore the enhancement mechanism of pollutants removal and filtration performance. Sludge properties were analyzed through various analytical measurements. The results showed that the improved sludge aggregation ability and the evolution of microbial communities affected sludge morphology in PAC-DMBR, as evidenced by the formation of large, regularly shaped and strengthened sludge flocs. The modifications of sludge characteristics promoted the formation process and filtration flux of the dynamic membrane (DM) layer. Additionally, PAC addition did not exert very significant influence on the propagation of eukaryotes (protists and metazoans) and microbial metabolic activity. High-throughput pyrosequencing results indicated that adding PAC improved the bacterial diversity in activated sludge, as PAC addition brought about additional microenvironment in the form of biological PAC (BPAC), which promoted the enrichment of Acinetobacter (13.9%), Comamonas (2.9%), Flavobacterium (0.31%) and Pseudomonas (0.62%), all contributing to sludge flocs formation and several (such as Acinetobacter) capable of biodegrading relatively complex organics. Therefore, PAC addition could favorably modify sludge properties from various aspects and thus enhance the DMBR performance.
Yuan, B, Zou, D, Yu, S, Jin, H, Qiang, W & Shen, J 2019, 'Defending Against Flow Table Overloading Attack in Software-Defined Networks', IEEE Transactions on Services Computing, vol. 12, no. 2, pp. 231-246.
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© 2008-2012 IEEE. The Software-Defined Network (SDN) is a new and promising network architecture. At the same time, SDN will surely become a new target of cyber attackers. In this paper, we point out one critical vulnerability in SDNs, the size of flow table, which is most likely to be attacked. Due to the expensive and power-hungry features of Ternary Content Addressable Memory (TCAM), a flow table usually has a limited size, which can be easily disabled by a flow table overloading attack (a transformed DDoS attack). To provide a security service in SDN, we proposed a QoS-aware mitigation strategy, namely, peer support strategy, which integrates the available idle flow table resource of the whole SDN system to mitigate such an attack on a single switch of the system. We established a practical mathematical model to represent the studied system, and conducted a thorough analysis for the system in various circumstances. Based on our analysis, we found that the proposed strategy can effectively defeat the flow table overloading attacks. Extensive simulations and testbed-based experiments solidly support our claims. Moreover, our work also shed light on the implementation of SDN networks against possible brute-force attacks.
Yuan, C, Tao, X, Li, N, Ni, W, Liu, RP & Zhang, P 2019, 'Analysis on Secrecy Capacity of Cooperative Non-Orthogonal Multiple Access With Proactive Jamming', IEEE Transactions on Vehicular Technology, vol. 68, no. 3, pp. 2682-2696.
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© 1967-2012 IEEE. This paper analyzes the secrecy capacity of a cooperative relaying system using non-orthogonal multiple access (NOMA). A new cooperative NOMA scheme is proposed, where the source actively sends jamming signals while the relay is forwarding, thereby enhancing the security of intended communication links. Closed-form expressions for the ergodic secrecy rate are derived in the presence of an eavesdropper. Asymptotic approximate expressions for the ergodic secrecy rate are established in high signal-to-noise ratio (SNR) regime, which provides insights on secure NOMA transmission. Numerical results reveal the critical condition, under which NOMA is able to outperform orthogonal multiple access (OMA) in terms of secrecy rate. The proposed NOMA scheme can improve the secrecy rate by about 78.1rm%.
Yuan, W, Wu, N, Guo, Q, Huang, X, Li, Y & Hanzo, L 2019, 'TOA-Based Passive Localization Constructed Over Factor Graphs: A Unified Framework', IEEE Transactions on Communications, vol. 67, no. 10, pp. 6952-6965.
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© 2019 IEEE. Passive localization based on time of arrival (TOA) measurements is investigated, where the transmitted signal is reflected by a passive target and then received at several distributed receivers. After collecting all measurements at receivers, we can determine the target location. The aim of this paper is to provide a unified factor graph-based framework for passive localization in wireless sensor networks based on TOA measurements. Relying on the linearization of range measurements, we construct a Forney-style factor graph model and conceive the corresponding Gaussian message passing algorithm to obtain the target location. It is shown that the factor graph can be readily modified for handling challenging scenarios such as uncertain receiver positions and link failures. Moreover, a distributed localization method based on consensus-aided operation is proposed for a large-scale resource constrained network operating without a fusion center. Furthermore, we derive the Cramér-Rao bound (CRB) to evaluate the performance of the proposed algorithm. Our simulation results verify the efficiency of the proposed unified approach and of its distributed implementation.
Yuan, X, Feng, Z, Ni, W, Wei, Z, Liu, RP & Zhang, JA 2019, 'Secrecy Rate Analysis Against Aerial Eavesdropper', IEEE Transactions on Communications, vol. 67, no. 10, pp. 7027-7042.
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© 2019 IEEE. This paper studies the threat that an aerial eavesdropper can pose to terrestrial wireless communications, from an information-theoretic point of view. The achievable ergodic and the average ϵ-outage secrecy rates with no channel state information at the transmitter (i.e., with no CSIT) are analyzed for a transmitter-receiver pair on the ground, in the presence of an aerial eavesdropper which flies a random trajectory following a smooth turn (ST) mobility model in a three-dimensional (3D) space. The ST mobility model induces a uniform distribution (of the eavesdropper's waypoints) within the considered 3D volume. Closed-form asymptotic approximations of the achievable secrecy rates are derived based on the almost sure convergence and non-trivial mathematical manipulations. Validated by simulations, our analysis is tight and reveals that the ground transmission is particularly vulnerable to aerial eavesdropping which can be carried out in a distance without being noticed. 3D spherical regions are identified, within which the secrecy rates vanish. This sheds useful insights to protect terrestrial wireless networks from aerial eavesdropping.
Yuan, Z, Dong, L, Gao, Q, Huang, Z, Wang, L, Wang, G & Yu, X 2019, 'SnSb alloy nanoparticles embedded in N-doped porous carbon nanofibers as a high-capacity anode material for lithium-ion batteries', Journal of Alloys and Compounds, vol. 777, pp. 775-783.
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© 2018 SnSb alloy is a promising anode material for lithium-ion batteries due to its high specific capacity. However, the large volume change in the process of charge/discharge causes significant pulverizing of SnSb alloy particles, which leads to a rapid capacity fading. This paper reports the synthesis of homogenous SnSb nanoparticles that are embedded in N-doped porous carbon nanofibers through electrospinning technique with LiN3 serving as poregen agent. This distinctive structure prevents the direct contact of SnSb nanoparticles with the electrolyte and provides enough space for the volume change of SnSb alloy during the Li+ insertion/extraction process, enabling this material to deliver a high reversible capacity of 892 mA h g−1 after 100th cycle at 100 mA g−1, and a stable capacity of 487 mA h g−1 after 1000 cycles at 2000 mA g−1. These results highlight the importance of the synergistic effect of SnSb alloy nanoparticles and N-doped porous carbon nanofibers for the high performance of lithium-ion batteries.
zahrani, SA, Islam, MS & Saha, SC 2019, 'A thermo-hydraulic characteristics investigation in corrugated plate heat exchanger', Energy Procedia, vol. 160, pp. 597-605.
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© 2019 The Authors. Published by Elsevier Ltd. The amount of heat transfer from plate heat exchanger (PHE) is much higher compared with other types of conventional heat exchangers due to the high surface area of each plate. This study aims to investigate the heat transfer characteristics in a commercial corrugated PHE for sinusoidal corrugation type. A computational fluid dynamics (CFD) has been used to simulate the fluid flow inside the PHE for 1-1 single (water-water) and two (air-water) phase flow, counter arrangement. An advanced meshing technique has been used to generate the mesh for the PHE and a proper grid refinement test has been performed for the generated mesh. The overall investigation has been conducted for 60°/60° chevron angle plate (β) for a wide range of Re (500 ≤ Re ≤3000) and Prandtl number (Pr) (0.72 ≤ Pr ≤ 7.5). The result is validated with the benchmark experimental data. The impact of Reynolds and Pr has been investigated. The CFD results illustrate that the Nusselt number (Nu) increases with increasing of Reynolds number, while f decreasing with increasing of Re. The effect of Pr on Nusselt number and isothermal friction factor is presented. The corresponding correlations of Nu and f are developed from the CFD results.
Zamani, H, Nadimi-Shahraki, MH & Gandomi, AH 2019, 'CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems', Applied Soft Computing, vol. 85, pp. 105583-105583.
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© 2019 Elsevier B.V. In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: CSA, BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design.
Zdankowski, P, McGloin, D & Swedlow, JR 2019, 'Full volume super-resolution imaging of thick mitotic spindle using 3D AO STED microscope', Biomedical Optics Express, vol. 10, no. 4, pp. 1999-1999.
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© 2019 Optical Society of America under the terms of the OSA Open Access Publishing. Stimulated emission depletion (STED) nanoscopy is one of a suite of modern optical microscopy techniques capable of bypassing the conventional diffraction limit in fluorescent imaging. STED makes use of a spiral phase mask to enable 2D super-resolution imaging whereas to achieve full volumetric 3D super-resolution an additional bottle-beam phase mask must be applied. The resolution achieved in biological samples 10 μm or thicker is limited by aberrations induced mainly by scattering due to refractive index heterogeneity in the sample. These aberrations impact the fidelity of both types of phase mask, and have limited the application of STED to thicker biological systems. Here we apply an automated adaptive optics solution to correct the performance of both STED masks, enhancing robustness and expanding the capabilities of this nanoscopic technique. Corroboration in terms of successful high-quality imaging of the full volume of a 15μm mitotic spindle with resolution of 50nm x 50nm x 150nm achieved in all three dimensions is presented.
Zeng, J, Lv, T, Liu, RP, Su, X, Beaulieu, NC & Guo, YJ 2019, 'Linear Minimum Error Probability Detection for Massive MU-MIMO With Imperfect CSI in URLLC', IEEE Transactions on Vehicular Technology, vol. 68, no. 11, pp. 11384-11388.
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© 1967-2012 IEEE. It is challenging to realize ultra-reliable and low latency communications (URLLC) under severe shadow fading and imperfect channel state information (CSI). However, reliability can be increased by exploiting space diversity from multiple receive antennas rather than retransmission with limited latency. Massive multi-user multiple-input-multiple-output (MU-MIMO) is studied to enable URLLC with imperfect CSI from least-square channel estimation. The linear minimum error probability (MEP) detector with a given length of pilots (LoP) is derived. Further, the LoP is optimized to minimize the error probability of the uplink with a limited number of channel uses, using the finite blocklength information theory and one-dimensional search methods. Numerical results verify that the proposed linear MEP detection incorporated in massive MU-MIMO improves reliability with limited latency and imperfect CSI.
Zeng, J, Lv, T, Ni, W, Liu, RP, Beaulieu, NC & Guo, YJ 2019, 'Ensuring Max–Min Fairness of UL SIMO-NOMA: A Rate Splitting Approach', IEEE Transactions on Vehicular Technology, vol. 68, no. 11, pp. 11080-11093.
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Zha, Q, Liang, H, Kou, G, Dong, Y & Yu, S 2019, 'A Feedback Mechanism With Bounded Confidence- Based Optimization Approach for Consensus Reaching in Multiple Attribute Large-Scale Group Decision-Making', IEEE Transactions on Computational Social Systems, vol. 6, no. 5, pp. 994-1006.
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© 2014 IEEE. Different feedback mechanisms have been developed in large-scale group decision-making (GDM) to provide the decision-makers with advices for preference adjustment with the aim of improving the group consensus level. However, the willingness of the decision-makers to accept these advices is rarely considered in the extant feedback mechanisms. In the field of opinion dynamics, this issue is studied by the bounded confidence model, which shows that the decision-makers only consider the preferences that differ from their own preferences not more than a certain confidence level. Following this idea, this article proposes a large-scale consensus model with a bounded confidence-based feedback mechanism to promote the consensus level among decision-makers with bounded confidences. Specifically, this feedback mechanism classifies the decision-makers into different clusters and provides the corresponding clusters with more acceptable advices based on a bounded confidence-based optimization approach. Finally, through the numerical example and the simulation analysis, the use of the model is introduced, and the effectiveness of the model is justified.
Zhan, J, Ge, XJ, Huang, S, Zhao, L, Wong, JKW & He, SX 2019, 'Improvement of the inspection-repair process with building information modelling and image classification', Facilities, vol. 37, no. 7/8, pp. 395-414.
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PurposeAutomated technologies have been applied to facility management (FM) practices to address labour demands of, and time consumed by, inputting and processing manual data. Less attention has been focussed on automation of visual information, such as images, when improving timely maintenance decisions. This study aims to develop image classification algorithms to improve information flow in the inspection-repair process through building information modelling (BIM).Design/methodology/approachTo improve and automate the inspection-repair process, image classification algorithms were used to connect images with a corresponding image database in a BIM knowledge repository. Quick response (QR) code decoding and Bag of Words were chosen to classify images in the system. Graphical user interfaces (GUIs) were developed to facilitate activity collaboration and communication. A pilot case study in an inspection-repair process was applied to demonstrate the applications of this system.FindingsThe system developed in this study associates the inspection-repair process with a digital three-dimensional (3D) model, GUIs, a BIM knowledge repository and image classification algorithms. By implementing the proposed application in a case study, the authors found that improvement of the inspection-repair process and automated image classification with a BIM knowledge repository (such as the one developed in this study) can enhance FM practices by increasing productivity and reducing time and costs associated with ecision-making.Originality/valueThis study introduces an innovative approach th...
Zhan, K, Chang, X, Guan, J, Chen, L, Ma, Z & Yang, Y 2019, 'Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features', IEEE Transactions on Cybernetics, vol. 49, no. 5, pp. 1826-1834.
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© 2018 IEEE. Multifeature learning has been a fundamental research problem in multimedia analysis. Most existing multifeature learning methods exploit graph, which must be computed beforehand, as input to uncover data distribution. These methods have two major problems confronted. First, graph construction requires calculating similarity based on nearby data pairs by a fixed function, e.g., the RBF kernel, but the intrinsic correlation among different data pairs varies constantly. Therefore, feature learning based on such predefined graphs may degrade, especially when there is dramatic correlation variation between nearby data pairs. Second, in most existing algorithms, each single-feature graph is computed independently and then combine them for learning, which ignores the correlation between multiple features. In this paper, a new unsupervised multifeature learning method is proposed to make the best utilization of the correlation among different features by jointly optimizing data correlation from multiple features in an adaptive way. As opposed to computing the affinity weight of data pairs by a fixed function, the weight of affinity graph is learned by a well-designed optimization problem. Additionally, the affinity graph of data pairs from different features is optimized in a global level to better leverage the correlation among different channels. In this way, the adaptive approach correlates the features of all features for a better learning process. Experimental results on real-world datasets demonstrate that our approach outperforms the state-of-the-art algorithms on leveraging multiple features for multimedia analysis.
Zhan, M, Liang, H, Kou, G, Dong, Y & Yu, S 2019, 'Impact of Social Network Structures on Uncertain Opinion Formation', IEEE Transactions on Computational Social Systems, vol. 6, no. 4, pp. 670-679.
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© 2019 IEEE. When people express their opinions about a certain issue, they often give uncertain opinions rather than exact opinions. Particularly, these uncertain opinions will evolve in social networks. Therefore, in this paper, we focus on investigating uncertain opinion formation with social networks under bounded confidence. Specifically, we define the uncertain opinions by numerical interval opinions, whose ranges are between zero and one, and the larger width of numerical interval opinions means the more uncertainty of the opinions. Meanwhile, we describe social network structures by ER random graphs with different agents' scales and network connected probabilities. Then, we present the detailed simulation experiments to reveal the strong impact of social network structures on uncertain opinion formation. Simulation results show that: 1) larger agents' scales will yield the smaller ratios of agents expressing the uncertain opinions and larger average widths of uncertain opinions; 2) the average stable time starts increasing and then decreases with the increase in the network connected probabilities; and 3) larger network connected probabilities will yield less opinion clusters and the smaller ratios of the extremely small clusters in all clusters. The obtained results are helpful for the government and public opinion management departments to understand and manage uncertain public opinion evolution effectively.
Zhang, B, Li, J, Quan, L, Chen, Y & Lü, Q 2019, 'Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network', Neurocomputing, vol. 357, pp. 86-100.
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© 2019 Elsevier B.V. Proteins often interact with each other and form protein complexes to carry out various biochemical activities. Knowledge of the interaction sites is helpful for understanding disease mechanisms and drug design. Accurate prediction of the interaction sites from protein sequences is still a challenging task and severe imbalance data also decreased the performance of computational methods. In this study, we propose to use a deep learning method for improving the imbalanced prediction of protein interaction sites. We develop a new simplified long short-term memory (SLSTM) network to implement a deep learning architecture (named DLPred). To deal with the imbalanced classification in the deep learning model, we explore three new ideas. First, our collection of the training data is to construct a set of protein sequences, instead of a set of just single residues, to retain the entire sequential completeness of each protein. Second, a new penalization factor is appended to the loss function such that the penalization to the non-interaction site loss can be effectively enhanced. Third, multi-task learning of interaction sites and residue solvent accessibility prediction are used for correcting the preference of the prediction model on the non-interaction sites. Our model is evaluated on three public datasets: Dset186, Dtestset72 and PDBtestset164. Compared with current state-of-the-art methods, DLPred is able to significantly improve the predictive accuracies and AUC values while improving the F-measure. The training dataset, test datasets, a standalone version of DLPred and online service are available at http://qianglab.scst.suda.edu.cn/dlp/.
Zhang, C, Wu, X, Zheng, X & Yu, S 2019, 'Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications', IEEE Access, vol. 7, pp. 11829-11843.
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© 2019 IEEE. It is well known that blink, yawn, and heart rate changes give clue about a human's mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject's facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver's driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way.
Zhang, C, Yu, JJQ & Liu, Y 2019, 'Spatial-Temporal Graph Attention Networks: A Deep Learning Approach for Traffic Forecasting', IEEE Access, vol. 7, pp. 166246-166256.
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Zhang, F, Lin, X, Zhang, Y, Qin, L & Zhang, W 2019, 'Efficient community discovery with user engagement and similarity.', VLDB J., vol. 28, no. 6, pp. 987-1012.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. In this paper, we investigate the problem of (k,r)-core which intends to find cohesive subgraphs on social networks considering both user engagement and similarity perspectives. In particular, we adopt the popular concept of k-core to guarantee the engagement of the users (vertices) in a group (subgraph) where each vertex in a (k,r)-core connects to at least k other vertices. Meanwhile, we consider the pairwise similarity among users based on their attributes. Efficient algorithms are proposed to enumerate all maximal (k,r)-cores and find the maximum (k,r)-core, where both problems are shown to be NP-hard. Effective pruning techniques substantially reduce the search space of two algorithms. A novel (k,k′)-core based (k,r)-core size upper bound enhances the performance of the maximum (k,r)-core computation. We also devise effective search orders for two algorithms with different search priorities for vertices. Besides, we study the diversified (k,r)-core search problem to find l maximal (k,r)-cores which cover the most vertices in total. These maximal (k,r)-cores are distinctive and informationally rich. An efficient algorithm is proposed with a guaranteed approximation ratio. We design a tight upper bound to prune unpromising partial (k,r)-cores. A new search order is designed to speed up the search. Initial candidates with large size are generated to further enhance the pruning power. Comprehensive experiments on real-life data demonstrate that the maximal (k,r)-cores enable us to find interesting cohesive subgraphs, and performance of three mining algorithms is effectively improved by all the proposed techniques.
Zhang, G, Li, Y, Wang, H & Wang, J 2019, 'Rheological Properties of Polyurethane-Based Magnetorheological Gels', Frontiers in Materials, vol. 6.
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© 2019 Zhang, Li, Wang and Wang. The paper tests the influence of mass fractions of carbonyl iron particles (CIPs) on the rheological properties of magnetorheological (MR) gels. Polyurethane-based MR gels with different weight fraction of CIPs, i.e., 40, 60, and 80%, were firstly prepared by mechanical mixing, respectively. The changes of shear stress and viscosity with shear rate under different magnetic flux density were tested and analyzed. It was found that the shear stress increases with mass fraction under magnetic flux density. The viscoelastic properties of MRGs were achieved by oscillatory shear measure. The effects of strain amplitude and frequency on viscoelastic of MRGs under different magnetic flux density were measured and analyzed. The study results shown that the elastic characteristics become more obvious with the increase of CIPs mass fraction. However, it has opposite effect on the viscous properties of materials.
Zhang, G, Tao, J, Qiu, X & Burnett, I 2019, 'Decentralized Two-Channel Active Noise Control for Single Frequency by Shaping Matrix Eigenvalues', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 1, pp. 44-52.
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© 2014 IEEE. In an active noise control (ANC) system, computational complexity is one major concern when designing practical control algorithms. For an ANC system with multiple secondary sources and error microphones, one approach to reducing computational complexity is to apply a decentralized control scheme rather than centralized approaches. A decentralized scheme attempts to control a number of small-size ANC subsystems independently. In this paper, we consider the decentralized control of a two-channel ANC system tackling a noise disturbance in the frequency domain, where each channel consists of one secondary source and one error microphone. We propose a decentralized control method that is able to achieve the same noise reduction performance as the centralized controller with guaranteed convergence. The key step in designing the control method is to properly shape the eigenvalues of a matrix that models the two-channel secondary paths for each frequency index.
Zhang, H, Dong, Y, Chiclana, F & Yu, S 2019, 'Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design', European Journal of Operational Research, vol. 275, no. 2, pp. 580-598.
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© 2018 Elsevier B.V. Consensus reaching processes (CRPs) aim to help decision-makers achieve agreement regarding the solution to a common decision problem, and consequently play an increasingly important role in the resolution of group decision making (GDM) problems. To date, a large number of CRPs have been reported. However, there is a lack of a general framework and criteria to evaluate the efficiency of the different CRPs. This paper aims to fill this gap in the research literature on CRPs. To achieve this goal, firstly, a comprehensive review regarding the different approaches to CRP is reported, and a series of CRPs as the comparison objects are presented. Secondly, the following comparison criteria for measuring the efficiency of CPRs are proposed: the number of adjusted decision-makers, the number of adjusted alternatives, the number of adjusted preference values, the distance between the original and the adjusted preference information (adjustment cost), and the number of negotiation rounds required to reach consensus. Following this, a detailed simulation experiment is designed to analyze the efficiency of different CRPs under the mentioned different comparison criteria. Furthermore, new multi-stage optimization-based CRPs are also developed, which the simulation experiment shows to have better comprehensive consensus efficiency in different GDM settings.
Zhang, H, Zhang, Q & Chen, W 2019, 'Bi-level programming model of truck congestion pricing at container terminals', Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 1, pp. 385-394.
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Zhang, H, Zhu, X, Li, Z & Yao, S 2019, 'Displacement-dependent nonlinear damping model in steel buildings with bolted joints', Advances in Structural Engineering, vol. 22, no. 5, pp. 1049-1061.
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The stick–slip phenomenon is commonly found at structural connections in steel buildings. It is a major damping mechanism in a structure with bolted joints and makes a significant contribution to the total structural damping. This article reviews the stick–slip damping model of an elastic single-degree-of-freedom system with one stick–slip component. It is observed that the damping ratios of the system with the stick–slip mechanism first quickly increase when experiencing a very small displacement and then slowly decrease. After the number of activated slip surfaces is assumed to be a linear function related to the structural displacement, the equivalent damping ratios of a structural system with numerous stick–slip components are derived. However, this displacement-dependent damping model is very difficult to be used for a structural dynamic analysis due to its inherent complexity. Therefore, a new displacement-dependent damping model for a structural dynamic analysis is proposed based on the viscous damping. A high-rise steel moment resisting frame with bolted joints subjected to an earthquake ground motion is taken as an example to verify the proposed method.
Zhang, H-P, Han, W, Tavakoli, J, Zhang, Y-P, Lin, X, Lu, X, Ma, Y & Tang, Y 2019, 'Understanding interfacial interactions of polydopamine and glass fiber and their enhancement mechanisms in epoxy-based laminates', Composites Part A: Applied Science and Manufacturing, vol. 116, pp. 62-71.
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Interfacial behavior greatly affects the bulk mechanical performance of fiber reinforced polymer laminates. In this study, polydopamine modified glass fiber was used to fabricate short glass fiber reinforced polymer (GFRP) laminates. The interactions between glass fiber and polydopamine were studied experimentally and theoretically by X-ray photoelectron spectroscopy (XPS) and density functional calculation respectively. Theoretical study clearly demonstrated that electronic interactions existed between polydopamine and glass fiber, indicating the hydrogen bonds/chemical interactions between them that were also demonstrated by XPS results. The enhanced interfacial interaction significantly benefited GFRP laminates, as demonstrated by various mechanical characterizations such as single fiber pull-out and Mode I interlaminar fracture toughness tests. Combining the theoretical and experimental studies indicated that polydopamine modification of glass fiber could be an easy and effective way to significantly improve the interfacial interactions between glass fiber and matrix and enhance the mechanical properties of GFRP laminates.
Zhang, H-W, Kok, VC, Chuang, S-C, Tseng, C-H, Lin, C-T, Li, T-C, Sung, F-C, Wen, CP, Hsiung, CA & Hsu, CY 2019, 'Long-term ambient hydrocarbons exposure and incidence of ischemic stroke', PLOS ONE, vol. 14, no. 12, pp. e0225363-e0225363.
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Exposure to air pollutants is known to have adverse effects on human health; however, little is known about the association between hydrocarbons in air and an ischemic stroke (IS) event. We investigated whether long-term exposure to airborne hydrocarbons, including volatile organic compounds, increased IS risk. This retrospective cohort study included 283,666 people aged 40 years or older in Taiwan. Cox proportional hazards regression analysis was used to fit single- and multiple-pollutant models for two targeted pollutants, total hydrocarbons (THC) and nonmethane hydrocarbons (NMHC), and estimated the risk of IS. Before controlling for multiple pollutants, hazard ratios (HRs) of IS with 95% confidence intervals for the overall population were 2.69 (2.64-2.74) at 0.16-ppm increase in THC and 1.62 (1.59-1.66) at 0.11-ppm increase in NMHC. For the multiple-pollutant models controlling for PM2.5, the adjusted HR was 3.64 (3.56-3.72) for THC and 2.21 (2.16-2.26) for NMHC. Our findings suggest that long-term exposure to THC and NMHC may be a risk factor for IS development.
Zhang, J, Lei, J, Wu, L, Huang, B & Li, Y 2019, 'Improved algorithm for hyperspectral endmember extraction and its FPGA implementation', Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, vol. 46, no. 4, pp. 22-27.
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The automatic target generation process (ATGP) is unsuitable for being deployed for high speed hardware implementation due to enormously complex inverse operations and the increasing scale of iterative operation problems. On the basis of an intensive study of the ATGP algorithm, a novel implementation framework is proposed in this paper, which takes advantage of simple regular matrix operations instead of increasingly complicated matrix inversion to update the orthogonal projection operator matrix. Furthermore, an accelerated design of the algorithm is implemented on FPGA by using high-level synthesis (HLS) for the first time. Experimental results demonstrate that the processing time achieved by the FPGA implementation is strictly real-time while retaining the same high detection accuracy.
Zhang, J, Li, L, Dorrell, DG & Guo, Y 2019, 'Modified PI controller with improved steady-state performance and comparison with PR controller on direct matrix converters', Chinese Journal of Electrical Engineering, vol. 5, no. 1, pp. 53-66.
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This paper proposes a modified proportional-integral (PI) controller and compares it with a proportional-resonant (PR) controller. These controllers are tested on a three-phase direct matrix converter (MC). The modified PI controller involves current feedforward together with space vector modulation (SVM) to control the MC output currents. This controller provides extra control flexibility in terms of the current error reduction, and it gives improved steady-state tracking performance. When the coefficient of current feedforward is equal to the load resistor (K = R), the steady-state error is effectively minimized even when regulating sinusoidal variables. The total harmonic distortion is also reduced. In order to comparatively evaluate the modified PI controller, a PR controller is designed and tested. Both the modified PI and PR controllers are implemented in the natural frame (abc) in a straightforward manner. This removes the coordinate transformations that are required in the stationary (αβ) and synchronous (dq) reference frame based control strategies. In addition, both controllers can handle the unbalanced conditions. The experimental and simulation results verify the feasibility and effectiveness of the proposed controllers.
Zhang, J, Li, L, Dorrell, DG, Norambuena, M & Rodriguez, J 2019, 'Predictive Voltage Control of Direct Matrix Converters With Improved Output Voltage for Renewable Distributed Generation', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 7, no. 1, pp. 296-308.
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© 2013 IEEE. This paper proposes a predictive voltage control strategy for a direct matrix converter used in a renewable energy distributed generation (DG) system. A direct matrix converter with LC filters is controlled in order to work as a stable voltage supply for loads. This is especially relevant for the stand-alone operation of a renewable DG where a stable sinusoidal voltage, with desired amplitude and frequency under various load conditions, is the main control objective. The model predictive control is employed to regulate the matrix converter so that it produces stable sinusoidal voltages for different loads. With predictive control, many other control objectives, e.g., input power factor, common-mode voltage, and switching frequency, can be achieved depending on the application. To reduce the number of required measurements and sensors, this paper utilizes observers and makes the use of the switch matrices. In addition, the voltage transfer ratio can be improved with the proposed strategy. The controller is tested under various conditions including intermittent disturbance, nonlinear loads, and unbalanced loads. The proposed controller is effective, simple, and easy to implement. The simulation and experimental results verify the effectiveness of the proposed scheme and control strategy. This proposed scheme can be potentially used in microgrid applications.
Zhang, J, Wu, Q, Zhang, J, Shen, C, Lu, J & Wu, Q 2019, 'Heritage image annotation via collective knowledge', Pattern Recognition, vol. 93, pp. 204-214.
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© 2019 Elsevier Ltd The automatic image annotation can provide semantic illustrations to understand image contents, and builds a foundation to develop algorithms that can search images within a large database. However, most current methods focus on solving the annotation problem by modeling the image visual content and tag semantic information, which overlooks the additional information, such as scene descriptions and locations. Moreover, the majority of current annotation datasets are visually consistent and only annotated by common visual objects and attributes, which makes the classic methods vulnerable to handle the more diverse image annotation. To address above issues, we propose to annotate images via collective knowledge, that is, we uncover relationships between the image and its neighbors by measuring similarities among metadata and conduct the metric learning to obtain the representations of image contents, we also generate semantic representations for images given collective semantic information from their neighbors. Two representations from different paradigms are embedded together to train an annotation model. We ground our model on the heritage image collection we collected from the library online open data. Annotations on the heritage image collection are not limited to common visual objects, and are highly relevant to historical events, and the diversity of the heritage image content is much larger than the current datasets, which makes it more suitable for this task. Comprehensive experimental results on the benchmark dataset indicate that the proposed model achieves the best performance compared to baselines and state-of-the-art methods.
Zhang, K, Qu, Z, Dong, Y, Lu, H, Leng, W, Wang, J & Zhang, W 2019, 'Research on a combined model based on linear and nonlinear features - A case study of wind speed forecasting', Renewable Energy, vol. 130, pp. 814-830.
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© 2018 Elsevier Ltd As one of the most promising sustainable energy sources, wind energy is being paid more attention by the researchers. Because of the volatility and instability of wind speed series, wind power integration faces a severe challenge; thus, an accurate wind energy forecasting plays a key role in smart grid planning and management. However, many traditional forecasting models do not consider the necessity and importance of data preprocessing and neglect the limitation of using a single forecasting model, which leads to poor forecasting accuracy. To solve these problems, a novel combined model based on two linear and four nonlinear forecasting algorithms is proposed to adapt both the linear and nonlinear characteristics of the wind energy time series. In addition, a modified Artificial Fish Swarm Algorithm and Ant Colony Optimization (AFSA-ACO) algorithm is proposed and employed to determine the optimal weight coefficients of the combined models. To verify the forecasting performance of the developed combined model, several experiments were implemented by using 10-min interval wind speed data in Shandong, China. Then, one-step (10-min), three-step (30-min) and five-step (50-min) predictions were conducted. The experimental results indicate that the developed combined model is remarkably superior to all benchmark models for the high precision and stability of wind-speed predictions.
Zhang, L, Liu, J, Luo, M, Chang, X, Zheng, Q & Hauptmann, AG 2019, 'Scheduled sampling for one-shot learning via matching network', Pattern Recognition, vol. 96, no. 8, pp. 106962-106962.
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Considering human can learn new object successfully from just one sample, one-shot learning, where each visual class just has one labeled sample for training, has attracted more and more attention. In the past years, most researchers achieve one-shot learning by training a matching network to map a small labeled support set and an unlabeled image to its label. The support set is combined by one image with the same label as unlabeled image and few images with other labels generated by random sampling. This random sampling strategy easily generates massive over-easy support sets in which most labels are less relevant to the label of unlabeled image. It leads to the limitation of matching network for one-shot prediction over indistinguishable label sets. For this issue, we propose a novel metric to evaluate the learning difficulty of support set, where this metric jointly considers the semantic diversity and similarity of visual labels. Based on the metric, we introduce a scheduled sampling strategy to train the matching network from easy to difficult. Extensive experimental results on three datasets, including mini-Imagenet, Birds and Flowers, indicate that our method could achieve significant improvements over other previous methods.
Zhang, L, Ma, C, Liu, L, Pan, J & Wang, Q 2019, 'Fabrication of novel particle electrode γ-Al2O3@ZIF-8 and its application for degradation of Rhodamine B', Water Science and Technology, vol. 80, no. 1, pp. 109-116.
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Abstract Due to the high Brunauer–Emmett–Teller (BET) surface area of zeolitic imidazolate framework (ZIF)-8, a secondary crystallization method was used to prepare a particle electrode of γ-Al2O3@ZIF-8. According to the results from a field emission scanning electron microscope (SEM) and X-ray diffractometer (XRD), the particle electrode of γ-Al2O3 was successfully loaded with ZIF-8, and the BET surface area (1,433 m2/g) of ZIF-8 was over ten times that of γ-Al2O3. The key operation parameters of cell voltage, pH, initial RhB concentration and electrolyte concentration were all optimized. The observed rate constant (kobs) of the pseudo-first-order kinetic model for the electrocatalytic oxidation (ECO) system with the particle electrode of γ-Al2O3@ZIF-8 (15.2 × 10−2 min−1) was over five times higher than that of the system with the traditional particle electrode of γ-Al2O3 (2.6 × 10−2 min−1). The loading of ZIF-8 on the surface of γ-Al2O3 played an important role in improving electrocatalytic activity for the degradation of Rhodamine B (RhB), and the RhB removal efficiency of the three-dimensional (3D) electrocatalytic system with the particle electrode of γ-Al2O3@ZIF-8 was 93.5% in 15 min, compared with 27.5% in 15 min for the particle electrode of γ-Al2O3. The RhB removal efficiency was kept over 85% after five cycles of reuse for the 3D electrocatalytic system with the particle electrode of γ-Al2O3@ZIF-8.
Zhang, M, Qiu, B, Kalhori, H & Qu, X 2019, 'Hybrid reconstruction method for indirect monitoring of an ice load of a steel gate in a cold region', Cold Regions Science and Technology, vol. 162, pp. 19-34.
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© 2019 Elsevier B.V. The steel gate of a hydraulic complex may be subjected to ice loads during freezing periods in cold regions, threatening the gate safety. The ice load on the gate is usually affected by several factors, including the ice thickness, snow cover, and changes in water level and temperature. The ice pressure distribution on the gate cannot be readily estimated by theoretical analysis or empirical formulae. Therefore, structural strain and local ice pressure data were collected over 140 days during the winter of 2016–2017 to investigate the structural deformation and local ice pressure distribution. A hybrid reconstruction method (HCM) was developed for establishing the ice pressure distribution using the monitoring data, and the effectiveness of the HCM was analysed based on several uniform load patterns and the Chebyshev polynomial functions. The ice pressure distributions on the gate were reconstructed during the entire monitoring period, considering the collected data for the lowest temperature of each day. The reconstructed ice pressure distribution, i.e., the equivalent and uniform ice pressure within every individual cell, was lower than 0.1 MPa in most parts of the gate.
Zhang, Q, Lu, J, Wu, D & Zhang, G 2019, 'A Cross-Domain Recommender System With Kernel-Induced Knowledge Transfer for Overlapping Entities', IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 7, pp. 1998-2012.
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© 2012 IEEE. The aim of recommender systems is to automatically identify user preferences within collected data, then use those preferences to make recommendations that help with decisions. However, recommender systems suffer from data sparsity problem, which is particularly prevalent in newly launched systems that have not yet had enough time to amass sufficient data. As a solution, cross-domain recommender systems transfer knowledge from a source domain with relatively rich data to assist recommendations in the target domain. These systems usually assume that the entities either fully overlap or do not overlap at all. In practice, it is more common for the entities in the two domains to partially overlap. Moreover, overlapping entities may have different expressions in each domain. Neglecting these two issues reduces prediction accuracy of cross-domain recommender systems in the target domain. To fully exploit partially overlapping entities and improve the accuracy of predictions, this paper presents a cross-domain recommender system based on kernel-induced knowledge transfer, called KerKT. Domain adaptation is used to adjust the feature spaces of overlapping entities, while diffusion kernel completion is used to correlate the non-overlapping entities between the two domains. With this approach, knowledge is effectively transferred through the overlapping entities, thus alleviating data sparsity issues. Experiments conducted on four data sets, each with three sparsity ratios, show that KerKT has 1.13%-20% better prediction accuracy compared with six benchmarks. In addition, the results indicate that transferring knowledge from the source domain to the target domain is both possible and beneficial with even small overlaps.
Zhang, Q, Shi, C, Niu, Z & Cao, L 2019, 'HCBC: A Hierarchical Case-Based Classifier Integrated with Conceptual Clustering', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 1, pp. 152-165.
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© 1989-2012 IEEE. The structured case representation improves case-based reasoning (CBR) by exploring structures in the case base and the relevance of case structures. Recent CBR classifiers have mostly been built upon the attribute-value case representation rather than structured case representation, in which the structural relations embodied in their representation structure are accordingly overlooked in improving the similarity measure. This results in retrieval inefficiency and limitations on the performance of CBR classifiers. This paper proposes a hierarchical case-based classifier, HCBC, which introduces a concept lattice to hierarchically organize cases. By exploiting structural case relations in the concept lattice, a novel dynamic weighting model is proposed to enhance the concept similarity measure. Based on this similarity measure, HCBC retrieves the top-K concepts that are most similar to a new case by using a bottom-up pruning-based recursive retrieval (PRR) algorithm. The concepts extracted in this way are applied to suggest a class label for the case by a weighted majority voting. Experimental results show that HCBC outperforms other classifiers in terms of classification performance and robustness on categorical data, and also works confidently well on numeric datasets. In addition, PRR effectively reduces the search space and greatly improves the retrieval efficiency of HCBC.
Zhang, Q, Wu, J, Zhang, P, Long, G & Zhang, C 2019, 'Salient Subsequence Learning for Time Series Clustering', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 9, pp. 2193-2207.
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IEEE Time series has been a popular research topic over the past decade. Salient subsequences of time series that can benefit the learning task, e.g. classification or clustering, are called shapelets. Shapelet-based time series learning extracts these types of salient subsequences with highly informative features from a time series. Most existing methods for shapelet discovery must scan a large pool of candidate subsequences, which is a time-consuming process. A recent work, Grabocka:KDD14, uses regression learning to discover shapelets in a time series; however, it only considers learning shapelets from labeled time series data. This paper proposes an Unsupervised Salient Subsequence Learning (USSL) model that discovers shapelets without the effort of labeling. We developed this new learning function by integrating the strengths of shapelet learning, shapelet regularization, spectral analysis and pseudo-label to simultaneously and automatically learn shapelets to help clustering unlabeled time series better. The optimization model is iteratively solved via a coordinate descent algorithm. Experiments show that our USSL can learn meaningful shapelets, with promising results on real-world and synthetic data that surpass current state-of-the-art unsupervised time series learning methods.
Zhang, R, Mu, C, Xu, M, Xu, L & Xu, X 2019, 'Facial Component-Landmark Detection With Weakly-Supervised LR-CNN', IEEE Access, vol. 7, pp. 10263-10277.
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Zhang, R, Mu, C, Xu, M, Xu, L, Shi, Q & Wang, J 2019, 'Synthetic IR Image Refinement Using Adversarial Learning With Bidirectional Mappings', IEEE Access, vol. 7, pp. 153734-153750.
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© 2019 IEEE. Collecting a large dataset of real infrared (IR) images is expensive, time-consuming, and even unavailable in some specific scenarios. With recent progress in machine learning, it has become more feasible to replace real IR images with qualified synthetic IR images in learning-based IR systems. However, this alternative may fail to achieve the desired performance, due to the gap between real and synthetic IR images. Inspired by adversarial learning for image-to-image translation, we propose the Synthetic IR Refinement Generative Adversarial Network (SIR-GAN) to narrow this gap. By learning the bidirectional mappings between two unpaired domains, the realism of the simulated IR images generated from the IR Simulator are significantly improved, where the source domain contains a large number of simulated IR images, where the target domain contains a limited quantity of real IR images. Specifically, driven by the idea of transferring infrared characteristic and protect target semantic information simultaneously, we propose a SIR refinement loss to consider an infrared loss and a structure loss further to the adversarial loss and the consistency loss. To further reduce the gap, stabilize training, and avoid artefacts, we modify the proposed algorithm by developing a training strategy, adding the U-net in the generators, using the dilated convolution in the discriminators and invoking the N-Adam acts as the optimizer. Qualitative, quantitative, and ablation study experiments demonstrate the superiority of the proposed approach compared with the state-of-the-art techniques in terms of realism and fidelity. In addition, our refined IR images are evaluated in the context of a feasibility study, where the accuracy of the trained classifier is significantly improved by adding our refined data into a small real-data training set.
Zhang, T, Bao, J-F, Zeng, R-Z, Yang, Y, Bao, L-L, Bao, F-H, Zhang, Y & Qin, F 2019, 'Long lifecycle MEMS double-clamped beam based on low stress graphene compound film', Sensors and Actuators A: Physical, vol. 288, pp. 39-46.
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© 2019 Elsevier B.V. Graphene based three-layer compound film on the silicon substrate is formed by gold deposition of electron beam evaporation (EBE) and graphene transfer. Processed with different high temperature annealing in nitrogen, the film with residual tensile stress of 52.58 MPa at 500 ℃ can be achieved by using an X-ray diffraction (XRD) method. Based on this low stress film, a series of long lifecycle MEMS double-clamped beams (DCBs) are fabricated by the standard MEMS manufacturing technology. The achieved beam can be turned on/off for up to 100 million times at the pull-in voltage of 30 V, which is compatible with the conventional, complementary metal-oxide-semiconductor (CMOS) circuit requirements.
Zhang, W, Liu, T, Ye, L, Ueland, M, Forbes, SL & Su, SW 2019, 'A novel data pre-processing method for odour detection and identification system', Sensors and Actuators A: Physical, vol. 287, pp. 113-120.
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© 2018 Elsevier B.V. This paper presents a novel electronic nose (E-nose) data pre-processing method, based on a recently developed non-parametric kernel-based modelling (KBM) approach. The proposed method is tested by an automated odour detection and classification system, named “NOS.E” developed by the NOS.E team in University of Technology Sydney. Experimental results show that when extracting the derivative-related features from signals collected by the NOS.E, the proposed non-parametric KBM odour data pre-processing method achieves more reliable and stable pre-processing results comparing with other pre-processing methods such as wavelet package correlation filter (WPCF), mean filter (MF), polynomial curve fitting (PCF) and locally weighted regression (LWR). Based on these derivative-related features, the NOS.E can achieve a 96.23% accuracy of classification with the popular Support Vector Machine (SVM) classifier.
Zhang, X, Fatahi, B, Khabbaz, H & Poon, B 2019, 'Assessment of the Internal Shaft Friction of Tubular Piles in Jointed Weak Rock Using the Discrete-Element Method', Journal of Performance of Constructed Facilities, vol. 33, no. 6, pp. 04019067-04019067.
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© 2019 American Society of Civil Engineers. This study focuses on the internal shaft friction of open-ended tubular piles induced by jointed weak rock plugs. To investigate the bearing mechanism of the plug, push-up load tests were carried out on the jointed mudstone inside a tubular pile. The discrete-element method (DEM) was used in order to consider heterogeneity and to reproduce the discrete nature of the rock mass. A flat-joint model was used to reproduce the mechanical behavior of mudstone, and a smooth-joint contact model was used to replicate natural joints. The push-up load tests were carried out using the calibrated properties of a weak mudstone. The effects of joint density and joint dip were examined in detail and, as expected, the push-up force of the rock plug was influenced by the joint properties because joint density and joint dip had to some extent affected the plug resistance. The existing joints reduced the push-up force when the joints were steep, whereas the horizontal joints had a minimal effect on altering the inner shaft friction compared with the intact rock mass. The reduced friction along the pile was amplified with joint density, while the exponential increase of vertical stress from the top of the rock plug to the bottom revealed that the inner shaft resistance was mainly mobilized at the bottom portion of the rock plug. The findings of this study increase our understanding of joint dip and joint density affecting the internal shaft resistance of open-ended tubular piles; this knowledge can be used further to develop a design methodology for open-ended tubular piles in weak rock while assessing plugging effects.
Zhang, X, Ji, J & Xu, J 2019, 'Parameter identification of time-delayed nonlinear systems: An integrated method with adaptive noise correction', Journal of the Franklin Institute, vol. 356, no. 11, pp. 5858-5880.
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© 2019 The Franklin Institute This paper proposes a novel method called the adaptive-noise-correction integrated parameter identification (ANCPI) for time-delayed nonlinear systems. Compared with the existing de-noising techniques, the significance of the proposed method is the use of the system itself to correct the noise-polluted components so that the accuracy of parameter identification is enhanced. To achieve the goal of adaptive noise correction, this study starts from the case of periodic response and then parameterizes the noise correction as the coefficient correction of harmonic basis. In this way, the parameter identification integrated with noise correction can be performed as the parameter optimization of the error function. For the convenience of application, a user-friendly program package is further provided and a detailed tutorial is presented in the supplementary material.
Zhang, X, Ji, J, Fu, J & Xu, J 2019, 'Denoising identification for nonlinear systems with distorted streaming', International Journal of Non-Linear Mechanics, vol. 117, pp. 103224-103224.
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© 2019 Elsevier Ltd Streaming usually happens in systems with asymmetric nonlinearity. It is a dynamic phenomenon that the midpoint of the motion shifts away from the equilibrium point of the system. It is also an ultra-low-frequency process so that its observation often distorts because of signal acquisition limitations. Extensive studies have shown that the precision of parameter identification will drop greatly if this distortion is not accurately corrected. Motivated by the intention of enhancing the parameter identification's precision via signal correction, the present paper proposes a novel approach called the orthonormal Legendre polynomial based denoising identification method (OLP-DIM). In this method, the distorted response is decomposed by the orthonormal Legendre polynomials. The polynomial coefficients corresponding to the distortion are treated as uncertain parameters and then jointly identified with the system parameters. Numerical and experimental examples with different responses show that the OLP-DIM returns parameters in excellent precision and, distinct from traditional parameter identification methods, accurately recovers the system's streaming.
Zhang, X, Lu, W, Li, F, Peng, X & Zhang, R 2019, 'Deep Feature Fusion Model for Sentence Semantic Matching', Computers, Materials & Continua, vol. 61, no. 2, pp. 601-616.
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© 2019 Tech Science Press. All rights reserved. Sentence semantic matching (SSM) is a fundamental research in solving natural language processing tasks such as question answering and machine translation. The latest SSM research benefits from deep learning techniques by incorporating attention mechanism to semantically match given sentences. However, how to fully capture the semantic context without losing significant features for sentence encoding is still a challenge. To address this challenge, we propose a deep feature fusion model and integrate it into the most popular deep learning architecture for sentence matching task. The integrated architecture mainly consists of embedding layer, deep feature fusion layer, matching layer and prediction layer. In addition, we also compare the commonly used loss function, and propose a novel hybrid loss function integrating MSE and cross entropy together, considering confidence interval and threshold setting to preserve the indistinguishable instances in training process. To evaluate our model performance, we experiment on two real world public data sets: LCQMC and Quora. The experiment results demonstrate that our model outperforms the most existing advanced deep learning models for sentence matching, benefited from our enhanced loss function and deep feature fusion model for capturing semantic context.
Zhang, X, Lv, T, Ren, Y, Ni, W, Beaulieu, NC & Guo, YJ 2019, 'Economical Caching for Scalable Videos in Cache-Enabled Heterogeneous Networks', IEEE Journal on Selected Areas in Communications, vol. 37, no. 7, pp. 1608-1621.
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© 1983-2012 IEEE. We develop the optimal economical caching schemes in cache-enabled heterogeneous networks, while delivering multimedia video services with personalized viewing qualities to mobile users. By applying scalable video coding (SVC), each video file to be requested is divided into one base layer (BL) and several enhancement layers (ELs). In order to assign different transmission tasks, the serving small-cell base stations (SBSs) are grouped into K clusters. The SBSs are able to cache and cooperatively transmit BL and EL contents to the user. We analytically derive expressions for successful transmission probability and ergodic service rate, and then the closed-form expression for EConomical Efficiency (ECE) is obtained. In order to enhance the ECE performance, we formulate the ECE optimization problems for two cases. In the first case, with equal cache size equipped at each SBS, the layer caching indicator is determined. Since this problem is NP-hard, after the l-{0} -norm approximation, the discrete optimization variables are relaxed to be continuous, and this relaxed problem is convex. Next, based on the optimal solution derived from the relaxed problem, we devise a greedy-strategy based heuristic algorithm to achieve the near-optimal layer caching indicators. In the second case, the cache size for each SBS, the layer size, and the layer caching indicator are jointly optimized. This problem is a mixed integer programming problem, which is more challenging. To effectively solve this problem, the original ECE maximization problem is divided into two subproblems. These two subproblems are iteratively solved until the original optimization problem is convergent. Numerical results verify the correctness of the theoretical derivations. Additionally, compared to the most popular layer placement strategy, the performance superiority of the proposed SVC-based caching schemes is testified.
Zhang, X, Xiang, X, Wang, Y, Ding, G, Xu, X & Yang, Z 2019, 'A Heterogeneous Integrated MEMS Inertial Switch With Compliant Cantilevers Fixed Electrode and Electrostatic Locking to Realize Stable On-State', Journal of Microelectromechanical Systems, vol. 28, no. 6, pp. 977-986.
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© 1992-2012 IEEE. A novel heterogeneous integrated inertial micro-switch has been designed with adjustable acceleration threshold and a stable 'on'-state due to a predefined bias voltage. The bias voltage is applied onto the large-area parallel-plate electrodes, which endows the movable proof-mass with electrostatic forces. With an external excitation acceleration, the movable electrode moves to the fixed electrode and it can be locked by the electrostatic force onto the compliant electrical contacts, which are composed of micro-cantilever array to eliminate the contact rebound during electrostatic pull-in process. Both the dynamic response of the proof-mass and the relationship between the bias voltage and the inertial excitation acceleration were analyzed using theoretical model and finite element simulation. A unique heterogeneous integration process including both the silicon-based and non-silicon surface micromachining processes was adopted to fabricate the switch. The tests using a standard dropping hammer system demonstrated that the switch could keep a stable switch-on at the 57 g excitation acceleration and 38 V bias voltage. As wide as 52% adjustment range of the acceleration threshold was obtained when the applied bias voltage was from 38 V to 44 V. The tested relationship between the bias voltage and the external acceleration was very consistent with the simulated relationship. [2019-0038].
Zhang, X, Yu, S, Zhang, J & Xu, Z 2019, 'Forwarding Rule Multiplexing for Scalable SDN-Based Internet of Things', IEEE Internet of Things Journal, vol. 6, no. 2, pp. 3373-3385.
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© 2014 IEEE. Internet of Things (IoT) provides a vast number of devices with heterogeneous characteristics connected to the Internet. As a promising networking paradigm that decouples control plane from data plane, software-defined networking (SDN) is an appropriate architecture for IoT. The SDN paradigm supports deploying traffic flows dynamically by a centralized controller to SDN switches. In particular, the controller configures forwarding rules of SDN switches to steer traffic. However, forwarding rules are usually stored in expensive and power hungry ternary content addressable memory (TCAM), which is very limited in quantity for SDN switches. Thus, the shortage of TCAM becomes a fatal bottleneck for scalable flow management for SDN-based IoT. To this end, we propose a method of forwarding rule multiplexing (FRM) to minimize the total number of forwarding rules in SDN-based IoT. We multiplex different traffic flows traversing through the same path into an aggregated flow with the label of VLAN ID. As a result, multiple forwarding rules could be merged into one multiplexed rule. We also extend the method to SDN protection against link failure, and reduce backup path forwarding rules. We formulate the FRM problem as an integer linear programming model. Since the problem is NP-hard, we design a polynomial algorithm using the Markov approximation technique. Theoretical analysis indicates that the polynomial algorithm generates near-optimal solution. The extensive emulation results show that the proposed Markov approximation-based algorithm reduces the number of forwarding rules by 15.73% in average compared with the benchmark algorithms.
Zhang, X, Zhang, S, Lin, J, Sun, F, Zhu, X, Yang, Y, Tong, X & Yang, H 2019, 'An Efficient Seismic Data Acquisition Based on Compressed Sensing Architecture With Generative Adversarial Networks', IEEE Access, vol. 7, pp. 105948-105961.
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© 2013 IEEE. Recently, large scale seismic data acquisition has been a critical method for scientific research and industrial production. However, due to the bottleneck on data transmission and the limitation of energy storage, it is hard to conduct large seismic data acquisition in a real-time way. So, in this paper, an efficient seismic data acquisition method, namely, compressed sensing architecture with generative adversarial networks (CSA-GAN), is proposed to tackle the two restrictions of collecting large scale seismic data. In the CSA-GAN, a data collection architecture based on compressed sensing theory is applied to reduce data traffic load of the whole system, as well as balance the data transmission. To make the compressed sensing procedure perform well in both data quality and compression ratio, a kind of generative adversarial networks is designed to learn the recovering map. According to our experiment results, a high data quality (about 30 dB) at the compression ratio of 16 is achieved by the proposed approach, which enables the system to afford 15 times more sensors and reduces the energy cost by means of data collection from N(N + 1)/2 to N2/16. These results show that the CSA-GAN can afford more sensors with the same bandwidth and consume less energy, via improving the efficiency seismic data acquisition.
Zhang, Y, Huang, Y, Porter, AL, Zhang, G & Lu, J 2019, 'Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study', Technological Forecasting and Social Change, vol. 146, pp. 795-807.
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© 2018 As one of the most impactful emerging technologies, big data analytics and its related applications are powering the development of information technologies and are significantly shaping thinking and behavior in today's interconnected world. Exploring the technological evolution of big data research is an effective way to enhance technology management and create value for research and development strategies for both government and industry. This paper uses a learning-enhanced bibliometric study to discover interactions in big data research by detecting and visualizing its evolutionary pathways. Concentrating on a set of 5840 articles derived from Web of Science covering the period between 2000 and 2015, text mining and bibliometric techniques are combined to profile the hotspots in big data research and its core constituents. A learning process is used to enhance the ability to identify the interactive relationships between topics in sequential time slices, revealing technological evolution and death. The outputs include a landscape of interactions within big data research from 2000 to 2015 with a detailed map of the evolutionary pathways of specific technologies. Empirical insights for related studies in science policy, innovation management, and entrepreneurship are also provided.
Zhang, Y, Liu, Q, He, Z, Zong, Z & Fang, J 2019, 'Dynamic impact response of aluminum honeycombs filled with Expanded Polypropylene foam', Composites Part B: Engineering, vol. 156, pp. 17-27.
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© 2018 The paper investigated the dynamic impact response and characteristics of aluminum honeycomb filled with EPP foam (Expanded polypropylene) experimentally and numerically. It was found that the initial peak strength and mean strength of the filled honeycomb were improved significantly attributable to the interaction effect between the aluminum honeycomb and the foam, but the specific energy absorption (SEA) decreased. For the filled specimens with the same foam density, the initial peak strength, mean strength and SEA increased with the increase in impact velocity. Compared with the characteristics in the static compression test, the initial peak strength in the dynamic impact test increased, whereas the mean strength and SEA decreased. The study showed that EPP foam filling was effective to improve the impact characteristics of the bare aluminum honeycomb. Numerical simulation for the dynamic impact of filled honeycombs was also explored. It accurately reproduced the deformation process and addressed the interaction between the wall and EPP foam. By comparison of the properties in different filling types, it showed the single-cell filling was a good choice to improve the load resistance while using the least filling material.
Zhang, Y, Lu, X & Li, J 2019, 'Single-sample face recognition under varying lighting conditions based on logarithmic total variation', Signal, Image and Video Processing, vol. 13, no. 4, pp. 657-665.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. The logarithmic total variation (LTV) algorithm is a classical algorithm that is proposed to address the illumination interference in face recognition. Some state-of-the-art techniques based on LTV assume that the illumination component mainly lies in the low-frequency features among face images. However, these techniques adopt unsuitable methods to process low-frequency features, resulting in final unsatisfactory recognition rates. In this paper, we propose an improved illumination normalization method based on the LTV method, called the RETINA&TH-LTV algorithm. In this algorithm, the retina model is utilized to eliminate most of the illumination component in low-frequency features. Then, an advanced contrast-limited adaptive histogram equalization technique is proposed to remove the residual lighting component. At the same time, through realizing threshold-value filtering on high-frequency features, the enhancement of facial features is achieved. Finally, the processed frequency features are combined to form a robust holistic feature image, which is then utilized for recognition. Insufficient training images in face recognition are also taken into consideration in this research. Comparative experiments for single-sample face recognition are conducted on YALE B, CMU PIE and our self-built driver databases. The nearest neighbor classifier and extended sparse representation classifier are employed as classification methods. The results indicate that the RETINA&TH-LTV algorithm has promising performance, especially in serious illumination and insufficient training sample conditions.
Zhang, Y, Lv, P, Lu, X & Li, J 2019, 'Face detection and alignment method for driver on highroad based on improved multi-task cascaded convolutional networks', Multimedia Tools and Applications, vol. 78, no. 18, pp. 26661-26679.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Driver’s face detection and alignment techniques in Intelligent Transportation System (ITS) under unlimited environment are challenging issues, which are conductive to supervising traffic order and maintaining public safety. This paper proposes the improved Multi-task Cascaded Convolutional Networks (ITS-MTCNN) to realize accurate face region detection and feature alignment of driver’s face on highway, predicting face and feature location via a coarse-to-fine pattern. Moreover, the improved regularization method and effective online hard sample mining technique are proposed in ITS-MTCNN method. Then, the training model and contrast experiment are conducted on our self-build traffic driver’s face database. Finally, the effectiveness of ITS-MTCNN method is validated by comparative experiments and verified under various complex highway conditions. At the same time, average alignment errors on left eye, right eye, nose, left mouth as well as right mouth of the proposed technique are performed. Experimental results show that ITS-MTCNN model shows satisfied performance compared to other state-of-the-art techniques used in driver’s face detection and alignment, keeping robust to the occlusion, varying pose and extreme illumination on highway.
Zhang, Y, Porter, A, Chiavetta, D, Newman, NC & Guo, Y 2019, 'Forecasting technical emergence: An introduction', Technological Forecasting and Social Change, vol. 146, pp. 626-627.
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Zhang, Y, Tehran, K, Scheuermann, A & Li, L 2019, 'Prediction of shrinkage behavior of soft soil using ramp loading consolidation theory', Japanese Geotechnical Society Special Publication, vol. 7, no. 2, pp. 215-218.
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Zhang, Y, Wang, J & Lu, H 2019, 'Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting', Energies, vol. 12, no. 10, pp. 1931-1931.
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Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model’s effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models.
Zhang, Y, Wang, M, Gottwalt, F, Saberi, M & Chang, E 2019, 'Ranking scientific articles based on bibliometric networks with a weighting scheme', Journal of Informetrics, vol. 13, no. 2, pp. 616-634.
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© 2019 Elsevier Ltd. All rights reserved. As the volume of scientific articles has grown rapidly over the last decades, evaluating their impact becomes critical for tracing valuable and significant research output. Many studies have proposed various ranking methods to estimate the prestige of academic papers using bibliometric methods. However, the weight of the links in bibliometric networks has been rarely considered for article ranking in existing literature. Such incomplete investigation in bibliometric methods could lead to biased ranking results. Therefore, a novel scientific article ranking algorithm, W-Rank, is introduced in this study proposing a weighting scheme. The scheme assigns weight to the links of citation network and authorship network by measuring citation relevance and author contribution. Combining the weighted bibliometric networks and a propagation algorithm, W-Rank is able to obtain article ranking results that are more reasonable than existing PageRank-based methods. Experiments are conducted on both arXiv hep-th and Microsoft Academic Graph datasets to verify the W-Rank and compare it with three renowned article ranking algorithms. Experimental results prove that the proposed weighting scheme assists the W-Rank in obtaining ranking results of higher accuracy and, in certain perspectives, outperforming the other algorithms.
Zhang, Y, Wang, M, Saberi, M & Chang, E 2019, 'From Big Scholarly Data to Solution-Oriented Knowledge Repository', Frontiers in Big Data, vol. 2.
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The volume of scientific articles grow rapidly, producing a scientific basis for understanding and identifying the research problems and the state-of-the-art solutions. Despite the considerable significance of the problem-solving information, existing scholarly recommending systems lack the ability to retrieve this information from the scientific articles for generating knowledge repositories and providing problem-solving recommendations. To address this issue, this paper proposes a novel framework to build solution-oriented knowledge repositories and provide recommendations to solve given research problems. The framework consists of three modules: a semantics based information extraction module mining research problems and solutions from massive academic papers; a knowledge assessment module based on the heterogeneous bibliometric graph and a ranking algorithm; and a knowledge repository generation module to produce solution-oriented maps with recommendations. Based on the framework, a prototype scholarly solution support system is implemented. A case study is carried out in the research field of intrusion detection, and the results demonstrate the effectiveness and efficiency of the proposed method.
Zhang, Z, Oberst, S & Lai, JCS 2019, 'A non-linear friction work formulation for the analysis of self-excited vibrations', Journal of Sound and Vibration, vol. 443, pp. 328-340.
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Even though much research has been devoted to understand friction-induced vibrations, its root cause is not yet fully understood. Reliable prediction of friction-induced unstable vibrations such as in brake squeal or hip squeak remains a challenge because of nonlinearities involved and because the complex eigenvalue analysis (CEA) widely used in industry is linear. The energy fed back into the system by friction has been shown to be useful for analysis of measurements and numerical simulations. In numerical simulations, the linearised method of feed-in energy, calculated purely based on friction work has provided some insights into the physical mechanism for instabilities. However, the dynamics due to friction-induced instabilities is highly nonlinear and damping may offset some or all of the excess friction energy provided to the system. By using a nonlinear 2-DOF dry friction oscillator, a nonlinear friction work formulation is proposed to demonstrate that in combination with viscous damping the energy budget provides an improved analysis capability over linearised friction work. The results highlight the potential of nonlinear friction work as a reliable tool to study friction-induced instabilities to gain deeper physical insights into squeal triggering mechanisms and to better understand the over- and under-predictive character inherent to linear methods.
Zhang, Z, Wu, Q, Wang, Y & Chen, F 2019, 'High-Quality Image Captioning With Fine-Grained and Semantic-Guided Visual Attention', IEEE Transactions on Multimedia, vol. 21, no. 7, pp. 1681-1693.
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© 1999-2012 IEEE. The soft-attention mechanism is regarded as one of the representative methods for image captioning. Based on the end-to-end convolutional neural network (CNN)-long short term memory (LSTM) framework, the soft-attention mechanism attempts to link the semantic representation in text (i.e., captioning) with relevant visual information in the image for the first time. Motivated by this approach, several state-of-the-art attention methods are proposed. However, due to the constraints of CNN architecture, the given image is only segmented to the fixed-resolution grid at a coarse level. The visual feature extracted from each grid indiscriminately fuses all inside objects and/or their portions. There is no semantic link between grid cells. In addition, the large area 'stuff' (e.g., the sky or a beach) cannot be represented using the current methods. To address these problems, this paper proposes a new model based on the fully convolutional network (FCN)-LSTM framework, which can generate an attention map at a fine-grained grid-wise resolution. Moreover, the visual feature of each grid cell is contributed only by the principal object. By adopting the grid-wise labels (i.e., semantic segmentation), the visual representations of different grid cells are correlated to each other. With the ability to attend to large area 'stuff,' our method can further summarize an additional semantic context from semantic labels. This method can provide comprehensive context information to the language LSTM decoder. In this way, a mechanism of fine-grained and semantic-guided visual attention is created, which can accurately link the relevant visual information with each semantic meaning inside the text. Demonstrated by three experiments including both qualitative and quantitative analyses, our model can generate captions of high quality, specifically high levels of accuracy, completeness, and diversity. Moreover, our model significantly outperforms all other meth...
Zhao, G, Li, Y, Xu, C, Han, Z, Xing, Y & Yu, S 2019, 'Joint Power Control and Channel Allocation for Interference Mitigation Based on Reinforcement Learning', IEEE Access, vol. 7, pp. 177254-177265.
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© 2013 IEEE. In dense Wireless Local Area Networks (WLANs), high-density Access Points (APs) bring severe interference that seriously affects the experience of users, resulting in lower throughput and poor connection quality. Due to the heavy computation workload raised by the sizable networking systems and the difficulty in estimating instantaneous Channel State Information (CSI), existing works are hard to solve interference problem. In this paper, we propose a Joint Power control and Channel allocation based on Reinforcement Learning (JPCRL) algorithm combining with statistical CSI to reduce interference adaptively. Firstly, we analyze the correlation between transmit power and channel, and formulate the interference optimization as a Mixed Integer Nonlinear Programming (MINLP) problem. Secondly, we use the statistical CSI method to take the power and channel state as the state and action space, the overall throughput increment as the reward function of Q-learning, and obtain the optimal joint optimization strategy through off-line training. Moreover, for the periodic reinforcement learning process leading to resource consumption, we design an event-driven mechanism of Q-learning, which triggers online learning to refresh the optimal policy by event-driven condition and the consumption of computing resources can be reduced. The evaluation results show that the proposed algorithm can effectively improve the throughput compared with the existing scheme.
Zhao, H, Tao, M, Li, X, Cao, W & Wu, C 2019, 'Estimation of spalling strength of sandstone under different pre-confining pressure by experiment and numerical simulation', International Journal of Impact Engineering, vol. 133, pp. 103359-103359.
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© 2019 Elsevier Ltd Spalling failure is a common underground engineering disaster, particularly in deep environments with high geo-stress and strong stress disturbances. Therefore, the further investigation of the spalling behaviour of rock under different pre-confining pressures is of considerable importance. In the presented work, a modified split Hopkinson pressure bar (SHPB) system is modified with a pre-confining loading device that can apply pre-static loads to Φ50 mm × 300 mm specimens. The system is employed to study the spalling characteristics of rock specimens subjected to full pre-confining pressure. Spalling tests of Φ50 mm × 300 mm rock specimens under different pre-confining pressures were conducted. The experimental results indicated that spalling failure was influenced by pre-confining pressure. Furthermore, numerical simulations with the same loading conditions as the experiments were conducted using the finite element software, LS-DYNA, and the spalling strengths of the rock specimens under different pre-confining pressures were obtained based on the improved stress wave analysis method. The results indicated that the stress wave analysis method in view of numerical simulation can effectively be used for calculating the spalling strength of rock and rock-like material under pre-confining pressure. The spalling strength of the rock specimens increased first and then decreased as the pre-confining pressure increased.
Zhao, J, Huang, S, Zhao, L, Chen, Y & Luo, X 2019, 'Conic Feature Based Simultaneous Localization and Mapping in Open Environment via 2D Lidar', IEEE Access, vol. 7, pp. 173703-173718.
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© 2013 IEEE. The conventional planar scan matching approach cannot cope well with the open environment as lacking of sufficient edges and corners. This paper presents a conic feature based simultaneous localization and mapping (SLAM) algorithm via 2D lidar which can adapt to an open environment nicely. The novelty of this work includes threefold: (1) defining a conic feature based parametrization approach; (2) developing a method to utilize feature's conic geometric information and odometry information since open environments are short of regular linear geometric features; (3) developing a factor graph based framework which can be adapted with the proposed parametrization. Simulation experiments and real environment experiments demonstrated that the proposed SLAM algorithm can get accurate and convincing results for the open environment and the map in our representation can express accurately the environment situation.
Zhao, L, Huang, S & Dissanayake, G 2019, 'Linear SLAM: Linearising the SLAM problems using submap joining', Automatica, vol. 100, pp. 231-246.
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© 2018 Elsevier Ltd The main contribution of this paper is a new submap joining based approach for solving large-scale Simultaneous Localization and Mapping (SLAM) problems. Each local submap is independently built using the local information through solving a small-scale SLAM; the joining of submaps mainly involves solving linear least squares and performing nonlinear coordinate transformations. Through approximating the local submap information as the state estimate and its corresponding information matrix, judiciously selecting the submap coordinate frames, and approximating the joining of a large number of submaps by joining only two maps at a time, either sequentially or in a more efficient Divide and Conquer manner, the nonlinear optimization process involved in most of the existing submap joining approaches is avoided. Thus the proposed submap joining algorithm does not require initial guess or iterations since linear least squares problems have closed-form solutions. The proposed Linear SLAM technique is applicable to feature-based SLAM, pose graph SLAM and D-SLAM, in both two and three dimensions, and does not require any assumption on the character of the covariance matrices. Simulations and experiments are performed to evaluate the proposed Linear SLAM algorithm. Results using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization algorithm started from an accurate initial guess. The C/C++ and MATLAB source codes of Linear SLAM are available on OpenSLAM.
Zhao, L-S, Zhou, W-H, Geng, X, Yuen, K-V & Fatahi, B 2019, 'A closed-form solution for column-supported embankments with geosynthetic reinforcement', Geotextiles and Geomembranes, vol. 47, no. 3, pp. 389-401.
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© 2019 Elsevier Ltd Soil arching effect results from the non-uniform stiffness in a geosynthetic-reinforced and column-supported embankment system. However, most theoretical models ignore the impact of modulus difference on the calculation of load transfer. In this study, a generalized mathematical model is presented to investigate the soil arching effect, with consideration given to the modulus ratio between columns and the surrounding soil. For simplification, a cylindrical unit cell is drawn to study the deformation compatibility among embankment fills, geosynthetics, columns, and subsoils. A deformed shape function is introduced to describe the relationship between the column and the adjacent soil. The measured data gained from a full-scale test are applied to demonstrate the application of this model. In the parametric study, certain influencing factors, such as column spacing, column length, embankment height, modulus ratio, and tensile strength of geosynthetic reinforcement, are analyzed to investigate the performance of the embankment system. This demonstrates that the inclusion of a geosynthetic reinforcement or enlargement of the modulus ratio can increase the load transfer efficiency. When enhancing the embankment height or applying an additional loading, the height of the load transfer platform tends to be reduced. However, a relatively long column has little impact on the load transfer platform.
Zhao, N, Ngo, HH, Li, Y, Wang, X, Yang, L, Jin, P & Sun, G 2019, 'A comprehensive simulation approach for pollutant bio-transformation in the gravity sewer', Frontiers of Environmental Science & Engineering, vol. 13, no. 4.
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© 2019, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature. Presently, several activated sludge models (ASMs) have been developed to describe a few biochemical processes. However, the commonly used ASM neither clearly describe the migratory transformation characteristics of fermentation nor depict the relationship between the carbon source and biochemical reactions. In addition, these models also do not describe both ammonification and the integrated metabolic processes in sewage transportation. In view of these limitations, we developed a new and comprehensive model that introduces anaerobic fermentation into the ASM and simulates the process of sulfate reduction, ammonification, hydrolysis, acidogenesis and methanogenesis in a gravity sewer. The model correctly predicts the transformation of organics including proteins, lipids, polysaccharides, etc. The simulation results show that the degradation of organics easily generates acetic acid in the sewer system and the high yield of acetic acid is closely linked to methanogenic metabolism. Moreover, propionic acid is the crucial substrate for sulfate reduction and ammonification tends to be affected by the concentration of amino acids. Our model provides a promising tool for simulating and predicting outcomes in response to variations in wastewater quality in sewers. [Figure not available: see fulltext.]
Zhao, S, Qiu, X, Lacey, J & Maisch, S 2019, 'Configuring fixed-coefficient active control systems for traffic noise reduction', Building and Environment, vol. 149, pp. 415-427.
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© 2018 Elsevier Ltd Practical implementation of active noise control (ANC) systems for outdoor traffic noise reduction remains rare. One challenge is the difficulty of configuring an ANC controller due to moving noise sources, which are typically located far from ANC systems. In this paper, a pseudo noise source method is proposed for configuring fixed-coefficient feedforward ANC systems for traffic noise control. First, a minimum of one pseudo noise source is placed near an ANC system to determine the control coefficients in the tuning stage. Second, the ANC systems are run to reduce the noise from far-field traffic noise sources using the optimal control coefficients in the cancelling stage. The feasibility and limitations of the proposed method are investigated by illustrating the effect of the pseudo noise source position on the noise reduction performance of the ANC system. The simulation results show that the performance of the ANC system increases with distance when the pseudo noise sources move farther from the system but approaches a constant when the pseudo noise sources are in the far field. The indoor experimental results are consistent with the simulation results. The outdoor experimental results of a six-channel coupled system show a noise reduction of 3 dB below 500 Hz at the position of a dummy head.
Zhao, Y, Chen, J, Wu, D, Teng, J, Sharma, N, Sajjanhar, A & Blumenstein, M 2019, 'Network Anomaly Detection by Using a Time-Decay Closed Frequent Pattern', Information, vol. 10, no. 8, pp. 262-262.
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Anomaly detection of network traffic flows is a non-trivial problem in the field of network security due to the complexity of network traffic. However, most machine learning-based detection methods focus on network anomaly detection but ignore the user anomaly behavior detection. In real scenarios, the anomaly network behavior may harm the user interests. In this paper, we propose an anomaly detection model based on time-decay closed frequent patterns to address this problem. The model mines closed frequent patterns from the network traffic of each user and uses a time-decay factor to distinguish the weight of current and historical network traffic. Because of the dynamic nature of user network behavior, a detection model update strategy is provided in the anomaly detection framework. Additionally, the closed frequent patterns can provide interpretable explanations for anomalies. Experimental results show that the proposed method can detect user behavior anomaly, and the network anomaly detection performance achieved by the proposed method is similar to the state-of-the-art methods and significantly better than the baseline methods.
Zhao, Y, Liu, D, Huang, W, Yang, Y, Ji, M, Nghiem, LD, Trinh, QT & Tran, NH 2019, 'Insights into biofilm carriers for biological wastewater treatment processes: Current state-of-the-art, challenges, and opportunities', Bioresource Technology, vol. 288, pp. 121619-121619.
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Biofilm carriers play an important role in attached growth systems for wastewater treatment processes. This study systematically summarizes the traditional and novel biofilm carriers utilized in biofilm-based wastewater treatment technology. The advantages and disadvantages of traditional biofilm carriers are evaluated and discussed in light of basic property, biocompatibility and applicability. The characteristics, applications performance, and mechanism of novel carriers (including slow-release carriers, hydrophilic/electrophilic modified carriers, magnetic carriers and redox mediator carriers) in wastewater biological treatment were deeply analyzed. Slow release biofilm carriers are used to provide a solid substrate and electron donor for the growth of microorganisms and denitrification for anoxic and/or anaerobic bioreactors. Carriers with hydrophilic/electrophilic modified surface are applied for promoting biofilm formation. Magnetic materials-based carriers are employed to shorten the start-up time of bioreactor. Biofilm carriers acting as redox mediators are used to accelerate biotransformation of recalcitrant pollutants in industrial wastewater.
Zhao, Z, Peng, H, Zhang, X, Zheng, Y, Chen, F, Fang, L & Li, J 2019, 'Identification of lung cancer gene markers through kernel maximum mean discrepancy and information entropy', BMC Medical Genomics, vol. 12, no. S8.
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AbstractBackgroundThe early diagnosis of lung cancer has been a critical problem in clinical practice for a long time and identifying differentially expressed gene as disease marker is a promising solution. However, the most existing gene differential expression analysis (DEA) methods have two main drawbacks: First, these methods are based on fixed statistical hypotheses and not always effective; Second, these methods can not identify a certain expression level boundary when there is no obvious expression level gap between control and experiment groups.MethodsThis paper proposed a novel approach to identify marker genes and gene expression level boundary for lung cancer. By calculating a kernel maximum mean discrepancy, our method can evaluate the expression differences between normal, normal adjacent to tumor (NAT) and tumor samples. For the potential marker genes, the expression level boundaries among different groups are defined with the information entropy method.ResultsCompared with two conventional methods t-test and fold change, the top average ranked genes selected by our method can achieve better performance under all metrics in the 10-fold cross-validation. Then GO and KEGG enrichment analysis are conducted to explore the biological function of the top 100 ranked genes. At last, we choose the top 10 average ranked genes as lung cancer markers and their expression boundaries are calculated and reported.ConclusionThe proposed approach is effective to identify gene markers for lung cancer diagnosis. It is not only more accurate than conventional DEA methods but also provides a reliable method to identify the gene expression level boundaries.
Zheng, D, Zhang, H, Andrew Zhang, J & Li, Y 2019, 'Consensus of the Second-order Multi-agent Systems under Asynchronous Switching with a Controller Fault', International Journal of Control, Automation and Systems, vol. 17, no. 1, pp. 136-144.
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© 2019, Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature. Asynchronous switching differing from asynchronous consensus may hinder the system to reach a consensus. This receives very limited attention, especially when the multi-agent systems have a controller fault. In order to analyze the consensus in this situation, this paper studies the consensus of the second-order multi-agent systems under asynchronous switching with a controller fault. We convert the consensus problems under asynchronous switching into stability problems and obtain important results for consensus with the aid of linear matrix inequalities. An example is given to illustrate the effect of asynchronous switching on the consensus, and to validate the analytical results in this paper.
Zheng, J, Luo, Z, Jiang, C & Gao, J 2019, 'Robust topology optimization for concurrent design of dynamic structures under hybrid uncertainties', Mechanical Systems and Signal Processing, vol. 120, pp. 540-559.
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© 2018 Elsevier Ltd A new robust topology optimization method based on level sets is developed for the concurrent design of dynamic structures composed of uniform periodic microstructures subject to random and interval hybrid uncertainties. A Hybrid Dimensional Reduction (HDR) method is proposed to estimate the interval mean and the interval variance of the uncertain objective function based on a bivariate dimension reduction scheme. The robust objective function is defined as a weighted sum of the mean and standard variance of the dynamic compliance under the worst case. The sensitivity information of the robust objective function with respect to the macro and micro design variables can then be obtained after the uncertainty analysis. Several examples are used to validate the effectiveness of the proposed robust topology optimization method.
Zheng, K, Liao, Z, Yoda, N, Fang, J, Chen, J, Zhang, Z, Zhong, J, Peck, C, Sasaki, K, Swain, MV & Li, Q 2019, 'Investigation on masticatory muscular functionality following oral reconstruction – An inverse identification approach', Journal of Biomechanics, vol. 90, pp. 1-8.
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© 2019 Elsevier Ltd The human masticatory system has received significant attention in the areas of biomechanics due to its sophisticated co-activation of a group of masticatory muscles which contribute to the fundamental oral functions. However, determination of each muscular force remains fairly challenging in vivo; the conventional data available may be inapplicable to patients who experience major oral interventions such as maxillofacial reconstruction, in which the resultant unsymmetrical anatomical structure invokes a more complex stomatognathic functioning system. Therefore, this study aimed to (1)establish an inverse identification procedure by incorporating the sequential Kriging optimization (SKO)algorithm, coupled with the patient-specific finite element analysis (FEA)in silico and occlusal force measurements at different time points over a course of rehabilitation in vivo; and (2)evaluate muscular functionality for a patient with mandibular reconstruction using a fibula free flap (FFF)procedure. The results from this study proved the hypothesis that the proposed method is of certain statistical advantage of utilizing occlusal force measurements, compared to the traditionally adopted optimality criteria approaches that are basically driven by minimizing the energy consumption of muscle systems engaged. Therefore, it is speculated that mastication may not be optimally controlled, in particular for maxillofacially reconstructed patients. For the abnormal muscular system in the patient with orofacial reconstruction, the study shows that in general, the magnitude of muscle forces fluctuates over the 28-month rehabilitation period regardless of the decreasing trend of the maximum muscular capacity. Such finding implies that the reduction of the masticatory muscle activities on the resection side might lead to non-physiological oral biomechanical responses, which can change the muscular activities for stabilizing the reconstructed mandible.
Zheng, L, Price, WE & Nghiem, LD 2019, 'Effects of fouling on separation performance by forward osmosis: the role of specific organic foulants', Environmental Science and Pollution Research, vol. 26, no. 33, pp. 33758-33769.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. In this study, forward osmosis (FO) membranes and fouling solutions were systematically characterized to elucidate the effects of organic fouling on the rejection of two pharmaceutically active compounds, namely, sulfamethoxazole and carbamazepine. Municipal wastewater resulted in a more severe flux decline compared to humic acid and sodium alginate fouling solutions. This result is consistent with the molecular weight distribution of these foulant solutions. Liquid chromatography with organic carbon detection analysis shows that municipal wastewater consists of mostly low molecular weight acids and neutrals, which produce a more compact cake layer on the membrane surface. By contrast, humic acid and sodium alginate consist of large molecular weight humic substances and biopolymers, respectively. The results also show that membrane fouling can significantly alter the membrane surface charge and hydrophobicity as well as the reverse salt flux. In particular, the reverse salt flux of a fouled membrane was significantly higher than that under clean conditions. Although the rejection of sulfamethoxazole and carbamazepine by FO membrane was high, a discernible impact of fouling on their rejection could still be observed. The results show that size exclusion is a major rejection mechanism of both sulfamethoxazole and carbamazepine. However, they respond to membrane fouling differently. Membrane fouling results in an increase in sulfamethoxazole rejection while carbamazepine rejection decreases due to membrane fouling.
Zheng, M, Duan, H, Dong, Q, Ni, B-J, Hu, S, Liu, Y, Huang, X & Yuan, Z 2019, 'Effects of ultrasonic treatment on the ammonia-oxidizing bacterial (AOB) growth kinetics', Science of The Total Environment, vol. 690, pp. 629-635.
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© 2019 Elsevier B.V. Ultrasound has in the past few decades found applications in a variety of disciplines including chemistry, medicine, physics, and to a much less extent microbiology. Our previous studies found that ultrasonic treatment increases the activity of ammonia-oxidizing bacteria (AOB) while suppressing nitrite-oxidizing bacteria (NOB), resulting in beneficial effects in wastewater treatment. In this study, the kinetic and microbiological features of nitrifying microorganisms in activated sludge intermittently treated with ultrasound were investigated to gain an improved understanding of the mechanism involved in ultrasound-induced stimulation of AOB kinetics. The nitrifying microorganisms were initially enriched over 100 days in a laboratory sequential batch reactor (SBR). Ultrasonic treatment of the sludge was then applied with the treatment time in each 12 h SBR cycle progressively increased from 4 to 24 min. Application of the treatment for 21 days led to a doubled maximum specific ammonia oxidation rate, and also the enhanced dominance of known AOB Nitrosomonas genus in the biomass. This stimulatory effect is well described by a modified enzyme catalyzed reaction model, showing a good linear relationship between the natural logarithm value of μmax,AOB and the applied ultrasonic energy density. This result suggests that ultrasonic treatment likely reduced the activation energy of key enzymes involved in ammonium oxidation.
Zheng, M, Jiang, T, Yang, W, Zou, Y, Wu, H, Liu, X, Zhu, F, Qian, R, Ling, D, McDonald, K, Shi, J & Shi, B 2019, 'The siRNAsome: A Cation‐Free and Versatile Nanostructure for siRNA and Drug Co‐delivery', Angewandte Chemie International Edition, vol. 58, no. 15, pp. 4938-4942.
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AbstractNanoparticles show great potential for drug delivery. However, suitable nanostructures capable of loading a range of drugs together with the co‐delivery of siRNAs, which avoid the problem of cation‐associated cytotoxicity, are lacking. Herein, we report an small interfering RNA (siRNA)‐based vesicle (siRNAsome), which consists of a hydrophilic siRNA shell, a thermal‐ and intracellular‐reduction‐sensitive hydrophobic median layer, and an empty aqueous interior that meets this need. The siRNAsome can serve as a versatile nanostructure to load drug agents with divergent chemical properties, therapeutic proteins as well as co‐delivering immobilized siRNAs without transfection agents. Importantly, the inherent thermal/reduction‐responsiveness enables controlled drug loading and release. When siRNAsomes are loaded with the hydrophilic drug doxorubicin hydrochloride and anti‐P‐glycoprotein siRNA, synergistic therapeutic activity is achieved in multidrug resistant cancer cells and a tumor model.
Zheng, M, Liu, Y, Wang, Y, Zhang, D, Zou, Y, Ruan, W, Yin, J, Tao, W, Park, JB & Shi, B 2019, 'ROS‐Responsive Polymeric siRNA Nanomedicine Stabilized by Triple Interactions for the Robust Glioblastoma Combinational RNAi Therapy', Advanced Materials, vol. 31, no. 37.
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AbstractSmall interfering RNA (siRNA) holds inherent advantages and great potential for treating refractory diseases. However, lack of suitable siRNA delivery systems that demonstrate excellent circulation stability and effective at‐site delivery ability is currently impeding siRNA therapeutic performance. Here, a polymeric siRNA nanomedicine (3I‐NM@siRNA) stabilized by triple interactions (electrostatic, hydrogen bond, and hydrophobic) is constructed. Incorporating extra hydrogen and hydrophobic interactions significantly improves the physiological stability compared to an siRNA nanomedicine analog that solely relies on the electrostatic interaction for stability. The developed 3I‐NM@siRNA nanomedicine demonstrates effective at‐site siRNA release resulting from tumoral reactive oxygen species (ROS)‐triggered sequential destabilization. Furthermore, the utility of 3I‐NM@siRNA for treating glioblastoma (GBM) by functionalizing 3I‐NM@siRNA nanomedicine with angiopep‐2 peptide is enhanced. The targeted Ang‐3I‐NM@siRNA exhibits superb blood–brain barrier penetration and potent tumor accumulation. Moreover, by cotargeting polo‐like kinase 1 and vascular endothelial growth factor receptor‐2, Ang‐3I‐NM@siRNA shows effective suppression of tumor growth and significantly improved survival time of nude mice bearing orthotopic GBM brain tumors. New siRNA nanomedicines featuring triple‐interaction stabilization together with inbuilt self‐destruct delivery ability provide a robust and potent platform for targeted GBM siRNA therapy, which may have utility for RNA interference therapy of other tumors or brain diseases.
Zheng, Y, Peng, H, Ghosh, S, Lan, C & Li, J 2019, 'Inverse similarity and reliable negative samples for drug side-effect prediction', BMC Bioinformatics, vol. 19, no. S13, pp. 554-554.
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© 2019 The Author(s). Background: In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly focus on leveraging validated drug side-effect relations for the prediction. The performance is severely impeded by the lack of reliable negative training data. Thus, a method to select reliable negative samples becomes vital in the performance improvement. Methods: Most of the existing computational prediction methods are essentially based on the assumption that similar drugs are inclined to share the same side-effects, which has given rise to remarkable performance. It is also rational to assume an inverse proposition that dissimilar drugs are less likely to share the same side-effects. Based on this inverse similarity hypothesis, we proposed a novel method to select highly-reliable negative samples for side-effect prediction. The first step of our method is to build a drug similarity integration framework to measure the similarity between drugs from different perspectives. This step integrates drug chemical structures, drug target proteins, drug substituents, and drug therapeutic information as features into a unified framework. Then, a similarity score between each candidate negative drug and validated positive drugs is calculated using the similarity integration framework. Those candidate negative drugs with lower similarity scores are preferentially selected as negative samples. Finally, both the validated positive drugs and the selected highly-reliable negative samples are used for predictions. Results: The performance of the proposed method was evaluated on simulative side-effect prediction of 917 DrugBank drugs, comparing with four machine-learning algorithms. Extensive experiments show that the drug similarity integration framework has superior capability in capturing drug features, ac...
Zheng, Y, Peng, H, Zhang, X, Zhao, Z, Gao, X & Li, J 2019, 'DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions', BMC Bioinformatics, vol. 20, no. S19.
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AbstractBackgroundDrug-drug interactions (DDIs) are a major concern in patients’ medication. It’s unfeasible to identify all potential DDIs using experimental methods which are time-consuming and expensive. Computational methods provide an effective strategy, however, facing challenges due to the lack of experimentally verified negative samples.ResultsTo address this problem, we propose a novel positive-unlabeled learning method named DDI-PULearn for large-scale drug-drug-interaction predictions. DDI-PULearn first generates seeds of reliable negatives via OCSVM (one-class support vector machine) under a high-recall constraint and via the cosine-similarity based KNN (k-nearest neighbors) as well. Then trained with all the labeled positives (i.e., the validated DDIs) and the generated seed negatives, DDI-PULearn employs an iterative SVM to identify a set of entire reliable negatives from the unlabeled samples (i.e., the unobserved DDIs). Following that, DDI-PULearn represents all the labeled positives and the identified negatives as vectors of abundant drug properties by a similarity-based method. Finally, DDI-PULearn transforms these vectors into a lower-dimensional space via PCA (principal component analysis) and utilizes the compressed vectors as input for binary classifications. The performance of DDI-PULearn is evaluated on simulative prediction for 149,878 possible interactions between 548 drugs, comparing with two baseline methods and five state-of-the-art methods. Related experiment results show that the proposed method for the representation of DDIs characterizes them accurately. DDI-PULearn achieves superior performance owing to the identified reliable negatives, outperforming all other methods significantly. In addition, the predicted novel DDIs suggest that DDI-PULearn is capable to identify novel DDIs.
Zheng, Y, Peng, H, Zhang, X, Zhao, Z, Gao, X & Li, J 2019, 'Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces', BMC Bioinformatics, vol. 20, no. S23, p. 605.
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AbstractBackgroundDetection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interactions (i.e., positive samples) for the predictions. Their performance is severely impeded by the lack of reliable negative samples.ResultsWe propose a method to construct highly-reliable negative samples for drug target prediction by a pairwise drug-target similarity measurement and OCSVM with a high-recall constraint. On one hand, we measure the pairwise similarity between every two drug-target interactions by combining the chemical similarity between their drugs and the Gene Ontology-based similarity between their targets. Then we calculate the accumulative similarity with all known drug-target interactions for each unobserved drug-target interaction. On the other hand, we obtain the signed distance from OCSVM learned from the known interactions with high recall (≥0.95) for each unobserved drug-target interaction. After normalizing all accumulative similarities and signed distances to the range [0,1], we compute the score for each unobserved drug-target interaction via averaging its accumulative similarity and signed distance. Unobserved interactions with lower scores are preferentially served as reliable negative samples for the classification algorithms. The performance of the proposed method is evaluated on the interaction data between 1094 drugs and 1556 target proteins. Extensive comparison experiments using four classical classifiers and one domain predictive method demonstrate the superior performance of the proposed method. A better decision boundary has been learned from the constructed reliable negative samples.Conclusions...
Zhou, JT, Pan, SJ & Tsang, IW 2019, 'A deep learning framework for Hybrid Heterogeneous Transfer Learning', Artificial Intelligence, vol. 275, pp. 310-328.
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© 2019 Elsevier B.V. Most previous methods in heterogeneous transfer learning learn a cross-domain feature mapping between different domains based on some cross-domain instance-correspondences. Such instance-correspondences are assumed to be representative in the source domain and the target domain, respectively. However, in many real-world scenarios, this assumption may not hold. As a result, the constructed feature mapping may not be precise, and thus the transformed source-domain labeled data using the feature mapping are not useful to build an accurate classifier for the target domain. In this paper, we offer a new heterogeneous transfer learning framework named Hybrid Heterogeneous Transfer Learning (HHTL), which allows the selection of corresponding instances across domains to be biased to the source or target domain. Our basic idea is that though the corresponding instances are biased in the original feature space, there may exist other feature spaces, projected onto which, the corresponding instances may become unbiased or representative to the source domain and the target domain, respectively. With such a representation, a more precise feature mapping across heterogeneous feature spaces can be learned for knowledge transfer. We design several deep-learning-based architectures and algorithms that enable learning aligned representations. Extensive experiments on two multilingual classification datasets verify the effectiveness of our proposed HHTL framework and algorithms compared with some state-of-the-art methods.
Zhou, JT, Tsang, IW, Ho, S-S & Müller, K-R 2019, 'N-ary decomposition for multi-class classification', Machine Learning, vol. 108, no. 5, pp. 809-830.
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© 2019, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature. A common way of solving a multi-class classification problem is to decompose it into a collection of simpler two-class problems. One major disadvantage is that with such a binary decomposition scheme it may be difficult to represent subtle between-class differences in many-class classification problems due to limited choices of binary-value partitions. To overcome this challenge, we propose a new decomposition method called N-ary decomposition that decomposes the original multi-class problem into a set of simpler multi-class subproblems. We theoretically show that the proposed N-ary decomposition could be unified into the framework of error correcting output codes and give the generalization error bound of an N-ary decomposition for multi-class classification. Extensive experimental results demonstrate the state-of-the-art performance of our approach.
Zhou, JT, Tsang, IW, Pan, SJ & Tan, M 2019, 'Multi-class Heterogeneous Domain Adaptation', Journal of Machine Learning Research, vol. 20, no. 57, pp. 1-31.
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A crucial issue in heterogeneous domain adaptation (HDA) is the ability to learn a feature mapping between different types of features across domains. Inspired by language translation, a word translated from one language corresponds to only a few words in another language, we present an efficient method named Sparse Heterogeneous Feature Representation (SHFR) in this paper for multi-class HDA to learn a sparse feature transformation between domains with multiple classes. Specifically, we formulate the problem of learning the feature transformation as a compressed sensing problem by building multiple binary classifiers in the target domain as various measurement sensors, which are decomposed from the target multi-class classification problem. We show that the estimation error of the learned transformation decreases with the increasing number of binary classifiers. In other words, for adaptation across heterogeneous domains to be successful, it is necessary to construct a sufficient number of incoherent binary classifiers from the original multi-class classification problem. To achieve this, we propose to apply the error correcting output correcting (ECOC) scheme to generate incoherent classifiers. To speed up the learning of the feature transformation across domains, we apply an efficient batch-mode algorithm to solve the resultant nonnegative sparse recovery problem. Theoretically, we present a generalization error bound of our proposed HDA method under a multi-class setting. Lastly, we conduct extensive experiments on both synthetic and real-world datasets to demonstrate the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy and training efficiency.
Zhou, W, Sutton, GJ, Andrew Zhang, J, Liu, RP & Pan, S 2019, 'Delay-Guaranteed Admission Control for LAA Coexisting With WiFi', IEEE Wireless Communications Letters, vol. 8, no. 4, pp. 1048-1051.
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© 2012 IEEE. Licensed-assisted-access (LAA) is used to extend the LTE link into the unlicensed band. How to guarantee the quality-of-service (QoS) for LTE devices in the unlicensed band is a challenging problem due to the listen-before-talk contention access in 5-GHz unlicensed bands. In this letter, we quantitatively analyze the medium access control delay for tagged LAA eNBs and propose a delay-guaranteed admission control scheme. We consider the freezing time of busy slots caused by collision or successful transmission, and introduce the exponential backoff mechanism for delay analysis. Validated by simulation results, our method provides important insights into the system admission performance and fairness of access.
Zhou, X, Huang, L, Zhang, Y & Yu, M 2019, 'A hybrid approach to detecting technological recombination based on text mining and patent network analysis', Scientometrics, vol. 121, no. 2, pp. 699-737.
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© 2019, Akadémiai Kiadó, Budapest, Hungary. Detecting promising technology groups for recombination holds the promise of great value for R&D managers and technology policymakers, especially if the technologies in question can be detected before they have been combined. However, predicting the future is always easier said than done. In this regard, Arthur’s theory (The nature of technology: what it is and how it evolves, Free Press, New York, 2009) on the nature of technologies and how science evolves, coupled with Kuhn’s theory of scientific revolutions (Kuhn in The structure of scientific revolutions, 1st edn, University of Chicago Press, Chicago, p 3, 1962), may serve as the basis of a shrewd methodological framework for forecasting recombinative innovation. These theories help us to set out quantifiable criteria and decomposable steps to identify research patterns at each stage of a scientific revolution. The first step in the framework is to construct a conceptual model of the target technology domain, which helps to refine a reasonable search strategy. With the model built, the landscape of a field—its communities, its technologies, and their interactions—is fleshed out through community detection and network analysis based on a set of quantifiable criteria. The aim is to map normal patterns of research in the domain under study so as to highlight which technologies might contribute to a structural deepening of technological recombinations. Probability analysis helps to detect and group candidate technologies for possible recombination and further manual analysis by experts. To demonstrate how the framework works in practice, we conducted an empirical study on AI research in China. We explored the development potential of recombinative technologies by zooming in on the top patent assignees in the field and their innovations. In conjunction with expert analysis, the results reveal the cooperative and competitive relationships among these technology ...
Zhou, X, Jin, W, Han, S-F, Li, X, Gao, S-H, Chen, C, Xie, G-J, Tu, R, Wang, Q & Wang, Q 2019, 'The mutation of Scenedesmus obliquus grown in municipal wastewater by laser combined with ultraviolet', Korean Journal of Chemical Engineering, vol. 36, no. 6, pp. 880-885.
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© 2019, The Korean Institute of Chemical Engineers. Mutagenetic breeding is an efficient technique for the enhancement of lipid productivity from microalgae. In this study, oil-rich microalga Scenedesmus obliquus were treated by Laser-UV composite mutagenesis. Among the 35 mutant strains, X5 was primely screened. Afterwards, a twice UV mutagenizing was operated on X5, and the optimal mutant strain X5-H13 was obtained. The growth rate, dry weight, lipid yield and lipid content of X5-H13 were 0.698×107 cells/mL·d, 0.99 g/L, 0.49 g/L and 48.8% while cultivated in municipal wastewater, respectively, which were increased by 45%, 58%, 109% and 32% than the original strain. The results of the subculture of repeated mutant showed that the biomass and lipid content of strain X5-H13 were up to 0.99 g/L and 48.8%. The growth of each generation was stable. Furthermore, the random amplified polymorphic DNA analysis indicated that the mutant strain X5-H13 was different from the starting strain, with their genetic similarity coefficient value of 0.815.
Zhou, X, Jin, W, Tu, R, Guo, Q, Han, S-F, Chen, C, Wang, Q, Liu, W, Jensen, PD & Wang, Q 2019, 'Optimization of microwave assisted lipid extraction from microalga Scenedesmus obliquus grown on municipal wastewater', Journal of Cleaner Production, vol. 221, pp. 502-508.
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© 2019 Elsevier Ltd Efficient cost effective lipid extraction from microalgae is a challenging topic for large scale production of microalgae-based biodiesel. In this study, a microwave-assisted lipid extraction process was applied to the oil-rich green microalga Scenedesmus obliquus grown on municipal wastewater. N-hexane/isopropanol solvent was used as alternative solvent. Optimal extraction parameters were determined as: operational temperature of 130 °C, extraction time of 0.25 h, solvent ratio of N-hexane/isopropanol of 3:2 (V:V), phase ratio of co-solvent/biomass was 50:1 (mL:g). Relative extraction rates of lipid and fatty acid methyl esters (FAMEs) achieved using microwave-assisted extraction (MAE) were 88.25% and 95.58%, respectively, which is higher than traditional water bath heating extraction process (WHE). In addition, compared with WHE, the apparent first order rate constant of MAE was enhanced by 18 times compared to traditional methods. Analysis using scanning electron microscopy indicated that disruption of the cell wall of Scenedesmus obliquus by microwave led to the enhancement of solvents’ penetration and lipid extraction.
Zhou, X, Li, S, Lu, M, Zeng, F, Zhu, M & Yu, Y 2019, 'New Fault Tolerance Method for Open-Phase PMSM', IEEE Access, vol. 7, pp. 146416-146427.
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© 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved. Once the motor stator winding is opened, balanced three-phase windings turn into unbalanced two-phases windings. Unfortunately, by conducting Clarke and Park transformation for open-phase PMSM, complete decoupling of the torque and flux cannot achieve. To maintain the rated torque, the two remained phase currents have to be modified as sinusoidal currents with 60◦ phase difference (not 120◦). As a result, the current controller design becomes complicated. In order to solve this problem, a new fault tolerance method for the open-phase PMSM is proposed in this paper. It is designed based on a novel reference frame transformation. Through proposed frame transformation, the modified sinusoidal time-varying current commands are turned into dc variables in the redefined synchronous rotating frame. Hence, the design of the open-phase PMSM current controller can be simplified. This method can deal with different phase open fault and different current control mode (id = 0 or id 6= 0 mode). In addition, considering that the neutral current ripple at usual switching frequencies may be very high, an optimal additional inductance that inserted into the neutral wire is designed. With the designed additional inductance, complete decoupling can be achieved. Experimental results confirm that the reliability and the performance of the PMSM drive can be improved distinctly with the proposed open-phase fault tolerance strategy.
Zhu, C, Mesiar, R, Yager, RR, Merigo, J, Qin, J, Feng, X & Jin, L 2019, 'Two-layer preference models with methodologies using induced aggregation in management administration and decision making', Journal of Intelligent & Fuzzy Systems, vol. 37, no. 1, pp. 1213-1221.
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In this work, we propose some two-layer preference models that can be appropriately applied in management problems such as the group decision making about predicting the future market share of certain product. By introducing the convex IOWA operator paradigm and some related properties and definitions, we list some detailed preference and inducing preference models to demonstrate and exemplify the proposed conceptual frame of two-layer preference model. The convex IOWA operator paradigm facilitates the modeling process and, from mathematical view, makes it stricter. When relevant inducing information and aggregation selection change, the proposed models can be easily adapted to accommodate more different applications in decision making and evaluation.
Zhu, H & Guo, YJ 2019, 'Wideband Filtering Phase Shifter Using Transversal Signal-Interference Techniques', IEEE Microwave and Wireless Components Letters, vol. 29, no. 4, pp. 252-254.
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© 2001-2012 IEEE. A wideband filtering phase shifter is presented in this letter using transversal signal-interference techniques. The proposed structure creates two signal-propagation paths by introducing extra coupling between two short-ended stubs. Cascaded coupled-line sections and parallel short-ended stubs can generate a constant passband and multiple transmission zeros (TZs) in the stopband and meanwhile provide the arbitrary value of phase shift within the filtering band range. The positions of TZs and phase shift range can be easily controlled. Explicit relations between the objective filtering band and the related parameters are given, and cases with different in-band phase shift values are studied. To validate the design, a prototype is built, simulated, and tested using microstrip lines. The experimental results agree with the predicted ones, demonstrating that the device can realize any given in-band phase shift with a wideband filtering response.
Zhu, H, Cheng, Z & Guo, YJ 2019, 'Design of Wideband In-Phase and Out-of-Phase Power Dividers Using Microstrip-to-Slotline Transitions and Slotline Resonators', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 4, pp. 1412-1424.
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© 2019 IEEE. A new class of in-phase and out-of-phase power dividers with constant equal-ripple frequency response and wide operating bandwidth is presented in this paper. The proposed design is based on microstrip-to-slotline transitions and slotline resonators. A slotted T-junction is adopted to split the power into two parts and obtain wideband isolation between the two output signals at the same time. The characteristic impedance of the transitions and resonators determines the operating bandwidth and in-band magnitude response. By reversing the placement direction of the slotline-to-microstrip transition, the electrical field is reversed, thus resulting in out-of-phase responses between output ports. A thorough analysis of the relations between the structure and the characteristic functions is provided to guide the selection of parameters of the structure in order to meet the design objectives. In the structure, simulation and measurement are conducted to verify the design method. For both in-phase and out-of-phase cases, more than 110% bandwidth has been achieved with excellent matching at all ports and isolation of output signals. Constant in-band ripple is obtained within the operating band of the power dividers, indicating that the proposed design can realise minimal power deviations, which is extremely desired in wireless systems.
Zhu, H, Lin, J-Y & Guo, YJ 2019, 'Filtering Balanced-to-Single-Ended Power Dividers With Wide Range and High Level of Common-Mode Suppression', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 12, pp. 5038-5048.
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© 1963-2012 IEEE. A new design approach for developing balanced-to-single-ended (BTSE) power dividers (PDs) to achieve high-level and wide range of common-mode (CM) suppression and minimum mode-conversion level is presented. The suppression of CM signals and mode conversion is realized by a microstrip-to-slotline transition, which is due to the orthogonality between the electric field of the microstrip line and slotline. Filtering responses are included in the differential-mode transmission performance, and multiple transmission zeros are generated by loading shunted coupled-line stubs. A multimode slotline resonator is used to provide multiple resonances in the passband, and these resonances can be easily controlled so that the operating bandwidth can be varied in a large frequency range. Based on this design approach, several BTSE PDs with different bandwidths are simulated in the EM environment. Prototypes are fabricated and tested to verify the design. The experimental results reveal that 200% fractional bandwidth of CM suppression is obtained. The CM suppression and mode-conversion levels are below-35 dB at all frequencies, which is extremely desired in differential circuits and systems.
Zhu, H, Sun, H, Jones, B, Ding, C & Guo, YJ 2019, 'Wideband Dual-Polarized Multiple Beam-Forming Antenna Arrays', IEEE Transactions on Antennas and Propagation, vol. 67, no. 3, pp. 1590-1604.
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© 1963-2012 IEEE. Wideband multibeam antenna arrays based on three-beam Butler matrices are presented in this paper. The proposed beam-forming arrays are particularly suited to increasing the capacity of 4G long-term evolution (LTE) base stations. Although dual-polarized arrays are widely used in LTE base stations, analog beam-forming arrays have not been realized before, due to the huge challenge of achieving wide operating bandwidth and stable array patterns. To tackle these problems, for the first time, we present a novel wideband multiple beam-forming antenna array based on Butler matrices. The described beam-forming networks produce three beams but the methods are applicable to larger networks. The essential part of the beam-forming array is a wideband three-beam Butler matrix, which comprises quadrature couplers and fixed wideband phase shifters. Wideband quadrature and phase shifters are developed using striplines, which provide the required power levels and phase differences at the outputs. To achieve the correct beamwidth and to obtain the required level of crossover between adjacent beams, beam-forming networks consisting of augmented three-beam Butler matrices using power dividers are presented to expand the number of output ports from three to five or six. Dual-polarized, three-beam antenna arrays with five and six elements covering LTE band are developed. Prototypes comprising beam-forming networks and arrays are tested according to LTE base station specification. The test results show close agreement with the simulation ones and compliance with LTE requirements. The designs presented are applicable to a wide range of wideband multibeam arrays.
Zhu, J, Yang, Y, Li, S, Liao, S & Xue, Q 2019, 'Dual-Band Dual Circularly Polarized Antenna Array Using FSS-Integrated Polarization Rotation AMC Ground for Vehicle Satellite Communications', IEEE Transactions on Vehicular Technology, vol. 68, no. 11, pp. 10742-10751.
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© 1967-2012 IEEE. This paper presents a new dual-band dual circularly polarized (CP) high gain patch antenna array for vehicle satellite communications. The array consists of 16 linearly polarized dual-band elements backed with a new frequency-selective-surface (FSS)-integrated polarization rotation ground. The polarization rotation ground is located underneath the array, which not only acts as a reflector to increase the array boresight gain but also enables the array's dual CP radiation. The electromagnetic (EM) waves reflected by the ground retard either 90° or 270° with respect to the EM waves of the array directly radiated to the upper sphere at two frequency bands, leading to the dual-band dual CP radiation. This is totally different from many of the former counterparts which are based on either the perturbation on the stacked patch or external feeding network. Measured results show the -10-dB impedance bandwidth is from 7.8 to more than 8.5-GHz for X-band and from 14 to 15.3-GHz for Ku-band. The 3-dB axial ratio bandwidth is from 8.15 to 8.35-GHz for the lower band (right-hand CP) and from 14.2 to 14.8-GHz for the higher band (left-hand CP), respectively.
Zhu, J, Yang, Y, Li, S, Liao, S & Xue, Q 2019, 'Single-Ended-Fed High-Gain LTCC Planar Aperture Antenna for 60 GHz Antenna-in-Package Applications', IEEE Transactions on Antennas and Propagation, vol. 67, no. 8, pp. 5154-5162.
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© 1963-2012 IEEE. This paper presents new single-ended-fed planar aperture antennas (SPAAs) using low-temperature co-fired ceramics (LTCC) process technology. The SPAA element is proposed first, which not only inherits the merits of the aperture antennas including high gain and wide bandwidth but also exhibits advantages of low profile and compact size. The aperture is excited by a cross-shaped patch, and a loop-shaped balun structure placed below the patch is introduced to convert the single-ended signal into differential one to drive the patch. In this way, the energy can propagate on the patch in a traveling waveform and illuminate the aperture with uniform E-field distributions. Therefore, the antenna achieves good electrical and radiation performances, which are comparable to its balanced-fed counterparts, while processing a simplified structure. Measured results demonstrate that the impedance bandwidth of the antenna covers the 60 GHz license-free band (57-64 GHz), and the maximum gain can reach 11.5 dBi with a cavity area of only about 27 mm2. Furthermore, the element is successfully extended to a 4 × 4 element array using a substrate-integrated-waveguide based feeding network to further increase the gain up to 20.4 dBi. The measured results show that the impedance bandwidth of the array is from 57.5 to 65.7 GHz and the radiation performances are very stable over the operating frequency band.
Zhu, L-F, Ke, L-L, Zhu, X-Q, Xiang, Y & Wang, Y-S 2019, 'Crack identification of functionally graded beams using continuous wavelet transform', Composite Structures, vol. 210, pp. 473-485.
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© 2018 Elsevier Ltd This paper proposes a new damage index for the crack identification of beams made of functionally graded materials (FGMs) by using the wavelet analysis. The damage index is defined based on the position of the wavelet coefficient modulus maxima in the scale space. The crack is assumed to be an open edge crack and is modeled by a massless rotational spring. It is assumed that the material properties follow exponential distributions along the beam thickness direction. The Timoshenko beam theory is employed to derive the governing equations which are solved analytically to obtain the frequency and mode shape of cracked FGM beams. Then, we apply the continuous wavelet transform (CWT) to the mode shapes of the cracked FGM beams. The locations of the cracks are determined from the sudden changes in the spatial variation of the damage index. An intensity factor, which relates to the size of the crack and the coefficient of the wavelet transform, is employed to estimate the crack depth. The effects of the crack size, the crack location and the Young's modulus ratio on the crack depth detection are investigated.
Zhu, Q, Coleman, P, Qiu, X, Wu, M, Yang, J & Burnett, I 2019, 'Robust Personal Audio Geometry Optimization in the SVD-Based Modal Domain', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 3, pp. 610-620.
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© 2014 IEEE. Personal audio generates sound zones in a shared space to provide private and personalized listening experiences with minimized interference between consumers. Regularization has been commonly used to increase the robustness of such systems against potential perturbations in the sound reproduction. However, the performance is limited by the system geometry such as the number and location of the loudspeakers and controlled zones. This paper proposes a geometry optimization method to find the most geometrically robust approach for personal audio amongst all available candidate system placements. The proposed method aims to approach the most 'natural' sound reproduction so that the solo control of the listening zone coincidently accompanies the preferred quiet zone. Being formulated in the SVD-based modal domain, the method is demonstrated by applications in three typical personal audio optimizations, i.e., the acoustic contrast control, the pressure matching, and the planarity control. Simulation results show that the proposed method can obtain the system geometry with better avoidance of 'occlusion,' improved robustness to regularization, and improved broadband equalization.
Zhu, S, Li, JC, Casciati, S & Li, J 2019, 'Special Issue on Smart Devices for Structural Control:Preface', Smart Structures and Systems, vol. 24, no. 1, p. I.
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Zhu, Y, Chambua, J, Lu, H, Shi, K & Niu, Z 2019, 'An opinion based cross‐regional meteorological event detection model', Weather, vol. 74, no. 2, pp. 51-55.
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Zhuang, Y, Zhu, G, Gong, Z, Wang, C & Huang, Y 2019, 'Experimental and numerical investigation of performance of an ethanol-gasoline dual-injection engine', Energy, vol. 186, pp. 115835-115835.
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© 2019 Elsevier Ltd Experiments and simulations were performed to investigate the effect of ethanol direct injection plus gasoline port injection (EDI + GPI) on engine performance. Gasoline direct injection plus GPI (GDI + GPI) was also tested as a reference to EDI + GPI. The experimental results showed that volumetric efficiency increased with the raise of direct injection ratio in both EDI + GPI and GDI + GPI conditions. The volumetric efficiency and IMEP of EDI + GPI were greater than that of GDI + GPI, due to the stronger charge cooling effect of EDI. Combustion process was improved by EDI when ethanol energy ratio (EER) was less than 42%, however further increase of EER led to the deterioration of combustion process. Simulation results showed that ethanol's high laminar flame speed played a dominate role to the improvement of combustion process. Although EDI negatively affected the equivalence ratio around spark plug, this disadvantage was offset by the high laminar flame speed of ethanol, resulting in shorter initial and major combustion durations. Simulation results also found that combustion process was deteriorated when EER was greater than 42%, which was mainly due to over-cooling and poor mixing of EDI. Regarding emissions, NO decreased while CO and HC increased with the raise of both EDI and GDI ratios.
Zuo, H, Lu, J, Zhang, G & Liu, F 2019, 'Fuzzy Transfer Learning Using an Infinite Gaussian Mixture Model and Active Learning', IEEE Transactions on Fuzzy Systems, vol. 27, no. 2, pp. 291-303.
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© 2018 IEEE. Transfer learning is gaining considerable attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain. Fuzzy transfer learning, which is based on fuzzy system (especially fuzzy rule-based models), has been developed because of its capability to deal with the uncertainty in transfer learning. However, two issues with fuzzy transfer learning have not yet been resolved: choosing an appropriate source domain and efficiently selecting labeled data for the target domain. This paper proposes an innovative method based on fuzzy rules that combines an infinite Gaussian mixture model (IGMM) with active learning to enhance the performance and generalizability of the constructed model. An IGMM is used to identify the data structures in the source and target domains providing a promising solution to the domain selection dilemma. Further, we exploit the interactive query strategy in active learning to correct imbalances in the knowledge to improve the generalizability of fuzzy learning models. Through experiments on synthetic datasets, we demonstrate the rationality of employing an IGMM and the effectiveness of applying an active learning technique. Additional experiments on real-world datasets further support the capabilities of the proposed method in practical situations.
Zuo, H, Lu, J, Zhang, G & Pedrycz, W 2019, 'Fuzzy Rule-Based Domain Adaptation in Homogeneous and Heterogeneous Spaces', IEEE Transactions on Fuzzy Systems, vol. 27, no. 2, pp. 348-361.
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© 2018 IEEE. Domain adaptation aims to leverage knowledge acquired from a related domain (called a source domain) to improve the efficiency of completing a prediction task (classification or regression) in the current domain (called the target domain), which has a different probability distribution from the source domain. Although domain adaptation has been widely studied, most existing research has focused on homogeneous domain adaptation, where both domains have identical feature spaces. Recently, a new challenge proposed in this area is heterogeneous domain adaptation where both the probability distributions and the feature spaces are different. Moreover, in both homogeneous and heterogeneous domain adaptation, the greatest efforts and major achievements have been made with classification tasks, while successful solutions for tackling regression problems are limited. This paper proposes two innovative fuzzy rule-based methods to deal with regression problems. The first method, called fuzzy homogeneous domain adaptation, handles homogeneous spaces while the second method, called fuzzy heterogeneous domain adaptation, handles heterogeneous spaces. Fuzzy rules are first generated from the source domain through a learning process; these rules, also known as knowledge, are then transferred to the target domain by establishing a latent feature space to minimize the gap between the feature spaces of the two domains. Through experiments on synthetic datasets, we demonstrate the effectiveness of both methods and discuss the impact of some of the significant parameters that affect performance. Experiments on real-world datasets also show that the proposed methods improve the performance of the target model over an existing source model or a model built using a small amount of target data.
Сілі, І 2019, 'THE PARAMETERS CALCULATION OF INTERACTION BETWEEN RADIO-PULSED MICROWAVE RADIATION AND PLANT ENVIRONMENT OF POTATO', Scientific bulletin of the Tavria State Agrotechnological University, vol. 9, no. 1.
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秀聡, 高, 奎太, 石, 浩永, 丸, 俊之, 田, 紗季, 野, 亮, 岡, Schell, A, Tran, TT, Aharonovich, I & 繁樹, 竹 2019, '六方晶窒化ホウ素中の単一結晶欠陥の双極子方向解析 Analysis of the dipole orientation of single defects in hexagonal boron nitrides', JSAP Annual Meetings Extended Abstracts, pp. 566-566.
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Abad, ZSH, Bano, M & Zowghi, D 1970, 'How much authenticity can be achieved in software engineering project based courses?', ICSE (SEET), IEEE / ACM, pp. 208-219.
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© 2019 IEEE. Software engineering (SE) students not only need sufficient technical knowledge and problem solving ability but also social and interpersonal skills in order to be industry ready. To prepare the students for the 'real world' the SE educators frequently use 'Authentic Assessment' and 'Project Based Learning (PBL)' approaches in their curricula. However, the level of 'authenticity' should vary within PBL courses offered in different years of a degree program. In this paper, we present and discuss the results of the data collected and analyzed from the first SE course offered to the students. The aim of our research is to explore how much authenticity can be achieved in the first SE course. Our study was conducted at the University of Calgary with 64 software development project teams, totaling 229 undergraduate students. The data is collected from three semesters (2016-2018) in order to assess and monitor students performance. The course design used seven authentic assessments that focused on students skills while covering a complete software development lifecycle. The results from data analysis show that students made progress in some areas of problem solving skills, however, they struggled in their social skills (e.g. people handling skills, negotiations skills and organizational skills), understanding software quality and adaptability.
Abbas, SM, Zahra, H, Hashmi, R, Esselle, KP & Volakis, JL 1970, 'Compact On-Body Antennas for Wearable Communication Systems', 2019 International Workshop on Antenna Technology (iWAT), 2019 International Workshop on Antenna Technology (iWAT), IEEE, Miami, FL, pp. 65-66.
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Abdo, P, Huynh, BP, Braytee, A & Taghipour, R 1970, 'Effect of Phase Change Material on Temperature in a Room Fitted With a Windcatcher', Volume 7: Fluids Engineering, ASME 2019 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers.
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Abstract Global warming and climate change have been considered as major challenges over the past few decades. Sustainable and renewable energy sources are nowadays needed to overcome the undesirable consequences of rapid development in the world. Phase change materials (PCM) are substances with high latent heat storage capacity which absorb or release the heat from or to the surrounding environment. They change from solid to liquid and vice versa. PCMs could be used as a passive cooling method which enhances energy efficiency in buildings. Integrating PCM with natural ventilation is investigated in this study by exploring the effect of phase change material on the temperature in a room fitted with a windcatcher. A chamber made of acrylic sheets fitted with a windcatcher is used to monitor the temperature variations. The dimensions of the chamber are 1250 × 1000 × 750 mm3. Phase change material is integrated respectively at the walls of the room, its floor and ceiling and within the windcatchers inlet channel. Temperature is measured at different locations inside the chamber. Wind is blown through the room using a fan with heating elements.
Abdo, P, Taghipour, R & Huynh, BP 1970, 'Three Dimensional Simulation of the Effect of Windcatcher’s Inlet Shape', Volume 2: Computational Fluid Dynamics, ASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference, American Society of Mechanical Engineers, San Francisco, CA, USA, pp. 1-8.
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Abstract Windcatcher has been used over centuries for providing natural ventilation using wind power, it is an effective passive method to provide healthy and comfortable indoor environment. The windcatcher’s function is based on the wind and on the stack effect resulting from temperature differences. Generally, it is difficult for wind to change its direction, and enter a room through usual openings, the windcatcher is designed to overcome such problems since they have vertical columns to help channel wind down to the inside of a building. The efficiency of a windcatcher is maximized by applying special forms of opening and exit. The openings depend on the windcatcher’s location and on its cross sectional area and shape such as square, rectangular, hexagonal or circular. In this study the effect of the inlet design is investigated to achieve better air flow and increase the efficiency of windcatchers. To achieve this, CFD (computational fluid dynamics) tool is used to simulate the air flow in a three dimensional room fitted with a windcatcher based on the different inlet designs. The divergent inlet has captured the highest air flow with a difference of approximately 3% compared to the uniform inlet and 5% difference compared to the bulging-convergent inlet.
Abdo, P, Taghipour, R & Huynh, BP 1970, 'Three Dimensional Simulation Of Ventilation Flow Through A Solar Windcatcher', Proceedings of The ASME - JSME - KSME Joint Fluids Engineering Conference 2019, The ASME - JSME - KSME Joint Fluids Engineering Conference 2019, ASME, San Francisco, CA, USA, pp. 1-6.
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Natural ventilation is the process of supplying and removing air through an indoor space by natural means. There are two types of natural ventilation occurring in buildings: winddriven ventilation and buoyancy driven or stack ventilation. The most efficient design for natural ventilation in buildings should implement both types of natural ventilation. Stack ventilation which is temperature induced is driven by buoyancy making it less dependent on wind and its direction. Heat emitted causes a temperature difference between two adjoining volumes of air, the warmer air will have lower density and be more buoyant thus will rise above the cold air creating an upward air stream. Combining the wind driven and the buoyancy driven ventilation will be investigated in this study through the use of a windcatcher natural ventilation system. Stack driven air rises as it leaves the windcatcher and it is replaced with fresh air from outside as it enters through the positively pressured windward side.
Abdollahi, A, Nezhad, MP & Pradhan, B 1970, 'Determining the desertification risks of the Mashhad regions using integrated indices based on the AHP method', 2019 13th International Conference on Sensing Technology (ICST), 2019 13th International Conference on Sensing Technology (ICST), IEEE, Macquarie Univ, Sydney, AUSTRALIA, pp. 1-6.
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Abdollahi, A, Nezhad, MP & Pradhan, B 1970, 'Investigation of the Vegetation Cover and the Vulnerability of the Mashhad Regions to Desertification by Using MODIS Image and EVI', 2019 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 2019 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), IEEE, Banda Aceh, Indonesia, pp. 46-49.
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© 2019 IEEE. Desertification is a natural phenomenon that threatens the biomass of the world in various forms, and the adverse effects of this phenomenon can be observed in different parts of the planet. Some events and complications of the earth's surface, such as vegetation coverage, have changed over time due to natural or human factors, thereby affecting the ecosystem's condition and performance. Vegetation coverage is a critical factor in the assessment of desertification, and continuous production of accurate vegetation maps is an important tool for monitoring natural resources and the environment. Therefore, this paper used MODIS images to investigate the vulnerability of the Mashhad regions in Iran to desertification according to the enhanced vegetation index (EVI) for various periods. Experimental results showed that the Mashhad regions had the highest vulnerability to desertification during 2001-2005, given the highest variation in the EVI in this period.
Abdollahi, M, Abolhasan, M, Shariati, N, Lipman, J, Jamalipour, A & Ni, W 1970, 'A Routing Protocol for SDN-based Multi-hop D2D Communications', 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), IEEE, USA, pp. 1-4.
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© 2019 IEEE. This paper presents a new Multi-hop Device-to-Device (MD2D) routing protocol, referred to as SMDRP (SDN-based Multi-hop D2D Routing Protocol), for SDN-based wireless networks. Our proposed protocol can be considered as a semi-distributed routing protocol, where an SDN controller manages and controls part of the overall MD2D routing functionality to increase scalability while enabling network operators to control and maintain the out-of-band packet forwarding network. This paper also extends prior work on the Hybrid SDN Architecture for Wireless Distributed Networks (HSAW) [1] and is adapted to the framework presented in this paper. In HSAW, since all link state information is flooded by the controller to the nodes, the network will experience scalability problem. In our approach, this problem is overcome by only passing the next hop for each active route to the mobile nodes. To investigate this, we performed a theoretical and simulation studies comparing HSAW with SMDRP. From our result, it can be seen that for larger density populated networks, SMDRP shows better scalability than HSAW. In addition, mobile nodes need less memory and energy for their communications.
Abdollahi, M, Gao, X, Mei, Y, Ghosh, S & Li, J 1970, 'Stratifying Risk of Coronary Artery Disease Using Discriminative Knowledge-Guided Medical Concept Pairings from Clinical Notes', PRICAI 2019: Trends in Artificial Intelligence, Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing, Cuvu, Yanuca Island, Fiji, pp. 457-473.
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© 2019, Springer Nature Switzerland AG. Document classification (DC) is one of the broadly investigated natural language processing tasks. Medical document classification can support doctors in making decision and improve medical services. Since the data in document classification often appear in raw form such as medical discharge notes, extracting meaningful information to use as features is a challenging task. There are many specialized words and expressions in medical documents which make them more challenging to analyze. The classification accuracy of available methods in medical field is not good enough. This work aims to improve the quality of the input feature sets to increase the accuracy. A new three-stage approach is proposed. In the first stage, the Unified Medical Language System (UMLS) which is a medical-specific dictionary is used to extract the meaningful phrases by considering disease or symptom concepts. In the second stage, all the possible pairs of the extracted concepts are created as new features. In the third stage, Particle Swarm Optimisation (PSO) is employed to select features from the extracted and constructed features in the previous stages. The experimental results show that the proposed three-stage method achieved substantial improvement over the existing medical DC approaches.
Abedin, B, Erfani, S, Milne, D, Beattie, A & Fenerty, K 1970, 'Unpacking support types in online health communities: An application of attraction-selection-attrition theory', Proceedings of the 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019, Pacific Asia Conference on Information Systems, AISEL, China 2, pp. 1-8.
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Online communities are increasingly becoming part of the healthcare ecosystem, as they allow patients, family members and carers to connect and support each other at any time and from any location. This support can take many forms, including information, advice, esteem support and solidarity. Prior research has identified the Attraction-Selection-Attrition Theory as a promising framework for modelling and explaining how participants join, participate, and leave organizations in general (and online communities specifically), and how the actions of individuals effect the organization as a whole. However, it has not previously been applied specifically to online health communities (i.e. those that focus on physical and/or mental health). We propose to gather empirical evidence from a large online community that provides support for Australians effected by cancer. In doing so, we hope to develop evidence-based policies and procedures for growing, maintaining and moderating these communities.
Abeywickrama, A, Indraratna, B & Rujikiatkamjorn, C 1970, 'Excess Pore-Water Pressure Generation and Mud Pumping in Railways Under Cyclic Loading', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 371-383.
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© 2019, Springer Nature Singapore Pte Ltd. High-speed heavy haul trains have become one of the most popular and economical modes of transportation in the modern world to cater for increased demand in freight for agricultural and mining activities. However, when these trains travel through vulnerable areas occupying soft subgrade formations, frequent maintenance is required to prevent differential settlement and localized failures of track. The poor performance of track caused by ballast fouling is also often observed where fines are fluidized and pumped into the ballast voids (mud pumping), which in turn create ballast pockets, mud holes and track instability. When saturated subgrade is subjected to short-term undrained cyclic loading, the pore-water pressure can accumulate inducing fine particles to migrate upwards into the ballast layer. Mud pumping causes millions of dollars of damage to heavy haul rail networks every year in Australia. This paper presents a critical review primarily focused on the role of excess pore-water pressure generation on mud pumping under cyclic loading. Mitigation of these issues can result in considerable savings to rail authorities on recurrent track maintenance activities.
Abeywickrama, HV, He, Y, Dutkiewicz, E & Jayawickrama, BA 1970, 'An Adaptive UAV Network for Increased User Coverage and Spectral Efficiency', 2019 IEEE Wireless Communications and Networking Conference (WCNC), 2019 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Marrakesh, Morocco, pp. 1-6.
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© 2019 IEEE. Unmanned Aerial Vehicles (UAVs) are fast becoming a popular choice in a variety of applications in wireless communication systems. UAV-mounted base stations (UAV-BSs) are an effective and cost-efficient solution for providing wireless connectivity where fixed infrastructure is not available or destroyed. We present a method of using UAV-BSs to provide coverage to mobile users in a fixed area. We propose an algorithm for predicting the user locations based on their mobility data and clustering the predicted locations, so that one UAV-BS would provide coverage to one user cluster. The proposed method, hence is similar to the UAV-BSs following the users to keep them under the coverage region. Simulation results show that the proposed method increases the user coverage by 47%-72% and increases the spectral efficiency by 43%-55% depending on the scenario and in addition, reduces the number of UAV-BSs required to provide coverage.
Abou Maroun, E, Daniel, J, Zowghi, D & Talaei-Khoei, A 1970, 'Blockchain in Supply Chain Management: Australian Manufacturer Case Study', Lecture Notes in Business Information Processing, Australasian Symposium on Service Research and Innovation, Springer International Publishing, Sydney, Australia, pp. 93-107.
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© 2019, Springer Nature Switzerland AG. The recent explosion of interest around Blockchain and capabilities of this technology to track all types of transaction more transparently and securely motivate us to explore the possibilities Blockchain offers across the supply chain. This paper examines whether Blockchain makes a good fit for use in an Australian manufacturer supply chain. To address this, the research uses Technology Acceptance Model (TAM) as a framework from the literature. Blockchain allows us to have permissioned or permission-less distributed ledgers where stakeholders can interact with each other. It details how Blockchain works and the mechanism of hash algorithms which allows for greater security of information. It also focuses on the supply chain management and looks at the intricacies of a manufacturers supply chain. We present a review of the processes in place of an electrical manufacturer and the problems faced in the supply chain. A model is proposed in using public and private Blockchains to overcome these issues. The proposed solution has the potential to bring greater transparency, validity across the supply chain, and improvement of communication between stakeholders involved. We also point out some potential issues that should be considered if adopting Blockchain.
Abou Maroun, E, Zowghi, D & Agarwal, R 1970, 'Challenges in forecasting uncertain product demand in supply chain: A systematic literature review', Managing the many faces of sustainable work, Annual Australian and New Zealand Academy of Management, ANZAM, Auckland, New Zealand.
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Forecasting for uncertain product demand in supply chain is challenging and statistical models alone cannot overcome the challenges faced. Our overall objective is to explore the challenges faced in forecasting uncertain product demand and examine extant literature by synthesizing the results of studies that have empirically investigated this complex phenomenon. We performed a Systematic Literature Review (SLR) following the well-known guidelines of the evidence-based paradigm which resulted in selecting 66 empirical studies. Our results are presented into two categories of internal and external challenges: 24 of the 66 studies express internal challenges, whilst 13 studies report external challenges, and 8 studies cover both internal and external challenges. We also present significant gaps identified in the research literature
Abrar, MH & Hasan, ASMM 1970, 'Power Generation from Waste in Chittagong City, Bangladesh- A Sustainable Approach to Mitigate The Energy Crisis', 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST), 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST), IEEE, pp. 237-241.
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Abu ul Fazal, M, Ferguson, S, Karim, S & Johnston, A 1970, 'Vinfomize', Proceedings of the 2019 3rd International Conference on Information System and Data Mining, ICISDM 2019: 2019 The 3rd International Conference on Information System and Data Mining, ACM, Houston, Texas, USA, pp. 143-147.
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© 2019 Association for Computing Machinery. In this paper, we discuss investigations conducted with 10 visually challenged users (VCUs) and 8 sighted users (SUs) that aimed to determine user's experience, interest and expectations from concurrent information communication systems. In the first study, we concurrently played two voice-based streams in continuous form in both the ears, and in the second study, we concurrently communicated one stream continuously in one ear and three news headlines as interval-based short interruptions in another ear. We first reported the participants' experience qualitatively and then based on the feedback received from the users, we proposed a framework that may help in developing systems to communicate multiple voice-based information to the users. It is expected that the application of this new framework to information systems that provide multiple concurrent communication will provide a better user experience for users subject to their contextual and perceptual needs and limitations.
Acuna, P, Ghias, A, Aguilera, RP, Lezana, P, Mcgrath, B, Merabet, A & Jayan, V 1970, 'Sequential Phase-Shifted Model Predictive Control for a Five-Level Flying Capacitor Converter', 2019 IEEE International Conference on Industrial Technology (ICIT), 2019 IEEE International Conference on Industrial Technology (ICIT), IEEE, Melbourne, Australia, pp. 533-538.
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© 2019 IEEE. This paper extends previous work on the Sequential Phase-Shifted Model Predictive Control (SPS-MPC) strategy into a Single-Phase Five-Level Flying Capacitor Converter (SP-FL-FCC). The use of SPS-MPC in an FL-FCC is not as straightforward as it first seems. Therefore, a sequential average model based on more than one active control input is derived. The proposed SPS-MPC strategy achieves a fixed switching frequency, which is beneficial regarding of semiconductor loss distribution, and also to ensure that a high-bandwidth is achieved that compares favorably to the finite-control-set MPC case. Simulation results of the proposed SPS-MPC strategy validate the current and FC voltages tracking control during the transient and steady-state conditions at a fixed switching frequency.
Acut, RVP, Hora, JA, Gerasta, OJL, Zhu, X & Dutkiewicz, E 1970, 'PV-TEG- WiFi Multiple Sources Design Energy Harvesting System for WSN Application', 2019 IEEE International Circuits and Systems Symposium (ICSyS), 2019 IEEE International Circuits and Systems Symposium (ICSyS), IEEE, pp. 1-5.
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© 2019 IEEE. Ambient energy harvesting is becoming an essential factor for wireless sensor nodes applications. Harvested energy from ambient sources, such as solar, indoor light, thermal energy, vibration, and radio frequencies (RF) provide a potential power capability that may supplement batteries to provide longevity to the sensor nodes. In an indoor setting, the intermittent availability of these sources will likely lessen the energy densities. Using only a single ambient-energy harvested source may not sustain the power requirement for the design of a battery-less wireless sensor node. Thus, this work investigates and design a power combiner circuit of the indoor light, thermal. The design utilized a cross-coupled charge pump operation to combined the same DC ambient sources, the PV cell, and TEG energy transducers. The system design is simulated using the 65 nm CMOS process technology. The simulation shows that the system is able to supply peak power of 1.69 mW at ±500Ω load. Peak power efficiency is 69% with 919 μA ILOAD.
Adak, C, Chaudhuri, BB, Lin, C-T & Blumenstein, M 1970, 'Detecting Named Entities in Unstructured Bengali Manuscript Images', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia, pp. 196-201.
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© 2019 IEEE. In this paper, we undertake a task to find named entities directly from unstructured handwritten document images without any intermediate text/character recognition. Here, we do not receive any assistance from natural language processing. Therefore, it becomes more challenging to detect the named entities. We work on Bengali script which brings some additional hurdles due to its own unique script characteristics. Here, we propose a new deep neural network-based architecture to extract the latent features from a text image. The embedding is then fed to a BLSTM (Bidirectional Long Short-Term Memory) layer. After that, the attention mechanism is adapted to an approach for named entity detection. We perform experimentation on two publicly-available offline handwriting repositories containing 420 Bengali handwritten pages in total. The experimental outcome of our system is quite impressive as it attains 95.43% balanced accuracy on overall named entity detection.
Adinolf, S, Wyeth, P, Brown, R & Altizer, R 1970, 'Towards Designing Agent Based Virtual Reality Applications for Cybersecurity Training', Proceedings of the 31st Australian Conference on Human-Computer-Interaction, OZCHI'19: 31ST AUSTRALIAN CONFERENCE ON HUMAN-COMPUTER-INTERACTION, ACM, pp. 452-456.
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Afzal, M, Lalbakhsh, A, Koli, NY & EsseIle, KP 1970, 'Antenna Beam Steering by Near-Field Phase Transformation: Comparison between Phase Transforming Printed Metasurfaces and Graded-Dielectric Plates', PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON ELECTROMAGNETICS IN ADVANCED APPLICATIONS (ICEAA), 21st International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE, SPAIN, Granada, pp. 593-595.
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Afzal, MU, Lalbakhsh, A & Esselle, KP 1970, 'Compact Beam-Steered Resonant-Cavity Antenna Using Near-Field Phase Transformation', 2019 14th Conference on Industrial and Information Systems (ICIIS), 2019 IEEE 14th Conference on Industrial and Information Systems (ICIIS), IEEE, pp. 1-4.
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Afzal, MU, Lalbakhsh, A, Hayat, T & Esselle, KP 1970, 'Recent Progress on Development of Near-Field Structures for Radio-Frequency Front-End Antennas', 2019 23rd International Conference on Applied Electromagnetics and Communications (ICECOM), 2019 23rd International Conference on Applied Electromagnetics and Communications (ICECOM), IEEE, pp. 1-5.
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© 2019 IEEE. The theory of near-field phase transformation equips antenna designers with a tool to carry out more accurate electric field transformation, for even geometrically complex radiating structures, within the near-field region. The theory has now been demonstrated with a range of free-standing structures or surfaces, designed using dielectrics or printed planar metasurfaces. The development on the near-field structures can be divided into three phases. The first phase was focused on narrow frequency band for demonstrating the concept. The second phase of the development is to increase the bandwidth of near-field structures that can cover frequency band required for commercial wireless applications. In the third phase, the overall cost of near-field structures is drastically reduced using advanced manufacturing technique such as additive manufacturing. Some of the highlights of the developments in near-field structures include increasing broadside gain of classical resonant-cavity antennas by ~ 9 dB and realization of antenna beam steering in a conical region having an apex angle of 102 °.
Afzal, MU, Lalbakhsh, A, Koli, NY & Esselle, KP 1970, 'Antenna Beam Steering by Near-Field Phase Transformation: Comparison between Phase Transforming Printed Metasurfaces and Graded-Dielectric Plates', 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE, pp. 0593-0595.
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© 2019 IEEE. The antenna beam-steering technique based on near-field phase transformation yields antenna systems that have superior characteristics compared to the traditional methods including both mechanical and electronic. Some of the unique attributes associated with this technology are its totally passive nature, low height profile, and extremely simple operating mechanism. The technology has been used to develop two generations of antenna systems. The first generation was developed using multilayered printed metasurfaces (PMs) and the second generation used graded-dielectric plates (GDPs). In terms of radiation performance, the two antenna systems are nearly identical. The height profile of a GDP based antenna is about one free-space wavelength more than the height of a PM based antenna. PMs can only be manufactured in specialised facilities while with rapid advancements in materials and 3D printing, it may be possible to cheaply develop GDPs.
Aghayarzadeh, M & Khabbaz, H 1970, 'Numerical simulation of concrete pile groups' response bored in cemented sand deposit under axial static load testing', E3S Web of Conferences, International Symposium on Deformation Characteristics of Geomaterials, EDP Sciences, Glasgow, UK, pp. 16011-16011.
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For a safe foundation to perform as desired, the ultimate strength of each pile must fulfil both structural and geotechnical requirements. Pile load testing is considered as a direct method of determining the ultimate bearing capacity of a pile. Pile groups are commonly used in foundation engineering and due to the difficulties and cost of full-scale load tests, most pile group tests are scaled down regardless of whether performed in the field or laboratory. In this paper, it is aimed to simulate the behaviour of concrete bored pile groups under axial static load testing using PLAXIS 3D software and to compare the obtained results with measured curves in an experimental study introduced in the literature. In numerical simulation, to account for the stiffness variation existing inside the pile group and to achieve a reasonable correlation between measured and predicted load-settlement curves three different analyses, including linear elastic, completely non-linear, and a combination of non-linear and linear analyses were performed. The results indicate that the combined non-linear and linear analysis seems a suitable analysis for pile group behaviour prediction.
Aghayarzadeh, M, Khabbaz, H & Fatahi, B 1970, 'Evaluation of Reaction Piles Effect on Test Piles in Static Load Testing Using Three-Dimensional Numerical Analysis', ASTM Special Technical Publication, International Conference on Stress Wave Theory and Testing Methods for Deep Foundations, ASTM International, San Diego, California, USA, pp. 68-80.
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Copyright © 2019 by ASTM International. Static load testing includes the direct measurement of pile head displacements when a physical test load is applied. It is known as the most fundamental form of pile load testing and generally considered as a benchmark for pile performance assessment. During static load testing, the load is commonly applied using a hydraulic jack acting against a reaction beam, which is restrained by an anchorage system. The anchorage system may be in the form of cable anchors or reaction piles installed into the ground to provide tension resistance. In this paper, PLAXIS 3D software incorporating elastic-perfectly-plastic Mohr-Coulomb and hardening-soil constitutive models is initially used to simulate a real static load test conducted in stiff overconsolidated clay. Then, in order to assess the effect of the reaction system on the test results, a similar model using the hardening-soil model is simulated. In the three-dimensional model, different numbers of reaction piles, identical to the test pile, are located in different distances from the test pile. Subsequently, the influences of spacing, length, diameter of reaction piles, and type of reaction piles on the load-displacement behavior of test piles are assessed. This paper can provide insight to practicing civil engineers on how to design the loading systems for static pile load tests.
Aguilera, RP, Acuna, P, Rojas, CA, Konstantinou, G & Pou, J 1970, 'Instantaneous Zero Sequence Voltage for Grid Energy Balancing Under Unbalanced Power Generation', 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 2019 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, Baltimore, MD, pp. 2572-2577.
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An instantaneous zero sequence voltage injection method is presented in this paper. The key novelty of this method lies in the ability to provide instantaneous zero sequence voltage references using stationary abc-framework variables with no numerical iterations and no phase-locked loops. This paper discusses its applicability, especially in the field of large-scale photovoltaic power plants integration, where the efforts are made to achieve grid energy balancing. As an application example, the proposed method is used to find suitable zero sequence voltage references to extract unbalanced power from each phase in star-connected cascaded H-bridge multilevel converters. Under same conditions, the proposed method allows reactive power control, in contrast to traditional approaches that only consider unity power factor operation. Experimental results are provided to verify the effectiveness of the proposed zero sequence voltage injection method.
Ahadi, A & Mathieson, L 1970, 'A Comparison of Three Popular Source code Similarity Tools for Detecting Student Plagiarism', Proceedings of the Twenty-First Australasian Computing Education Conference, ACE'19: Twenty-First Australasian Computing Education Conference, ACM, Australia, pp. 112-117.
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© 2019 Association for Computing Machinery. This paper investigates automated code plagiarism detection in the context of an undergraduate level data structures and algorithms module. We compare three software tools which aim to detect plagiarism in the students' programming source code. We evaluate the performance of these tools on an individual basis and the degree of agreement between them. Based on this evaluation we show that the degree of agreement between these tools is relatively low. We also report the challenges faced during utilization of these methods and suggest possible future improvements for tools of this kind. The discrepancies in the results obtained by these detection techniques were used to devise guidelines for effectively detecting code plagiarism.
Ahadi, A, Lister, R & Mathieson, L 1970, 'ArAl', Proceedings of the Twenty-First Australasian Computing Education Conference, ACE'19: Twenty-First Australasian Computing Education Conference, ACM, Australia, pp. 118-125.
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© 2019 Association for Computing Machinery. Several systems that collect data from students' problem solving processes exist. Within computing education research, such data has been used for multiple purposes, ranging from assessing students' problem solving strategies to detecting struggling students. To date, however, the majority of the analysis has been conducted by individual researchers or research groups using case by case methodologies. Our belief is that with increasing possibilities for data collection from students' learning process, researchers and instructors will benefit from ready-made analysis tools. In this study, we present ArAl, an online machine learning based platform for analyzing programming source code snapshot data. The benefit of ArAl is two-fold. The computing education researcher can use ArAl to analyze the source code snapshot data collected from their own institute. Also, the website provides a collection of well-documented machine learning and statistics based tools to investigate possible correlation between different variables. The presented web-portal is available at online-analysisdemo. herokuapp.com. This tool could be applied in many different subject areas given appropriate performance data.
Akter, N, Li, A, Shi, R, Phu, J, Perry, S, Fletcher, J & Roy, M 1970, 'A feature agnostic based glaucoma diagnosis from OCT images with deep learning technique', 2019 Meeting of the American Academy of Optometry, Orlando, Florida, USA.
Al Taee, AA, Khushaba, RN & Al-Jumaily, A 1970, 'Spatially Filtered Low-Density EMG and Time-Domain Descriptors Improves Hand Movement Recognition', 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Berlin, Germany, pp. 2671-2674.
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Surface Electromyogram (EMG) pattern recognition has long been utilized for controlling multifunctional myoelectric prostheses. In such an application, a number of EMG channels are usually utilized to acquire more information about the underlying activity of the remaining muscles in the amputee stump. However, despite the multichannel nature of this application, the extracted features are usually acquired from each channel individually, without consideration for the interaction between the different muscles recruited to achieve a specific movement. In this paper, we proposed an approach of spatial filtering, denoted as Range Spatial Filtering (RSF), to increase the number of EMG channels available for feature extraction, by considering the range of all possible logical combinations of each n channels. The proposed RSF method is then combined with conventional time-domain (TD) feature extraction, as an extension of the conventional single channel TD features that are heavily considered in this field. We then show how the addition of a new feature, specifically the minimum absolute value of the range of each two windowed EMG signals, can significantly reduce the different patterns misclassification rate achieved by conventional TD features (with and without our RSF method). The performance of the proposed method is verified on EMG data collected from nine transradial amputees (seven traumatic and two congenital), with six grip and finger movements, for three different levels of forces (low, medium, and high). The classification results showed significant reduction in classification error rates compared to other methods (nearly 10% for some individual TD features and 5% for combined TD features, with Bonferroni corrected p-values <; 0.01).
Alajlouni, D, Bliuc, D, Tran, T, Nguyen, T, Eisman, J & Center, J 1970, 'ROLE OF INDIVIDUAL COMPONENTS OF SARCOPENIA IN FRACTURE RISK PREDICTION IN ELDERLY WOMEN AND MEN', OSTEOPOROSIS INTERNATIONAL, IOF-Regional 7th Asia-Pacific Osteoporosis Conference, SPRINGER LONDON LTD, AUSTRALIA, Sydney, pp. S91-S91.
Alajlouni, D, Tuan, N, Eisman, J, Center, J, Bliuc, D & Thach, T 1970, 'The Role of Individual Components of Sarcopenia and Their Rate of Decline in Fracture Risk in Elderly Women and Men', JOURNAL OF BONE AND MINERAL RESEARCH, Annual Meeting of the American-Society-for-Bone-and Mineral Research, WILEY, FL, Orlando, pp. 124-124.
Alanazi, F, Gay, V & Alturki, R 1970, 'Tag Based Recommendation Systems for Tourism in Saudi Arabia', VISION 2025: EDUCATION EXCELLENCE AND MANAGEMENT OF INNOVATIONS THROUGH SUSTAINABLE ECONOMIC COMPETITIVE ADVANTAGE, 34th International-Business-Information-Management-Association (IBIMA) Conference, INT BUSINESS INFORMATION MANAGEMENT ASSOC-IBIMA, Madrid, SPAIN, pp. 6492-6500.
Aldini, S, Akella, A, Singh, AK, Wang, Y-K, Carmichael, M, Liu, D & Lin, C-T 1970, 'Effect of Mechanical Resistance on Cognitive Conflict in Physical Human-Robot Collaboration', 2019 International Conference on Robotics and Automation (ICRA), 2019 International Conference on Robotics and Automation (ICRA), IEEE, Canada, pp. 6137-6143.
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© 2019 IEEE. Physical Human-Robot Collaboration (pHRC) is about the interaction between one or more human operator(s) and one or more robot(s) in direct contact and voluntarily exchanging forces to accomplish a common task. In any pHRC, the intuitiveness of the interaction has always been a priority, so that the operator can comfortably and safely interact with the robot. So far, the intuitiveness has always been described in a qualitative way. In this paper, we suggest an objective way to evaluate intuitiveness, known as prediction error negativity (PEN) using electroencephalogram (EEG). PEN is defined as a negative deflection in event related potential (ERP) due to cognitive conflict, as a consequence of a mismatch between perception and reality. Experimental results showed that the forces exchanged between robot and human during pHRC modulate the amplitude of PEN, representing different levels of cognitive conflict. We also found that PEN amplitude significantly decreases (mathrm {p} lt 0.05) when a mechanical resistance is being applied smoothly and more time in advance before an invisible obstacle, when compared to a scenario in which the resistance is applied abruptly before the obstacle. These results indicate that an earlier and smoother resistance reduces the conflict level. Consequently, this suggests that smoother changes in resistance make the interaction more intuitive.
Al-Doghman, F, Chaczko, Z, Brooke, W & Gordon, LC 1970, 'Social Consensus-inspired Aggregation Algorithms for Edge Computing', 2019 3rd Cyber Security in Networking Conference (CSNet), 2019 3rd Cyber Security in Networking Conference (CSNet), IEEE, pp. 138-141.
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© 2019 IEEE. The current interest about the∗nternet of Things (IoT) evokes the establishment of infinite services giving huge, active, and varied information sets. Within it, an enormous mass of heterogeneous data are generated and interchanged by billions of device which can yield to an enormous information traffic jam and affects network efficiency. To get over this issue, there's a necessity for an effective, smart, distributed, and in-network technique that uses a cooperative effort to aggregate data along the pathway from the network edge to its sink. we tend to propose an information organization blueprint that systematizes data aggregation and transmission within the bounds of the Edge domain from the front-end until the Cloud. A social consensus technique obtained by applying statistical analysis is employed within the blueprint to get and update a policy concerning a way to aggregate and transmit data according to the order of information consumption inside the network. The Propose technique, consensus Aggregation, uses statistical Machine Learning to consolidate the approach and appraise its performance. inside the normal operation of the approach, data aggregation is performed with the utilization of data distribution. A notable information delivery efficiency was obtained with a nominal loss in precision as the blueprint was tested inside a particular environment as a case study. The conclusion of the strategy showed that the consensus approach overcome the individual ones in several directions.
Aleidi, AI & Chandran, D 1970, 'Key drivers for women in technology entrepreneurship: Insights from Saudi Arabia', Proceedings of the 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019.
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IT entrepreneurs represent a valuable source to the societies. They prompt socio-economic growth, and innovation. Despite the increasing awareness of this importance, evidence indicates that women engagement in technology entrepreneurship is scant, which has received limited attention in both information systems and female entrepreneurship literature. Drawing on the theory of planned behaviour, this research highlights the existing gap by analyzing influential aspects that affect the decision-making process of women tech-entrepreneurs in the Saudi context. Hypotheses were tested using survey data that has been collected from different Saudi female public universities as well as technology incubators, and entrepreneurship programs. Findings from PLS support the core entrepreneurial intention model and highlight the important role of traditional determinants of intention. In addition, the research findings highlight and contribute a new understanding of the value of IT factors for women in increasing their entrepreneurial intention and subsequent decisions, actions, and outcomes.
Al-Ghattas, H & Marjanovic, O 1970, 'How Can Analytics Drive Organisational Resilience in the CME Sector', 14th International Cooperative Alliance Asia-Pacific Research Conference, Newcastle, Australia.
Ali, AR, Budka, M & Gabrys, B 1970, 'A Meta-Reinforcement Learning Approach to Optimize Parameters and Hyper-parameters Simultaneously', PRICAI 2019: Trends in Artificial Intelligence, Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing, Yanuca Island, Fiji,, pp. 93-106.
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© 2019, Springer Nature Switzerland AG. In the last few years, we have witnessed a resurgence of interest in neural networks. The state-of-the-art deep neural network architectures are however challenging to design from scratch and requiring computationally costly empirical evaluations. Hence, there has been a lot of research effort dedicated to effective utilisation and adaptation of previously proposed architectures either by using transfer learning or by modifying the original architecture. The ultimate goal of designing a network architecture is to achieve the best possible accuracy for a given task or group of related tasks. Although there have been some efforts to automate network architecture design process, most of the existing solutions are still very computationally intensive. This work presents a framework to automatically find a good set of hyper-parameters resulting in reasonably good accuracy, which at the same time is less computationally expensive than the existing approaches. The idea presented here is to frame the hyper-parameter selection and tuning within the reinforcement learning regime. Thus, the parameters of a meta-learner, RNN, and hyper-parameters of the target network are tuned simultaneously. Our meta-learner is being updated using policy network and simultaneously generates a tuple of hyper-parameters which are utilized by another network. The network is trained on a given task for a number of steps and produces validation accuracy whose delta is used as reward. The reward along with the state of the network, comprising statistics of network’s final layer outcome and training loss, are fed back to the meta-learner which in turn generates a tuned tuple of hyper-parameters for the next time-step. Therefore, the effectiveness of a recommended tuple can be tested very quickly rather than waiting for the network to converge. This approach produces accuracy close to the state-of-the-art approach and is found to be comparatively l...
Ali, AR, Budka, M & Gabrys, B 1970, 'Towards Meta-learning of Deep Architectures for Efficient Domain Adaptation', PRICAI 2019: Trends in Artificial Intelligence, Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing, Yanuca Island, Fiji., pp. 66-79.
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© 2019, Springer Nature Switzerland AG. This paper proposes an efficient domain adaption approach using deep learning along with transfer and meta-level learning. The objective is to identify how many blocks (i.e. groups of consecutive layers) of a pre-trained image classification network need to be fine-tuned based on the characteristics of the new task. In order to investigate it, a number of experiments have been conducted using different pre-trained networks and image datasets. The networks were fine-tuned, starting from the blocks containing the output layers and progressively moving towards the input layer, on various tasks with characteristics different from the original task. The amount of fine-tuning of a pre-trained network (i.e. the number of top layers requiring adaptation) is usually dependent on the complexity, size, and domain similarity of the original and new tasks. Considering these characteristics, a question arises of how many blocks of the network need to be fine-tuned to get maximum possible accuracy? Which of a number of available pre-trained networks require fine-tuning of the minimum number of blocks to achieve this accuracy? The experiments, that involve three network architectures each divided into 10 blocks on average and five datasets, empirically confirm the intuition that there exists a relationship between the similarity of the original and new tasks and the depth of network needed to fine-tune in order to achieve accuracy comparable with that of a model trained from scratch. Further analysis shows that the fine-tuning of the final top blocks of the network, which represent the high-level features, is sufficient in most of the cases. Moreover, we have empirically verified that less similar tasks require fine-tuning of deeper portions of the network, which however is still better than training a network from scratch.
Ali, J, Khalid, AS, Yafi, E, Musa, S & Ahmed, W 1970, 'Towards a secure behavior modeling for IoT networks using blockchain', CEUR Workshop Proceedings, pp. 244-258.
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In recent years, Internet of Things (IoT) occupies a vital aspect of our daily lives. IoT networks composed of smart-devices which communicate and exchange the information without the physical intervention of humans. Due to such proliferation and autonomous nature of IoT systems make the devices more vulnerable and prone to a severe kind of threats. In this paper, we propose a behavior, capturing and verification procedures in Blockchainsupported smart-IoT systems that can show the trust-level confidence to outside networks. We proposed our own custom Behavior Monitor and implement on a selected node that can extract the activity of each device and analyzes the behavior using deep machine learning strategy. Besides, we deploy Trusted Execution Technology (TEE) which can provide a secure execution environment (enclave) for sensitive application code and data on blockchain. Finally, in evaluation, we analyze various IoT devices data that is infected by Mirai attack. The evaluation results demonstrate the ability of our proposed method in terms of accuracy and time required for detection.
Aljarajreh, H, Lu, DD-C & Tse, CK 1970, 'Synthesis of Dual-Input Single-Output DC/DC Converters', 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, Sapporo, Japan.
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© 2019 IEEE This paper presents a topological study using power flow diagrams to derive all possible basic and non-isolated double-input single-output (DISO) converters. Unlike most reported DISO converters with one bidirectional port, this paper considers up to two bidirectional ports. The paper focuses on providing a general guideline of all power flow combinations and corresponding converter configurations. After eliminating the impractical configurations due to their indirect connection to some ports and their multiple conversion stages, three converter configurations have been identified and corresponding circuit realizations are demonstrated.
Aljohani, N & Chandran, D 1970, 'Adoption of M-Health Applications: The Saudi Arabian Healthcare Perspectives', ACIS 2019 Proceedings, Australiasian Conference on Information Systems, ACIS, Perth Western Australia, pp. 1-8.
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Despite the vital role that mobile applications will play in the implementation of healthcare plans in the Saudi Vision 2030, several factors may influence the process. Due to the conflict of interest, lack of exposure, resistance to change, as well as limited technical knowledge of the apps, the Saudi Arabian society may inadvertently impede the government’s objectives. All the challenges could be related to individual perceptions, technical complexities, social influence, as well as organizational reliability and preparedness. The earlier the authorities identify the issues and respond to them, the faster it will be to succeed in the implementation of mobile health (m-health) and subsequent attainment of the Vision 2030 health goals. This study conducted a review of literature in this context. The proposed model and factors identified will be tested to understand patients’ perceptions of m-health applications. The results will be beneficial to increase the adoption rates of m-health in Saudi Arabia
Aljohani, N & Chandran, D 2019, 'Adoption of M-Health Applications: The Saudi Arabian Healthcare Perspectives', 30th Australiasian Conference on Information Systems, Perth Western Australia.
Aljohani, N & Chandran, D 1970, 'The Role of Individuals and Social Awareness in Adopting Mobile Health Applications in Saudi Arabia: A Review and Preliminary Findings', the 34th International Business Information Management Association Conference, Madrid, Spain, pp. 4841-4846.
Al-Najjar, HAH, Kalantar, B, Pradhan, B & Saeidi, V 1970, 'Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms', Earth Resources and Environmental Remote Sensing/GIS Applications X, Earth Resources and Environmental Remote Sensing/GIS Applications X, SPIE, Strasbourg, FRANCE, pp. 19-19.
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Altulyan, MS, Huang, C, Yao, L, Wang, X, Kanhere, S & Cao, Y 1970, 'Reminder Care System: An Activity-Aware Cross-Device Recommendation System', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Advanced Data Mining and Applications, Springer International Publishing, Dalian, China, pp. 207-220.
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Alzheimer’s disease (AD) affects large numbers of elderly people worldwide and represents a significant social and economic burden on society, particularly in relation to the need for long term care facilities. These costs can be reduced by enabling people with AD to live independently at home for a longer time. The use of recommendation systems for the Internet of Things (IoT) in the context of smart homes can contribute to this goal. In this paper, we present the Reminder Care System (RCS), a research prototype of a recommendation system for the IoT for elderly people with cognitive disabilities. RCS exploits daily activities that are captured and learned from IoT devices to provide personalised recommendations. The experimental results indicate that RCS can inform the development of real-world IoT applications.
Alturki, R & Gay, V 1970, 'Augmented and virtual reality in mobile fitness applications: A survey', EAI International Conference on Future Intelligent Vehicular Technologies, EAI International Conference on Future Intelligent Vehicular Technologies, ISLAMABAD, PAKISTAN, pp. 67-75.
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Obesity is a major issue around the world. It is the main reason for several chronic diseases. Obesity can be stopped by encouraging people to do physical activities and making behaviour intervention regarding lifestyle. Mobile fitness apps are emerging because of the unique features that are provided. They are seen as a vital tool to motivate people suffering from obesity to perform physical activities and make behaviour intervention regarding health and fitness. Augmented reality (AR) and virtual reality (VR) technologies have been used successfully in different kinds of mobile apps. This paper presents a systematic review of some of the most recent AG and VR researches in mobile apps. It discusses the main findings of applying both technologies in different fields of mobile apps. Based on this systematic review, a fitness mobile app for obese individuals that consider both AR and VR technology will be developed.
Alturki, R & Gay, V 1970, 'Usability attributes for mobile applications: A systematic review', EAI International Conference on Future Intelligent Vehicular Technologies, EAI International Conference on Future Intelligent Vehicular Technologies, Islamabad, Pakistan, pp. 53-62.
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The usability of mobile applications (apps) is an emerging area of research because of the increasing use of mobile devices around the world. App development is challenging because each application has its own purpose, and each individual user has different needs and expectations from the apps. There are various apps available for each purpose, and the success of the application depends on its usefulness. This paper presents a systematic review of some of the most contemporary apps and highlights their usability attributes. It discusses usability models, frameworks and guidelines outlined in previous research for designing apps with enhanced usability characteristics. Based on this research, comprehensive guidelines for mobile apps’ usability can then be provided.
Al-Zu'bi, MM, Mohan, AS & Ling, SSH 1970, 'Influence of Tissue Anisotropy on Molecular Communication.', EMBC, the 41st International Engineering in Medicine and Biology Conference, IEEE, Germany, pp. 2921-2924.
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Many biological tissues inside the human body exhibit highly anisotropic diffusion properties; for example, tissues of the nervous system and white matter in the brain. Here, we present an improved molecular communication model by introducing the tissue anisotropy to model diffusive molecular channel for nanomachine communications. We present a stochastic particle-based simulation model for molecular communication in three-dimensional (3D) anisotropic diffusive biological microenvironments and validate with analytical expressions. We also derive expressions for peak amplitude and peak time for the received molecular signal. The results demonstrate that the channel impulse response in anisotropic biological media depend significantly on the diffusion tensor as well as on the locations of the nanomachines.
Amin, BMR, Rahman, MS & Hossain, MJ 1970, 'Impact Assessment of Credible Contingency and Cyber Attack on Australian 14-Generator Interconnected Power System', 2019 IEEE Power & Energy Society General Meeting (PESGM), 2019 IEEE Power & Energy Society General Meeting (PESGM), IEEE, Atlanta, GA, USA, pp. 1-5.
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© 2019 IEEE. This paper analyses the impacts of credible contingency and an event of cyber attack on the dynamic performance of a real large-scale interconnected power grid. Any credible contingency, for example, short circuit fault or unnatural behaviour of protective devices due to cyber intrusion could create catastrophic consequences and even complete blackout to the power systems. In order to protect power systems against cyber events, it is necessary to analyse the impacts of both faults and cyber attacks on the dynamic behaviour of the power system to identify cyber events from credible contingencies. In this paper, a simplified model of an Australian 14-generator interconnected system is considered as a testbed and MATLAB/Simulink Simpowersystems Toolbox is used for the analyses. A real-life incident of faults has considered as case study and an event of a cyber attack on protection relay function is simulated to explore the possible similar impacts on the same page. The systematic analyses of different properties of the system will help to design the detection and counter measure techniques to ensure the system is protected from cyber threats.
Amin, U, Hossain, MJ, Fernandez, E, Mahmud, K & Tiezheng, G 1970, 'A Contract-based Trading Model for Electricity Suppliers in Smart Grids', 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP), 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP), IEEE, New Delhi, India, pp. 1-5.
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© 2019 IEEE. This paper proposes an approach to categorize electricity suppliers (ESs) for energy trading between ESs and a single aggregator. A principal-agents game model is developed to model the interactions between an aggregator and different categories of ESs by considering the benefits of both parties. In a proposed game, the aggregator as a principal will purchase a certain amount of power from different-category ESs with the cheapest pricing options available, and at the same time the ESs, acting as agents will maximize their utilities by selling their power to the aggregator instead of feeding the grid at a low rate. The developed optimal contract-based scheme, which can be implemented distributed manner, allows different-category ESs to sell their power at different prices based on their unit production cost to maximize their benefits, and the total cost to the aggregator is minimized. Numerical analysis confirms the effectiveness of the proposed ESs categorizing framework in the development of a contract-based incentive mechanism for energy trading.
Anshu, A, Berta, M, Jain, R & Tomamichel, M 1970, 'Second-Order Characterizations via Partial Smoothing', 2019 IEEE International Symposium on Information Theory (ISIT), 2019 IEEE International Symposium on Information Theory (ISIT), IEEE, pp. 937-941.
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© 2019 IEEE. Smooth entropies are a tool for quantifying resource trade-offs in information theory and cryptography. However, in typical multi-partite problems some of the sub-systems are often left unchanged and this is not reflected by the standard smoothing of information measures over a ball of close states. We propose to smooth instead only over a ball of close states which also have some of the reduced states on the relevant sub-systems fixed. This partial smoothing of information measures naturally allows to give more refined characterizations of various information-theoretic problems in the one-shot setting. As a consequence, we can derive asymptotic second-order characterizations for tasks such as privacy amplification against classical side information or classical state splitting. For quantum problems like state merging the general resource trade-off is tightly characterized by partially smoothed information measures as well.
Anwar, M, Gill, A & Beydoun, G 1970, 'Using Adaptive Enterprise Architecture Framework for Defining the Adaptable Identity Ecosystem Architecture', https://aisel.aisnet.org/acis2019/, Australasian Conference on Information Systems, AIS, Perth, pp. 1-11.
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Digital identity management is often used to handle fraud detection and hence reduce identity thefts. However, using digital identity management presents additional challenges in terms of privacy of the identity owner meanwhile managing the security of the verification. In this paper, drawing on adaptive enterprise architecture (EA) with an ecosystem approach to digital identity, we describe an identity ecosystem (IdE) architecture to handle identity management (IdM) while safeguarding security and privacy. This study is a part of the larger action design research project with our industry partner DZ. We have used Adaptive EA as a baseline to define a privacy aware adaptive IdE to make ID operations more efficient and improve the delivery of services in the public and private sector. The value of the anticipated architecture is in its generic yet comprehensive structure, component orientation and layered approach which aim to enable the contemporary IdM
Anwar, MJ & Gill, AQ 1970, 'A Review of the Seven Modelling Approaches for Digital Ecosystem Architecture.', CBI (1), IEEE Conference on Business Informatics, IEEE, Moscow, Russia, pp. 94-103.
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A dynamic digital ecosystem is an interrelated network of organisations, people and/or entities that interact and collaborate for value co-creation. The challenge is how to effectively model the digital ecosystems operating in a highly complex and dynamic environment. There are several modelling approaches to choose from. There is a need to evaluate the existing modelling approaches to support the modelling of digital ecosystems. This paper evaluates the scope and coverage of the selected seven modelling approaches (Adaptive Enterprise Architecture, ArchiMate, TOGAF, FAML, ISO/IEC/IEEE 42010, SABSA, and ITIL) for modelling the digital ecosystems. Adaptive enterprise architecture is taken as a reference architecture for this review due to its higher relevance to digital ecosystem layers. The results of this review indicate that every modelling methodology is different in scope and coverage and demands the integration and tailoring of a context specific modelling approaches to provide the type of support needed for digital ecosystems.
Anwar, MJ, Gill, AQ & Beydoun, G 1970, 'Using Adaptive Enterprise Architecture Framework for Defining the Adaptable Identity Ecosystem Architecture', ACIS 2019 Proceedings - 30th Australasian Conference on Information Systems, pp. 890-900.
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Digital identity management is often used to handle fraud detection and hence reduce identity thefts. However, using digital identity management presents additional challenges in terms of privacy of the identity owner meanwhile managing the security of the verification. In this paper, drawing on adaptive enterprise architecture (EA) with an ecosystem approach to digital identity, we describe an identity ecosystem (IdE) architecture to handle identity management (IdM) while safeguarding security and privacy. This study is a part of the larger action design research project with our industry partner DZ. We have used adaptive EA as a theoretical lens to define a privacy aware adaptive IdM with a view to improve the Id operations and delivery of services in the public and private sector. The value of the anticipated architecture is in its generic yet comprehensive structure, component orientation and layered approach which aim to enable the contemporary IdM.
Apostolopoulou, M, Armaghani, DJ, Bakolas, A, Douvika, MG, Moropoulou, A & Asteris, PG 1970, 'Compressive strength of natural hydraulic lime mortars using soft computing techniques', Procedia Structural Integrity, Elsevier BV, pp. 914-923.
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Argha, A, Su, SW, Liu, Y & Celler, BG 1970, 'Control Allocation Based Sliding Mode Fault Tolerant Control', 2019 American Control Conference (ACC), 2019 American Control Conference (ACC), IEEE, Philadelphia, PA, USA, pp. 3752-3757.
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© 2019 American Automatic Control Council. This paper describes a novel fault tolerant control using robust sliding mode control strategy. This scheme can also be employed as actuator redundancy management for over-actuated uncertain linear systems. In contrast to many existing methods in the literature that assume the control input matrix is not of full rank such that it can be factorised into two matrices, this scheme can be applied to systems whose control input matrix has full rank. The so-called virtual control, in this scheme, is designed to be robust against uncertainties emanating from visibility of the control allocator to the controller and imperfection in the estimated effectiveness gain. Then using a static real-time control allocator, the obtained virtual control signal is redistributed among remaining (redundant or non-faulty) set of actuators. The proposed scheme is a unified, control allocation-based fault tolerant control which does not need to reconfigure the control system in the case of actuator fault or failure. The effectiveness of the proposed schemes is discussed with a numerical example.
Ariyachandra, T, Marjanovic, O & Dinter, B 1970, 'Introduction to the Minitrack on Business Intelligence, Business Analytics and Big Data: Innovation, Deployment, and Management', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii (Virtual), pp. 5846-5847.
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Armaghani, DJ, Hatzigeorgiou, GD, Karamani, C, Skentou, A, Zoumpoulaki, I & Asteris, PG 1970, 'Soft computing-based techniques for concrete beams shear strength', Procedia Structural Integrity, Elsevier BV, pp. 924-933.
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Armstrong, T & Leong, TW 1970, 'SNS and the Lived Experiences of Queer Youth', Proceedings of the 31st Australian Conference on Human-Computer-Interaction, OZCHI'19: 31ST AUSTRALIAN CONFERENCE ON HUMAN-COMPUTER-INTERACTION, ACM, Fremantle, Australia, pp. 376-380.
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© 2019 Association for Computing Machinery. Technology design has not adequately included a queer perspective, even though digital technologies such as social networking sites (SNS) have been shown to play vital roles in the lives and well-being of queer people. SNS provide queer people with a means to explore their identities, learn about queerness and connect to others with similar experiences. However, SNS use can also have detrimental effects, exposing queer people to harm and victimisation. To date, there is not much effort in HCI to understand the experiences of queer people with SNS. As a result, we lack understanding of how SNS and other social technologies could be designed in ways that are supportive of queer people's well-being. The findings from this exploratory study reveal how particular digital technologies can have complex effects in shaping queer people's experiences and their well-being.
Arukgoda, J, Ranasinghe, R & Dissanayake, G 1970, 'Representation of Uncertain Occupancy Maps with High Level Feature Vectors', 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), IEEE, Vancouver, BC, Canada, pp. 1035-1041.
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© 2019 IEEE. This paper presents a novel method for representing an uncertain occupancy map using a 'feature vector' and an associated covariance matrix. Input required is a point cloud generated using observations from a sensor captured at different locations in the environment. Both the sensor locations and the measurements themselves may have an associated uncertainty. The output is a set of coefficients and their uncertainties of a cubic spline approximation to the distance function of the environment, thereby resulting in a compact parametric representation of the environment geometry. Cubic spline coefficients are computed by solving a non-linear least squares problem that enforces the Eikonal equation over the space in which the environment geometry is defined, and zero boundary condition at each observation in the point cloud. It is argued that a feature based representation of point cloud maps acquired from uncertain locations using noisy sensors has the potential to open up a new direction in robot mapping, localisation and SLAM. Numerical examples are presented to illustrate the proposed technique.
Arukgoda, J, Ranasinghe, R & Dissanayake, G 1970, 'Robot Localisation in 3D Environments Using Sparse Range Measurements', 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, Hong Kong, pp. 551-558.
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© 2019 IEEE. This paper presents an algorithm for mobile robot localisation given a map of a 3D environment and a sparse set of range-bearing measurements. The environment is represented using a spline approximation of its vector distance function (VDF). For a given location in the environment, VDF encodes the distance to the nearest occupied region along three orthogonal axes. VDF is first obtained from an occupancy voxel map and its three components are then approximated in the least-square sense using a set of three dimensional cubic b-splines, providing a rich and continuous representation of the environment. First and second order derivatives of the VDF are also computed and stored. The difference between an observed range measurement in a given direction and its expected value is formulated as a function of the robot location and the spline coefficients representing the VDF. This leads to a non-linear least-squares optimization problem that can be solved to localise the robot given a set of such measurements. It is demonstrated that a sparse set of range-bearing measurements, an order of magnitude smaller than what is typically available from 3D range sensor is adequate to achieve accurate localisation. The algorithm presented is illustrated using a number of examples including a single point range sensor mounted on a pan-tilt head to localise a robot moving in an indoor environment.
Arunachalam, S, Chakraborty, S, Lee, T, Paraashar, M & De Wolf, R 1970, 'Two new results about quantum exact learning', Leibniz International Proceedings in Informatics, LIPIcs, International Colloquium on Automata, Languages, and Programming, Greece.
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We present two new results about exact learning by quantum computers. First, we show how to exactly learn a k-Fourier-sparse n-bit Boolean function from O(k1.5(log k)2) uniform quantum examples for that function. This improves over the bound of Θe (kn) uniformly random classical examples (Haviv and Regev, CCC'15). Our main tool is an improvement of Chang's lemma for sparse Boolean functions. Second, we show that if a concept class C can be exactly learned using Q quantum membership queries, then it can also be learned using O logQ2Q log |C| classical membership queries. This improves the previous-best simulation result (Servedio-Gortler, SICOMP'04) by a log Q-factor.
Ashtari, S, Tofigh, F, Abolhasan, M, Lipman, J & Ni, W 1970, 'Efficient Cellular Base Stations Sleep Mode Control Using Image Matching', 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), IEEE, Kuala Lumpur, MALAYSIA, pp. 1-7.
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© 2019 IEEE. Green cellular network helps to decrease environmental pollution. In contrast, massive connectivity and demand for higher data rate promise the presence of new generation of cellular system (5G) and small cell networks. Hence, expectation on increasing the number of base stations (BSs), which leads to increase in energy usage. One way to improve energy consumption is by shutting down the redundant BSs while sustaining the Quality-of-Service (QoS) for each user. In this paper, we propose a dynamic structural algorithm based on transportation problem, to switch on/off the BSs in cellular networks without compromising its coverage, and maintain the networks load by neighboring cells. We use weighted graphs to translate our problem as a transportation problem and then use linear programming to solve it. The cost of transport, turning a BS into sleep mode, is illustrated as a function of energy usage,coverage area and load on the BSs. Running the propose method consecutively provides the maximum number of BSs whom are at sleep mode. The methodology explained in this paper reduces energy consumption to almost 40%, whereas maintaining all the existing loads in the network.
Athayde, J, De Silva Wijayaratna, K & Robson, E 1970, 'Employment Decentralisation in Sydney', Australian Institute of Traffic Planning and Management 2019 National Conference, Adelaide, Australia.
Attar, M, Kang, K & Sohaib, O 1970, 'Knowledge Sharing Practices, Intellectual Capital and Organizational Performance', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Grand Wailea, United States, pp. 5578-5587.
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Although knowledge sharing and intellectual capital are significant factors for long-term success of an organization, existing literature rarely examines the relationship between knowledge sharing practices intellectual capital (IC) as constitutive elements of a knowledge environment leading to enhanced operational performance. The main aim of this paper is to explore whether knowledge sharing practices (types, approaches, and process) and intellectual capital affect organizational operational performance. Findings suggest that knowledge sharing types and knowledge sharing process influence intellectual capital of an organization. Moreover, intellectual capital influences organizational operational performance. However, knowledge sharing approaches, i.e. codification and personalization strategies have no effect on intellectual capital.
Attar, M, Kang, K & Sohaib, O 1970, 'Knowledge Sharing Practices, Intellectual Capital and Organizational Performance.', HICSS, Pacific Asia Conference on Information Systems, ScholarSpace, Yokohama, Japan, pp. 1-10.
Aung, TWW, Huo, H & Sui, Y 1970, 'Interactive Traceability Links Visualization using Hierarchical Trace Map', 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), IEEE, Cleveland, Ohio.
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Traceability links between various software artifacts of a system aid software engineers in system comprehension, verification and change impact analysis. Establishing trace links between software artifacts manually is an error-prone and costly task. Recently, studies in automated traceability link recovery area have received broad attention in the software maintenance community aiming to overcome the challenges of manual trace links maintenance process. In these studies, the trace links results generated by an automated trace recovery approach are presented either in a bland textual matrix format (e.g., tabular format) or two-dimensional graphical formats (e.g. tree view, hierarchical leaf node). Therefore, it is challenging for software engineers to explore the inter-relationships between various artifacts at once (e.g., which test cases and source code files/methods are related to a particular requirement). In this position paper, we propose a hierarchical trace map visualization technique to explore inter-relationships between various artifacts at once naturally and intuitively
Awan, Z, Kahlke, T, Ralph, P & Kennedy, P 1970, 'Chemical Named Entity Recognition with Deep Contextualized Neural Embeddings', Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 11th International Conference on Knowledge Discovery and Information Retrieval, SCITEPRESS - Science and Technology Publications, Austria, pp. 135-144.
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Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved Chemical named entity recognition (ChemNER) is a preliminary step in chemical information extraction pipelines. ChemNER has been approached using rule-based, dictionary-based, and feature-engineered based machine learning, and more recently also deep learning based methods. Traditional word-embeddings, like word2vec and Glove, are inherently problematic because they ignore the context in which an entity appears. Contextualized embeddings called embedded language models (ELMo) have been recently introduced to represent contextual information of a word in its embedding space. In this work, we quantify the impact of contextualized embeddings for ChemNER by using Bi-LSTM-CRF (bidirectional long short term memory networks - conditional random fields) networks. We benchmarked our approach using four well-known corpora for chemical named entity recognition. Our results show that incorporation of ELMo results in statistically significant improvements in F1 score in all of the tested datasets.
Ayachit, A, Hasan, SU, Siwakoti, YP, Abdul-Hak, M, Kazimierczuk, MK & Blaabjerg, F 1970, 'Coupled-Inductor Bidirectional DC-DC Converter for EV Charging Applications with Wide Voltage Conversion Ratio and Low Parts Count', 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 2019 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, Baltimore, MD, USA, pp. 1174-1179.
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© 2019 IEEE. This paper proposes a new bidirectional dc-dc converter and presents its application for electric-vehicle (EV) charging applications. Using the benefits of a coupled-inductor, the proposed converter exhibits a wide voltage conversion ratio in both buck and boost modes, while using lesser number of components compared to other unidirectional and bidirectional converter counterparts. The steady-state analysis of the converter is presented and the steady-state waveforms are derived. The expressions for the dc voltage and current transfer functions, current and voltage stresses of the semiconductor components, and the design expressions for the converter passive components are derived. Design of a 3.2 kW, 380 V at Low-Voltage (LV) side and 48 V at High-Voltage (HV) side bidirectional converter is shown and verified by simulations. Simulation show efficiencies greater than 95% for both operating modes. A laboratory prototype of a small-scale 300 W bidirectional dc-dc converter with 40 V at LV-side and 300 V at HV-side is considered. The experimental results to support the theoretical predictions are given.
Azizivahed, A, Ghavidel, S, Ghadi, MJ, Li, L & Zhang, J 1970, 'Multi-Objective Energy Management Approach Considering Energy Storages in Distribution Networks with Respect to Voltage Security', 2019 IEEE International Conference on Industrial Technology (ICIT), 2019 IEEE International Conference on Industrial Technology (ICIT), IEEE, Melbourne, pp. 661-666.
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© 2019 IEEE. The presence of electrical energy storage (EES) units in distribution systems has potentials to improve the network profiles (e.g. bus voltage and branch current profiles) as well as to reduce operational cost and power losses. This paper presents a novel approach to determine the optimal charging/discharging schedule of EES units in distribution systems by employing multi-objective optimization methods, aiming at reducing operation cost and enhancing radial distribution networks security. In this regard, a voltage stability index (VSI) to improve the radial network security is presented as a separate objective function. In order to assess effectiveness and applicability of the proposed method, it is applied to a 33-bus IEEE standard distribution test system and then the obtained results are compared with existing methodologies.
Baba, AA, Hashmi, RM, Esselle, KP & Attygalle, M 1970, 'A Passive Beam Reconfigurable Antenna System for Millimeter-wave Applications', 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, IEEE, pp. 691-692.
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© 2019 IEEE. This paper presents the performance of a pathfinder beam reconfigurable antenna system for mm-wave applications. The system is completely passive and can provide continuous three-dimensional beam scanning in a conical region of ±40° from boresight direction. It uses a pair of near-field dielectric structures, inspired from optical prisms, introducing a pre-calculated phase gradient to the incoming wave front from a source antenna, and redirecting the radiated beam to an arbitrary direction with in a conical region with an apex angle of 80°. The proposed system can provide a peak gain of 20.7 dBi and does not require expensive phase shifters and a power distribution network.
Baba, AA, Hashmi, RM, Esselle, KP & Matekovits, L 1970, 'All-Dielectric Compact Superstrates for High-Gain Resonant-Cavity Antennas: Designs & Measurements', 2019 URSI International Symposium on Electromagnetic Theory (EMTS), 2019 URSI International Symposium on Electromagnetic Theory (EMTS), IEEE, pp. 1-4.
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© 2019 URSI. This paper presents the designs and measurements of two compact single-layer all-dielectric resonant-cavity antennas (RCAs). Both the antennas are compact (footprint < 5.5λ20) and low in profile (overall height < 0.9λ0). The first RCA consists of a single-layer partially reflecting superstrate (PRS) in which thickness and permittivity vary from the center towards the edge of the PRS. Four commercially available dielectric materials are used to achieve this permittivity variation. This RCA demonstrates a measured peak directivity of 20.7 dBi and its 3dB directivity bandwidth extends from 12.75-19 GHz, which is 57% at the center frequency. The second RCA, made out of a single dielectric material demonstrates a measured peak directivity of 20.3 dBi and its measured 3dB directivity bandwidth is 55.9%. This class of compact single-layer RCAs, with a directivity bandwidth product per unit area (DBP/A) of greater than 1200, successfully overcomes the trade-off between directivity, bandwidth, profile and footprint and breaks the challenging barrier that has existed for RCAs over the last decade (and other planar high-gain antennas).
Bah, AO, Bird, TS & Qin, P 1970, 'A Low Profile Tightly Coupled Antenna Array with 80° Scanning for Multifunctional Applications', 2019 IEEE International Symposium on Phased Array System & Technology (PAST), 2019 IEEE International Symposium on Phased Array System & Technology (PAST), IEEE, Waltham, MA, USA, pp. 1-4.
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A wideband wide scanning antenna array for application in multifunctional phased arrays is presented. The dipoles and balun are printed on both sides of a single RT/Duroid™ 6010 substrate with a relative dielectric constant of 10.2. Optimized designs of two thicknesses of a metasurface-based wide angle impedance matching layer are presented, facilitating the highest figure of merit values in phased array antennas. The feed network, composed of meandered impedance transformer and balun sections, are constructed from Klopfenstein tapered microstrip lines. The overall height of the array above the ground plane is 0.087 $\lambda_{\mathrm{L}}$ , where $\lambda_{\mathrm{L}}$ is the wavelength at the lowest frequency of operation. For the single sided metasurface design, scanning to 80° along the E-plane and 55° along the H-plane over a 5.5:1 impedance bandwidth (0.77 GHz-4.2 GHz) was achieved assuming an active VSWR value of 3.1.
Bai, L, Yao, L, Kanhere, SS, Wang, X & Sheng, QZ 1970, 'STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, pp. 1981-1987.
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Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a short-term encoder to derive the next-step prediction for generating multi-step prediction; 3) an attention-based output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.
Bai, L, Yao, L, Kanhere, SS, Wang, X, Liu, W & Yang, Z 1970, 'Spatio-Temporal Graph Convolutional and Recurrent Networks for Citywide Passenger Demand Prediction', Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM '19: The 28th ACM International Conference on Information and Knowledge Management, ACM, Beijing, China, pp. 2293-2296.
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© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. Online ride-sharing platforms have become a critical part of the urban transportation system. Accurately recommending hotspots to drivers in such platforms is essential to help drivers find passengers and improve users' experience, which calls for efficient passenger demand prediction strategy. However, predicting multi-step passenger demand is challenging due to its high dynamicity, complex dependencies along spatial and temporal dimensions, and sensitivity to external factors (meteorological data and time meta). We propose an end-to-end deep learning framework to address the above problems. Our model comprises three components in pipeline: 1) a cascade graph convolutional recurrent neural network to accurately extract the spatial-temporal correlations within citywide historical passenger demand data; 2) two multi-layer LSTM networks to represent the external meteorological data and time meta, respectively; 3) an encoder-decoder module to fuse the above two parts and decode the representation to predict over multi-steps into the future. The experimental results on three real-world datasets demonstrate that our model can achieve accurate prediction and outperform the most discriminative state-of-the-art methods.
Bai, L, Yao, L, Kanhere, SS, Yang, Z, Chu, J & Wang, X 1970, 'Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Macau, China, pp. 29-42.
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© Springer Nature Switzerland AG 2019. Accurate prediction of passenger demands for taxis is vital for reducing the waiting time of passengers and drivers in large cities as we move towards smart transportation systems. However, existing works are limited in fully utilizing multi-modal features. First, these models either include excessive data from weakly correlated regions or neglect the correlations with similar but spatially distant regions. Second, they incorporate the influence of external factors (e.g., weather, holidays) in a simplistic manner by directly mapping external features to demands through fully-connected layers and thus result in substantial bias as the influence of external factors is not unified. To tackle these problems, we propose an end-to-end multi-task deep learning model for passenger demand prediction. First, we select similar regions for each target region based on their Point-of-Interest (PoI) information or historical demand and utilize Convolutional Neural Networks (CNN) to extract their spatial correlations. Second, we map external factors to future demand levels as part of the multi-task learning framework to further boost prediction accuracy. We conduct experiments on a large-scale real-world dataset collected from a city in China with a population of 1.5 million. The results demonstrate that our model significantly outperforms the state-of-the-art and a set of baseline methods.
Bai, X, Feng, X, Ni, J, Beretov, J, Deng, J, Zhu, Y, Graham, P & Li, Y 1970, 'Abstract 4754: CHTOP is a novel therapeutic target for chemoresistant epithelial ovarian cancer therapy', Experimental and Molecular Therapeutics, Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA, American Association for Cancer Research, pp. 4754-4754.
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Baidya, R, Aguilera, RP, Karamanakos, P, Acuna, P, Rojas, C, Geyer, T & Lu, DD-C 1970, 'Dealing with Suboptimality in Multistep Model Predictive Control for Transient Operations', 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 2019 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, Baltimore, MD, USA, pp. 3780-3785.
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© 2019 IEEE. Recently, a computational issue of sphere decoding algorithm (SDA) during transient operation of multistep model predictive control has been addressed in [1] and achieved its real-time implementation in [2] for a medium-voltage electrical drive system. This is achieved by projecting the unconstrained solution onto the convex-hull of the finite control set during transient operation. Therefore, a new initial sphere that guarantees feasibility and includes a significant smaller number of candidate solutions is obtained. This reduces the computation time required to solve the optimization problem. However, the reduction of the computational burden comes at the expense of (mild) suboptimal results [3]. This paper analyses the possibility to obtain a suboptimal solution by the SDA based optimization during transient operation. To deal with this suboptimality issue, this work explores the possibility to enlarge the convex-hull, whose size is by definition tied to the original finite control set. Therefore, in this work, the convex-hull is treated as a SDA initialization parameter during transient operation. As will be demonstrated, enlarging the convex-hull size reduces the possibility to obtain a suboptimal solution during the transient operation retaining, thus, the optimality during the whole converter operation.
Balnave, N, Patmore, G & Marjanovic, O 1970, 'Death, Taxes and Demutualisation: Perspectives from the Australian Visual Atlas Co-operative History Project', 11th Annual Conference of Academic Association of Historians in Australian and New Zealand Business Schools (AAHANZBS), Auckland, NZ.
Bandara, M, Behnaz, A & Rabhi, FA 1970, 'RVO - The Research Variable Ontology', Springer International Publishing, pp. 412-426.
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Bandara, M, Rabhi, FA, Meymandpour, R & Demirors, O 1970, 'A Digital Interaction Framework for Managing Knowledge Intensive Business Processes', Springer International Publishing, pp. 108-122.
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Bani Musa, GM, Alnajjar, F, Al-Jumaily, A & Shimoda, S 1970, 'Upper Limb Recovery Prediction After Stroke Rehabilitation Based on Regression Method', Converging Clinical and Engineering Research on Neurorehabilitation III, International Conference on NeuroRehabilitation, Springer International Publishing, Pisa, Italy, pp. 380-384.
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In this paper, we investigate the possibility of a machine-learning algorithm using the Support Victor Machine Regression (SVMR) to predict the motor functional recovery of moderate post stroke patients during their rehabilitation program. To train the model, we used the recorded electromyography (EMG) signals from the upper limb muscles of the patients during their initial rehabilitation sessions. Then we tested the trained model to predict the later muscles performance of the patient during the same sessions. The results of this pilot study were promising; data were, to some extent, predictable. We believe such research direction could be essential to motivate the patient to complete the designed rehabilitation program and can assist the therapist to innovate proper rehabilitation menu for individual patients.
Bannink, T, Briet, J, Buhrman, H, Lee, T & Labib, F 1970, 'Bounding quantum-classical separations for classes of nonlocal games', Symposium on Theoretical Aspects of Computer Science, Germany.
Bano, M & Zowghi, D 1970, 'Gender disparity in the governance of software engineering conferences.', GE@ICSE, International Workshop on Gender Equality in Software Engineering (GE), IEEE / ACM, Montreal, Canada, pp. 21-24.
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© 2019 IEEE. In this paper, we discuss gender disparity in software engineering (SE) conferences. We have examined the roles of General Chair, Program Chair, and main track Program Committee members in six highly ranked conferences in SE for a period of ten years in order to understand the pattern of gender disparity in visible roles. We also present the opinions elicited from ten participants on this topic, who have served at some of these SE conferences in leadership roles. Our aim is to reflect on the current state and initiate the debate, on gender equality in SE conferences.
Baral, P, Indraratna, B & Rujikiatkamjorn, C 1970, 'An Elastic Visco-Plastic Model for Soft Soil with Reference to Radial Consolidation', Geotechnics for Transportation Infrastructure, International Symposium on Transportation Geotechnics, Springer Singapore, Delhi (India), India, pp. 369-380.
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© Springer Nature Singapore Pte Ltd 2019. The time-dependent stress–strain behaviour of soft soil due to its viscous nature affects its long-term settlement and pore water dissipation. A novel mathematical model developed using the Peaceman–Rachford ADI scheme (P–R FD Scheme) can describe the visco-plastic behaviour of soft clay with a non-Darcian flow function; this model is a combination of the basic radial consolidation equation developed by Barron and Bjerrum’s time-equivalent (Bjerrum in Geotechnique 17:81–118, 1967) concept that incorporates Yin and Graham’s (Can Geotech J 26:199–209, 1989b) visco-plastic parameters. The settlement and excess pore water pressure obtained from this model are then compared with preexisting models such as a Class C prediction for the Ballina trial embankment at National Field Testing Facility (NFTF). This elastic visco-plastic model provides better results in terms of settlement and pore water pressure with the field data, although the excess pore water pressure that did not dissipate after one year is mainly due to the piezometers becoming biologically and chemically clogged in terrain with acid sulphate soil (ASS).
Barthelmey, A, Lee, E, Hana, R & Deuse, J 1970, 'Dynamic digital twin for predictive maintenance in flexible production systems', IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, IEEE, Lisbon, Portugal, pp. 4209-4214.
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© 2019 IEEE. Technical innovations to improve production systems flexibility have been extensively investigated and are already available to industrial practice. However, the production companies' internal structures and processes must also meet these flexibility requirements in order for system adjustments to be efficiently carried out. Due to the direct influence on availability, performance and quality losses, maintenance is particularly relevant. Therefore, this paper considers the effects of flexibility on the promising strategy of predictive maintenance. With the digital twin, its update service and a labelling interface, the essential components of a predictive maintenance system for flexible production systems are introduced in detail. This application is based on a reference plant in the form of a flexible hybrid assembly system. The outlook discusses the transferability to other industrial use cases.
Basavaraja, V, Shivakumara, P, Guru, DS, Pal, U, Lu, T & Blumenstein, M 1970, 'Age Estimation using Disconnectedness Features in Handwriting', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia, pp. 1131-1136.
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© 2019 IEEE. Real-time applications of handwriting analysis have increased drastically in the fields of forensic and information security because of accurate cues. One of such applications is human age estimation based on handwriting for the purpose of immigrant checking. In this paper, we have proposed a new method for age estimation using handwriting analysis using Hu invariant moments and disconnectedness features. To make the proposed method robust to both ruled and un-ruled documents, we propose to explore intersection point detection in Canny edge images of each input document, which results in text components. For each text component pair, we propose Hu invariant moments for extracting disconnectedness features, which in fact measure multi-shape components based on distance, shape and mutual position analysis of components. Furthermore, iterative k-means clustering is proposed for the classification of different age groups. Experimental results on our dataset and some standard datasets, namely, IAM and KHATT, show that the proposed method is effective and outperforms the state-of-the-art methods.
Bautista, M, Zhu, H, Zhu, X & Yang, Y 1970, 'Design of Self-Coupling Enhanced Resonator in $0.13-\mu\mathrm{m}$ (Bi)-CMOS Technology', 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT), 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT), IEEE, Guangzhou, China, pp. 1-3.
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© 2019 IEEE. Design of a compact on-chip resonator operating at mm-wave region is presented in this paper. Unlike the previously published ones in the literature, this work is implemented using a planar structure, which requires no broadside coupling. Instead, the resonance is generated through enhanced self-coupling. To further demonstrate the feasibility of using this approach in practice, the designed resonator is fabricated in a standard 0.13- mumathrm{m} (Bi)-CMOS technology. The measured results show that it can generate a notch at 47 GHz with the attenuation better than 28 dB due to the enhanced self-resonant frequency. The chip size, excluding the pads, is only 0.096times 0.294 text{mm}{2}.
Bautista, MG, Zhu, H, Zhu, X, Yang, Y, Sun, Y, Dutkiewicz, E & Zhang, F 1970, 'Millimeter-Wave BPFs Design using Quasi-Lumped Elements in 0.13-μm (Bi)-CMOS Technology', 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, Sapporo, Japan, pp. 1-5.
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© 2019 IEEE A design methodology using quasi-lumped elements for compact millimeter-wave on-chip bandpass filter (BPF) is presented in this work. To implement BPF using this approach, a novel inductor cell is presented first and then using this cell along with metal-insulator-metal (MIM) capacitors, two BPFs are designed. For the purpose of proof-of-concept, all three designs are implemented and fabricated in a standard 0.13-µm (Bi)-CMOS technology. The measurements show that the inductor cell generates a notch at 47 GHz with a chip size of 0.096 × 0.294 mm2 without pads. Moreover, the 1st BPF has the center frequency at 27 GHz with an insertion loss of 2.5 dB and it has one transmission zero at 58 GHz with a peak attenuation of 23 dB. Unlike the 1st design, the 2nd design has two transmission zeros. The center frequency of this BPF is located at 29 GHz with a minimum insertion loss of 3.5 dB. Without the measurement pads, the chip sizes of the two BPFs are 0.076 × 0.296 mm2 and 0.096 × 0.296 mm2, respectively.
Becerril, L, Guertler, M & Longa, E 1970, 'Developing Design Methods - a Conceptual Requirement Framework', Proceedings of the Design Society: International Conference on Engineering Design, Proceedings of the Design Society: International Conference on Engineering Design, Cambridge University Press (CUP), Delft, Netherlands, pp. 1463-1472.
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AbstractDesign methods can provide valuable support in structuring and solving complex product design problems. However, the application and the transfer of methods from academia to industry is limited. To date, research has tended to focus on solving this through improved method selection, method adaptation and training. The development of design methods itself has attracted surprisingly low attention. This paper closes this gap and adds a quite new perspective of systematic requirement management of method development. However, the variety of methods, method users and application contexts is a key challenge and does not allow for a universal set of requirements. Thus, this paper transfers the concept of solution-neutral requirements frameworks, which are established in product design, to method development. The framework is derived from analysing and structuring different requirements found in literature. Different requirement sub-/categories allow for accommodating the varying levels of detail of requirements. The framework works like a checklist and helps design researchers to consider the most important requirement categories, which subsequently can be detailed project-specifically.
Begh, MAW, Liegmann, E, Karamanakos, PP, Mahajan, A, Siwakoti, YP & Kennel, R 1970, 'Indirect Model Predictive Control of a Three-Phase Grid-Connected Siwakoti-H Inverter.', IECON, Annual Conference of the IEEE Industrial Electronics Society, IEEE, Lisbon, Portugal, pp. 411-416.
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The Siwakoti-H flying-capacitor inverter (sFCI) is a potential candidate for photovoltaic applications, specifically for the transformerless grid-connected systems. One of the main challenges in the control of a sFCI is to maintain the flying capacitor voltage within prescribed limits while balancing the voltages on the three flying capacitors. This paper proposes an indirect model predictive control strategy for a three-phase sFCI connected to the grid via an LCL-filter. By linearizing the system model, the nonlinearities introduced due to the dynamics of the flying capacitor are neglected. Moreover, by not directly controlling the switches, but rather manipulating the modulating signal, the optimization problem can be formulated as a quadratic program (QP) and solved in a computationally efficient manner. The explicit solution computed by the controller makes the realtime implementation feasible by employing a carrier-based pulse width modulator (CB-PWM). The presented results illustrate the steady-state and dynamic performance of the controller.
Begh, MAW, Liegmann, E, Mahajan, A, Palanisamy, A, Siwakoti, YP, Karamanakos, P, Abdelrahem, M & Kennel, R 1970, 'Design of state-feedback controller for a single-phase grid-connected Siwakoti-h inverter with LCL filter', PCIM Europe Conference Proceedings, International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management, IEEE, Nuremberg, Germany, pp. 1587-1594.
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Grid-tied transformerless inverters for photovoltaic (PV) systems have attained a prominent share in the distributed power generation applications on both the domestic and utility scale. This arises the need of a transformerless inverter with less hardware components and complexity. This paper presents a state-feedback current controller (SFCC) for a flying-capacitor based transformerless inverter (Siwakoti-H topology). Compared to the conventional proportional-integral (PI) based control, the state-feedback current control provides a better dynamic performance for a single-phase grid-tied converter equipped with an LCL filter. The controller is designed using pole placement to set the dominant behavior of the converter, which actively damps the resonant frequency of the LCL filter. The controller is extended using dc voltage feedforward compensation (DVFC) to maintain the voltage ripple of the flying capacitor within the limits, and an integral state for improved disturbance rejection. Simulation results illustrate the steady-state and dynamic performance of the controller as well as the inherent damping of the LCL filter resonance. Experimental results are presented to verify the steady-state operation of the system.
Begum, H, Ali, A & Lee, JE-Y 1970, 'Mass Sensitivity Measurements of a Novel High Q-Factor Disk Resonator for Liquid-Phase Sensing Applications', 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII), 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII), IEEE.
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Behnaz, A, Bandara, M, Rabhi, FA & Peat, M 1970, 'A Statistical Learning Ontology for Managing Analytics Knowledge', Springer International Publishing, pp. 180-194.
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Bell, J & Leong, TW 1970, 'Collaborative Futures', Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI '19: CHI Conference on Human Factors in Computing Systems, ACM, Glasgow, Scotland, pp. 1-13.
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© 2019 Association for Computing Machinery. Designing new technologies to support the lived experience of dementia is of increasing interest within HCI. While there is guidance on qualitative research methods to use in areas such as dementia, there is a need for more appropriate ways to research in the younger demographic. In Younger Onset Dementia (YOD), the circumstances and experiences are markedly different from dementia in the later stage of life – requiring a different approach. This paper presents insights into the methods and approaches used in a fieldwork with five people living with YOD; where they engaged as co-researchers in a co-directed inquiry into their lived experiences. Through this, we make a number of methodological contributions to HCI and Participatory Action Research (PAR) for research in the YOD setting. This includes productive approaches that are sensitive, respectful and empowering to the participants. It also extends current approaches to using probes in HCI and dementia research.
Best, G & Hollinger, GA 1970, 'Decentralised self-organising maps for the online orienteering problem with neighbourhoods', 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), IEEE, pp. 139-141.
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Beydoun, G 1970, 'Trends and future of computer modeling and simulation: ICCMS 2019', ACM International Conference Proceeding Series, p. VII.
Beydoun, G 1970, 'Trends and future of computer modeling and simulation: ICCMS 2019', ACM International Conference Proceeding Series, p. VII.
Binh, HTT, Toulouse, M, Yu, S, Bui, M, Ha, LM, Hu, Z & Thang, HQ 1970, 'Foreword', ACM International Conference Proceeding Series, pp. VII-VIII.
Binh, HTT, Toulouse, M, Yu, S, Bui, M, Ha, LM, Hu, Z & Thang, HQ 1970, 'Foreword', ACM International Conference Proceeding Series, pp. VII-VIII.
Bliemel, M, Agarwal, R, Bajada, C, Subhadrammal, D, Pugalia, S & Francis, J 1970, 'Entrepreneurial Ecosystems: Dynamics and Metrics', University-Industry Engagement Conference, Sydney, NSW.
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This paper presents our review of the literature and industry reports in relation to attempts to quantify and measure entrepreneurial ecosystems. Public interest and research on entrepreneurial ecosystems (EEs) has exploded in recent years, with many different conceptualisations of EEs. How they are opera-tionalised and quantified remains a challenge. However, having a reliable metric for the state or health of an EE remains of great interest to policy makers and researchers alike. In this study, we review the emerg-ing literature on EEs with a focus on attempts to quantify what they are and how they work. While there is an emerging concesus or synthesis of what EEs are, the literature and reports on their quantification remain scattered. Many quantitative studies are based on the practicality of using data with very limited availability. Others use macro-level or aggregated individual level data to make inferences about what occurs at the level of the firm, their immediate network, or how these interactions play out across the ecosystem across a very diverse set of actors. While startups are the primary outcome and primary stakeholder in EEs, the broader literature recognises that startups do not operate in isolation, and that their emergence depends on the actions and interaction with other stakeholders, such as larger corpora-tions, universities, government and other incumbents. A single-minded obsession about the number of startups and their fundings deprives policy makers and researchers the ability to study the whole system or context in which they exist and create jobs, wealth and innovations.
Bliemel, M, Schweitzer, J, Mery Keitel, A, Green, R, Nicolas, L, Miles, M, Moroko, L, Groeger, L & Griffith, S 1970, 'Herding cats to co-create cross-university courses in record time', University-Industry Engagement Conference, Sydney, NSW.
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The Navigator, a core unit of the new AUD$25 million state-funded Sydney School of Entrepreneurship (SSE), provides an example of how a new course is co-developed and co-delivered by an interdisciplinary group of academics across multiple universities to multiple cohorts of students from all 12 higher-education institutions (HEIs) across the State of New South Wales (NSW). What is unique about this course is the extremely diverse inter-organisational environment hosted by SSE and the speed at which the unit was designed, often adjusted only hours ahead of delivery. While the operational details of SSE still require attention, the cross-institutional collaboration to develop The Navigator is recognised as best-practice in co-development of state- or even nation-wide curriculum.
Bluff, A & Johnston, A 1970, 'Devising Interactive Theatre', Proceedings of the 2019 on Designing Interactive Systems Conference, DIS '19: Designing Interactive Systems Conference 2019, ACM, San Diego, CA, USA, pp. 279-289.
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© 2019 Copyright held by the owner/author(s). This paper presents a case study of a long-term collaboration between a physical performance company and interactive digital artists. The collaboration has resulted in the creation of five major performance works which have toured internationally over several years. We argue that the interactive systems can be considered a 'material' which changes over time, shaping performer actions and being shaped by them in return. Based on detailed interviews with key stakeholders and our own personal reflections, we have identified several 'trajectories' that have evolved over the duration of each individual production and the entire body of work. These trajectories address a number of perspectives including the way performers interact with the system, the relationship between the dramaturgy and the interaction palette and the way the stakeholders conceive of the interactive system. The evolution of the technology itself has also been examined in terms of aesthetic capability, performance robustness, operational cost and complexity across the entire duration of the collaboration.
Bluff, A & Johnston, A 1970, 'Don’t Panic: Recursive Interactions in a Miniature Metaworld', Proceedings of the 17th International Conference on Virtual-Reality Continuum and its Applications in Industry, VRCAI '19: The 17th International Conference on Virtual-Reality Continuum and its Applications in Industry, ACM, Brisbane, Australia, pp. 1-9.
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© 2019 Association for Computing Machinery. Metaworld is a new recursive interaction paradigm for virtual reality, where a miniature display (or 3D map) of the virtual world is presented to the user as a miniature model that itself lives inside the virtual world. The miniature model is interactive and every action which occurs on the miniature world similarly occurs to the greater virtual world and vice-versa. We implemented the metaworld concept in the virtual reality application MetaCity, a city designing sandbox where users can reach into a miniature model and move the cars and skyscrapers. Design considerations of how to display and interact with the miniature model are presented, and a technical implementation of the miniature world is described. The metaworld concept was informally and playfully tested in the MetaCity which revealed a number of novel interactions that enable the user to navigate quickly through large spaces, re-scale objects in the world and manipulate the very fabric of the world itself. These interactions are discussed within the context of four major categories-Experiential Planning, Interdimensional Transformations, Power of the Gods and Self Manipulation.
Bolaji, BO, Adeleke, AE, Adu, MR, Olanipekun, MU & Akinnibosun, E 1970, 'Theoretical Investigation of Energy-Saving Potential of Eco-Friendly R430A, R440A and R450A Refrigerants in a Domestic Refrigerator', Iranian Journal of Science and Technology, Transactions of Mechanical Engineering, International Symposium on Automation and Robotics in Construction, Springer Science and Business Media LLC, Sydney Australia, pp. 103-112.
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The objective of this study is to explore an optimal strategy on energy consumption for a direct expansion (DX) air-conditioning system by using a refrigerant pump in the liquid line to allow the system to operate at a lower condensing pressure. An existing DX rooftop package of a commercial building located in a hot and dry climate zone is used for data collection. The theoretical-empirical modelling approach is used to obtain system model, from which the proposed strategy is formulated. A numerical algorithm is developed to analyse the system transient performance, using an iterative loop. As a minimum pressure differential is required across the expansion device, liquid pressure amplification (LPA) devices can be used on DX systems that operate with fixed head pressure control. They can be fitted to new or existing systems. Results show that the LPA approach is more effective when the ambient temperature is falling, with electricity saving around 25.3% in average.
Bonthu, RK, Aguilera, RP, Pham, H, Phung, MD & Ha, QP 1970, 'Energy Cost Optimization in Microgrids Using Model Predictive Control and Mixed Integer Linear Programming', 2019 IEEE International Conference on Industrial Technology (ICIT), 2019 IEEE International Conference on Industrial Technology (ICIT), IEEE, Melbourne, Australia, pp. 1113-1118.
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© 2019 IEEE. This paper presents a model predictive control (MPC) approach based on the mixed integer linear programming (MILP) to develop an optimal power management strategy (PMS) for minimizing the electricity bill of commercial buildings in a domestic on-grid system. The optimal PMS is first formulated as a MILP-MPC with time-varying constraints. The constraints are then linearized at each sampling time so that a receding horizon principle can be used to determine the control input applied to the plant and update the model. The time-varying efficiency of power electronic converters is evaluated for each time interval and assumed to be persistent for the prediction time horizon. The numerical results show that the proposed MILP-MPC strategy with variable efficiency is effective in utilizing photovoltaic power generation to save the cost on electricity for buildings.
Boroon, L, Abedin, B & Erfani, S 1970, 'Addiction to Social Network Site Use: An Information Technology Identity Perspective', ACIS 2019 Proceedings, Australasian Conference on Information Systems, AIS Electronic Library (AISeL)., Perth, pp. 1-8.
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As the popularity of social network sites (SNSs) has grown substantially over the past years, several negative effects of using SNSs have been experienced by users and reported by Information Systems (IS) researchers. Addiction to SNSs is one of such negative experiences, which has widely been considered from a psychopathology perspective. While increasingly there is more studies in IS on this phenomenon,it is still unclear what characterises addiction to SNSs and what may influence it. This in-progress study adopts an information technology (IT) identity perspective and applies Dual Systems Theory as well as Protection Motivation Theory to provide an initial understanding of what impacts SNS addiction and how to combat it from an IT/SNS identity perspective. To achieve these objectives, we reviewed theliterature and proposed a preliminary framework of addiction to SNSs use. We then offer discuss research implications and propose ideas for future studies.
Bożejko, W, Chaczko, Z, Nadybski, P & Wodecki, M 1970, 'Meta-heuristic Task Scheduling Algorithm for Computing Cluster with 2D Packing Problem Approach', Advances in Intelligent Systems and Computing, International Conference on Dependability and Complex Systems, Springer International Publishing, Brunów, Poland, pp. 74-82.
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© Springer International Publishing AG, part of Springer Nature 2019. In this paper we present a mathematical model and an algorithm for solving a task scheduling problem in computing cluster. The problem is considered as a 2D packing problem. Each multi-node task is treated as a set of separate subtasks with common constrains. For optimization the tabu search metaheuristic algorithm is applied.
Braun, R, Bone, D, Brookes, W, Trede, F & Hadgraft, R 1970, 'Studios in DE and EE at UTS: Structure and Rationale.', ITHET, International Conference on Information Technology Based Higher Education and Training, IEEE, Magdeburg, Germany, pp. 1-6.
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© 2019 IEEE. We describe the Studios we have introduced into our Data and Electronic Engineering programs. We explain the purpose of the Studios, and the structure of activities. We describe the rationale for the significant components. We comment on the success of the components, and lessons learned.
Braun, R, Brookes, W, Hadgraft, R & Chaczko, Z 1970, 'Assessment Design for Studio-Based Learning', Proceedings of the Twenty-First Australasian Computing Education Conference, ACE'19: Twenty-First Australasian Computing Education Conference, ACM, Sydney, Australia, pp. 106-111.
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© 2019 Association for Computing Machinery. Studio-based learning is not new to computing education, however as the ecosystem of available Open Educational Resources (OERs) expands, the capacity and desire for student self-directed learning is growing. However increasing student autonomy in how and when learning takes place creates challenges around assessment. This paper introduces the design of assessment tasks to support studiobased learning at undergraduate level. It describes an example of using learning contracts and portfolio-based assessment for evaluating individual and team performance. The paper presents some initial observations of the approach taken, and its transferability to other areas of the curriculum.
Bravyi, S, Gosset, D, Koenig, R & Tomamichel, M 1970, 'Quantum advantage with noisy shallow circuits in 3D', Nature Physics (2020), Annual Symposium on Foundations of Computer Science, IEEE Computer Society Press, Baltimore, MD, USA, USA, pp. 995-999.
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Prior work has shown that there exists a relation problem which can be solvedwith certainty by a constant-depth quantum circuit composed of geometricallylocal gates in two dimensions, but cannot be solved with high probability byany classical constant depth circuit composed of bounded fan-in gates. Here weprovide two extensions of this result. Firstly, we show that a separation incomputational power persists even when the constant-depth quantum circuit isrestricted to geometrically local gates in one dimension. The correspondingquantum algorithm is the simplest we know of which achieves a quantum advantageof this type. It may also be more practical for future implementations. Oursecond, main result, is that a separation persists even if the shallow quantumcircuit is corrupted by noise. We construct a relation problem which can besolved with near certainty using a noisy constant-depth quantum circuitcomposed of geometrically local gates in three dimensions, provided the noiserate is below a certain constant threshold value. On the other hand, theproblem cannot be solved with high probability by a noise-free classicalcircuit of constant depth. A key component of the proof is a quantumerror-correcting code which admits constant-depth logical Clifford gates andsingle-shot logical state preparation. We show that the surface code meetsthese criteria. To this end, we provide a protocol for single-shot logicalstate preparation in the surface code which may be of independent interest.
Bravyi, S, Gosset, D, Koenig, R & Tomamichel, M 1970, 'Quantum Advantage with Noisy Shallow Circuits in 3D', 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS), 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS), IEEE, Baltimore, USA, pp. 995-999.
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Broomhead, T, Cremean, L, Ridoux, J & Veitch, D 1970, 'Virtualize everything but time', Proceedings of the 9th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2010, pp. 451-464.
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We propose a new timekeeping architecture for virtualized systems, in the context of Xen. Built upon a feed-forward based RADclock synchronization algorithm, it ensures that the clocks in each OS sharing the hardware derive from a single central clock in a resource effective way, and that this clock is both accurate and robust. A key advantage is simple, seamless VM migration with consistent time. In contrast, the current Xen approach for timekeeping behaves very poorly under live migration, posing a major problem for applications such as financial transactions, gaming, and network measurement, which are critically dependent on reliable timekeeping. We also provide a detailed examination of the HPET and Xen Clocksource counters. Results are validated using a hardware-supported testbed.
Brunker, A, Catchpoole, D, Kennedy, P, Simoff, S & Nguyen, QV 1970, 'Two-Dimensional Immersive Cohort Analysis Supporting Personalised Medical Treatment', 2019 23rd International Conference in Information Visualization – Part II, 2019 23rd International Conference in Information Visualization – Part II, IEEE, Adelaide, Australia, pp. 34-41.
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© 2019 IEEE. Genomic data are large and complex which are challenges to visualize them effectively on ordinary screens due to the limited display spaces. Large and high resolution displays could enable the capability to show more information at once for better comprehension from the visualization. This paper presents a two-dimensional interactive visualization system and supporting algorithm for multi-dimensional large genomic data analysis that can be used in both ordinary displays or immersive environments. We provide both view of the entire patient cohort in the similarity space and the genomic details currently for comparison among the patients. Through the similarity space and on the selected genes of interest, we are able to perceive the genetic similarity throughout the cohort. From the linked heat map visualisation of the selected genes, we apply hierarchical clustering on both the horizontal and vertical axes to group together the genetically similar patients. We demonstrate the effectiveness of the visualization with two case studies on pediatric cancer patients suffering from Acute Lymphoblastic Leukemia (ALL) and from Rhabdomyosarcoma (RMS)
Burridge, J, Lowe, D, Willey, K & kay, J 1970, 'Defeating Hawthorne in tech-enabled education: Passive observation of student behaviour with a remote laboratory', Australasian Association for Engineering Education Conference, Brisbane, Australia.
Butcher, R & Sirivivatnanon, V 1970, 'Influence of shape and grading of manufactured sand on the workability and compressive strength of concrete', FIB 2018 - Proceedings for the 2018 fib Congress: Better, Smarter, Stronger, pp. 3050-3060.
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In this study, two methods of producing manufactured sands from the same rock source were evaluated in terms of the resulting shape and grading of the sands, and their effects on the workability and compressive strength of cement mortar and concrete. The two sands were used to blend with a natural sand to produce cement mortars at fixed sand to cement (S/C) and water to cement ratio (W/C). The shape and grading of the two sands were found to affect the New Zealand flow cone time and air void (RMS T279) and consequently the flow and compressive strength of the mortars. The sand blends were also used to produce a standard grade concrete with equal slump. The efficiency of the two sands in concrete production was measured in term of water demand of the concrete. The economic viability of each sand production method is reflected in comparing the quantity of cement and fly ash required to produce each cubic meter of a standard grade concrete.
Cai, Z, Tang, X, Li, Z, Zhang, T, Liu, Y & Yang, Y 1970, 'A Low Phase Noise Differential Oscillator Employing Stub-Loaded Nested Split-Ring Resonator Inspired Balanced Bandpass Filter', 2019 IEEE MTT-S International Microwave Symposium (IMS), 2019 IEEE/MTT-S International Microwave Symposium - IMS 2019, IEEE, Boston, MA, USA, pp. 967-970.
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© 2019 IEEE. This paper presents a low phase noise differential oscillator by employing a balanced feedback bandpass filter (BPF) designed with the proposed stub-loaded nested split-ring resonator (SLNSRR). The proposed balanced BPF functions as a frequency stabilization element in its feedback loop. Taking advantage of the balanced structure, a high group delay is obtained by introducing a transition zero near the upper passband of feedback loop filter. The proposed differential oscillator can present a differential output with low phase noise performance. For proof of the concept, a 2 GHz differential oscillator has been designed, fabricated and measured. The measured results show that the 180o out-of-phase differential signals are obtained with almost the same peak-peak voltage among two channels. The output power is 9.18 dBm when oscillates at 2.004 GHz with the second harmonic suppression of 40.63 dB. The measured phase noise is -126.72 dBc/Hz at 100 kHz frequency offset. The figure-of-merit (FOM) at 100 kHz is -196.19 dBc/Hz. The phase noise performance of proposed differential oscillator is one of the best among open literatures.
Calam, RCM, Hora, JA, Gerasta, OJL, Zhu, X & Dutkiewicz, E 1970, 'A Self-Calibrating Off-Time Controller for WSN/IoT Synchronous Non-Inverting Buck-Boost DC-to-DC Converter Application', 2019 IEEE International Circuits and Systems Symposium (ICSyS), 2019 IEEE International Circuits and Systems Symposium (ICSyS), IEEE, pp. 1-4.
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© 2019 IEEE. Inappropriate turning-off of synchronous switches in DC-to-DC converters operating in discontinuous conduction mode (DCM) degrades the overall efficiency of the converter due to the power losses in either body-diode conduction or reverse inductor current. This paper presents a self-calibrating off-time controller using a digitally-controlled delay element (DCDE) for synchronous DC-to-DC converter. An up-down counter that is controlled by the polarity of the inductor current is utilized to drive the binary-weighted DCDE. The programmable DCDE is used to generate a self-calibrating off-time pulse to turn-off the synchronous switches very close to the zero-crossing of the inductor current. The design and simulation of the proposed method is implemented using 65nm CMOS Technology process in Synopsys Custom Designer tool. When implemented, the measured accuracy of the detection is in order of less than ±10mA. The average power consumption of the proposed system is less than 10 uW and is expected to be much lower at lighter loads.
Cancino, CA, Amirbagheri, K, Merigó, JM & Dessouky, Y 1970, 'Evolution of the academic research on supply chain and global warming', Proceedings of International Conference on Computers and Industrial Engineering, CIE.
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The aim of this work is to study supply chain publications with a focus on global warming effects using a bibliometric approach. The study uses the Web of Science Core Collection database to analyze the bibliometric data from 1994 to 2018. The main objective is to identify the leading trends in this area by analyzing the most significant journals, papers, institutions and supra-regions. This work also develops a graphical mapping of the bibliographic material by using visualization of similarities (VOS) viewer software. With this software, the study analyses co-citations of journals and co-occurrence of author keywords. The results show the growth of the development of supply chain models that consider global warming factors between 2014-2018, which is consistent with the general public awareness of climate change. The researchers from Imperial College London and Hong Kong Polytechnic University have the greatest number of publications in this area. In terms of supra-regions, more than 25% of the publications come from Asian universities, followed by American and British universities with 20%. Given the growing global concern about the effects of supply chains on global warming, it is expected that the number of publications from different parts of the world and the greater number of citations will strongly increase.
Cao, M & Zhang, Q 1970, 'A Empirical Study of Programming Behaviours on Large Scale Online Learning', 2019 14th International Conference on Computer Science & Education (ICCSE), 2019 14th International Conference on Computer Science & Education (ICCSE), IEEE, CANADA, Ontario Tech Univ, Toronto, pp. 889-894.
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Cao, Y & Veitch, D 1970, 'Where on Earth Are the Best-50 Time Servers?', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Passive and Active Network Measurement, Springer International Publishing, Chile, pp. 101-115.
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© 2019, Springer Nature Switzerland AG. We present a list of the Best-50 public IPv4 time servers by mining a high-resolution dataset of Stratum-1 servers for Availability, Stratum Constancy, Leap Performance, and Clock Error, broken down by continent. We find that a server with ideal leap performance, high availability, and low stratum variation is often clock error-free, but this is no guarantee. We discuss the relevance and lifetime of our findings, the scalability of our approach, and implications for load balancing and server ranking.
Cao, Z, Chang, Y-C, Prasad, M, Tanveer, M & Lin, C-T 1970, 'Tensor Decomposition for EEG Signals Retrieval', 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), IEEE, Bari, Italy, pp. 2423-2427.
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© 2019 IEEE. Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial scenarios, yet few approaches aiming to a temporal reconstruction with lower loss. The goal of this study is to retrieve the temporal EEG signals independently which was overlooked in data pre-processing. We considered EEG signals are impinging on tensor-based approach, named nonlinear Canonical Polyadic Decomposition (CPD). In this study, we collected EEG signals during a resting-state task. Then, we defined that the source signals are original EEG signals and the generated tensor is perturbed by Gaussian noise with a signal-to-noise ratio of 0 dB. The sources are separated using a basic nonnegative CPD and the relative errors on the estimates of the factor matrices. Comparing the similarities between the source signals and their recovered versions, the results showed significantly high correlation over 95%. Our findings reveal the possibility of recoverable temporal signals in EEG applications.
Carmichael, MG, Aldini, S, Khonasty, R, Tran, A, Reeks, C, Liu, D, Waldron, KJ & Dissanayake, G 1970, 'The ANBOT: An Intelligent Robotic Co-worker for Industrial Abrasive Blasting', 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Macau, China, pp. 8026-8033.
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© 2019 IEEE. We present the ANBOT, an intelligent robotic coworker for physical human-robot collaboration. The ANBOT system assists workers performing industrial abrasive blasting, shielding them from the large forces experienced during this physically demanding task. The co-operative robotic system combines the strength and endurance of robots with the decision making of skilled workers. The inherent challenges in human-robot collaboration, combined with the difficult blasting environment required novel design decisions to be made and new solutions to be developed. These include an approach for handling kinematic singularities in a manner suitable for human-robot co-operation, estimating worker pose under poor visibility conditions, and an intuitive control scheme that adapts the robotic assistance based on the estimated strength of the worker. In this work we summarise the ANBOT system and present findings from preliminary site trials. The trials included several real industrial blasting tasks under the control of a skilled abrasive blasting worker who had no experience working alongside a robot. Results demonstrate the suitability of the ANBOT for practical industrial applications.
Cetindamar, D, Kocaoglu, D, Lammers, T & Merigo, JM 1970, 'A Bibliometric Analysis of Technology Management Research at PICMET for 2009–2018', 2019 Portland International Conference on Management of Engineering and Technology (PICMET), 2019 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE, Portland, Oregon, pp. 1-7.
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© 2019 PICMET. The Portland International Centre for Management of Engineering and Technology (PICMET) was established in 1989. It has since become one of the leading organizations in the field of management of engineering and technology in the world. PICMET provides a strong platform for academicians, industry professionals and government representatives to exchange new knowledge derived from both research and implementation of technology management. To celebrate its 30-year journey, and to show the trends in technology management research and implementation over the past ten years (2009-2018), this paper presents a bibliometric analysis of the more than 3000 papers accepted for inclusion in PICMET conferences. The study highlights the topics, authors, journals and countries where significant research on technology management is conducted.
Chaczko, Z, Klempous, R, Rozenblit, J, Chiu, C, Kluwak, K & Smutnicki, C 1970, 'Enabling Design of Middleware for Massive Scale IOT-based Systems', 2019 IEEE 23rd International Conference on Intelligent Engineering Systems (INES), 2019 IEEE 23rd International Conference on Intelligent Engineering Systems (INES), IEEE, Gödöllő, Hungary, pp. 000219-000223.
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Recently, the Internet of Things (IoT) technology has rapidly advanced to the stage where it is feasible to discover, locate and identify various smart sensors and devices based on the context, situation, characteristics, and relevancy to query for their data or control actions. Taking things a step further when developing Large Scale Applications requires that two serious issues be overcome. The first issue is to find a solution for data sensing and collection from a massive number of various ubiquitous devices when converging these into the next generation networks. The second important issue is to deal with the “Big Data” that arrive from a very large number of sources. This research emphasizes the need for finding a solution for a large scale data aggregation and delivery. The paper introduces biomimetic design methods for data aggregation in the context of large scale IoT-based systems.
Chaczko, Z, Wajs-Chaczko, P, Tien, D & Haidar, Y 1970, 'Contents', 2019 International Conference on Machine Learning and Cybernetics (ICMLC), 2019 International Conference on Machine Learning and Cybernetics (ICMLC), IEEE, Kobe, Japan, pp. 1-8.
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Monitoring the presence of micro-plastics in human and animal habitats is fast becoming an important research theme due to a need to preserve healthy ecosystems. Microplastics pollute the environment and can represent a serious threat for biological organisms including the human body, as they can be inadvertently consumed through the food chain. To perceive and understand the level of microplastics pollution threats in the environment there is a need to design and develop reliable methodologies and tools that can detect and classify the different types of microplastics. This paper presents results of our work related to the exploration of methods and techniques useful for detecting suspicious objects in their respective ecosystems captured in hyperspectral images and then classifying these objects with the use of Neural Networks technique.
Chai, R, Tran, Y, Ling, SH, Craig, A & Nguyen, HT 1970, 'Combining ICA Clustering and Power Spectral Density for Feature Extraction of Mental Fatigue of Spinal Cord Injury Patients', 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Berlin, Germany, pp. 530-533.
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This paper presents the combination of clustering-based independent component analysis (ICASSO) and power spectral density (PSD) as a features extractor of mental fatigue from spinal cord injury (SCI) patients. Initially, the results show that SCI and abled-bodied groups have no differences in EEG for alert and mental fatigue states. Further, the coefficient determination (R2) is calculated for testing the variation of data alert vs. fatigue on the SCI group, resulting in a lower R2 for proposed combination of ICASSO and PSD method compared to the PSD method only. With the lower R2 values, this shows that the proposed method ICASSO and PSD is able to provide superior distinction for separating fatigue vs. alert data variation. The statistical significance is found across four EEG bands and EEG channels.
Chandran, D & Aleidi, A 1970, 'Exploring Antecedents of Female IT Entrepreneurial Intentions in the Saudi Context', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences.
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Chang, L & Qin, L 1970, 'Cohesive Subgraph Computation Over Large Sparse Graphs', 2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019 IEEE 35th International Conference on Data Engineering (ICDE), IEEE, Macao, pp. 2068-2071.
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With the rapid development of information technology, huge volumes of graph data are accumulated. Real graphs are usually sparsely connected from a global point of view, but typically contain subgraphs that are locally densely connected. It is of great importance to identify dense (i.e., cohesive) subgraphs in a large sparse graph. Cohesive subgraph computation can either be the main goal of a graph analysis task, or act as a preprocessing step aiming to reduce/trim the graph by removing sparse/unimportant parts such that more complex and time-consuming analysis can be conducted. In the literature, the cohesiveness of a subgraph is usually measured by the minimum degree, the average degree, or their higher-order variants. Cohesive subgraph computation based on different cohesiveness measures extracts subgraphs with different properties, and also requires different levels of computational efforts. In this tutorial, we survey the models and state-of-the-art algorithms for efficient cohesive subgraph computation based on different cohesiveness measures. We discuss details of the algorithms, including time complexity and implementation matters. Finally, we present open problems for future research.
Chang, Y, Li, Z, Luo, L, Luo, S, Sowmya, A, Wang, Y & Chen, F 1970, 'Exploring Latent Structure Similarity for Bayesian Nonparameteric Model with Mixture of NHPP Sequence', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Neural Information Processing, Springer International Publishing, Sydney, NSW, Australia, pp. 432-444.
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© 2019, Springer Nature Switzerland AG. Temporal point process data has been widely observed in many applications including finance, health, and infrastructures, so that it has become an important topic in data analytics domain. Generally, a point process only records occurrence of a type of event as 1 or 0. To interpret the temporal point process, it is important to estimate the intensity of the occurrence of events, which is challenging especially when the intensity is dynamic over time, for example non-homogeneous Poisson process (NHPP) which is exactly what we will analyse in this paper. We performed a joint task to determine which two NHPP sequences are in the same group and to estimate the intensity resides in that group. Distance dependent Chinese Restaurant Process (ddCRP) provides a prior to cluster data points within a Bayesian nonparametric framework, alleviating the required knowledge to set the number of clusters which is sensitive in clustering problems. However, the distance in previous studies of ddCRP is designed for data points, in this paper such distance is measured by dynamic time warping (DTW) due to its wide application in ordinary time series (e.g. observed values are in $$\mathcal {R}$$). The empirical study using synthetic and real-world datasets shows promising outcome compared with the alternative techniques.
Chang, Y, Li, Z, Zhang, B, Luo, L, Sowmya, A, Wang, Y & Chen, F 1970, 'Recovering DTW Distance Between Noise Superposed NHPP', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Macau, China, pp. 229-241.
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© Springer Nature Switzerland AG 2019. Unmarked event data is increasingly popular in temporal modeling, containing only the timestamp of each event occurrence without specifying the class or description of the events. A sequence of event is usually modeled as the realization from a latent intensity series. When the intensity varies, the events follow the Non-Homogeneous Poisson Process (NHPP). To analyze a sequence of such kind of events, an important task is to measure the similarity between two sequences based on their intensities. To avoid the difficulties of estimating the latent intensities, we measure the similarity using timestamps by Dynamic Time Warping (DTW), which can also resolve the issue that observations between two sequences are not aligned in time. Furthermore, real event data always has superposed noise, e.g. when comparing the purchase behaviour of two customers, we can be mislead if one customer visits market more often because of some occasional shopping events. We shall recover the DTW distance between two noise-superposed NHPP sequences to evaluate the similarity between them. We proposed two strategies, which are removing noise events on all possibilities before calculating the DTW distance, and integrating the noise removal into the DTW calculation in dynamic programming. We compare empirical performance of all the methods and quantitatively show that the proposed methods can recover the DTW distance effectively and efficiently.
Chang, Y-C, Dostovalova, A, Lin, C-T & Kim, J 1970, 'Intelligent Multi-agent Coordination and Learning', 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), IEEE, Bari, Italy, pp. 1431-1436.
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We present a hierarchical neural-fuzzy system for precision coordination of multiple mobile agents for simultaneous arrival at their destination positions in a cluttered urban environment. We assume that each agent is equipped with a 2D scanning LiDAR to make movement decisions based on local distance and bearing information. Two solution approaches are considered and compared. Both of them are structured around a hierarchical arrangement of controller modules to enable synchronisation of the agents arrival times while avoiding collision with obstacles. The first approach is based on cascading SONFIN (Self-Organizing Neural Fuzzy Inference Network) controllers, and the second approach considers the use of an LSTM (Long ShortTerm Memory) recurrent neural network module alongside SONFIN modules. Parameters of all the controllers are optimised using the Particle Swarm optimization algorithm. A physics-based simulator, Webots, is used as a training and testing environment for the two learning models to facilitate the deployment of codes to hardware which will follow in the next phase of our research.
Chen, F, Pan, S, Jiang, J, Huo, H & Long, G 1970, 'DAGCN: Dual Attention Graph Convolutional Networks', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-8.
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Graph convolutional networks (GCNs) have recently become one of the most powerful tools for graph analytics tasks in numerous applications, ranging from social networks and natural language processing to bioinformatics and chemoinformatics, thanks to their ability to capture the complex relationships between concepts. At present, the vast majority of GCNs use a neighborhood aggregation framework to learn a continuous and compact vector, then performing a pooling operation to generalize graph embedding for the classification task. These approaches have two disadvantages in the graph classification task: (1)when only the largest sub-graph structure (k-hop neighbor) is used for neighborhood aggregation, a large amount of early-stage information is lost during the graph convolution step; (2) simple average/sum pooling or max pooling utilized, which loses the characteristics of each node and the topology between nodes. In this paper, we propose a novel framework called, dual attention graph convolutional networks (DAGCN) to address these problems. DAGCN automatically learns the importance of neighbors at different hops using a novel attention graph convolution layer, and then employs a second attention component, a self-attention pooling layer, to generalize the graph representation from the various aspects of a matrix graph embedding. The dual attention network is trained in an end-to-end manner for the graph classification task. We compare our model with state-of-the-art graph kernels and other deep learning methods. The experimental results show that our framework not only outperforms other baselines but also achieves a better rate of convergence.
Chen, K, Yao, L, Zhang, D, Chang, X, Long, G & Wang, S 1970, 'Distributionally Robust Semi-Supervised Learning for People-Centric Sensing', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, Hawaii USA, pp. 3321-3328.
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Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, humangenerated data inherently suffer from distribution shift in semi-supervised learning due to the diverse biological conditions and behavior patterns of humans. To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data. Considering both the discrepancy and the consistency between the labeled data and the unlabeled data, we learn the latent features that reduce person-specific discrepancy and preserve task-specific consistency. We evaluate our model in a variety of people-centric recognition tasks on real-world datasets, including intention recognition, activity recognition, muscular movement recognition and gesture recognition. The experiment results demonstrate that the proposed model outperforms the state-of-the-art methods.
Chen, L, Liu, Y & Guo, YJ 1970, 'Efficient Frequency-invariant Beam Pattern Synthesis With Multiple Space-frequency Nulls', 2019 IEEE International Conference on Computational Electromagnetics (ICCEM), 2019 IEEE International Conference on Computational Electromagnetics (ICCEM), IEEE, Shanghai, China.
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© 2019 IEEE. This paper presents a modified Fourier transform method to synthesize a broadband array with frequency-invariant (FI) mainlobe and multiple space-frequency nulls. In this method, a iterative Fourier transform is used to design multiple optimized reference patterns which have the same mainlobe shape but with different sidelobe or null distributions. These reference patterns are used for describing different radiation requirements in different sub-bands of the whole frequency band. Then, a desired broadband spatial spectral distribution can be obtained by matching it to each reference pattern at each sub-band, and a broadband excitation distribution for generating the desired FI pattern can be efficiently found by performing an inverse fast Fourier transform (IFFT) on the broadband spatial spectral distribution. An example for synthesizing a FI beam pattern with two space-frequency nulls is provided to verify the effectiveness and efficiency of the proposed method.
Chen, L, Wang, Y, Liu, Y & Guo, YJ 1970, 'Synthesis of Frequency-invariant Beam Patterns under Accurate Sidelobe Control by Second-order Cone Programming', 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall), 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall), IEEE, Xiamen, China, pp. 2260-2263.
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It is shown in this work that the FI pattern synthesis can be treated as an optimization problem for minimizing the mainlobe frequency variation. To control both the mainlobe and sidelobe regions, we introduce several constraints imposed on the broadband pattern, called the look-direction constraint, the spatial response variation constraint and the sidelobe constraint, respectively. The whole optimization process needs to perform the SOCP solver. A synthesis of FI pattern with low sidelobe level (SLL) is given to validate the accuracy and effectiveness of the proposed method.
Chen, S-L, Karmokar, DK, Ziolkowski, RW & Guo, YJ 1970, 'Wide-Angle Wideband Frequency-Independent Beam-Scanning Leaky Wave Antenna', 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE, Granada, Spain, pp. 0554-0557.
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© 2019 IEEE. Frequency-independent beam scanning leaky-wave antennas (LWAs) that can operate over a specific frequency band are highly desirable for future wireless systems. A composite right/left-handed (CRLH) LWA is developed in this paper that facilitates these functionalities. It utilizes two groups of varactor diodes to realize the frequency-independent beam scanning capability. The optimized reconfigurable CRLH LWA and its simple DC biasing network achieves a simulated frequency-independent beam that scans over 100° at each frequency point between 4.75 and 5.25 GHz. An antenna prototype was fabricated and tested. The measured results at 5.0 GHz confirm its simulated performance characteristics.
Chen, S-L, Karmokar, DK, Ziolkowski, RW & Guo, YJ 1970, 'Wide-Angle Wideband Frequency-Independent Beam-Scanning Leaky Wave Antenna', PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON ELECTROMAGNETICS IN ADVANCED APPLICATIONS (ICEAA), 21st International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE, SPAIN, Granada, pp. 554-557.
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Chen, X, Cao, X, Xu, Z, Zhang, Y, Shang, S & Zhang, W 1970, 'Accelerate MaxBRkNN Search by kNN Estimation', 2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019 IEEE 35th International Conference on Data Engineering (ICDE), IEEE, Macao, Macao, pp. 1730-1733.
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© 2019 IEEE. Given a set of server points (e.g., locations) P and a set of client points (e.g., users) O, the problem of maximizing bichromatic reverse k-nearest neighbor (MaxBRkNN) aims to find a region for setting up a new service site such that it can influence the most clients, i.e., it is in the kNN results of most client points. All existing studies first compute the kNN of client points and then perform the MaxBRkNN search. However, computing kNN for all clients is extremely time consuming especially on large datasets. Observing this, we develop an approach which computes kNN for only promising clients by utilising a two-level grid index (ADPGI) to reduce the cost substantially. Empirical studies on both real and synthetic datasets show that our proposed exact algorithm is 3 to 5 times faster than two state-of-the-art MaxBRkNN algorithms.
Chen, X, Huang, C, Zhang, X, Wang, X, Liu, W & Yao, L 1970, 'Expert2Vec: Distributed Expert Representation Learning in Question Answering Community', Advanced Data Mining and Applications (LNAI), International Conference on Advanced Data Mining and Applications, Springer International Publishing, Dalian, China, pp. 288-301.
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© 2019, Springer Nature Switzerland AG. Community question answering (CQA) has attracted increasing attention recently due to its potential as a de facto knowledge base. Expert finding in CQA websites also has considerably board applications. Stack Overflow is one of the most popular question answering platforms, which is often utilized by recent studies on the recommendation of the domain expert. Despite the substantial progress seen recently, it still lacks relevant research on the direct representation of expert users. Hence hereby we propose Expert2Vec, a distributed Expert Representation learning in question answering community to boost the recommendation of the domain expert. Word2Vec is used to preprocess the Stack Overflow dataset, which helps to generate representations of domain topics. Weight rankings are then extracted based on domains and variational autoencoder (VAE) is unitized to generate representations of user-topic information. This finally adopts the reinforcement learning framework with the user-topic matrix to improve it internally. Experiments show the adequate performance of our proposed approaches in the recommendation system.
Chen, Y, An, P, Huang, X, Meng, C & Wu, Q 1970, 'Modified Baseline for Light Field Stitching', 2019 IEEE Visual Communications and Image Processing (VCIP), 2019 IEEE Visual Communications and Image Processing (VCIP), IEEE, Australia, pp. 1-4.
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© 2019 IEEE. In traditional 2D image stitching, the baseline method usually means global homography via Direct Linear Transformation (DLT) on inliers. In this paper, a modified baseline method for light field (LF) stitching is proposed to stitch two LFs. The depth map and the center sub-Aperture image (SAI) are used to filter the feature points of the entire LF. The global 4D homography is then calculated by DLT to align all SAIs corresponding to the same angular domain coordinates of two LFs. Finally, the improved Markov Random Field (MRF) energy considering the global LF is used to find the seam of 2D SAIs instead of computational 4D graph cut. Experimental results show that the proposed method can effectively stitch the 4D LFs, and preserve the consistency of the angular and spatial domains of the stitched LF compared with implementing 2D image stitching to the corresponding SAIs. Moreover, the method proposed in this paper can easily extend all advanced 2D image stitching methods to 4D LF, so that the acquired LF can have larger field of view and wider applications.
Chen, Y, Huang, S, Fitch, R, Zhao, L, Yu, H & Yang, D 1970, 'On-line 3D active pose-graph SLAM based on key poses using graph topology and sub-maps', 2019 International Conference on Robotics and Automation (ICRA), 2019 International Conference on Robotics and Automation (ICRA), IEEE, Montreal, CANADA, pp. 169-175.
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© 2019 IEEE. In this paper, we present an on-line active pose-graph simultaneous localization and mapping (SLAM) frame-work for robots in three-dimensional (3D) environments using graph topology and sub-maps. This framework aims to find the best trajectory for loop-closure by re-visiting old poses based on the T-optimality and D-optimality metrics of the Fisher information matrix (FIM) in pose-graph SLAM. In order to reduce computational complexity, graph topologies are introduced, including weighted node degree (T-optimality metric) and weighted tree-connectivity (D-optimality metric), to choose a candidate trajectory and several key poses. With the help of the key poses, a sampling-based path planning method and a continuous-time trajectory optimization method are combined hierarchically and applied in the whole framework. So as to further improve the real-time capability of the method, the sub-map joining method is used in the estimation and planning process for large-scale active SLAM problems. In simulations and experiments, we validate our approach by comparing against existing methods, and we demonstrate the on-line planning part using a quad-rotor unmanned aerial vehicle (UAV).
Chen, Z & Zhu, X 1970, 'Integration of mm-wave Antennas on Fan-Out Wafer Level Packaging (FOWLP) for Automotive Radar Applications', 2019 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA), 2019 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA), IEEE, Chengdu, China, pp. 168-169.
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The integration of mm-wave antennas on fan-out wafer level packaging (FOWLP) for automotive radar applications is presented in this paper. The size of the package is 12 x 15 x 0.45 mm 3 . The package includes seven antenna arrays realized with three redistribution layers (RDL). Three patch antenna arrays are used as transmitting antennas and four patch antenna arrays are used as receiving antennas. The simulation results show that the proposed antenna achieves a 10-dB input impedance bandwidth of 5.5 GHz and a maximal peak realized gain of 9.9 dBi at 77.6 GHz. The angle resolution of the MIMO radar can be improved to 9.6°.
Chen, Z, Zhu, X & Xu, L 1970, 'Integration of mm-wave Antennas on Fan-Out Wafer Level Packaging (FOWLP) for Automotive Radar Applications', 2019 IEEE Asia-Pacific Microwave Conference (APMC), 2019 IEEE Asia-Pacific Microwave Conference (APMC), IEEE, Singapore, pp. 1607-1609.
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© 2019 IEEE. This paper presents the integration of mm-wave antennas on fan-out wafer level packaging (FOWLP) for automotive radar applications. The size of the package is 24x24.5x0.3 mm3, Three microstrip grid array antennas are used as transmitting antennas and four patch sub arrays are used as receiving antennas. The antennas are fed by chip signal which is coupled through a slot in the ground plane. The simulation results show maximal peak realized gain of 14.28 dBi and 11.1 dBi for transmitting and receiving antennas, respectively. The angle resolution of the MIMO radar can be improved to 9.6°.
Cheng, T, Lu, DD-C & Siwakoti, Y 1970, 'Electro-Thermal Modeling of a Boost Converter Considering Device Self-heating', 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), IEEE, Singapore, pp. 1-6.
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Thermal management is one of the most critical aspects of the design of high power density converters. Recently, significant efforts and progress have been made in developing electro-thermal models for power semiconductor devices as they are very sensitive to temperature changes. Passive components such as inductors and capacitors are also investigated, since they are temperature-dependent. However, most published work focuses on electro-thermal model of a single power device or a module only instead of a whole converter, which is more realistic in terms of converter design. Hence, in this work, a datasheet informed electro-thermal model is proposed for a boost converter. It is achieved by adding additional behavior models to the existing electrical model of each power device to reflect the temperature incurred electrical behavioral change. Loss model and RC network are used to estimate the temperature change. This forms a power loss and temperature feedback loop. The advantages of this work are ease of integration with existing electrical models in the SPICE library, and elimination of the complicated physical properties of the power devices, but fully utilizes the device datasheet information and mathematical method.
Cheng, X, Wang, H, Hua, J, Zhang, M, Xu, G, Yi, L & Sui, Y 1970, 'Static Detection of Control-Flow-Related Vulnerabilities Using Graph Embedding', 2019 24th International Conference on Engineering of Complex Computer Systems (ICECCS), 2019 24th International Conference on Engineering of Complex Computer Systems (ICECCS), IEEE, Guangzhou, China, pp. 41-50.
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© 2019 IEEE. Static vulnerability detection has shown its effectiveness in detecting well-defined low-level memory errors. However, high-level control-flow related (CFR) vulnerabilities, such as insufficient control flow management (CWE-691), business logic errors (CWE-840), and program behavioral problems (CWE-438), which are often caused by a wide variety of bad programming practices, posing a great challenge for existing general static analysis solutions. This paper presents a new deep-learning-based graph embedding approach to accurate detection of CFR vulnerabilities. Our approach makes a new attempt by applying a recent graph convolutional network to embed code fragments in a compact and low-dimensional representation that preserves high-level control-flow information of a vulnerable program. We have conducted our experiments using 8,368 real-world vulnerable programs by comparing our approach with several traditional static vulnerability detectors and state-of-the-art machine-learning-based approaches. The experimental results show the effectiveness of our approach in terms of both accuracy and recall. Our research has shed light on the promising direction of combining program analysis with deep learning techniques to address the general static analysis challenges.
Cheng, Y, Yang, L, Yu, S & Ma, J 1970, 'Achieving Efficient and Verifiable Assured Deletion for Outsourced Data Based on Access Right Revocation', Cryptology and Network Security, International Conference on Cryptology and Network Security, Springer International Publishing, Fuzhou, China, pp. 392-411.
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© 2019, Springer Nature Switzerland AG. With the growing use of cloud storage facilities, outsourced data security becomes a major concern. However, assured deletion for outsourced data, as an important issue for users, but received less attention in academia and industry. Most of traditional deletion solutions require specific data organization forms or storage media, and are not applicable for outsourced data. Moreover, existing access control schemes for cloud which used ciphertext-policy attribute-based encryption (CPABE), focused on fine-grained access control, and completely ignored data deletion. In this paper, we aim to design an effective data deletion scheme that can be applied to any CPABE built on linear secret sharing-scheme. However, the challenge is how to maintain the traits of traditional CPABE while implementing a universal deletion method. To address this challenge, we propose a policy graph to describe relationships among users, policies, attributes, and files and introduce a new deletion concept for CPABE: when all users are unauthorized for a file, we say that the file is deleted. Then, we extend an efficient and verifiable deletion scheme on a CPABE. Specifically, we give an effective method to select key attributes and update the relevant parts of ciphertext so that all users become unauthorized. Furthermore, we verify the cipher update performed by third-party server through merkle trees. We also demonstrate its universality and prove the security under q-BDHE assumption. Finally, the performance evaluation and simulation results reveal that our solution achieves better performance compared with other schemes.
Chevinly, JS, Siwakoti, YP, Forouzesh, M & Blaabjerg, F 1970, 'A Novel Single-Phase Flying-Inductor Buck-Boost Inverter', 2019 10th International Conference on Power Electronics and ECCE Asia (ICPE 2019 - ECCE Asia), 2019 10th International Conference on Power Electronics and ECCE Asia (ICPE 2019 - ECCE Asia), IEEE, Busan, Korea (South), pp. 1065-1070.
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In this paper a new single-phase flying-inductor buck-boost inverter is proposed. The inverter operates on the principle of flying-inductor, and it has the capability to buck or boost the input voltage without increasing the number of required components. In addition, the inverter is free from electrolytic capacitors, which helps to improve reliability and lifetime of the inverter. The operating principle of the proposed inverter has been analyzed and discussed. Finally, experimental results from a 250 W prototype validates the performance of the proposed topology.
Chou, K-P, Lin, C-T & Lin, W-C 1970, 'A self-adaptive artificial bee colony algorithm with local search for TSK-type neuro-fuzzy system training', 2019 IEEE Congress on Evolutionary Computation (CEC), 2019 IEEE Congress on Evolutionary Computation (CEC), IEEE, Wellington, NEW ZEALAND, pp. 1502-1509.
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© 2019 IEEE. In this paper, we introduce a self-adaptive artificial bee colony (ABC) algorithm for learning the parameters of a Takagi-Sugeno-Kang-type (TSK-type) neuro-fuzzy system (NFS). The proposed NFS learns fuzzy rules for the premise part of the fuzzy system using an adaptive clustering method according to the input-output data at hand for establishing the network structure. All the free parameters in the NFS, including the premise and the following TSK-type consequent parameters, are optimized by the modified ABC (MABC) algorithm. Experiments involve two parts, including numerical optimization problems and dynamic system identification problems. In the first part of investigations, the proposed MABC compares to the standard ABC on mathematical optimization problems. In the remaining experiments, the performance of the proposed method is verified with other metaheuristic methods, including differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and standard ABC, to evaluate the effectiveness and feasibility of the system. The simulation results show that the proposed method provides better approximation results than those obtained by competitors methods.
Choudhary, K, Rujikiatkamjorn, C, Indraratna, B & Choudhury, PK 1970, 'Analytical Modeling of Indian-Made Biodegradable Jute Drains for Soft Soil Stabilization: Progress and Challenges', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 99-109.
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© Springer Nature Singapore Pte Ltd. 2019. Installation of vertical drains in soft soil is probably the most popular preloading method of ground improvement today. These drains reduce the consolidation time of the soil by providing alternative pathways to relieve the pore water pressure in the soil quickly thus reducing construction time. Jute drains have been introduced as an environmentally friendly alternative to synthetic drains in recent times. However, owing to higher absorption capacity of jute and their tendency to degrade in soil their consolidation behavior can be vastly different from that of synthetic drains. In this review, the paper provides in detail the properties of jute drains along with significant developments that have been achieved over the years in understanding their consolidation behavior. The clogging and degradation behavior in these drains is investigated in relation to the limitations in analytical modeling. This article aimed to discuss not only the challenges associated with modeling this phenomenon but also suggests approaches by which this problem can be solved.
Chow, D, Liu, A, Zhang, G & Lu, J 1970, 'Knowledge graph-based entity importance learning for multi-stream regression on Australian fuel price forecasting', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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© 2019 IEEE. A knowledge graph (KG) represents a collection of interlinked descriptions of entities. It has become a key focus for organising and utilising this type of data for applications. Many graph embedding techniques have been proposed to simplify the manipulation while preserving the inherent structure of the KG. However, scant attention has been given to the investigation of the importance of the entities (the nodes of KGs). In this paper, we propose a novel entities importance learning framework that investigates how to weight the entities and use them as a prior knowledge for solving multi-stream regression problems. The framework consists of KG feature extraction, multi-stream correlation analysis, and entity importance learning. To evaluate the proposed method, we implemented the framework based on Wikidata and applied it to Australian retail fuel price forecasting. The experiment results indicate that the proposed method reduces prediction error, which supports the weighted knowledge graph information as a means for improving machine learning model accuracy.
Chu, H-N & Pham, T-M 1970, 'Joint Optimization of Gateway Placement and Multi-hop Routing for the Internet of Things', 2019 6th NAFOSTED Conference on Information and Computer Science (NICS), 2019 6th NAFOSTED Conference on Information and Computer Science (NICS), IEEE, Posts & Telecommunicat Inst Technol Hanoi, Hanoi, VIETNAM, pp. 88-93.
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Chu, P, Zhang, JA, Wang, X, Fang, G & Wang, D 1970, 'Semi-Persistent V2X Resource Allocation with Traffic Prediction in Two-Tier Cellular Networks', 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), IEEE, Kuala Lumpur, Malaysia, pp. 1-6.
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© 2019 IEEE. In a dense urban area, conventional cellular V2X communications require frequent and heavy resource allocation, which can lead to processing congestion and large delay. In this paper, we propose a semi- persistent resource allocation scheme using the least minimum mean square error (LMMSE) traffic prediction in a two-tier network. The two-tier network architecture includes a central macro base station (MBS) and multiple roadside units (RSU). In the proposed scheme, the MBS pre-allocates persistent resource to RSUs based on predicted traffic, and then allocate dynamic resource upon real-time requests from vehicles through RSUs. We formulate an optimization problem for minimizing the total bandwidth under latency constraints and provide an optimal solution to the problem. Simulation is conducted for both artificially generated and real-world data, and the results validate the effectiveness of the proposed semi- persistent scheme.
Chua, BB & Zhang, Y 1970, 'Predicting open source programming language repository file survivability from forking data', Proceedings of the 15th International Symposium on Open Collaboration, OpenSym '19: The 15th International Symposium on Open Collaboration, ACM, Skövde Sweden, pp. 1-8.
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Very few studies have looked at repositories' programming language survivability in response to forking conditions. A high number of repository programming languages does not alone ensure good forking performance. To address this issue and assist project owners in adopting the right programming language, it is necessary to predict programming language survivability from forking in repositories. This paper therefore addresses two related questions: are there statistically meaningful patterns within repository data and, if so, can these patterns be used to predict programming language survival? To answer these questions we analysed 47,000 forking instances in 1000 GitHub projects. We used Euclidean distance applied in the K-Nearest Neighbour algorithm to predict the distance between repository file longevity and forking conditions. We found three pattern types ('once-only', intermittent or steady) and propose reasons for short-lived programming languages.
Chubb, CT, Korzekwa, K & Tomamichel, M 1970, 'Moderate deviation analysis of majorisation-based resource interconversion', 2019 IEEE International Symposium on Information Theory (ISIT), 2019 IEEE International Symposium on Information Theory (ISIT), IEEE, France, pp. 3002-3006.
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© 2019 IEEE. We consider the problem of interconverting a finite amount of resources within all theories whose single-shot transformation rules are based on a majorisation relation, e.g. the resource theories of entanglement and coherence (for pure state transformations), as well as thermodynamics (for energy-incoherent transformations). When only finite resources are available we expect to see a non-trivial trade-off between the rate rn at which n copies of a resource state ρ can be transformed into nrn copies of another resource state σ, and the error level ϵn of the interconversion process, as a function of n. In this work we derive the optimal trade-off in the so-called moderate deviation regime, where the rate of interconversion rn approaches its optimum in the asymptotic limit of unbounded resources (n → ∞), while the error ϵn vanishes in the same limit. We find that the moderate deviation analysis exhibits a resonance behaviour which implies that certain pairs of resource states can be interconverted at the asymptotically optimal rate with negligible error, even in the finite n regime.
Coluccia, A, Fascista, A, Schumann, A, Sommer, L, Ghenescu, M, Piatrik, T, De Cubber, G, Nalamati, M, Kapoor, A, Saqib, M, Sharma, N, Blumenstein, M, Magoulianitis, V, Ataloglou, D, Dimou, A, Zarpalas, D, Daras, P, Craye, C, Ardjoune, S, De la Iglesia, D, Mendez, M, Dosil, R & Gonzalez, I 1970, 'Drone-vs-Bird Detection Challenge at IEEE AVSS2019', 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, Taipei, Taiwan.
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© 2019 IEEE. This paper presents the second edition of the 'drone-vs-bird' detection challenge, launched within the activities of the 16-th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). The challenge's goal is to detect one or more drones appearing at some point in video sequences where birds may be also present, together with motion in background or foreground. Submitted algorithms should raise an alarm and provide a position estimate only when a drone is present, while not issuing alarms on birds, nor being confused by the rest of the scene. This paper reports on the challenge results on the 2019 dataset, which extends the first edition dataset provided by the SafeShore project with additional footage under different conditions.
Cong, HP, Perry, S & HoangVan, X 1970, 'A low complexity Wyner-Ziv coding solution for Light Field image transmission and storage', 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, Jeju, Korea, pp. 1-5.
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Compressing Light Field (LF) imaging data is a challenging but very important task for both LF image transmission and storage applications. In this paper, we propose a novel coding solution for LF images using the well-known Wyner-Ziv (WZ) information theorem. First, the LF image is decomposed into a fourth-dimensional LF (4D-LF) data format. Using a spiral scanning procedure, a pseudo-sequence of 4D-LF is generated. This sequence is then compressed in a distributed coding manner as specified in the WZ theorem. Secondly, a novel adaptive frame skipping algorithm is introduced to further explore the high correlation between 4D-LF pseudo-sequences. Experimental results show that the proposed LF image compression solution is able to achieve a significant performance improvement with respect to the standard, notably around 54% bitrate saving when compared with the standard High Efficiency Video Coding (HEVC) Intra benchmark while requiring less computational complexity.
Conway, D, Taib, R, Harris, M, Berkovsky, S, Yu, K & Chen, F 1970, 'A qualitative investigation of bank employee experiences of information security and phishing', Proceedings of the 13th Symposium on Usable Privacy and Security, SOUPS 2017, Symposium on Usable Privacy and Security, USENIX Association, Santa Clara, California, pp. 115-129.
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Staff behaviour is increasingly understood to be an important determinant of an organisations' vulnerability to information security breaches. In parallel to the HCI and CSCW literature, models drawn from cognitive and health psychology have suggested a number of mental variables that predict staff response to security threats. This study began with these models, but engaged in a broader, discovery-orientated, qualitative investigation of how these variables were experienced, interacted subjectively, and what further variables might be of relevance. We conducted in-depth, semi-structured interviews consisting of open and closed questions with staff from a financial services institution under conditions of strict anonymity. Results include a number of findings such as a possible association between highly visible security procedures and low perceptions of vulnerability leading to poor security practices. We also found self-efficacy was a strong determinant of staff sharing stories of negative experiences and variances in the number of non-relevant emails that they process. These findings lead to a richer, deeper understanding of staff experiences in relation to information security and phishing.
Croaker, P, Venning, J, Karimi, M, Brandner, P, Doolan, C & Kessissoglou, N 1970, 'Noise from a blunt edged flat plate in a reverberant water tunnel', Proceedings of the International Congress on Acoustics, 23RD INTERNATIONAL CONGRESS ON ACOUSTICS, EAA, Aachen, Germany, pp. 5320-5326.
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An experimental and numerical investigation of the flow around and noise produced by a blunt edged flat plate in a reverberant water tunnel is presented. The flow is at a Reynolds number based on chord of 6.8 × 106 and a Mach number of 5.3 × 10-3. Experimental measurements were taken in the Australian Maritime College Cavitation Research Laboratory closed recirculating variable-pressure water tunnel. Hydrophones mounted in the tunnel wall and pressure sensors attached to the plate were used to measure the pressures generated by the turbulent flow over the plate. A large eddy simulation was also conducted, with hydrodynamic pressures on the surface of the plate extracted and combined with Curle's acoustic analogy to predict the pressure fluctuations on the wall of the water tunnel. The numerical predictions are found to agree well with the experimental measurements.
Dang, LC & Khabbaz, H 1970, 'Experimental Investigation on the Compaction and Compressible Properties of Expansive Soil Reinforced with Bagasse Fibre and Lime', Recent Advancements on Expansive Soils: Proceedings of the 2nd GeoMEast International Congress and Exhibition on Sustainable Civil Infrastructures, Egypt 2018 – The Official International Congress of the Soil-Structure Interaction Group in Egypt (SSIGE), GeoMEast International Congress and Exhibition on Sustainable Civil Infrastructures, Egypt 2018 – The Official International Congress of the Soil-Structure Interaction Group in Egypt (SSIGE), Springer International Publishing, Egypt, pp. 64-78.
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This paper presents a laboratory investigation into the mechanical characteristics of expansive soil reinforced with randomly distributed bagasse fibre and lime combination. Bagasse fibre, an agricultural waste by-product left after crushing of sugar-cane for juice extraction, was employed in this investigation as a reinforcing component for expansive soil reinforcement. Several series of laboratory experiments including standard compaction and consolidation tests were carried out on untreated soil and soil samples mixed with various contents of bagasse fibre in a wide range from 0% to 2% and a certain amount of 2.5% lime. The experimental results were used to comprehend the effects of adding bagasse fibre on the compaction and compressible properties of fibre reinforced soils with lime stabilisation. The compaction test results indicate that the addition of bagasse fibre, hydrated lime, and their combination decreased the dry density of stabilised soils. Moreover, the obtained results of the consolidation tests reveal that the reinforcement of expansive soil with bagasse fibre improved the pre-consolidation pressure, meanwhile tended to reduce the compression characteristics of the lime stabilised soils as bagasse fibre content increased from 0% to 1%. However, an excessive increase in bagasse fibre content beyond 1% to 2% was found to result in a slight reduction of the compressibility of lime-soil mixtures reinforced with bagasse fibre. The findings of this research provide a deeper insight into promoting applications of an agricultural waste by-product of bagasse fibre as a low-cost and eco-friendly material for treatment of expansive soils and fill materials for sustainable construction development in the field of civil infrastructure foundations.
Dang, LC & Khabbaz, H 1970, 'Shear Strength Behaviour of Bagasse Fibre Reinforced Expansive Soil', IACGE 2018, International Conference on Geotechnical and Earthquake Engineering 2018, American Society of Civil Engineers, Chongqing, China, pp. 393-402.
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© 2019 American Society of Civil Engineers. This paper presents an experimental investigation on the shear strength behaviour of expansive soil reinforced with bagasse fibre. Bagasse fibre, an agricultural waste by-product left after crushing of sugar-cane for juice extraction, was employed in this study as a reinforcing component for expansive soil reinforcement. The expansive soil used in this investigation was collected from Queensland, Australia. To experimentally investigate the influence of bagasse fibre reinforcement on the shear strength of expansive soil, a series of non-reinforced and fibre reinforced soil samples was prepared by changing randomly distributed bagasse fibre content from 0% to 2%. An array of intensive experimental tests using triaxial compression apparatus was carried out and its results are presented and discussed in further detail. The experimental results reveal that bagasse fibre reinforcement exhibited significant effects on shear strength characteristics of reinforced soils in terms of relationships between deviatoric stress and axial shear strain, ultimate deviatoric strength, and shear strength parameters.
Dang, LC, Dang, CC & Khabbaz, H 1970, 'Numerical Modelling of Embankment Supported by Fibre Reinforced Load Transfer Platform and Cement Mixed Columns Reinforced Soft Soil', 17th European Conference on Soil Mechanics and Geotechnical Engineering, ECSMGE 2019 - Proceedings.
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This paper presents the numerical modelling of a new ground modification technique utilising fibre reinforced load transfer platform (FRLTP) and columns supported (CS) embankment constructed on top of multilayers of soft soil. To investigate the influences of thickness and tensile strength of FRLTP on the embankment behaviour, a series of finite element analyses (FEA) was conducted on the full geometry of a CS embankment reinforced without or with an FRLTP. The FRLTP thickness varied in a range of 0∼3 m, and the FRLTP tensile strength ranged from 10 kPa to 240 kPa, which were considered in this numerical modelling. The numerical results reveal that an increase in the FRLTP thickness significantly improved the stress concentration ratio between columns and surrounding soil, meanwhile resulted in a considerable reduction of the lateral deformation and hence, effectively improved the stability of the embankment system. The findings of the parametric study also indicate that when the FRLTP tensile strength increased in the investigated range, the embankment lateral displacement was found to reduce to a certainly low value, and then it remained almost unchanged. It is also found that the time-dependent embankment behaviour was considerably affected by the changes in the tensile strength and the thickness of FRLTP.
Dart, S, Blackmore, K, Willey, K, Gardner, A, Jose, S, Sharma, R, Trad, S & Jolly, L 1970, 'Moving from crime and punishment to success and reward: Transitioning from technical to educational research', Proceedings of the 8th Research in Engineering Education Symposium, REES 2019 - Making Connections, Research in Engineering Education Symposium, Cape Town, South Africa, pp. 329-338.
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Many engineering academics interested in quality teaching and learning dabble with educational research. Some go further leaving their technical research field behind to embark head-long into what for many is an initially bewildering and conceptually challenging domain. Often peers perceive this transition as a crime (giving up on real engineering) liable to be punished with reduced access to funding and institutional recognition for one's research. The Australasian Association for Engineering Education (AAEE) has been sponsoring a Winter School in Engineering Education Research Methods since 2011, to help engineering academics change their transition story from one of crime and punishment to success and reward. While helpful, this transition is not a simple matter of learning new techniques but of altering one's perspective and habits of thinking and behaviour. Many participants find this both challenging and at least initially, a lonely pursuit. In this paper, participants in the 2018 school ask the question 'what enables and hinders the transition to educational research'.
Das, A, Pal, U, Blumenstein, M, Wang, C, He, Y, Zhu, Y & Sun, Z 1970, 'Sclera Segmentation Benchmarking Competition in Cross-resolution Environment', 2019 International Conference on Biometrics (ICB), 2019 International Conference on Biometrics (ICB), IEEE, Crete, Greece, pp. 1-7.
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© 2019 IEEE. This paper summarizes the results of the Sclera Segmentation Benchmarking Competition (SSBC 2019). It was organized in the context of the 12th IAPR International Conference on Biometrics (ICB 2019). The aim of this competition was to record the developments on sclera segmentation in the cross-resolution environment (sclera trait captured using multiple acquiring sensors with different image resolutions). Additionally, the competition also aimed to gain the attention of researchers on this subject of research.For the purpose of benchmarking, we have employed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1). The second dataset is the Mobile Sclera Dataset (MSD), and in this dataset, images were captured using.a mobile phone rear camera of 8-megapixels. Baseline manual segmentation masks of the sclera images from both the datasets were developed.Precision and recall-based measures were employed to evaluate the effectiveness and ranking of the submitted segmentation techniques. Four algorithms were submitted to address the segmentation task. In this paper we analyzed the results produced by these algorithms/systems, and we have defined a way forward for this problem. Both the datasets along with some of the accompanying ground truth/baseline masks will be freely available for research purposes.
Dasgupta, A, Gill, A & Hussain, F 1970, 'A Conceptual Framework for Data Governance in IoT-enabled Digital IS Ecosystems', Proceedings of the 8th International Conference on Data Science, Technology and Applications, 8th International Conference on Data Science, Technology and Applications, SCITEPRESS - Science and Technology Publications, Prague, Czech Republic, pp. 209-216.
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Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved There is a growing interest in the use of Internet of Things (IoT) in information systems (IS). Data or information governance is a critical component of IoT enabled digital IS ecosystem. There is insufficient guidance available on how to effectively establish data governance for IoT enabled digital IS ecosystem. The introduction of new regulations related to privacy such as General Data Protection Regulation (GDPR) as well as existing regulations such as Health Insurance Portability and Accountability Act (HIPPA) has added complexity to this issue of data governance. This could possibly hinder the effective IoT adoption in healthcare digital IS ecosystem. This paper enhances the 4I framework, which is iteratively developed and updated using the design science research (DSR) method to address this pressing need for organizations to have a robust governance model to provide the coverage across the entire data lifecycle in IoT-enabled digital IS ecosystem. The 4I framework has four major phases: Identify, Insulate, Inspect and Improve. The application of this framework is demonstrated with the help of a Healthcare case study. It is anticipated that the proposed framework can help the practitioners to identify, insulate, inspect and improve governance of data in IoT enabled digital IS ecosystem.
Dasgupta, A, Gill, AQ & Hussain, FK 1970, 'A Review of General Data Protection Regulation for Supply Chain Ecosystem.', IMIS, International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Springer, Sydney, Australia, pp. 456-465.
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© 2020, Springer Nature Switzerland AG. The data-intensive digital supply chain management (SCM) ecosystems seem to be impacted by the recent changes in the regulations and advancement in technologies such as Artificial Intelligence, Big Data, Analytics, Networking, IoT including proliferation of less expensive hardware devices. There is limited guidance available on how to govern the logistics sector, particularly from a regulatory compliance perspective. Through this paper, we investigate the impact of General Data Protection Regulation (GDPR) on digitized SCM. The key questions are: What are the GPDR specific legal obligations? What is the best approach to manage data access, quality, privacy, security and ownership effectively in SCM? This research paper aims to assist researchers and practitioners to understand the impact of GDPR on SCM, provide the 4I (Identify, Insulate, Inspect, Improve) Framework and its applicability to streamline the GDPR compliance activities.
Dawson, N, Rizoiu, M-A, Johnston, B & Williams, M-A 1970, 'Adaptively selecting occupations to detect skill shortages from online job ads', 2019 IEEE International Conference on Big Data (Big Data), IEEE International Conference on Big Data, Los Angeles, CA, USA, pp. 1-7.
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Labour demand and skill shortages have historically been difficult to assessgiven the high costs of conducting representative surveys and the inherentdelays of these indicators. This is particularly consequential for fastdeveloping skills and occupations, such as those relating to Data Science andAnalytics (DSA). This paper develops a data-driven solution to detecting skillshortages from online job advertisements (ads) data. We first propose a methodto generate sets of highly similar skills based on a set of seed skills fromjob ads. This provides researchers with a novel method to adaptively selectoccupations based on granular skills data. Next, we apply this adaptive skillssimilarity technique to a dataset of over 6.7 million Australian job ads inorder to identify occupations with the highest proportions of DSA skills. Thisuncovers 306,577 DSA job ads across 23 occupational classes from 2012-2019.Finally, we propose five variables for detecting skill shortages from onlinejob ads: (1) posting frequency; (2) salary levels; (3) education requirements;(4) experience demands; and (5) job ad posting predictability. This contributesfurther evidence to the goal of detecting skills shortages in real-time. Inconducting this analysis, we also find strong evidence of skills shortages inAustralia for highly technical DSA skills and occupations. These resultsprovide insights to Data Science researchers, educators, and policy-makers fromother advanced economies about the types of skills that should be cultivated tomeet growing DSA labour demands in the future.
Dawson, N, Rizoiu, M-A, Johnston, B & Williams, M-A 2019, 'Adaptively selecting occupations to detect skill shortages from online job ads', 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), IEEE International Conference on Big Data (Big Data), IEEE, Los Angeles, CA, pp. 1637-1643.
De Silva Wijayaratna, S, Huang, Y & De Silva Wijayaratna, K 1970, 'Applications of Public Transport Accessibility Levels (PTAL) in Sydney', Australian Institute of Traffic Planning and Management National Conference, Adelaide, Australia.
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Encouraging public transport adoption and utilisation is imperative to achieving sustainable and mobile cities. The concept of public transport accessibility and its measurement is a complex topic that has received considerable attention in research and practice. One metric, the Public Transport Accessibility Level (PTAL), is used extensively around the world and has been increasingly used in Australia to inform parking rates, trip generation and transport impact assessments. This study reviews the relationship between PTAL and public transport use at a number of selected sites in Sydney to consider the suitability of PTAL in practice. The paper then proposes potential factors which could be further developed to provide a more robust assessment of public transport accessibility.
Deng, W, Chen, W, Clement, S, Guller, A, Zhao, Z, Engel, A & Goldys, EM 1970, 'Controlled drug release from a X-ray triggered liposomal delivery platform for colorectal cancer treatment (Conference Presentation)', Optical Methods for Tumor Treatment and Detection: Mechanisms and Techniques in Photodynamic Therapy XXVIII, Optical Methods for Tumor Treatment and Detection: Mechanisms and Techniques in Photodynamic Therapy XXVIII, SPIE, pp. 21-21.
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Derix, EC & Leong, TW 1970, 'Towards a Probe Design Framework', Proceedings of the 31st Australian Conference on Human-Computer-Interaction, OZCHI'19: 31ST AUSTRALIAN CONFERENCE ON HUMAN-COMPUTER-INTERACTION, ACM, Fremantle, Australia, pp. 117-127.
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© 2019 Association for Computing Machinery. Since their introduction, probes have been widely used in HCI. Despite this, there have not been much reflections and discussions about the design thinking behind their creation and use. There is also a lack of actionable guidance on designing and using probes. This lack may have contributed to some concerns that the method has been misinterpreted and misunderstood. We reviewed HCI literature surrounding probes and found one of the few papers that offers a nascent framework for probe design and use. We used it to guide the design of a collection of probes and reflected on the framework's usefulness. We extend this framework by offering a more useful way of visualizing and working with probe design properties. We also provide further clarity and advice on how others may think and approach the design and use of probes more effectively, especially those turning to probes for the first time.
Ding, C, Sun, H-H, Jay Guo, Y & Jones, B 1970, 'Enabling the Co-Existence of Multiband Antenna Arrays', 2019 IEEE International Symposium on Phased Array System & Technology (PAST), 2019 IEEE International Symposium on Phased Array System & Technology (PAST), IEEE, Waltham, MA USA, pp. 1-4.
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This paper identifies a kind of interaction mechanism that has not been well addressed before, the crossband scattering in multi-band antenna arrays. First the crossband scattering effect is demonstrated on an interleaved dual- band base station antenna array section as an example. Then an effective de-scattering method is proposed, which is to insert chokes on low band antennas. The working mechanism and principle of the chokes are also presented in this paper. Finally, this method is applied on the base station antenna array section to demonstrate its effectiveness.
Ding, C, Wang, K & Guo, YJ 1970, 'Building Antennas on Perovskite Solar Cell (PSC) for Hybrid Solar/EM Wireless Energy Harvesting and Transfer', 2018 Asian Wireless Power Transfer Workshop (AWPT), 2018 Asian Wireless Power Transfer Workshop (AWPT), Sendai, Japan.
Ding, L, Fardjahromi, MA, Bazaz, SR, Asadnia, M, Vesey, G & Warkiani, ME 1970, 'A 3D printed modular microfluidic device for large scale cell harvesting from bioreactors', 23rd International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019, pp. 604-605.
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Mesenchymal stem cell (MSC) therapy is a research hotspot nowadays due to its multiple therapeutic effects and novel application in diseases treatments. However, the current industrial technologies for their culture and harvesting are labour-intensive, time-consuming and very expensive. Herein, we presented a 3D printed modular microfluidic system consisting of micromixers and spiral channel units for cell harvesting which will greatly reduce the time and cost needed for cell harvesting, washing and cultivation. At the meantime, this system is automatic, making it compatible to the current Good Manufacturing Practice (cGMP).
Dinh, TH, Alsheikh, MA, Gong, S, Niyato, D, Han, Z & Liang, Y-C 1970, 'Defend Jamming Attacks: How to Make Enemies Become Friends', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, pp. 1-6.
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In this paper, we consider a smart jammer that only attacks the channel if it detects activities of legitimate devices on that channel. To cope with smart jamming attacks, we propose an intelligent deception strategy in which the legitimate device will send fake transmissions to lure the jammer. Then, if the jammer launches attacks to the channel, the legitimate device can either backscatter the jamming signals to transmit data or harvest energy from the jamming signals for future active transmission. In this way, we can not only undermine the attack ability of the jammer, but also leverage jamming attacks as means to enhance system performance. In addition, to find an optimal defense strategy for the legitimate device under uncertainty of wireless environment as well as incomplete information from the jammer, we develop Q-learning and deep Q-learning algorithms based on the Markov decision process. Through simulation results, we demonstrate that our proposed solution is able to not only deal with smart jamming attacks, but also successfully leverage jamming attacks to improve the system performance.
Do, Q, Verma, S, Chen, F & Liu, W 1970, 'Multiple Knowledge Transfer for Cross-Domain Recommendation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing, Cuvu, Yanuca Island, Fij, pp. 529-542.
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© 2019, Springer Nature Switzerland AG. Collaborative filtering based recommendation systems rely on underlying similarities among users and items across multiple dataset and hence requires sufficiently large amount of ratings data to achieve accurate and reliable results. However, newly established businesses do not have sufficient ratings data and hence this requirement is rarely met. In this research, we propose Multiple Latent Clusters (MultLC) transfer to exploit the correlations among multiple datasets that do not necessarily have an identical dimension of information. In particular, we transfer different aspects of knowledge across different data sources where while transferring each aspect from a source to the target, we only soft-transfer common latent clusters while preserving unique (domain-specific) latent clusters of the target. By soft-transfer, we mean that we minimize the difference among the shared clusters (while not making them identical). Comprehensive experiments on real-world datasets demonstrate the effectiveness of our proposed MultLC over other widely utilized cross-domain recommendation algorithms. The performance improvements demonstrate the benefits of transferring knowledge from multiple sources while preserving the unique information of the target-domain for cross-domain recommendations.
Dolmark, T, Sohaib, O & Beydon, G 1970, 'The Effect of Technology Readiness on Individual Absorptive Capacity for Knowledge Transfer.', ISD, International Conference on Information Systems Development, ISEN Yncréa Méditerranée / Association for Information Systems, France, pp. 1-7.
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Recipient’s Absorptive Capacity (ACAP) remains an under-researched barrier to knowledge transfer in organisations. The Technology Readiness (TR) dimensions appear to align with individual ACAP as it measures an individual's propensity to use technology. Hence, this research-in-progress discusses that the TR dimensions correlate to individual ACAP. As universities are centres of knowledge generation and its transfer, they are an ideal context for this research. Accordingly, a conceptual framework is developed that serves as a basis for deriving knowledge transfer, aimed at achieving individuals technology absorptive capacity.
Dong, M, Wang, J, Huang, Y, Yu, D, Su, K, Zhou, K, Shao, J, Wen, S & Wang, C 1970, 'Temporal Feature Augmented Network for Video Instance Segmentation', 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), IEEE, Seoul, SOUTH KOREA, pp. 721-724.
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Dong, M, Yao, L, Wang, X, Benatallah, B & Huang, C 1970, 'Similarity-Aware Deep Attentive Model for Clickbait Detection', Advances in Knowledge Discovery and Data Mining (LNAI), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Macau, China, pp. 56-69.
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© Springer Nature Switzerland AG 2019. Clickbait is a type of web content advertisements designed to entice readers into clicking accompanying links. Usually, such links will lead to articles that are either misleading or non-informative, making the detection of clickbait essential for our daily lives. Automated clickbait detection is a relatively new research topic. Most recent work handles the clickbait detection problem with deep learning approaches to extract features from the meta-data of content. However, little attention has been paid to the relationship between the misleading titles and the target content, which we found to be an important clue for enhancing clickbait detection. In this work, we propose a deep similarity-aware attentive model to capture and represent such similarities with better expressiveness. In particular, we present the ways of either using similarity only or integrating it with other available quality features for the clickbait detection. We evaluate our model on two benchmark datasets, and the experimental results demonstrate the effectiveness of our approach by outperforming a series of competitive state-of-the-arts and baseline methods.
Dong, M, Yao, L, Wang, X, Benatallah, B, Zhang, X & Sheng, QZ 1970, 'Dual-stream Self-Attentive Random Forest for False Information Detection', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, HUNGARY, pp. 1-8.
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Du, A, Huang, X, Zhang, J, Yao, L & Wu, Q 1970, 'Kpsnet: Keypoint Detection and Feature Extraction for Point Cloud Registration', 2019 IEEE International Conference on Image Processing (ICIP), 2019 IEEE International Conference on Image Processing (ICIP), IEEE, Taipei, Taiwan, pp. 2576-2580.
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© 2019 IEEE. This paper presents the KPSNet, a KeyPoint Siamese Network to simultaneously learn task-desirable keypoint detector and feature extractor. The keypoint detector is optimized to predict a score vector, which signifies the probability of each candidate being a keypoint. The feature extractor is optimized to learn robust features of keypoints by exploiting the correspondence between the keypoints generated from two inputs, respectively. For training, the KPSNet does not require to manually annotate keypoints and local patches pairwise. Instead, we design an alignment module to establish the correspondence between the two inputs and generate positive and negative samples on-the-fly. Therefore, our method can be easily extended to new scenes. We test the proposed method on the open-source benchmark and experiments show the validity of our method.
Duong, TQ, Nguyen, LD, Tuan, HD & Hanzo, L 1970, 'Learning-Aided Realtime Performance Optimisation of Cognitive UAV-Assisted Disaster Communication', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA, pp. 1-6.
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In this work, we propose efficient optimisation methods for relay-assisted unmanned aerial vehicles (UAVs) in cognitive radio networks (CRNs) to cope with the network destruction in the event of a natural disaster. Our model considers real- time optimisation in embedded UAV-CRN communication involved in recovering wireless communication services. Particularly, by conceiving advanced optimisation techniques and training deep neural networks, our solutions become capable of supporting real-time applications in disaster recovery scenarios. Our algorithms impose low computational complexity, hence, have a low execution time in solving real- time optimisation problems. Numerical results demonstrate the benefits of our approaches proposed for UAV-CRN.
Dutta, LK, Xiong, J, Gui, L, Liu, B & Shi, Z 1970, 'On Hit Rate Improving and Energy Consumption Minimizing in Cache-Based Convergent Overlay Network on High-speed Train', 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, Jeju, Korea (South), pp. 1-6.
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© 2019 IEEE. Content caching and energy consumption to protract user device lifetime while bolstering the hit rate when mobility is beyond 300Kmph is challenging. In this paper, we consider a cache-based convergent overlay network comprising cellular base stations and terrestrial broadcast networks as effective means to deliver the services in high-speed train(HST). Most popular contents with high Zipf rank are pushed and cached using broadcast network in user devices and relay system on HST to offload the network traffic and bring user experience upfront leveraging energy. Presence of cache in relay system (RS), users can get the services without delay. However, if user's request for contents are served by cellular network from cloud, they face throughput bottleneck with delay and increased transmission time. This eventually leads increase in user device energy consumption. A popular contents caching scheme with constraints of location, limited storage size and limited power of user device is modeled as closed form expression to maximize users local hit rate with minimal power consumption. we propose an algorithm to cache the contents in user device to minimize total energy consumption of user devices. Moreover, user's content activity behavior is retained to minimize user device's energy by dynamic cache space algorithm. Simulation results justify that the proposed scheme can effectively improve cache hit rate and reduce the power consumption of user devices.
Ebeling, C, Skinner, B & Bluff, A 1970, 'Xploro', ACM SIGGRAPH 2019 Appy Hour, SIGGRAPH '19: Special Interest Group on Computer Graphics and Interactive Techniques Conference, ACM, Los Angeles, California, pp. 1-2.
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© 2019 Copyright Held by the Owner/Author(s). Xploro is an educational game for iOS which combines augmentedreality (AR) technology and spatial computing multi-user game-play mechanics. It was created by the UTS Animal Logic Academy (UTSALA) 2018 cohort to educate children aged 8-12 in a fun and social way. Xploro uses emerging augmented reality technology to create the hide and seek fun of 'Wheres Wally' alongside educational aspects similar to 'Carmen Sandiego'.
El-Ghattas, H & Marjanovic, O 1970, 'How can analytics drive organizational resilience in the CME sector?', 14th International Co-operative Alliance Asia-Pacific Research Conference, Newcastle Australia, 12 Dec 2019 - 14 Dec 2019. 12 Dec 2019, Newcastle Australia.
El-Hawat, O, Fatahi, B & Edmonds, C 1970, 'Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions', 7th International Conference on Earthquake Geotechnical Engineering, CRC Press, Roma, Italy, pp. 2241-2248.
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El-Hawat, O, Fatahi, B & Edmonds, C 1970, 'The Effectiveness of Restrainers to Enhance the Seismic Performance of Bridges with Rocking Foundations', Australian Earthquake Engineering Society 2019 Conference, Newcastle, NSW, Australia.
Erfani, S, Erfani, SM & Ramin, K 1970, 'A Smartphone Health Application To Facilitate Falls Prevention Practices For Older Adults', Proceedings of the 27th European Conference on Information Systems (ECIS), European Conference on Information Systems, AISEL, Stockholm & Uppsala, Sweden, pp. 1-11.
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Falls pose a serious threat to older adults’ health and their quality of life. Web-based technologies such as smartphones have emerged as vital tools for health-related behavioural interventions, but little is known about the potential benefits of a smartphone health application (app) in applying falls prevention practices for older adults. The research presented in this paper sought to answer the question: what are the key features needed in a smartphone health app intended to support falls prevention practices for older adults, increase their autonomy and improve their quality of life? A comprehensive literature review of studies conducted in public health, aged care, mobile health and mobile app design disciplines was undertaken and a conceptual framework for a smartphone app was proposed. The framework depicts the features of a smartphone app that can facilitate the implementation of falls prevention practices, including exercise programs; establishing a healthy diet and falls prevention education. Translation of the conceptual framework into a practical app will reduce falls in older adults, improve their sense of belongingness, and consequently enable better autonomy and quality of life.
Erfani, SS, Erfani, SM & Ramin, K 1970, 'Facebook support groups for ovarian cancer carers: A qualitative evaluation', 25th Americas Conference on Information Systems, AMCIS 2019, Americas Conference on Information Systems, Curran, Cancun, Mexico., pp. 1560-1564.
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A cancer diagnosis takes a great toll on the health of both patients and their carers. Online cancer support groups, including cancer support Facebook groups, have evolved as new sources of support for cancer patients and their carers. However, little is known about how cancer carers make use of such online resources. Most research attention has been paid to Facebook support groups for cancer patients. This research is designed to determine the content of communication in Ovarian Facebook pages, and the impact of those communications on carers of ovarian cancer patients. The study will contribute to knowledge about how cancer patients’ carers use Facebook cancer support groups and the impact of this use on their health and quality of life.
Fan, H, Zhu, L & Yang, Y 1970, 'Cubic LSTMs for Video Prediction', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, HI, pp. 8263-8270.
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Predicting future frames in videos has become a promising direction of research for both computer vision and robot learning communities. The core of this problem involves moving object capture and future motion prediction. While object capture specifies which objects are moving in videos, motion prediction describes their future dynamics. Motivated by this analysis, we propose a Cubic Long Short-Term Memory (CubicLSTM) unit for video prediction. CubicLSTM consists of three branches, i.e., a spatial branch for capturing moving objects, a temporal branch for processing motions, and an output branch for combining the first two branches to generate predicted frames. Stacking multiple CubicLSTM units along the spatial branch and output branch, and then evolving along the temporal branch can form a cubic recurrent neural network (CubicRNN). Experiment shows that CubicRNN produces more accurate video predictions than prior methods on both synthetic and real-world datasets.
Fan, X, Li, B, Sisson, SA, Li, C & Chen, L 1970, 'Scalable deep generative relational models with high-order node dependence', Advances in Neural Information Processing Systems, Vancouver, Canada.
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We propose a probabilistic framework for modelling and exploring the latent structure of relational data. Given feature information for the nodes in a network, the scalable deep generative relational model (SDREM) builds a deep network architecture that can approximate potential nonlinear mappings between nodes' feature information and the nodes' latent representations. Our contribution is two-fold: (1) We incorporate high-order neighbourhood structure information to generate the latent representations at each node, which vary smoothly over the network. (2) Due to the Dirichlet random variable structure of the latent representations, we introduce a novel data augmentation trick which permits efficient Gibbs sampling. The SDREM can be used for large sparse networks as its computational cost scales with the number of positive links. We demonstrate its competitive performance through improved link prediction performance on a range of real-world datasets.
Fang, Z, Lu, J, Liu, F & Zhang, G 1970, 'Unsupervised Domain Adaptation with Sphere Retracting Transformation', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-8.
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© 2019 IEEE. Unsupervised domain adaptation aims to leverage the knowledge in training data (source domain) to improve the performance of tasks in the remaining unlabeled data (target domain) by mitigating the effect of the distribution discrepancy. Existing approaches resolve this problem mainly by 1) mapping data into a latent space where the distribution discrepancy between two domains is reduced; or 2) reducing the domain shift by weighting the source domain. However, most of these approaches share a common issue that they neglect inter-class margins while matching distributions, which has a significant impact on classification performance. In this paper, we analyze the issue from the theoretical aspect and propose a novel unsupervised domain adaptation approach: Sphere Retracting Transformation (SRT), which reduces the distribution discrepancy and increases inter-class margins. We implement SRT, according to our theoretical analysis by (1) assigning class-specific weights for data in the source domain, and (2) minimizing the intra-class variations. Experiments confirm that the SRT approach outperforms several competitive approaches for standard domain adaptation benchmarks.
Faro, B, Abedin, B & Kozanoglu, DC 1970, 'Continuous Transformation of Public Sector Organisations in the Digital Era.', AMCIS, Americas Conference on Information Systems, Association for Information Systems, Cancun, Mexico, pp. 1-5.
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© 2019 Association for Information Systems. All rights reserved. Public-sector organisations need to continuously transform to retain their legitimacy by meeting their obligations to citizens, central governments, and laws. Digital era brings new challenges for public-sector organisations who historically are slow in adoption of changes. This is significant as policymakers are concerned that unexpected disruptions could take away their governance power. This research in progress aims to clarify how public-sector organisations respond to digital transformation drivers. The literature review and expert interviews highlight that organisations require both existing and novel organisational capabilities to utilise digital technologies in order to respond to transformation drivers. This research highlights the gap related to organisational capabilities for existing and novel organisational forms.
Faro, B, Abedin, B & Kozanoglu, DC 1970, 'Continuous transformation of public–sector organisations in the digital era', 25th Americas Conference on Information Systems, AMCIS 2019.
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Public-sector organisations need to continuously transform to retain their legitimacy by meeting their obligations to citizens, central governments, and laws. Digital era brings new challenges for public-sector organisations who historically are slow in adoption of changes. This is significant as policymakers are concerned that unexpected disruptions could take away their governance power. This research in progress aims to clarify how public-sector organisations respond to digital transformation drivers. The literature review and expert interviews highlight that organisations require both existing and novel organisational capabilities to utilise digital technologies in order to respond to transformation drivers. This research highlights the gap related to organisational capabilities for existing and novel organisational forms.
Fei, X, Li, K, Yu, S & Li, K 1970, 'An Economical and High-Quality Encryption Scheme for Cloud Servers with GPUs', 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), IEEE, Gold Coast, Australia, pp. 214-219.
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Motivated by cloud servers undertaking heavy encryption of outsourced data for diverse devices, an Economical and High-Quality Encryption Scheme is proposed to alleviate the burden of energy consumption of the servers meanwhile to keep high-quality services. The objective of the scheme is to minimize the cost that combines economy and service quality. For achieving this objective, a two-phase scoring mechanism is proposed. And then based on the above methods and the scoring mechanism, an algorithm achieving the scheme is designed. To evaluate the scheme, some experiments are performed on a heterogeneous platform. The experimental results show that the encryption algorithm can save energy consumption by 47.8% and slightly improve delay rate by 0.93/10000 on average compared with the original one.
Fell, L, Dehdashti, S, Bruza, P & Moreira, C 1970, 'An Experimental Protocol to Derive and Validate a Quantum Model of Decision-Making', Proceedings of the 41st Annual Meeting of the Cognitive Science Society: Creativity + Cognition + Computation, CogSci 2019, pp. 1724-1730.
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This study utilises an experiment famous in quantum physics, the Stern-Gerlach experiment, to inform the structure of an experimental protocol from which a quantum cognitive decision model can be developed. The'quantumness' of this model is tested by computing a discrete quasi-probabilistic Wigner function. Based on theory from quantum physics, our hypothesis is that the Stern-Gerlach protocol will admit negative values in the Wigner function, thus signalling that the cognitive decision model is quantum. A crowdsourced experiment of two images was used to collect decisions around three questions related to image trustworthiness. The resultant data was used to instantiate the quantum model and compute the Wigner function. Negative values in the Wigner functions of both images were encountered, thus substantiating our hypothesis. Findings also revealed that the quantum cognitive model was a more accurate predictor of decisions when compared to predictions computed using Bayes' rule.
Ferrari, A, Spoletini, P, Bano, M & Zowghi, D 1970, 'Learning Requirements Elicitation Interviews with Role-Playing, Self-Assessment and Peer-Review.', RE, International Requirements Engineering Conference, IEEE, Jeju Island, Korea (South), pp. 28-39.
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© 2019 IEEE. Interviews are largely used in the practice of requirements elicitation. Nevertheless, performing an effective interview often depends on soft-skills, and on knowledge acquired through experience. When it comes to requirements engineering education and training (REET), limited resources and few well-founded pedagogical approaches are available to allow students to acquire and improve their skills as interviewers. This paper presents a novel pedagogical approach that combines role-playing, peer-review and self-assessment to enable students to reflect on their mistakes, and improve their interview skills. We evaluate the approach through a controlled quasi-experiment. The study shows that the approach significantly reduces the amount of mistakes made by the students. Feedback from the participants confirms the usefulness and easiness of the proposed training. This work contributes to the body of knowledge of REET with an empirically evaluated method for teaching inter-views. Furthermore, we share the pedagogical material used, to enable other educators to apply and possibly tailor the approach.
Fisher, KE, Yafi, E, Maitland, C & Xu, Y 1970, 'Al Osool', Proceedings of the 9th International Conference on Communities & Technologies - Transforming Communities, C&T 2019: The 9th International Conference on Communities & Technologies - Transforming Communities, ACM, pp. 273-282.
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Fitch, R, Katupitiya, J & Whitty, M 1970, 'FOREWORD', IFAC PAPERSONLINE, 6th International-Federation-of-Automatic-Control (IFAC) Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL), ELSEVIER, AUSTRALIA, Sydney, pp. VI-VI.
Fitzsimons, J, Ji, Z, Vidick, T & Yuen, H 1970, 'Quantum proof systems for iterated exponential time, and beyond', Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, STOC '19: 51st Annual ACM SIGACT Symposium on the Theory of Computing, ACM, Phoenix, AZ, USA, pp. 473-480.
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© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. We show that any language solvable in nondeterministic time exp(exp(· · · exp(n))), where the number of iterated exponentials is an arbitrary function R(n), can be decided by a multiprover interactive proof system with a classical polynomial-time verifier and a constant number of quantum entangled provers, with completeness 1 and soundness 1 − exp(−C exp(· · · exp(n))), where the number of iterated exponentials is R(n) − 1 and C > 0 is a universal constant. The result was previously known for R = 1 and R = 2; we obtain it for any time-constructible function R. The result is based on a compression technique for interactive proof systems with entangled provers that significantly simplifies and strengthens a protocol compression result of Ji (STOC’17). As a separate consequence of this technique we obtain a different proof of Slofstra’s recent result on the uncomputability of the entangled value of multiprover games (Forum of Mathematics, Pi 2019). Finally, we show that even minor improvements to our compression result would yield remarkable consequences in computational complexity theory and the foundations of quantum mechanics: first, it would imply that the class MIP∗ contains all computable languages; second, it would provide a negative resolution to a multipartite version of Tsirelson’s problem on the relation between the commuting operator and tensor product models for quantum correlations.
Franzò, S, Frattini, F, Cagno, E & Trianni, A 1970, 'TOWARDS A COMPREHENSIVE ANALYSIS OF ENERGY EFFICIENCY POLICIES FOR BUILDINGS: LESSONS LEARNT FROM THE ITALIAN TAX RELIEF SCHEME', Energy Proceedings.
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Energy efficiency is deemed to play a crucial role in improving sustainability. Within the current debate on the design of more effective policies to promote energy efficiency in industry and society, the aim of this paper is to carry out an exhaustive evaluation of the Italian tax relief scheme by a specifically developed comprehensive multi-stakeholder cost-benefit evaluation framework. The framework considers the entire set of stakeholders involved in a broad set of cost-benefit items. The application of the evaluation framework in the Italian context shows that tax relief scheme had a positive impact for energy users and players in the energy efficiency value chain, while the State and energy utilities suffered from a negative cost-benefit balance. In particular, results seem to call for a business model transformation for the energy efficiency value chain, where utilities may counterbalance a reduction in their original business (marketed energy) through a greater role in offering energy efficiency value-added services to final users. The findings, beside providing policy-makers with useful insights on the (re)design of energy efficiency incentive mechanisms, also contribute to future academic research on the topic.
Froissard, J-C, Liu, D, Richards, D & Atif, A 1970, 'A learning analytics pilot in Moodle and its impact on developing organisational capacity in a university', ASCILITE Publications, ASCILITE, Australasian Society for Computers in Learning in Tertiary Education, Toowoomba, QLD, pp. 73-77.
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Moodle is used as a learning management system around the world. However, integrated learning analytics solutions for Moodle that provide actionable information and allow teachers to efficiently use it to connect with their students are lacking. The enhanced Moodle Engagement Analytics Plugin (MEAP), presented at ASCILITE2015, enabled teachers to identify and contact students at-risk of not completing their units. Here, we discuss a pilot using MEAP in 36 units at Macquarie University, a metropolitan Australian university. We use existing models for developing organisational capacity in learning analytics and to embed learning analytics into the practice of teaching and learning to discuss a range of issues arising from the pilot. We outline the interaction and interdependency of five stages during the pilot: technology infrastructure, analytics tools and applications; policies, processes, practices and workflows; values and skills; culture and behaviour; and leadership. We conclude that one of the most significant stages is to develop a culture and behaviour around learning analytics.
Fryc, S, Liu, L & Vidal Calleja, T 1970, 'Efficient Pipeline for Mobile Brick Picking', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Adelaide, pp. 1-8.
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Autonomous mobile manipulation is gaining more and more attention for a range of application including disaster response, logistics, manufacturing and construction because removes work space limitation and allows object handling. A key challenge in mobile manipulation is the interaction between motion planning and perception that will deliver stable and efficient solutions. In this work, we are interested in the problem of picking a single brick shaped object from an unstructured pile using a mo- bile manipulator and a 3D camera system. We propose a robust multi-stage pipeline for a efficient, collision-free brick picking given the object pose. The key contribution of this work is a scoring function used to find the most suitable configuration considering the integrated kinematic chain of mobile base and manipulator arm. Realistic simulation results show the proposed pipeline has 100% success rate as opposed to a standard off-the-shelf solution, which has high-failure rates.
Fryc, S, Liu, L & Vidal-Calleja, T 1970, 'Robust pipeline for mobile brick picking', Australasian Conference on Robotics and Automation, ACRA.
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In this work, we are interested in the problem of picking a single brick shaped object from an unstructured pile using a mobile manipulator and a 3D camera system. We propose a robust multi-stage pipeline for efficient, collision-free brick picking given the pose of a target object. The key contribution of this work is a scoring function used to find the most suitable configuration considering the integrated kinematic chain of a mobile base and manipulator arm. Realistic simulation results show the proposed pipeline has 100% success rate as opposed to a standard off-the-shelf solution, which has high-failure rates.
Gaisbauer, W, Raffe, WL, Garcia, JA & Hlavacs, H 1970, 'Procedural Generation of Video Game Cities for Specific Video Game Genres Using WaveFunctionCollapse (WFC)', Extended Abstracts of the Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts, CHI PLAY '19: The Annual Symposium on Computer-Human Interaction in Play, ACM, Barcelona, SPAIN, pp. 397-404.
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Copyright held by the owner/author(s). Virtual cities as background scenarios can be used for many 3D video game genres like action. However, the procedural generation of virtual cities for specific video game genres is an on-going research problem. In this paper, we seek to establish a grounding for future work into city generation for specific game genres by exploring how game designers approach existing generation tool-sets. Firstly, we look at the video game city Skara Brae from the party-based role-playing game The Bard’s Tale and try to replicate it using the Wave Function Collapse (WFC) approach to procedural generation. We show in two experimental conditions which parameters for WFC are suitable for replicating the city. Secondly, a pilot user study with eight users shows how they approach creating different video game cities after they preselect a video game genre. The users’ video game level ideas are then discussed, and different output levels are generated using WFC.
Galea, M & Vidal-Calleja, T 1970, 'Point cloud edge detection and template matching with 1D gradient descent for wall pose estimation', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Adelaide, Australia, pp. 1-10.
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Mobile manipulation in unstructured construction environments involves a range of complex robotic problems. We address a perception requirement for autonomous brick placement; estimating the pose of a partially built wall to facilitate the placement of the subsequent brick. Our method uses RGB-D data to extract the surface edge points of the wall and classify them as horizontally or vertically aligned. The contribution of this paper encompasses a wall template that encapsulates its surface edge features and a novel 1D gradient descent template matching algorithm for pose estimation. We apply our method in mobile manipulator brick placement, demonstrating its robotic applications. Evaluation methods prove the efficacy of the proposed framework, both quantitatively and qualitatively and using both simulated and real data.
Gamal, M, Abolhasan, M, jafarizadeh, S, Lipman, J & Ni, W 1970, 'Mapping and Scheduling of Virtual Network Functions using Multi Objective Optimization Algorithm', 2019 19th International Symposium on Communications and Information Technologies (ISCIT), 2019 19th International Symposium on Communications and Information Technologies (ISCIT), IEEE, Ho Chi Minh City, Vietnam, pp. 328-333.
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© 2019 IEEE. Within the context of Software-Defined Networking (SDN), the problem of resource allocation for a set of incoming Virtual Network Functions (VNF) service requests has been the focus of many studies. In this paper, a new optimization model has been developed to find the near to optimal mapping and scheduling for the incoming VNF service requests. This model while considering delay, aims to achieve three objectives functions, namely, minimizing the transmission delays occurring in every link, minimizing the processing capacity for every Virtual Machine (VM) and minimizing the processing delay at every VM. The resultant problem is formulated as a multi-objective optimization problem and the developed solution is based on a multi-objective evolutionary algorithm utilizing the decomposition algorithm. Simulation results illustrate that the resulting algorithm is scalable while considering delay and it outperforms the genetic bandwidth link allocation (GA-BA) and genetic non-bandwidth link allocation (GA-NBA) algorithms.
Gamal, M, Jafarizadeh, S, Abolhasan, M, Lipman, J & Ni, W 1970, 'Mapping and Scheduling for Non-Uniform Arrival of Virtual Network Function (VNF) Requests', 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), IEEE, Honolulu, HI, USA, pp. 1-6.
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© 2019 IEEE. As a new research concept for both academia and industry, there are several challenges faced by the Network Function Virtualization (NFV). One such challenge is to find the optimal mapping and scheduling for the incoming service requests which is the focus of this study. This optimization has been done by maximizing the number of accepted service requests, minimizing the number of bottleneck links and the overall processing time. The resultant problem is formulated as a multi- objective optimization problem, and two novel algorithms based on genetic algorithm have been developed. Through simulations, it has been shown that the developed algorithms can converge to the near to optimal solutions and they are scalable to large networks.
Gao, J, Li, H, Luo, Z, Li, P & Gao, L 1970, 'Isogeometric Density Field Method for Topology Optimization of Micro-architected Materials', 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE, Porto, Portugal, pp. 524-529.
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© 2019 IEEE. In this paper, an isogeometric density field method is proposed for the design of micro-architected materials with the specific mechanical properties, consisting of the maximum bulk and shear modulus and the Negative Poisson's ratio (NPR). Firstly, the non-uniform rational B-splines (NURBS) basis functions are employed to construct the density field function (DFF), where the Shepard function is used to improve the smoothness of the nodal densities assigned to control points. The NURBS basis functions and the Shepard function can ensure the sufficient continuity and smoothness of the DFF, owing to their significant properties. The optimization formulation for micro-architected materials is developed using the DFF, where the isogeometric analysis (IGA) is applied to evaluate the unknown structural responses. Effective macroscopic properties of materials are predicted by the asymptotic homogenization method. The same NURBS basis functions used in the IGA and the DFF can keep the consistency of the geometric model and analysis model, which provides the unique benefits. Numerical examples are used to demonstrate the effectiveness of the proposed topology optimization approach for micro-architected materials.
Gao, X, Zhang, T, Du, J & Guo, YJ 1970, 'Ultrasensitive Terahertz High-Tc Superconducting Receivers', 2019 IEEE MTT-S International Wireless Symposium (IWS), 2019 IEEE MTT-S International Wireless Symposium (IWS), IEEE, Guangzhou, PEOPLES R CHINA, pp. 1-3.
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© 2019 IEEE. This paper reviews our major technical achievement in high-Tc superconducting (HTS) terahertz (THz) receivers or heterodyne mixers in recent years. By virtue of innovative on-chip antenna/circuit designs, accurate device modelling and simulation, and advanced YBa2Cu3O7-x (YBCO) step-edge junction technology, we have successfully developed a series of HTS Josephson THz mixers with superior performance in terms of operating temperature, intermediate-frequency (IF) bandwidth, conversion gain and noise temperature. These mixers serve as promising receiver frontends for THz wireless communication and sensing systems.
Garcia, JA, Sundara, N, Tabor, G, Gay, VC & Leong, TW 1970, 'Solitaire Fitness: Design of an asynchronous exergame for the elderly to enhance cognitive and physical ability', 2019 IEEE 7th International Conference on Serious Games and Applications for Health (SeGAH), 2019 IEEE 7th International Conference on Serious Games and Applications for Health (SeGAH), IEEE, IEEE, pp. 1-6.
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© 2019 IEEE. The use of exergames has shown positive results in encouraging the elderly to increase their motivation towards physical activity and rehabilitation. These games usually offer playful routines that require players to perform full body movements in order to interact with the game. While this is often well-received by elderly users, this approach has some limitations that can lead to negative effects in the aged cohort. The main one being, that gameplay and exercise must happen concurrently. This, unfortunately, places limitations on the elderly users and limits the range of exercises that can be delivered. Also, prior studies have revealed that while the aged cohort often finds this approach enjoyable, they are more inclined to exercise in more traditional ways. This paper describes the design and development of an asynchronous game, called Solitaire Fitness, where physical exercise and cognitive gameplay do not occur at the same time. The game is designed to enhance both cognitive and physical abilities. It seamlessly links a well-established card game, solitaire, and let the elderly chose the form of exercise they are familiar with and let them exercise at their own pace, allow them to fully immerse in gameplay, and ultimately increase their motivation towards an healthy active lifestyle.
Gärdenfors, P, Williams, MA, Johnston, B, Billingsley, R, Vitale, J, Peppas, P & Clark, J 1970, 'Event boards as tools for holistic AI', CEUR Workshop Proceedings, International Workshop on Artificial Intelligence and Cognition, CEUR, Palermo, Italy, pp. 1-10.
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We propose a novel architecture for holistic AI systems that integrate machine learning and knowledge representation. We extend an earlier proposal to divide representations into symbolic, conceptual and subconceptual levels. The key idea is to use event boards representing components of events as an analogy to blackboards found in earlier AI systems. The event components are ‘thematic roles’ such as agent, patient, recipient, action, and result. They are represented in terms of vectors of conceptual spaces rather than in symbolic form that has been used previously. A control level, including an attention mechanism decides which processes are run.
Ge, M, Pineda, JA, Sheng, D, Burton, GJ & Li, N 1970, 'Collapse behaviour of compacted loess: role of the stress level on soil microstructure', Japanese Geotechnical Society Special Publication, The Japanese Geotechnical Society, pp. 209-214.
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© 2019 7th Asia-Pacific Conference on Unsaturated Soils, AP-UNSAT 2019. All rights reserved. The paper presents preliminary results of an experimental study aimed at evaluating the influence of soil microstructure on the collapse behaviour of compacted loess from Xi'an, Shannxi province, China. Collapse behaviour was evaluated from one-dimensional compression tests in which compacted specimens were loaded to different vertical stresses, under constant water content conditions, prior soaking. Mercury intrusion porosimetry (MIP) tests and Scanning Electron Microscopy (SEM) analysis reveals a strong influence of the stress level on the soil microstructure formed by soaking under zero lateral deformation conditions.
Gehrke, L, Akman, S, Lopes, P, Chen, A, Singh, AK, Chen, H-T, Lin, C-T & Gramann, K 1970, 'Detecting Visuo-Haptic Mismatches in Virtual Reality using the Prediction Error Negativity of Event-Related Brain Potentials', Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI '19: CHI Conference on Human Factors in Computing Systems, ACM, Glasgow, SCOTLAND, pp. 1-11.
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© 2019 Copyright held by the owner/author(s). Designing immersion is the key challenge in virtual reality; this challenge has driven advancements in displays, rendering and recently, haptics. To increase our sense of physical immersion, for instance, vibrotactile gloves render the sense of touching, while electrical muscle stimulation (EMS) renders forces. Unfortunately, the established metric to assess the effectiveness of haptic devices relies on the user’s subjective interpretation of unspecific, yet standardized, questions. Here, we explore a new approach to detect a conflict in visuo-haptic integration (e.g., inadequate haptic feedback based on poorly configured collision detection) using electroencephalography (EEG). We propose analyzing event-related potentials (ERPs) during interaction with virtual objects. In our study, participants touched virtual objects in three conditions and received either no haptic feedback, vibration, or vibration and EMS feedback. To provoke a brain response in unrealistic VR interaction, we also presented the feedback prematurely in 25% of the trials. We found that the early negativity component of the ERP (so called prediction error) was more pronounced in the mismatch trials, indicating we successfully detected haptic conflicts using our technique. Our results are a first step towards using ERPs to automatically detect visuo-haptic mismatches in VR, such as those that can cause a loss of the user’s immersion.
Gentil, CL, Vidal-Calleja, T & Huang, S 1970, 'IN2LAMA: INertial Lidar Localisation And MApping', 2019 International Conference on Robotics and Automation (ICRA), 2019 International Conference on Robotics and Automation (ICRA), IEEE, Montreal, pp. 6388-6394.
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© 2019 IEEE. In this paper, we introduce a probabilistic framework for INertial Lidar Localisation And MApping (IN2LAMA). Most of today's lidars are based on spinning mechanisms that do not capture snapshots of the environment. As a result, movement of the sensor can occur while scanning. Without a good estimation of this motion, the resulting point clouds might be distorted. In the lidar mapping literature, a constant velocity motion model is commonly assumed. This is an approximation that does not necessarily always hold. The key idea of the proposed framework is to exploit preintegrated measurements over upsampled inertial data to handle motion distortion without the need for any explicit motion-model. It tightly integrates inertial and lidar data in a batch on-manifold optimisation formulation. Using temporally precise upsampled preintegrated measurement allows frame-to-frame planar and edge features association. Moreover, features are re-computed when the estimate of the state changes, consolidating front-end and back-end interaction. We validate the effectiveness of the approach through simulated and real data.
Gentile, C, Kesteven, S, Wu, J, Bursill, C, Davies, MJ & Figtree, G 1970, 'A Novel Cellular and Genetic Approach to Investigate the Cardioprotective Role Played by Endothelial Nitric Oxide Synthase in Myocardial Infarction', CIRCULATION RESEARCH, 14th Annual American-Heart-Association's Basic Cardiovascular Sciences (BCVS) Scientific Sessions - Integrative Approaches to Complex Cardiovascular Diseases, LIPPINCOTT WILLIAMS & WILKINS, Boston, MA.
Ghadi, MJ, Li, L, Zhan, J, Chen, L, Huang, Q & Li, C 1970, 'A Review on the Development of Concentrated Solar Power and its Integration in Coal-Fired Power Plants', 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), IEEE, Chengdu, China, pp. 1106-1111.
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© 2019 IEEE. Concentrated solar power (CSP) technology has attracted the attention of researchers worldwide recently, due to falling prices and much lower operating costs. With the capability of providing high-temperature steam and being dispatchable when coupled with thermal storage, CSP systems are becoming promising technologies for increasing the generation efficiency of different types of power plants. This paper provides a review of the worldwide growth of CSP technologies. After providing a summary on current CSP technologies, development of CSP in some countries is reviewed. Then, possible joint-operations of CSP with different types of power plants are examined. Finally, their application on coal-fired power plants (CFPP) in terms of optimal location for integration, and criteria for CSP utilization based on the size of CFPP are investigated. This review study can assist power system planners to reach a deeper vision of CSP potential benefits for hybrid power generation, especially with thermal power plants, as well as a quick overview of successful projects globally.
Ghanbarikarekani, M, Zeibots, M & Qu, X 1970, 'Optimization of Signalized Intersections Equipped with LRV Signal Priority Systems by Minimizing Cars’ Stop Time', 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019 IEEE Intelligent Transportation Systems Conference - ITSC, IEEE, Auckland, New Zealand, pp. 4230-4235.
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There are some strategies suggested to improve the performance of intersections and increase the demand for public vehicles by providing them priority. In order to achieve this goal, several policies have been used such as Transit Signal Priority (TSP) system for Light Rail Vehicle (LRV). LRV signal priority is a timing strategy that gives priority to LRVs at signalized intersections. More specifically, this strategy is based on changing the sequence of phases, extending green time and reducing red time of LRV's phase. Although this method has considerable benefits for LRVs, it penalizes private vehicles by increasing their delay and stop time at intersections. This paper aims to propose a model to improve LRV signal priority systems. The modifying model for LRV signal priority systems minimizes the green extension and red reduction of LRV's phase by using linear programming (LP) method to calculate an optimal speed for LRVs reaching the stop line. Consequently, LRVs are prioritized while the performance of private vehicles would be improved.
Gharehchaei, M, Akbarnezhad, A, Chilwesa, M, Castel, A, Lloyd, R & Foster, S 1970, 'A genetic algorithm to identify the optimal concrete mix for the elements subject to risk of early age thermal cracking', FIB 2018 - Proceedings for the 2018 fib Congress: Better, Smarter, Stronger, FIB Congress, Fédération internationale du béton, Melbourne, Australia, pp. 3351-3360.
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The mismatch between the rate of heat generation due to cement hydration and the rate of heat dissipation through conduction and convection may result in considerable temperature gradient within mass concrete and concrete elements with high cement content. This temperature gradient may in turn lead to considerable thermal stresses in concrete at its early ages when it has not achieved its full capacity to resist tensile stress, leading to early age thermal cracking in concrete. Among various measures investigated to minimize the risk of early age thermal cracking, optimizing the concrete mixes and use of supplementary cementitious materials are usually favoured by the industry mainly because these methods do not require changes to the construction method and plan. However, regulating the internal heat generation to reduce the risk of thermal cracking is not considered as an objective in existing mix deign approaches which have been designed to achieve target mechanical properties. In this paper, a mathematical optimization model based on genetic algorithms is developed to identify the optimal mix for typical concrete elements subject to risk of early age thermal cracking. The optimization model is designed to reduce the temperature gradient within the concrete without comprising the development of mechanical properties of concrete. The proposed optimization method is applied to a case study involving identifying the optimal mix design for a large concrete raft. The reduction in the risk of thermal cracking due to use of optimal mix, rather than originally planned mix, is verified through numerical simulation.
Ghosh, S & Lee, JE-Y 1970, 'Eleventh Order Lamb Wave Mode Biconvex Piezoelectric Lorentz Force Magnetometer for Scaling Up Responsivity and Bandwidth', 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII), 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII), IEEE, pp. 146-149.
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Giabbanelli, PJ, Voinov, AA, Castellani, B & Tornberg, P 1970, 'Ideal, Best, and Emerging Practices in Creating Artificial Societies', 2019 Spring Simulation Conference (SpringSim), 2019 Spring Simulation Conference (SpringSim), IEEE, Tucson, AZ, USA, pp. 1-12.
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© 2019 Society for Modeling & Simulation International (SCS). Artificial societies used to guide and evaluate policies should be built by following “best practices”. However, this goal may be challenged by the complexity of artificial societies and the interdependence of their sub-systems (e.g., built environment, social norms). We created a list of seven practices based on simulation methods, specific aspects of quantitative individual models, and data-driven modeling. By evaluating published models for public health with respect to these ideal practices, we noted significant gaps between current and ideal practices on key items such as replicability and uncertainty. We outlined opportunities to address such gaps, such as integrative models and advances in the computational machinery used to build simulations.
Giovanangeli, N, Piyathilaka, L, Kodagoda, S, Thiyagarajan, K, Barclay, S & Vitanage, D 1970, 'Design and Development of Drill-Resistance Sensor Technology for Accurately Measuring Microbiologically Corroded Concrete Depths', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 36th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), Canada, pp. 735-735.
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© 2019 International Association for Automation and Robotics in Construction I.A.A.R.C. All rights reserved. Microbial corrosion of concrete is a severe problem that significantly reduces the service life of underground sewers in countries around the globe. Therefore, water utilities are actively looking for in-situ sensors that can quantify the biologically induced concrete corrosion levels, in order to carry out preventive maintenance before any catastrophic failures. As a solution, this paper introduces a drill-resistance based sensor that can accurately measure the depth of the microbiologically corroded concrete layer. A prototype sensor was developed and evaluated in laboratory test conditions. The lab experiments proved that the developed sensor has the ability to measure the depth of the microbiologically corroded concrete with millimeter level of accuracy. Additionally, the sensor can also locate and accurately measure the size of concrete aggregates as well as potential cracks, effectively creating a sub-surface ‘scan’ of the concrete at the targeted point of interest. Therefore, providing valuable extra information for assessing the condition of the sewer concrete.
Goldsmith, R & Jin, X 1970, 'Using action research to explore postgraduate transition and develop disciplinary literacies for engineering students', Students transitions achievement retention & success 2019, Unistars.org, Melbourne, pp. 1-5.
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Unprecedented changes in higher education in the 21st century both in teaching and learning practices and in student cohorts have contributed to the development of transition strategies and pedagogies for first year undergraduate students. However, there has been little acknowledgement of the need for similar approaches for commencing postgraduate students, despite research which indicates that postgraduate cohorts are very diverse and do not necessarily have the ‘expert status’ accorded to them. This is especially the case in engineering and IT faculties, where students from a multitude of undergraduate and language backgrounds enrol in postgraduate studies for a wide range of reasons. In response to this, we devised an initiative which seeks to scaffold support for postgraduate coursework engineering students in research practices, academic writing practices and problem-based learning. An action research approach has been adopted to implement this initiative, and we will encourage students to become participant researchers.
Gong, C, Shi, K & Niu, Z 1970, 'Hierarchical Text-Label Integrated Attention Network for Document Classification', Proceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference, HPCCT 2019: 2019 The 3rd High Performance Computing and Cluster Technologies Conference, ACM, pp. 254-260.
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Gong, S, Oberst, S & Wang, X 1970, 'Dynamic analysis of vibrating flip-flow screens equipped with support and shear rubber springs', Journal of Physics: Conference Series, Recent Advances in Structural Dynamics, IOP Publishing, Lyon, France, pp. 012061-012061.
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Abstract Vibrating flip-flow screens provide an effective means of screening highly viscous or fine materials, and the dynamic characteristics of the main and the floating screen frames are largely responsible for a flip-flow screen’s screen performance and its processing capacity. An accurate dynamic model of the rubber shear springs used within the frame of the screen is critical for its dynamic analysis – to understand deficiencies and improve its performance. In this paper, the Sjöberg model is used to predict the frequency-and amplitude-dependent behaviour of the rubber shear springs. A friction model represents the amplitude dependency of the rubber shear springs. The fractional derivative model is used to describe its frequency dependency with its elasticity being represented by a linear spring. This model is further validated by cyclic tests of the rubber shear springs. Furthermore, dynamic response of the VFFS have been analysed using the Sjöberg model and the Kelvin-Voigt model, respectively. Experimental results indicate that dynamic response of VFFS can be better predict using the Sjöberg model than Kelvin-Voigt model in time region as well as in the frequency domain.
Gowripalan, N, Cao, J, Sirivivatnanon, V & South, W 1970, 'Assessment of ASR expansions using an ultra-accelerated test', 29th Biennial Conference of the Concrete Institute of Australia, 29th Biennial Conference of the Concrete Institute of Australia, Sydney Australia.
Grigorev, A, Severiukhina, O & Derevitskii, I 1970, 'Anomaly Detection Using Adaptive Suppression', Procedia Computer Science, Elsevier BV, pp. 274-282.
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Guan, Z, Qin, H, Yager, K, Choo, Y & Yu, D 1970, 'Automatic X-ray scattering image annotation via double-view Fourier-Bessel convolutional networks', British Machine Vision Conference 2018, BMVC 2018.
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X-ray scattering is a key technique towards material analysis and discovery. Modern x-ray facilities are producing x-ray scattering images at such an unprecedented rate that machine aided intelligent analysis is required for scientific discovery. This paper articulates a novel physics-aware image feature transform, Fourier-Bessel transform (FBT), in conjunction with deep representation learning, to tackle the problem of annotating x-ray scattering images with a diverse label set of physics characteristics. We devise a novel joint inference model, Double-View Fourier-Bessel Convolutional Neural Network (DVFB-CNN) to integrate feature learning in both polar frequency and image domains. For polar frequency analysis, we develop an FBT estimation algorithm for partially observed x-ray images, and train a dedicated CNN to extract structural information from FBT. We demonstrate that our deep Fourier-Bessel features well complement standard convolutional features, and the joint network (i.e., DVFB-CNN) improves mean average precision by 13% in multilabel annotation. We also conduct transfer learning on real experimental datasets to further confirm that our joint model is well generalizable.
Gui, L, Liang, X, Chang, X & Hauptmann, AG 1970, 'Adaptive context-aware reinforced agent for handwritten text recognition', British Machine Vision Conference 2018, BMVC 2018.
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Handwritten text recognition has been a ubiquitous research problem in the field of computer vision. Most existing approaches focus on the recognition of handwritten words without considering the cursive nature and significant differences in the writing of individuals. In this paper, we address these problems by leveraging an adaptive context-aware reinforced agent which learns the actions to determine the scales of context regions during inference. We formulate our approach in a reinforcement learning framework. Specifically, we construct the action set with a number of context lengths. Given an image feature sequence, our model is trained to adaptively choose the optimal context length according to the observed state. An attention mechanism is then used to selectively attend the context region. Our model can generalize well from recognizing isolated words to recognizing individual lines of text while remain low computation overheads. We conduct extensive experiments on three large-scale handwritten text recognition datasets. The experimental results show that our proposed model is superior to the state-of-the-art alternatives.
Gunatilake, A, Piyathilaka, L, Kodagoda, S, Barclay, S & Vitanage, D 1970, 'Real-Time 3D Profiling with RGB-D Mapping in Pipelines Using Stereo Camera Vision and Structured IR Laser Ring', Proceedings of the 14th IEEE Conference on Industrial Electronics and Applications, ICIEA 2019, IEEE Conference on Industrial Electronics and Applications, IEEE, Xi'an, China, pp. 916-921.
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This paper is focused on delivering a solution that can scan and reconstructthe 3D profile of a pipeline in real-time using a crawler robot. A structuredinfrared (IR) laser ring projector and a stereo camera system are used togenerate the 3D profile of the pipe as the robot moves inside the pipe. Theproposed stereo system does not require field calibrations and it is notaffected by the lateral movement of the robot, hence capable of producing anaccurate 3D map. The wavelength of the IR light source is chosen to be nonoverlapping with the visible spectrum of the color camera. Hence RGB colorvalues of the depth can be obtained by projecting the 3D map into the colorimage frame. The proposed system is implemented in Robotic Operating System(ROS) producing real-time RGB-D maps with defects. The defect map exploitdifferences in ovality enabling real-time identification of structural defectssuch as surface corrosion in pipe infrastructure. The lab experiments showedthe proposed laser profiling system can detect ovality changes of the pipe withmillimeter level of accuracy and resolution.
Guo, K, Yu, H, Chai, R, Nguyen, H & Su, SW 1970, 'A Hybrid Physiological Approach of Emotional Reaction Detection Using Combined FCM and SVM Classifier', 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Berlin, Germany, pp. 7088-7091.
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© 2019 IEEE. Users' emotional reaction capturing is one of the primary issues for brain computer interface applications. Despite the intuitive feedback provided by the qualitative methods, emotional reactions are expected to be detected and classified quantitatively. Based on the human emotion representation on physiological signal, this paper offers an hybrid approach combining electroencephalogram (EEG) and facial expression together to classify the human emotion. Several advanced signal processing techniques are used to simplify the data and extract the features involving local binary patterns (LBP), Compressed Sensing (CS) and Wavelet Transform (WT). A novel machine learning algorithm, combined Fuzzy Cognitive Maps (FCM) and Support Vector Machine (SVM) are implemented to recognise the feature patterns. The result illustrates a stable emotion classification system with 75.64% accuracy. This design can provide fast and precise emotional feedback, which would further improve the communication between human and computer.
Guo, T, Zhu, X, Wang, Y & Chen, F 1970, 'Discriminative Sample Generation for Deep Imbalanced Learning', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, Macao, pp. 2406-2412.
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In this paper, we propose a discriminative variational autoencoder (DVAE) to assist deep learning from data with imbalanced class distributions. DVAE is designed to alleviate the class imbalance by explicitly learning class boundaries between training samples, and uses learned class boundaries to guide the feature learning and sample generation. To learn class boundaries, DVAE learns a latent two-component mixture distributor, conditioned by the class labels, so the latent features can help differentiate minority class vs. majority class samples. In order to balance the training data for deep learning to emphasize on the minority class, we combine DVAE and generative adversarial networks (GAN) to form a unified model, DVAAN, which generates synthetic instances close to the class boundaries as training data to learn latent features and update the model. Experiments and comparisons confirm that DVAAN significantly alleviates the class imbalance and delivers accurate models for deep learning from imbalanced data.
Gupta, D, Sarma, HJ, Mishra, K & Prasad, M 1970, 'Regularized Universum twin support vector machine for classification of EEG Signal', 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), IEEE, Bari, Italy, pp. 2298-2304.
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© 2019 IEEE. Electroencephalogram signal is the signal used for the detection of a neurological disorder as epilepsy disorder, sleep disorder and many more. The types of EEG signal gives the hidden information regarding the distribution of the data that may consist of a large volume of the poor and noisy signal. In order to reduce the outlier effects and noise, incorporation of prior knowledge in the model, universum may help and enhance the better generalization ability of the model. This paper proposes a regularized universum twin support vector machine (RUTWSVM) for classification of the healthy and seizure EEG signals. Here, the selection of the universum data points is obtained in two ways (i). Universum data has been generated from the healthy and seizure EEG signals itself and (ii). Interictal EEG signal has been used as universum data which may help to handle the outlier effects. Further, various feature selection techniques are applied to extract the important noise free features from the EEG signals. We have performed a comparative analysis of proposed RUTWSVM with USVM and UTWSVM to classify the EEG signals as well as benchmark real-world datasets in an optimum way. The experiment results clearly exhibit the applicability and usability of the proposed RUTWSVM with interictal EEG signals as universum data points as well as benchmark real-world datasets.
Gurca, A, Ravishankar, MN & Pradhan, S 1970, 'Evolving organizational design enterprises in developing countries: A dialectic perspective', Responsible Leadership in Rising Economies, Bled, Slovenia.
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Digital enterprises in developing countries (DECDs) must meet challenging demands from clients, while generating positive outcomes for society. This implies a maintaining several dialectic tensions in a sustainable equilibrium. Drawing on an in-depth qualitative case study, we identify and discuss three tensions pertinent to DECDs: positive social impact-profitability, accuracy-scalability and trust-vigilance. We then emphasize interdependencies between these tensions and explain that addressing one tension may indirectly enhance or constrain other tensions. We further provide empirical evidence on the three tensions and we show how DECD organizational design evolves over time in response to these tensions.
Ha, TQ, Al-Kilidar, H & Leveaux, R 1970, 'A review of issues surrounding the adoption of technologies by SMEs in Vietnam', Proceedings of the 33rd International Business Information Management Association Conference, IBIMA 2019: Education Excellence and Innovation Management through Vision 2020, 33rd International-Business-Information-Management-Association (IBIMA) Conference, INT BUSINESS INFORMATION MANAGEMENT ASSOC-IBIMA, Granada, SPAIN, pp. 2694-2701.
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Technology adoption is key to economic improvement. Despite the importance of SMEs, the technology adoption in SMEs in Vietnam is significantly lower than other ASEAN countries.. This review of literature identifies two main inhibitors to technology adoption in Vietnam and Cambodia namely language and education. Both Vietnam and Cambodia were colonized by France in the mid-19th through to the mid-20th century, which made those two countries late adapters of the English language compared to Singapore, Malaysia, and Thailand which were colonized and subsequently had alliances with Britain. Education is a crucial element in the ability to understand and use new technologies by individuals, and it is a skill that is developed through education. This review, identified a relationship between the quality of the education system and the technology readiness in a number of countries in Southeast Asia.
Ha, TV & Hoang, DB 1970, 'Toward an Active Aging Society: An IT Model to Engage the Aging Population', 2019 International Conference on Information Networking (ICOIN), 2019 International Conference on Information Networking (ICOIN), IEEE, Kuala Lumpur, Malaysia, pp. 375-380.
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Many countries around the world are expecting a growing number of elderly people as the society is aging over time. This shift is expected to create a large impact on our health and social security system. The cost of having an increasing proportion of elderly people is emerging as a challenge for governments, so much that our government is encouraging people to stay in the workforce longer. As a result, the aging population requires a solution that allows them to remain productive and keeping them mentally healthy. Existing solutions rely on the benefits of social networks or service networks to keep them active and improve mental health. However, these solutions fail to allow elderly people to act as a value contributor for the society. This paper proposes the design of a new model that allows elderly people to actively and collaboratively provide value to the society through an assistive platform that integrates a service network with a social network. This model combines the advantages of the social network to connect them and utilize the advantages of the service network to create opportunities for elderly people to offer their skills and knowledge to exchange benefits with other users. The proposed model can be used as a mean to engage seniors to the community, allowing them to generate value for themselves and the community while staying mentally healthy.
Hadgraft, R, Francis, B, Brown, T, Fitch, R & Halkon, B 1970, 'Renewing Mechanical and Mechatronics Programs', AAEE2019, AAEE2019, Brisbane, Australia.
Hadgraft, R, Francis, B, Lawson, J, Jarman, R & Araci, JT 1970, 'Summer studios - Lessons from a 'small bet' in student-led learning', Proceedings of the 46th SEFI Annual Conference 2018: Creativity, Innovation and Entrepreneurship for Engineering Education Excellence, SEFI Annual Conference, SEDI, Copenhagen, Denmark, pp. 815-823.
Halkon, B 1970, 'On the possibility of UAV-mounted LDVS for response-only dynamic characterisation of remote infrastructure', 8th IOMAC - International Operational Modal Analysis Conference, Proceedings, International Operational Modal Analysis Conference, Curran, Copenhagen, pp. 547-551.
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Laser Doppler vibrometers are technically well suited to general application but they offer special benefits in a variety of challenging measurement scenarios which are now well documented and accepted. An interesting and potentially powerful example of such a challenging measurement scenario is one where the laser vibrometer is mounted on/in an unmanned aerial vehicle in order that autonomous measurement campaigns can be undertaken in remote and/or harsh environments. One important challenge to overcome in such a scenario is the measurement sensitivity to vibration of the instrument itself or indeed of any steering optics used to point the probe laser beam toward the target of interest. In this paper, recently reported means by which this measurement sensitivity can be rectified by simultaneously obtained correction measurements will be described. Specifically, this development opens up the possibility of laser Doppler vibrometry from unmanned aerial vehicles for response-only dynamic assessment of remote infrastructure, a measurement challenge of significant potential value.
Halkon, B, Rauter, A, Oberst, S & Marburg, S 1970, 'Research and development of an air-puff excitation system for lightweight structures', 8th IOMAC - International Operational Modal Analysis Conference, Proceedings, International Operational Modal Analysis Conference, Curran Associates, Copenhagen, Denmark, pp. 627-634.
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Lightweight, thin-walled structures appear in numerous engineering and natural structures. Due to their sensitivity, vibration excitation by, now traditional, contacting techniques, such as modally-tuned impact hammers or electrodynamic shakers, to investigate their dynamics is challenging since it typically adds substantial mass and/or stiffness at the excitation location. The research presented in this article, therefore, is intended to yield a system for the non-contact excitation of thin-walled structures through small, controlled blasts of air. An air-puff system, consisting of two fast-acting solenoid-controlled valves, a small air outlet nozzle and bespoke control software with a programmable valve control sequence, is researched and developed. The excitation impulse characteristics are investigated experimentally and described in detail for varying input control parameters. Ultimately, suitability of the system for the excitation of thin-walled structures is explored, for both a 3D-printed micro-satellite panel and a natural bee honeycomb, with promising results when compared to that of an impact hammer.
Hamilton, TJ, Kavehei, O, Asadnia, M, Kan, A, Wabnitz, A, Luke, R & Gargiulo, GD 1970, 'Towards a Low-Power, Minimally-Invasive Nerve Regeneration', 2019 INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING RESEARCH & PRACTICE (ICEERP-2019), International Conference on Electrical Engineering Research and Practice (ICEERP) / 5th World Congress of the Global-Circle-for-Scientific-Technological-and-Management-Research (GCSTMR), IEEE, AUSTRALIA, Sydney, pp. 13-16.
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Hamilton, TJ, Kavehei, O, Asadnia, M, Kan, A, Wabnitz, A, Luke, R & Gargiulo, GD 1970, 'Towards a Low-Power, Minimally-Invasive Nerve Regeneration', 2019 International Conference on Electrical Engineering Research & Practice (ICEERP), 2019 International Conference on Electrical Engineering Research & Practice (ICEERP), IEEE, pp. 1-4.
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© 2019 IEEE. In this paper we will explore current advances and the future directions of nerve regeneration implants. We will discuss the challenges of developing implants which can be chronically implanted for long-term nerve regeneration and stimulation. We will also discuss the need for developing such implants to be low-power and minimally invasive. We will compare solutions proposed in the literature as well as develop some criteria which we believe will maximize the success of future implant development.
Haque, MN, Mathieson, L & Moscato, P 1970, 'A memetic algorithm approach to network alignment', Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '19: Genetic and Evolutionary Computation Conference, ACM, Prague, Czech Republic, pp. 258-265.
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© 2019 Association for Computing Machinery. Given two graphs modelling related, but possibly distinct, networks, the alignment of the networks can help identify signiicant structures and substructures which may relate to the functional purpose of the network components. The Network Alignment Problem is the NP-hard computational formalisation of this goal and is a useful technique in a variety of data mining and knowledge discovery domains. In this paper we develop a memetic algorithm to solve the Network Alignment Problem and demonstrate the efectiveness of the approach on a series of biological networks against the existing state of the art alignment tools. We also demonstrate the use of network alignment as a clustering and classiication tool on two mental health disorder diagnostic databases.
Harcombe, DM, Ruppert, MG & Fleming, AJ 1970, 'Modeling and Noise Analysis of a Microcantilever-based Mass Sensor', 2019 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), 2019 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), IEEE, pp. 1-6.
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Hasan, MN, Simorangkir, RBVB, Esselle, KP & Shad, S 1970, 'Differentially Fed CDRA Array with Phase Inverter for High Gain and Reduced Cross Polarization', 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, IEEE, pp. 65-66.
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© 2019 IEEE. This paper presents a compact novel 2×2 CDRA array with high gain and reduced cross polarization. By feeding the array elements differentially, the reduction of cross polarization and the enhancement of gain are achieved. The feeding network employs an on-board 180° phase inverter as a balun for differential feeding of array elements. The simulated results show that the proposed array achieves-10 dB impedance bandwidth of 5.78-5.9 GHz with a broadside radiation pattern having a peak realized gain of 11.07 dBi at 5.84 GHz. The cross-polarization levels in the boresight direction are-30.65 dB and-29.5 dB in YZ and XZ plane, respectively at 5.84 GHz. The proposed CDRA array is compact, making it suitable for portable devices.
Hashmi, RM, Baba, AA, Esselle, KP, Gonzalez Marin, J & Hesselbarth, J 1970, 'On the design of broadband resonant cavity antennas with feeds suitable for integration with millimeter-wave transceiver chips', 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE, pp. 0621-0622.
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© 2019 IEEE. This paper demonstrates the resonant cavity antenna's (RCA) integration capability with the millimeter-wave (mm-wave) transceiver chips. A broadband RCA fed by a probe-fed microstrip patch, which is readily integrated with transceiver chips, is designed at 60 GHz. It exhibits a peak broadside gain of 19 dBi with a VSWR 2:1 bandwidth ranging from 57 GHz to beyond 70 GHz. Radiation performance of the RCA fed by a traditional waveguide aperture is also presented to demonstrate the feasibility of the proposed RCA in mm-wave practical applications.
Hassan, W, Gautam, S, Lu, DD-C & Xiao, W 1970, 'Analysis, Design, and Experimental Verification of High Step-up DC-DC Converter to Interface Renewable Energy Sources into DC Nanogrid', 2019 IEEE International Conference on Industrial Technology (ICIT), 2019 IEEE International Conference on Industrial Technology (ICIT), IEEE, pp. 1649-1654.
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© 2019 IEEE. This paper proposes a new non-isolated, high step-up DC-DC converter to interface renewable sources into DC microgrid. The topology utilizes the coupled inductor and switched capacitor techniques to achieve high step-up voltage conversion ratio. The leakage energy is directly transferred to output to avoid voltage spikes across the switch. The switching devices have relatively low voltage stresses. In addition, the coupled inductor alleviated the reverse recovery problem of the diode. The key features include high efficiency, low voltage stresses, and low component count and cost. The steady-state analysis and operation of the proposed converter are presented in detail. Finally, a 200 W prototype circuit operating at a switching frequency of 100 kHz is built in the laboratory to verify the performance. The experimental results substantiate the theoretical analysis and show a peak efficiency of 96.90%.
Hassan, W, Hasan, R, Lu, DD-C, Xiao, W & Soon, JL 1970, 'Performance Enhancement of High Step-up DC-DC Converter to Attain High Efficiency and Low Voltage Stress', 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), IEEE, Singapore, pp. 1-6.
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This study proposes a new high voltage gain and high-efficiency dc-dc converter to interface renewable energy resources into dc nanogrid. The proposed topology is formed by a coupled inductor to achieve high voltage gain and low stress on the active switch. The switch voltage stress is significantly low compared to the output voltage. Thus, efficiency is improved by utilizing a low voltage rating MOSFET. Furthermore, the utilization of couple inductor eliminated the reverse recovery losses of diodes. The converter consists of the least number of components that decrease the overall system cost. The steady-state operation and analysis of the proposed converter are discussed comprehensively. The experimental performance is verified by building and testing a prototype in the laboratory. The experimental results prove the consistency with the theoretical analysis. The converter depicts a peak efficiency of 97.10% in the laboratory.
Hassoun, M, Fatahi, B & Mirlatifi, S 1970, 'Seismic effectiveness of different restraining systems for skewed bridge decks supported on elastomeric bearings', Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019, International Conference on Earthquake Geotechnical Engineering, CRC Press, Rome, Italy, pp. 2812-2819.
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Past earthquakes have shown that isolated bridges are susceptible to excessive movements at the expansion joints. Reconnaissance reports have exposed various isolated bridges that have collapsed due to this excessive movement in a phenomenon known as unseating. This problem is particularly evident in skewed bridges as the bridge experience coupled lateral and rotational movements. The study seeks to understand the effectiveness of various restrainers, namely cable restrainers and viscous dampers in limiting both lateral and rotational movements of the bridge deck. In order to approximate the complex behavior of isolated restrained bridges, the models used in this study include the effect of soil-structure interaction behind the abutment backwall and the pile foundations. Moreover, the inelastic behavior of highly damaged bridge elements such as abutment backwall, pier and shear-keys are studied, and results are presented.
Hawchar, L, Stewart, MG, Nolan, P, Sweeney, F & Ryan, PC 1970, 'Climate change risk for Irish timber power pole networks', 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019.
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The latest IPCC report states that warming of the climate system is unequivocal, and this warming may lead to increased risk of breakdown of infrastructure networks due to extreme weather. Before appropriate action can be taken for power infrastructure in this regard, we must first understand existing risk, and then try to predict potential climate related changes in risk. The work described in this paper examines both existing vulnerability, and potential future vulnerability, for a notional network of Irish timber power poles. These power pole networks represent important critical infrastructure assets, both nationally, and internationally. There are currently approximately two million timber power poles in service in Ireland, five million timber power poles in service in Australia, worth over $10 billion, and approximately 200 million treated power poles in service in the United States. The impacts of climate change on Irish power poles will be examined herein using a Monte-Carlo event-based sequential model, which incorporates structural reliability, deterioration, climatic effects and network maintenance. The hazards of interest are storm winds and timber decay - both of which may worsen due to a changing climate.
Hayati, H & Eager, D 1970, 'Additional injury prevention criteria for impact attenuation surfacing used in children’s playgrounds', 14th Australasian Injury Prevention and Safety Promotion, 14th Australasian Injury Prevention and Safety Promotion, Brisbane.
Hayati, H, Eager, D & Walker, P 1970, 'An impact attenuation surfacing test to analyse the dynamic behaviour of greyhound racetrack sand surface', WEC2019: World Engineers Convention 2019, World Engineers Conventio, Engineers Australia, Melbourne, Australia, pp. 391-401.
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The underfoot surface affects the dynamics of legged locomotion of any kind from a legged robot to a racing greyhound. In racing greyhounds, the surface is one of the leading risk factors contributing to life-threatening injuries. The current standard type of material used in greyhound racing tracks is sand. Two variables affect the sand functional behaviour, namely: the moisture content; and rate of compaction. This paper analyses the effect of altering the moisture content and density on the dynamic behaviour of the sand surface. This paper also presents a method to obtain the mechanical coefficients of the surface via an standard impact test which was applied as an input in the legged locomotion simulation over compliant terrains. The results show that a sand sample with the 20% moisture content and density of 1.35 g/cm3 has the most favourable behaviour with regards to injury prevention.
He, Y, Jayawickrama, BA & Dutkiewicz, E 1970, 'Distributed Power Allocation Algorithm for General Authorised Access in Spectrum Access System', 2019 IEEE Wireless Communications and Networking Conference (WCNC), 2019 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, Marrakesh, Morocco, pp. 1-6.
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© 2019 IEEE. To meet the capacity needs of the next generation wireless communications, U.S. Federal Communications Commission has recently introduced Spectrum Access System. Spectrum is shared between three tiers - Incumbents, Priority Access Licensees (PAL) and General Authorised Access (GAA) Licensees. When the incumbents are absent, PAL and GAA share the spectrum under the constraint that GAA ensure the aggregate interference to PAL is no more than -80 dBm within the PAL protection area. Currently GAA users are required to report their geolocations. However, geolocation is private information that GAA may not be willing to share. We propose a distributed GAA power allocation algorithm that does not require centralised coordination on sharing locations with other GAA users via SAS. We analytically proved the critical point of the interference along the PAL protection area to avoid calculating the interference on every points of the area. We proposed exclusion zone, transitional zone and open zone for GAA users to calculate the self-determined transmit power. Simulation results show that our method meets the interference requirement and achieve more than 90% of capacity approximation to the optimal centralised method, while completely masking the GAA locations.
Hesamian, MH, Jia, W, He, X & Kennedy, PJ 1970, 'Atrous Convolution for Binary Semantic Segmentation of Lung Nodule', ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Brighton. UK, pp. 1015-1019.
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© 2019 IEEE. Accurately estimating the size of tumours and reproducing their boundaries from lung CT images provides crucial information for early diagnosis, staging and evaluating patients response to cancer therapy. This paper presents an advanced solution to segment lung nodules from CT images by employing a deep residual network structure with Atrous convolution. The Atrous convolution increases the field of view of the filters and helps to improve classification accuracy. Moreover, in order to address the significant class imbalance issue between the nodule pixels and background non-nodule pixels, a weighted loss function is proposed. We evaluate our proposed solution on the widely adopted benchmark dataset LIDC. A promising result of an average DCS of 81.24% is achieved, outperforming the state of the arts. This demonstrates the effectiveness and importance of applying the Atrous convolution and weighted loss for such problems.
Hirschmanner, M, Tsiourti, C, Patten, T & Vincze, M 1970, 'Virtual Reality Teleoperation of a Humanoid Robot Using Markerless Human Upper Body Pose Imitation', 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids), IEEE, pp. 259-265.
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Ho, P, Wijayaratna, K & Dixit, V 1970, 'Understanding driver behavior in response to variable message signs for smart motorway management systems', Proceedings of the 24th International Conference of Hong Kong Society for Transportation Studies, HKSTS 2019: Transport and Smart Cities, The 24th International Conference of Hong Kong Society for Transportation Studies, Hong Kong, pp. 73-80.
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Variable message signs (VMS) are a key component of information systems used to manage congestion. To improve the accuracy of real time predictions for decision support systems, it is important to understand and model driver behavior in response to messages displayed on VMS during incidents. In this empirical study, an aggregate analysis was undertaken to study the impact of VMS on off-ramp diversion behavior on the M4 Motorway in Sydney, Australia. Hypothesis testing was first used to confirm the statistically significant impact of VMS on diversion proportions revealing a shift of up to 3.5%. A linear regression model was then estimated using over 4000 observed incident messages to identify influencing factors such as downstream congestion, accident events, and the content and duration of messages. The results provide objective insight into the aggregate diversion behaviour. This has implications in providing more accurate predictions and evaluation of incident management strategies.
Hoang, VT, Phung, MD, Dinh, TH, Zhu, Q & Ha, QP 1970, 'Reconfigurable Multi-UAV Formation Using Angle-Encoded PSO', 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), IEEE, Vancouver, BC, Canada, pp. 1670-1675.
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© 2019 IEEE. In this paper, we propose an algorithm for the formation of multiple UAVs used in vision-based inspection of infrastructure. A path planning algorithm is first developed by using a variant of the particle swarm optimisation, named θ-PSO, to generate a feasible path for the overall formation configuration taken into account the constraints for visual inspection. Here, we introduced a cost function that includes various constraints on flight safety and visual inspection. A reconfigurable topology is then added based on the use of intermediate waypoints to allow the formation to avoid collision with obstacles during operation. The planned path and formation are then combined to derive the trajectory and velocity profiles for each UAV. Experiments have been conducted for the task of inspecting a light rail bridge. The results confirmed the validity and effectiveness of the proposed algorithm.
Ho-Le, T, Tran, T, Center, J, Eisman, J & Nguyen, T 1970, 'MULTIMORBILITY CONTRIBUTES TO POST-FRACTURE MORTALITY: A LATENT CLASS ANALYSIS', OSTEOPOROSIS INTERNATIONAL, IOF-Regional 7th Asia-Pacific Osteoporosis Conference, SPRINGER LONDON LTD, AUSTRALIA, Sydney, pp. S99-S99.
Hora, JA, Arellano, AC, Zhu, X & Dutkiewicz, E 1970, 'Design of Buck Converter with Dead-time Control and Automatic Power-Down System for WSN Application', 2019 IEEE Wireless Power Transfer Conference (WPTC), 2019 IEEE Wireless Power Transfer Conference (WPTC), IEEE, pp. 582-586.
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© 2019 IEEE. A buck converter design with an automatic power-down technique and dead-time control system intended for low power application such as a wireless sensor network is proposed. With an input voltage range of 1V to 1.2V, the buck converter regulated the output voltage at 0.8V. This buck converter operates in a pulse-width modulation technique at load current range of 1mA-100mA. The output voltage ripple measured is 7.5 m V with the peak efficiency is 94.98 %. The quiescent current (mathrm{I}-{mathrm{q}}) of this proposed design is about 5mu mathrm{A}. The line and load regulation is 0.195 mV/V and 0.61mV/mA, respectively. The circuit core layout dimension is 179 mumathrm{m} and 120mu mathrm{m} 65nm CMOS technology.
Hora, JA, Darell Ang, J, Zhu, X & Dutkiewicz, E 1970, 'A Highly Linear OTA with 759 µS gm for RF Transceiver Application', 2019 19th International Symposium on Communications and Information Technologies (ISCIT), 2019 19th International Symposium on Communications and Information Technologies (ISCIT), IEEE, Ho Chi Minh City, Vietnam, Vietnam, pp. 595-598.
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© 2019 IEEE. This paper presents a design of a highly linear fully differential operational transconductance amplifier (OTA) in 65nm CMOS Technology Process for RF transceiver application. To improve the linearity and the output common-mode voltage of the design OTA, a cross-coupled differential pair, and differential active loading were applied, respectively. The cross-coupled differential pair was taken from a single-ended OTA and utilized to form a fully differential OTA topology with two current mirrors. The presented OTA has a constant transconductance (gm) of 759 μS at Vin of 0V, a VDD of 1.2V and is linear at input voltages of -0.5V to 0.5V. The OTA core circuit has a gain of 22.4 dB, and unity-gain bandwidth of 280 MHz at a load of 0.2 pF and control voltage of 0.5V. The output common-mode voltage is kept close to Vdd/2 of 0.6V. The final circuit layout of the transconductance core with negative resistance circuit has dimensions of 37.22 μm x 64.59 μm. This paper also presents a pseudo-differential topology OTA for comparison purposes.
Hora, JA, Mayormita, MA, Rebollos, JRC, Zhu, X & Dutkiewicz, E 1970, 'On-Chip Inductor-Less Indoor Light Energy Harvester with Improved Efficiency for WSN/IoT Device Design', 2019 13th International Conference on Sensing Technology (ICST), 2019 13th International Conference on Sensing Technology (ICST), IEEE, Sydney, Australia, pp. 1-6.
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© 2019 IEEE. An on-chip inductor-less indoor light energy harvester circuit block for internet-of-things wireless sensor node device design is implemented in 65nm CMOS process technology. The design of the indoor light energy harvester comprises a bootstrapped ring oscillator, a two-phase non-overlapping clock generator, a tapered buffer, a multi-stage differential-drive CMOS rectifier, a charge controlling circuit and a voltage regulator. The system boosts an input of 500 mV from a photovoltaic cell without using a typical boost converter circuit that employs an inductor element. Hence, a simplified on-chip design charges an external 1.3-V rechargeable battery. The use of a multi-stage differential-drive rectifier eliminates the need for expensive on-chip inductors. A charge control circuit is implemented to maintain the battery voltage and avoid overcharging, thus improving battery life. A low-dropout voltage regulator further regulates the battery voltage to produce a stable dc voltage. The chip core design has a total area of 1342μm×1011μm. The output of the harvesting system is a regulated 0.9 V supply with 1.05 mA current at full load.
Hora, JA, Zhu, X & Dutkiewicz, E 1970, '2.4 GHz CMOS Design RF-to-DC Energy Harvesting with Charge Control System for WSN Application', 2019 IEEE Wireless Power Transfer Conference (WPTC), 2019 IEEE Wireless Power Transfer Conference (WPTC), IEEE, pp. 49-54.
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© 2019 IEEE. This paper presents an RF-to-DC energy harvester in the Wi-Fi band. The energy harvester is meant to charge the 1.2V battery of the wireless sensor node device. The system design consists of three main circuit blocks: A low-dropout (LDO) voltage regulator, a charge control circuit and multistage differential-drive rectifier. The maximum PCE attained by the rectifier alone is 31.43%. The charge control circuit maintains the voltage within 1.3V-l.4V, while the LDO provides a stable and regulated output of 1.2V. The designed energy harvester has a minimum RF input power of -2.04 dBm. The chip layout of the overall design has a dimension of 1.2mm × 1.1mm.
Hora, JA, Zhu, X & Dutkiewicz, E 1970, 'Design of High Voltage Output for CMOS Voltage Rectifier for Energy Harvesting Design', 2019 IEEE Wireless Power Transfer Conference (WPTC), 2019 IEEE Wireless Power Transfer Conference (WPTC), IEEE, pp. 40-44.
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© 2019 IEEE. This paper presents a modified design of CMOS differential voltage multiplier circuit block for energy harvesting circuit for wireless sensor networks (WSN) application. The design simulation and layout was carried out using 65nm CMOS process. The extraction of high DC voltage from rectifier block is always a severe bottleneck for energy harvesting. In this work, a simple mechanism to eliminate (Vth) of the MOS transistor by adding an auxiliary PMOS transistor is proposed. Also, an additional two capacitor (Cs) is split and connected to the differential output. Moreover, the conventional and modified voltage multiplier was simulated and implemented with three stages with a load capacitance of 100pF. The simulation result shows that the modified voltage multiplier obtain a higher voltage conversion ratio (Gv) of 3.96, while the conventional voltage multiplier only obtained a Gv of 2.96. Accordingly, the proposed modified rectifier circuit achieved a peak efficiency of 22.41 % and can able to operate a device with a power requirement of 1.2V to 1.8V and with a continuous output current of 3mA.
Hora, JA, Zhu, X & Dutkiewicz, E 1970, 'Simplified Over- Temperature Protection Circuit Structure for WSN/IoT Device Power Management', 2019 13th International Conference on Sensing Technology (ICST), 2019 13th International Conference on Sensing Technology (ICST), IEEE, Sydney, Australia,, pp. 1-4.
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© 2019 IEEE. This paper presents a modified structure of the over-temperature protective circuit integrated into the power management converter design for wireless sensor node devices. The design is focused on realizing a simplified circuit structure that is compatible with standard CMOS technology structure and evaluating the over-temperature threshold to assure needed accuracy. HSPICE simulation using Monte Carlo Analysis for the bandgap voltage reference is used to get a better estimation, process variation, and reliability. The target design temperature threshold is obtained at approximately 150 °C, which is the standard chip testing value at worst temperature consideration. Post-layout simulation of the proposed circuit design structure is carried out using 65nm 1P9M CMOS 1.2V/2.5V logic CMOS technology. And it is co-integrated in the power management circuit design for the system-on-chip wireless sensor node device.
Hu, H, Ghosh, S, Siwakoti, Y & Long, T 1970, 'Generalized Multilevel Converter in DC-DC Application', 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 2019 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, pp. 5137-5143.
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© 2019 IEEE. In this paper, a novel non-isolated generalized multilevel DC-DC converter aiming for applications of connecting Low Voltage (LV) DC to Medium Voltage (MV) DC in high power has been presented. The proposed converter can achieve high voltage transfer ratio between LV and MV, and reduced DC inductance and output capacitance requirement by using low voltage power electronic devices with series interleaved switching techniques. The operating rules have been established and the switching states and switching sequences have been analyzed and selected. The design consideration and simulation results are presented for a 2 kW prototype with a voltage transfer ratio of 8. A 2 kW, 4-level converter has been designed and fabricated to validate the proposed converter. Experimental results are reported at 100 V input, 2 kW load.
Hu, L, Jian, S, Cao, L, Gu, Z, Chen, Q & Amirbekyan, A 1970, 'HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, Hawaii USA, pp. 3830-3837.
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Classic recommender systems face challenges in addressing the data sparsity and cold-start problems with only modeling the user-item relation. An essential direction is to incorporate and understand the additional heterogeneous relations, e.g., user-user and item-item relations, since each user-item interaction is often influenced by other users and items, which form the user’s/item’s influential contexts. This induces important yet challenging issues, including modeling heterogeneous relations, interactions, and the strength of the influence from users/items in the influential contexts. To this end, we design Influential-Context Aggregation Units (ICAU) to aggregate the user-user/item-item relations within a given context as the influential context embeddings. Accordingly, we propose a Heterogeneous relations-Embedded Recommender System (HERS) based on ICAUs to model and interpret the underlying motivation of user-item interactions by considering user-user and item-item influences. The experiments on two real-world datasets show the highly improved recommendation quality made by HERS and its superiority in handling the cold-start problem. In addition, we demonstrate the interpretability of modeling influential contexts in explaining the recommendation results.
Huang, B, Fatahi, B & Terzaghi, S 1970, 'Investigating effects of wave incident angle and joint depth on ground surface motion considering multiple reflections via coupled discrete element and finite difference method', Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019, International Conference on Earthquake Geotechnical Engineering (VII ICEGE), CRC Press, Roma, Italy, pp. 2883-2890.
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In this study, the effects of incident angle and joint depth on the ground surface motion induced by the shear wave propagation across the rock mass were numerically assessed. A three-dimensional coupled discrete element-finite difference model consisting of a 40-m deep rock mass with a single joint, was developed using 3DEC software. The continuously yielding joint model was adopted to replicate the nonlinear behaviour of the joint under the influence of the seismic wave. Moreover, the role of the multiple reflections occurring between the ground surface and the joint in the ground surface motion was determined via the comparison between the models with and without the presence of the free surface. The results of this parametric study showed that a larger incident angle could lead to the amplification of both the horizontal and vertical components of the peak particle velocity captured on the ground surface. In addition, it was found that the multiple reflections can significantly amplify the ground surface motion, particularly when the joint with the shallow depth was present. Hence, it is critical for practicing engineers to take into account the joint spatial properties such as the joint orientation and depth, in conjunction with the multiple reflections, when making the prediction of the ground surface motion.
Huang, B, Fatahi, B & Zargarbashi, S 1970, 'Coupled discrete element and finite difference modelling of wave propagation across rock mass considering multiple reflections between ground surface and joint', 13th Australia New Zealand Conference on Geomechanics, Australia New Zealand Conference on Geomechanics, Australian Geomechanics Society, Perth, Australia, pp. 889-896.
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Ground surface motion induced by the upward propagating seismic waves could cause severe damage to structures on ground surface. The propagation of seismic wave could be significantly affected by the presence of joints embedded in a rock mass. The joints could attenuate and slow down the travelling wave. Moreover, the multiple wave reflections occurring between the ground surface and the joint could significantly alter the behaviour of the wave travelling through the near-ground rock mass. In this study, the S-wave propagating through the jointed rock mass and its induced ground surface motion were numerically investigated. A three-dimensional coupled discrete element-finite difference model was developed using 3DEC software to study seismic wave propagation across a 30-m deep rock mass with a single horizontal joint in the middle. Continuously yielding joint model was adopted to capture the nonlinear progressive damage of joints under shear. The influences of the shear stress ratio and the frequency of the incident S-wave on the wave propagation across the rock mass and the associated ground surface motion were studied, considering multiple wave reflections between the free ground surface and rock joint. Moreover, a comparison was made between the models with and without the presence of the free surface, to better understand the impact of the multiple reflections on the wave propagation and the ground surface motion. It was showed that the multiple reflections could conspicuously intensify the wave propagation across the rock mass, and therefore amplifying the ground surface motion, particularly when the frequency was low. Moreover, the ground surface motion became insensitive to the variation of the shear stress ratio when the ratio was either too small or too large, in conjunction with the multiple wave reflections. Hence, the effect of multiple reflections should be carefully considered, when predicting the ground surface motion caused by the wave p...
Huang, H, Liu, Y, Chen, L, Qin, P-Y & Guo, YJ 1970, 'Synthesis of a Dipole Array with Optimally End-fire Directive Pattern', 2019 IEEE International Conference on Computational Electromagnetics (ICCEM), 2019 IEEE International Conference on Computational Electromagnetics (ICCEM), IEEE, Shanghai, China, pp. 1-3.
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© 2019 IEEE. In this work, a formula is derived for generating optimally directive pattern for an arbitrary antenna array including mutual coupling effect, and it is then applied to design the optimally directive four-element end-fire dipole array. Synthesis results show that the obtained directivity coefficient is about 2dB and 3.5dB higher than those of ordinary and hansen-woodyard four-element end-fire dipole arrays.
Huang, H, Zhang, J, Zhang, J, Wu, Q & Xu, J 1970, 'Compare More Nuanced: Pairwise Alignment Bilinear Network for Few-Shot Fine-Grained Learning', 2019 IEEE International Conference on Multimedia and Expo (ICME), 2019 IEEE International Conference on Multimedia and Expo (ICME), IEEE, Shanghai, China, pp. 91-96.
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© 2019 IEEE. The recognition ability of human beings is developed in a progressive way. Usually, children learn to discriminate various objects from coarse to fine-grained with limited supervision. Inspired by this learning process, we propose a simple yet effective model for the Few-Shot Fine-Grained (FSFG) recognition, which tries to tackle the challenging fine-grained recognition task using meta-learning. The proposed method, named Pairwise Alignment Bilinear Network (PABN), is an end-to-end deep neural network. Unlike traditional deep bilinear networks for fine-grained classification, which adopt the self-bilinear pooling to capture the subtle features of images, the proposed model uses a novel pairwise bilinear pooling to compare the nuanced differences between base images and query images for learning a deep distance metric. In order to match base image features with query image features, we design feature alignment losses before the proposed pairwise bilinear pooling. Experiment results on four fine-grained classification datasets and one generic few-shot dataset demonstrate that the proposed model outperforms both the state-of-the-art few-shot fine-grained and general few-shot methods.
Huang, P-Y, Chang, X & Hauptmann, A 1970, 'Multi-Head Attention with Diversity for Learning Grounded Multilingual Multimodal Representations', Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, Hong Kong, HONG KONG, pp. 1461-1467.
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With the aim of promoting and understanding the multilingual version of image search, we leverage visual object detection and propose a model with diverse multi-head attention to learn grounded multilingual multimodal representations. Specifically, our model attends to different types of textual semantics in two languages and visual objects for fine-grained alignments between sentences and images. We introduce a new objective function which explicitly encourages attention diversity to learn an improved visual-semantic embedding space. We evaluate our model in the German-Image and English-Image matching tasks on the Multi30K dataset, and in the Semantic Textual Similarity task with the English descriptions of visual content. Results show that our model yields a significant performance gain over other methods in all of the three tasks.
Huang, P-Y, Kang, G, Liu, W, Chang, X & Hauptmann, AG 1970, 'Annotation Efficient Cross-Modal Retrieval with Adversarial Attentive Alignment', Proceedings of the 27th ACM International Conference on Multimedia, MM '19: The 27th ACM International Conference on Multimedia, ACM, Nice, FRANCE, pp. 1758-1767.
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Visual-semantic embeddings are central to many multimedia applications such as cross-modal retrieval between visual data and natural language descriptions. Conventionally, learning a joint embedding space relies on large parallel multimodal corpora. Since massive human annotation is expensive to obtain, there is a strong motivation in developing versatile algorithms to learn from large corpora with fewer annotations. In this paper, we propose a novel framework to leverage automatically extracted regional semantics from un-annotated images as additional weak supervision to learn visual-semantic embeddings. The proposed model employs adversarial attentive alignments to close the inherent heterogeneous gaps between annotated and un-annotated portions of visual and textual domains. To demonstrate its superiority, we conduct extensive experiments on sparsely annotated multimodal corpora. The experimental results show that the proposed model outperforms state-of-the-art visual-semantic embedding models by a significant margin for cross-modal retrieval tasks on the sparse Flickr30k and MS-COCO datasets. It is also worth noting that, despite using only 20% of the annotations, the proposed model can achieve competitive performance (Recall at 10 > 80.0% for 1K and > 70.0% for 5K text-to-image retrieval) compared to the benchmarks trained with the complete annotations.
Huang, P-Y, Vaibhav, Chang, X & Hauptmann, AG 1970, 'Improving What Cross-Modal Retrieval Models Learn through Object-Oriented Inter- and Intra-Modal Attention Networks', Proceedings of the 2019 on International Conference on Multimedia Retrieval, ICMR '19: International Conference on Multimedia Retrieval, ACM, Ottawa, CANADA, pp. 244-252.
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Although significant progress has been made for cross-modal retrieval models in recent years, few have explored what those models truly learn and what makes one model superior to another. Start by training two state-of-the-art text-to-image retrieval models with adversarial text inputs, we investigate and quantify the importance of syntactic structure and lexical information in learning the joint visual-semantic embedding space for cross-modal retrieval. The results show that the retrieval power mainly comes from localizing and connecting the visual objects and their cross-modal counterparts, the textual phrases. Inspired by this observation, we propose a novel model which employs object-oriented encoders along with inter- and intra-modal attention networks to improve inter-modal dependencies for cross-modal retrieval. In addition, we develop a new multimodal structure-preserving objective which additionally emphasizes intra-modal hard negative examples to promote intra-modal discrepancies. Extensive experiments show that the proposed approach outperforms the existing best method by a large margin (16.4% and 6.7% relatively with Recall@1 in the text-toimage retrieval task on the Flickr30K dataset and the MS-COCO dataset respectively).
Huang, S, Lu, W, Zhou, Y, Yu, S, Zhang, Y, Shi, X & Chen, Z 1970, 'An Automatic Slope-Calibrated Ramp Generator for Single-Slope ADCs', 2019 IEEE 13th International Conference on ASIC (ASICON), 2019 IEEE 13th International Conference on ASIC (ASICON), IEEE, PEOPLES R CHINA, Chongqing, pp. 1-4.
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Huang, W, Xu, RYD & Oppermann, I 1970, 'Efficient Diversified Mini-Batch Selection using Variable High-layer Features', Proceedings of Machine Learning Research, pp. 300-315.
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Stochastic Gradient Descent (SGD) has been widely adopted in training Deep Neural networks of various structures. Instead of using a full dataset, a so-called mini-batch is selected during each gradient descent iteration. This aims to speed up the learning when a large number of training data is present. Without the knowledge of its true underlying distribution, one often samples the data indices uniformly. Recently, researchers applied a diversified mini-batch selection scheme through the use of Determinantal Point Process (DPP), in order to avoid having highly correlated samples in one batch (Zhang et al. (2017)). Despite its success, the attempts were restrictive in the sense that they used fixed features to construct the Gram-matrix for DPP; using the raw or fixed higher-layer features limited the amount of potential improvement over the convergence rate. In this paper, we instead proposed to use variable higher-layer features which are updated at each iteration when the parameter changes. To avoid the high computation cost, several contributions have been made to speed up the computation of DPP sampling, including: (1) using hierarchical sampling to break down a single DPP sampling with large Gram-matrix into many DPP samplings of much smaller Gram-matrix and (2) using Markov k-DPP to encourage diversity across iterations. Empirical results show a much more diversified mini batch in each iteration in addition to a much improved convergence compared with the previous approach.
Huang, W, Xu, RYD & Oppermann, I 1970, 'Realistic Image Generation using Region-phrase Attention', Proceedings of Machine Learning Research, pp. 284-299.
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The Generative Adversarial Network (GAN) has achieved remarkable progress in generating synthetic images from text, especially since the use of the attention mechanism. The current state-of-the-art algorithm applies attentions between individual regular-grid regions of an image and words of a sentence. These approaches are sufficient to generate images that contain a single object in its foreground. However, natural languages often involve complex foreground objects and the background may also constitute a variable portion of the generated image. In this case, the regular-grid region based image attention weights may not necessarily concentrate on the intended foreground region(s), which in turn, results in an unnatural looking image. Additionally, individual words such as “a”, “blue” and “shirt” do not necessarily provide a full visual context unless they are applied together. For this reason, in our paper, we proposed a novel method in which we introduced an additional set of natural attentions between object-grid regions and word phrases. The object-grid region is defined by a set of auxiliary bounding boxes. They serve as superior location indicators to where the alignment and attention should be drawn with the word phrases. We perform experiments on the Microsoft Common Objects in Context (MSCOCO) dataset and prove that our proposed approach is capable of generating more realistic images compared with the current state-of-the-art algorithms.
Huang, X 1970, 'Towards Terabit Wireless Communications', 2019 Australian Communications Theory Workshop, 2019 Australian Communications Theory Workshop, Sydney, Australia.
Huang, X, Fan, L, Wu, Q, Zhang, J & Yuan, C 1970, 'Fast Registration for cross-source point clouds by using weak regional affinity and pixel-wise refinement', Proceedings - IEEE International Conference on Multimedia and Expo, IEEE International Conference on Multimedia and Expo, IEEE, Shanghai, China, pp. 1552-1557.
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Many types of 3D acquisition sensors have emerged in recent years and pointcloud has been widely used in many areas. Accurate and fast registration ofcross-source 3D point clouds from different sensors is an emerged researchproblem in computer vision. This problem is extremely challenging becausecross-source point clouds contain a mixture of various variances, such asdensity, partial overlap, large noise and outliers, viewpoint changing. In thispaper, an algorithm is proposed to align cross-source point clouds with bothhigh accuracy and high efficiency. There are two main contributions: firstly,two components, the weak region affinity and pixel-wise refinement, areproposed to maintain the global and local information of 3D point clouds. Then,these two components are integrated into an iterative tensor-based registrationalgorithm to solve the cross-source point cloud registration problem. Weconduct experiments on synthetic cross-source benchmark dataset and realcross-source datasets. Comparison with six state-of-the-art methods, theproposed method obtains both higher efficiency and accuracy.
Huang, X, Zhang, H, Zhang, JA, Guo, YJ, Song, R-L, Xu, X-F, Wang, C-T, Lu, Z & Wu, W 1970, 'Dual Pulse Shaping Transmission with Complementary Nyquist Pulses', 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), IEEE, Honolulu, HI, USA, pp. 1-6.
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© 2019 IEEE. The concept of complementary Nyquist pulse is introduced in this paper. Making use of a half rate Nyquist pulse and its complementary one, a dual pulse shaping transmission scheme is proposed, which achieves full Nyquist rate transmission with only a half of the sampling rate required by conventional Nyquist pulse shaping. This is essential for realizing high-speed digital communication systems with available and affordable data conversion devices. The condition for cross-symbol interference free transmission with the proposed dual pulse shaping is proved in theory, and two classes of ideal complementary Nyquist pulses are formulated assuming raised-cosine pulse shaping. Simulation results are also presented to demonstrate the improved spectral efficiency with dual pulse shaping and compare other system performance against conventional Nyquist pulse shaping.
Huang, Y, Song, R, Chen, W, Yu, H, Argha, A, Celler, BG & Su, S 1970, 'The effects of different tracking tasks on muscle synergy through visual feedback', 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Berlin, Germany, pp. 417-420.
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© 2019 IEEE. By recruiting a modular organization of muscle with relative activities, the arm motion can be indicated by the neural system and generated for performing a variety of motor tasks. In this study, a Non-negative Matrix Factorization with initial estimation is applied to identify and extract primary muscle synergies and their activation patterns from the processed EMG recordings during three multidirectional tracking tasks with visual feedback interaction. The effects of task variety and tracking accuracy by visual feedback on muscle synergies and their activation patterns are explored by statistic analysis. The results showed that only the task variety affected what synergies were indicated by the neural system, but both task variety and visual feedback affected the duration and magnitude of the primary synergies. Thus, for active rehabilitation application, it is advised that if the purpose is to enhance the synergy indication from the neural system, the task completion accuracy should be emphasized, but if the purpose is to expand the motion area, the task variety should be diversified.
Huang, Y, Wu, Q, Xu, J & Zhong, Y 1970, 'Celebrities-ReID: A Benchmark for Clothes Variation in Long-Term Person Re-Identification', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-8.
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© 2019 IEEE. This paper considers person re-identification (re-ID) in the case of long-time gap (i.e., long-term re-ID) that concentrates on the challenge of clothes variation of each person. We introduce a new dataset, named Celebrities-reID to handle that challenge. Compared with current datasets, the proposed Celebrities-reID dataset is featured in two aspects. First, it contains 590 persons with 10,842 images, and each person does not wear the same clothing twice, making it the largest clothes variation person re-ID dataset to date. Second, a comprehensive evaluation using state of the arts is carried out to verify the feasibility and new challenge exposed by this dataset. In addition, we propose a benchmark approach to the dataset where a two-step fine-tuning strategy on human body parts is introduced to tackle the challenge of clothes variation. In experiments, we evaluate the feasibility and quality of the proposed Celebrities-reID dataset. The experimental results demonstrate that the proposed benchmark approach is not only able to best tackle clothes variation shown in our dataset but also achieves competitive performance on a widely used person re-ID dataset Market1501, which further proves the reliability of the proposed benchmark approach.
Huang, Y, Wu, Q, Xu, J & Zhong, Y 1970, 'SBSGAN: Suppression of Inter-Domain Background Shift for Person Re-Identification', 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Seoul, South Korea, pp. 9526-9535.
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© 2019 IEEE. Cross-domain person re-identification (re-ID) is challenging due to the bias between training and testing domains. We observe that if backgrounds in the training and testing datasets are very different, it dramatically introduces difficulties to extract robust pedestrian features, and thus compromises the cross-domain person re-ID performance. In this paper, we formulate such problems as a background shift problem. A Suppression of Background Shift Generative Adversarial Network (SBSGAN) is proposed to generate images with suppressed backgrounds. Unlike simply removing backgrounds using binary masks, SBSGAN allows the generator to decide whether pixels should be preserved or suppressed to reduce segmentation errors caused by noisy foreground masks. Additionally, we take ID-related cues, such as vehicles and companions into consideration. With high-quality generated images, a Densely Associated 2-Stream (DA-2S) network is introduced with Inter Stream Densely Connection (ISDC) modules to strengthen the complementarity of the generated data and ID-related cues. The experiments show that the proposed method achieves competitive performance on three re-ID datasets, i.e., Market-1501, DukeMTMC-reID, and CUHK03, under the cross-domain person re-ID scenario.
Huang, Z & Zhang, Q 1970, 'Skew Correction of Handwritten Chinese Character Based on ResNet', 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), IEEE, PEOPLES R CHINA, Shenzhen, pp. 223-227.
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Hyun, J-S, Carmichael, MG, Tran, A, Zhang, S & Liu, D 1970, 'Evaluation of Fast, High-detail Projected Light 3D Sensing for Robots in Construction', 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, Xi'an, China, pp. 1262-1267.
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© 2019 IEEE. Robots used on-site in construction need to perceive the surrounding environment to operate autonomously. This is challenging as the construction environment is often less than ideal due to changing lighting conditions, turbid air, and the need to detect fine details. In this work we evaluate a custom made projected light 3D sensor system for suitability and practicality in enabling autonomous robotics for construction. A series of tests are performed to evaluate the sensor based on ability to capture environmental details, operate robustly in challenging lighting conditions, and make accurate geometric measurements. Analysis shows that high fidelity measurements with accuracy in the order of millimeters can be obtained, making the technology a promising solution for robots operating in construction environments.
Idrees, MO & Pradhan, B 1970, 'Frontier in Three-Dimensional Cave Reconstruction—3D Meshing Versus Textured Rendering', GCEC 2017: Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 1029-1038.
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© Springer Nature Singapore Pte Ltd. 2019. Underground caves and their specific structures are important for geomorphological studies. This paper investigates the capabilities of a new modelling approach advanced for true-to-life three-dimensional (3D) reconstruction of cave with full resolution scan relative to 3D meshing. The cave was surveyed using terrestrial laser scanner (TLS) to acquire high resolution scans. The data was processed to generate a 3D-mesh model and textured 3D model using sub-sampled points and full resolution scan respectively. Based on both point and solid surface representation, comparative analysis of the strengths and weaknesses of the two approaches were examined in terms of data processing efficiency, visualization, interactivity and geomorphological feature representation and identification. The result shows that full scan point representation offers advantage for dynamic visualization over the decimated xyz point data because of high density of points and availability of other surface information like point normal, intensity and height which can be visualized in colour scale. For the reconstructed surface, mesh model is better with respect to interactivity and morphometric but 3D rendering shows superiority in visual reality and identification of micro detail of features with high precision. Complementary use of the two will provide better understanding of the cave, its development and processes.
Indraratna, B, Baral, P, Qi, Y, Ngo, T, Rujikiatkamjorn, C & Ferreia, F 1970, 'Advances in Ground Improvement and Principles of Track Geomechanics for Future Railways', Proc. 17th African Regional Conference on Soil Mechanics and Geotechnical Engineering, 17th African Regional Conference on Soil Mechanics and Geotechnical Engineering, Cape Town, pp. 21-36.
Indraratna, B, Ngo, NT, Sun, Q, Rujikiatkamjorn, C & Ferreira, FB 1970, 'Concepts and Methodologies for Track Improvement and Associated Physical Modelling and Field Monitoring', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, New Delhi, India, pp. 219-246.
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© 2019, Springer Nature Singapore Pte Ltd. As the heavy haul freight trains become longer and heavier, ballast grain experience pronounced breakage and deformation, resulting in the deterioration of the ballasted track substructure. Suitable soil stabilisation approaches using geosynthetics and/or energy-absorbing rubber mats are commonly employed to enhance the stability and longevity of ballasted tracks. This paper reviews the research studies that have been conducted at the University of Wollongong on track technology using advanced laboratory and computational modelling, as well as real-life health monitoring of selected track sections. Full-scale instrumented field monitoring supported by Australian rail organisations has been carried out to obtain measurements of actual stresses and displacements and thereby evaluate track performance supplemented by computational models. In the past decade, the authors have tested varied types of geosynthetics and rubber mats both in the laboratory and in the field where these geoinclusions were put underneath the ballast layer in tracks built on various subgrade types (i.e. soft and hard subgrades). Stresses induced by traffic, ballast degradation, vertical and lateral displacements of the ballast aggregates were routinely recorded using extensive instrumentation systems. These results provide suitable approaches that can be considered into current track design for future heavy and long freight train travelling at higher speeds.
Indraratna, B, Qi, Y, Jayasuriya, C, Heitor, A & Sinniah, KN 1970, 'Use of Rubber Tyre Elements in Track Stabilization', 15th International Conference on Geotechnical Engineering, Larhore, Pakistan.
Inibhunu, C, Jalali, R, Doyle, I, Gates, A, Madill, J & McGregor, C 1970, 'Adaptive API for Real-Time Streaming Analytics as a Service', 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Berlin, Germany, pp. 3472-3477.
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A significant amount of physiological data is generated from bedside monitors and sensors in neonatal intensive units (NICU) every second, however facilitating the ingestion of such data into multiple analytical processes in a real time streaming architecture remains a central challenge for systems that seek effective scaling of real-time data streams. In this paper we demonstrate an adaptive streaming application program interface (API) that provides real time streams of data for consumption by multiple analytics services enabling real-time exploration and knowledge discovery from live data streams. We have designed, developed and evaluated an adaptive API with multiple ingestion of data streamed out of bedside monitors that is passed to a middleware for standardization and structuring and finally distributed as a service for multiple analytical services to consume and perform further processing. This approach allows, (a) multiple applications to process the same data streams using multiple algorithms, (b) easy scalability to manage diverse data streams, (c) processing of analytics for each patient monitored at the NICU, (d) ability to integrate analytics that seek to evaluate multiple patients at the same point in time, and (e) a robust automated process with no manual interruptions that effectively adapts to changing data volumes when bedside monitors increases or the amount of data emitted by a monitor changes. The proposed architecture has been instantiated within the Artemis Platform which provides a framework for real-time high speed physiological data collection from multiple and diverse bed side monitors and sensors in NICUs from multiple hospitals. Results indicate this is a robust approach that can scale effectively as data volumes increase or data sources change.
Irfan, S, Dushmantha, T & Michael, H 1970, 'Novel Half-Patch based 1-D Periodic Structure with Better Control over Stop Bandwidth', 2019 IEEE Asia-Pacific Microwave Conference (APMC), 2019 IEEE Asia-Pacific Microwave Conference (APMC), IEEE, SINGAPORE, pp. 1712-1714.
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Islam, M, Mithulananthan, N, Hossain, J & Bhumkittipich, K 1970, 'Short-term Voltage Stability of Distribution Grids With Medium-scale PV Plants due to Asymmetrical Faults', 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), IEEE, Bangkok, Thailand, pp. 130-135.
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© 2019 IEEE. With the increasing penetration of photo-voltaic (PV) units into electrical grids, particularly in distribution networks (DNs), the concern of short-term voltage instability (STVI) are growing in the presence of induction motor (IM) loads. On the event of unsymmetrical faults, STVI issues could be more complicated as the next-generation PV systems would require negative sequence power injection into the grid in conjunction with positive one. Therefore, this paper comprehensively investigates the impact of negative sequence power on the short-term voltage stability (STVS) of DNs. The method of characterizing an unbalanced fault and supplementary controls for PV systems are developed. Different case studies are conducted on a balanced IEEE 4 bus and an unbalanced IEEE 13 bus system by injecting different level of negative sequence power considering with and without peak current limitation of the PV converters. It is observed that STVS is likely to be weakened in case of large negative sequence power penetration, while injecting high positive sequence power can cause excessive voltage swell resulting inverter disconnections. Therefore, both positive and negative sequence powers need to be injected optimally to ensure the system's security following a fault.
Islam, M, Mithulananthan, N, Hossain, MJ & Bhumkittipich, K 1970, 'A New Grid-support Strategy with PV Units to Enhance Short-term Voltage Stability', 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), IEEE, Bangkok, Thailand, pp. 142-147.
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© 2019 IEEE. Modern grid codes demand the integration of voltage support capability with photo-voltaic (PV) generators to ensure a secure and reliable grid operation. On the other hand, short-term voltage instability (STVI) of distribution networks (DNs) is one of the key issues to be addressed due to the rising proportion of induction motor (IM) loads. However, the literature lacks an extensive analysis of short-term voltage stability (STVS) following an unsymmetrical fault in a DN, as well as an effective voltage-support strategy for PV units to improve the STVS while mitigating the excessive voltage swell. Therefore, at first, this paper thoroughly investigates the STVS of a DN subjected to an unbalanced fault. It is perceived that voltage support through conventional methods can increase the risk of STVI and excessive voltage swell. Secondly, a new voltage-support strategy is proposed based on the negative sequence voltage at the point of common coupling (PCC) to improve the STVS and to limit the voltage swell within requirement. The key features of the proposed method are (1) fast and accurate estimation of a network's impedance at PCC is not required, and (2) can be re-designed considering the network behaviors. The proposed method is validated on two IEEE benchmark test systems, and the provided results designate the effectiveness in improving the STVS and alleviating over voltage issues in a DN.
Islam, MR, Helen Lu, H, Hossain, MJ & Li, L 1970, 'Improving Power Quality of Distributed PV-EV Distribution Grid by Mitigating Unbalance', 2019 IEEE International Conference on Industrial Technology (ICIT), 2019 IEEE International Conference on Industrial Technology (ICIT), IEEE, Melbourne, Australia, pp. 643-648.
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© 2019 IEEE. Increasing price of fossil fuels and public awareness encouraged many countries to use clean technologies in transport and electricity generation sector. The advent of smart meters can identify unbalance in PV-EV distribution grid which is a great concern for Distribution Service Operators (DSOs). Several researchers have accessed the degree of unbalance and impact of unbalance on distribution grids considering either distributed PV or EVs. Moreover, a few research work has been done for mitigating unbalance till now. This paper measures unbalance due to unequal distribution of loads and sources among three phases and assess the impact of unbalance on power quality of the PV-EV distribution system by considering different PV and EV penetration levels using DigSILENT Power Factory simulation software. An improved method is proposed to mitigate unbalance using Genetic Algorithm by optimizing load distribution among phases. Finally, the efficacy of the proposed method is evaluated considering unequally distributed residential and EV load scenarios, and it is found that the proposed method can reduce a significant amount of unbalance at all the buses of the distribution grid.
Islam, MR, Lu, H, Fang, G, Li, L & Hossain, MJ 1970, 'Optimal Dispatch of Electrical Vehicle and PV Power to Improve the Power Quality of an Unbalanced Distribution Grid', 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), IEEE, Shenzhen, China, pp. 258-263.
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© 2019 IEEE. In the smart grid, the distributed generations play an important role to manage the distribution grid. The renewable energy sources such as PV solar, wind, etc. and the Electric Vehicle's Energy Storage are the prominent distributed generation sources. The distributed generation (DG) reduces power loss and improves the voltage profile and reliability of a low voltage (LV) distribution grid. However, optimal placement and sizing of DGs need to be planned properly. Several researchers planned to place single or multiple DGs at the optimum node with an optimal amount of power dispatch assuming balanced distribution grid. But the DGs are connected at all node/buses which require an optimum amount of power dispatch and distribution grids are seldom balance. Moreover, a few research have been conducted for optimizing DG dispatch in an unbalanced distribution grid. This paper proposes a method to improve voltage profile and reduce the total power loss by optimizing the PV and EVs power dispatch in an unbalanced distribution grid. This study will solve the optimization problem using the Differential evolution (DE) optimization algorithm and compares the performance with the Genetic algorithm (GA). Finally, the efficacy of the proposed method is evaluated by applying to an Australian distribution grid. The proposed method reduces 55.72% real power loss of the network. It is also found that the proposed method improves the bus voltage up to 7.65% and increase the bus voltage above 0.95 p.u at all the nodes.
Islam, MR, Lu, H, Hossain, MJ & Li, L 1970, 'Multi-objective Dynamic Phase re-configuration Technique to Mitigate the Unbalance Due to Penetration of Electric Vehicles', 2019 9th International Conference on Power and Energy Systems (ICPES), 2019 9th International Conference on Power and Energy Systems (ICPES), IEEE, Perth, AUSTRALIA, pp. 1-5.
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Islam, MR, Lu, H, Hossain, MJ & Li, L 1970, 'Reducing Neutral Current of a higher EV Penetrated Unbalanced Distribution Grid', 2019 9th International Conference on Power and Energy Systems (ICPES), 2019 9th International Conference on Power and Energy Systems (ICPES), IEEE, Perth, AUSTRALIA, pp. 1-5.
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Islam, MR, Lu, HH, Hossain, MJ & Li, L 1970, 'A Comparison of Performance of GA, PSO and Differential Evolution Algorithms for Dynamic Phase Reconfiguration Technology of a Smart Grid', 2019 IEEE Congress on Evolutionary Computation (CEC), 2019 IEEE Congress on Evolutionary Computation (CEC), IEEE, Wellington, New Zealand, pp. 858-865.
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© 2019 IEEE. Increasing penetration of Distributed Generations (Photovoltaic solar energy (PV), Wind energy, and Battery Energy Storage) and PEVs (Plug-in Electric Vehicles) into smart grid induce network imbalance which reduces power quality. The uncertainty of demand-generation requires balancing for mitigating network imbalance. Several researchers have used various optimization methods for mitigating unbalance. Moreover, a few researchers have done comparative studies of optimization methods for mitigating unbalance till now. This paper proposes a method to mitigate unbalance and reduce the total power loss by optimizing load distribution among phases. This paper compares the performance of Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms on the application of phase balancing. Finally, the efficacy of these algorithms are evaluated for the proposed unbalance mitigation technique, and it is found that the proposed technique using DE algorithm can reduce a significant amount of unbalance at all the buses of the distribution grid with less computational effort.
Islam, MR, Lu., HH, Hossain, MJ & Li, L 1970, 'Compensating Neutral Current, Voltage Unbalance and Improving Voltage of an Unbalanced Distribution Grid Connected with EV and Renewable Energy Sources', 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), IEEE, Harbin, China, pp. 1-5.
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© 2019 IEEE. Coordinating electric vehicle (EV) charging offers several possible solutions, e.g., charging or discharging rate, and schedule time to improve performances of the distribution network. But EV charging or discharging schedule can be affected due to the punctuality of EV users and equipment failures. The growing penetration of EVs is expected to affect the distribution network performances (voltage unbalance, neutral current, and voltage) as well as generation scheduling due to EV uncertainties. Most of the proposed EV charging control strategies improve the network performance ignoring comfortability (change charging or discharging rate) and lack of punctuality of EV users. This paper investigates the impact of EV uncertainty on the imbalance of the network in a higher penetrated distribution grid. A centralized control algorithm is proposed to coordinate EVs and DESs service point of connection (SPOC) among phases to mitigate the network imbalance and improve the voltage. Using the proposed control approach, the candidate DES number is reduced to participate, whereas EV users do not require to participate. Results obtained using the proposed control approach shows that the neutral current reduces 82.98%, voltage unbalance up to 99.08% and improve voltage up to 17.08%.
Jamborsalamati, P, Moghimi, M, Hossain, J & Lu, J 1970, 'Design and Implementation of a Hierarchical Hybrid Communication Platform for Multi-Microgrid Applications', Sustainability in Energy and Buildings 2018 Proceedings of the 10th International Conference in Sustainability on Energy and Buildings (SEB’18), International Conference in Sustainability on Energy and Buildings, Springer International Publishing, Gold Coast, Australia, pp. 199-208.
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© Springer Nature Switzerland AG 2019. This paper presents a hierarchical hybrid communication platform for Multi-Microgrid (MMG) optimization applications. The main purpose of the implemented platform is to attach multiple Microgrids (MGs) to each other by adding an Internet of Things (IoT) gateway to each MG. This enables bi-directional data exchange among the MGs through the IoT gateway for optimal operation of the MGs with respect to each other. Considering the scale of the data acquisition in MMG optimization problems, utilization of a cloud-based platform for extensive data sharing and post-processing of the aggregated data is vital. The proposed platform has adopted Modbus protocol for communications between the devices inside each MG, local controllers, and the MG Central Controller (MGCC). The Message Queue Telemetry Transport (MQTT) protocol is used for data sharing among the MGCCs and HTTP requests for interactions with a cloud server in a hybrid platform. The cloud server has an interface to MATLAB and the hierarchical architecture is implemented in a co-simulation platform with Python and MATLAB. Results show the efficacy of the implemented platform for MMG optimization applications.
Jaschek, C, Beckmann, T, Garcia, JA & Raffe, WL 1970, 'Mysterious Murder - MCTS-driven Murder Mystery Generation', 2019 IEEE Conference on Games (CoG), 2019 IEEE Conference on Games (CoG), IEEE, London, United Kingdom, pp. 1-8.
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© 2019 IEEE. We present an approach to procedurally generate the narrative of a simple murder mystery. As a basis for the simulation, we use a rule evaluation system inspired by Ceptre, which employs linear logic to resolve valid actions during each step of the simulation. We extend Ceptre's system with a concept of believable agents to make consecutive actions appear to have a causal connection so that players can comprehend the flow of events. The parts of the generated narratives are then presented to a player whose task it is to figure out who the murderer in this story could have been. Rather than aiming to replace highly authored narratives, this project generates puzzles, which may contain emerging arcs of a story as perceived by the player. While we found that even a simple rule set can create stories that are interesting to reason about, we expect that this type of system is flexible enough to create considerably more engaging stories if enough time is invested in authoring more complex rule sets.
Jauregi Unanue, I, Zare Borzeshi, E, Esmaili, N & Piccardi, M 1970, 'ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems', Proceedings of the 2019 Conference of the North, Proceedings of the 2019 Conference of the North, Association for Computational Linguistics, Minneapolis, pp. 430-436.
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Regularization of neural machine translation is still a significant problem, especially in low-resource settings. To mollify this problem, we propose regressing word embeddings (ReWE) as a new regularization technique in a system that is jointly trained to predict the next word in the translation (categorical value) and its word embedding (continuous value).
Such a joint training allows the proposed system to learn the distributional properties represented by the word embeddings, empirically improving the generalization to unseen sentences. Experiments over three translation datasets have showed a consistent improvement over a strong baseline, ranging between 0.91 and 2.54 BLEU points, and also a marked
improvement over a state-of-the-art system.
Jayasuriya, M, Dissanayake, G, Ranasinghe, R & Gandhi, N 1970, 'Leveraging Deep Learning Based Object Detection for Localising Autonomous Personal Mobility Devices in Sparse Maps.', ITSC, IEEE Intelligent Transportation Systems Conference, IEEE, Auckland, New Zealand, pp. 4081-4086.
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© 2019 IEEE. This paper presents a low cost, resource efficient localisation approach for autonomous driving in GPS denied environments. One of the most challenging aspects of traditional landmark based localisation in the context of autonomous driving, is the necessity to accurately and frequently detect landmarks. We leverage the state of the art deep learning framework, YOLO (You Only Look Once), to carry out this important perceptual task using data obtained from monocular cameras. Extracted bearing only information from the YOLO framework, and vehicle odometry, is fused using an Extended Kalman Filter (EKF) to generate an estimate of the location of the autonomous vehicle, together with it's associated uncertainty. This approach enables us to achieve real-time sub metre localisation accuracy, using only a sparse map of an outdoor urban environment. The broader motivation of this research is to improve the safety and reliability of Personal Mobility Devices (PMDs) through autonomous technology. Thus, all the ideas presented here are demonstrated using an instrumented mobility scooter platform.
Jayathilaka, P, Indraratna, B & Heitor, A 1970, 'Influence that Osmotic Suction and Tree Roots has on the Stability of Coastal Soils', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 669-680.
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© Springer Nature Singapore Pte Ltd 2019. The contribution made by osmotic suction to unsaturated shear strength analysis has not been considered for the past few decades. Osmotic suction is generated by the salt in pore water, especially in coastal environments, and it can be more significant than matric suction. Tree roots can also induce osmotic and matric suction by continuous transpiration, and when these saline and rooted environments are combined under unsaturated conditions, they can challenge conventional shear strength models. Electrical resistivity can be used as a proper tool to evaluate the properties of soil in a large scale. This review summarizes the historical development of studies related to osmotic suction as well as the present situation of osmotic suction for soil shear strength.
Jena, R & Pradhan, B 1970, 'A Model To Detect Forest Change Relating To Mining Using Google Earth Engine Application In Belitung Island, Indonesia', 2019 6th International Conference on Space Science and Communication (IconSpace), 2019 6th International Conference on Space Science and Communication (IconSpace), IEEE, Johor Bahru, Malaysia, pp. 47-52.
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© 2019 IEEE. Belitung Island is one of the biodiversity hotspots in Indonesia that is best known for its multi-use landscape, tourism, large agricultural land, tin mining and all other activities. The main earning possibility of local people of the island is most efficiently lies in coastal activities and tin mining. Main challenges are persistent cloud cover over the steep and vegetated terrain that creates a problem in forest change mapping. This research was conducted to identify and visually analyse the forest loss or gain due to tin mining activity and settlement along with the consequences of illegal logging using the Google earth engine application. Furthermore, this study will also help to understand the areas of water bodies filled after mining making it inactive. Therefore, NDVI and MNDWI analysis have been conducted to calculate the index values using the (GEE) Google earth engine and graphically presented. Landsat +ETM, MODIS global land cover, Hansen global forest change and other remote sensing data applied to conduct this research. The results obtained from this study shows that the width of forestry land cover is decreased gradually from 2012 to 2017 and the active tin mining, agricultural land, and settlement are widely increased. The inactive tin-mined areas are filled with water that can be well understood from the elevation modelling. Furthermore, the forest gain is also increasing mildly as per the results of change detection in forest gain analysis from 2012 to 2017. This clearly indicates the change of forest resulting due to the active tin mining and inactive tin-mined water filled land as well as the human settlement.
Jena, R & Pradhan, B 1970, 'Earthquake Vulnerability Assessment using Expert-based Approach in GIS', 2019 6th International Conference on Space Science and Communication (IconSpace), 2019 6th International Conference on Space Science and Communication (IconSpace), IEEE, Johor Bahru, Malaysia, pp. 53-56.
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© 2019 IEEE. Several techniques of earthquake vulnerability assessment exist for the evaluation of social, economic and buildings vulnerability that has been carried out to investigate their suitability in the application of earthquake risk assessment. The primary challenge is the prediction of earthquakes that is almost seemingly impossible in the current time. The main concern is mitigation and preparedness, which is dominant for the human, animals, and environment. However, exposed assets and the determination of their fragilities/vulnerabilities are essential and will be challenging in the future for the viability and reliability during the assessment of rapid loss. Therefore, this study proposes an expert's decision-based approach for the assessment of earthquake vulnerability in Banda Aceh city, Indonesia that could help in future risk assessment on a city scale. It was analysed that the proposed method adequately satisfies all the necessary criteria that can be involved in earthquake vulnerability assessment in Banda Aceh city to reduce the earthquake impacts. The results shows that the proposed method is good for city-scale earthquake vulnerability assessment with significant consistency ratio of 0.04. This research observes the current practices involved in regional and urban earthquake vulnerability assessment.
Ji, LY, Qin, PY, Guo, YJ, Genovesi, S, Zhu, HL & Zong, Y 1970, 'A Reconfigurable Partially Reflective Surface Antenna with Enhanced Beam Steering Capability', 13th European Conference on Antennas and Propagation, EuCAP 2019, European Conference on Antennas and Propagation, IEEE, Krakow, Poland.
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A reconfigurable partially reflective surface (PRS) antenna with improved beam steering capability is proposed in this paper. Compared with our previous paper, the beam-steering angle can be enhanced from ±5° to ±17° with less active elements and a much smaller gain variation. It is realized by employing a compact reconfigurable metasurface as the PRS structure, which is located atop a probe-fed square patch antenna. A prototype antenna operating at 5.5 GHz is fabricated and measured. Good agreement between the simulated and measured results for the input reflection coefficients and radiation patterns is achieved, which validates the feasibility of the design principle.
Ji, S, Pan, S, Long, G, Li, X, Jiang, J & Huang, Z 1970, 'Learning Private Neural Language Modeling with Attentive Aggregation', International Joint Conference on Neural Networks, IEEE, Budapest, Hungary.
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Mobile keyboard suggestion is typically regarded as a word-level language modeling problem. Centralized machine learning techniques require the collection of massive user data for training purposes, which may raise privacy concerns in relation to users' sensitive data. Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestions by training models on distributed clients rather than training them on a central server. To obtain a global model for prediction, existing FL algorithms simply average the client models and ignore the importance of each client during model aggregation. Furthermore, there is no optimization for learning a well-generalized global model on the central server. To solve these problems, we propose a novel model aggregation with an attention mechanism considering the contribution of client models to the global model, together with an optimization technique during server aggregation. Our proposed attentive aggregation method minimizes the weighted distance between the server model and client models by iteratively updating parameters while attending to the distance between the server model and client models. Experiments on two popular language modeling datasets and a social media dataset show that our proposed method outperforms its counterparts in terms of perplexity and communication cost in most settings of comparison.
Ji, S, Pan, S, Long, G, Li, X, Jiang, J & Huang, Z 1970, 'Learning Private Neural Language Modeling with Attentive Aggregation', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, HUNGARY, pp. 1-8.
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Mobile keyboard suggestion is typically regarded as a word-level language modeling problem. Centralized machine learning techniques require the collection of massive user data for training purposes, which may raise privacy concerns in relation to users' sensitive data. Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestions by training models on distributed clients rather than training them on a central server. To obtain a global model for prediction, existing FL algorithms simply average the client models and ignore the importance of each client during model aggregation. Furthermore, there is no optimization for learning a well-generalized global model on the central server. To solve these problems, we propose a novel model aggregation with an attention mechanism considering the contribution of client models to the global model, together with an optimization technique during server aggregation. Our proposed attentive aggregation method minimizes the weighted distance between the server model and client models by iteratively updating parameters while attending to the distance between the server model and client models. Experiments on two popular language modeling datasets and a social media dataset show that our proposed method outperforms its counterparts in terms of perplexity and communication cost in most settings of comparison.
Ji, Z, Qiao, Y, Song, F & Yun, A 1970, 'General Linear Group Action on Tensors: A Candidate for Post-quantum Cryptography', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Theory of Cryptography, Springer International Publishing, Nuremberg, pp. 251-281.
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© 2019, International Association for Cryptologic Research. Starting from the one-way group action framework of Brassard and Yung (Crypto’90), we revisit building cryptography based on group actions. Several previous candidates for one-way group actions no longer stand, due to progress both on classical algorithms (e.g., graph isomorphism) and quantum algorithms (e.g., discrete logarithm). We propose the general linear group action on tensors as a new candidate to build cryptography based on group actions. Recent works (Futorny–Grochow–Sergeichuk Lin. Alg. Appl., 2019) suggest that the underlying algorithmic problem, the tensor isomorphism problem, is the hardest one among several isomorphism testing problems arising from areas including coding theory, computational group theory, and multivariate cryptography. We present evidence to justify the viability of this proposal from comprehensive study of the state-of-art heuristic algorithms, theoretical algorithms, hardness results, as well as quantum algorithms. We then introduce a new notion called pseudorandom group actions to further develop group-action based cryptography. Briefly speaking, given a group G acting on a set S, we assume that it is hard to distinguish two distributions of (s, t) either uniformly chosen from S × S, or where s is randomly chosen from S and t is the result of applying a random group action of gεG on s. This subsumes the classical Decisional Diffie-Hellman assumption when specialized to a particular group action. We carefully analyze various attack strategies that support instantiating this assumption by the general linear group action on tensors. Finally, we construct several cryptographic primitives such as digital signatures and pseudorandom functions. We give quantum security proofs based on the one-way group action assumption and the pseudorandom group action assumption.
Jia, B, Niu, K, Hou, X, Li, N, Peng, X, Gu, P & Jia, R 1970, 'Prediction for Student Academic Performance Using SMNaive Bayes Model', Lecture Notes in Computer Science, Advanced Data Mining and Applications, Springer International Publishing, Dalian, China, pp. 712-725.
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Predicting students academic performance is very important for students future development. There are a large number of students who can not graduate from colleges on time for various reasons every year. Nowadays, a large volume of students academic data has been generated in the process of promoting education informatization from the field of education. It becomes critical to predict student performance and ensure students to graduate on time by taking the best of these data. Machine learning models that predict students performance are widely available. However, some existing machine learning models still have the problem of low accuracy in predicting students performance. To solve this problem, we proposes a SMNaive Bayes (SMNB) model, which integrates Sequential Minimal Optimization (SMO) and Naive Bayes to make the prediction result more accurate. The basic idea is that the model predicts the performance of students professional courses via their basic course performance in the previous stage. In particular, SMO algorithm is leveraged to predict students academic performance of the first step and produces the results of the prediction; Naive Bayes then makes decision about the inconsistent results of the initial prediction; Lastly, the final results of students professional course performance prediction are produced. To test the effectiveness of our proposed model, we have conducted extensive experiments to compare SMNB against four prediction methods. The experimental results demonstrate that the proposed SMNB model is superior to all the compared methods.
Jian, S, Hu, L, Cao, L, Lu, K & Gao, H 1970, 'Evolutionarily Learning Multi-Aspect Interactions and Influences from Network Structure and Node Content', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, Hawaii USA, pp. 598-605.
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The formation of a complex network is highly driven by multi-aspect node influences and interactions, reflected on network structures and the content embodied in network nodes. Limited work has jointly modeled all these aspects, which typically focuses on topological structures but overlooks the heterogeneous interactions behind node linkage and contributions of node content to the interactive heterogeneities. Here, we propose a multi-aspect interaction and influence-unified evolutionary coupled system (MAI-ECS) for network representation by involving node content and linkage-based network structure. MAI-ECS jointly and iteratively learns two systems: a multi-aspect interaction learning system to capture heterogeneous hidden interactions between nodes and an influence propagation system to capture multiaspect node influences and their propagation between nodes. MAI-ECS couples, unifies and optimizes the two systems toward an effective representation of explicit node content and network structure, and implicit node interactions and influences. MAI-ECS shows superior performance in node classification and link prediction in comparison with the stateof-the-art methods on two real-world datasets. Further, we demonstrate the semantic interpretability of the results generated by MAI-ECS.
Jimenez, KJP, Hora, JA, Gerasta, OJL, Zhu, X & Dutkiewicz, E 1970, 'Self-Biased 2.4 GHz CMOS RF-to-DC Converter with 80% Efficiency and −22.04 dBm Sensitivity for Wi-Fi Energy Harvesting', 2019 IEEE International Circuits and Systems Symposium (ICSyS), 2019 IEEE International Circuits and Systems Symposium (ICSyS), IEEE, pp. 1-4.
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© 2019 IEEE. One of the significant disadvantages of RF energy harvesting is having a low power density in comparison to other ambient energy sources. The rectifier is the core of an RF energy harvesting system since it converts and boosts weak RF power to usable DC power. This study introduces a design of an efficiency enhanced RF-to-DC power converter with impedance matched to ±50Ω. The circuit design is based on two circuit design architectures, namely: the fully cross-coupled rectifier and self-biased technique, and the combined with LC matching circuit to obtain high power conversion efficiency. The design simulation is implemented using 65 nm CMOS Technology process. The performance of the proposed circuit design has achieved a peak power conversion efficiency of 80% at -14.4 dBm with a minimum input power of -22.04 dBm at 2.4 GHz for a load resistance of 20 kΩ and 10 pF load.
Jing Liu, Qimei Cui, Jian Zhang, Wei Ni, Zesong Fei & Yong Li 1970, 'Tradeoff Between Reliability and Channel Utilization Efficiency for 5G NR in Unlicensed Spectrum', IET 8th International Conference on Wireless, Mobile & Multimedia Networks, IET 8th International Conference on Wireless, Mobile & Multimedia Networks, Institution of Engineering and Technology.
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Johir, M 1970, 'Waste Water Recycling and Management', Springer Singapore.
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John, BM & Jayan Chirayath Kurian, J 1970, 'Making the world a better place with Mixed Reality in Education,', Perth.
John, BM & Jayan Chirayath Kurian, J 1970, 'Mixed Reality in the Information Systems pedagogy: An Authentic Learning Experience', Munich.
Jorquera, E, Rodríguez, JF, Saco, PM & Timmermans, H 1970, 'Assessment of the impact of cyclones on the annual sediment budget in a pacific island catchment using a hydro-sedimentological model', 23rd International Congress on Modelling and Simulation - Supporting Evidence-Based Decision Making: The Role of Modelling and Simulation, MODSIM 2019, pp. 972-978.
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Pacific Islands are one of the world hotspots for climate change, with sea level rise (SLR) and increases in tropical cyclones (TC) activity posing a serious threat to coastal areas and ecosystems. Precipitation and extreme sea level events associated with TC generate floods that cause damage to agriculture, home and businesses and also produce considerable amounts of sediment that end up in the adjacent coastal areas. Our study focuses on coastal wetlands that receive sediments from the Dreketi River catchment on the northern coast of Vanua Levu, Fiji which are likely to be heavily affected by climate change. Recent studies have identified this area of the coast as a storm tide high-risk zone, and also that the Dreketi River catchment contributes most of the sediment to the adjacent Great Sea Reef (GSR) or Cakaulevu. The purpose of this work is to identify the impact of TC on the annual sediment yield through a physically-based hydro-sedimentological model. To address this, the period from 1970 to 2017 was simulated daily with SWAT, obtaining flow and sediment discharges at the outlet of Dreketi River catchment. For the same period, the cyclones within a radius of 600 Km of the barycentre of the catchment were analysed using the Southwest Pacific Enhanced Archive of Tropical Cyclones (SPEArTC). Two types of analysis were performed. The first one focused on the meteorological data, and the aim was to relate the maximum rainfall in the catchment with TC. The second one was based on the results of the hydro-sedimentological model assessing two aspects; i) which percentage of the annual sediment budget can be explained by TC, and ii) in how many cases the maximum annual sediment yield is due to a TC. Regarding the meteorological data, three meteorological stations were analysed with focus on the maximum daily rainfall. It was found that a TC caused the extreme values in each station in 10, 13 and 15 out of 45 years, respectively. However, the modelling res...
Kalantar, B, Ueda, N, Al-Najjar, HAH, Gibril, MBA, Lay, US & Motevalli, A 1970, 'AN EVALUATION OF LANDSLIDE SUSCEPTIBILITY MAPPING USING REMOTE SENSING DATA AND MACHINE LEARNING ALGORITHMS IN IRAN', ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Geospatial Week, Copernicus GmbH, The Netherlands, pp. 503-511.
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Abstract. Landslide is painstaking as one of the most prevalent and devastating forms of mass movement that affects man and his environment. The specific objective of this research paper is to investigate the application and performances of some selected machine learning algorithms (MLA) in landslide susceptibility mapping, in Dodangeh watershed, Iran. A 112 sample point of the past landslide, occurrence or inventory data was generated from the existing and field observations. In addition, fourteen landslide-conditioning parameters were derived from DEM and other topographic databases for the modelling process. These conditioning parameters include total curvature, profile curvature, plan curvature, slope, aspect, altitude, topographic wetness index (TWI), topographic roughness index (TRI), stream transport index (STI), stream power index (SPI), lithology, land use, distance to stream, distance to the fault. Meanwhile, factor analysis was employed to optimize the landslide conditioning parameters and the inventory data, by assessing the multi-collinearity effects and outlier detections respectively. The inventory data is divided into 70% (78) training dataset and 30% (34) test dataset for model validation. The receiver operating characteristics (ROC) curve or area under curve (AUC) value was used for assessing the model's performance. The findings reveal that TRI has 0.89 collinearity effect based on variance-inflated factor (VIF) and based on Gini factor optimization total curvature is not significant in the model development, therefore the two parameters are excluded from the modelling. All the selected MLAs (RF, BRT, and DT) shown promising performances on landslide susceptibility mapping in Dodangeh watershed, Iran. The ROC curve for training and validation for RF are 86% success rate and 83% prediction rate implies the best model performance compared to BRT and DT, with ROC curve of 72% and 70% prediction rate, respectively. In conclusion, ...
Kalantar, B, Ueda, N, Al-Najjar, HAH, Halin, AA, Ahmadi, P & Gibril, MBA 1970, 'On the effects of different groundwater inventory scenarios for spring potential mapping in Haraz, northern Iran', Earth Resources and Environmental Remote Sensing/GIS Applications X, Earth Resources and Environmental Remote Sensing/GIS Applications X, SPIE.
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© 2019 SPIE. This study investigates the effectiveness of using groundwater inventory data for groundwater spring potential mapping in the Haraz watershed located in Norther Iran. From a total of 917 groundwater inventory dataset, six random inventory scenarios of 917, 690, 450, 230, 92, and 46 were generated. We trained two learning classifiers, namely the Support Vector Machine (SVM) and Random Forest (RF) based on each scenario to determine which one(s) would be more suitable for spring potential mapping. In each of the scenarios, 70% of the dataset was used for training whereas 30% was used for testing. The end results (classified maps) for each classifier and their respective dataset were quantitatively assessed based on the Area under Curve (AUC) metric. The prediction accuracies for the spring potential maps being produced for each scenario ranged from 0.693 to 0.736 using the SVM, and 0.608 to 0.895 for RF. Our findings indicate that 46 random points of inventory data did not produce a desirable outcome. On the contrary, more points yield better results, i.e. 450 random points produced the highest ROC when using SVM (0.736) followed by 917 and 690 random points using RF (0.895 and 0.877, respectively).
Kalantar, B, Ueda, N, Al-Najjar, HAH, Moayedi, H, Halin, AA & Mansor, S 1970, 'UAV AND LIDAR IMAGE REGISTRATION: A SURF-BASED APPROACH FOR GROUND CONTROL POINTS SELECTION', The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Geospatial Week, Copernicus GmbH, The Netherlands, pp. 413-418.
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Abstract. Multisource remote sensing image data provides synthesized information to support many applications including land cover mapping, urban planning, water resource management, and GIS modelling. Effectively utilizing such images however requires proper image registration, which in turn highly relies on accurate ground control points (GCP) selection. This study evaluates the performance of the interest point descriptor SURF (Speeded-Up Robust Features) for GCPs selection from UAV and LiDAR images. The main motivation for using SURF is due to it being invariant to scaling, blur and illumination, and partially invariant to rotation and view point changes. We also consider features generated by the Sobel and Canny edge detectors as complements to potentially increase the accuracy of feature matching between the UAV and LiDAR images. From our experiments, the red channel (Band-3) produces the most accurate and practical results in terms of registration, while adding the edge features seems to produce lacklustre results.
Kalantar, B, Ueda, N, Lay, US, Al-Najjar, HAH & Halin, AA 1970, 'Conditioning Factors Determination for Landslide Susceptibility Mapping Using Support Vector Machine Learning', IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Yokohama, Japan, pp. 9626-9629.
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This study investigates the effectiveness of two sets of landslide conditioning variable(s). Fourteen landslide conditioning variables were considered in this study where they were duly divided into two sets G1 and G2. Two Support Vector Machine (SVM) classifiers were constructed based on each dataset (SVM-G1 and SVM-G2) in order to determine which set would be more suitable for landslide susceptibility prediction. In total, 160 landslide inventory datasets of the study area were used where 70% was used for SVM training and 30% for testing. The intra-relationships between parameters were explored based on variance inflation factors (VIF), Pearson's correlation and Cohen's kappa analysis. Other evaluation metrics are the area under curve (AUC).
Kalhori, H, Halkon, B & Alamdari, MM 1970, 'Wavelet transform-based strategy for identifying impact force on a composite panel', Proceedings of the 26th International Congress on Sound and Vibration, ICSV 2019, International Congress on Sound and Vibration, Montreal, Canada.
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An algorithm based on wavelet analysis for automatically estimating the location and magnitude of impact forces exerted on a rectangular carbon fibre-epoxy honeycomb composite panel is developed. The technique employs a single piezoelectric sensor mounted distant from the impact zone and presumes that an impact is applied at one of several pre-established locations. Furthermore, it is presumed that the recorded vibration response is the superposition of the simultaneous 'assumed' impacts at these locations, with the aim of simultaneously identifying the actual impact location and force magnitude through an under-determined regularisation scheme. The algorithm aims to detect the most probable impact location amongst the spurious locations. Since a normal impact introduces a narrow-band time-localised event with high energy, the wavelet transform is an effective tool to locate this event, with the wavelet coefficient representing how closely correlated the wavelet is with the reconstructed forces. The larger the coefficient is in absolute value, the greater the similarity. As a case study, an under-determined problem with four potential impact locations is considered. The results demonstrate successful localisation and reconstruction of the impact force using both orthogonal and non-orthogonal wavelets
Kane, DM, Snowdon, B, Blamires, SJ & Little, DJ 1970, 'Orb web spider silks: how their optics affects potential visibility', AOS Australian Conference on Optical Fibre Technology (ACOFT) and Australian Conference on Optics, Lasers, and Spectroscopy (ACOLS) 2019, AOS Australian Conference on Optical Fibre Technology (ACOFT) and Australian Conference on Optics, Lasers, and Spectroscopy (ACOLS) 2019, SPIE.
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Kanekol, T, Tsuruminel, Y, Poon, J, Onuki, Y, Dai, Y, Kawabata, K & Matsubara, T 1970, 'Learning Deep Dynamical Models of a Waste Incineration Plant from In-furnace Images and Process Data', 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), IEEE, pp. 873-878.
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Karimi, M, Croaker, P, Kessissoglou, N, Robin, O, Atalla, N, Berry, A, Maxit, L, Skvortsov, A & Marburg, S 1970, 'A numerical and experimental study of vibroacoustic responses of a panel excited by a turbulent boundary layer', Proceedings of the 26th International Congress on Sound and Vibration, ICSV 2019.
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A hybrid finite element method (FEM) - boundary element method (BEM) technique is used to predict the structural and acoustic responses of a panel in low Mach number flow. Analytical expressions are used to estimate the turbulent boundary layer (TBL) parameters over the surface of the panel. The spectrum of the wall pressure fluctuations is evaluated from the TBL parameters and by using semi-empirical models from literature. The wall pressure field underneath the TBL is synthesized by realisations of uncorrelated wall plane waves. The FEM-BEM approach is adopted to compute the structural and acoustic responses of the panel for each realisation of uncorrelated wall plane waves. The responses are then obtained from an ensemble average of the different realisations. Numerical results are compared with experimental data obtained in an anechoic wind tunnel at the Université de Sherbrooke. The proposed technique for predicting the vibroacoustic responses of a structure in turbulent flow is computationally efficient
Karimi, M, Croaker, P, Kessissoglou, N, Robin, O, Atalla, N, Berry, A, Maxit, L, Skvortsov, A & Marburg, S 1970, 'A numerical and experimental study of vibroacoustic responses of a panel excited by a turbulent boundary layer', Proceedings of the 26th International Congress on Sound and Vibration, ICSV 2019, International COngress on Sound and Vibration, Curran, Montreal, Canada, pp. 2820-2825.
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© Proceedings of the 26th International Congress on Sound and Vibration, ICSV 2019. All rights reserved. A hybrid finite element method (FEM) - boundary element method (BEM) technique is used to predict the structural and acoustic responses of a panel in low Mach number flow. Analytical expressions are used to estimate the turbulent boundary layer (TBL) parameters over the surface of the panel. The spectrum of the wall pressure fluctuations is evaluated from the TBL parameters and by using semi-empirical models from literature. The wall pressure field underneath the TBL is synthesized by realisations of uncorrelated wall plane waves. The FEM-BEM approach is adopted to compute the structural and acoustic responses of the panel for each realisation of uncorrelated wall plane waves. The responses are then obtained from an ensemble average of the different realisations. Numerical results are compared with experimental data obtained in an anechoic wind tunnel at the Université de Sherbrooke. The proposed technique for predicting the vibroacoustic responses of a structure in turbulent flow is computationally efficient
Karmokar, DK, Bird, TS, Guo, YJ & Esselle, KP 1970, 'A Binary-switch Controlled Periodic Half-width Leaky-wave Antenna for Fixed Frequency Beam Steering near the Endfire Region', 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring), 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring), IEEE, Rome, Italy, pp. 1799-1803.
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© 2019 IEEE. A leaky-wave antenna (LWA) with fixed-frequency beam scanning capabilities is presented in this paper. The main structure is a half-width microstrip LWA (HW-MLWA), and the direction of the main beam at a fixed operating frequency is controlled by using a group of gap capacitors and binary switches. Different binary switching patterns are applied to change the reactance profile of the radiating structure and hence beam scanning at a fixed frequency is achieved in discrete steps. Results from the full-wave simulation demonstrate that the radiating antenna beam can be steered from 38 to 68 in a discrete step at 7 GHz.
Karmokar, DK, Chen, S-L, Qin, P-Y & Guo, YJ 1970, 'Open-Stopband Suppression and Cross-Polarization Reduction of a Substrate Integrated Waveguide Leaky-Wave Antenna', 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC), 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC), IEEE, New Delhi, INDIA, pp. 1-4.
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© 2019 URSI. All rights reserved. Most leaky-wave antennas (LWAs) suffer from significant gain degradation when the main beam points towards broadside. This is because an open stopband (OSB) restricts broadside radiation. In this paper, a method to suppress the OSB of a periodic substrate integrated waveguide (SIW) LWA is discussed. By simultaneously introducing a slot and a partially radiating wall in each unit cell the impedance in the OSB region has been matched and hence a continuous beam scan through broadside is achieved. The developed LWA can scan its main beam from 74° continuously to +40° when the frequency varies from 7.45 to 10.55 GHz, with a broadside gain and a level of cross-polarization for the broadside beam of 10.8 dBi and 21.37 dB, respectively.
Kashif, M, Hossain, MJ, Nawazish Ali, SM, Sharma, V & Nizami, MSH 1970, 'Harmonic Identification based on DSC and MAF for Three-phase Shunt Active Power Filter', 2019 29th Australasian Universities Power Engineering Conference (AUPEC), 2019 29th Australasian Universities Power Engineering Conference (AUPEC), IEEE, Nadi, Fiji, pp. 1-6.
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© 2019 IEEE. Harmonic current identification is very important in control of Active Power Filters. The dq Synchronous Reference Frame (SRF) based reference-current extraction technique has been widely utilized for this purpose. The dynamic performance of reference-current detection is dependent on numerical filters. In this paper, the two most popular filters, Delayed Signal Cancellation (DSC) and Moving Average Filter (MAF) are utilized in the rotating reference frame for both fundamental component identification and selective harmonic identification. A comprehensive comparison of the performance of these two filters is then carried out. Experimental results from digital implementation are provided to substantiate the theoretical analysis and simulation results.
Kashif, M, Hossain, MJ, Sharma, V, Ali, SMN & Khan, A 1970, 'Neutral-point Voltage Control of Three-level NPC Inverter for Three-phase APF based on Zero-sequence Voltage Injection', 2019 INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING RESEARCH & PRACTICE (ICEERP-2019), International Conference on Electrical Engineering Research and Practice (ICEERP) / 5th World Congress of the Global-Circle-for-Scientific-Technological-and-Management-Research (GCSTMR), IEEE, AUSTRALIA, Sydney, pp. 77-81.
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Kashif, M, Hossain, MJ, Sharma, V, Nawazish Ali, SM & Khan, A 1970, 'Neutral-point Voltage Control of Three-level NPC Inverter for Three-phase APF based on Zero-sequence Voltage Injection', 2019 International Conference on Electrical Engineering Research & Practice (ICEERP), 2019 International Conference on Electrical Engineering Research & Practice (ICEERP), IEEE, Sydney, Australia, pp. 1-5.
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© 2019 IEEE. Active Power Filters (APF) have already adopted the three-level inverter topology in medium-voltage and high-power applications for solving power-quality problems. The Neutral-point voltage clamped (NPC) Inverter because of its robustness has become a matured and broadly used topology. It is necessary to maintain the neutral-point voltage at the DC-side as close to zero as possible. The focus of this paper is on the neutral-point voltage control of the three-level NPC inverter based on multicarrier PWM by manipulating the dwell time of small vectors by injecting a zero-sequence voltage into the modulating signal. The effectiveness of the presented method on a three-level NPC inverter is validated via simulation in MATLAB/Simulink. The results confirm the efficacy of the method in maintaining the neutral-point voltage at a minimum value with the desired overall good APF compensation characteristics.
Katic, M, Cetindamar, D, Agarwal, R & Sick, N 1970, 'Operationalising Ambidexterity: The Role of 'Better' Management Practices in High-Variety, Low-Volume Manufacturing', 2019 Portland International Conference on Management of Engineering and Technology (PICMET), 2019 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE, Portland, Oregon, pp. 1-8.
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Kekirigoda, A, Hui, K-P, Cheng, Q, Lin, Z, Zhang, JA, Nguyen, DN & Huang, X 1970, 'Massive MIMO for Tactical Ad-hoc Networks in RF Contested Environments', MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM), MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM), IEEE, Norfolk, VA, pp. 658-663.
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Survivability of wireless communications segments in tactical military networks is an enormous challenge in the present and future defence forces, especially as these networks usually operate in radio frequency (RF) contested environments. Therefore, it is necessary to develop techniques to provide effective and efficient communication in RF contested environments. Massive multiple-input-multiple-output (MIMO) techniques use a large number of antennas enabling higher degrees of freedom that can improve communications network's survivability and efficiency compared to conventional MIMO or single antenna systems. This paper presents a novel massive MIMO communications system which enhances the throughput of the network, reduces the bit-error-rate and mitigates the interference from high powered jammers. Simulation results in contested environments verify the effectiveness of this system.
Khan, MNH, Siwakoti, YP, Li, L & Khan, SA 1970, 'Switched-Capacitor Integrated Single-Phase (2N+1)-Levels Boost Inverter for Grid-Tied Photovoltaic (PV) Applications.', ICIT, IEEE International Conference on Industrial Technology, IEEE, Melbourne, pp. 1655-1660.
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© 2019 IEEE. This paper presents a switched-capacitor integrated (2N+1)-level (N≥2) boost inverter for single-phase photovoltaic (PV) applications. It consists of N modular switching cells, where each cell consists of two switched capacitors and three active switching elements. A boost converter at the front side of the switching cells helps to maintain the capacitor voltage balance during different operation modes. With this arrangement, the inverter is capable to generate 2N+1 output voltage levels, and able to accommodate a wide range of input voltage. Detailed analysis followed by simulation and experimental results of a 5-level inverter as an example is presented to verify the proposed concept. Further, comparison with other multilevel inverter topologies is presented to show the merit of the proposed concept.
Khan, SA, Guo, Y, Habib Khan, MN, Siwakoti, Y & Zhu, J 1970, 'Model Predictive Control without Weighting Factors for T-type Multilevel Inverters with Magnetic-Link and Series Stacked AC-DC Modules', 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 2019 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, Baltimore, MD, USA, pp. 5603-5609.
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© 2019 IEEE. This paper presents a multiport magnetic-link based T-type multilevel inverter topology and associated control scheme. The proposed structure comprises of series stacked AC-DC modules connected to a high-frequency magnetic-link, which boost the input DC-link voltage level significantly, and generates the required dc-link voltages for the multilevel inverter stage. The desired number of the output voltage level can be realized by cascading series stacked AC-DC modules and bidirectional switches. Due to inherent voltage balancing capability of the magnetic-link based structure, this topology does not require any control scheme to balance the series connected capacitors in the DC-bus. Thus, it reduces the control complexity. Moreover, the magnetically isolated structure eliminates the leakage and DC current injection into the grid from DC sources, like photovoltaic (PV) module. The proposed structure has the capability to integrate multiple sources operating with different voltage levels, and consequently, reduces the number of components and control complexity. In this work, multilevel voltage synthesizing, active and reactive power control capability are realized by using the finite control set model predictive control (FCS-MPC) algorithm. A prototype multilevel inverter incorporating three stacked AC-DC modules, designed for seven levels operation has been built and tested to verify the circuit performance and associated control scheme.
Khan, TA, Ling, SH & Mohan, AS 1970, 'Advanced Gravitational Search Algorithm with Modified Exploitation Strategy', 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), IEEE, Bari, Italy, pp. 1056-1061.
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Gravitational search algorithm (GSA) is a novel technique as compared to other heuristic methods and depends pon the gravitational forces between masses. It showed better performance in terms of convergence but has slow exploitation ability due to the fitness function effect on masses; they are getting heavier after every iteration. Therefore, masses are getting closer to each other and nullify the gravitational forces on each other avoiding them from swiftly exploiting the optimum. In order to solve this problem in this paper, an advanced gravitational search algorithm (AGSA) with modified exploitation strategy is proposed. The reason for the modification is that the agents will reach the optimum point swiftly and the convergence is much faster as compared to the standard and other improved versions of GSA available in the literature. AGSA is also compared with the standard and modified Particle Swarm optimization algorithm in this paper. Five benchmark functions have been implemented to assess the efficiency of the presented algorithm. In addition, a standard, constrained, design problem of a pressure vessel design is also used to examine the efficiency of the proposed technique. Simulation results empirically validated that the presented algorithm has remarkably better results in accordance with convergence and solution stability when compared to the other methods.
Khan, TA, Ling, SH, Tram, N & Sanagavarapu, AM 1970, 'A Modified Particle Swarm Optimization Algorithm for Solving DNA Problem', 2019 60th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), 2019 60th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), IEEE, Riga, LATVIA, pp. 1-5.
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© 2019 IEEE. DNA Sequencing is a complex problem since DNA computation depends on the biochemical reactions of DNA molecules that resulted in an improper or unwanted results. Thus, researchers are focusing to make the molecular computation for the DNA sequences design problem more consistent. Designing of DNA sequencing consists of several difficult and inconsistent designing parameters and typical optimization approaches do not perform well. As a result, a Modified Particle Swarm Optimization Algorithm (MPSO) is proposed to elucidate this problem. Four objective functions that are continuity, similarity, hairpin, and H-measure are used to evaluate this multi-objective problem. Different methods are used to explain DNA problem but to solve it with MPSO the multi-objective problem is converted into a single-objective problem. MPSO is presented to minimize the objective functions subject to two constraints. Average and Standard deviation values of the objective functions are used to calculate the efficiency of the presented method. The results obtained are compared with the other approaches and it showed that MPSO gives better performance.
Khan, TA, Zain-Ul-Abideen, K & Ling, SH 1970, 'A Hybrid Advanced PSO-Neural Network System', 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), IEEE, Bari, ITALY, pp. 1626-1630.
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In this paper, a combination of Advanced Particle Swarm Optimization (APSO) and Neural Network are presented to compensate the drawbacks of both the techniques and utilize the strong attributes to form a hybrid system called Hybrid Advance Particle Swarm Optimization-Neural Network System (HAPSONNS). APSO is used for the training of the neural network. In the initial phases of the search, PSO has swift convergence for global optimum, but later it suffers from slow convergence around the global optimum position. On the contrary, the gradient method attains prior to convergence around the global optimum point, therefore, attaining better accuracy in terms of convergence. This paper elucidates the usage of APSO applied to feedforward neural network to improve the classification accuracy of the network and also decreases the network training time.
Khan, TA, Zain-Ul-Abideen, K & Ling, SH 1970, 'A Modified Particle Swarm Optimization Algorithm Used for Feature Selection of UCI Biomedical Data Sets', 2019 60th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), 2019 60th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), IEEE, Riga, LATVIA, pp. 1-4.
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Khoa, NLD, Tian, H, Wang, Y & Chen, F 1970, 'Online Data Fusion Using Incremental Tensor Learning', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Macau, China, pp. 357-369.
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© Springer Nature Switzerland AG 2019. Despite the advances in Structural Health Monitoring (SHM) which provides actionable information on the current and future states of infrastructures, it is still challenging to fuse data properly from heterogeneous sources for robust damage identification. To address this challenge, the sensor data fusion in SHM is formulated as an incremental tensor learning problem in this paper. A novel method for online data fusion from heterogeneous sources based on incrementally-coupled tensor learning has been proposed. When new data are available, decomposed component matrices from multiple tensors are updated collectively and incrementally. A case study in SHM has been developed for sensor data fusion and online damage identification, where the SHM data are formed as multiple tensors to which the proposed data fusion method is applied, followed by a one-class support vector machine for damage detection. The effectiveness of the proposed method has been validated through experiments using synthetic data and data obtained from a real-life bridge. The results have demonstrated that the proposed fusion method is more robust to noise, and able to detect, assess and localize damage better than the use of individual data sources.
Khuat, TT & Gabrys, B 1970, 'Accelerated Training Algorithms of General Fuzzy Min-Max Neural Network Using GPU for Very High Dimensional Data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Neural Information Processing, Springer International Publishing, Sydney, Australia, pp. 583-595.
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© Springer Nature Switzerland AG 2019. One of the issues of training a general fuzzy min-max neural network (GFMM) on very high dimensional data is a long training time even if the number of samples is relatively low. This is a quite common problem shared by many prototype-based methods requiring frequently repeated distance or similarity calculations. This paper proposes the method of accelerating the learning algorithms of the GFMM by, first, reformulating and representing them in a format allowing for their parallel execution and subsequently leveraging the computational power of the graphics processing unit (GPU). The original implementation of GFMM is modified by matrix computations to be executed on the GPU for the very high-dimensional datasets. The empirical results on two very high-dimensional datasets indicated that the training and testing processes performed on Nvidia Quadro P5000 GPU were from 10 to 35 times faster compared to those running serially on the Xeon CPU while retaining the same classification accuracy.
Kim, I, De Silva Wijayaratna, K & Jian, S 1970, 'Travel Behaviour variation across Sydney', Australian Institute of Traffic Planning and Management 2019 National Conference, Adelaide, Australia.
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Increasing numbers of people are opting to move to the outskirts of metropolitan areas and into “semi-regional” areas of Greater Sydney as a result of rapid population growth. In addition, public transport service provisions differ across the network where frequencies and capacities change between stations and lines. Hence, communities adjusttheir travel behaviour to align with the availability of services within their locality, particularly affecting departure times. Thus, this behaviour raises the key research question, “Do Semi-Regional Sydney commuters have more consistent work travel patterns than those living in Metropolitan Sydney?” This was investigated by using the average total travel time of frequent rail commuters and a novel accessibility metric, “Tapon Time Deviation”, based on the tap on times from smart card data. This metric was used to quantify travel behaviour consistency and gain a better understanding of geographic impacts on individuals travel characteristics.
Kiyani, A, Hashmi, RM & Esselle, KP 1970, 'Closely-Spaced Resonant Cavity Antennas For Meeting ETSI Class-2 Specifications', 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, IEEE, pp. 1119-1120.
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© 2019 IEEE. Dense, wideband and high-gain Resonant Cavity Antenna Arrays (RCAAs) are presented in this paper. Two array topologies (comprising square & radial configurations) made out of a Transverse Permittivity Gradient Superstrate (TPGS) are specifically investigated for meeting ETSI Class-2 specifications. Their boresight performance is characterized on the basis of peak directivity, 3dB directivity bandwidth, and most importantly the far-field radiation pattern envelope (RPE) masks. In comparison to a 9 × 9 square array, a radial array (having 91 elements) is shown to achieve the peak directivity of 37 dBi with a 3dB directivity bandwidth of more than 20%. In addition, the radial array demonstrated the potential of satisfying minimum ETSI Class-2 antenna requirements with appropriate RPEs up to 50° and SLLs as low as-17 dBi.
Kocbek, S & Gabrys, B 1970, 'Automated Machine Learning Techniques in Prognostics of Railway Track Defects', 2019 International Conference on Data Mining Workshops (ICDMW), 2019 International Conference on Data Mining Workshops (ICDMW), IEEE, China, pp. 777-784.
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© 2019 IEEE. The readiness and usefulness of Automated Machine Learning (AutoML) methods in classification of railway track defects is explored. Safety of railway networks is the top priority in the railroad industry, and track defects are a common cause of train accidents and service disruptions around the world. Effective classification and prediction of these defects based on historical inspection data can help in planning maintenance activities before critical defects occur. This increases safety of the network and lowers costs of the maintenance. The experimental analysis carried out on data from an international predictive modelling competition has shown that the proposed AutoML approaches resulted in an improved performance in comparison to the competition winning solutions and have an excellent potential for building robust predictive models in railway industry.
Kocbek, S, Kocbek, P, Zupanic, T, Stiglic, G & Gabrys, B 1970, 'Using (Automated) Machine Learning and Drug Prescription Records to Predict Mortality and Polypharmacy in Older Type 2 Diabetes Mellitus Patients', Communications in Computer and Information Science, International Conference on Neural Information Processing, Springer International Publishing, Sydney, NSW, Australia, pp. 624-632.
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We analyse a large drug prescription dataset and test the hypothesis that drug prescription data can be used to predict further complications in older patients newly diagnosed with type 2 diabetes mellitus. More specifically, we focus on mortality and polypharmacy prediction. We also examine the balance between interpretability and predictive performance for both prediction tasks, and compare performance of interpretable models with models generated with automated methods. Our results show good predictive performance in the polypharmacy prediction task with AUC of 0.859 (95% CI: 0.857–0.861). On the other hand, we were only able to achieve the average predictive performance for mortality prediction task with AUC of 0.754 (0.747–0.761). It was also shown that adding additional drug related features increased the performance only in the polypharmacy prediction task, while additional information on prescribed drugs did not influence the performance in the mortality prediction. Despite the limited success in mortality prediction, this study demonstrates the added value of the systematic collection and use of Electronic Health Record (EHR) data in solving the problem of polypharmacy related complications in older age.
Koli, NY, Afzal, MU, Esselle, KP & Zahidul Islam, M 1970, 'A Radial Line Slot Array Antenna with Improved Radiation Patterns for Satellite Communication', 13th European Conference on Antennas and Propagation, EuCAP 2019, 13th European Conference on Antennas and Propagation (EuCAP), IEEE, Krakow, POLAND.
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In this paper we have investigated aperture field distribution of a radial line slot array (RLSA) antenna to improve the radiation pattern quality. A circularly polarised (CP) RLSA antenna was designed with tapered amplitude distribution. The distribution was obtained by manipulating the slot lengths on the antenna aperture based on a slot coupling analysis. The antenna has achieved a peak directivity of 31.7dBic and a peak gain of 31.3 dBic at 19.3 GHz. A significant improvement has been achieved in reducing side lobe levels. The antenna has demonstrated a side lobe level of -28.8 dB in φ = 0° plane and -32.2 dB in φ = 90° plane at 19.3 GHz.
Koli, NY, Afzal, MU, Esselle, KP & Zahidul Islam, M 1970, 'Analyzing the Coupling from Radiating Slots in a Double-Layered Radial Line Slot Array Antenna', 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, IEEE, pp. 1427-1428.
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© 2019 IEEE. This paper investigates power coupling out of radiating slots in a double-layered circularly polarised radial line slot array (RLSA) antenna. The antenna is composed of three plates which form a folded radial waveguide and supports inward-travelling waves. Top plate of the antenna has radiating slots. The slots are designed to intercept the currents on the radial waveguide and produce a circularly polarised broadside beam. The slot length is varied using coupling factor in order to achieve a uniform aperture distribution. The simulation results show that the antenna has fairly uniform phase distribution in its near field. The far-field result indicates a peak directivity of 26.4 dBic at 20 GHz with a good pattern quality and a side lobe level of-21.8 dB.
Koli, NY, Afzal, MU, Esselle, KP, Matekovits, L & Islam, Z 1970, 'Investigating Small Aperture Radial Line Slot Array Antennas for Medium Gain Communication Links', 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA), 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE, pp. 0613-0616.
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This paper investigates the performance of a relatively small aperture circularly polarized (CP) radial line slot array (RLSA) antenna with medium gain for the Ku-band. The antenna is composed of two parallel metal plates which form a parallel-plate waveguide and supports rotationally symmetric transverse electromagnetic (TEM) travelling wave. The top plate consists of radiating slot elements. The slots are arrayed on the aperture in a way to produce a circularly polarised broadside beam. The antenna was designed at the central operating frequency of 11 GHz having a circular aperture with radius of 90 mm. The predicted results indicated that the return magnitude of reflection coefficient is less than -10 dB in a operating frequency band from 10.5 GHz to 11.5 GHz. The antenna has achieved a peak directivity of 22.4 dBic with a peak gain of 22.1 dBic at 11 GHz. The CP-RLSA antenna has a predicted aperture efficiency of 40%, and a total efficiency of 93% at 11 GHz.
Koli, NY, Afzal, MU, Esselle, KP, Matekovits, L & Islam, Z 1970, 'Investigating Small Aperture Radial Line Slot Array Antennas for Medium Gain Communication Links', PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON ELECTROMAGNETICS IN ADVANCED APPLICATIONS (ICEAA), 21st International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE, SPAIN, Granada, pp. 613-616.
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Kong, Q, Rizoiu, M-A & Xie, L 1970, 'Modeling Information Cascades with Self-exciting Processes via Generalized Epidemic Models', WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining, 13th Annual ACM International Conference on Web Search and Data Mining (WSDM), ASSOC COMPUTING MACHINERY, Houston, TX, pp. 286-294.
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Epidemic models and self-exciting processes are two types of models used todescribe diffusion phenomena online and offline. These models were originallydeveloped in different scientific communities, and their commonalities areunder-explored. This work establishes, for the first time, a general connectionbetween the two model classes via three new mathematical components. The firstis a generalized version of stochastic Susceptible-Infected-Recovered (SIR)model with arbitrary recovery time distributions; the second is therelationship between the (latent and arbitrary) recovery time distribution,recovery hazard function, and the infection kernel of self-exciting processes;the third includes methods for simulating, fitting, evaluating and predictingthe generalized process. On three large Twitter diffusion datasets, we conductgoodness-of-fit tests and holdout log-likelihood evaluation of self-excitingprocesses with three infection kernels --- exponential, power-law and TsallisQ-exponential. We show that the modeling performance of the infection kernelsvaries with respect to the temporal structures of diffusions, and also withrespect to user behavior, such as the likelihood of being bots. We furtherimprove the prediction of popularity by combining two models that areidentified as complementary by the goodness-of-fit tests.
Krätzig, O, Franzkowiak, V & Sick, N 1970, 'A multi-level perspective approach to facilitate sustainable transitions – The way of German OEMs to electric vehicles', XXX ISPIM Innovation Conference: Celebrating Innovation - 500 Years since Da Vinci, Florence, Italy.
Kridalukmana, R, Lu, H & Naderpour, M 1970, 'Component-Based Transparency to Comprehend Intelligent Agent Behaviour for Human-Autonomy Teaming', 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), IEEE, pp. 771-777.
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Krüger, M, Aal, K, Wulf, V, Tachtler, FM, Talhouk, R, Duarte, AMB, Fisher, KE, Yafi, E & Charitonos, K 1970, 'Technology at/of the border', Proceedings of the 9th International Conference on Communities & Technologies - Transforming Communities, C&T 2019: The 9th International Conference on Communities & Technologies - Transforming Communities, ACM, pp. 336-342.
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Kulasinghe, A, Kapeleris, J, Kenny, L, Warkiani, M, Vela, I, Thiery, J-P, O'Byrne, K & Punyadeera, C 1970, 'Abstract 1333: Isolation, characterization and expansion of circulating tumor cells in solid cancers', Cancer Research, American Association for Cancer Research (AACR), pp. 1333-1333.
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Abstract Metastasis in cancer patients is reflected by measurable levels of circulating tumor cells (CTCs) in the blood of cancer patients. CTCs represent cancer cells from the primary and metastatic sites, thereby providing a comprehensive representation of the tumor burden of an individual patient. Recent advancements have shown that PD-1/PD-L1 immune checkpoint therapies have durable responses in a number of solid tumor types. Our study was designed to use multiple CTC enrichment platforms for the capture of CTCs and novel culture formulations for the ex vivo expansion of CTCs. Head and Neck cancer (n=300) and lung cancer (n=80) patients were recruited to investigate the prognostic role of CTCs. We evaluated multiple CTC isolation technologies (CellSearch, filtration, CD45 depletion, inertial microfluidics) using matched patient samples which showed that epitope-independent CTC isolation captured a greater proportion of CTCs. Molecular alterations present in the primary tissue were confirmed in the CTCs by 3D-DNA FISH (EGFR-amplification, ALK-translocations). In HNC, the presence of CTC clusters associated with the development of distant metastatic disease (P=0.0313). HNC CTC-positive patients had shorter progression free survival (PFS) (Hazard ratio [HR]: 4.946; 95% confidence internal [CI]:1.571-15.57; P=0.0063) and PD-L1-positive CTCs were found to be significantly associated with worse outcome ([HR]:5.159; 95% [CI]:1.011-26.33; P=0.0485). In a proof of principle study, we were able to demonstrate for the first time, short-term patient derived CTC cultures outside the patient’s body from 7/18 HNC samples (4/7 HPV-positive). Recently, we have preliminary data that suggests that PD-L1 is frequently expressed on CTCs in HNC and lung cancer and an immunoscore may be able to identify patients likely to benefit ...
Laccone, F, Malomo, L, Froli, M, Cignoni, P & Pietroni, N 1970, 'Concept and cable-tensioning optimization of post-tensioned shells made of structural glass', IASS Symposium 2019 - 60th Anniversary Symposium of the International Association for Shell and Spatial Structures; Structural Membranes 2019 - 9th International Conference on Textile Composites and Inflatable Structures, FORM and FORCE, pp. 2188-2195.
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Shells made of structural glass are charming objects from both the aesthetics and the engineering point of view. However, they pose two significant challenges: the first one is to assure adequate safety and redundancy concerning possible global collapse; the second one is to guarantee the economy for replacing collapsed components. To address both requirements, this research explores a novel concept where triangular panels of structural glass are both post-tensioned and reinforced to create 3D free-form systems. Hence, the filigree steel truss, made of edges reinforcements, is sized in performance-based perspective to bear at least the weight of all panels in the occurrence of simultaneous cracks (worst-case scenario). The panels are post-tensioned using a set of edge-aligned cables that add beneficial compressive stress on the surface. The cable placement and pre-loads are optimized to minimize the tensile stress acting on the shell and match the manufacturing constraints. These shells optimize material usage by providing not only a transparent and fascinating building separation but also load-bearing capabilities. Visual and structural lightness are improved to grid shell competitors.
Laccone, F, Malomo, L, Pérez, J, Pietroni, N, Ponchio, F, Bickel, B & Cignoni, P 1970, 'FlexMaps Pavilion: A twisted arc made of mesostructured flat flexible panels', IASS Symposium 2019 - 60th Anniversary Symposium of the International Association for Shell and Spatial Structures; Structural Membranes 2019 - 9th International Conference on Textile Composites and Inflatable Structures, FORM and FORCE, 60th Anniversary Symposium of the International-Association-for-Shell-and-Spatial-Structures (IASS SYMPOSIUM) / 9th International Conference on Textile Composites and Inflatable Structures (STRUCTURAL MEMBRANES), INT CENTER NUMERICAL METHODS ENGINEERING, Barcelona, SPAIN, pp. 509-515.
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Bending-active structures are able to efficiently produce complex curved shapes starting from flat panels. The desired deformation of the panels derives from the proper selection of their elastic properties. Optimized panels, called FlexMaps, are designed such that, once they are bent and assembled, the resulting static equilibrium configuration matches a desired input 3D shape. The FlexMaps elastic properties are controlled by locally varying spiraling geometric mesostructures, which are optimized in size and shape to match the global curvature (i.e., bending requests) of the target shape. The design pipeline starts from a quad mesh representing the input 3D shape, which defines the edge size and the total amount of spirals: every quad will embed one spiral. Then, an optimization algorithm tunes the geometry of the spirals by using a simplified pre-computed rod model. This rod model is derived from a non-linear regression algorithm which approximates the non-linear behavior of solid FEM spiral models subject to hundreds of load combinations. This innovative pipeline has been applied to the project of a lightweight plywood pavilion named FlexMaps Pavilion, which is a single-layer piecewise twisted arc that fits a bounding box of 3.90x3.96x3.25 meters.
Lai, L, Qing, Z, Yang, Z, Jin, X, Lai, Z, Wang, R, Hao, K, Lin, X, Qin, L, Zhang, W, Zhang, Y, Qian, Z & Zhou, J 1970, 'Distributed Subgraph Matching on Timely Dataflow.', Proc. VLDB Endow., pp. 1099-1112.
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Recently there emerge many distributed algorithms that aim at solving subgraph matching at scale. Existing algorithm-level comparisons failed to provide a systematic view of distributed subgraph matching mainly due to the intertwining of strategy and optimization. In this paper, we identify four strategies and three general-purpose optimizations from representative state-of-the-art algorithms. We implement the four strategies with the optimizations based on the common Timely dataflow system for systematic strategy-level comparison. Our implementation covers all representative algorithms. We conduct extensive experiments for both un-labelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.
Lai, Y, Sutjipto, S, Carmichael, M & Paul, G 1970, 'Heuristic Detection of Recovery Progress using Robotic Data', 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), IEEE, Bangkok, Thailand, pp. 506-511.
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Assessment methods for rehabilitation and recovery have recently been the focal point of research for medical professionals and engineers alike. Current assessment protocols rely on historical ordinal metrics which have been disputed despite their inter-rater reliability. Contemporary kinematic measures have allowed for new approaches to assess recovery progress. However, the abundance of data has deterred medical professionals from adopting these new protocols. This paper presents a method, based on the RMSE-LWSS (Longest Warping Subsequence) score, to distinguish outliers from systemic change for updating the personalized exercise path for users. By treating change detection as a classification problem, the incorporation of a compromised path based on the user's current capability is possible. Experiments were conducted to verify the efficacy of the method, comparing against statistical techniques for change detection and classification of pre-determined paths. The paper highlights how readily available data, rather than complex sensor systems, can be utilized to improve the robustness of personalization capabilities for robotic rehabilitation systems.
Lama, S, Pradhan, S & Shrestha, A 1970, 'An e-Tourism Adoption Model & Its Implications for Tourism Industry in Nepal', https://link.springer.com/chapter/10.1007/978-3-030-05940-8_23, e-Tourism Conference, Springer International Publishing, Nicosia, Cyprus, pp. 291-303.
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Although Nepal has tremendous tourism opportunities, the small and medium tourism enterprises (SMTEs) that constitute the largest percentage of tourism service providers, are lagging behind in e-Tourism adoption. This research conducts a comprehensive analysis of existing literature to propose an e-Tourism adoption model based on the Technology-Organisation-Environment and e-Readiness models. This model is supported by empirical data using qualitative in-depth interviews with seven key stakeholders and quantitative survey with 198 SMTEs. An operational model is outlined to identify the barriers and motivators for e-Tourism adoption in Nepal. Implications of this model for key stakeholders such as the government, tourism organisations and tourism associations are discussed. As Nepal moves to a federal political structure, the findings and recommendation from this research are expected to help policy makers, tourism associations and SMTEs to develop specific e-Tourism based programs in order to provide superior services to tourists.
Lammers, T, Tomidei, L & Trianni, A 1970, 'Towards a Novel Framework of Barriers and Drivers for Digital Transformation in Industrial Supply Chains', 2019 Portland International Conference on Management of Engineering and Technology (PICMET), 2019 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE, Portland, pp. 1-6.
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© 2019 PICMET. Businesses across all sectors are facing the complexity of an increasingly digital economy. Digital transformation offers vast opportunities to businesses and entire supply chains. While many investments are targeted at the organization level, the supply chain perspective can lead to even greater impacts on business performance. However, as supply chains involve interconnections between multiple actors, comprehensive digitalization initiatives at this level are very complex. Several strategic factors affect decision-making around digital investments. For this reason, a framework that categorizes all these factors is needed in order to help managers build digitalization strategies for their supply chains. In this paper, based on a review of existing literature, we give indications for a framework encompassing barriers to and drivers for digital transformation in the context of industrial supply chains. Our framework preliminarily allocates these factors by using two dimensions. The first one classifies them using several categories: financial, knowledge and skills, regulatory, technological, market, organizational, and cultural. The second dimension classifies determinants at the level on which actions can be made, i.e. market, supply chain, or organization. The framework can support organizations to exploit the opportunities provided by digitalization of supply chains and will help managers understand the complexity involved.
Lamsam, L, Zhang, M, Carmichael, M, Bhambvani, H, Connolly, ID, Hernandez-Boussard, T, Veeravagu, A & Ratliff, JK 1970, 'Impact of Centers for Medicare and Medicaid Services Non-Reimbursement on Hospital-Acquired Conditions Following Spine Procedures', NEUROSURGERY, Annual Meeting of the Congress-of-Neurological-Surgeons, OXFORD UNIV PRESS INC, CA, San Francisco, pp. 27-27.
Lau, CW, Nguyen, QV, Qu, Z, Simoff, S & Catchpoole, D 1970, 'Immersive Intelligence Genomic Data Visualisation', Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2019: Australasian Computer Science Week 2019, ACM, pp. 1-10.
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© 2019 Association for Computing Machinery. Genomics data are very complex and could contain crucial information about a disease or how a treatment method may perform well on one but not on another. Understanding such genomic data would enable better insight into the correlation between genes and diseases, which could facilitate personalised treatments for the patients. Although visualisations have been increasingly used in the genomic analysis, there is still limited research work on interactive visualisations on immersive platforms, such as in Augmented and Virtual Reality. This paper presents a new interactive visualisation and navigation of genomics data in such environments. We provide an overview of the patient cohort in 3D genetic similarity-space as well as the views of the genes of interests for detail study. The visualisation employs avatars to represent the patients to enhance the realistic look-and-feel of the patients in the immersive environments. We illustrate the effectiveness of our platform through a childhood cancer dataset, B-cell Acute Lymphoblastic Leukaemia.
Lawrence, C, Leong, TW, Brereton, M, Taylor, JL, Bidwell, N & Wadley, G 1970, 'Indigenous HCl', Proceedings of the 31st Australian Conference on Human-Computer-Interaction, OZCHI'19: 31ST AUSTRALIAN CONFERENCE ON HUMAN-COMPUTER-INTERACTION, ACM, Fremantle, Australia, pp. 17-19.
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© 2019 Association for Computing Machinery. The Workshop on Indigenous HCI aims to bring together researchers and practitioners (Indigenous and otherwise) who work with Indigenous people and communities on technology projects and human-computer interaction (HCI) research The workshop is oriented to establishing connections and supporting discussion; to enabling participants to network and to share ideas and experiences in a diverse environment. As such, we invite academic and industry participants as well as community representatives. We also recognize the importance of all aspects of the technology ecosystem including teaching, research, design, development and implementation. We invite those who are interested to do technology work with Indigenous communities but are unsure of how to begin. Our long-Term aims are to build on earlier work to establish Indigenous HCI as an ongoing theme in Australian HCI. and at OzCHI. and to shape a set of guidelines for doing this work.
Lay, US & Pradhan, B 1970, 'Identification of Debris Flow Initiation Zones Using Topographic Model and Airborne Laser Scanning Data', GCEC 2017 Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 915-940.
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© Springer Nature Singapore Pte Ltd. 2019. Empirical multivariate predictive models represent an important tool to estimate debris flow initiation areas. Most of the approaches used in modelling debris flows propagation and deposit phases required identifying release (starting point) area or source area. Initiation areas offer a good overview to point out where field investigation should be conducted to establish a detailed hazard map. These zones, usually, are arbitrarily chosen which affect the model outputs; hence, there is a need to have accurate and automated means of identifying the release area. In addition to this, the resolution of the terrain dataset also affects the results of the detection of source areas. In this study, airborne laser scanning (ALS) data was used because of its robustness in providing detailed terrain attributes at high resolution. Primary and secondary conditioning parameters were derived from digital elevation model (DEM) as input into the modelling process. Three models were executed at different spatial resolution scales: 5, 10 and 15 m, respectively. MARSpline multivariate data mining predictive approach was implemented using morphometric indices and topographical derived parameter as independent variables. A statistics validation was calculated to estimate the optimal pixel size, 1200 randomly sample data were generated from existing inventory data. Debris flows and no-debris flows were categorized, and the transform to continuous integer (1 and 0), respectively. To achieve this, the data set was divided into two, 70% (840) for the training dataset and 30% (360) for validation. The best model was selected based on the model performance using the generalized cross validation (GCV) and the receiver operating characteristic (ROC) curve/area under curve (AUC) values. Conditioning parameters were numerically optimized to identify the arbitrarily maximum model basis function for eleven variables, using MARSplines analysis (algorithm)...
Lay, US, Jibrin, G, Tijani, I & Pradhan, B 1970, 'Geomorphometric Analysis of Landform Pattern Using Topographic Position and ASTER GDEM', GCEC 2017 Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 1139-1160.
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© Springer Nature Singapore Pte Ltd. 2019. A number of research have been carried out on geomorphology using a conventional approach to classify the landform; this has a tendency of producing misleading result, due to ruggedness and inaccessibility of the terrain. Geographic Information System (GIS) and remote sensing techniques are capable of generating automated landform classes using Topographic Position Index techniques (TPI). This research is set to achieve the following objectives: to categorize landform elements and to illustrate the complexity of the terrain in Negeri Sembilan state based on ASTER GDEM with 30 m resolution. TPI-based algorithm for landscape classification was applied to slope position and landform classification automation. We used 300 and 3000 neighbourhood size on the TPI grids to determine the landform categories. To quantify the spatial pattern of topographic position, Deviation from mean elevation (DEV) is adopted. Maximum Elevation Deviation was selected to measure the spatial landscape pattern at the maximum (3000) scale of the absolute DEV value within the scale (DEVmax), and finally, high-pass filter algorithm was used to identify the extreme topography (ridges/valleys). The combination of the TPI and slope position of DEV that formed the landform classification results show four prominent landform classes these include canyons, U-shape valley, local ridges/ hill valleys, and mountaintops/high ridges. The slope position classes revealed only two (valley/cliff base and ridges/canyons edge) classes based on slope position index. The canyons had the maximum of 63% and minimum was U-shaped valley with 1.04% for the landform of the area of interest. To achieve better results, there is a need to utilize a high spatial resolution remotely sensed DEM derived data and sensitivity analysis need to be incorporated. For that, laser scanning data is capable of improving the results.
Le, AT, Tran, LC, Huang, X & Guo, YJ 1970, 'Authors', 2019 29th International Telecommunication Networks and Applications Conference (ITNAC), 2019 29th International Telecommunication Networks and Applications Conference (ITNAC), IEEE, Auckland, New Zealand, pp. 1-5.
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Le, AT, Tran, LC, Huang, X & Guo, YJ 1970, 'Beam-Based Analog Self-Interference Cancellation with Auxiliary Transmit Chains in Full-Duplex MIMO Systems', 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), IEEE, Cannes, France, pp. 1-5.
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© 2019 IEEE. Analog domain cancellation has been considered as the most important step to mitigate self-interference (SI) in full-duplex (FD) radios. However, in FD multiple-input multiple-output (MIMO) systems, this method faces a critical issue of complexity since the number of cancellation circuits increases quadratically with the number of antennas. In this paper, we propose a beam-based radio frequency SI cancellation architecture which uses adaptive filters to significantly reduce the complexity. Data symbols for all the beams are up-converted by auxiliary transmit chains to provide reference signals for all adaptive filters. Hence, the number of cancellation circuits becomes proportional to the number of transmit beams which are much smaller than that of transmit antennas. We then show that the interference suppression ratio in this architecture is neither affected by the number of beams nor transmit or receive antennas. Instead, it is decided by the performance of the adaptive filter. Simulations are conducted to confirm the theoretical analyses.
Le, TM, Dang, LC & Khabbaz, H 1970, 'Combined Effects of Bottom Ash and Lime on Behaviour of Expansive Soil', Recent Advancements on Expansive Soils, International Congress and Exhibition on Sustainable Civil Infrastructures, Springer International Publishing, Cairo, Egypt, pp. 28-44.
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This study illustrates the effectiveness of combining bottom ash and hydrated lime to enhance the engineering properties of expansive soil. The bottom ash was collected from Eraring Power Station in New South Wales, Australia, as a by-product of coal-fired power stations, and soil specimens were used as artificial soil including kaolinite, bentonite and fine sand in a reasonable ratio to stimulate soil samples with characteristics of expansive soil. The stabilised soil samples were prepared by altering the bottom ash content from 0% to 30% on a dry weight basis of expansive soil as well as with constant percentage of 5% in hydrated lime. Through conducting a series of experimental tests including linear shrinkage and unconfined compressive strength (UCS) in various curing time, the shrinkage and strength behaviour of treated soils were investigated and compared with untreated soil samples. The results revealed that the combination of bottom ash and hydrated lime significantly reduced the linear shrinkage, while it increased the strength of expansive soil. The use of bottom ash alone is not recommended due to a slight increase of linear shrinkage and a minor negative impact on the soil strength. The optimum content of combined bottom ash and hydrated lime to stabilise expansive soils is also presented.
Le, TM, Dang, LC & Khabbaz, H 1970, 'Strength Characteristics of Lime and Bottom Ash Reinforced Expansive Soils', Geo-Congress 2019, Eighth International Conference on Case Histories in Geotechnical Engineering, American Society of Civil Engineers, Philadelphia, Pennsylvania, pp. 352-362.
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© 2019 American Society of Civil Engineers. The primary aim of study is to evaluate the influence of hydrated lime and bottom ash on strength properties of expansive soil by two different kinds of dry-weight-based ratios. In this research, bottom ash is used as a stabilizing agent for expansive soil stabilization due to the potential benefits of its additional pozzolanic components in combination of hydrated lime. Bottom ash is a by-product of coal-fired process which was collected from Eraring Power Station in New South Wales, Australia. Meanwhile, expansive soils were artificially prepared using a proper combination of kaolinite, bentonite, and Sydney fine sand to constitute soil samples typically representing expansive soil in the region. To determine the optimum combination ratio of bottom ash to lime to stabilize expansive soil, different contents of randomly distributed bottom ash from 5% to 30% were mixed with soil and 5% hydrated lime to investigate the engineering behaviour of stabilized expansive soils. It is noted that the additive contents of lime and bottom ash, adopted in this study, were calculated based on both the dry weight of soil alone and the total weight of bottom ash-lime-soil admixture for the comparison purpose. The results of indirect tensile (IDT) strength and California bearing ratio (CBR) tests after various curing times are presented and discussed. The experimental findings show that a ratio of 5% lime to 20% bottom ash mixed with expansive soil is considered as their optimum combination ratio for achieving the maximum bearing capacity and tensile strength for soil-dry-weight-based additives. However, the optimum combination ratio of 5% lime to 25% bottom ash is determined in the case of the entire mixture-dry-weight-based additives. It is concluded that applying the latter optimum ratio to a combination of lime and bottom ash can improve the expansive soil strength better than the former.
Lee, KMB, Yoo, C, Hollings, B, Anstee, S, Huang, S & Fitch, R 1970, 'Online Estimation of Ocean Current from Sparse GPS Data for Underwater Vehicles', Proceedings - IEEE International Conference on Robotics and Automation, International Conference on Robotics and Automation (ICRA), IEEE, Montreal, CANADA, pp. 3443-3449.
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Underwater robots are subject to position drift due to the effect of oceancurrents and the lack of accurate localisation while submerged. We areinterested in exploiting such position drift to estimate the ocean current inthe surrounding area, thereby assisting navigation and planning. We present aGaussian process~(GP)-based expectation-maximisation~(EM) algorithm thatestimates the underlying ocean current using sparse GPS data obtained on thesurface and dead-reckoned position estimates. We first develop a specialised GPregression scheme that exploits the incompressibility of ocean currents tocounteract the underdetermined nature of the problem. We then use the proposedregression scheme in an EM algorithm that estimates the best-fitting oceancurrent in between each GPS fix. The proposed algorithm is validated insimulation and on a real dataset, and is shown to be capable of reconstructingthe underlying ocean current field. We expect to use this algorithm to closethe loop between planning and estimation for underwater navigation in unknownocean currents.
Lee, SS, Lim, CS, Siwakoti, YP, Idris, NRN, Alsofyani, IM & Lee, K-B 1970, 'A New Unity-Gain 5-Level Active Neutral-Point-Clamped (UG-5L-ANPC) Inverter', 2019 IEEE Conference on Energy Conversion (CENCON), 2019 IEEE Conference on Energy Conversion (CENCON), IEEE, Yogyakarta, Indonesia, pp. 213-217.
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© 2019 IEEE. The active neutral-point-clamped (ANPC) inverter is a popular multilevel inverter for various industry applications. In a recent attempt, an improved topology that integrates a flying capacitor to enhance the voltage gain from half to unity has been presented [11]. Retaining the benefit of unity-gain, this paper proposes a new ANPC topology with two improvements. Firstly, the voltage stress of the flying capacitor is reduced by half. Secondly, the charging of the flying capacitor at 0 level is made possible to achieve uniform charging over the power cycle. Comprehensive analysis is presented and experimental results of a prototype are presented for validation. Finally, the extension of the topology with increased number of output voltage levels generation is briefly discussed.
Lee, SS, Shen Lim, C, Siwakoti, YP & Lee, K-B 1970, 'Single-Stage Common-Ground Boost Inverter (S2CGBI) for Solar Photovoltaic Systems', 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 2019 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, Baltimore, MD, USA, pp. 4229-4233.
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© 2019 IEEE. This paper presents a new transformerless inverter topology which is termed as a single-stage common-ground boost inverter (S2CGBI) for single-phase solar photovoltaic (PV) systems. The proposed topology provides a common-ground for both the dc source and ac output to eliminate the leakage current induced by the parasitic capacitance of PV cells. Voltage-boosting is made possible with the incorporation of only one inductor, which renders the realization of single-stage power conversion. The proposed pulse width modulation (PWM) technique is capable of charging the inductor with a constant duty-cycle while guaranteeing ac output generation. The operation of the proposed S2CGBI is analyzed. Simulation and experimental results are provided to validate the effectiveness of the proposed topology and modulation scheme.
Lee, T, Bannink, T, Briët, J, Buhrman, H & Labib, F 1970, 'Bounding Quantum-Classical Separations for Classes of Nonlocal Games', LIPIcs : Leibniz International Proceedings in Informatics, International Symposium on Theoretical Aspects of Computer Science, Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik, Berlin, Germany, pp. 1-11.
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We bound separations between the entangled and classical values for several classes of nonlocal t-player games. Our motivating question is whether there is a family of t-player XOR games for which the entangled bias is 1 but for which the classical bias goes down to 0, for fixed t. Answering this question would have important consequences in the study of multi-party communication complexity, as a positive answer would imply an unbounded separation between randomized communication complexity with and without entanglement. Our contribution to answering the question is identifying several general classes of games for which the classical bias can not go to zero when the entangled bias stays above a constant threshold. This rules out the possibility of using these games to answer our motivating question. A previously studied set of XOR games, known not to give a positive answer to the question, are those for which there is a quantum strategy that attains value 1 using a so-called Schmidt state. We generalize this class to mod-m games and show that their classical value is always at least 1/m + (m-1)/m t^{1-t}. Secondly, for free XOR games, in which the input distribution is of product form, we show beta(G) >= beta^*(G)^{2^t} where beta(G) and beta^*(G) are the classical and entangled biases of the game respectively. We also introduce so-called line games, an example of which is a slight modification of the Magic Square game, and show that they can not give a positive answer to the question either. Finally we look at two-player unique games and show that if the entangled value is 1-epsilon then the classical value is at least 1-O(sqrt{epsilon log k}) where k is the number of outputs in the game. Our proofs use semidefinite-programming techniques, the Gowers inverse theorem and hypergraph norms.
Lei, Y & Sui, Y 1970, 'Fast and Precise Handling of Positive Weight Cycles for Field-Sensitive Pointer Analysis', Static Analysis, International Static Analysis Symposium, Springer International Publishing, Porto, Portugal, pp. 27-47.
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© Springer Nature Switzerland AG 2019. By distinguishing the fields of an object, Andersen’s field-sensitive pointer analysis yields better precision than its field-insensitive counterpart. A typical field-sensitive solution to inclusion-based pointer analysis for C/C++ is to add positive weights to the edges in Andersen’s constraint graph to model field access. However, the precise modeling is at the cost of introducing a new type of constraint cycles, called positive weight cycles (PWCs). A PWC, which contains at least one positive weight constraint, can cause infinite and redundant field derivations of an object unless the number of its fields is bounded by a pre-defined value. PWCs significantly affect analysis performance when analyzing large C/C++ programs with heavy use of structs and classes.para This paper presents Dea, a fast and precise approach to handling of PWCs that significantly accelerates existing field-sensitive pointer analyses by using a new field collapsing technique that captures the derivation equivalence of fields derived from the same object when resolving a PWC.para Two fields are derivation equivalent in a PWC if they are always pointed to by the same variables (nodes) in this PWC. A stride-based field representation is proposed to identify and collapse derivation equivalent fields into one, avoiding redundant field derivations with significantly fewer field objects during points-to propagation. We have conducted experiments using 11 open-source C/C++ programs. The evaluation shows that Dea is on average 7.1X faster than Pearce et al.’s field-sensitive analysis (Pkh), obtaining the best speedup of 11.0X while maintaining the same precision.
Leong, TW, Lawrence, C & Wadley, G 1970, 'Designing for diversity in Aboriginal Australia', Proceedings of the 31st Australian Conference on Human-Computer-Interaction, OZCHI'19: 31ST AUSTRALIAN CONFERENCE ON HUMAN-COMPUTER-INTERACTION, ACM, Fremantle, Australia, pp. 418-422.
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© 2019 Association for Computing Machinery. Aboriginal Australians have been colonized for over 230 years. As a result, many have been disconnected from their communities and identity. This paper reports on a national-scale HCI project that aims to design technology that allows Aboriginal Australians to reconnect with their communities and to reaffirm their Aboriginal identity. Our project faces significant challenges, some due to the effects of colonization and some due to the great (and underrecognized) diversity of Aboriginal Australia. In this paper, we report the design phase of our project, and discuss some of these challenges we faced. Through this, we offer insights for HCI designers and researchers undertaking similar work.
Leveaux, R & Kang, K 1970, 'An Examination of the Delivery of Sports Taekwondo Referee and Coach Education with Emphasis Towards the Oceania Region', VISION 2025: EDUCATION EXCELLENCE AND MANAGEMENT OF INNOVATIONS THROUGH SUSTAINABLE ECONOMIC COMPETITIVE ADVANTAGE, 34th International-Business-Information-Management-Association (IBIMA) Conference, INT BUSINESS INFORMATION MANAGEMENT ASSOC-IBIMA, SPAIN, Madrid, pp. 7140-7150.
Lewis, C, Krivokapic-Skoko, B & Marjanovic, O 1970, 'Saving rural communities through tourism marketing cooperatives', 14th International Cooperatives Alliance Asia-Pacific Research Conference., Newcastle.
Li, C, Zhang, F, Zhang, Y, Qin, L, Zhang, W & Lin, X 1970, 'Efficient Progressive Minimum k-core Search.', Proc. VLDB Endow., ASSOC COMPUTING MACHINERY, Tokyo, JAPAN, pp. 362-375.
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Li, G, Zhu, L, Liu, P & Yang, Y 1970, 'Entangled Transformer for Image Captioning', 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Long Beach, pp. 8928-8937.
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Li, G, Zhu, L, Liu, P & Yang, Y 1970, 'Entangled transformer for image captioning', Proceedings of the IEEE International Conference on Computer Vision, IEEE/CVF International Conference on Computer Vision, IEEE, Seoul, Korea (South), pp. 8927-8936.
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In image captioning, the typical attention mechanisms are arduous to identify the equivalent visual signals especially when predicting highly abstract words. This phenomenon is known as the semantic gap between vision and language. This problem can be overcome by providing semantic attributes that are homologous to language. Thanks to the inherent recurrent nature and gated operating mechanism, Recurrent Neural Network (RNN) and its variants are the dominating architectures in image captioning. However, when designing elaborate attention mechanisms to integrate visual inputs and semantic attributes, RNN-like variants become unflexible due to their complexities. In this paper, we investigate a Transformer-based sequence modeling framework, built only with attention layers and feedforward layers. To bridge the semantic gap, we introduce EnTangled Attention (ETA) that enables the Transformer to exploit semantic and visual information simultaneously. Furthermore, Gated Bilateral Controller (GBC) is proposed to guide the interactions between the multimodal information. We name our model as ETA-Transformer. Remarkably, ETA-Transformer achieves state-of-the-art performance on the MSCOCO image captioning dataset. The ablation studies validate the improvements of our proposed modules.
Li, H, Wang, TQ, Huang, X & Zhang, JA 1970, 'Enhanced AoA Estimation Using Localized Hybrid Dual-Polarized Arrays', 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), IEEE, Honolulu, HI, USA, pp. 1-6.
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© 2019 IEEE. With balanced system performance, implementation complexity and hardware cost, hybrid antenna array is regarded as an enabling technology for massive multiple-input and multiple-output communication systems in millimeter wave (mmWave) frequencies. Angle-of-arrival (AoA) estimation using a localized hybrid array faces the challenges of the phase ambiguity problem due to its localized nature of array structure and susceptibility to noises. This paper discusses AoA estimation in an mmWave system employing dual-polarized antennas. We propose an enhanced AoA estimation algorithm using a localized hybrid dual-polarized array for a polarized mmWave signal. First, the use of dual-polarized arrays effectively strengthens the calibration of differential signals and resulting signal-to-noise ratio with coherent polarization combining, leading to an enhanced estimate of the phase offset between adjacent subarrays. Second, given the phase offset, an initial AoA estimate can be obtained, which is used to update the phase offset. By employing the updated one, the AoA is re- estimated with improved accuracy. The closed-form mean square error (MSE) lower bounds of AoA estimation are derived and compared with simulated MSEs. The simulation results show that the proposed algorithm in combination with hybrid dual- polarized arrays significantly improves the estimation accuracy compared with the state of the art.
Li, H-Y, Xu, J-X, Zhang, XY & Yang, Y 1970, 'Design of Low-Loss Single-Pole Single-Throw and Single-Pole Double-Throw Filtering Switches Using Coaxial Resonators', 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT), 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT), IEEE, Guangzhou, China, pp. 1-3.
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© 2019 IEEE. In this paper, a single-pole single-throw (SPST) filtering switch and a single-pole double-throw (SPDT) filtering switch are designed using coaxial resonators. The ON- and OFF-states are achieved by controlling the PIN diodes embedded in the coaxial filter structures. For the SPST filtering switch, four PIN diodes are all turned off to achieve the ON-state, functioning as same as a conventional four-order coaxial filter, where the cross-coupling is employed to realize high-selectivity filtering responses. When the four PIN diodes are turned on, the SPDT switch works in the OFF-state. Capacitors are connected to the coaxial resonators to change the resonant frequencies of the resonators, thus the passband cannot be formed, resulting in a high isolation between the two ports. For demonstration, a SPST filtering switch is fabricated. Measured results show a low insertion loss of less than 1 dB and a high isolation of better than 60 dB. The excellent performance makes it attractive in wireless systems. Moreover, a SPDT filtering switch is also proposed. Compared with the state-of-the-art works using DR-based resonators, the proposed design shows a more compact size.
Li, J & Wu, C 1970, 'Experimental and numerical study on Basalt scale aluminium foam', 7th International Conference on Protection of Structures against Hazards, Hanoi.
Li, JT & Zhu, XQ 1970, 'Bridge Damage Identification via Interaction Forces of the Vehicle-bridge System using Vehicle Axle Responses', 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings, The 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Missouri University of Science and Technology, ST. Louis, USA, pp. 1266-1272.
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Bridge damage identification using vibration responses of a passing vehicle is an attractive but challenging task. Instrumented vehicle carries out a drive-by inspection of the bridge and requires no bridge instrumentation which is convenient and cost effective. Extracting useful bridge structural health information from vehicle responses is where the challenges exist. A twostep strategy is proposed to identify the bridge damage using vehicle axle responses. Dual Kalman filter (DKF) is adopted to identify the interaction forces at the contact points between vehicle and bridge structure. The parameters of vehicle system are required and only the dynamic responses of the axles are measured. Local anomalies of the structure are estimated from the interaction forces in the second step. The damage is defined as the elemental flexural stiffness reduction. Interaction force sensitivity analysis is performed and realized with a regularization technique. Numerical example is presented where a half-car model is used and the bridge is modelled using finite element model. The identification results show that the proposed strategy is efficient for interaction force identification and damage detection with only responses of the vehicle axles.
Li, K, Kanhere, SS, Ni, W, Tovar, E & Guizani, M 1970, 'Proactive Eavesdropping via Jamming for Trajectory Tracking of UAVs', 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC), IEEE, pp. 477-482.
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Li, K, Lu, L, Ni, W, Tovar, E & Guizani, M 1970, 'Cooperative Secret Key Generation for Platoon-Based Vehicular Communications', ICC 2019 - 2019 IEEE International Conference on Communications (ICC), ICC 2019 - 2019 IEEE International Conference on Communications (ICC), IEEE.
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Li, L, Liu, Z, Zhang, J & Zhou, X 1970, 'Learn Image Object Co-segmentation with Multi-scale Feature Fusion', 2019 IEEE Visual Communications and Image Processing (VCIP), 2019 IEEE Visual Communications and Image Processing (VCIP), IEEE, Sydney, Australia, pp. 1-4.
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© 2019 IEEE. Image object co-segmentation aims to segment common objects in a group of images. This paper proposes a novel neural network, which extracts multi-scale convolutional features at multiple layers via a modified VGG network and fuses them both within and across images as the intra-image and the inter-image features. Then these two kinds of features are further fused at each scale as the multi-scale co-features of common objects, and finally the multi-scale co-features are summed up and upsampled to obtain the co-segmentation results. To simplify the network and reduce the rapidly rising resource cost along with the inputs, the reduced input size, less downsampling and dilation convolution are adopted in the proposed model. Experimental results on the public dataset demonstrate that the proposed model achieves a comparable performance to the state-of-The-Art co-segmentation methods while the computation cost has been effectively reduced.
Li, Q, Wu, Q & Liu, X 1970, 'Multi-scale and Hierarchical Embedding for Polarity Shift Sensitive Sentiment Classification', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Artificial Intelligence and Security, Springer International Publishing, New York, NY, USA, pp. 227-238.
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© 2019, Springer Nature Switzerland AG. Appropriate paragraph embedding is critical for sentiment classification. However, the embedding for paragraph with polarity shift is very challenging and insufficiently explored. In this paper, a MUlti-Scale and Hierarchical embedding method, MUSH, is proposed to learn a more accurate paragraph embedding for polarity shift sensitive sentiment classification. MUSH adopts CNN with multi-size filters to reveal multi-scale sentiment atoms and utilizes hierarchical multi-line CNN-RNN structures to simultaneously capture polarity shift in both sentence level and paragraph level. Extensive experiments on four large real-world data sets demonstrate that the MUSH-enabled sentiment classification significantly enhances the accuracy compared with three state-of-the-art and four baseline competitors.
Li, Q, Wu, Q, Zhu, C & Zhang, J 1970, 'Bi-Level Masked Multi-scale CNN-RNN Networks for Short Text Representation', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia, pp. 888-893.
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Representing short text is becoming extremely important for a variety of valuable applications. However, representing short text is critical yet challenging because it involves lots of informal words and typos (i.e. the noise problem) but only a few vocabularies in each text (i.e. the sparsity problem). Most of the existing work on representing short text relies on noise recognition and sparsity expansion. However, the noises in short text are with various forms and changing fast, but, most of the current methods may fail to adaptively recognize the noise. Also, it is hard to explicitly expand a sparse text to a high-quality dense text. In this paper, we tackle the noise and sparsity problems in short text representation by learning multi-grain noise-tolerant patterns and then embedding the most significant patterns in a text as its representation. To achieve this goal, we propose a bi-level multi-scale masked CNN-RNN network to embed the most significant multi-grain noise-tolerant relations among words and characters in a text into a dense vector space. Comprehensive experiments on five large real-world data sets demonstrate our method significantly outperforms the state-of-the-art competitors.
Li, Q, Wu, Q, Zhu, C, Zhang, J & Zhao, W 1970, 'An Inferable Representation Learning for Fraud Review Detection with Cold-start Problem', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-8.
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© 2019 IEEE. Fraud review significantly damages the business reputation and also customers' trust to certain products. It has become a serious problem existing on the current social media. Various efforts have been put in to tackle such problems. However, in the case of cold-start where a review is posted by a new user who just pops up on the social media, common fraud detection methods may fail because most of them are heavily depended on the information about the user's historical behavior and its social relation to other users, yet such information is lacking in the cold-start case. This paper presents a novel Joint-bEhavior-and-Social-relaTion-infERable (JESTER) embedding method to leverage the user reviewing behavior and social relations for cold-start fraud review detection. JESTER embeds the deep characteristics of existing user behavior and social relations of users and items in an inferable user-item-review-rating representation space where the representation of a new user can be efficiently inferred by a closed-form solution and reflects the user's most probable behavior and social relations. Thus, a cold-start fraud review can be effectively detected accordingly. Our experiments show JESTER (i) performs significantly better in detecting fraud reviews on four real-life social media data sets, and (ii) effectively infers new user representation in the cold-start problem, compared to three state-of-the-art and two baseline competitors.
Li, Q, Wu, Q, Zhu, C, Zhang, J & Zhao, W 1970, 'Unsupervised User Behavior Representation for Fraud Review Detection with Cold-Start Problem', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, China, pp. 222-236.
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© Springer Nature Switzerland AG 2019. Detecting fraud review is becoming extremely important in order to provide reliable information in cyberspace, in which, however, handling cold-start problem is a critical and urgent challenge since the case of cold-start fraud review rarely provides sufficient information for further assessing its authenticity. Existing work on detecting cold-start cases relies on the limited contents of the review posted by the user and a traditional classifier to make the decision. However, simply modeling review is not reliable since reviews can be easily manipulated. Also, it is hard to obtain high-quality labeled data for training the classifier. In this paper, we tackle cold-start problems by (1) using a user’s behavior representation rather than review contents to measure authenticity, which further (2) consider user social relations with other existing users when posting reviews. The method is completely (3) unsupervised. Comprehensive experiments on Yelp data sets demonstrate our method significantly outperforms the state-of-the-art methods.
Li, R-H, Dai, Q, Wang, G, Ming, Z, Qin, L & Yu, JX 1970, 'Improved Algorithms for Maximal Clique Search in Uncertain Networks.', ICDE, IEEE 35th International Conference on Data Engineering, IEEE, Macao, Macao, pp. 1178-1189.
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Enumerating maximal cliques from an uncertain graph is a fundamental problem in uncertain graph analysis. Given an uncertain graph G, a set of nodes C in G is a maximal (k, τ)-clique if (1) |C|>k and C is a clique with probability at least τ, and (2) C is a maximal node set meeting (1). The state-of-the-art algorithm for enumerating all maximal (k, τ)-cliques is very costly when handling large uncertain graphs, as its time complexity is proportional to 2^n where n is the number of nodes in the uncertain graph. To overcome this issue, we propose two new core-based pruning algorithms to reduce the uncertain graph size without missing any maximal (k, τ)-clique. We also develop a novel cut-based optimization technique to further improve the pruning performance of the core-based pruning algorithms. Based on these pruning techniques, we propose an improved algorithm to enumerate all maximal (k, τ)-cliques, and a new algorithm with several novel upper-bounding techniques to compute one of maximum (k, τ)-cliques from the pruned uncertain graph. The results of extensive experiments on six real-world datasets demonstrate the efficiency and effectiveness of the proposed algorithms.
Li, W, Qiao, M, Qin, L, Zhang, Y, Chang, L & Lin, X 1970, 'Scaling Distance Labeling on Small-World Networks.', SIGMOD Conference, ACM SIGMOD International Conference on Management of Data (SIGMOD), ACM, Amsterdam, NETHERLANDS, pp. 1060-1077.
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© 2019 Association for Computing Machinery. Distance labeling approaches are widely adopted to speed up the online performance of shortest distance queries. The construction of the distance labeling, however, can be exhaustive especially on big graphs. For a major category of large graphs, small-world networks, the state-of-the-art approach is Pruned Landmark Labeling (PLL). PLL prunes distance labels based on a node order and directly constructs the pruned labels by performing breadth-first searches in the node order. The pruning technique, as well as the index construction, has a strong sequential nature which hinders PLL from being parallelized. It becomes an urgent issue on massive small-world networks whose index can hardly be constructed by a single thread within a reasonable time. This paper scales distance labeling on small-world networks by proposing a Parallel Shortest-distance Labeling (PSL) scheme and further reducing the index size by exploiting graph and label properties. PSL insightfully converts the PLL's node-order dependency to a shortest-distance dependence, which leads to a propagation-based parallel labeling in D rounds where D denotes the diameter of the graph. Extensive experimental results verify our efficiency on billion-scale graphs and near-linear speedup in a multi-core environment.
Li, Y, Long, G, Shen, T, Zhou, T, Yao, L, Huo, H & Jiang, J 1970, 'Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction'.
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Distantly supervised relation extraction intrinsically suffers from noisylabels due to the strong assumption of distant supervision. Most prior worksadopt a selective attention mechanism over sentences in a bag to denoise fromwrongly labeled data, which however could be incompetent when there is only onesentence in a bag. In this paper, we propose a brand-new light-weight neuralframework to address the distantly supervised relation extraction problem andalleviate the defects in previous selective attention framework. Specifically,in the proposed framework, 1) we use an entity-aware word embedding method tointegrate both relative position information and head/tail entity embeddings,aiming to highlight the essence of entities for this task; 2) we develop aself-attention mechanism to capture the rich contextual dependencies as acomplement for local dependencies captured by piecewise CNN; and 3) instead ofusing selective attention, we design a pooling-equipped gate, which is based onrich contextual representations, as an aggregator to generate bag-levelrepresentation for final relation classification. Compared to selectiveattention, one major advantage of the proposed gating mechanism is that, itperforms stably and promisingly even if only one sentence appears in a bag andthus keeps the consistency across all training examples. The experiments on NYTdataset demonstrate that our approach achieves a new state-of-the-artperformance in terms of both AUC and top-n precision metrics.
Li, Z, Hong, J, Kim, J & Yu, C 1970, 'Control Design and Analysis of An Epidemic SEIV Model upon Adaptive Network', 2019 18th European Control Conference (ECC), 2019 18th European Control Conference (ECC), IEEE, Naples, Italy, pp. 2492-2497.
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This paper focuses on the control design and stability analysis of a Susceptible-Exposed-Infected-Vigilant (SEIV) epidemic model via adaptive complex networks. The network is designed empirically as a state-dependent network, where the network structure keeps changing to inhibit the epidemic propagation. The recovery rate and the disease prevention rate are chosen as the control scheme in the epidemic system, both of which are closely associated with medical resources allocation. People may cut the connection with an infected neighbor and reduce the frequency to go out when an epidemic occurs. In order to formulate this behavior, an adaptive network structure is presented which is designed to be consistent with real human contact behaviors under epidemic prevalence. A candidate Lyapunov function is employed to analyze the system stability and guarantee the extinction of the epidemic. Simulation results are shown to illustrate the high efficiency and validity of the parameter control and the adaptive network design.
Li, Z, Hong, J, Kim, J & Yu, C 1970, 'Control Design and Stability Analysis of a Two-Infectious-State Awareness Epidemic Model', 2019 12th Asian Control Conference, ASCC 2019, Asian Control Conference, IEEE, Kitakyushu-shi, Japan, pp. 704-709.
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This paper focuses on the control design and stability analysis of an awareness Susceptible-Exposed-Infected-Vigilant (SEIV) epidemic system which has two infectious states over arbitrary directed networks. A state feedback controller is firstly applied to the generalized SEIV model with human awareness, and explores the medical treatment usage against the epidemic propagation. After applying the control scheme into the epidemic system, the epidemic threshold condition is found to guarantee the exponential stability of the system. Simulation results are illustrated to verify the threshold condition as well as the performance of the control design which is able to reduce the epidemic outbreak and effectively inhibiting the epidemic dissemination.
Li, Z, Liu, W, Chang, X, Yao, L, Prakash, M & Zhang, H 1970, 'Domain-Aware Unsupervised Cross-dataset Person Re-identification', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 15th International Conference on Advanced Data Mining and Applications, Springer International Publishing, Dalian, China, pp. 406-420.
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© 2019, Springer Nature Switzerland AG. We focus on the person re-identification (re-id) problem of matching people across non-overlapping camera views. While most existing works rely on the abundance of labeled exemplars, we consider a more difficult unsupervised scenario, where no labeled exemplar is provided. One solution for unsupervised re-id that attracts much attention in the recent researches is cross-dataset transfer learning. It utilizes knowledge from multiple source datasets from different domains to enhance the unsupervised learning performance on the target domain. In previous works, much effect is taken on extraction of the generic and robust common appearances representations across domains. However, we observe that there also particular appearances in different domains. Simply ignoring these domain-unique appearances will misleading the matching schema in re-id application. Few unsupervised cross-dataset algorithms are proposed to learn the common appearances across multiple domains, even less of them consider the domain-unique representations. In this paper, we propose a novel domain-aware representation learning algorithm for unsupervised cross-dataset person re-id problem. The proposed algorithm not only learns a common appearances across-datasets but also captures the domain-unique appearances on the target dataset via minimization of the overlapped signal supports across different domains. Extensive experimental studies on benchmark datasets show superior performances of our algorithm over state-of-the-art algorithms. Sample analysis on selected samples also verifies the ability of diversity learning of our algorithm.
Li, Z, Zhang, J, Wu, Q, Gong, Y, Yi, J & Kirsch, C 1970, 'Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points', Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, Anchorage AK USA, pp. 2848-2856.
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© 2019 Association for Computing Machinery. Railway points are among the key components of railway infrastructure. As a part of signal equipment, points control the routes of trains at railway junctions, having a significant impact on the reliability, capacity, and punctuality of rail transport. Meanwhile, they are also one of the most fragile parts in railway systems. Points failures cause a large portion of railway incidents. Traditionally, maintenance of points is based on a fixed time interval or raised after the equipment failures. Instead, it would be of great value if we could forecast points' failures and take action beforehand, min-imising any negative effect. To date, most of the existing prediction methods are either lab-based or relying on specially installed sensors which makes them infeasible for large-scale implementation. Besides, they often use data from only one source. We, therefore, explore a new way that integrates multi-source data which are ready to hand to fulfil this task. We conducted our case study based on Sydney Trains rail network which is an extensive network of passenger and freight railways. Unfortunately, the real-world data are usually incomplete due to various reasons, e.g., faults in the database, operational errors or transmission faults. Besides, railway points differ in their locations, types and some other properties, which means it is hard to use a unified model to predict their failures. Aiming at this challenging task, we firstly constructed a dataset from multiple sources and selected key features with the help of domain experts. In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels. We present a robust multiple kernel learning algorithm for predicting points failures. Our model takes into account the missing pattern of data as well as the inherent variance on different sets of railway points. Extensive experiments demonstrate the superiority of our algorit...
Li, Z, Zou, Y, Wang, G & Zhang, J 1970, 'Scale-Informed Density Estimation for Dense Crowd Counting', 2019 IEEE Visual Communications and Image Processing (VCIP), 2019 IEEE Visual Communications and Image Processing (VCIP), IEEE, Sydney, Australia, pp. 1-4.
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© 2019 IEEE. Dense crowd counting (DCC) remains challenging due to the scale variation and occlusion. Several deep learning based DCC methods have achieved the state-of-Arts on public datasets. However, experimental results show that the scale variation is still the main factor to hinder the DCC performance. In this work, we propose a scale-informed dense crowd counting method focusing on handling the negative effect caused by scale variation. More specifically, we propose a method to obtain the scale information of the patch from its GT density maps via estimating the mean value of the Gaussian kernel width and then a scale-classifier is deigned and trained accordingly. Moreover, with the estimated scale information, two sub-nets are dedicatedly deigned to learn the density maps for large-scale head patch and small-scale patch separately. Experimental results validate the performance of our proposed method which achieves the best performance on three dense crowd datasets.
Liang, B, Vitanage, D, Doolan, C, Li, Z, Taib, R, Mathews, G, Wang, Y, Lu, S, Chen, F, Hua, T & Peters, A 1970, 'Predicting Water Quality for the Woronora Delivery Network with Sparse Samples', 2019 IEEE International Conference on Data Mining (ICDM), 2019 IEEE International Conference on Data Mining (ICDM), IEEE, Beijing, China, pp. 1210-1215.
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© 2019 IEEE. Monitoring drinking water quality in the entire delivery network, mainly indicated by total chlorine (TC), is a critical component of overall water supply management. However, it is extremely difficult to collect sufficient TC data from the network at customer sites, which makes it sparse for comprehensive modelling. This paper details an approach that provides TC prediction within the entire Woronora delivery network in Sydney in the next 24 hours. First, the hydraulic system is employed to capture the topology of the delivery network, so that the water travel time can be estimated using predicted water demand. The travel time links the upstream (reservoir) data to the downstream (resident) data. Then, a two-step strategy is proposed as a semi-parametric method to determine the crucial factors and build Bayesian model for TC decay to predict TC with the travel time. Lastly, the uncertainties of both data and the model are analysed to define the boundaries of prediction for better decision making. Several operational stages are involved when the approach is being deployed, including prediction interpretation, interactive tool development for water quality mapping and visualisation, and proactive optimisation. This has established a successful initiative to improve the overall water supply management for the entire Woronora delivery network.
Liang, B, Zheng, L & Li, X 1970, 'Sequential Deep Learning for Action Recognition with Synthetic Multi-view Data from Depth Maps', Communications in Computer and Information Science, Workshops of the International Conference on Advanced Information Networking and Applications, Springer Singapore, Matsue, Japan, pp. 360-371.
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© Springer Nature Singapore Pte Ltd. 2019. Recurrent neural network (RNN) has proven successful recently in action recognition. However, depth sequences are of high dimensionality and contain rich human dynamics, which makes traditional RNNs difficult to capture complex action information. This paper addresses the problem of human action recognition from sequences of depth maps using sequential deep learning. The proposed method first synthesizes multi-view depth sequences by rotating 3D point clouds from depth maps. Each depth sequence is then split into short-term temporal segments. For each segment, a multi-view depth motion template (MVDMT), which compresses the segment to a motion template, is constructed for short-term multi-view action representation. The MVDMT effectively characterizes the multi-view appearance and motion patterns within a short-term duration. Convolutional Neural Network (CNN) models are leveraged to extract features from MVDMT, and a CNN-RNN network is subsequently employed to learn an effective representation for sequential patterns of the multi-view depth sequence. The proposed multi-view sequential deep learning framework can simultaneously capture spatial-temporal appearance and motion features in the depth sequence. The proposed method has been evaluated on the MSR Action3D and MSR Action Pairs datasets, achieving promising results compared with the state-of-the-art methods based on depth data.
Liang, B-J, Zheng, Y-Y, Ma, X-M & Zhang, Q 1970, 'Construction and Technology Research of Cross Platform Mobile Medical Image Reading System', 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), IEEE, PEOPLES R CHINA, Chongqing, pp. 384-387.
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Liao, Q, Wang, D, Holewa, H & Xu, M 1970, 'Squeezed Bilinear Pooling for Fine-Grained Visual Categorization', 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), IEEE, South Korea, pp. 728-732.
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Lin, A, Lu, J, Xuan, J, Zhu, F & Zhang, G 1970, 'One-Stage Deep Instrumental Variable Method for Causal Inference from Observational Data', 2019 IEEE International Conference on Data Mining (ICDM), 2019 IEEE International Conference on Data Mining (ICDM), IEEE, Beijing, China, pp. 419-428.
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© 2019 IEEE. Causal inference from observational data aims to estimate causal effects when controlled experimentation is not feasible, but it faces challenges when unobserved confounders exist. The instrumental variable method resolves this problem by introducing a variable that is correlated with the treatment and affects the outcome only through the treatment. However, existing instrumental variable methods require two stages to separately estimate the conditional treatment distribution and the outcome generating function, which is not sufficiently effective. This paper presents a one-stage approach to jointly estimate the treatment distribution and the outcome generating function through a cleverly designed deep neural network structure. This study is the first to merge the two stages to leverage the outcome to the treatment distribution estimation. Further, the new deep neural network architecture is designed with two strategies (i.e., shared and separate) of learning a confounder representation account for different observational data. Such network architecture can unveil complex relationships between confounders, treatments, and outcomes. Experimental results show that our proposed method outperforms the state-of-the-art methods. It has a wide range of applications, from medical treatment design to policy making, population regulation and beyond.
Lin, J, Sun, G, Shen, J, Cui, T, Yu, P, Xu, D, Li, L & Beydoun, G 1970, 'Towards the Readiness of Learning Analytics Data for Micro Learning.', SCC, International Conference on Services Computing, Springer, San Diego, CA, pp. 66-76.
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© Springer Nature Switzerland AG 2019. With the development of data mining and machine learning techniques, data-driven based technology-enhanced learning (TEL) has drawn wider attention. Researchers aim to use established or novel computational methods to solve educational problems in the ‘big data’ era. However, the readiness of data appears to be the bottleneck of the TEL development and very little research focuses on investigating the data scarcity and inappropriateness in the TEL research. This paper is investigating an emerging research topic in the TEL domain, namely micro learning. Micro learning consists of various technical themes that have been widely studied in the TEL research field. In this paper, we firstly propose a micro learning system, which includes recommendation, segmentation, annotation, and several learning-related prediction and analysis modules. For each module of the system, this paper reviews representative literature and discusses the data sources used in these studies to pinpoint their current problems and shortcomings, which might be debacles for more effective research outcomes. Accordingly, the data requirements and challenges for learning analytics in micro learning are also investigated. From a research contribution perspective, this paper serves as a basis to depict and understand the current status of the readiness of data sources for the research of micro learning.
Lin, J-Y, Wong, S-W, Yang, Y & Zhu, L 1970, 'Cavity Balanced-to-Unbalanced Magic-T with Filtering Response', 2019 IEEE MTT-S International Microwave Symposium (IMS), 2019 IEEE/MTT-S International Microwave Symposium - IMS 2019, IEEE, Boston, MA, USA, pp. 444-447.
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© 2019 IEEE. In this paper, a design of balanced-to-unbalanced magic-T with filtering response is proposed for the first time. The function of the proposed magic-T is composed of an in-phase balanced-to-unbalanced power divider and an out-of-phase balanced-to-unbalanced power divider. Three fundamental modes, namely, TE011, TE101, and TM110, of the triple-mode resonators (TMRs) are excited to provide the odd- and even-symmetric field distributions so that in-phase and out-of-phase responses can be achieved. It is noteworthy that the common-mode suppression can be achieved at the balanced ports, while high isolation is achieved at the single-end ports. To verify the concept, the proposed balanced-to-unbalanced magic-T structure is fabricated and measured. Good matching between simulated and measured results shows the validity and accuracy of the proposed design methodology.
Lin, Z, Lv, T, Zhang, JA & Liu, RP 1970, '3D Wideband mmWave Localization for 5G Massive MIMO Systems', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA, pp. 1-6.
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© 2019 IEEE. This paper proposes a novel 3D localization method for wideband mmWave massive MIMO systems. A high dimensional linear interpolation (HDLI)-based preprocessing is first proposed to transform the frequency-associated dynamical array response vectors into the common counterparts at the reference frequency. Through this method, the received data in all frequency bands can be processed jointly, and thus the high temporal resolution provided by wideband mmWave systems can be fully exploited for position estimation. To reduce the computational complexity in the process of the parameter estimation, we then present a wideband beamspace (WBS)-based parameter estimation algorithm to estimate the angle and delay in the low-dimensional beamspace. By exploiting the quasi- optical propagation at the mmWave frequencies, a novel positioning scheme is also designed to determine the 3D location of the target. According to our analysis and simulation results, the proposed method is capable of achieving significantly reduced computational complexity, while maintaining high localization accuracy.
Ling, SH, Makgawinata, H, Monsivais, FH, dos Santos Goncalves Lourenco, A, Lyu, J & Chai, R 1970, 'Classification of EEG Motor Imagery Tasks Using Convolution Neural Networks', 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Berlin, Germany, pp. 758-761.
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Electroencephalograph (EEG) is a highly nonlinear data and very difficult to be classified. The EEG signal is commonly used in the area of Brain-Computer Interface (BCI). The signal can be used as an operative command for directional movements for a powered wheelchair to assist people with disability in performing the daily activity.In this paper, we aim to classify Electroencephalograph EEG signals extracted from subjects which had been trained to perform four Motoric Imagery (MI) tasks for two classes. The classification will be processed via a Convolutional Neural Network (CNN) utilising all 22 electrodes based on 10-20 system placement. The EEG datasets will be transformed into scaleogram using Continuous Wavelet Transform (CWT) method.We evaluated two different types of image configuration, i.e. layered and stacked input datasets. Our procedure starts from denoising the EEG signals, employing Bump CWT from 8-32 Hz brain wave. Our CNN architecture is based on the Visual Geometry Group (VGG-16) network. Our results show that layered image dataset yields a high accuracy with an average of 68.33% for two classes classification.
Liu, B, Xiong, J, Wu, Y, Ding, M & Wu, CM 1970, 'Protecting Multimedia Privacy from Both Humans and AI', 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, Jeju, Korea (South).
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© 2019 IEEE. With the development of artificial intelligence (AI), multimedia privacy issues have become more challenging than ever. AI-assisted malicious entities can steal private information from multimedia data more easily than humans. Traditional multimedia privacy protection only considers the situation when humans are the adversaries, therefore they are ineffective against AI-assisted attackers. In this paper, we develop a new framework and new algorithms that can protect image privacy from both humans and AI. We combine the idea of adversarial image perturbation which is effective against AI and the obfuscation technique for human adversaries. Experiments show that our proposed methods work well for all types of attackers.
Liu, B, Yuan, L, Lin, X, Qin, L, Zhang, W & Zhou, J 1970, 'Efficient (a,β)-core Computation: an Index-based Approach.', WWW, World Wide Web Conference, ACM, San Francisco CA USA, pp. 1130-1141.
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The problem of computing (α, β)-core in a bipartite graph for given α and β is a fundamental problem in bipartite graph analysis and can be used in many applications such as online group recommendation, fraudsters detection, etc. Existing solution to computing (α, β)-core needs to traverse the entire bipartite graph once. Considering the real bipartite graph can be very large and the requests to compute (α, β)-core can be issued frequently in real applications, the existing solution is too expensive to compute the (α, β)-core. In this paper, we present an efficient algorithm based on a novel index such that the algorithm runs in linear time regarding the result size (thus, the algorithm is optimal since it needs at least linear time to output the result). We prove that the index only requires O(m) space where m is the number of edges in the bipartite graph. Moreover, we devise an efficient algorithm with time complexity O(δ·m) for index construction where δ is bounded by √m and is much smaller than √m in practice. We also discuss efficient algorithms to maintain the index when the bipartite graph is dynamically updated and parallel implementation of the index construction algorithm. The experimental results on real and synthetic graphs (more than 1 billion edges) demonstrate that our algorithms achieve up to 5 orders of magnitude speedup for computing (α, β)-core and up to 3 orders of magnitude speedup for index construction, respectively, compared with existing techniques.
Liu, B, Zhu, T, Zhou, W, Wang, K, Zhou, H & Ding, M 1970, 'Protecting Privacy-Sensitive Locations in Trajectories with Correlated Positions', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA, pp. 1-6.
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The location privacy issue has become a critical research topic recently. The existing solutions do not solve one typical problem in practice: people may only want to protect certain privacy-sensitive locations among a group of temporal and spatial correlated points in a trajectory. As an effort towards this issue, we analyze the impact of space-time relationship on location privacy preservation. In addition, we propose new privacy definitions to better evaluate the privacy level and prove that the target location's privacy can be enhanced by randomizing its time and space related points. Moreover, under the constraint of the total noise power, the problem of obfuscating a location in a temporal and spatial correlated trajectory is formulated as finding the best noise allocation vector which can achieve the highest privacy level. This problem is solved by our proposed location privacy preserving method which applies differential privacy scheme on a series of points with noise budget allocation. Lastly, the performance of the proposed scheme is evaluated by simulations.
Liu, C, Ngo, NT & Indraratna, B 1970, 'Improved Performance of Railroad Ballast Using Geogrids', International Symposium on Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 151-163.
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© Springer Nature Singapore Pte Ltd. 2019. Geogrids are commonly used to stabilise and reinforce ballast, and over the various laboratory tests have been carried out to determine how geogrids affect the interface between geogrid and ballast aggregates. This paper presents a critical review and interpretation of the results of large-scale direct shear tests and cyclic tests on key parameters such as the interlocking effects of aperture size and the location of geogrids. Field investigations from sites at Bulli and Singleton as well as findings from Discrete Element Modelling, including the influence zone of geogrid and the linear relationship between geometric anisotropy and stress ratio, are examined and discussed. It also includes a presentation and discussion of analytical modelling for quantifying the geogrid reinforcing effect (pullout tests).
Liu, DYT, Atif, A, Froissard, JC & Richards, D 1970, 'An enhanced learning analytics plugin for Moodle: Student engagement and personalised intervention', ASCILITE 2015 - Australasian Society for Computers in Learning and Tertiary Education, Conference Proceedings, ASCILITE, ASCILITE, Perth, Australia, pp. 180-189.
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Moodle, an open source Learning Management System (LMS), collects a large amount of data on student interactions within it, including content, assessments, and communication. Some of these data can be used as proxy indicators of student engagement, as well as predictors for performance. However, these data are difficult to interrogate and even more difficult to action from within Moodle. We therefore describe a design-based research narrative to develop an enhanced version of an open source Moodle Engagement Analytics Plugin (MEAP). Working with the needs of unit convenors and student support staff, we sought to improve the available information, the way it is represented, and create affordances for action based on this. The enhanced MEAP (MEAP+) allows analyses of gradebook data, assessment submissions, login metrics, and forum interactions, as well as direct action through personalised emails to students based on these analyses.
Liu, F, Zhang, G & Lu, J 1970, 'A Novel Fuzzy Neural Network for Unsupervised Domain Adaptation in Heterogeneous Scenarios', 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, New Orleans, LA, USA, pp. 1-6.
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© 2019 IEEE. How to leverage knowledge from labelled domain (source) to help classify unlabeled domain (target) is a key problem in the machine learning field. Unsupervised domain adaptation (UDA) provides a solution to this problem and has been well developed for two homogeneous domains. However, when the target domain is unlabeled and heterogeneous with the source domain, current UDA models cannot accurately transfer knowledge from a source domain to a target domain. Benefiting from development of neural networks, this paper presents a new neural network, shared fuzzy equivalence relations neural network (SFER-NN), to address the heterogeneous UDA (HeUDA) problem. SFER-NN transfers knowledge across two domains according to shared fuzzy equivalence relations that can simultaneously cluster features of two domains into several categories. Based on the clustered categories, SFER-NN is constructed to minimize the discrepancy between two domains. Compared to previous works, SFER-NN is more capable of minimizing discrepancy between two domains. As a result of this advantage, SFER-NN delivers a better performance than previous studies using two public datasets.
Liu, H, Zhu, X, Lu, M & Yeo, KS 1970, 'Design of a Voltage-Controlled Programmable-Gain Amplifier in 65-nm CMOS Technology', 2019 IEEE MTT-S International Microwave Symposium (IMS), 2019 IEEE/MTT-S International Microwave Symposium - IMS 2019, IEEE, Boston, USA, pp. 87-90.
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A voltage-controlled programmable-gain amplifier (VC-PGA) is designed in this work. The power consumption of the VC-PGA is binary-weighted. In contrast to conventional PGAs, the gain step of the designed PGA can be continuously tuned by a control voltage. To prove the concept, an analog baseband chain is implemented in 65 nm CMOS technology, which consists of a switchable-order filter with the VC-PGA. The measurements show that the frequency responses can be configured as either 5 th or 7 th order with 16 gain steps. The bandwidth is approximately 50 MHz for all cases and the gain step can be continuously tuned between 0 and 3 dB. The core area is only 0.18 μm 2 .
Liu, J & Wu, C 1970, 'Numerical study of ceramic balls protected ultrahigh performance concrete targets subjected to projectile impact', 13th International Conference on Shock and Impact Loads on Structures, SILOS 2019, pp. 325-334.
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Ceramic materials have excellent mechanical properties such as light weight, great hardness and high compressive strength. This paper presents a numerical study on dynamic response of ceramic balls protected ultra-high performance concrete (UHPC) targets subjected to the high-velocity rigid projectile impact using the coupled smoothed particle hydrodynamics-finite element (SPH-FE) method in LS-DYNA. Numerical models are firstly validated, and then parametric studies are conducted to explore the effect of diameter, spatial arrangement and material type of ceramic balls as well as the impact position on the dynamic performance of UHPC targets. Compared with other existing UHPC slabs at the striking velocities from 500 m/s to 850 m/s, UHPC slabs protected with 6-layer hex-pack arranged ceramic balls with the diameter of 20 mm is most effective in terms of reducing the depth of penetration (DOP).
Liu, J, Chaczko, Z, Braun, R & Gudzbeler, G 1970, 'Collaborative RFID Agent Simulation in Dynamic Environment', 2019 18th International Conference on Information Technology Based Higher Education and Training (ITHET), 2019 18th International Conference on Information Technology Based Higher Education and Training (ITHET), IEEE, Magdeburg, Germany, Germany, pp. 1-4.
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© 2019 IEEE. This paper discusses the design and implementation process of applying morphogenetic and reflexive agents models into the software architecture of radio frequency identification (RFID) agent simulation environment framework (RMAP), as well as, illustrate the agent internal interaction mechanism, and thus allowing multiple agents to work collaboratively to achieve the same goal at least effort. The RMAP can be extensively used for training of medical professionals, developers, students, first responders, emergency services, etc. The RMAP simulation framework is an attempt to synthesize on technological issues related to RFID based solutions in order to 'train' and 'support' medical practitioners and developers.
Liu, J, Lu, J, Hossain, MJ & Li, H 1970, 'A Commercial Building Based Microgrid Performance Investigation', Sustainability in Energy and Buildings 2018 Proceedings of the 10th International Conference in Sustainability on Energy and Buildings (SEB’18), International Conference in Sustainability on Energy and Buildings, Springer International Publishing, Gold Coast, Australia, pp. 209-217.
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© Springer Nature Switzerland AG 2019. In order to overcome the problems which distributed generations bring to the power system, the microgrid concept is proposed. For the power quality consideration, the introduction of the static synchronous compensators and the active power filters may lead to complexity and extra cost to the system. Therefore, an appropriate control strategy for the microgrid is developed in this paper to combine the power quality improvement function to the interlinking converter. This paper investigated the performance of a commercial building based microgrid system. The proposed control strategy enables the microgrid to achieve the active power, reactive power, and harmonics compensation functions. Different scenarios are carried out through simulation based on the real case data. The simulation results show that the microgrid performs effectively with the grid under different cases.
Liu, J, Zhan, B, Wang, S, Ying, S, Liu, T, Li, Y, Ying, M & Zhan, N 1970, 'Formal Verification of Quantum Algorithms Using Quantum Hoare Logic.', CAV (2), 31st International Conference on Computer-Aided Verification (CAV), Springer, New York, NY, pp. 187-207.
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© The Author(s) 2019. We formalize the theory of quantum Hoare logic (QHL) [TOPLAS 33(6),19], an extension of Hoare logic for reasoning about quantum programs. In particular, we formalize the syntax and semantics of quantum programs in Isabelle/HOL, write down the rules of quantum Hoare logic, and verify the soundness and completeness of the deduction system for partial correctness of quantum programs. As preliminary work, we formalize some necessary mathematical background in linear algebra, and define tensor products of vectors and matrices on quantum variables. As an application, we verify the correctness of Grover’s search algorithm. To our best knowledge, this is the first time a Hoare logic for quantum programs is formalized in an interactive theorem prover, and used to verify the correctness of a nontrivial quantum algorithm.
Liu, J, Zhan, B, Wang, S, Ying, S, Liu, T, Li, Y, Ying, M & Zhan, N 1970, 'Quantum Hoare Logic.', Arch. Formal Proofs.
Liu, L, Zhou, T, Long, G, Jiang, J & Zhang, C 1970, 'Learning to Propagate for Graph Meta-Learning', Advances in Neural Information Processing Systems 32 (NIPS 2019), Conference on Neural Information Processing Systems, NIPS, Canada, pp. 1-12.
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Meta-learning extracts common knowledge from learning different tasks anduses it for unseen tasks. It can significantly improve tasks that suffer frominsufficient training data, e.g., few shot learning. In most meta-learningmethods, tasks are implicitly related by sharing parameters or optimizer. Inthis paper, we show that a meta-learner that explicitly relates tasks on agraph describing the relations of their output dimensions (e.g., classes) cansignificantly improve few shot learning. The graph's structure is usually freeor cheap to obtain but has rarely been explored in previous works. We develop anovel meta-learner of this type for prototype-based classification, in which aprototype is generated for each class, such that the nearest neighbor searchamong the prototypes produces an accurate classification. The meta-learner,called 'Gated Propagation Network (GPN)', learns to propagate messages betweenprototypes of different classes on the graph, so that learning the prototype ofeach class benefits from the data of other related classes. In GPN, anattention mechanism aggregates messages from neighboring classes of each class,with a gate choosing between the aggregated message and the message from theclass itself. We train GPN on a sequence of tasks from many-shot to few shotgenerated by subgraph sampling. During training, it is able to reuse and updatepreviously achieved prototypes from the memory in a life-long learning cycle.In experiments, under different training-test discrepancy and test taskgeneration settings, GPN outperforms recent meta-learning methods on twobenchmark datasets. The code of GPN and dataset generation is available athttps://github.com/liulu112601/Gated-Propagation-Net.
Liu, L, Zhou, T, Long, G, Jiang, J & Zhang, C 1970, 'Learning to Propagate for Graph Meta-Learning', ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), 33rd Conference on Neural Information Processing Systems (NeurIPS), NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), CANADA, Vancouver.
Liu, L, Zhou, T, Long, G, Jiang, J, Yao, L & Zhang, C 1970, 'Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph', The 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China.
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A variety of machine learning applications expect to achieve rapid learningfrom a limited number of labeled data. However, the success of most currentmodels is the result of heavy training on big data. Meta-learning addressesthis problem by extracting common knowledge across different tasks that can bequickly adapted to new tasks. However, they do not fully exploreweakly-supervised information, which is usually free or cheap to collect. Inthis paper, we show that weakly-labeled data can significantly improve theperformance of meta-learning on few-shot classification. We propose prototypepropagation network (PPN) trained on few-shot tasks together with dataannotated by coarse-label. Given a category graph of the targeted fine-classesand some weakly-labeled coarse-classes, PPN learns an attention mechanism whichpropagates the prototype of one class to another on the graph, so that theK-nearest neighbor (KNN) classifier defined on the propagated prototypesresults in high accuracy across different few-shot tasks. The training tasksare generated by subgraph sampling, and the training objective is obtained byaccumulating the level-wise classification loss on the subgraph. The resultinggraph of prototypes can be continually re-used and updated for new tasks andclasses. We also introduce two practical test/inference settings which differaccording to whether the test task can leverage any weakly-supervisedinformation as in training. On two benchmarks, PPN significantly outperformsmost recent few-shot learning methods in different settings, even when they arealso allowed to train on weakly-labeled data.
Liu, L, Zhou, T, Long, G, Jiang, J, Yao, L & Zhang, C 1970, 'Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph', PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 28th International Joint Conference on Artificial Intelligence, IJCAI-INT JOINT CONF ARTIF INTELL, PEOPLES R CHINA, Macao, pp. 3015-3022.
Liu, W, Wang, H, Zhang, Y, Wang, W & Qin, L 1970, 'I-LSH: I/O Efficient c-Approximate Nearest Neighbor Search in High-Dimensional Space.', ICDE, IEEE 35th International Conference on Data Engineering, IEEE, Macao, pp. 1670-1673.
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© 2019 IEEE. Nearest Neighbor search has been well solved in low-dimensional space, but is challenging in high-dimensional space due to the curse of dimensionality. As a trade-off between efficiency and result accuracy, a variety of c-approximate nearest neighbor (c-ANN) algorithms have been proposed to return a c-approximate NN with confident at least δ. We observe that existing c-ANN search algorithms have some limitations on I/O efficiency when their indexes are resided on the external memory, which is critical for handling large scale high-dimensional data. In this paper, we introduce an incremental search based c-ANN search algorithm, named I-LSH. Unlike the previous LSH methods, which expand the bucket width in an exponential way, I-LSH adopts a more natural search strategy to incrementally access the hash values of the objects. We provide rigorous theoretical analysis to underpin our incremental search strategy. Our comprehensive experiment results show that, compared with state-of-the-art I/O efficient c-ANN techniques, our algorithm can achieve much better I/O efficiency under the same theoretical guarantee.
Liu, W, Wen, D, Wang, H, Zhang, F & Wang, X 1970, 'Skyline Nearest Neighbor Search on Multi-layer Graphs', 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW), IEEE, Macao, Macao, pp. 259-265.
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© 2019 IEEE. Nearest neighbor search is a fundamental problem in graph theory. In real-world applications, the multi-layer graph model is extensively studied to reveal the multi-dimensional relations between the graph entities. In this paper, we formulate a new problem named skyline nearest neighbor search on multi-layer graphs. Given a query vertex u, we aim to compute a set of skyline vertices that are not dominated by other vertices in terms of the shortest distance on all graph layers. We propose an early-termination algorithm instead of naively adopting the traditional skyline procedure as a subroutine. We also investigate the rule to optimize search order in the algorithm and further improve the algorithmic efficiency. The experimental results demonstrate that the optimization strategies work well on different graphs and can speed up the algorithm significantly.
Liu, Y, Liu, F, Qin, P-Y & Guo, YJ 1970, 'Recent development in nonuniformly spaced array synthesis methods', 2019 IEEE International Symposium on Phased Array System & Technology (PAST), 2019 IEEE International Symposium on Phased Array System & Technology (PAST), IEEE, Waltham, MA, USA,, pp. 1-5.
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© 2019 IEEE. Synthesis of sparse arrays with reduced number of elements are significant for some applications where the available space, weight and the cost of the antenna system is very limited. In recent years, a variety of advanced techniques have been presented to deal with sparse array synthesis problems in either narrow and wideband cases. This paper presents a brief review of these techniques, and gives rough comparative study on some of sparse array synthesis methods.
Liu, Y, Luo, Q, Li, M & Guo, YJ 1970, 'Thinned Massive Antenna Array for 5G Millimeter-Wave Communications', 13th European Conference on Antennas and Propagation, EuCAP 2019, European Conference on Antennas and Propagation, IEEE, Krakow, Poland.
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massive antenna array is one of the key technologies for 5G millimeter-wave communications. In this paper, a modified iterative FFT is introduced to obtain thinned massive arrays. An example is given for synthesizing a 128-element thinned array with U-slot microstrip antenna working at 27.5-28.5 GHz. Simulated results show that the thinned array has improved beam resolution and sidelobe performance than those for a conventional 128-element array.
Liu, Y, Yan, Y, Chen, L, Han, Y & Yang, Y 1970, 'Adaptive sparse confidence-weighted learning for online feature selection', 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence, ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, Honolulu, HI, pp. 4408-4415.
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In this paper, we propose a new online feature selection algorithm for streaming data. We aim to focus on the following two problems which remain unaddressed in literature. First, most existing online feature selection algorithms merely utilize the first-order information of the data streams, regardless of the fact that second-order information explores the correlations between features and significantly improves the performance. Second, most online feature selection algorithms are based on the balanced data presumption, which is not true in many real-world applications. For example, in fraud detection, the number of positive examples are much less than negative examples because most cases are not fraud. The balanced assumption will make the selected features biased towards the majority class and fail to detect the fraud cases. We propose an Adaptive Sparse Confidence-Weighted (ASCW) algorithm to solve the aforementioned two problems. We first introduce an `0-norm constraint into the second-order confidence-weighted (CW) learning for feature selection. Then the original loss is substituted with a cost-sensitive loss function to address the imbalanced data issue. Furthermore, our algorithm maintains multiple sparse CW learner with the corresponding cost vector to dynamically select an optimal cost. We theoretically enhance the theory of sparse CW learning and analyze the performance behavior in F-measure. Empirical studies show the superior performance over the state-of-the-art online learning methods in the online-batch setting.
Lowe, D, Machet, T, Wilkinson, T & Johnston, A 1970, 'Diversity and gender enrolment patterns in an undergraduate engineering program', Proceedings of the 46th SEFI Annual Conference 2018: Creativity, Innovation and Entrepreneurship for Engineering Education Excellence, SEFI Annual Conference, Copenhagen, Denmark, pp. 261-269.
Lowe, D, Machet, T, Willey, K & Berger, A 1970, 'The use of constructive alignment in the design of laboratory activities', Proceedings of the 46th SEFI Annual Conference 2018: Creativity, Innovation and Entrepreneurship for Engineering Education Excellence, 46th SEFI Annual Conference, European Society for Engineering Education, Copenhagen, Denmark, pp. 1016-1023.
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Constructive Alignment has been widely shown to be a valuable approach to educational design, and yet there has been very limited exploration of the application of CA to the design of laboratory experiences. An analysis of existing student information on laboratory experiments shows that most student guides focus on describing the experimental activities to be carried out and often fail to provide clarity in the intended learning outcomes that are being targeted or connect these to assessment activities that allow students to assess their progress in achievement of those learning outcomes. We have argued that the application of CA to laboratory experiments will allow much more effective design. Future work in this area would focus on assessing the extent to which improved student learning outcomes can be achieved through the application of CA.
Lu, S, Oberst, S, Zhang, G & Luo, Z 1970, 'Period adding bifurcations in dynamic pricing processes', 2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), IEEE, Shenzhen, China, pp. 1-6.
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Price information enables consumers to anticipate a price and to make purchasing decisions based on their price expectations, which are critical for agents with pricing decisions or price regulations. A company with pricing decisions can aim to optimise the short-term or the long-term revenue, each of which leads to different pricing strategies thereby different price expectations. Two key ingredients play important roles in the choosing of the short-term or the long-term optimisation objectives: the maximal revenue and the robustness of the chosen pricing strategy against market volatility. However the robustness is rarely identified in a volatile market. Here, we investigate the robustness of optimal pricing strategies with the short-term or long-term optimisation objectives through the analysis of nonlinear dynamics of price expectations. Bifurcation diagrams and period diagrams are introduced to compare the change in dynamics of the optomal pricing strategies. Our results highlight that period adding bifurcations occur during the dynamic pricing processes studied. These bifurcations would challenge the robustness of an optimal pricing strategy. The consideration of the long-term revenue allows a company to charge a higher price, which in turn increases the revenue. However, the consideration of the short-term revenue can reduce the occurrence of period adding bifurcations, contributing to a robust pricing strategy. For a company, this strategy is a robust guarantee of optimal revenue in a volatile market; for consumers, this strategy avoids rapid changes in price and reduce their dissatisfaction of price variations.
Luo, S & Sugiyama, M 1970, 'Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions', Proceedings of the AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), pp. 4488-4495.
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Hierarchical probabilistic models are able to use a large number of parameters to create a model with a high representation power. However, it is well known that increasing the number of parameters also increases the complexity of the model which leads to a bias-variance trade-off. Although it is a classical problem, the bias-variance trade-off between hiddenlayers and higher-order interactions have not been well studied. In our study, we propose an efficient inference algorithm for the log-linear formulation of the higher-order Boltzmann machine using a combination of Gibbs sampling and annealed importance sampling. We then perform a bias-variance decomposition to study the differences in hidden layers and higher-order interactions. Our results have shown that using hidden layers and higher-order interactions have a comparable error with a similar order of magnitude and using higherorder interactions produce less variance for smaller sample size.
Luo, S, Chu, VW, Li, Z, Wang, Y, Zhou, J, Chen, F & Wong, RK 1970, 'Multitask Learning for Sparse Failure Prediction', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Macau, China, pp. 3-14.
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© Springer Nature Switzerland AG 2019. Sparsity is a problem which occurs inherently in many real-world datasets. Sparsity induces an imbalance in data, which has an adverse effect on machine learning and hence reducing the predictability. Previously, strong assumptions were made by domain experts on the model parameters by using their experience to overcome sparsity, albeit assumptions are subjective. Differently, we propose a multi-task learning solution which is able to automatically learn model parameters from a common latent structure of the data from related domains. Despite related, datasets commonly have overlapped but dissimilar feature spaces and therefore cannot simply be combined into a single dataset. Our proposed model, namely hierarchical Dirichlet process mixture of hierarchical beta process (HDP-HBP), learns tasks with a common model parameter for the failure prediction model using hierarchical Dirichlet process. Our model uses recorded failure history to make failure predictions on a water supply network. Multi-task learning is used to gain additional information from the failure records of water supply networks managed by other utility companies to improve prediction in one network. We achieve superior accuracy for sparse predictions compared to previous state-of-the-art models and have demonstrated the capability to be used in risk management to proactively repair critical infrastructure.
Luo, Y, Zhang, JA, Huang, S, Pan, J & Huang, X 1970, 'Quantization with Combined Codebook for Hybrid Array using Two-Phase-Shifter Structure', ICC 2019 - 2019 IEEE International Conference on Communications (ICC), ICC 2019 - 2019 IEEE International Conference on Communications (ICC), IEEE, Shanghai, China, pp. 1-6.
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© 2019 IEEE. We propose a novel joint quantization scheme for hybrid antenna array systems using the two-phase-shifter (2-PS) structure, where two phase shifters are combined to represent one beamforming weight. Conventional quantization using a single phase shifter for each beamforming weight cannot represent the magnitude. We propose a new codebook design that combines the two codebooks of the two phase shifters in the recently proposed 2-PS structure. We also study the scaling problem of the beamforming vector and propose a low-complexity searching algorithm for finding a near-optimal scalar based on element-wise quantization. The mean squared quantization error and signal-to-noise ratio (SNR) degradation are derived analytically. Simulation results validate the accuracy of the analytical results and the effectiveness of the proposed quantization methods.
Luo, Y, Zhang, JA, Ni, W, Pan, J & Huang, X 1970, 'Constrained Multibeam Optimization for Joint Communication and Radio Sensing', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Hawaii, USA, pp. 1-6.
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Multibeam technology has recently been proposed for joint communication and radio sensing (JCAS) in millimeter wave systems using analog antenna arrays. Generation of the multibeam satisfying both communication and sensing requirements is yet to be developed. In this paper, we develop closed-form solutions for optimizing the coefficient that combines communication and sensing subbeams to generate a multibeam. Our solutions maximize the received signal power for communication, in the cases (1) without constraint on sensing beamforming (BF) waveform, (2) with minimum BF gain constraints on discrete sensing directions, and (3) with a minimum total power constraint on a range of sensing directions. Simulation results are provided and validate the effectiveness of the proposed solutions.
Luu, V-H, Dao, M-S, Nguyen, TN-T, Perry, S & Zettsu, K 1970, 'Semi-supervised Convolutional Neural Networks for Flood Mapping using Multi-modal Remote Sensing Data', 2019 6th NAFOSTED Conference on Information and Computer Science (NICS), 2019 6th NAFOSTED Conference on Information and Computer Science (NICS), IEEE, Hanoi, Vietnam, pp. 342-347.
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When floods hit populated areas, quick detection of flooded areas is crucial for initial response by local government, residents, and volunteers. Space-borne polarimetric synthetic aperture radar (PolSAR) is an authoritative data sources for flood mapping since it can be acquired immediately after a disaster even at night time or cloudy weather. Conventionally, a lot of domain-specific heuristic knowledge has been applied for PolSAR flood mapping, but their performance still suffers from confusing pixels caused by irregular reflections of radar waves. Optical images are another data source that can be used to detect flooded areas due to their high spectral correlation with the open water surface. However, they are often affected by day, night, or severe weather conditions (i.e., cloud). This paper presents a convolution neural network (CNN) based multimodal approach utilizing the advantages of both PolSAR and optical images for flood mapping. First, reference training data is retrieved from optical images by manual annotation. Since clouds may appear in the optical image, only areas with a clear view of flooded or non-flooded are annotated. Then, a semisupervised polarimetric-features-aided CNN is utilized for flood mapping using PolSAR data. The proposed model not only can handle the issue of learning with incomplete ground truth but also can leverage a large portion of unlabelled pixels for learning. Moreover, our model takes the advantages of expert knowledge on scattering interpretation to incorporate polarimetric-features as the input. Experiments results are given for the flood event that occurred in Sendai, Japan, on 12th March 2011. The experiments show that our framework can map flooded area with high accuracy (F1 = 96:12) and outperform conventional flood mapping methods.
Lv, X, Withayachumnankul, W & Fumeaux, C 1970, 'Terahertz Absorber Design Adopting Metallic FSS in Sub-Skin-Depth Thickness', 2019 IEEE Asia-Pacific Microwave Conference (APMC), 2019 IEEE Asia-Pacific Microwave Conference (APMC), IEEE, pp. 628-630.
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Lyu, J, Ling, SH, Banerjee, S, Zheng, JJY, Lai, K-L, Yang, D, Zheng, Y-P & Su, S 1970, '3D Ultrasound Spine Image Selection Using Convolution Learning-to-Rank Algorithm', 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Berlin, GERMANY, pp. 4799-4802.
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© 2019 IEEE. 3D Ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. However, the coronal images from different depths of a 3D ultrasound image have different imaging definitions. So there is a need to select the coronal image that would give the best image definition. Also, manual selection of coronal images is time-consuming and limited to the discretion and capability of the assessor. Therefore, in this paper, we have developed a convolution learning-to-rank algorithm to select the best ultrasound images automatically using raw ultrasound images. The ranking is done based on the curve angle of the spinal cord. Firstly, we approached the image selection problem as a ranking problem; ranked based on probability of an image to be a good image. Here, we use the RankNet, a pairwise learning-to-rank method, to rank the images automatically. Secondly, we replaced the backbone of the RankNet, which is the traditional artificial neural network (ANN), with convolution neural network (CNN) to improve the feature extracting ability for the successive iterations. The experimental result shows that the proposed convolutional RankNet achieves the perfect accuracy of 100% while conventional DenseNet achieved 35% only. This proves that the convolutional RankNet is more suitable to highlight the best quality of ultrasound image from multiple mediocre ones.
Madhisetty, S & Williams, M-A 1970, 'Managing Privacy Through Key Performance Indicators When Photos and Videos Are Shared via Social Media', Advances in Intelligent Systems and Computing, Science and Information Conference, Springer International Publishing, London, United Kingdom, pp. 1103-1117.
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© Springer Nature Switzerland AG 2019. There are many definitions of privacy. What is considered sensitive varies from individual to individual. When a document is shared it may reveal certain information, the exchange of information is grounded with a specific context. This contextual grounding may not be afforded when photos and videos are shared, because they may contain rich semantic and syntactic information coded as tacit knowledge. Identifying sensitive information in a photo or a video is a major problem; therefore, rather than making assumptions about what is sensitive in a photo or a video, this research asked a group of study participants why they share content and what their concerns are (if any)? This enabled inferences to be made about categories of sensitivity in accordance with the participants’ responses. Interview data was gathered and Grounded Theory was applied. The following themes emerged from the data: a major theme, in which no privacy concerns were developed, three sub-themes in which varying levels of privacy concerns were developed and key performance indicators which manage levels of privacy were determined. This paper focuses on the main themes’ key performance indicators and how they can manage privacy when photos and videos are shared over social media.
Madhisetty, S & Williams, M-A 1970, 'The Role of Trust and Control in Managing Privacy When Photos and Videos Are Stored or Shared', Advances in Intelligent Systems and Computing, Future Technologies Conference, Springer International Publishing, Vancouver, BC (Canada), pp. 127-140.
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© Springer Nature Switzerland AG 2019. A photo or a video could contain sensitive information coded as tacit information, which makes it difficult gauge, the loss of privacy, if such photo or a video were shared. Social media applications like Facebook, Twitter, WhatsApp and many more such applications are becoming popular. The instant sharing of information via photos and videos is making the management of issues which rise out of loss of privacy more difficult. Many users of social media trust that their content will not be misused other than purposes that were originally intended. This paper discusses not only about how much of that trust is real and how much of it was forced, but demonstrates the reasoning behind forced trust. These interferences were made after data collection via interviews and data analysis using Grounded Theory.
Madhisetty, S, Williams, M-A, Massy-Greene, J, Franco, L & El Khoury, M 1970, 'How to Manage Privacy in Photos after Publication', Proceedings of the 21st International Conference on Enterprise Information Systems, 21st International Conference on Enterprise Information Systems, SCITEPRESS - Science and Technology Publications, Greece, pp. 162-168.
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Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved. Photos and videos once published may stay available for people to view it unless they are deleted by the publisher of the photograph. If the content is downloaded and uploaded by others then they lose all the privacy settings once afforded by the publisher of the photograph or video via social media settings. This means that they could be modified or in some cases misused by others. Photos also contain tacit information, which cannot be completely interpreted at the time of their publication. Sensitive information may be revealed to others as the information is coded as tacit information. Tacit information allows different interpretations and creates difficulty in understanding loss of privacy. Free flow and availability of tacit information embedded in a photograph could have serious privacy problems. Our solution discussed in this paper illuminates the difficulty of managing privacy due the tacit information embedded in a photo. It also provides an offline solution for the photograph such that it cannot be modified or altered and gets automatically deleted over a period of time. By extending the Exif data of a photograph by incorporating an in-built feature of automatic deletion, and the access to an image by scrambling the image via adding a hash value. Only a customized application can unscramble the image therefore making it available. This intends to provide a novel offline solution to manage the availability of the image post publication.
Makhdoom, I, Zhou, I, Abolhasan, M, Lipman, J & Ni, W 1970, 'PrivySharing: A Blockchain-based Framework for Integrity and Privacy-preserving Data Sharing in Smart Cities', Proceedings of the 16th International Joint Conference on e-Business and Telecommunications, 16th International Conference on Security and Cryptography, SCITEPRESS - Science and Technology Publications, Prague, Czech Republic, pp. 363-371.
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Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved The ubiquitous use of Internet of Things (IoT) ranges from industrial control systems to e-Health, e-commerce, smart cities, supply chain management, smart cars, cyber-physical systems and a lot more. However, the data collected and processed by IoT systems especially the ones with centralized control are vulnerable to availability, integrity, and privacy threats. Hence, we present “PrivySharing,” a blockchain-based innovative framework for integrity and privacy-preserving IoT data sharing in a smart city environment. The proposed scheme is distinct from existing technologies on many aspects. The data privacy is preserved by dividing the blockchain network into various channels, where every channel processes a specific type of data such as health, smart car, smart energy or financial data. Moreover, access to user data within a channel is controlled by embedding access control rules in the smart contracts. In addition, users' data within a channel is further isolated and secured by using private data collection. Likewise, the REST API that enables clients to interact with the blockchain network has dual security in the form of an API Key and OAuth 2.0. The proposed solution also conforms to some of the significant requirements outlined in the European Union General Data Protection Regulation. Lastly, we present a system of reward in the form of a digital token “PrivyCoin” for the users for sharing their data with the stakeholders/third parties.
Malik, N, Nanda, P, He, X & Liu, R 1970, 'Trust and Reputation in Vehicular Networks: A Smart Contract-Based Approach', 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), IEEE, Rotorua, New Zealand, pp. 34-41.
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© 2019 IEEE. Appending digital signatures and certificates to messages guarantee data integrity and ensure non-repudiation, but do not identify greedy authenticated nodes. Trust evolves if some reputable and trusted node verifies the node, data and evaluates the trustworthiness of the node using an accurate metric. But, even if the verifying party is a trusted centralized party, there is opacity and obscurity in computed reputation rating. The trusted party maps it with the node's identity, but how is it evaluated and what inputs derive the reputation rating remains hidden, thus concealment of transparency leads to privacy. Besides, the malevolent nodes might collude together for defamatory actions against reliable nodes, and eventually bad mouth these nodes or praise malicious nodes collaboratively. Thus, we cannot always assume the fairness of the nodes as the rating they give to any node might not be a fair one. In this paper, we propose a smart contract-based approach to update and query the reputation of nodes, stored and maintained by IPFS distributed storage. The use case particularly deals with an emergency scenario, dealing against colluding attacks. Our scheme is implemented using MATLAB simulation. The results show how smart contracts are capable of accurately identifying trustworthy nodes and record the reputation of a node transparently and immutably.
Mannen, T, Ha, PN & Wada, K 1970, 'Performance Evaluation of a Boost Integrated Three-Phase PV Inverter Operating With Current Unfolding Principle', 2019 21st European Conference on Power Electronics and Applications (EPE '19 ECCE Europe), 2019 21st European Conference on Power Electronics and Applications (EPE '19 ECCE Europe), IEEE, Genova, Italy, pp. 1-8.
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This paper proposes a high-efficiency boost integrated three-phase PV inverter. The proposed inverter can reduce total number of switching and increase its efficiency. The 1.2-kW experimental verifications confirm the validity of the proposed inverter and exhibit a great loss reduction of approximately 60% compared to the conventional boost-converter and three-phase-inverter approach
Mao, K, Niu, J, Liu, X, Yu, S & Zhao, L 1970, 'Word2Cluster: A New Multi-Label Text Clustering Algorithm with an Adaptive Clusters Number', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, pp. 1-6.
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Text clustering has been widely used in many Natural Language Processing (NLP) applications such as text summarization and news recommendation. However, most of the current algorithms need to predefine a clustering number, which is difficult to obtain. Moreover, the mutli-label clustering is useful in multiple clustering tasks in many applications, but related works are rarely available. Although several studies have attempted to solve above two problems, there is a need for methods that can solve the two issues simultaneously. Therefore, we propose a new text clustering algorithm called Word2Cluster. Word2Cluster can automatically generate an adaptive number of clusters and support multi-label clustering. To test the performance of Wrod2Cluster, we build a Chinese text dataset, Hotline, according to real world applications. To evaluate the clustering results better, we propose an improved evaluation method based on basic accuracy, precision and recall for multi-label text clustering. Experimental results on a Chinese text dataset (Hotline) and a public English text dataset (Reuters) demonstrate that our algorithm can achieve better F1-measure and runs faster than the state-of- the-art baselines.
Mao, T, Mihăiţă, AS & Cai, C 1970, 'Traffic Signal Control Optimisation under Severe Incident Conditions using Genetic Algorithm', ITS World Congress 2019 (ITSWC2019), Singapore, ITS World Congress, Singapore, pp. 1-14.
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Traffic control optimization is a challenging task for various traffic centres in the world and majority of approaches focus only on applying adaptive methods under normal (recurrent) traffic conditions. But optimizing the control plans when severe incidents occur still remains a hard topic to address, especially if a high number of lanes or entire intersections are affected. This paper aims at tackling this problem and presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. The approach relies on deploying genetic algorithms (GA) by considering the phase durations as decision variables and the objective function to minimize as the total travel time in the network. Firstly, we develop the GA algorithm on a signalized testbed network under recurrent traffic conditions, with the purpose of fine-tuning the algorithm for crossover, mutation, fitness calculation, and obtain the optimal phase durations. Secondly, we apply the optimal signal timings previously found under severe incidents affecting the traffic flow in the network but without any further optimization. Lastly, we further apply the GA optimization under incident conditions and show that our approach improved the total travel time by almost 40.76%.
Marjanovic, O & Zhu, J 1970, 'A Taxonomy of Platform Co-ops: Towards an Understanding of Different Value Creation Mechanisms', 14th International Cooperatives Alliance Asia Pacific Research Conference, Newcastle Australia.
Marjanovic, O, Cecez-Kecmanovic, D & Vidgen, R 1970, 'Towards Algorithmic Justice', Pre-ICIS European Journal of Information Systems Workshop, Munich Germany.
Marjanovic, O, Dinter, B & Ariyachandra, T 1970, 'Introduction to the Minitrack on Business Intelligence, Business Analytics and Big Data: Innovation, Deployment and Management', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences.
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Marjanovic, O, Dinter, B & Ariyachandra, T 1970, 'Proceedings of the 52nd Hawaii International Conference on System Sciences', Proceedings of the Annual Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii.
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Marjanovic, O, Zhu, J, Krivokapic-Skoko, B & Clifford, L 1970, 'Will The Real Data Coop Stand Up? Data Cooperatives in the Coop Sector - Current Challenges and Future Opportunities', 14th International Co-operative Alliance Asia-Pacific Research Conference, Newcastle Australia.
Mau, J, Afshar, S, Hamilton, T, van Schaik, A, Lussana, R, Panella, A, Trumpf, J & Delic, DV 1970, 'Embedded implementation of a random feature detecting network for real-time classification of time-of-flight SPAD array recordings', Laser Radar Technology and Applications XXIV, Laser Radar Technology and Applications XXIV, SPIE, pp. 7-7.
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© 2019 SPIE. A real time program is implemented to classify different model airplanes imaged using a 32x32 SPAD array camera in time-of-flight mode. The algorithm uses random feature extractors in series with a linear classifier and is implemented on the NVIDIA Jetson TX2 platform, a power efficient embedded computing device. The algorithm is trained by calculating the classification matrix using a simple pseudoinverse operation on collected image data with known corresponding object labels. The implementation in this work uses a combination of serial and parallel processes and is optimized for classifying airplane models imaged by the SPAD and laser system. The performance of different numbers of convolutional filters is tested in real time. The classification accuracy reaches up to 98.7% and the execution time on the TX2 varies between 34.30 and 73.55 ms depending on the number of convolutional filters used. Furthermore, image acquisition and classification use 5.1 W of power on the TX2 board. Along with its small size and low weight, the TX2 platform can be exploited for high-speed operation in applications that require classification of aerial targets where the SPAD imaging system and embedded device are mounted on a UAS.
Mazzurco, A, Daniel, SA & Smith, J 1970, 'Development of socio-technical and co-design expertise in engineering students', Proceedings of the 8th Research in Engineering Education Symposium, REES 2019 - Making Connections, pp. 339-348.
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Universities are challenged to educate engineers with a broad set of attributes, including socio-technical and co-design expertise, which will enable them to tackle wicked problems. In this study, we ask: To what extent do courses on human-centred design and systems engineering analysis impact students' development of socio-technical and co-design expertise? We used scenariobased assessment in a pre-/post-design to evaluate the development of these two attributes in two separate units at two Australian universities. The results show some small changes in the responses students gave to the scenario-based tool, at the end of each course. However, the analysis showed that students were still distant from the optimal levels of socio-technical and co-design expertise required of graduates. Therefore, we suggest that such one-off courses are insufficient to develop socio-technical and design expertise. Instead, we argue that engineering programs need to integrate opportunities to develop such expertise throughout all year levels.
McGregor, C & Majola, PX 1970, 'Opportunities for a Cloud Based Health Analytics as a Service for Eastern Cape Initiation Schools in South Africa', 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), IEEE, Cordoba, Spain, pp. 531-534.
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© 2019 IEEE. Traditional male circumcision in the contemporary South Africa has become the focus of the government and media due to the large number of initiates severely injured or dying during the initiation period, which happens twice a year. Deaths and penile amputations are a feature of every circumcision season, as a result of sepsis, gangrene and dehydration amongst other diseases. This paper proposes a Cloud based Health Analytics as a Service for Eastern Cape initiation schools in South Africa to assist in saving lives and preserving the customs. The proposed Artemis platform will assist in acquiring physiological data of initiates before and during initiation to provide early insights of many conditions that can develop during initiation. Big data analytics based on Clinical Decision Support System such as Artemis provides real-time online analytics with knowledge extraction component that supports data mining and enables clinical research of various conditions. Conversely, Artemis has challenges for lower resource settings, which will be explored in this paper.
Medawela, S, Indraratna, B, Pathirage, U & Heitor, A 1970, 'Controlling Soil and Water Acidity in Acid Sulfate Soil Terrains Using Permeable Reactive Barriers', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 413-426.
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© 2019, Springer Nature Singapore Pte Ltd. There are about 12–14 million ha of acid sulfate soils (ASSs) found throughout the length and breadth of the earth. Bridge and building foundations, pipelines, culverts, and other buried infrastructure in such acidic environments are deteriorated when they are exposed to higher acidity, which is generated due to leaching of sulfuric acid from ASS. Thus, acidic groundwater should be properly treated to avoid detrimental effects on natural environment and strenuous efforts on repairing damaged manmade structures. Since the early 90s, permeable reactive barriers (PRBs) were implemented in several places worldwide and it was proven that PRBs are capable of competently treating poor-quality groundwater with various contaminants. While the acidic groundwater flows through a PRB, contaminants (toxic cations) are removed by mineral precipitation and due to the chemical reactions occur, a near-neutral pH is maintained in the effluent. Nevertheless, longevity of the PRB is alleviated due to coupled clogging in porous media. Physical, chemical, and biological clogging mechanisms and the existing PRB design criteria have been critically reviewed in this paper, including precursory numerical models. It is imperative to extend existing equations and models combining all possible clogging mechanisms, to assure the maximum acid removal capacity of a PRB. Hence, water and soil quality would be enhanced to make the land safe for transport and other infrastructure developments.
Meena, MS, Singh, P, Rana, A, Mery, D & Prasad, M 1970, 'A Robust Face Recognition System for One Sample Problem', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Rim Symposium on Image and Video Technology, Springer International Publishing, Sydney, NSW, Australia, pp. 13-26.
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© 2019, Springer Nature Switzerland AG. Most of the practical applications have limited number of image samples of individuals for face verification and recognition process such as passport, driving licenses, photo ID etc. So use of computer system becomes challenging task, when image samples available per person for training and testing of system are limited. We are proposing a robust face recognition system based on Tetrolet, Local Directional Pattern (LDP) and Cat Swam Optimization (CSO) to solve this problem. Initially, the input image is pre-processed to extract region of interest using filtering method. This image is then given to the proposed descriptor, namely Tetrolet-LDP to extract the features of the image. The features are subjected to classification using the proposed classification module, called Cat Swarm Optimization based 2-Dimensional Hidden Markov Model (CSO-based 2DHMM) in which the CSO trains the 2D-HMM. The performance is analyzed using the metrics, such as accuracy, False Rejection Rate (FRR), & False Acceptance Rate (FAR) and the system achieves high accuracy of 99.65%, and less FRR and FAR of 0.0033 and 0.003 for training percentage variation and 99.65%, 0.0035 and 0.004 for k-Fold Validation.
Mendelson, N, Nikolay, N, Xu, ZQ, Tran, TT, Sadzak, N, Bohm, F, Sontheimer, B, Benson, O, Toth, M & Aharonovich, I 1970, 'Tuning of Quantum Emitters in Hexagonal Boron Nitride', 2019 Conference on Lasers and Electro-Optics, CLEO 2019 - Proceedings.
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We demonstrate two different techniques to tune quantum emitters in hBN, achieving record tuning magnitudes for a solid state quantum emitter, as well as dynamic and reversible modulation of the emitters through both methods).
Mendelson, N, Nikolay, N, Xu, ZQ, Tran, TT, Sadzak, N, Böhm, F, Sontheimer, B, Benson, O, Toth, M & Aharonovich, I 1970, 'Tuning of quantum emitters in hexagonal boron nitride', Optics InfoBase Conference Papers.
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We demonstrate two different techniques to tune quantum emitters in hBN, achieving record tuning magnitudes for a solid state quantum emitter, as well as dynamic and reversible modulation of the emitters through both methods).
Mendelson, N, Nikolay, N, Xu, Z-Q, Tran, TT, Sadzak, N, Böhm, F, Sontheimer, B, Benson, O, Toth, M & Aharonovich, I 1970, 'Tuning of Quantum Emitters in Hexagonal Boron Nitride', Conference on Lasers and Electro-Optics, CLEO: QELS_Fundamental Science, OSA, pp. FM4A.5-FM4A.5.
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Meng, L, Lin, C-T, Jung, T-P & Wu, D 1970, 'Neural Information Processing', Neural Information Processing 26th International Conference, ICONIP 2019 Sydney, NSW, Australia, December 12–15, 2019 Proceedings, Part I, International Conference on Neural Information Processing of the Asia-Pacific Neural Network Society, Springer International Publishing, Australia, pp. 476-490.
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Machine learning has achieved great success in many applications, including electroencephalogram (EEG) based brain-computer interfaces (BCIs). Unfortunately, many machine learning models are vulnerable to adversarial examples, which are crafted by adding deliberately designed perturbations to the original inputs. Many adversarial attack approaches for classification problems have been proposed, but few have considered target adversarial attacks for regression problems. This paper proposes two such approaches. More specifically, we consider white-box target attacks for regression problems, where we know all information about the regression model to be attacked, and want to design small perturbations to change the regression output by a pre-determined amount. Experiments on two BCI regression problems verified that both approaches are effective. Moreover, adversarial examples generated from both approaches are also transferable, which means that we can use adversarial examples generated from one known regression model to attack an unknown regression model, i.e., to perform black-box attacks. To our knowledge, this is the first study on adversarial attacks for EEG-based BCI regression problems, which calls for more attention on the security of BCI systems.
Mezaal, MR, Pradhan, B, Shafri, HZM, Mojaddadi, H & Yusoff, ZM 1970, 'Optimized Hierarchical Rule-Based Classification for Differentiating Shallow and Deep-Seated Landslide Using High-Resolution LiDAR Data', GCEC 2017: Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 825-848.
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© Springer Nature Singapore Pte Ltd. 2019. Landslide is one of the most devastating natural disasters across the world with serious negative impact on its inhabitants and the environs. Landslide is considered as a type of soil erosion which could be shallow, deep-seated, cut slope, bare soil, and so on. Distinguishing between these types of soil erosions in dense vegetation terrain like Cameron Highlands Malaysia is still a challenging issue. Thus, it is difficult to differentiate between these erosion types using traditional techniques in locations with dense vegetation. Light detection and ranging (LiDAR) can detect variations in terrain and provide detailed topographic information on locations behind dense vegetation. This paper presents a hierarchical rule-based classification to obtain accurate map of landslide types. The performance of the hierarchical rule set classification using LiDAR data, orthophoto, texture, and geometric features for distinguishing between the classes would be evaluated. Fuzzy logic supervised approach (FbSP) was employed to optimize the segmentation parameters such as scale, shape, and compactness. Consequently, a correlation-based feature selection technique was used to select relevant features to develop the rule sets. In addition, in other to differentiate between deep-seated cover under shadow and normal shadow, the band ration was created by dividing the intensity over the green band. The overall accuracy and the kappa coefficient of the hierarchal rule set classification were found to be 90.41 and 0.86%, respectively, for site A. More so, the hierarchal rule sets were evaluated using another site named site B, and the overall accuracy and the kappa coefficient were found to be 87.33 and 0.81%, respectively. Based on these results, it is demonstrated that the proposed methodology is highly effective in improving the classification accuracy. The LiDAR DEM data, visible bands, texture, and geometric features considerably influenc...
Mhiesan, H, Mantooth, A & Siwakoti, YP 1970, 'A Fault-Tolerant Hybrid Cascaded H-Bridge Topology', 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 2019 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, Baltimore, MD, USA, pp. 6376-6381.
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© 2019 IEEE. This paper presents the fault-tolerant operation for a cascaded H-bridge (CHB) inverter. The added features ensure reliable and robust operation in the event of a fault. The proposed strategy uses an additional cross-coupled CHB (X-CHB) unit in companion with the existing CHB to support the output voltage and ensure continuity of operation in case of an open/short circuit fault. The operation of the proposed X-CHB inverter is described in detail. Simulation and experimental verification of the proposed concept is demonstrated using a seven-level CHB. Both simulation and experimental results validate the fault-tolerant operation of the CHB for a battery energy storage system (BESS) in case of switch faults such as open/short-circuit switch faults or dc-source or battery failure.
Mihaita, A-S, Li, H, He, Z & Rizoiu, M-A 1970, 'Motorway Traffic Flow Prediction using Advanced Deep Learning', 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, IEEE, pp. 1683-1690.
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Congestion prediction represents a major priority for traffic managementcentres around the world to ensure timely incident response handling. Theincreasing amounts of generated traffic data have been used to train machinelearning predictors for traffic, however this is a challenging task due tointer-dependencies of traffic flow both in time and space. Recently, deeplearning techniques have shown significant prediction improvements overtraditional models, however open questions remain around their applicability,accuracy and parameter tuning. This paper proposes an advanced deep learningframework for simultaneously predicting the traffic flow on a large number ofmonitoring stations along a highly circulated motorway in Sydney, Australia,including exit and entry loop count stations, and over varying training andprediction time horizons. The spatial and temporal features extracted from the36.34 million data points are used in various deep learning architectures thatexploit their spatial structure (convolutional neuronal networks), theirtemporal dynamics (recurrent neuronal networks), or both through a hybridspatio-temporal modelling (CNN-LSTM). We show that our deep learning modelsconsistently outperform traditional methods, and we conduct a comparativeanalysis of the optimal time horizon of historical data required to predicttraffic flow at different time points in the future.
Mihaita, A-S, Liu, Z, Cai, C & Rizoiu, M-A 1970, 'Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting', ITS World Congress 2019 (ITSWC2019), Singapore.
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Predicting traffic incident duration is a major challenge for many trafficcentres around the world. Most research studies focus on predicting theincident duration on motorways rather than arterial roads, due to a highnetwork complexity and lack of data. In this paper we propose a bi-levelframework for predicting the accident duration on arterial road networks inSydney, based on operational requirements of incident clearance target which isless than 45 minutes. Using incident baseline information, we first deploy aclassification method using various ensemble tree models in order to predictwhether a new incident will be cleared in less than 45min or not. If theincident was classified as short-term, then various regression models aredeveloped for predicting the actual incident duration in minutes byincorporating various traffic flow features. After outlier removal andintensive model hyper-parameter tuning through randomized search andcross-validation, we show that the extreme gradient boost approach outperformedall models, including the gradient-boosted decision-trees by almost 53%.Finally, we perform a feature importance evaluation for incident durationprediction and show that the best prediction results are obtained whenleveraging the real-time traffic flow in vicinity road sections to the reportedaccident location.
Mirtalaie, MA, Hussain, OK, Chang, E & Hussain, FK 1970, 'A Fine-Grained Ontology-Based Sentiment Aggregation Approach', Advances in Intelligent Systems and Computing, International Conference on Complex, Intelligent, and Software Intensive Systems, Springer International Publishing, Japan, pp. 252-262.
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© 2019, Springer International Publishing AG, part of Springer Nature. Sentiment analysis techniques are widely used to capture the voice of customers about different products/services. Aspect or feature-based sentiment detection tools as one of the sentiment analyses’ types are developed to find the customers’ opinions about various features of a product. However, as a product may contain many features, presenting the final obtained results to the users is a challenge. Even though this issue is addressed in the literature by developing different sentiment aggregation methods, their results are mostly presented at the basic-level features of a product. This may cause in losing customers’ opinion about at minor sub-features. However, as the performance of a basic feature is dependent on those of its different sub-features, we propose an approach which aggregates the extracted results at a fine-grained level features using a product ontology tree. We interpret the polarity of each feature as a satisfaction score which can help managers in investigating the weaknesses of their products even at minor levels in a more informed way.
Mirzababaei, M, Decourcy, T & Fatahi, B 1970, 'Sustainable Use of Reclaimed Ballast Rejects for Construction of Rail Corridor Access Road-an Australian Experience', Springer International Publishing, pp. 257-269.
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Mishra, DK, Jabbari Ghadi, M, Li, L & Zhang, J 1970, 'Proposing a Framework for Resilient Active Distribution Systems using Withstand, Respond, Adapt, and Prevent Element', 2019 29th Australasian Universities Power Engineering Conference (AUPEC), 2019 29th Australasian Universities Power Engineering Conference (AUPEC), IEEE, Nadi, Fiji, pp. 1-6.
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© 2019 IEEE. The increasing frequency of natural disasters and man-made attacks have increased power outages worldwide. Thus, a resilient infrastructure must be constructed to reduce power system damages which directly impacts on the social and economic lives of people. In this paper, a new framework called withstand, respond, adapt, and prevent (WRAP) is presented to evaluate and improve the resilience of distribution networks following a review on existing studies. This resilience enhancement may happen through microgrid and multi- microgrid development in planning or operation stages. Each element of the WRAP framework is responsible for the improvement of the power system resilience in terms of its own attributes and resilience evaluation index. Furthermore, the WRAP framework is defined on the basis of a flowchart with respect to conditional statements. The WRAP framework can be a helpful solution in measuring the resiliency of the power system in terms of robustness, rapidity, adaptability, and predictability. Finally, a case study considering energy-not- supplied as a resilience evaluation index is presented.
Mishra, DK, Panigrahi, TK, Mohanty, A & Ray, PK 1970, 'Integrating Concentrating Solar Plant-Based System in Multi-area AGC Using LabVIEW', Springer Singapore, pp. 675-686.
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Moayedi, H, Foong, LK, Nazir, R & Pradhan, B 1970, 'Investigation of Aqueous and Light Non-aqueous Phase Liquid in Fractured Double-Porosity Soil', Springer International Publishing, pp. 207-210.
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Moayedi, H, Nazir, R, Foong, LK, Mosallanezhad, M & Pradhan, B 1970, 'Experimental Investigation of Several Different Types of Soil Erosion Protection Systems', Springer International Publishing, pp. 481-483.
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Mohammed, TU, Mahmood, AH, Ahmed, SS & Pasha, M 1970, 'Clay-burnt coarse aggregate: Production and utilization in concrete', Sustainable Construction Materials and Technologies.
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Scarcity of natural aggregates in Bangladesh has led the construction industry to rely prominently on crushed brick coarse aggregates. The crushing procedure involves material waste and lacks means to control the grading, which results in poor quality concrete. These drawbacks could be addressed if graded aggregates could be made by burning clay. With this background, suitable soil was sampled, molded and burnt at high temperatures (850°C, 900°C, and 950°C) to make clay-burnt coarse aggregates (CBCA) of different sizes (20 mm, 15 mm, 10 mm and 5 mm). Physical properties of the aggregates were investigated and cylindrical concrete specimens were made with both CBCA and other locally available aggregates. Aggregates produced at 950°C showed better aggregate properties compared to first class brick aggregates. CBCA made concrete resulted in higher compressive strength compared to stone and brick aggregates. Direct production of CBCA has the potential to enhance overall construction quality in Bangladesh.
Mohebali, B, Tahmassebi, A, Gandomi, AH & Meyer-Bäse, A 1970, 'A big data inspired preprocessing scheme for bandwidth use optimization in smart cities applications using Raspberry Pi', Big Data: Learning, Analytics, and Applications, Big Data: Learning, Analytics, and Applications, SPIE, Baltimore, MD, pp. 1-1.
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Mojsilović, N & Stewart, MG 1970, 'Influence of workmanship on the compressive strength of structural masonry', 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019.
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Structural masonry is a composite material that consists of brick/block units and mortar. Often, masonry is treated as a homogeneously material. The key mechanical characteristic of structural masonry is its compressive strength perpendicular to the bed joints. Estimating or even predicting this material property is thus an issue of central importance to assessing the reliability of masonry structures. As part of a course on structural masonry taught at ETH in Zurich, students are given an opportunity to do some practical work. During one lecture (one and half-hours) students are divided in smaller groups (five to six students) and each group is asked to build a standard masonry specimen (according to the European testing standard EN-1052-1) in the structural laboratory of the Institute of Structural Engineering. Simultaneously, two professional masons, which are instructing/helping students during the exercise, are asked to build one specimen. Such practical work has been performed every year since 2007, usually with clay block masonry, but also with calcium-silicate and AAC masonry. After the prescribed curing time all specimens are tested and the corresponding results (masonry compressive strength) are discussed with students. This paper presents the results and statistics of these test series. Special attention is paid to the influence of workmanship. Namely, strengths obtained from tests on specimens built by professional masons are, for all series, more or less near the mean values in spite of the fact that almost all students are without any skills as masons. The reasons for such distribution of the results are investigated and the findings are presented.
Mokhtar, ES, Pradhan, B, Ghazali, AH & Shafri, HZM 1970, 'Assessing Vertical Accuracy and the Impact of Water Surface Elevation from Different DEM Datasets', GCEC 2017 Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference 2017, Springer Singapore, Malaysia, pp. 849-862.
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© Springer Nature Singapore Pte Ltd. 2019. Digital elevation models (DEMs) are essential to provide continuous terrain elevation for water surface elevation (WSE) with a variety of horizontal and vertical accuracies in flood inundation modelling. The WSE forecasting depends on the appropriateness of the DEM data used. The comparative methodology is applied to various DEM sources: LiDAR and IFSAR DEM based on different types of land use at each of the cross-sectional lines. The accuracy of the IFSAR DEMs was assessed with LiDAR data, which is a high-precision DEM and was applied in hydraulic modelling to simulate the WSE in Padang Terap, Kedah, Malaysia. Furthermore, Bjerklie’s model is used as predicted discharge to support the analysis. The relationship of the DEMs is established by natural logarithm (ln). Then, the equation is interpolated on the original and resampled IFSAR DEMs to improve the medium-resolution data for WSE delineation. Next, the WSE was validated with observed WSE obtained along the upstream (Kuala Nerang) to the downstream parts (Kampung Kubu) Kedah using R 2 , mean absolute error (MAE), and root-mean-square error (RMSE). By applying this method, the WSE can be improved by considering uncertainties and lead to produce a better flood hazard map using medium-high-resolution images.
Moylan, E, De Silva Wijayaratna, K, Jian, S & Waller, ST 1970, 'The Unreliability of journey-time reliability measurements', Australian Institute of Traffic Planning and Management 2019 National Conference, Adelaide, Australia.
Mukhtar, NM & Lu, DD-C 1970, 'Comparative Study of Isolated and Symmetrical Bidirectional DC-DC Converters based on Flyback and Forward Topologies', 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), IEEE, pp. 1-7.
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This paper presents a comparative study of isolated and symmetrical bidirectional dc-dc converters based on flyback and forward topologies. It covers those converters with dissipative and non-dissipative snubbers, active clamped switches and two-switch configuration. The parameters used for this study include conversion efficiency, component count, component stress and gate driving requirements. It is found that some structures produce high circulation current which should be avoided. The study has identified two promising converter structures which eliminate the problems and produce high efficiency with reasonable component count.
Munasinghe, N, Miles, L & Paul, G 1970, 'Direct-Write Fabrication of Wear Profiling IoT Sensor for 3D Printed Industrial Equipment', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 36th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), Banff, Canada, pp. 862-869.
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© 2019 International Association for Automation and Robotics in Construction I.A.A.R.C. All rights reserved. Additive Manufacturing (AM), also known as 3D printing, is an emerging technology, not only as a prototyping technology, but also to manufacture complete products. Gravity Separation Spirals (GSS) are used in the mining industry to separate slurry into different density components. Currently, spirals are manufactured using moulded polyurethane on fibreglass substructure, or injection moulding. These methods incur significant tooling cost and lead times making them difficult to customise, and they are labour-intensive and can expose workers to hazardous materials. Thus, a 3D printer is under development that can print spirals directly, enabling mass customisation. Furthermore, sensors can be embedded into spirals to measure the operational conditions for predictive maintenance, and to collect data that can improve future manufacturing processes. The localisation of abrasive wear in the GSS is an essential factor in optimising parameters such as suitable material, print thickness, and infill density and thus extend the lifetime and performance of future manufactured spirals. This paper presents the details of a wear sensor, which can be 3D printed directly into the spiral using conductive material. Experimental results show that the sensor can both measure the amount of wear and identify the location of the wear in both the horizontal and vertical axes. Additionally, it is shown that the accuracy can be adjusted according to the requirements by changing the number and spacing of wear lines.
Munasinghe, N, Woods, M, Miles, L & Paul, G 1970, '3-D Printed Strain Sensor for Structural Health Monitoring', 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), IEEE, Bangkok, pp. 275-280.
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Additive manufacturing, or 3D printing, is evolving from a technology that can only aid rapid prototyping, to one that can be used to directly manufacture large-scale, real-world equipment. Gravity Separation Spirals (GSS) are vital to the mining industry for separating mineral-rich slurry into its different density components. In order to overcome inherent drawbacks of the traditional mould base manufacturing methods, including significant tooling costs, limited customisation and worker exposure to hazardous materials, a 3D printer is under development to directly print spirals. By embedding small Internet of Things (IoT) sensors inside the GSS, it is possible to remotely determine the operation conditions, predict faults, and use collected data to optimise production output. This work presents a 3D printed strain sensor, which can be directly printed into the GSS. This approach uses a carbon-based conductive filament to print a strain gauge on top of a Polylactic Acid (PLA) base material. Printed sensors have been tested using an Instron E10000 testing machine with an optical extensometer to improve accuracy. Testing was conducted by both loading and unloading conditions to understand the effect of hysteresis. Test results show a near-linear relationship between strain and measured resistance, and show a 6.05% increase in resistance after the test, which indicates minor hysteresis. Moreover, the impact of viscoelastic behaviour is identified, where the resistance response lags the strain. Results from both conductive and non-conductive material show the impact of the conductive carbon upon the tensile strength, which will help to inform future decisions about sensor placement.
Murray, RA, Foy, G & Clemon, L 1970, 'Dimensional comparison of a cold spray additive manufacturing simulation tool', Solid Freeform Fabrication 2019: Proceedings of the 30th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, SFF 2019, Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference, University of Texas, Austin, Texas USA, pp. 1333-1339.
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High-velocity particle spray greatly increases metal additive manufacturing deposition speed over other commercial methods. Accurate prediction and measurement of this process will improve process control. A LightSPEE3D machine fabricated symmetric copper components. On-board software predicts the build geometry (.stl) given the input geometry and the build settings. Assessment of prediction accuracy is needed to enable rapid part design and print setting optimization. White-light 3D-scanning and high-fidelity optical microscopy scans are compared to the simulation and intended 20mm cubes using hausdorf distance: 1. Control-repeated scans: 0.38±0.48mm, max:2.25mm 2. Intended-original vs. scans: 1.42±1.58mm, max:6.72mm 3. Software-predicted vs. scans: 0.44±0.66mm, max:3.97mm Discrepancies up to 6.72mm and asymmetric fabrication artifacts were identified. The reduction in the hausdorf distance for simulation vs intended-original, and larger distance of the simulation compared to control, indicate the simulation tool may enable rapid optimization given over/under spray quantification. Recommendations for reducing asymmetric fabrication artifacts and over/underspray are provided.
Murthy, V & Marjanovic, O 1970, 'Intellectual Capital Performance: A Study of a contract employment set-up', 18th Australasian Centre for Social and Environmental Accounting Research (A-CSEAR) Conference, Sydney.
N., HP, Mannen, T & Wada, K 1970, 'A Current Source Three-Phase AC-AC Converter using Current Unfolding and Active Damping Principles', 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 2019 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, Baltimore, MD, USA, pp. 6239-6245.
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This paper proposes a new current source three-phase AC to AC converter based on current unfolding and active damping principles. The proposed converter consists of two bidirectional three-phase AC/DC converters connecting in a Back-To-Back (BTB) configuration. Each of them is similar to a bidirectional SWISS rectifier but operating in current source mode with current unfolding and active damping techniques and thus does not need to employ passive ac filter and damping resistor. Comparing to a conventional BTB current source three-phase converter, this approach requires a total of 32 switches but only eight of them are operated at high frequency, thus resulting in a significant reduction in switching losses. The other 24 switches only operate at low frequency, and therefore have negligible switching losses, long life-time and can be parallel easily to reduce conduction losses. As a result, the proposed converter is expected to deliver high efficiency at small volume. This paper discusses the basic operating principle and control method for the converter. Simulation study confirms stable operation of the proposed inverter during both transient and steady states.
Naji, M, Al-Ani, A, Braytee, A, Anaissi, A & Kennedy, P 1970, 'Queue Formation Augmented with Particle Swarm Optimisation to Improve Waiting Time in Airport Security Screening', Advances in Intelligent Systems and Computing, Workshops of the 33rd International Conference on Advanced Information Networking and Applications, Springer International Publishing, Japan, pp. 923-935.
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© 2019, Springer Nature Switzerland AG. Airport security screening processes are essential to ensure the safety of both passengers and the aviation industry. Security at airports has improved noticeably in recent years through the utilisation of state-of-the-art technologies and highly trained security officers. However, maintaining a high level of security can be costly to operate and implement. It may also lead to delays for passengers and airlines. This paper proposes a novel queue formation method based on a queueing theory model augmented with a particle swarm optimisation method known as QQT-PSO to improve the average waiting time in airport security areas. Extensive experiments were conducted using real-world datasets collected from Sydney airport. Compared to the existing system, our method significantly reduces the average waiting time and operating cost by 11.89% compared to the one-queue formation.
Nalamati, M, Kapoor, A, Saqib, M, Sharma, N & Blumenstein, M 1970, 'Drone Detection in Long-Range Surveillance Videos', 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), IEEE, Taipei, Taiwan.
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© 2019 IEEE. The usage of small drones/UAVs has significantly increased recently. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. The similarity in the appearance of small drone and birds in complex background makes it challenging to detect drones in surveillance videos. This paper addresses the challenge of detecting small drones in surveillance videos using popular and advanced deep learning-based object detection methods. Different CNN-based architectures such as ResNet-101 and Inception with Faster-RCNN, as well as Single Shot Detector (SSD) model was used for experiments. Due to sparse data available for experiments, pre-trained models were used while training the CNNs using transfer learning. Best results were obtained from experiments using Faster-RCNN with the base architecture of ResNet-101. Experimental analysis on different CNN architectures is presented in the paper, along with the visual analysis of the test dataset.
Nan, Y, Huang, X & Guo, YJ 1970, 'A Fast Piecewise Constant Doppler Algorithm for Generalized Continuous Wave Synthetic Aperture Radar', 2019 International Radar Conference (RADAR), 2019 International Radar Conference (RADAR), IEEE, Toulon, France, pp. 1-5.
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The generalized continuous wave synthetic aperture radar (GCW-SAR) adopts one-dimensional data recording without the slow time dimension and hence offers many advantages compared with the conventional SAR system. In this paper, a fast piecewise constant Doppler algorithm is proposed based on further zero-th order approximation on top of the linear approximation of the slant range, leading to a flexible azimuth imaging spacing. Significant reduction of the complexity can be achieved by extending the azimuth imaging spacing and downsampling the received signal in digital domain. Simulation results validate the advantages of the proposed algorithm.
Nanda, A, Nanda, P, Obaidat, MS, He, X & Puthal, D 1970, 'A Novel Multi-Path Anonymous Randomized Key Distribution Scheme for Geo Distributed Networks', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Hilton Waikoloa Village, Hawaii, USA, pp. 1-6.
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A major concern in distributed networks is the ability to provide acceptable levels of security. This is achieved by using encryption and authentication mechanisms that depend on encryption keys. However, given the ever-expanding nature of the network, it is difficult to keep setting up authorities that can aid the key-exchange process. This paper presents a novel
solution to the challenge of exchanging keys of a large, distributed network without the need to set up additional authorities. The key-exchange scheme presented takes advantage of features such as packet anonymity, random selection and a multi-path approach for the exchange process. The paper also discusses the effectiveness of the proposed scheme against various
threat scenarios.
Naseem, U & Musial, K 1970, 'DICE: Deep Intelligent Contextual Embedding for Twitter Sentiment Analysis', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia, pp. 953-958.
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© 2019 IEEE. The sentiment analysis of the social media-based short text (e.g., Twitter messages) is very valuable for many good reasons, explored increasingly in different communities such as text analysis, social media analysis, and recommendation. However, it is challenging as tweet-like social media text is often short, informal and noisy, and involves language ambiguity such as polysemy. The existing sentiment analysis approaches are mainly for document and clean textual data. Accordingly, we propose a Deep Intelligent Contextual Embedding (DICE), which enhances the tweet quality by handling noises within contexts, and then integrates four embeddings to involve polysemy in context, semantics, syntax, and sentiment knowledge of words in a tweet. DICE is then fed to a Bi-directional Long Short Term Memory (BiLSTM) network with attention to determine the sentiment of a tweet. The experimental results show that our model outperforms several baselines of both classic classifiers and combinations of various word embedding models in the sentiment analysis of airline-related tweets.
Nayak, A, Mishra, S, Hossain, J & Nizami, MSH 1970, 'Output Feedback Adaptive Control for Inter-area Oscillation Damping Under Power System Uncertainties', 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), IEEE, pp. 1-6.
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© 2019 IEEE. The power system is inherently a complex nonlinear system and experiences continuous changes in operating conditions due to sudden variations in load demand. The increasing integration of renewable power sources in current grids brings new dynamics and increases complexity in developing reliable control strategies. In addition, the variability of renewable generation introduce uncertainty and therefore, an advanced controller is required to ensure the systems stability. The wide area measurement systems (WAMS) has made the remote signal much readily available, thus improving the overall systems observability. With considering the changing systems dynamics, an output feedback model reference adaptive damping controller is designed and implemented in this paper. The results show the controllers effectiveness to handle the parametric and nonparametric uncertainties of the system while obtaining satisfactory damping action on inter-area oscillations.
Neshat, M, Alexander, B, Sergiienko, NY & Wagner, M 1970, 'A hybrid evolutionary algorithm framework for optimising power take off and placements of wave energy converters', Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '19: Genetic and Evolutionary Computation Conference, ACM, pp. 1293-1301.
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Ngo, CQ, Chai, R, Nguyen, TV, Jones, TW & Nguyen, HT 1970, 'Nocturnal Hypoglycemia Detection using EEG Spectral Moments under Natural Occurrence Conditions', 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Berlin, Germany, pp. 7177-7180.
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This paper is concerned with a study of hypoglycemia under natural occurrence conditions at night time. Five adolescents with type 1 diabetes (T1D) participated in the experiments. Patients' blood glucose profiles were interpolated to estimate the intermediate values. The proposed system used spectral moments of electroencephalogram (EEG) signals from central and occipital areas as features for detecting hypoglycemia. We found that hypoglycemia could be detected non-invasively using EEG spectral moments. During hypoglycemic episodes, theta moments increased significantly (P<; 0.005) whereas beta moments decreased significantly (P<; 0.001). Based on the optimal network architecture associated with the highest log evidence, the proposed optimal Bayesian neural network resulted in a sensitivity of 82% and a specificity of 52%. In addition, the estimated blood glucose profiles showed a significant correlation (P<; 1e-6) with interpolated blood glucose values in the test set.
Ngo, CQ, Chai, R, Nguyen, TV, Jones, TW & Nguyen, HT 1970, 'Nocturnal Hypoglycemia Detection using Optimal Bayesian Algorithm in an EEG Spectral Moments Based System', 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Berlin, Germany, pp. 5439-5442.
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This paper presents a hypoglycemia detection system using electroencephalogram (EEG) spectral moments from 8 patients with type 1 diabetes (T1D) at night time. Four channels (C3, C4, O1, and O2) associated with glycemic episodes were analyzed. Spectral moments were applied to EEG signal and its corresponding speed and acceleration. During hypoglycemia, theta moments increased significantly (P<; 0.001) and alpha moments decreased significantly (P<; 0.001). The system used an optimal Bayesian neural network for detecting hypoglycemic episodes. Based on the optimal network architecture with the highest log evidence, the final classification results for the test set were 79% and 51% in sensitivity and specificity, respectively. Essentially, the estimated blood glucose profiles correlated significantly to actual values in the test set (P<; 0.0001). Using error grid analysis, 93% of the estimated values were clinically acceptable.
Ngo, NT & Indraratna, B 1970, 'Interface Behavior of Geogrid-Reinforced Sub-ballast: Laboratory and Discrete Element Modeling', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 195-209.
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© Springer Nature Singapore Pte Ltd. 2019. This paper shows a study on the interface behavior of biaxial geogrids and sub-ballast using a direct shear box and computational modeling. A series of large-scale direct shear tests are performed on sub-ballast (capping layer) with and without geogrid inclusions. The laboratory test data indicate that the interface shear strength is mainly decided by applied normal stresses and types of geosynthetics tested. Discrete element modeling approach is used to investigate the interface shear behavior of the sub-ballast subjected to direct shear loads. Irregular-shaped sub-ballast particles are modeled by clumping of many spheres together in pre-determined sizes and positions. Biaxial geogrids are simulated in the DEM by bonding small balls together to build desired geogrid shapes and opening apertures. The numerical results reasonably match with the measured test data, showing that the introduced DEM model can simulate the interface behavior of sub-ballast stabilized by the geogrids. In addition, the triaxial geogrid presents the highest interface shear strength compared to the biaxial geogrids; and this can be associated with the symmetric geometry of grids’ apertures that can distribute load in all directions. Evolutions of contact forces of unreinforced/reinforced sub-ballast specimens and contour strain distributions during shear tests are also investigated.
Ngo, QT, Minh Dang, DN, Le-Trung, Q & Lam, DK 1970, 'A Novel Directional MAC in Restricted Access Window for IEEE 802.11ah Networks', 2019 26th International Conference on Telecommunications (ICT), 2019 26th International Conference on Telecommunications (ICT), IEEE, pp. 167-171.
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Nguyen Thi Kim, P, Tran, H, Fitzgerald, D, Tran, T, Graham, S & Marais, B 1970, 'Characterisation of children hospitalised with pneumonia in central Vietnam: A prospective study', Paediatric respiratory epidemiology, ERS International Congress 2019 abstracts, European Respiratory Society, pp. PA296-PA296.
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Nguyen Thi Kim, P, Tran, H, Tran, T, Fitzgerald, D, Graham, S & Marais, B 1970, 'Reducing unnecessary antibiotic use and hospitalization in children with pneumonia', Paediatric respiratory epidemiology, ERS International Congress 2019 abstracts, European Respiratory Society, pp. PA1041-PA1041.
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Nguyen, D-A, Bui, D-H, Iacopi, F & Tran, X-T 1970, 'An Efficient Event-driven Neuromorphic Architecture for Deep Spiking Neural Networks', 2019 32nd IEEE International System-on-Chip Conference (SOCC), 2019 32nd IEEE International System-on-Chip Conference (SOCC), IEEE, Singapore, Singapore, pp. 144-149.
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© 2019 IEEE. Deep Neural Networks (DNNs) have been successfully applied to various real-world machine learning applications. However, performing large DNN inference tasks in real-time remains a challenge due to its substantial computational costs. Recently, Spiking Neural Networks (SNNs) have emerged as an alternative way of processing DNN'fs task. Due to its eventbased, data-driven computation, SNN reduces both inference latency and complexity. With efficient conversion methods from traditional DNN, SNN exhibits similar accuracy, while leveraging many state-of-the-art network models and training methods. In this work, an efficient neuromorphic hardware architecture for image recognition task is presented. To preserve accuracy, the analog-to-spiking conversion algorithm is adopted. The system aims to minimize hardware area cost and power consumption, enabling neuromorphic hardware processing in edge devices. Simulation results have shown that, with the MNIST digit recognition task, the system has achieved × 20 reduction in terms of core area cost compared to the state-of-the-art works, with an accuracy of 94.4%, core area of 15 μ m2 at a maximum frequency of 250 MHz.
Nguyen, L & Miro, JV 1970, 'Acoustic Sensor Networks and Mobile Robotics for Sound Source Localization', 2019 IEEE 15th International Conference on Control and Automation (ICCA), 2019 IEEE 15th International Conference on Control and Automation (ICCA), IEEE, Edinburgh, UK, pp. 1453-1458.
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© 2019 IEEE. Localizing a sound source is a fundamental but still challenging issue in many applications, where sound information is gathered by static and local microphone sensors. Therefore, this work proposes a new system by exploiting advances in sensor networks and robotics to more accurately address the problem of sound source localization. By the use of the network infrastructure, acoustic sensors are more efficient to spatially monitor acoustical phenomena. Furthermore, a mobile robot is proposed to carry an extra microphone array in order to collect more acoustic signals when it travels around the environment. Driving the robot is guided by the need to increase the quality of the data gathered by the static acoustic sensors, which leads to better probabilistic fusion of all the information gained, so that an increasingly accurate map of the sound source can be built. The proposed system has been validated in a real-life environment, where the obtained results are highly promising.
Nguyen, L, Miro, JV & Qiu, X 1970, 'Can a Robot Hear the Shape and Dimensions of a Room?', IEEE International Conference on Intelligent Robots and Systems, IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Macau, China, pp. 5346-5351.
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Knowing the geometry of a space is desirable for many applications, e.g.sound source localization, sound field reproduction or auralization. Incircumstances where only acoustic signals can be obtained, estimating thegeometry of a room is a challenging proposition. Existing methods have beenproposed to reconstruct a room from the room impulse responses (RIRs). However,the sound source and microphones must be deployed in a feasible region of theroom for it to work, which is impractical when the room is unknown. This workpropose to employ a robot equipped with a sound source and four acousticsensors, to follow a proposed path planning strategy to moves around the roomto collect first image sources for room geometry estimation. The strategy caneffectively drives the robot from a random initial location through the room sothat the room geometry is guaranteed to be revealed. Effectiveness of theproposed approach is extensively validated in a synthetic environment, wherethe results obtained are highly promising.
Nguyen, L, Miro, JV, Shi, L & Vidal-Calleja, T 1970, 'Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation', 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), IEEE International Conference on Cybernetics and Intelligent Systems, and Robotics, Automation and Mechatronics, IEEE, Bangkok, Thailand.
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Rapidly estimating the remaining wall thickness (RWT) is paramount for thenon-destructive condition assessment evaluation of large critical metallicpipelines. A robotic vehicle with embedded magnetism-based sensors has beendeveloped to traverse the inside of a pipeline and conduct inspections at thelocation of a break. However its sensing speed is constrained by the magneticprinciple of operation, thus slowing down the overall operation in seekingdense RWT mapping. To ameliorate this drawback, this work proposes the partialscanning of the pipe and then employing Gaussian Processes (GPs) to infer RWTat the unseen pipe sections. Since GP prediction assumes to have normallydistributed input data - which does correspond with real RWT measurements -Gaussian mixture (GM) models are proven in this work as fitting marginaldistributions to effectively capture the probability of any RWT value in theinspected data. The effectiveness of the proposed approach is extensivelyvalidated from real-world data collected in collaboration with a water utilityfrom a cast iron water main pipeline in Sydney, Australia.
Nguyen, M, Kim, S, Tran, TT, Kianinia, M, Xu, Z, Wang, D, Yang, A, Aharonovich, I, Toth, M & Odom, T 1970, 'Nanophotonic integration of hexagonal boron nitride (Conference Presentation)', 2D Photonic Materials and Devices II, 2D Photonic Materials and Devices II, SPIE, pp. 5-5.
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Nguyen, MH, Hà, MH, Hoang, DT, Nguyen, DN, Dutkiewicz, E & Tran, TT 1970, 'An Efficient Algorithm for the k-Dominating Set Problem on Very Large-Scale Networks (Extended Abstract)', Computational Data and Social Networks (LNCS), International Conference on Computational Data and Social Networks, Springer International Publishing, Ho Chi Minh City, Vietnam, pp. 74-76.
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© Springer Nature Switzerland AG 2019. The minimum dominating set problem (MDSP) aims to construct the minimum-size subset $$D \subset V$$ of a graph $$G = (V, E)$$ such that every vertex has at least one neighbor in D. The problem is proved to be NP-hard [5]. In a recent industrial application, we encountered a more general variant of MDSP that extends the neighborhood relationship as follows: a vertex is a k-neighbor of another if there exists a linking path through no more than k edges between them. This problem is called the minimum k-dominating set problem (MkDSP) and the dominating set is denoted as $$D:k$$. The MkDSP can be used to model applications in social networks [2] and design of wireless sensor networks [3]. In our case, a telecommunication company uses the problem model to supervise a large social network up to 17 millions nodes via a dominating subset in which k is set to 3.
Nguyen, N-T, Van Huynh, N, Hoang, DT, Nguyen, DN, Nguyen, N-H, Nguyen, Q-T & Dutkiewicz, E 1970, 'Energy Management and Time Scheduling for Heterogeneous IoT Wireless-Powered Backscatter Networks', ICC 2019 - 2019 IEEE International Conference on Communications (ICC), ICC 2019 - 2019 IEEE International Conference on Communications (ICC), IEEE, Shanghai, pp. 1-6.
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© 2019 IEEE. In this paper, we propose a novel approach to jointly address energy management and network throughput maximization problems for heterogeneous IoT low-power wireless communication networks. In particular, we consider a low-power communication network in which the IoT devices can harvest energy from a dedicated RF energy source to support their transmissions or backscatter the signals of the RF energy source to transmit information to the gateway. Different IoT devices may have dissimilar hardware configurations, and thus they may have various communications types and energy requirements. In addition, the RF energy source may have a limited energy supply source which needs to be minimized. Thus, to maximize the network throughput, we need to jointly optimize energy usage and operation time for the IoT devices under different energy demands and communication constraints. However, this optimization problem is non-convex due to the strong relation between energy supplied by the RF energy source and the IoT communication time, and thus obtaining the optimal solution is intractable. To address this problem, we study the relation between energy supply and communication time, and then transform the non-convex optimization problem to an equivalent convex-optimization problem which can achieve the optimal solution. Through simulation results, we show that our solution can achieve greater network throughputs (up to five times) than those of other conventional methods, e.g., TDMA. In addition, the simulation results also reveal some important information in controlling energy supply and managing low-power IoT devices in heterogeneous wireless communication networks.
Nguyen, QD, Khan, MSH & Castel, A 1970, 'Carbonation of concrete using ferronickel slag as fine aggregate', FIB 2018 - Proceedings for the 2018 fib Congress: Better, Smarter, Stronger, pp. 2716-2722.
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This paper aims to investigate the carbonation resistance of concrete using ferronickel slag (FNS) as fine aggregate replacement. FNS fine aggregate substituted 50% by mass of natural aggregate and fly ash replaced 25% by mass of cement to produce the low carbon concrete. Mechanical and durability properties of FNS concrete were investigated and an environmental chamber was utilized to accelerate carbonation with 1% CO2. Concrete pH profile and phenolphthalein indicator test were conducted to evaluate the carbonation depth of concrete. Overall, the replacement of 50% fine aggregate by FNS increased the mechanical and durability properties of concrete. Moreover, the utilization of FNS aggregate can offset the detrimental effect of fly ash on concrete resistance against carbonation. The FNS concrete outperformed in comparison with plain concrete at all exposure time. This outcome presents the possibility of FNS as a low carbon concrete in exposure condition where carbonation corrosion can be an issue.
Nguyen, T, Indraratna, B & Carter, J 1970, 'Influence of soil clogging on the performance of jute fibre drains installed in Ballina clay', In Proceedings of the 13th ANZ Geomechanics-Australia New Zealand Conference on Geomechanics, the 13th ANZ Geomechanics-Australia New Zealand Conference on Geomechanics, Perth.
Nguyen, TAH, Ngo, HH, Guo, WS, Pham, TQ, Cao, TH & Nguyen, THH 1970, 'Applicability of zirconium loaded okara in the removal and recovery of phosphorus from municipal wastewater', IOP Conference Series: Earth and Environmental Science, International Forum on Sustainable Future in Asia, IOP Publishing, Hanoi, Vietnam, pp. 012004-012004.
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© 2019 Published under licence by IOP Publishing Ltd. Recently, there is a new trend to consider wastewater as a precious resource. Since phosphorus is a limited non-renewable element, and MAP (Magnesium Ammonium Phosphate - MgNH4PO4.6H2O) is a valuable slow-release fertilizer, the recovery of phosphorous as MAP has received special attention from scientists all over the world. However, the application of this process with municipal wastewater is still a challenge, due to low concentration of phosphorus and high volume of municipal wastewater. This study investigates the potential of reclaiming MAP from municipal wastewater by combination of adsorption and crystallization. Soybean milk residue (okara) was loaded with Zirconium (Zr) to prepare the adsorbent (ZLO). Adsorption and desorption experiments were conducted in a semi-pilot scale ZLO packed colum system. Effects of P: N: Mg molar ratios, chemical sources and temperature on the formation of MAP were examined in an attempt to identify the optimal crystallization conditions. The attained precipitate was characterized using XRD, SEM, FTIR techniques. It was found that the ZLO packed column adsorption-desorption system could pre-concentrate phosphorus from municipal wastewater up to 28.36 times, fitting well the minimum requirement (50 mg P/L) for the economical MAP recovery. Up to 95.19% of dissolved phosphorus in desorption solution was recovered at pH = 9, Mg: N: P molar ratio = 2:2:1, using a combination of MgCl2.6H2O and NH4Cl. The harvested MAP exhibited high purity (92.59%), high P-availability (89% by mass), and extremely low levels of heavy metals. The results prove that it is viable to recover MAP from municipal wastewater by employing ZLO as adsorbent, followed by crystallization. This paves the way for mining phosphorus from municipal wastewater and reducing okara as an agricultural byproduct in a green way.
Nguyen, TN, Yu, Y, Li, J, Gowripalan, N & Sirivivatnanon, V 1970, 'Mechanical properties of ASR affected concrete: a critical review', Concrete 2019, Concrete 2019, Sydney.
Nguyen, TV, Tran, TS, Center, JR & Eisman, JA 1970, 'Small individual-level increase in bone mineral density translated into substantial population-level decrease in fracture incidence: revisiting Goeffrey Rose's axiom', JOURNAL OF BONE AND MINERAL RESEARCH, Annual Meeting of the American-Society-for-Bone-and Mineral Research, WILEY, Orlando, FL, pp. 250-250.
Nguyet, LM, Kandasamy, J & Minh, DQ 1970, 'Assessing the Impact of Inundation Preventing Construction on River Morphology of Can Giuoc River in Long An Province', IOP Conference Series: Earth and Environmental Science, International science and technology conference, IOP Publishing, Russky Island, Russian Federation, pp. 022221-022221.
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Abstract To attend to inundation prevention in Ho Chi Minh city, the Prime Minister approved the irrigation plan to prevent inundation in which there is a solution of constructing Thu Bo tidal drainage in Long An province. Thu Bo drainage is expected to have a total width of 200m, including 120m with drainage threshold -8m and 80m with drainage threshold -4,0m. Thu Bo drainage will be constructed on Can Giuoc River which is a national waterway that has been planned (two level 3 waterways from Ho Chi Minh city to Kien Luong and to Ca Mau). The article presents the result of MIKE 21 model study to assess the impact of inundation preventing construction on river morphology of Can Giuoc River in Long An province. The result indicates that the location of the drainage has caused local erosion due to narrow riverbed, flow velocity here increases, hence there are erosions in front of and behind the drainage. The sphere of erosion influence causes riverbed modification towards the upstream and downstream about 180 – 200m, especially in the time of drainage operation, there are significant differences between the upstream and downstream water level, which causes local erosion, therefore it is essential to have riverbed and river bank strengthening reinforcement measures to stabilize riverbed.
Ni, W, Rao, Q & Luo, D 1970, 'Video Human Behaviour Recognition Based on Improved SVM_KNN for Traceability of Planting Industry', Springer International Publishing, pp. 474-482.
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Nia, AG, Lu, J, Zhang, Q & Ribeiro, M 1970, 'A Framework for a Large-Scale B2B Recommender System', 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), IEEE, pp. 337-343.
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The aim of a recommender system is to suggest relevant items in order to improve purchasing experience and minimise information overload. Despite extensive research in the area of B2C recommender systems, business-to-business (B2B) distributors can not directly benefit from the results. Mainly because the data from these large scale retailers is not publicly available to researchers and also their problems are not widely known to the outside world. These companies have complex structures for their items and customers, e.g. the huge number of items and customers leading to data sparsity or the high level of accuracy required in recommending safety items. Furthermore, one of the key requirements for such businesses is bulk recommendations to be able to meet their market demands. A unique hybrid approach to recommendation with an emphasis on knowledge components is needed for such businesses. It is critical to have a careful analysis of item-category specific features for any recommendation as well as the customer context. In this paper, we propose a large scale B2B recommender framework to address the above requirements.
Nizami, MSH, Hossain, MJ, Amin, BMR, Kashif, M, Fernandez, E & Mahmud, K 1970, 'Transactive Energy Trading of Residential Prosumers Using Battery Energy Storage Systems', 2019 IEEE Milan PowerTech, 2019 IEEE Milan PowerTech, IEEE, Milan, Italy, pp. 1-6.
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© 2019 IEEE. In a transactive energy (TE) framework, prosumers can participate in peer-to-peer (P2P) energy trading with neighbors. TE also allows prosumers to participate in grid services by trading their excess energy or energy consumption flexibility with the grid operators, energy suppliers, and third-party energy companies (e.g., Aggregators). This paper presents a novel bidding strategy for small-scale residential prosumers for energy trading in the day-ahead TE market using the flexibilities of residential battery energy storage systems to maximize the profit from energy trading. The bidding model is formulated as a bi-level optimization problem that determines energy trading bids to maximize profits for the prosumer in the upper level, while the lower-level problem schedules the operation of residential storage units with respect to minimum storage degradation and optimum user comfort. A comprehensive storage model is developed that incorporates the operational constraints and the degradation of storage units when they undergo frequent charge-discharge cycles for the energy trading. The proposed bidding model is evaluated via a case study for a typical Australian prosumer and results indicate the efficacy of the proposed model in terms of profit maximization for the prosumer while satisfying user preferences and constraints related to the operation of the storage units.
Nizami, MSH, Hossain, MJ, Rafique, S, Mahmud, K, Irshad, UB & Town, G 1970, 'A Multi-agent system based residential electric vehicle management system for grid-support service', 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), IEEE, Genova, Italy, pp. 1-6.
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© 2019 IEEE. With a spike in popularity and sales, the electric vehicles (EVs) have revolutionized the transportation industry. As EV technology advances, the EVs are becoming more accessible and affordable. Therefore, a rapid proliferation of light-duty EVs have been noticed in the residential sector. Even though the increased charging demand of EVs is manageable in large-scale, the low-voltage (LV) residential networks might not be capable of managing localized capacity issues of large scale EV integration. Dynamic electricity tariff coupled with demand response and smart charging management can provide grid assistance to some extent. However, uncoordinated charging, if clustered in a residential distribution feeder, can risk grid assets because of overloading and can even jeopardize the reliability of the network by violating voltage constraints. This paper proposes a coordinated residential EV management system for power grid support. Charging and discharging of residential EV batteries are coordinated and optimized to address grid overloading during peak demand periods and voltage constraint violations. The EV management for grid support is formulated as a mixed-integer programming based optimization problem to minimize the inconveniences of EV owner while providing grid assistance. The proposed methodology is evaluated via a case study based on a residential feeder in Sydney, Australia with actual load demand data. The simulation results indicate the efficacy of the proposed EV management method for mitigating grid overloading and maintaining desired bus voltages.
Noehring, F, Woestmann, R, Wienzek, T & Deuse, J 1970, 'Socio-Technical Capability Assessment to Support Implementation of Cyber-Physical Production Systems in Line with People and Organization', Advances in Intelligent Systems and Computing, AHFE 2018 International Conference on Human Factors and Systems Interaction, Springer International Publishing, USA, pp. 299-311.
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© Springer International Publishing AG, part of Springer Nature 2019. Cyber-Physical Production Systems (CPPS) enable the intelligent, horizontal and vertical interconnection of people, machines and objects throughout the enterprise in real-time by information and communication technologies providing a basis for increasing transparency and productivity of production processes. However, especially small and medium-sized enterprises with limited resources and personal competencies need support in planning and evaluation of CPPS. Former developments, as the CIM-era, showed that changes in production systems focusing only on technology failed. Due to the interconnection of CPPS, a holistic approach, taking likewise humans, technology and organization into account is necessary. This paper presents requirements as well as an evaluation of existing approaches. Furthermore, this paper presents the approach of a socio-technical capability assessment, enabling companies to evaluate effects of CPPS as well as deriving implementation measures. It concludes with a validation based on a use case of a worker information system.
Norman, M, Shafri, HZM, Pradhan, B & Yusuf, B 1970, 'Improved Building Roof Type Classification Using Correlation-Based Feature Selection and Gain Ratio Algorithms', GCEC 2017 Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 863-873.
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© Springer Nature Singapore Pte Ltd. 2019. Of late, application of data mining for pattern recognition and feature classification is fast becoming an essential technique in remote sensing research. Accurate feature selection is a necessary step to improve the accuracy of classification. This process depends on the number of feature attributes available for interactive synthesis of common characteristics that discriminate different features. Geographic object-based image analysis (GEOBIA) has made it possible to derive varieties of object attribute for this purpose; however, the analysis is more computationally intensive. The aim of this study is to develop feature selection technique that will provide the most suitable attributes to identify different roofing materials and their conditions. First, the feature importance was evaluated using gain ratio algorithm, and the result was ranked, leading to selection of the optimal feature subset. Then, the quality of the selected features was assessed using correlation-based feature selection (CFS). The classification results using SVM classifier produced an overall accuracy of 83.16%. The study has shown that the ability to exploit rich image feature attribute through optimization process improves accurate extraction of roof material with greater reliability.
Nosouhi, MR, Yu, S, Grobler, M, Zhu, Q & Xiang, Y 1970, 'Blockchain–Based Location Proof Generation and Verification', IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE.
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In location-sensitive applications, service providers need to verify the location of users in order to provide them with access to a service or benefit. This provides dishonest users with an incentive to cheat on their location by submitting fake location claims. To address this issue, a number of location proof mechanisms have been proposed in literature to date. However, they are faced with different security and privacy challenges. In this paper, we utilize the unique features of the blockchain technology to design a decentralized architecture in which mobile users act as witnesses and generate location proofs for other users. In the proposed scheme, a location proof is issued as part of a transaction that is broadcasted into a peer-to-peer network where it can be picked up by verifiers for further verification. Once a transaction is successfully verified, it is stored in a public ledger. Our security and privacy analysis shows that the proposed scheme preserves users' privacy and achieves a reliable performance against Prover-Prover and Prover-Witness collusions. Moreover, our prototype implementation on the Android platform shows that the location proof generation process in the proposed scheme is faster than the current decentralized schemes and requires low computational resources.
Nosouhi, MR, Yu, S, Sood, K & Grobler, M 1970, 'HSDC–Net: Secure Anonymous Messaging in Online Social Networks', 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), IEEE, Rotorua, New Zealand, pp. 350-357.
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© 2019 IEEE. Hiding contents of users' messages has been successfully addressed before, while anonymization of message senders remains a challenge since users do not usually trust ISPs and messaging application providers. To resolve this challenge, several solutions have been proposed so far. Among them, the Dining Cryptographers network protocol (DC-net) provides the strongest anonymity guarantees. However, DC-net suffers from two critical issues that makes it impractical, i.e., (1) collision possibility and (2) vulnerability against disruptions. Apart from that, we noticed a third critical issue during our investigation. (3) DC-net users can be deanonymized after they publish at least three messages. We name this problem the short stability issue and prove that anonymity is provided only for a few cycles of message publishing. As far as we know, this problem has not been identified in the previous research works. In this paper, we propose Harmonized and Stable DC-net (HSDC-net), a self-organizing protocol for anonymous communications. In our protocol design, we first resolve the short stability issue and obtain SDC-net, a stable extension of DC-net. Then, we integrate the Slot Reservation and Disruption Management sub-protocols into SDC-net to overcome the collision and security issues, respectively. The obtained HSDC-net protocol can also be integrated into blockchain-based cryptocurrencies (e.g. Bitcoin) to mix multiple transactions (belonging to different users) into a single transaction in such a way that the source of each payment is unknown. This preserves privacy of blockchain users. Our prototype implementation shows that HSDC-net achieves low latencies that makes it a practical protocol.
Nsiah-Baafi, E, Vessalas, K, Thomas, P & Sirivivatnanon, V 1970, 'Investigating the Alkali Threshold of Potentially Reactive Aggregates for Use in ASR Risk-Free Concretes', 29th Biennial National Conference of the Concrete Institute of Australia, Sydney.
Nsiah-Baafi, E, Vessalas, K, Thomas, P & Sirivivatnanon, V 1970, 'Investigation of Alkali Threshold Limits and Blended Aggregate in ASR Risk-Assessed Concretes', Concrete New Zealand Conference 2019, Concrete New Zealand Conference, ConcreteNZ, Dunedin, New Zealand.
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Concrete structures are designed for a specific design life to tolerate deterioration caused from various aggressive environmental loads such as carbon dioxide, chloride and aggressive soil conditions. The approach to prevent deterioration in concrete due to alkali-silica reaction (ASR) is by the avoidance of any such dissolution reaction taking place in concrete. ASR can in part be prevented by limiting the alkali content and restricting the use of potentially reactive aggregates. In this paper, the alkali threshold of several aggregates originating from New Zealand were determined using a modified version of RILEM AAR-3.2 and AAR-7.1. The AAR-2 accelerated mortar bar test (AMBT at 80°C) and AAR-3.2 concrete prism test (CPT at 38°C) were replaced with Australian Standard AS 1141.60.1 and 60.2 test methods, respectively, to evaluate expansion. Additional accelerated CPT in accordance with AAR-4.1 (ACPT at 60°C) was also conducted to examine the adequacy of shortening the test period. Petrographic examination taken before and after expansion testing was also carried out to qualify the presence of reactive silica and ASR gel contributing to expansion. The findings of this study suggest the potential for specifying the alkali threshold in concrete based on the reactivity classification of aggregates allowing a relaxation of the CCANZ Technical Report TR 3 alkali limit of 2.5 kg/m3 that is currently in place in New Zealand. This approach allows greater flexibility in the use of potentially reactive aggregates as sustainable concreting making materials.
Nsiah-Baafi, E, Vessalas, K, Thomas, P & Sirivivatnanon, V 1970, 'Mitigating Alkali Silica reactions in the absence of SCMs: A review of empirical studies', FIB 2018 - Proceedings for the 2018 fib Congress: Better, Smarter, Stronger, The International Federation for Structural Concrete 5th International fib Congress, Melbourne, pp. 3829-3844.
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The mechanism and severity of alkali-silica reaction (ASR) is subjective to the conditions of the availability of moisture and sufficient alkali content, and the presence of reactive aggregates. Since the 1940s, key focus has been placed on the reduction of alkali content by way of addition of supplementary cementitious materials (SCMs). However, the cost of SCMs and the realization that the availability of these materials could become limited in the untold future has influenced some researchers to investigate the development of protocols for the use of aggregates minimizing the likelihood of potential severe ASR. This paper presents a summary and review of the various strategies that have been adopted in recent years for the mitigation of ASR without utilising the addition of SCMs.
Ojha, S, Gudi, SLKC, Vitale, J, Williams, M-A & Johnston, B 1970, 'I Remember What You Did: A Behavioural Guide-Robot', Advances in Intelligent Systems and Computing, International Conference on Robot Intelligence Technology and Applications, Springer International Publishing, Kuala Lumpur, Malaysia, pp. 273-282.
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© Springer International Publishing AG, part of Springer Nature 2019. Robots are coming closer to human society following the birth of emerging field called Social Robotics. Social Robotics is a branch of robotics that specifically pertains to the design and development of robots that can be employed in human society for the welfare of mankind. The applications of social robots may range from household domains such as elderly and child care to educational domains like personal psychological training and tutoring. It is crucial to note that if such robots are intended to work closely with young children, it is extremely important to make sure that these robots teach not only the facts but also important social aspects like knowing what is right and what is wrong. It is because we do not want to produce a generation of kids that knows only the facts but not morality. In this paper, we present a mechanism used in our computational model (i.e EEGS) for social robots, in which emotions and behavioural response of the robot depends on how one has previously treated a robot. For example, if one has previously treated a robot in a good manner, it will respond accordingly while if one has previously mistreated the robot, it will make the person realise the issue. A robot with such a quality can be very useful in teaching good manners to the future generation of kids.
Omar, A, Beydoun, G, Win, KT, Shukla, N & Baker, G 1970, 'Socio-Technical Perspective on Managing Type II Diabetes', ACIS 2019 Proceedings - 30th Australasian Conference on Information Systems, Australasian Conference on Information Systems, AIS, Perth, pp. 840-850.
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Social attributes such as education level, family history or place of residence all place a strong role in the probability of a person developing type II diabetes later in life. The aim of this paper is to develop a knowledge system based to use social attributes to estimate the prevalence of type II diabetes in a given area in Australia to support public health policymaking. The focus of this paper is towards answering the research question How can social determinants associated with type II diabetes, be used to incrementally develop a supporting knowledge-based system (KBS)? The contribution of this paper is two folds: 1. The problem domain is analysed and a suitable KBS development framework is chosen 2. A prototype is developed and presented. Initial results with preliminary data confirm the validity of the approach.
Ona, ED, Cuesta-Gomez, A, Garcia, JA, Raffe, W, Sanchez-Herrera, P, Cano-de-la-Cuerda, R & Jardon, A 1970, 'Evaluating A VR-based Box and Blocks Test for Automatic Assessment of Manual Dexterity: A Preliminary Study in Parkinson’s Disease', 2019 IEEE 7th International Conference on Serious Games and Applications for Health (SeGAH), 2019 IEEE 7th International Conference on Serious Games and Applications for Health (SeGAH), IEEE, Kyoto, Japan, pp. 1-6.
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© 2019 IEEE. Opportunities of using Virtual Reality (VR) technology for the automation of clinical procedures in general, and for the assessment of motor function in particular, have not been fully explored in Parkinson' disease (PD). For that purpose, a game-like version of the Box and Blocks Test (BBT) for automatic assessment of hand motor function in VR was built. This system uses the Leap Motion Controller (LMC) for hand tracking and the Oculus Rift for a fully immersive experience. In this paper, we focus on evaluating the capabilities of our VR-BBT to reliably measure the manual dexterity in a sample of PD patients. For this study, a group of nine individuals in mild to moderate stage of PD were recruited. Participants were asked to perform both the physical BBT and the VR-BBT systems. Correlation analysis of collected data was carried out comparing the BBT and VR-BBT assessments. The test-retest reliability was also explored for the scores gathered with the virtual tool. Statistical analysis proved that the performance data collected by the game-like system correlated with the validated measures of the physical BBT, with a strong test-retest reliability. This fact suggests that the virtual version of the BBT could be used as a valid and reliable indicator for health improvements.
Ou, K, Pineda, JA, Liu, X & Sheng, D 1970, 'Osmotic effects on the microstructure of Ashfield shale', Japanese Geotechnical Society Special Publication, The Japanese Geotechnical Society, pp. 669-674.
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© 2019 7th Asia-Pacific Conference on Unsaturated Soils, AP-UNSAT 2019. All rights reserved. Preliminary results of a comprehensive microstructural investigation aimed at studying the influence of osmotic effects in Ashfield shale, a low permeability sedimentary rock from the Sydney Basin (Australia), are presented in the paper. Natural rock specimens were exposed to different brine solutions to assess their influence on rock microstructure. Qualitative as well as quantitative experimental techniques were used to evaluate changes in mineralogical composition (XRD analysis), Cation Exchange Capacity (CEC), cation/ion concentration (chromatographic analysis), specific surface, structural arrangement (Scanning Electron Microscopy) as well as pore size distribution (Mercury Intrusion Porosimetry). Test results show an important influence of the applied osmotic potential on specific surface, CEC and pore size distribution and to a lesser extend the structural arrangement assessed via SEM. These changes occur without important variations in the mineralogical composition of the rock.
Pan, L, Scheerlinck, C, Yu, X, Hartley, R, Liu, M & Dai, Y 1970, 'Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera', 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 6813-6822.
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Parajuli, N, Gunawardana, U, Gargiulo, G, Ulloa, DF, Sreenivasan, N, Naik, G, Bifulco, P, Esposito, D, Savino, S, Cesarelli, M & Hamilton, T 1970, 'Electrodeless FSR Linear Envelope Signal for Muscle Contraction Measurement', 2019 International Conference on Electrical Engineering Research & Practice (ICEERP), 2019 International Conference on Electrical Engineering Research & Practice (ICEERP), IEEE, pp. 1-5.
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Parajuli, N, Ulloa, DF, Sreenivasan, N, Naik, G, Bifulco, P, Esposito, D, Savino, S, Cesarelli, M, Hamilton, T, Gunawardana, U & Gargiulo, G 1970, 'Electrodeless FSR linear envelope signal for muscle contraction measurement', 2019 INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING RESEARCH & PRACTICE (ICEERP-2019), International Conference on Electrical Engineering Research and Practice (ICEERP) / 5th World Congress of the Global-Circle-for-Scientific-Technological-and-Management-Research (GCSTMR), IEEE, AUSTRALIA, Sydney, pp. 25-29.
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Park, K, Patten, T & Vincze, M 1970, 'Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation', 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE.
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Estimating the 6D pose of objects using only RGB images remains challengingbecause of problems such as occlusion and symmetries. It is also difficult toconstruct 3D models with precise texture without expert knowledge orspecialized scanning devices. To address these problems, we propose a novelpose estimation method, Pix2Pose, that predicts the 3D coordinates of eachobject pixel without textured models. An auto-encoder architecture is designedto estimate the 3D coordinates and expected errors per pixel. These pixel-wisepredictions are then used in multiple stages to form 2D-3D correspondences todirectly compute poses with the PnP algorithm with RANSAC iterations. Ourmethod is robust to occlusion by leveraging recent achievements in generativeadversarial training to precisely recover occluded parts. Furthermore, a novelloss function, the transformer loss, is proposed to handle symmetric objects byguiding predictions to the closest symmetric pose. Evaluations on threedifferent benchmark datasets containing symmetric and occluded objects show ourmethod outperforms the state of the art using only RGB images.
Park, K, Patten, T, Prankl, J & Vincze, M 1970, 'Multi-Task Template Matching for Object Detection, Segmentation and Pose Estimation Using Depth Images', 2019 International Conference on Robotics and Automation (ICRA), 2019 International Conference on Robotics and Automation (ICRA), IEEE.
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Parnell, J & Sommer, R 1970, 'Setting noise objectives for outdoor music festivals in rural locations', Australian Acoustical Society Annual Conference, AAS 2018, pp. 223-231.
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Unwanted music from outdoor events is considered a form of noise pollution which presents a unique set of challenges for regulators when compared to other environmental noise sources. Unlike noise generated by sources such as transport or industry where lower levels are always desirable, there is a minimum level of music below which patron experience will be unacceptable. The challenge for regulators therefore lies in balancing the need for entertainment, against the impacts of outdoor music on the surrounding population. Regulators and organisers of outdoor music festivals in rural environments are generally required to comply with receiver-based noise limits in noise catchments which range from very low backgrounds to those which may have dominant natural or transportation noise. With this in mind, this paper describes the approach undertaken to develop a practical and realistic set of noise objectives for a music festival site on the north coast of NSW, Australia.
Passerini, K, Bartolacci, MR, Bandera, C & Chandran, D 1970, 'Innovation and entrepreneurship theory and practice mini-track', Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 5358-5359.
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This mini-track examines both the theory and practice of knowledge management in organizations where innovation and an entrepreneurial structure require its successful application. Entrepreneurs often create knowledge but fail to capture it for future use. Organizations that have the ability to innovate in their early stages of existence and capture the knowledge they create are far better positioned to survive in the long run.
Pelchen, T & Lister, R 1970, 'On the Frequency of Words Used in Answers to Explain in Plain English Questions by Novice Programmers', Proceedings of the Twenty-First Australasian Computing Education Conference, ACE'19: Twenty-First Australasian Computing Education Conference, ACM, Sydney NSW Australia, pp. 11-20.
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Most previous research studies using Explain in Plain English questions have focussed on categorising the answers of novice programmers according to the SOLO taxonomy, and/or the relationship between explaining code and writing code. In this paper, we study the words used in the explanations of novice programmers. Our data is from twelve Explain in plain English questions presented to over three hundred students in an exam at the end of the students' first semester of programming. For each question, we compare the frequency of certain words used in correct answers, between students who scored a perfect twelve on all the Explain in plain English questions and students with lower scores. We report a number of statistically significant differences in word frequency between the students who answered all questions correctly and students who did not. The students who answered all twelve questions correctly tended to be more precise, more comprehensive, and more likely to choose words not explicitly in the code, but instead words that are an abstraction beyond the code.
Peng, J, Liu, D, Parnell, J & Kessissoglou, N 1970, 'An Australian case study on the estimation of heavy vehicle noise emission on grade', Proceedings of the International Congress on Acoustics, pp. 3735-3739.
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Heavy vehicles are considered the primary determinant of night-time noise disturbance, particularly along principal freight routes. To capture the dynamic influence of heavy vehicles associated with variation in speed and road grade on noise emission, heavy vehicle kinematic variables need to be incorporated within a road traffic noise emission model. These kinematic variables in turn assist with accurate estimation of engine noise and rolling noise. An existing prediction method that considers the driving speed profiles of articulated trucks is the American FHWA TNM road traffic noise model. However, it can only consider a fixed set of speed profiles based on a single heavy vehicle power-to-weight ratio. As such, the model is limited and does not accurately represent the longer and heavier vehicle combinations that dominate the Australian freight haul fleet. In this work, a road traffic noise prediction model which includes the equation of motion for a typical Australian heavy vehicle operating on grade is presented. A case study based on a principal freight route in New South Wales, Australia, is presented to illustrate the predicted variations in engine noise and rolling noise throughout the heavy vehicle's journey.
Peng, X, Long, G, Pan, S, Jiang, J & Niu, Z 1970, 'Attentive Dual Embedding for Understanding Medical Concepts in Electronic Health Records', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-8.
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Electronic health records contain a wealth of information on a patient’s healthcare over many visits, such as diagnoses, treatments, drugs administered, and so on. The untapped potential of these data in healthcare analytics is vast. However, given that much of medical information is a cause and effect science, new embedding methods are required to ensure the learning representations reflect the comprehensive interplays between medical concepts and their relationships over time. Unlike one-hot encoding, a distributed representation should preserve these complex interactions as high-quality inputs for machine learning-based healthcare analytics tasks. Therefore, we propose a novel attentive dual embedding method called MC2Vec. MC2Vec captures the proximity relationships between medical concepts through a two-step optimization framework that recursively refines the embedding for superior output. The framework comprises a Skip-gram model to generate the initial embedding and an attentive CBOW model to fine-tune the embedding with temporal information gleaned from sequences of patient visits. Experiments with two public datasets demonstrate that MC2Vec’s produces embeddings of higher quality than five state-of-the-art methods.
Peng, X, Long, G, Shen, T, Wang, S, Jiang, J & Blumenstein, M 1970, 'Temporal Self-Attention Network for Medical Concept Embedding', Proceedings - IEEE International Conference on Data Mining, ICDM, International Conference on Data Mining, Beijing, China, pp. 498-507.
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In longitudinal electronic health records (EHRs), the event records of apatient are distributed over a long period of time and the temporal relationsbetween the events reflect sufficient domain knowledge to benefit predictiontasks such as the rate of inpatient mortality. Medical concept embedding as afeature extraction method that transforms a set of medical concepts with aspecific time stamp into a vector, which will be fed into a supervised learningalgorithm. The quality of the embedding significantly determines the learningperformance over the medical data. In this paper, we propose a medical conceptembedding method based on applying a self-attention mechanism to represent eachmedical concept. We propose a novel attention mechanism which captures thecontextual information and temporal relationships between medical concepts. Alight-weight neural net, 'Temporal Self-Attention Network (TeSAN)', is thenproposed to learn medical concept embedding based solely on the proposedattention mechanism. To test the effectiveness of our proposed methods, we haveconducted clustering and prediction tasks on two public EHRs datasets comparingTeSAN against five state-of-the-art embedding methods. The experimental resultsdemonstrate that the proposed TeSAN model is superior to all the comparedmethods. To the best of our knowledge, this work is the first to exploittemporal self-attentive relations between medical events.
Perry, S, Pinheiro, A, Dumic, E & da Silva Cruz, LA 1970, 'Study of Subjective and Objective Quality Evaluation of 3D Point Cloud Data by the JPEG Committee', Electronic Imaging, Image Quality and System Performance XVI, Society for Imaging Science & Technology, Burlingame, CA, USA, pp. 312-312.
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The SC29/WG1 (JPEG) Committee within ISO/IEC is currently working on developing standards for the storage, compression and transmission of 3D point cloud information. To support the creation of these standards, the committee has created a database of 3D point clouds representing various quality levels and use-cases and examined a range of 2D and 3D objective quality measures. The examined quality measures are correlated with subjective judgments for a number of compression levels. In this paper we describe the database created, tests performed and key observations on the problems of 3D point cloud quality assessment.
Pfeiffer, S, Ebrahimian, D, Herse, S, Le, TN, Leong, S, Lu, B, Powell, K, Raza, SA, Sang, T, Sawant, I, Tonkin, M, Vinaviles, C, Vu, TD, Yang, Q, Billingsley, R, Clark, J, Johnston, B, Madhisetty, S, McLaren, N, Peppas, P, Vitale, J & Williams, M-A 1970, 'UTS Unleashed! RoboCup@Home SSPL Champions 2019', RoboCup 2019: Robot World Cup XXIII, Robot World Cup, Springer International Publishing, Sydney, NSW, Australia, pp. 603-615.
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This paper summarizes the approaches employed by Team UTS Unleashed! to take First Place in the 2019 RoboCup@Home Social Standard Platform League. First, our system architecture is introduced. Next, our approach to basic skills needed for a strong performance in the competition. We describe several implementations for tests participation. Finally our software development methodology is discussed.
Pham Ngoc, T, Fatahi, B & Khabbaz, H 1970, 'Impact of Liquid Whey Waste on Strength and Stiffness of Cement Treated Clay', New Developments in Soil Characterization and Soil Stability, Civil Infrastructures Confronting Severe Weathers and Climate Changes Conference, Springer International Publishing, Hangzhou, China, pp. 1-10.
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The reuse of whey waste, a by-product of the dairy industry, is an emerging issue due to the environmental impacts. Some previous experimental studies have indicated that whey waste can be used as an admixture for cement-based materials, including mortar and concrete, to reduce the setting time and increase the workability, thus reduce the amount of required cement. However, influence of whey waste on cemented soil has not received sufficient attention. This study investigates variations of unconfined compressive strength (UCS) and Young's modulus (E) of cemented Kaolin clay when water in cement slurry was replaced by different whey waste proportions. Unconfined compression tests were conducted on treated specimens after two different curing times, namely 14 days and 56 days. Stress-strain relationship in each test was used to compute UCS and E at different dosages of cement and whey waste. Results of the experiments show improvements of UCS and E only for specimens when less than 10% water in cement slurry was replaced by liquid whey waste at 56 day-curing age, regardless of cement dosage. For the other cases, the presence of whey waste resulted in reductions of both UCS and E, indicating that although whey waste can be used to improve mechanical properties of cement treated clay, the optimum dosage should be selected very carefully to minimize the adverse effects. Different responses of UCS and E with curing age, dosages of cement and liquid whey waste are explained while discussing about the effects of lactose (milk sugar) available in whey waste acting as a retarding agent.
Pham, M, Hoang, DB & Chaczko, Z 1970, 'Realization of congestion-aware energy-aware virtual link embedding', 2019 29th International Telecommunication Networks and Applications Conference (ITNAC), 2019 29th International Telecommunication Networks and Applications Conference (ITNAC), IEEE, Auckland, New Zealand, pp. 1-6.
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Network virtualization is an inherent component of future internets. Network resources are virtualized and provisioned to users on demand. The virtual network embedding entails two processes: node mapping and link mapping. However, efficient and practical solutions to the link mapping problem in software-defined networks (SDN) and data centers are still lacking. This paper proposes a solution to the link mapping (LiM) process that can dynamically interact with the routing protocols of the substrate network to allocate virtual link requests to the underlying substrate links, satisfies optimizing cost, minimizing energy consumption, and avoiding congestion (CEVNE) concurrently. CEVNE LiM is realized as a composite application on top of the SDN controller running the Segment Routing (SR) application. The performance of the CEVNE LiM algorithm is compared with the k-shortest path link mapping algorithm and shows its superior performance in terms of the overall runtime, the average path length, the average node stress, the average link stress, and the overall energy consumption.
Pham, TT & Dutkiewicz, E 1970, 'Quantify Physiologic Interactions Using Network Analysis', Computational Science and Its Applications – ICCSA 2019, International Conference on Computational Science and Its Applications, Springer International Publishing, Saint Petersburg, Russia, pp. 142-151.
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© 2019, Springer Nature Switzerland AG. To better understand the neural interactions amongst human organ systems, this work provides a framework of data analysis to quantify forms of neural signalling. We explore network interactions among the human brain and motor controlling. The main objective of this work is to provoke unique challenges in the emerging Network Physiology field. The proposed method applies network analysis techniques including power coherence for connectivity discovering and correlation measurement for profiling relationships. We used a well-designed dataset of 50 subjects over 14 different scenarios for each individual. We found network models for these interactions and observed informative network behaviours. The information can be used to study impaired communications that can lead to dysfunction of organs or the entire system such as sepsis.
Pham, TT, Takalkar, MA, Xu, M, Hoang, DT, Truong, HA, Dutkiewicz, E & Perry, S 1970, 'Airborne Object Detection Using Hyperspectral Imaging: Deep Learning Review', Computational Science and Its Applications – ICCSA 2019, International Conference on Computational Science and Its Applications, Springer International Publishing, Saint Petersburg, Russia, pp. 306-321.
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Hyperspectral images have been increasingly important in object detection applications especially in remote sensing scenarios. Machine learning algorithms have become emerging tools for hyperspectral image analysis. The high dimensionality of hyperspectral images and the availability of simulated spectral sample libraries make deep learning an appealing approach. This report reviews recent data processing and object detection methods in the area including hand-crafted and automated feature extraction based on deep learning neural networks. The accuracy performances were compared according to existing reports as well as our own experiments (i.e., re-implementing and testing on new datasets). CNN models provided reliable performance of over 97% detection accuracy across a large set of HSI collections. A wide range of data were used: a rural area (Indian Pines data), an urban area (Pavia University), a wetland region (Botswana), an industrial field (Kennedy Space Center), to a farm site (Salinas). Note that, the Botswana set was not reviewed in recent works, thus high accuracy selected methods were newly compared in this work. A plain CNN model was also found to be able to perform comparably to its more complex variants in target detection applications.
Phan, NM, Indraratna, B & Nguyen, T 1970, 'The response of granular soil to increasing hydraulic gradient through LBM-DEM coupling', In Proceedings of the 9th Asian Young Geotechnical Engineers (9YGEC), Lahore.
Phuong, N, Hoang, T, Fitzgerald, D, Thach, T, Graham, S & Marais, B 1970, 'CHARACTERISATION OF CHILDREN HOSPITALSED WITH PNEUMONIA IN CENTRAL VIETNAM: A PROSPECTIVE STUDY', RESPIROLOGY, WILEY, pp. 14-15.
Phuong, N, Hoang, T, Fitzgerald, D, Thach, T, Graham, S & Marais, B 1970, 'PREDICTORS OF 'UNLIKELY BACTERIAL PNEUMONIA' AND 'ADVERSE OUTCOME' IN VIETNAMESE CHILDREN ADMITTED WITH PNEUMONIA', RESPIROLOGY, WILEY, pp. 74-75.
Pietroni, N, Bickel, B, Malomo, L & Cignoni, P 1970, 'State of the art on stylized fabrication', SIGGRAPH Asia 2019 Courses, SA '19: SIGGRAPH Asia 2019, ACM, Brisbane, Australia, pp. 1-1.
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© 2019 Copyright held by the owner/author(s). Digital fabrication devices are powerful tools for creating tangible reproductions of 3D digital models. Most available printing technologies aim at producing an accurate copy of a tridimensional shape. However, fabrication technologies can also be used to create a stylistic representation of a digital shape. We refer to this class of methods as stylized fabrication methods. These methods abstract geometric and physical features of a given shape to create an unconventional representation, to produce an optical illusion, or to devise a particular interaction with the fabricated model. In this course, we classify and overview this broad and emerging class of approaches and also propose possible directions for future research.
Pietroni, N, Bickel, B, Malomo, L & Cignoni, P 1970, 'State of the art on stylized fabrication.', SIGGRAPH Asia, ACM, pp. 118:1-118:1.
Pilaka, M, Bandara, M & Mansoor, E 1970, 'A Semantic Model Based Framework for Regulatory Reporting Process Management', Springer International Publishing, pp. 149-164.
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Pilaka, M, Rabhi, FA & Bandara, M 1970, 'Semantic Process Based Framework for Regulatory Reporting Process Management', 9th International Conference on Advances in Computing and Information Technology (ACITY 2019), 9th International Conference on Advances in Computing and Information Technology, Aircc Publishing Corporation, pp. 183-201.
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Pileggi, SF, Peña, FC, Villamil, M-D-P & Beydoun, G 1970, 'Analysing the Trade-Off Between Computational Performance and Representation Richness in Ontology-Based Systems.', ICCS (5), International Conference on Computational Science, Springer, Faro, Portugal, pp. 237-250.
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As the result of the intense research activity of the past decade, Semantic Web technology has achieved a notable popularity and maturity. This technology is leading the evolution of the Web via interoperability by providing structured metadata. Because of the adoption of rich data models on a large scale to support the representation of complex relationships among concepts and automatic reasoning, the computational performance of ontology-based systems can significantly vary. In the evaluation of such a performance, a number of critical factors should be considered. Within this paper, we provide an empirical framework that yields an extensive analysis of the computational performance of ontology-based systems. The analysis can be seen as a decision tool in managing the constraints of representational requirements versus reasoning performance. Our approach adopts synthetic ontologies characterised by an increasing level of complexity up to OWL 2 DL. The benefits and the limitations of this approach are discussed in the paper.
Pineda, JA & Sheng, D 1970, 'Environmental degradation of clayey rocks', Japanese Geotechnical Society Special Publication, The Japanese Geotechnical Society, pp. 8-20.
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© 2019 7th Asia-Pacific Conference on Unsaturated Soils, AP-UNSAT 2019. All rights reserved. This paper explores the mechanisms that lead to the degradation of clayey rocks when exposed to environmental effects as those caused by unloading and cyclic variations in relative humidity. The following aspects are evaluated: (i) the number of applied RH cycles, N, (ii) the amplitude of relative humidity cycles, RH, (iii) the stress level (p-ua) and (iv) the effect of the fluid used to induce rock saturation (liquid water or vapour). The implementation of nonconventional experimental techniques for inducing and tracking rock degradation, at 'macro' and 'micro' scales, is described. An experimentally-based framework of behaviour is presented which may be used in practice for the evaluation of the degradation potential of clayey rocks.
Piyathilaka, L, Sooriyaarachchi, B, Kodagoda, S & Thiyagarajan, K 1970, 'Capacitive Sensor Based 2D Subsurface Imaging Technology for Non Destructive Evaluation of Building Surfaces', PROCEEDINGS OF THE IEEE 2019 9TH INTERNATIONAL CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS (CIS) ROBOTICS, AUTOMATION AND MECHATRONICS (RAM) (CIS & RAM 2019), 9th IEEE International Conference on Cybernetics and Intelligent Systems (CIS) / IEEE Conference on Robotics, Automation and Mechatronics (RAM), IEEE, Bangkok, THAILAND, pp. 287-292.
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Understanding the underlying structure of building surfaces like walls andfloors is essential when carrying out building maintenance and modificationwork. To facilitate such work, this paper introduces a capacitive sensor-basedtechnology which can conduct non-destructive evaluation of building surfaces.The novelty of this sensor is that it can generate a real-time 2D subsurfaceimage which can be used to understand structure beneath the top surface. FiniteElement Analysis (FEA) simulations are done to understand the best sensor headconfiguration that gives optimum results. Hardware and software components arecustom-built to facilitate real-time imaging capability. The sensor isvalidated by laboratory tests, which revealed the ability of the proposedcapacitive sensing technology to see through common building materials likewood and concrete. The 2D image generated by the sensor is found to be usefulin understanding the subsurface structure beneath the top surface.
Poblete, P, Pereda, J, Nunez, F & Aguilera, RP 1970, 'Distributed Current Control of Cascaded Multilevel Inverters', 2019 IEEE International Conference on Industrial Technology (ICIT), 2019 IEEE International Conference on Industrial Technology (ICIT), IEEE, Melbourne, AUSTRALIA, pp. 1509-1514.
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Pokhrel, SR, Sood, K, Yu, S & Nosouhi, MR 1970, 'Policy-based Bigdata Security and QoS Framework for SDN/IoT: An Analytic Approach', IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, Paris, France, pp. 73-78.
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© 2019 IEEE. With the explosive growth of Internet of Things (IoT) using WiFi networks along with their huge data flows (especially Bigdata using TCP connections), the significant challenges are the application performance and network security. Bigdata comes in form of varying volume, velocity, etc. and is very challenging to manage with traditional networks. Therefore, we advocate Software-defined networking (SDN) paradigm in this paper. Using SDN, firstly, from security perspective, we are able to diagnose Bigdata TCP streams that may come from both attack or non-attack sources. Secondly, when the Bigdata TCP streams come from legitimate sources, SDN can help in maintaining Quality of Service (QoS) to particular flow or application. In this paper, we have proposed a Policy-based framework that maintains the security as well the flow specific QoS requirement in SDN enabled IoT network. In our network settings, we proposed an algorithm at WiFi Access Point (AP) or at network edge router, to learn the incoming traffic from different Things and then takes appropriate action/s based on the policies in place. A mathematical model is developed considering TCP CUBIC streams over WiFi networks to understand and evaluate our idea. Our extensive simulation results demonstrate how we jointly enhance the security and effectively maintain the desired QoS of the streams in real time.
Poon, J, Cui, Y, Ooga, J, Ogawa, A & Matsubara, T 1970, 'Probabilistic Active Filtering for Object Search in Clutter', 2019 International Conference on Robotics and Automation (ICRA), 2019 International Conference on Robotics and Automation (ICRA), IEEE, Montreal, QC, Canada, pp. 7256-7261.
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© 2019 IEEE. This paper proposes a probabilistic approach for object search in clutter. Due to heavy occlusions, it is vital for an agent to be able to gradually reduce uncertainty in observations of the objects in its workspace by systematically rearranging them. Probabilistic methodologies present a promising sample-efficient alternative to handle the massively complex state-action space that inherently comes with this problem, avoiding the need for both exhaustive training samples and the accompanying heuristics for traversing a large-scale model during runtime. We approach the object search problem by extending a Gaussian Process active filtering strategy with an additional model for capturing state dynamics as the objects are moved over the course of the activity. This allows viable models to be built upon relatively scarce training data, while the complexity of the action space is also reduced by shifting objects over relatively short distances. Validation in both simulation and with a real Baxter robot with a limited number of training samples demonstrates the efficacy of the proposed approach.
Poostchi, H & Piccardi, M 1970, 'A multi-constraint structured hinge loss for named-entity recognition', Proceedings of the Australasian Language Technology Workshop, Annual Workshop of the Australasian Language Technology Association, ACLWEB, Sydney, NSW, Australia, pp. 41-46.
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The negative log-likelihood or cross entropy is the usual training objective of NLP models owing to its versatility and empirical performance. However, training objectives which directly target the performance measure used to evaluate the task have the potential to lead to higher empirical accuracy. For this reason, in this short paper we propose using a multi-constraint structured hinge loss as the training objective of a contemporary namedentity recognition (NER) model. Experimental results over the challenging OntoNotes 5.0 dataset have shown that the proposed objective has been able to achieve an improvement of 0.62 CoNLL score points at a complete parity of testing set-up.
Poostchi, H, Borzeshi, EZ & Piccardi, M 1970, 'BILSTM-CRF for Persian named-entity recognition armanpersonercorpus: The first entity-annotated Persian dataset', LREC 2018 - 11th International Conference on Language Resources and Evaluation, Language Resources and Evaluation Conference, European Language Resources Association (ELRA, Miyazaki, Japan, pp. 4427-4431.
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Named-entity recognition (NER) can still be regarded as work in progress for a number of Asian languages due to the scarcity of annotated corpora. For this reason, with this paper we publicly release an entity-annotated Persian dataset and we present a performing approach for Persian NER based on a deep learning architecture. In addition to the entity-annotated dataset, we release a number of word embeddings (including GloVe, skip-gram, CBOW and Hellinger PCA) trained on a sizable collation of Persian text. The combination of the deep learning architecture (a BiLSTM-CRF) and the pre-trained word embeddings has allowed us to achieve a 77.45% CoNLL F1 score, a result that is more than 12 percentage points higher than the best previous result and interesting in absolute terms.
Pour, AB, Park, T-YS, Park, Y, Hong, JK & Pradhan, B 1970, 'Application of Constrained Energy Minimization (CEM) algorithm to ASTER data for alteration mineral mapping', IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Japan, pp. 6760-6763.
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Pour, AB, Park, T-YS, Park, Y, Hong, JK & Pradhan, B 1970, 'Fusion of DPCA and ICA algorithms for mineral detection using Landsat-8 spectral bands', IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Yokohama, Japan, pp. 6067-6070.
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© 2019 IEEE. Fusion of Directed Principal Component Analysis (DPCA) and Independent Component Analysis (ICA) algorithms was applied to some selected Landsat-8 mineral indices for mapping gossan and clay-rich zones for Zn-Pb exploration in the Franklinian Basin of North Greenland. This region contains a unique potential for exploration of world-class zinc deposits. Numerous potential zones for Zn-Pb deposits were identified using the fusion technique of DPCA/ICA. Their identification was based on detecting alteration mineral patterns consist of ferric iron, ferrous iron and clay minerals within a background of sedimentary rocks using Landsat-8 spectral bands. Several zones of gossan and clay mineral assemblages were identified in the trough sequences of the Citronen Fjord, Peary Land that may represent potential undiscovered CD Zn-Pb deposits and warrant further investigation. This investigation indicates that satellite remote sensing mapping techniques can aid in identifying unknown/undiscovered Zn-Pb sulfide deposits in the High Arctic Franklinian Basin by targeting the location of alteration zones that are feasible location for mineral occurrences.
Pourpanah, F, Wang, R, Wang, X, Shi, Y & Yazdani, D 1970, 'mBSO: A Multi-Population Brain Storm Optimization for Multimodal Dynamic Optimization Problems', 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 673-679.
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Brain Storm Optimization (BSO), which is an effective swarm intelligence method inspired by the human brainstorming process, has shown promising results in solving static optimization problems. However, The search spaces of many real-world problems change over time, in which the original BSO and its variants are not able to cope with. This paper extends BSO as an adaptive multi-population based algorithm, i.e., mBSO, to solve dynamic optimization problems (DOPs). Firstly, a modified BSO, which uses new update mechanisms independent from the maximum number of iterations and objective space grouping method, is proposed. Then, the modified BSO is embedded in a multi-population framework. Several mechanisms such as convergence detection, exclusion, and re-diversification are employed to address the challenging issues of DOPs. The moving peaks benchmark (MPB) is used to evaluate the performance of mBSO along with comparison with other state-of-the-art methods. The outcome indicates the efficiency of the proposed mBSO in locating optima and tracking them after environmental changes.
Poursafar, N, Taghizadeh, S & Hossain, MJ 1970, 'An Optimized Power Management System for an Islanded DC Microgrid', 2019 29th Australasian Universities Power Engineering Conference (AUPEC), 2019 29th Australasian Universities Power Engineering Conference (AUPEC), IEEE, Nadi, FIJI, pp. 1-6.
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Pradhan, B 1970, 'Geospatial Computation for Urban Applications', 2019 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 2019 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), IEEE, pp. 1-1.
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Pradhan, S, Ehnis, C & Lama, S 1970, 'Towards a Digital Platform to Support/Enhance Community-based Tourism in Developing Countries - Findings from Nepal', ACIS 2019 Proceedings - 30th Australasian Conference on Information Systems, Australasian Conference on Information Systems, AIS, Perth, Australia, pp. 961-971.
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Real social impact is not possible without the engagement of the local communities. The paper describes the first phase of an engaged research project in which we develop a digital platform which is able to support and enhance Community-Based Tourism in Developing Countries. With the help of a local community in Nepal, we co-develop and understand the requirements which need to be included in a digital platform to support Community-Based Tourism in Developing Countries. The data is collected through three focus groups which explore “Categories of Local Tourism products/services”, “Education, Training, and Awareness Raising”, and “Design structures of a Digital Platform”. The participants of the focus groups were community leaders, local business owners, entrepreneurs, and tourism association representatives. The findings contribute to our understanding of supporting local entrepreneurism through digital platforms and help to make the world a better place with Information Systems.
Prior, J, Laudari, S & Leaney, J 1970, 'What is the Effect of a Software Studio Experience on a Student's Employability?', Proceedings of the Twenty-First Australasian Computing Education Conference, ACE'19: Twenty-First Australasian Computing Education Conference, ACM, Australia, pp. 28-36.
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© 2019 Association for Computing Machinery. Our software studio demonstrably increases students' employability, according to the empirical findings of this study, and an evaluation of these findings based on the CareerEDGE Employability Development Profile. We provide a studio environment in which students work in mixed teams on real software projects for clients, under the guidance of industry and academic mentors. This study used open-ended interviews and ethnographic observations in the studio sessions to understand employability success. Skills found important for employability include: Collaboration and communication, project management, supporting each other to resolve technical issues, seeking help from industry mentors and academics, social aspects of work (working with clients and mentors), reflection skills and technical skills. These skills were compared with the CareerEDGE Employability Development Profile and found to give good coverage of employability skills. Contributions made by this study to computing education are: • A deep empirical understanding of students' perspectives and what they value about their employability as a result of participating in the software studio • An evaluation of our findings against the CareerEDGE employability framework, in a technical learning environment • Findings from an investigation that is complementary to students' perspectives collected in accordance with the CareerEDGE approach, where the data is collected via a questionnaire with 5-point Likert scale responses; our interviews were open-ended and accompanied by ethnographic observations.
Prysyazhnyuk, A, McGregor, C, Chernikova, A & Rusanov, V 1970, 'A sliding window real-time processing approach for analysis of heart rate variability during spaceflight', Proceedings of the International Astronautical Congress, IAC.
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The paradigm of technological disruption continues to pave the way for innovative technology that has the capacity to acquire comprehensive real-time physiological and environmental data and present endless opportunities to study physiological processes and mechanisms, aid clinical discovery and advance the field of preventative and corrective medicine both on Earth and during spaceflight. Missions of increased distance and duration, as well as ad-hoc emergency situations that render the space crew to remain in space for long periods of time with reduced number of team members necessitate deployment of comprehensive clinical-decision support systems aboard the space station, to preserve and maintain the well-being of the crew, and ensure successful execution of mission objectives and safe return to Earth. In prior work, we presented the use of Artemis, big-data analytics platform for real-time analysis of adaption to conditions of spaceflight, to assess the levels of stress imposed on the human body and identify the state of well-being and any deviation from the norm that becomes apparent prior to onset of clinical symptoms. Conventional methods of adaption assessment were limited to 5-minute windows of data, which were historically averaged to a single hourly and daily value. The capability of Artemis to support analysis of high-frequency, high-volume and high-velocity data present new opportunities for analysis of heart rate variability during spaceflight. As such, we propose the use of a 5-minute sliding window-based analysis of heart rate variability for assessment of adaption during spaceflight. This method would support investigation of stressor-induced responses (i.e. physical load, task activity, environmental) to help identify the exact onset of the highest strain of regulatory mechanisms and assess activity of various components of the autonomic nervous system. In addition, 5-minute sliding window analysis would provide more insight into recov...
Pshtiwan Shakor, Shami Nejadi & Gavin Paul 1970, 'An Investigation into the Effects of Deposition Orientation of Material on the Mechanical Behaviours of the Cementitious Powder and Gypsum Powder in Inkjet 3D Printing', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 36th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), Banff, AB, Canada, pp. 42-49.
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© 2019 International Association for Automation and Robotics in Construction I.A.A.R.C. All rights reserved. Three-Dimensional Printing (3DP) is widely used and continues to be rapidly developed and adopted, in several industries, including construction industry. Inkjet 3DP is the approach which offers the most promising and immediate opportunities for integrating the benefits of additive manufacturing technic into the construction field. The ability to readily modify the orientation angle that the printed material is deposited is one of the most advantageous features in a 3DP scaffold compared with conventional methods. The orientation angle has a significant effect on the mechanical behaviours of the printed specimens. Therefore, this paper focuses on printing in different orientations somehow to compare various mechanical properties and to characterise a selection of common construction materials including gypsum (ZP 151) and cement mortar (CP). The optimum strength for the gypsum specimens in compression and flexural strength was observed in the (0° and 90°) and (0°) in the X-Z plane, respectively. According to the experimental results, the compression and flexural strength for ZP 151 are recorded at (11.59±1.18 and 11.78±1.19) MPa and 15.57±0.71 MPa, respectively. Conversely, the highest strength in compression and flexural strength are observed in the (90°) and (0°) degrees in the X-Z plane for the cement mortar, respectively. Moreover, it has been discovered that the compression and flexural strengths for CP are recorded as 19.44±0.11 MPa and 4.06±0.08 MPa, respectively. In addition, the dimensional effect for various w/c ratio has been monitored and examined.
Pugalia, S & Cetindamar Kozanoglu, D 1970, 'Tearing down the double glass ceiling for the women immigrant entrepreneurs in high-tech industry', Cairns, Australia, pp. 1-14.
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Although the number of women-owned firms is growing, there still remains the gap in the technology sector. The purpose of the present study is to explore the barriers faced by women-entrepreneurs due to their immigrant and ethnicity status. The paper presents a literature review in order to shed light on the possible causes of the lower number of women immigrant entrepreneurs particularly in high-tech sectors. Given the human, financial and network disadvantages faced by women vis-a-vis men, the immigrant status escalates the barriers further and create additional layer of 'glass ceiling' to pass for women who want to start a technology-based venture. In other words, immigrant women face a set of invisible barriers to advancement in their entrepreneurial career in high-technology sectors. The paper points out the existence of barriers and calls for researchers to find out ways to tear down these glass ceilings in order to empower woman and support their contribution to society as UN 2030 Agenda for Sustainable Development argues.
Puthal, D, Mohanty, SP, Nanda, P, Kougianos, E & Das, G 1970, 'Proof-of-Authentication for Scalable Blockchain in Resource-Constrained Distributed Systems', 2019 IEEE International Conference on Consumer Electronics (ICCE), 2019 IEEE International Conference on Consumer Electronics (ICCE), IEEE, Las Vegas, NV, USA, pp. 1-5.
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© 2019 IEEE. Resource -constrained distributed systems such as the Internet of Things (IoT), edge computing and fog computing are deployed for real-time monitoring and evaluation. Current security solutions are problematic when there is a centralized controlling entity. The blockchain provides decentralized security architectures using proof-of-work (PoW). Proof-of-work is an expensive process for IoT and edge computing due to the deployment of resource-constrained devices. This paper presents a novel consensus algorithm called Proof-of-Authentication (PoAh) to replace Proof-of-Work and introduce authentication in such environments to make the blockchain application-specific. This paper implemented the Proof-of-Authentication system to evaluate its sustainability and applicability for the IoT and edge computing. The evaluation process is conducted in both simulation and real-time testbeds to evaluate performance. Finally, the process of Proof-of-Authentication and its integration with blockchain in resource-constrained distributed systems is discussed. Our proposed PoAh, while running in limited computer resources (e.g. single-board computing devices like the Raspberry Pi) has a latency in the order of 3 secs.
Qahl, M, Hawryszkiewycz, I, Binsawad, M & Rehman, J 1970, 'Factors Affecting the Saudi Arabian Higher Education Creative Environment', ACIS 2019 Proceedings - 30th Australasian Conference on Information Systems, 30th Australasian Conference on Information Systems, Perth Western Australia, pp. 155-161.
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Creativity is an essential pillar for individuals in higher education institutions (HEIs). The promotion of a research culture in universities necessitates an environment supporting new ideas and innovation by encouraging their staff to be more creative. The Saudi government is trying to improve its higher education system by encouraging creative environments to support socioeconomic development and achieve a transformation from an oil-based to a knowledge-based economy. The purpose of this study is to identify the factors that contribute to creativity and innovation among academic staff in Saudi Arabian HEIs. Therefore, this research-in-progress paper discusses how a specific set of organizational, individual and technological factors can support the achievement of a creative environment. Accordingly, a conceptual framework is proposed that lay the foundation for a creative environment in Saudi Arabian HEIs, with the ultimate aim of building an innovative environment in Saudi Arabian HEIs.
Qi, Y, Indraratna, B, Heitor, A & Rujikiatkamjorn, C 1970, 'Recycled Rubber Derivatives for Resilient Transport Corridors', 15th International Conference on Geotechnical Engineering, Lahore, Pakistan.
Qi, Y, Indraratna, B, Heitor, A & Vinod, JS 1970, 'The Influence of Rubber Crumbs on the Energy Absorbing Property of Waste Mixtures', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 271-281.
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© Springer Nature Singapore Pte Ltd. 2019. The practical application of waste materials such as steel furnace slag (SFS) and coal wash (CW) is becoming more prevalent in civil engineering. While the addition of rubber crumbs (RC) derived from waste tyres can influence the geotechnical properties of the mixtures of SFS and CW significantly, especially the energy absorbing property. In this paper, the energy absorbing property of the SFS + CW + RC mixtures under static loading has been evaluated by the strain energy density. As expected, the energy absorbing capacity of the waste mixture increases with the addition of RC. To further illustrate the influence of rubber crumbs on the energy absorbing property of the waste mixtures, particle degradation has also be examined after finishing the triaxial tests. It has been found that the addition of RC can significantly reduce the particle breakage of the waste mixtures. Therefore, with high energy absorbing property, the SFS + CW + RC mixtures can be further extended to dynamic loading projects, such as railway capping layer.
Qin, C, Zhang, JA, Huang, X & Guo, YJ 1970, 'Angle-of-Arrival Acquisition and Tracking via Virtual Subarrays in an Analog Array', 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), IEEE, Honolulu, HI, USA, pp. 1-5.
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© 2019 IEEE. Angle-of-arrival (AoA) estimation is a challenging problem for analog antenna arrays. Typical algorithms use beam scanning and sweeping, which can be time-consuming, and the resolution is limited to the scanning step. In this paper, we propose a virtual-subarray based AoA estimation scheme, which divides an analog array into two virtual subarrays and can obtain a direct AoA estimate from every two temporal measurements. We propose different subarray constructions which lead to different range and accuracy of estimation. We provide detailed beamforming vector designs for these constructions and provide a performance lower bound for the estimator. We also present how to apply the estimator to AoA acquisition and tracking. Simulation results demonstrate that the proposed scheme significantly outperforms existing ones when the signal-to-noise ratio is not very low.
Qin, H & Stewart, MG 1970, 'Wind fragility of roof cladding and trusses for Australian contemporary housing', 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019.
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This study develops a reliability-based fragility method to predict the roof damage for contemporary houses in non-cyclonic regions of Australia. The overloading of roof connections is considered as the limit state, and deemed to cause the roof sheeting loss and roof truss failures. A finite element method is employed to evaluate the wind uplift forces in roof connections. The finite element model consists of metal roof sheets and battens, timber roof trusses, wall top plates, and the cladding-to-batten, batten-to-rafter/truss and rafter/truss-to-wall connections. The finite element method is able to capture the load sharing and redistribution of the roof system. A Monte Carlo simulation in conjunction with the finite element method are employed to conduct the wind fragility assessment, which enables the probabilistic characterization of wind demands, uplift capacities and structural response for roof connections. The proposed fragility method is illustrated on a representative contemporary house built in suburbs of Brisbane and Melbourne. At a 500-year gust wind speed, considerable damage to roof cladding and trusses is predicted for the representative contemporary house in Brisbane with windward wall dominant openings.
Qin, H, Li, R-H, Wang, G, Qin, L, Cheng, Y & Yuan, Y 1970, 'Mining Periodic Cliques in Temporal Networks.', ICDE, International Conference on Data Engineering, IEEE, Macao, Macao, pp. 1130-1141.
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© 2019 IEEE. Periodicity is a frequently happening phenomenon for social interactions in temporal networks. Mining periodic communities are essential to understanding periodic group behaviors in temporal networks. Unfortunately, most previous studies for community mining in temporal networks ignore the periodic patterns of communities. In this paper, we study a problem of seeking periodic communities in a temporal network, where each edge is associated with a set of timestamps. We propose a novel model, called maximal σ-periodic k-clique, that represents a periodic community in temporal networks. Specifically, a maximal σ-periodic k-clique is a clique with size larger than k that appears at least σ times periodically in the temporal graph. We show that the problem of enumerating all those periodic cliques is NP-hard. To compute all of them efficiently, we first develop two effective graph reduction techniques to significantly prune the temporal graph. Then, we present an efficient enumeration algorithm to enumerate all maximal σ-periodic k-cliques in the reduced graph. The results of extensive experiments on five real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms.
Qiu, X, Zhu, Q, Wang, S & Zhong, J 1970, 'A case study on the new reverberation room built in University of Technology Sydney', Proceedings of the International Congress on Acoustics, International Congress on Acoustics, EEA, Aachen, Germany, pp. 4051-4058.
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A new reverberation room has just been built at Centre for Audio, Acoustics and Vibration in University of Technology Sydney. This paper reports some key parameters of the room, which include the background noise and its spectrum, the number of modes in low frequency bands, the cut-off frequency, the standard deviation of the spatial variations of the reverberant field, the decay curves of the sound pressure level versus time, the reverberation times, and the absorption coefficients. The volume of the room is approximately 232 m3 with a total surface area of approximately 247 m2. The averaged overall background noise level inside the room is 23.1 dB and 18.1 dBA, the number of modes is greater than 30 in one-third-octave bands with centre frequencies of 160 Hz and above, the standard deviation of the sound pressure levels is less than 1.5 dB in one-third-octave bands with centre frequencies of 125 Hz and above, and the decay curves of sound-pressure level versus time is linear in all one-third-octave bands. The reverberation time of the room can be as long as 20 s in the low frequency range around 100 Hz, and the shortest reverberation time is about 3.1 s in the high frequency range around 5000 Hz. The equivalent sound absorption areas of the empty room in all one-third-octave bands are smaller than the threshold values specified in the standard, and the curve is smooth without any dips or peaks which differ by more than 15% from the mean values of two adjacent one-third-octave bands. The sound absorption coefficients in all frequency bands of the empty reverberation room are less than 0.02.
Qu, Y, Yu, S, Zhang, J, Binh, HTT, Gao, L & Zhou, W 1970, 'GAN-DP: Generative Adversarial Net Driven Differentially Privacy-Preserving Big Data Publishing', ICC 2019 - 2019 IEEE International Conference on Communications (ICC), ICC 2019 - 2019 IEEE International Conference on Communications (ICC), IEEE, Shanghai, China, pp. 1-6.
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© 2019 IEEE. Increasing massive volume of data are generated every single second in this big data era. With big data from multiple sources, adversaries continuously mine private information for potential benefits. Motivated by this, we propose a generative adversarial net (GAN) driven noise generation method under the framework of differential privacy. We add one more perceptron, which is a specifically devised differential privacy identifier. After the generator produces the noise, the discriminator and the proposed identifier game with each other to derive the Nash Equilibrium. Extensive experimental results demonstrate the proposed model meets differential privacy constraints and upgrade data utility simultaneously.
Qu, Z, Zhou, Y, Nguyen, QV & Catchpoole, DR 1970, 'Using Visualization to Illustrate Machine Learning Models for Genomic Data', Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2019: Australasian Computer Science Week 2019, ACM, pp. 1-8.
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Massive amounts of genomic data are created for the advent of Next Generation Sequencing technologies. Visualizing these complex genomic data requires not only simply plotting of data but should also invite a decision or a choice. Machine learning has the ability to make prediction and aid in decision-making. Machine learning and visualization are both effective ways to deal with big data but focus on different purposes. Machine learning applies statistical learning techniques to automatically identify patterns in data to make highly accurate predictions while visualization can leverage the human perceptual system to interpret and uncover hidden patterns in big data. Clinicians, experts and researchers intend to use both visualization and machine learning to analyze their complex genomic data, but it is a serious challenge for them to understand and trust machine learning models in the medical industry. This paper overcomes this problem by combining intelligent and interactive visualization with machine learning models. Our prototype not only visualizes the complex genomics data in a meaningful 3D similarity space, but also illustrates the machine learning models and the real-time prediction results. Interactions and connections between the machine learning model and the 3D scatter plot are also developed and illustrated.
Quan, R, Dong, X, Wu, Y, Zhu, L & Yang, Y 1970, 'Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification', Proceedings of the IEEE International Conference on Computer Vision, IEEE/CVF International Conference on Computer Vision, IEEE, Seoul, Korea (South), pp. 3749-3758.
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Prevailing deep convolutional neural networks (CNNs) for personre-IDentification (reID) are usually built upon ResNet or VGG backbones, whichwere originally designed for classification. Because reID is different fromclassification, the architecture should be modified accordingly. We propose toautomatically search for a CNN architecture that is specifically suitable forthe reID task. There are three aspects to be tackled. First, body structuralinformation plays an important role in reID but it is not encoded in backbones.Second, Neural Architecture Search (NAS) automates the process of architecturedesign without human effort, but no existing NAS methods incorporate thestructure information of input images. Third, reID is essentially a retrievaltask but current NAS algorithms are merely designed for classification. Tosolve these problems, we propose a retrieval-based search algorithm over aspecifically designed reID search space, named Auto-ReID. Our Auto-ReID enablesthe automated approach to find an efficient and effective CNN architecture forreID. Extensive experiments demonstrate that the searched architecture achievesstate-of-the-art performance while reducing 50% parameters and 53% FLOPscompared to others.
Qureshi, S & Braun, R 1970, 'Mininet Topology: Mirror of the Optical Switch Fabric', 2019 29th International Telecommunication Networks and Applications Conference (ITNAC), 2019 29th International Telecommunication Networks and Applications Conference (ITNAC), IEEE, New Zealand, pp. 1-6.
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Software Defined Networks (SDN) is a new approach to change the conventional networking and is being researched in the various networking domains. To test and prototype SDN based concepts, a lightweight and closer to reality option is Mininet emulator. Mininet emulates SDN behavior by creating a virtual network of elements using Network Namespaces on a single Linux kernel machine. In this work, we have developed a Mininet topology that emulates the structure of a WDM Switch. The topology mirrors the paths that can be used by the wavelengths in a WDM switch fabric. The SDN controller can find a path for communication between hosts through this network. We simulated our Mininet topology, which mirrors an architecture for three wavelengths. The Ping command results show that only a set of hosts can be reached out by a particular host; which is the requirement of a WDM switch, and this verifies that Mininet topology is mapping a WDM switch.
Radhakrishnan, M & Misra, A 1970, 'Can Earables Support Effective User Engagement during Weight-Based Gym Exercises?', Proceedings of the 1st International Workshop on Earable Computing, UbiComp '19: The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, pp. 42-47.
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Radhakrishnan, M, Smailagic, A, French, B, Siewiorek, DP & Balan, RK 1970, 'Design and Assessment of Myoelectric Games for Prosthesis Training of Upper Limb Amputees', 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, pp. 151-157.
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Rafique, S, Hasan Nizami, MS, Bin Irshad, U, Hossain, J & Town, G 1970, 'An aggregator-based-strategy to minimize the cost of energy consumption by optimal utilization of energy resources in an apartment building', 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), IEEE, Genova, Italy, pp. 1-5.
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© 2019 IEEE. Buildings and transport consume two thirds of the total global energy. It is desirable to maximize the use of renewable generation in these sectors, and to optimize the use of that energy by managing diverse sources and loads. This is particularly challenging in high-density residential premises where the space for such infrastructure is limited, and storage can have significant impact on energy utilization and demand. In this paper, we have proposed an aggregator-based-strategy (ABS) to optimally utilize the available energy resources and storage in an apartment building with twenty households, each having an electric vehicle (EV), and an aggregated solar photovoltaic (PV) energy and stationary battery storage (BS) system. The strategy is flexible and can be applied to any building with EVs, solar PV and BS to minimize the cost of energy consumption without compromising the flexibility of energy usage or travel requirements. The model also accounts for the battery capacity degradation and its associated cost to make it more realistic. The model is evaluated using real data and the results show that the strategy not only reduces the cost of energy consumption but also reduces the amount of energy drawn from the grid significantly.
Rafique, S, Nizami, MSH, Irshad, UB, Hossain, MJ & Town, GE 1970, 'A customer-based-strategy to minimize the cost of energy consumption by optimal utilization of energy resources in an apartment building', IOP Conference Series: Earth and Environmental Science, International Conference on Smart Power & Internet Energy Systems, IOP Publishing, Melbourne, Australia, pp. 012018-012018.
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Abstract Global energy consumption in heating and cooling of buildings and in the transport sector together accounts for approximately two-thirds of total energy consumption. Consequently, it is important to maximize the use of renewable generation energy in these sectors, and to optimize the use of that energy by managing diverse sources and loads. This is particularly challenging in high-density residential premises where the space for such infrastructure is limited, and storage can have significant impact on energy utilization and demand. In this paper, we describe a customer-based strategy (CBS) to optimize the usage of the available energy resources in such scenarios. The effectiveness of the strategy was validated for an apartment block of 20 households with photovoltaic generation (PV) and stationary battery storage (BS) systems, each with a vehicle-to-grid (V2G) capable electric vehicle (EV). The modelling used real data for customer demand and included the cost of battery degradation and expected vehicle usage in optimizing resource scheduling. Substantial savings in energy costs were shown to be possible for each customer.
Ragazzon, MRP, Messineo, S, Gravdahl, JT, Harcombe, DM & Ruppert, MG 1970, 'Generalized Lyapunov Demodulator for Amplitude and Phase Estimation by the Internal Model Principle', IFAC-PapersOnLine, Elsevier BV, pp. 247-252.
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Rahman, M, Ahmed, F & Abdur Rahman, AM 1970, 'Low-Cost Low-Profile Planar Patch Antenna for Ultra-Wideband Applications', 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), IEEE, pp. 1-4.
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Rahman, M, Ahmed, F & Rahman, AMA 1970, 'A Low-Cost WLAN Band Notched Planar Patch Antenna for Ultra-Wideband Applications', 2019 IEEE International Conference on Telecommunications and Photonics (ICTP), 2019 IEEE International Conference on Telecommunications and Photonics (ICTP), IEEE, pp. 1-4.
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Rahman, MA, Singh, P, Muniyandi, RC, Mery, D & Prasad, M 1970, 'Prostate Cancer Classification Based on Best First Search and Taguchi Feature Selection Method', Image and Video Technology, Pacific-Rim Symposium on Image and Video Technology, Springer International Publishing, Sydney, NSW, Australia, pp. 325-336.
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© 2019, Springer Nature Switzerland AG. Prostate cancer is the second most common cancer occurring in men worldwide, about 1 in 41 men will die because of prostate cancer. Death rates of prostate cancer increases with age. Even though, it being a serious condition only about 1 man in 9 will be diagnosed with prostate cancer during his lifetime. Accurate and early diagnosis can help clinician to treat the cancer better and save lives. This paper proposes two phases feature selection method to enhance prostate cancer early diagnosis based on artificial neural network. In the first phase, Best First Search method is used to extract the relevant features from original dataset. In the second phase, Taguchi method is used to select the most important feature from the already extracted features from Best First Search method. A public available prostate cancer benchmark dataset is used for experiment, which contains two classes of data normal and abnormal. The proposed method outperforms other existing methods on prostate cancer benchmark dataset with classification accuracy of 98.6%. The proposed approach can help clinicians to reach at more accurate and early diagnosis of different stages of prostate cancer and so that they make most suitable treatment decision to save lives of patients and prevent death due to prostate cancer.
Rahman, ML, Cui, P-F, Zhang, JA, Huang, X, Guo, YJ & Lu, Z 1970, 'Joint Communication and Radar Sensing in 5G Mobile Network by Compressive Sensing', 2019 19th International Symposium on Communications and Information Technologies (ISCIT), 2019 19th International Symposium on Communications and Information Technologies (ISCIT), IEEE, pp. 599-604.
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© 2019 IEEE. There is growing interest in integrating communication and radar sensing into one system. However, very limited results are reported on how to realize sensing using complicated mobile signals when joint communication and radar sensing (JCAS) is applied to mobile networks. This paper studies radar sensing using one-dimension (1D) to 3D compressive sensing (CS) techniques, referring to signals compatible with latest fifth generation (5G) new radio (NR) standard. We demonstrate that radio sensing using both downlink and uplink 5G signals can be realized with reasonable performance using these CS techniques, and highlight the respective advantages and disadvantages of these techniques.1
Ram, R & Rizoiu, M-A 1970, 'A social science-grounded approach for quantifying online social influence', Australian Social Network Analysis Conference (ASNAC’19), pp. 2-2.
Ranjbar Zahedani, M, Keshavarzi, A, Khabbaz, H & Ball, J 1970, 'Flow Structures Around a Circular Bridge Pier with a Submerged Prismat Upstream', World Congress on Civil, Structural, and Environmental Engineering, The 4th World Congress on Civil, Structural, and Environmental Engineering, Avestia Publishing.
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Previous investigations have indicated that local scour around bridge piers and abutments causes around 60% of waterway bridge failures. In order to decrease the potential of pier-scour failure, the authors previously proposed an upstream prism as a new countermeasure against local scour. The proposed prism was examined in a comprehensive experimental program to find the most efficient size, submergence ratio, and installation location of the prism. The experimental results showed that the submerged prism could reduce around 40% of the maximum scour depth, and 60% of the scour-hole volume. In this study, in order to find out how this submerged prism affects the flow structure around the pier and reduces the pier-scour, the flow structure analysis was conducted using particle image velocimetry (PIV). The velocity components were measured for two cases of a single circular pier with and without the submerged prism. Analysis of the results indicated that the proposed prism could change the flow structure at the upstream and downstream of the pier. In fact, this submerged prism formed a wake region behind itself, and the bridge pier was located at this wake region. The produced wake resisted the down-flow at the upstream side of the pier and also disturbed the formation of the horseshoe vortices around the pier. In addition, this submerged prism reduced the strength of wake vortices behind the pier. Consequently, the pier-scour was significantly reduced by the substantial changes in the flow structure.
Rauch, A, Paris, C, Repesse, Y, Borel-Derlon, A, Rugeri, L, Harroche, A, Ternisien, C, Castet, S, Lebreton, A, Pan-Petesch, B, Volot, F, Clayssens, S, Chamouni, P, Gay, V, Berger, C, Desprez, D, Biron-Andreani, C, Veyradier, A, Goudemand, J & Susen, S 1970, 'EXPLORATION AND TREATMENT STRATEGIES FOR GASTROINTESTINAL BLEEDING IN VON WILLEBRAND DISEASE OVER A 3-YEAR PERIOD: A RETROSPECTIVE NATIONAL SURVEY FROM THE FRENCH REFERENCE CENTER FOR VWD', HAEMATOLOGICA, Joint Conference of 10th BIC International Conference (BIC) and 3rd International Conference on Inhibitors in Hemophilia (Inhibitors), FERRATA STORTI FOUNDATION, ITALY, Genoa, pp. 22-23.
Rehman, HU, Yafi, E, Nazir, M & Mustafa, K 1970, 'Security Assurance Against Cybercrime Ransomware', Springer International Publishing, pp. 21-34.
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Rehman, J, Hawryszkiewycz, I, Sohaib, O & Namisango, F 1970, 'Rethinking Intellectual Capital in Professional Service Firms: A Triple Bottom-line Perspective on Value-creation', ACIS 2019 Proceedings - 30th Australasian Conference on Information Systems, 30th Australasian Conference on Information Systems, Perth, pp. 649-655.
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In contemporary business environment, the formulation of an effective strategy aimed at driving and sustaining value requires effective management of the intellectual resources as a basis for achieving strategic advantage. Given the paradigm shift in the factors of productivity within the knowledge landscape, it’s crucial for the Professional Service Firms (PSFs) to be mindful of their Intellectual Capital (IC) potential as a basis for the competitive advantage and value-creation. Drawing upon Resource-Based-View of the firm, this research attempts to revisit value-creation concept and investigate how IC resources can be integrated and utilized to achieve broader value outcomes for various organizational stakeholders. In view of that, the proposed research aims to develop a methodological value-creation framework in the service firms by epistemologically reflecting on the IC-derived value outcomes in the multi-stakeholder context.
Ren, F, Wu, C & Lok, TS 1970, 'Preface', 13th International Conference on Shock and Impact Loads on Structures, SILOS 2019, p. iv.
Ren, L, Wang, F, Li, L, Liu, X & Zhang, Y 1970, 'Design Optimization of Distributed PV Power Station Based on the Efficiency Modelling and Analysis', 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), IEEE, Singapore, pp. 1-6.
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Based on the large amount of data from a 3 MW distributed photovoltaic power station and a module-level experimental platform, the mathematical model of the performance ratio varying with the irradiation is established in this paper. The performance ratio value of the power station is 0.59, which is far below the engineering experience value. Therefore, the inverter efficiency, photovoltaic module efficiency and photovoltaic array efficiency are analyzed individually, and the focus is laid on the loss of performance ratio that caused by the voltage mismatch between the photovoltaic array and the maximum power point tracking of the inverter. In order to improve the performance ratio, the number of modules in series are designed and optimized. The research results are conducive to guide the optimal design of distributed photovoltaic power plants and significantly increase the power generation capacity.
Rippingale, S, Johnston, A & Bluff, A 1970, 'Hybrid animation production and the dream of flight', SIGGRAPH ASIA Art Gallery/Art Papers, SA '19: SIGGRAPH Asia 2019, ACM.
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© 2019 Simon Rippingale, Andrew Johnston and Andrew Bluff. Through a detailed account of a recent practice-based research project - a short animation project called Jasper, this paper explores how a hybrid analogue/digital production approach can generate a unique and engaging visual style - one that sits between the tangible, handcrafted feel of miniatures and the cleanness, fluidity and flexibility of computer-generated animation. The author examines the new creative possibilities and challenges that a hybrid animation production approach presents and also outlines various technical platforms encountered during the production of Jasper, including motion-controlled camera systems, 3D printing, game engines, point cloud scans and augmented reality.
Rizeei, HM, Pradhan, B & Saharkhiz, MA 1970, 'Surface Runoff Estimation and Prediction Regarding LULC and Climate Dynamics Using Coupled LTM, Optimized ARIMA and Distributed-GIS-Based SCS-CN Models at Tropical Region', GCEC 2017 Proceedings of the 1st Global Civil Engineering Conference, Global Civil Engineering Conference, Springer Singapore, Kuala Lumpur, Malaysia, pp. 1103-1126.
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© Springer Nature Singapore Pte Ltd. 2019. The integration of precipitation intensity and LULC forecasting have played a significant role in prospect surface runoff, allowing for an extension of the lead time that enables a more timely implementation of the control measures. The current study proposes a full-package model to monitor the changes in surface runoff in addition to forecasting the future surface runoff based on LULC and precipitation factors. On one hand, six different LULC classes from Spot-5 satellite image were extracted by object-based Support Vector Machine (SVM) classifier. Conjointly, Land Transformation Model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020. On the other hand, ARIMA model was applied to the analysis and forecasting the rainfall trends. The parameters of ARIMA time series model were calibrated and fitted statistically to minimize the prediction uncertainty by latest Taguchi method. Rainfall and streamflow data recorded in eight nearby gauging stations were engaged to train, forecast, and calibrate the climate hydrological models. Then, distributed-GIS-based SCS-CN model was applied to simulate the maximum probable surface runoff for 2000, 2010, and 2020. The comparison results showed that first, deforestation and urbanization have occurred upon the given time and it is anticipated to increase as well. Second, the amount of rainfall has been nonstationary declined till 2015 and this trend is estimated to continue till 2020. Third, due to the damaging changes in LULC and climate, the surface runoff has also increased till 2010 and it is forecasted to gradually exceed.
Roboredo, C, Thomas, P, Vessalas, K & Sirivivatnanon, V 1970, 'Alkali limit in cement with supplementary cementing materials - A review', FIB 2018 - Proceedings for the 2018 fib Congress: Better, Smarter, Stronger, The International Federation for Structural Concrete Congress, Conrete Institute, Melbourne, pp. 3702-3708.
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The alkali silica reaction (ASR) may cause deleterious cracking in concretes as a result of the reactions of reactive aggregates in concrete systems that contain elevated alkali contents. Current strategies applied in the mitigation of ASR are based on limiting the alkali content (Na2Oe) of the cement and concrete and through the screening of aggregates with additional surety provided by the use of supplementary cementitious materials (SCMs) in the partial replacement of cement. These strategies pose significant issues for the construction materials industry through increased manufacturing costs and reduction in volumes of viable raw materials that meet the imposed criteria. The effective mitigation of deleterious ASR using SCMs should change the focus of regulators and standards authorities to risk management through the assessment of the risk profile of a concrete mix in a particular application. Using a risk profile to assess alkali limits has the potential to relax alkali limits in cements. To achieve this aim a deep understanding of ASR in cement-SCM-aggregate concrete mixes is required through laboratory testing correlated with long-term field performance. This paper reviews ASR, reactivity assessment of aggregates and the role of SCMs in ASR mitigation and proposes a change in the focus to a balanced alkali limit based on assessed risk for the occurrence of deleterious ASR.
Ruppert, MG, Routley, BS, Fleming, AJ, Yong, YK & Fantner, GE 1970, 'Model-based Q Factor Control for Photothermally Excited Microcantilevers', 2019 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), 2019 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), IEEE, pp. 1-6.
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Saha, SC, Islam, MS, Rahimi-Gorji, M & Molla, MM 1970, 'Aerosol particle transport and deposition in a CT-scan based mouth-throat model', AIP Conference Proceedings, 8TH BSME INTERNATIONAL CONFERENCE ON THERMAL ENGINEERING, AIP Publishing, Dhaka, Bangladesh.
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© 2019 Author(s). A precise understanding of the aerosol particle transport and deposition (TD) in the realistic mouth-throat model is important for the respiratory health risk assessment and effective delivery of the aerosol medicine to the targeted positions of the lung. A wide range of studies have developed the particle TD framework for both idealized and non-idealized extra-thoracic airways. However, all of the existing in silico and experimental model reports a significant amount of aerosol particles are deposit at the extra-thoracic airways and the existing drug delivery device can deliver only 12 percent of the aerosol drug to the targeted position of the lung. This study aims to increase the efficiency of the targeted drug delivery by developing a realistic particle transport model for CT-Scan based mouth-throat replica. A 3-D realistic mouth-throat model is developed from the CT-Scan DiCom images of a healthy adult cast. High-Quality computational cells are generated for the replica model and the proper grid refinement test has been performed. ANSYS Fluent (19.1) solver is used for the particle TD computation. Tecplot and MATLAB software are used for the post-processing purpose. The numerical results report that the breathing pattern and particle diameter influences the overall particle TD in the mouth-throat model. The numerical results also depict different deposition hot spots for the mouth-throat model, which will eventually help to design a better drug delivery device. The numerical results reported that only 13.67 percent of the 10-μm diameter particles are deposited at the mouth-throat model at 15 lpm flow rate and which indicate that the remaining particles will move to the beyond airways. The present results along with more case studies will develop the understanding of the realistic particle deposition in the extrathoracic airways.
Sahoo, A, Arunan, A, Mahmud, K, Ravishankar, J, Nizami, MSH & Hossain, MJ 1970, 'Teager-Huang based Fault Detection in Inverter-interfaced AC Microgrid', 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), IEEE, pp. 1-5.
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© 2019 IEEE. The limited fault current tolerance of inverters in AC Microgrids demands the necessity of faster and accurate fault detections. At the point of common coupling, the occurrence of various symmetrical and unsymmetrical faults degrades the performance and robustness of the inverter-interfaced local controllers. To achieve faster fault detection in inverter-based AC microgrid, this paper proposes a combined technique that includes two well-known signal processing techniques such as teager energy operator and Hilbert-Huang transform. The combined principle is called a Teager-Huang technique, which can detect different line faults using the teager energy of the Hilbert-Huang based empirical mode decomposed signals. The fault detection technique is verified by creating faults at the inverter connection point to the grid, using MATLAB/SIMULINK and PLECS.
Saki, M, Abolhasan, M & Lipman, J 1970, 'A Big Sensor Data Offloading Scheme in Rail Networks', 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), IEEE, Kuala Lumpur, Malaysia, Malaysia, pp. 1-6.
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© 2019 IEEE. In this paper, we propose an offloading scheme to transfer massive stored sensor data from rolling stock to railway data centers. We apply a delayed offloading strategy for non-critical stored data assuming that the critical data has been already separated through an appropriate edge processing task and has been sent via a real-time communication such as cellular networks. We propose train stations as potential and feasible spots for data offloading via available wireless local area networks (WLAN) such as existing WiFi network at stations. Thus, stations will not only be the places of passenger exchange but also data exchange. We develop an analytical model customized for the proposed offloading strategy in rail applications. Then we validate the performance of our model through simulation in various scenarios in Omnet. The simulation results shows an accuracy of %98.67 for the proposed analytical model with reference to the simulation results in Omnetpp. Additionally, by using our proposed scheme, we can theoretically offload up to 5.43 GB per each stopping station.
Salamai, A, Hussain, O & Saberi, M 1970, 'Decision Support System for Risk Assessment Using Fuzzy Inference in Supply Chain Big Data', 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS), IEEE, Shenzhen, China, pp. 248-253.
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© 2019 IEEE. Currently, organisations find it difficult to design a Decision Support System (DSS) that can predict various operational risks, such as financial and quality issues, with operational risks responsible for significant economic losses and damage to an organisation's reputation in the market. This paper proposes a new DSS for risk assessment, called the Fuzzy Inference DSS (FIDSS) mechanism, which uses fuzzy inference methods based on an organisation's big data collection. It includes the Emerging Association Patterns (EAP) technique that identifies the important features of each risk event. Then, the Mamdani fuzzy inference technique and several membership functions are evaluated using the firm's data sources. The FIDSS mechanism can enhance an organisation's decision-making processes by quantifying the severity of a risk as low, medium or high. When it automatically predicts a medium or high level, it assists organisations in taking further actions that reduce this severity level.
Samal, PB, Jack Soh, P & Zakaria, Z 1970, 'Compact and Wearable Microstrip-based Textile Antenna with Full Ground Plane Designed for WBAN-UWB 802.15.6 Application', 13th European Conference on Antennas and Propagation, EuCAP 2019.
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The design and evaluation of a microstrip-based textile antenna for the IEEE 802.15.6 Wireless Body Area Network Ultrawideband (WBAN-UWB) application is presented in this paper. This textile antenna is designed with an innovative and compact UWB radiator on top of the overall structure with a full ground plane on its reverse side. The radiator based on a microstrip patch combined with multiple miniaturization methods resulted in a simple topology and a compact size of 39 mm x 42 mm x 3.34 mm to facilitate fabrication using simple tools. Meanwhile, the full ground plane enables the antenna operation in the vicinity of the human body with minimal body coupling and radiation towards it, ensuring operational safety. Besides the mandatory WBAN-UWB low and high band channels, the designed antenna also operated in five other high band channels, exhibiting a total bandwidth of 3.4 GHz.
Samaras, E & Johnston, A 1970, 'Off-lining to tape is not archiving', ACM SIGGRAPH 2019 Art Gallery, SIGGRAPH '19: Special Interest Group on Computer Graphics and Interactive Techniques Conference, ACM, Los Angeles, California, pp. 1-7.
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© 2019 ISAST This paper examines digital asset archiving and preservation practice in the visual effects (VFX) industry. The authors briefly summarize media archaeology theory and provide an overview of how VFX studios presently archive project assets and records, based on case study and interview research conducted with expert VFX practitioners from leading international studios. In addition, the authors propose that current practice could be improved by adopting archival science methods, including digital preservation practices. Doing so will support media archaeology studies of digital cultures over time and ensure that the legacy of VFX creative and technical production thrives for future generations.
Samavati, M, Palmer, AW, Hill, AJ & Seiler, KM 1970, 'Improvements in plan-driven truck dispatching systems for surface mining', Mining Goes Digital - Proceedings of the 39th international symposium on Application of Computers and Operations Research in the Mineral Industry, APCOM 2019, International Symposium Application of Computers and Operations Research in the Mineral Industry, CRC Press, Wroclaw, Poland, pp. 357-366.
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© 2019 Taylor & Francis Group, London. Truck dispatching systems have been widely used since the 1970s for truck assignments in open pit mines, in order to coordinate material movement from diggers to process plants and stockpiles. Existing commercial solutions are commonly plan-driven systems, which operate by making real-time truck assignments while maintaining conformance to an established haulage allocation plan. Such systems typically comprise programming models to deter-mine the allocation plan as a set of material flow rates. A significant limitation of these methods is that they optimise flow rates only for instantaneous production. These approaches ignore upcoming events and changes in availability and performance, limiting the ability to optimise flows around such events. The method proposed in this paper extends existing approaches by modelling material flows over a future time-horizon, incorporating upcoming events, disrup-tions, and changes in resources. Results are presented comparing an established algorithm with the proposed method, showing improved handling of disruption events.
SAND, SG, SACO, PM, WEN, LI, SAINTILAN, N, KUCZERA, G, RICCARD, G & RODRIGUEZ, JF 1970, 'PREDICTING THE RESILIENCE OF DRYLAND WETLANDS AFFECTED BY DROUGHTS', 38th IAHR World Congress - 'Water: Connecting the World', 38th IAHR World Congress, The International Association for Hydro-Environment Engineering and Research (IAHR), pp. 4486-4494.
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Sang, L, Xu, M, Qian, S & Wu, X 1970, 'AAANE: Attention-Based Adversarial Autoencoder for Multi-scale Network Embedding', Advances in Knowledge Discovery and Data Mining 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part III, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, China, pp. 3-14.
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© Springer Nature Switzerland AG 2019. Network embedding represents nodes in a continuous vector space and preserves structure information from a network. Existing methods usually adopt a one-size-fits-all approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in embedding learning. In this paper, we propose an Attention-based Adversarial Autoencoder Network Embedding (AAANE) framework, which promotes the collaboration of different scales and lets them vote for robust representations. The proposed AAANE consists of two components: (1) an attention-based autoencoder that effectively capture the highly non-linear network structure, which can de-emphasize irrelevant scales during training, and (2) an adversarial regularization guides the autoencoder in learning robust representations by matching the posterior distribution of the latent embeddings to a given prior distribution. Experimental results on real-world networks show that the proposed approach outperforms strong baselines.
Saputra, YM, Hoang, DT, Nguyen, DN & Dutkiewicz, E 1970, 'JOCAR: A Jointly Optimal Caching and Routing Framework for Cooperative Edge Caching Networks', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Hawaii.
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We propose a jointly optimal caching and routing framework (JOCAR) for a cooperative mobile edge caching network. This novel network architecture enables mobile edge servers/nodes (MENs) to collaborate in not only caching but also routing contents to users, in order to simultaneously minimize the total content-access delay for all mobile users and reduce the traffic on the backhaul network. To that end, we first formulate an access- delay minimization problem by jointly optimizing the content caching and routing decisions while accounting for various network configurations. Solving this problem requires us to deal with a nested dual optimization due to the strong mutual dependence between content caching and routing decisions. To tackle it, we first transform the nested dual problem to an equivalent mixed-integer nonlinear programming (MINLP) problem. Then, we design a branch-and-bound based algorithm with the interior-point method to find the near-optimal policy for the MINLP problem. Extensive simulations show that JOCAR can reduce the total average delay and increase the cache hit rate for the whole network by more than 40% and by four times, respectively, compared with other conventional policies.
Saputra, YM, Hoang, DT, Nguyen, DN, Dutkiewicz, E, Mueck, MD & Srikanteswara, S 1970, 'Energy Demand Prediction with Federated Learning for Electric Vehicle Networks', 2019 IEEE Global Communications Conference (GLOBECOM), IEEE Global Communications Conference, IEEE, Hawaii.
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In this paper, we propose novel approaches using state-of-the-art machinelearning techniques, aiming at predicting energy demand for electric vehicle(EV) networks. These methods can learn and find the correlation of complexhidden features to improve the prediction accuracy. First, we propose an energydemand learning (EDL)-based prediction solution in which a charging stationprovider (CSP) gathers information from all charging stations (CSs) and thenperforms the EDL algorithm to predict the energy demand for the consideredarea. However, this approach requires frequent data sharing between the CSs andthe CSP, thereby driving communication overhead and privacy issues for the EVsand CSs. To address this problem, we propose a federated energy demand learning(FEDL) approach which allows the CSs sharing their information withoutrevealing real datasets. Specifically, the CSs only need to send their trainedmodels to the CSP for processing. In this case, we can significantly reduce thecommunication overhead and effectively protect data privacy for the EV users.To further improve the effectiveness of the FEDL, we then introduce a novelclustering-based EDL approach for EV networks by grouping the CSs into clustersbefore applying the EDL algorithms. Through experimental results, we show thatour proposed approaches can improve the accuracy of energy demand prediction upto 24.63% and decrease communication overhead by 83.4% compared with otherbaseline machine learning algorithms.
Sarikaya, A, Erkmen, RE, Gowripalan, N, Sirivivatnanon, V & South, W 1970, 'A Plastic-Damage Model for Concrete under Cyclic Loads', Concrete 2019, Concrete 2019, Sydney, Australia.
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A constitutive model based on a novel coupled elastoplastic-damage framework is adopted for the modelling of concrete under cyclic loads. Coupled elastoplastic-damage models have been used to capture both the material degradation and the permanent deformations under inelastic deformations. In this study, a multisurface plasticity framework is implemented for the modelling of concrete under compressive and tensile cyclic loads. The elastoplastic-damage framework is based on the ‘direct-coupling’ method in which an a-priori relationship between the total strain and the damage strain is postulated. The model is easy to calibrate since it utilises the same yield and potential functions for plasticity and damage calculations. Concrete is modelled using a pair of yield surfaces in order to capture its compressive and tensile behaviour while utilising corresponding isotropic damage variables to capture the stiffness degradations in the compressive and tensile regimes. Material parameters are calibrated using uniaxially loaded concrete experiments. The results are compared with experimental and numerical data provided in the literature.
Sawhney, R, Shah, RR, Bhatia, V, Lin, C-T, Aggarwal, S & Prasad, M 1970, 'Exploring the Impact of Evolutionary Computing based Feature Selection in Suicidal Ideation Detection', 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, New Orleans, LA, USA, pp. 1-6.
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© 2019 IEEE. The ubiquitous availability of smartphones and the increasing popularity of social media provide a platform for users to express their feelings, including suicidal ideation. Suicide prevention by suicidal ideation detection on social media lights the path to controlling the rapidly increasing suicide rates amongst youth. This paper proposes a diverse set of features and investigates into feature selection using the Firefly algorithm to build an efficient and robust supervised approach to classifying tweets with suicidal ideation. The development of a suicidal language to create three diverse, manually annotated datasets leads to the validation of the proposed model. An in-depth result and error analysis lead to an accurate system for monitoring suicidal ideation on social media along with the discovery of optimal feature subsets and selection methods using a penalty based Firefly algorithm.
Sayem, ASM & Esselle, KP 1970, 'A Unique, Compact, Lightweight, Flexible and Unobtrusive Antenna for the Applications in Wireless Body Area Networks', 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, pp. 1-4.
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© 2019 IEEE. A novel design of a wearable antenna is proposed in this paper. The proposed antenna is optically transparent, flexible, compact and lightweight which are the desirable characteristics of the wearable antennas. Square ring type design is proposed for our antenna. Ring antenna is compact in size compared to patch antenna and it radiates at broadside direction like a patch antenna. Our proposed antenna operates at 5.8 GHz Wi-Fi band. Full ground plane is utilized in the design to minimize the back radiation of the antenna and thus minimizes the interaction of the antenna radiation with human-body. The proposed materials for the antenna realization are transparent conductive mesh, VeilShield for the antenna conductive parts and polydimethylsiloxane (PDMS) for the substrate and protective coatings of the antenna. Both VeilShield and PDMS are conformal and have very good light transmittance. So, our proposed antenna has flexibility and optical transparency and thus it will be a promising candidate for the applications in wireless body area networks that need unobtrusive appearance.
Sayem, ASM, Simorangkir, RBVB, Esselle, KP & Hashmi, RM 1970, 'Feasibility Study of PDMS Embedded Transparent Conductive Fabric for the Realization of Transparent Flexible Antennas', 13th European Conference on Antennas and Propagation, EuCAP 2019, 13th European Conference on Antennas and Propagation (EuCAP), IEEE, Krakow, POLAND.
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In this paper the suitability and effectiveness of transparent conductive fabric for design and realization of transparent wearable antennas is studied. In contrast to the other expensive and non-flexible transparent conducting materials, transparent conductive fabric is an effective alternative for the realization of flexible transparent antennas. When embedded in polydimethylsiloxane (PDMS) this fabric becomes mechanically robust against repeated bending which is a requirement for many wearable devices. The performance of the transparent conductive fabric embedded in PDMS is evaluated for the wearable antenna application by fabricating a simple patch antenna operating at 2.45 GHz and testing its performance. The antenna prototype has been fabricated by using transparent conductive fabric VeilShield to form the conducting parts including the radiating element and PDMS as the substrate and encapsulation. Experimental investigations of the antenna demonstrate the applicability of the proposed material for the realization of transparent wearable antenna through a simple and inexpensive fabrication process.
Schmitt, J, Hahn, F & Deuse, J 1970, 'Practical Framework for Advanced Quality-based Process Control in Interlinked Manufacturing Processes', 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), IEEE, Macau, pp. 511-515.
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As the economic manufacturing of high-quality products becomes an increasingly crucial competitive factor, corresponding quality assurance measures are gaining a growing interest. Even though research interest and industrial demand are both high, there is a large gap between methological approaches and practical applicability that needs to be closed. In this paper we therefore present a practical framework for advanced quality-based process control (AQPC) in interlinked manufacturing processes. Machine learning algorithms are used to predict the expected product quality based on recorded process parameters. That information then serves as an input for the derivation of optimal control decisions. Therefore, we formulate a mathematical optimization model including different options such as order reassignment and process parameter adaption to determine an optimal set of control decisions. We then break down the optimization into a gradual procedure that allows an application-specific integration into manufacturing.
Schreiberhuber, S, Prankl, J, Patten, T & Vincze, M 1970, 'ScalableFusion: High-resolution Mesh-based Real-time 3D Reconstruction', 2019 International Conference on Robotics and Automation (ICRA), 2019 International Conference on Robotics and Automation (ICRA), IEEE, pp. 140-146.
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Seifollahi, S, Piccardi, M, Borzeshi, EZ & Kruger, B 1970, 'Taxonomy-Augmented Features for Document Clustering', Communications in Computer and Information Science, Australasian Conference on Data Mining, Springer Singapore, Bathurst, NSW, Australia, pp. 241-252.
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© Springer Nature Singapore Pte Ltd. 2019. In document clustering, individual documents are typically represented by feature vectors based on term-frequency or bag-of-word models. However, such feature vectors intrinsically dismiss the order of the words in the document and suffer from very high dimensionality. For these reasons, in this paper we present novel taxonomy-augmented features that enjoy two promising characteristics: (1) they leverage semantic word embeddings to take the word order into account, and (2) they reduce the feature dimensionality to a very manageable size. Our feature extraction approach consists of three main steps: first, we apply a word embedding technique to represent the words in a word embedding space. Second, we partition the word vocabulary into a hierarchy of clusters by using k-means hierarchically. Lastly, the individual documents are projected to the hierarchy and a compact feature vector is extracted. We propose two methods for generating the features: the first uses all the clusters in the hierarchy and results in a feature vector whose dimensionality is equal to the number of the clusters. The second uses a small set of user-defined words and results in an even smaller feature vector whose dimensionality is equal to the size of the set. Numerical experiments on document clustering show that the proposed approach is capable of achieving comparable or even higher accuracy than conventional feature vectors with a much more compact representation.
Sethibe, T, Abedin, B, Milne, D & Marjanovic, O 1970, 'A conceptual framework of digital empowerment of informal carers: An expert elicitation study', Proceedings of the 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019, Pacific Asia Conference on Information Systems, AISEL, Xi’an, China.
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Many studies on online health communities (OHCs) have focused on patients' well-being. The capabilities of OHCs to effect other psychosocial states like empowerment have been under-explored. Additionally, the study of empowerment of other healthcare stakeholders, specifically informal carers, has not attracted much study. This is despite evidence that carers use OHCs as an information and self-care resource in dealing with the stress and strain of caregiving. It is not clear how moderator support may influence carer empowerment. We propose a conceptual model to explore how moderated OHCs may influence empowerment of carers. In order to assess the model and support its robustness, this paper uses expert interviews of academics and industry professionals, with the view to focusing the research as well as operationalise the model. Results suggest a favourable acceptance of the model by experts, and thematic analysis of their conversations generated an additional construct.
Shafiei, S, Mihăiţă, AS & Cai, C 1970, 'Demand Estimation and Prediction for Short-term Traffic Forecasting in Existence of Non-recurrent Incidents', ITS World Congress 2019 (ITSWC2019), Singapore, ITS World Congress, Singapore, pp. 1-6.
Shah, S & Goyal, M 1970, 'Anomaly Detection in Social Media Using Recurrent Neural Network', Computational Science – ICCS 2019 (LNCS), International Conference on Computational Science, Springer International Publishing, Faro, Portugal, pp. 74-83.
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In today’s information environment there is an increasing reliance on online and social media in the acquisition, dissemination and consumption of news. Specifically, the utilization of social media platforms such as Facebook and Twitter has increased as a cutting edge medium for breaking news. On the other hand, the low cost, easy access and rapid propagation of news through social media makes the platform more sensitive to fake and anomalous reporting. The propagation of fake and anomalous news is not some benign exercise. The extensive spread of fake news has the potential to do serious and real damage to individuals and society. As a result, the detection of fake news in social media has become a vibrant and important field of research. In this paper, a novel application of machine learning approaches to the detection and classification of fake and anomalous data are considered. An initial clustering step with the K-Nearest Neighbor (KNN) algorithm is proposed before training the result with a Recurrent Neural Network (RNN). The results of a preliminary application of the KNN phase before the RNN phase produces a quantitative and measureable improvement in the detection of outliers, and as such is more effective in detecting anomalies or outliers against the test dataset of 2016 US Presidential Election predictions.
Shaham, S, Ding, M, Liu, B, Lin, Z & Li, J 1970, 'Machine Learning Aided Anonymization of Spatiotemporal Trajectory Datasets', IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, pp. 1-6.
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© 2019 IEEE. The big data era requires a growing number of companies to publish their data publicly. Preserving the privacy of users while publishing these data has become a critical problem. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Such datasets are extremely sensitive as users' personal information such as home address, workplace and shopping habits can be inferred from them. In this paper, we propose an approach for anonymization of spatiotemporal trajectory datasets. The proposed approach is based on generalization entailing alignment and clustering of trajectories. We propose to apply k'-means algorithm for clustering trajectories by developing a technique that makes it possible. We also significantly reduce the information loss during the alignment by incorporating multiple sequence alignment instead of pairwise sequence alignment used in the literature. We analyze the performance of our proposed approach by applying it to Geolife dataset, which includes GPS logs of over 180 users in Beijing, China. Our experiments indicate the robustness of our framework compared to prior works.
Shaham, S, Ding, M, Liu, B, Lin, Z & Li, J 1970, 'Transition-Entropy: A Novel Metric for Privacy Preservation in Location-Based Services', IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, pp. 1-6.
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© 2019 IEEE. The advent of location-based services has created the need for preserving the location privacy of users. An adversary such as an untrusted location-based server can monitor the queried locations by a user to infer sensitive information such as the user's home address, health conditions, shopping habits, etc. To address this issue, dummy-based algorithms have been developed to increase the anonymity of users, and thus, protecting their privacy. Unfortunately, the existing algorithms only consider a limited amount of side information known by the adversary whereas they may face more serious challenges in practice. In this paper, we consider a new type of side information based on consecutive location changes of users, and propose a new metric called transition-entropy to investigate the location privacy preservation. Furthermore, we develop a greedy algorithm to significantly improve the transition-entropy performance for a given dummy generation algorithm. Via experiments conducted on a real-life dataset, we evaluate the performance of the proposed metric and algorithm.
Shakeri Hossein Abad, Z, Gervasi, V, Zowghi, D & H. Far, B 1970, 'Supporting Analysts by Dynamic Extraction and Classification of Requirements-Related Knowledge', 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), IEEE, Montreal, QC, Canada, pp. 442-453.
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© 2019 IEEE. In many software development projects, analysts are required to deal with systems’ requirements from unfamiliar domains. Familiarity with the domain is necessary in order to get full leverage from interaction with stakeholders and for extracting relevant information from the existing project documents. Accurate and timely extraction and classification of requirements knowledge support analysts in this challenging scenario. Our approach is to mine real-time interaction records and project documents for the relevant phrasal units about the requirements related topics being discussed during elicitation. We propose to use both generative and discriminating methods. To extract the relevant terms, we leverage the flexibility and power of Weighted Finite State Transducers (WFSTs) in dynamic modelling of natural language processing tasks. We used an extended version of Support Vector Machines (SVMs) with variable-sized feature vectors to efficiently and dynamically extract and classify requirements-related knowledge from the existing documents. To evaluate the performance of our approach intuitively and quantitatively, we used edit distance and precision/recall metrics. We show in three case studies that the snippets extracted by our method are intuitively relevant and reasonably accurate. Furthermore, we found that statistical and linguistic parameters such as smoothing methods, and words contiguity and order features can impact the performance of both extraction and classification tasks.
Shakor, P, Nejadi, S & Paul, G 1970, 'Effect of Elevated Temperatures as a Means of Curing in Inkjet 3D Printed Mortar Specimens', Biennial National Conference of the Concrete Institute of Australia, Concrete Institute of Australia, Sydney, Australia.
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Inkjet (Powder-based) three-dimensional printing (3DP) shows significant promise in concrete construction applications. The accuracy, speed, and capability to build complicated geometries are the most beneficial features of inkjet 3DP. Therefore, inkjet 3DP needs to be carefully studied and evaluated with construction goals in mind and employed in real-world applications, where it is most appropriate. This paper focuses on the important aspect of curing 3DP specimens. It discusses the enhanced mechanical properties of the mortar that are unlocked through a heat-curing process. Experiments have been conducted on cubic mortar samples that have been printed and cured in an oven at a range of different temperatures (e.g. 40, 60, 80, 90, 100°C). The results of the experimental tests have shown that 80°C is the optimum heat-curing temperature to achieve the highest compressive strength and flexural strength of the printed samples. These tests have been performed on two different dimensions of the cubic specimens 20x20x20mm, 50x50x50mm and on prism specimens with the dimensions of 160x40x40mm. The inkjet 3DP process and the post-processing curing are discussed. Additionally, 3D scanning of the printed specimens is employed and the surface roughness profiles of the 3DP specimens are presented.
Shang, J, Xu, W, Lee, C-H, Yuan, X, Zhang, P & Lin, J 1970, 'Delay Estimation of UAV Communications Based on Fountain Codes', 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), IEEE, pp. 1-6.
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Shaoxiong Ji, ZH 1970, 'Learning Private Neural Language Modeling with Attentive Aggregation', The 2019 International Joint Conference on Neural Networks (IJCNN 2019).
Shdifat, B, Cetindamar, D & Erfani, S 1970, 'A Literature Review on Big Data Analytics Capabilities', 2019 Portland International Conference on Management of Engineering and Technology (PICMET), 2019 Portland International Conference on Management of Engineering and Technology (PICMET), IEEE, Portland, Oregon, pp. 1-6.
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Many researchers and practitioners are interested in big data due to its transformational potential for achieving competitive advantage. Recent studies indicate that business achieves competitive advantage not only by investments on technology infrastructure but also by creating technological and organizational capabilities. In the light of the Resource-based View theory, this paper aims to find out "what capabilities have been required to build big data analytics?" by conducting an in-depth literature review. We adopted a systematic literature review approach and studied academic articles published between 2010 and 2018. We used Scopus and Web of Science (WoS) databases to find published studies related to big data analytics capabilities, twenty-five (25) of which met the selection criteria. Results showed capabilities of big data analytics fall into two major categories: human and infrastructure capability
Shen, T, Geng, X, Qin, T, Guo, D, Tang, D, Duan, N, Long, G & Jiang, D 1970, 'Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base', EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference, The 2019 Conference on Empirical Methods in Natural Language Processing, Hong Kong, China, pp. 2442-2451.
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We consider the problem of conversational question answering over alarge-scale knowledge base. To handle huge entity vocabulary of a large-scaleknowledge base, recent neural semantic parsing based approaches usuallydecompose the task into several subtasks and then solve them sequentially,which leads to following issues: 1) errors in earlier subtasks will bepropagated and negatively affect downstream ones; and 2) each subtask cannotnaturally share supervision signals with others. To tackle these issues, wepropose an innovative multi-task learning framework where a pointer-equippedsemantic parsing model is designed to resolve coreference in conversations, andnaturally empower joint learning with a novel type-aware entity detectionmodel. The proposed framework thus enables shared supervisions and alleviatesthe effect of error propagation. Experiments on a large-scale conversationalquestion answering dataset containing 1.6M question answering pairs over 12.8Mentities show that the proposed framework improves overall F1 score from 67% to79% compared with previous state-of-the-art work.
Shen, T, Geng, X, Qin, T, Guo, D, Tang, D, Duan, N, Long, G & Jiang, D 1970, 'Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base', Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics.
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Shen, T, Geng, X, Qin, T, Long, G, Jiang, J & Jiang, D 1970, 'Effective Search of Logical Forms for Weakly Supervised Knowledge-Based Question Answering', Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}, International Joint Conferences on Artificial Intelligence Organization, Yokohama, Japan, pp. 2227-2233.
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Many algorithms for Knowledge-Based Question Answering (KBQA) depend onsemantic parsing, which translates a question to its logical form. When onlyweak supervision is provided, it is usually necessary to search valid logicalforms for model training. However, a complex question typically involves a hugesearch space, which creates two main problems: 1) the solutions limited bycomputation time and memory usually reduce the success rate of the search, and2) spurious logical forms in the search results degrade the quality of trainingdata. These two problems lead to a poorly-trained semantic parsing model. Inthis work, we propose an effective search method for weakly supervised KBQAbased on operator prediction for questions. With search space constrained bypredicted operators, sufficient search paths can be explored, more validlogical forms can be derived, and operators possibly causing spurious logicalforms can be avoided. As a result, a larger proportion of questions in a weaklysupervised training set are equipped with logical forms, and fewer spuriouslogical forms are generated. Such high-quality training data directlycontributes to a better semantic parsing model. Experimental results on one ofthe largest KBQA datasets (i.e., CSQA) verify the effectiveness of ourapproach: improving the precision from 67% to 72% and the recall from 67% to72% in terms of the overall score.
Shi, L, Li, S, Cao, L, Yang, L & Pan, G 1970, 'TBQ(σ): Improving efficiency of trace utilization for off-policy reinforcement learning', Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, International Joint Conference on Autonomous Agents and Multiagent Systems, IFAAMAS, Montreal, Canada, pp. 1025-1032.
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OfF-policy reinforcement learning with eligibility traces faces is challenging because of the discrepancy between target policy and behavior policy One common approach is to measure the difference between two policies in a probabilistic way, such as importance sampling and tree-backup However, existing off-policy learning methods based on probabilistic policy measurement are inefficient when utilizing traces under a greedy target policy, which is ineffective for control problems The traces are cut immediately when a non-greedy action is taken, which may lose the advantage of eligibility traccs and slow down the learning process Alternatively, some non-probabdistic measurement methods such as General Q(A) and Naive Q(A) never cut traces, but face convergence problems in practice To address the above issues, this paper introduces a new method named TBQ(a) which effectively unifies the tree-backup algorithm and Naive Q(A) By introducing a new parameter a to illustrate the degree of utilizing traces, TBQ(
Shi, X, Liu, D, Chen, Z, Chen, G, Huang, S, Lu, W & Zhang, Y 1970, 'A Low-Power Single-Slope based 14-bit Column-Level ADC for 384×288 Uncooled Infrared Imager', 2019 IEEE 13th International Conference on ASIC (ASICON), 2019 IEEE 13th International Conference on ASIC (ASICON), IEEE, PEOPLES R CHINA, Chongqing, pp. 1-4.
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Shi, Y, Liu, L, Yu, X & Li, H 1970, 'Spatial-aware feature aggregation for cross-view image based geo-localization', Advances in Neural Information Processing Systems.
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Recent works show that it is possible to train a deep network to determine the geographic location of a ground-level image (e.g., a Google street-view panorama) by matching it against a satellite map covering the wide geographic area of interest. Conventional deep networks, which often cast the problem as a metric embedding task, however, suffer from poor performance in terms of low recall rates. One of the key reasons is the vast differences between the two view modalities, i.e., ground view versus aerial/satellite view. They not only exhibit very different visual appearances, but also have distinctive geometric configurations. Existing deep methods overlook those appearance and geometric differences, and instead use a brute force training procedure, leading to inferior performance. In this paper, we develop a new deep network to explicitly address these inherent differences between ground and aerial views. We observe that pixels lying on the same azimuth direction in an aerial image approximately correspond to a vertical image column in the ground view image. Thus, we propose a two-step approach to exploit this prior. The first step is to apply a regular polar transform to warp an aerial image such that its domain is closer to that of a ground-view panorama. Note that polar transform as a pure geometric transformation is agnostic to scene content, hence cannot bring the two domains into full alignment. Then, we add a subsequent spatial-attention mechanism which brings corresponding deep features closer in the embedding space. To improve the robustness of feature representation, we introduce a feature aggregation strategy via learning multiple spatial embeddings. By the above two-step approach, we achieve more discriminative deep representations, facilitating cross-view Geo-localization more accurate. Our experiments on standard benchmark datasets show significant performance boosting, achieving more than doubled recall rate compared with the previous ...
Shi, Y, Tuan, HD & Savkin, AV 1970, 'Mixed Integer Nonlinear Programming for Joint Coordination of Plug-in Electrical Vehicles Charging and Smart Grid Operations', 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, pp. 1-6.
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© 2019 IEEE. The joint coordination of plug-in electric vehicles(PEVs) charging and grid power control is to minimize both PEVs' charging cost and energy generation cost in meeting both PEVs' power demands and power grid operational constraints. A bang-bang PEV charging strategy is adopted to exploit its simple online implementation, which requires computation of a mixed integer nonlinear programming problem (MINP) in binary variables of the PEV charging and continuous variables of the grid voltages. A novel solver for this challenging MINP is proposed. Its efficiency is shown by numerical simulations.
Shi, Y, Xu, D, Pan, Y, Tsang, IW & Pan, S 1970, 'Label Embedding with Partial Heterogeneous Contexts', Proceedings of the AAAI Conference on Artificial Intelligence, 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, HI, pp. 4926-4933.
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Label embedding plays an important role in many real-world applications. To enhance the label relatedness captured by the embeddings, multiple contexts can be adopted. However, these contexts are heterogeneous and often partially observed in practical tasks, imposing significant challenges to capture the overall relatedness among labels. In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges. Categorizing heterogeneous contexts into two groups, relational context and descriptive context, we design tailor-made matrix factorization formula to effectively exploit the label relatedness in each context. With a shared embedding principle across heterogeneous contexts, the label relatedness is selectively aligned in a shared space. Due to our elegant formulation, PHCLE overcomes the partial context problem and can nicely incorporate more contexts, which both cannot be tackled with existing multi-context label embedding methods. An effective alternative optimization algorithm is further derived to solve the sparse matrix factorization problem. Experimental results demonstrate that the label embeddings obtained with PHCLE achieve superb performance in image classification task and exhibit good interpretability in the downstream label similarity analysis and image understanding task.
Shi, Z, Zhang, JA, Xu, R & Cheng, Q 1970, 'Deep Learning Networks for Human Activity Recognition with CSI Correlation Feature Extraction', ICC 2019 - 2019 IEEE International Conference on Communications (ICC), ICC 2019 - 2019 IEEE International Conference on Communications (ICC), IEEE, Shanghai, China, pp. 1-6.
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© 2019 IEEE. Device free WiFi Sensing using channel state information (CSI) has been shown great potentials for human activity recognition (HAR). However, extracting reliable and concise feature signals remains as a challenging problem, especially in a dynamic and complex environment. In this paper, we propose a novel scheme for CSI-based HAR using deep learning network (CH-DLN), with an innovative CSI correlation feature extraction (CCFE) method. The CCFE method pre-processes the signals input to the DLN in two steps. Firstly, it uses a recursive algorithm to reduce non-activity-related information from the signal and hence enhance the activity-dependent signals. Secondly, it computes the correlation over both the time and frequency domain to disclose better signal structure and compress the signal. From such enhanced and compressed signals, we utilize the recurrent neural networking (RNN) to automatically extract deeper features, and then apply the softmax regression algorithm for classifying activities. Through extensive experimental results, our proposed scheme is shown to outperform state-of-the-art methods in recognition accuracy, with much less training time.
Shiri, F, Yu, X, Porikli, F, Hartley, R & Koniusz, P 1970, 'Recovering Faces From Portraits with Auxiliary Facial Attributes', 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, pp. 406-415.
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Shirvani, F, Perez, P, Beydoun, G, Campbell, P & Scott, W 1970, 'Application of Design Science Research to Design a Modelling Approach for Procurement of Infrastructure Systems.', SysCon, IEEE International Systems Conference, IEEE, Orlando, FL, USA, pp. 1-7.
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© 2019 IEEE. Model-driven approaches are widely used in managing the complex domains such as infrastructure systems or disaster management. The foundation of conducting a systematic research is designing a methodology that pertinently covers the steps of research from problem definition to solution proposal and then identifying or tailoring a method for developing and validating the solution. This paper explains the application of Design Science for conducting a research which aims at providing a model-driven approach for addressing the complexities of infrastructure procurement projects. So firstly the design science artefacts are adopted for designing the method for this research. Then the steps of this method are explained briefly along with description of how each step is applied in this research. The core of this method is proposing a process for developing and validating the metamodels which is designed based on combination of other metamodeling processes.
Shu, Y & Xu, G 1970, 'Emotion Recognition from Music Enhanced by Domain Knowledge', PRICAI 2019: Trends in Artificial Intelligence, PACIFIC RIM INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, Springer International Publishing, Yanuca Island, Cuvu, Fiji, pp. 121-134.
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Music elements have been widely used to influence the audiences’ emotional experience by its music grammar. However, these domain knowledge, has not been thoroughly explored as music grammar for music emotion analyses in previous work. In this paper, we propose a novel method to analyze music emotion via utilizing the domain knowledge of music elements. Specifically, we first summarize the domain knowledge of music elements and infer probabilistic dependencies
between different main musical elements and emotions from the summarized
music theory. Then, we transfer the domain knowledge to constraints,
and formulate affective music analysis as a constrained optimization problem.
Experimental results on the Music in 2015 database and the AMG1608 database
demonstrate that the proposed music content analyses method outperforms the
state-of-the-art performance prediction methods.
Siami, M, Naderpour, M & Lu, J 1970, 'A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis', 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, New Orleans, LA, USA, pp. 1-6.
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© 2019 IEEE. Mobile app development in recent years has resulted in new products and features to improve human life. Mobile telematics is one such development that encompasses multidisciplinary fields for transportation safety. The application of mobile telematics has been explored in many areas, such as insurance and road safety. However, to the best of our knowledge, its application in gender detection has not been explored. This paper proposes a Choquet fuzzy integral vertical bagging classifier that detects gender through mobile telematics. In this model, different random forest classifiers are trained by randomly generated features with rough set theory, and the top three classifiers are fused using the Choquet fuzzy integral. The model is implemented and evaluated on a real dataset. The empirical results indicate that the Choquet fuzzy integral vertical bagging classifier outperforms other classifiers.
Singh, K, Afzal, MU, Bulger, D, Kovaleva, M & Esselle, KP 1970, 'Optimization of Beam-Steering Metasurfaces Using Modified Cross-Entropy Algorithm', 2019 URSI International Symposium on Electromagnetic Theory (EMTS), 2019 URSI International Symposium on Electromagnetic Theory (EMTS), IEEE, San Diego, CA, pp. 1-4.
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Singh, K, Afzal, MU, Bulger, D, Kovaleva, M & Esselle, KP 1970, 'Optimizing Amplitude Distribution in a Feed Array to Control Side-Lobe Levels of a Beam-Steering Metasurface', 2019 URSI International Symposium on Electromagnetic Theory (EMTS), 2019 URSI International Symposium on Electromagnetic Theory (EMTS), IEEE, San Diego, CA, pp. 1-4.
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Singh, K, Afzal, MU, Esselle, KP & Kovaleva, M 1970, 'Towards Decreasing Side Lobes Produced by Near-Field Phase Gradient Metasurfaces', 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, IEEE, pp. 1207-1208.
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© 2019 IEEE. When steering or tilting the beam of a planar high-gain antenna using a near-field phase gradient metasurface, undesirable side lobes are often noted. These side lobes can be predicted reasonably well by exciting the metasurface with a normally incident uniform plane wave but direct optimisation of such an electrically large metasurface to reduce strongest undesirable side lobes is computationally very expensive. Here we have applied a more efficient approach to reduce the levels of all side lobes below-20 dB.
Singh, M, Indraratna, B & Rujikiatkamjorn, C 1970, 'Use of Geosynthetics in Mitigating the Effects of Mud Pumping: A Railway Perspective', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 609-618.
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© Springer Nature Singapore Pte Ltd 2019. In Australia, where the major network of railways traverses along the coastal regions, millions of dollars are spent on track maintenance annually to mitigate track differential settlement. One of the recurring problems faced with ballasted tracks on estuarine soils is mud pumping. Mud pumping is a complex phenomenon involving the migration of fine soft subgrade particles into the coarser ballast/sub-ballast layer. The problem has been widely reported and is of interest among the railway engineers over the last couple of decades. The migration of fines causes excessive settlements and track degradation leading to track instability, thereby incurring excessive maintenance costs. The primary objective of this paper was to assess the existing remediation measures for mud pumping reported. The current mitigation techniques range from the in situ mixing of additives to the use of geosynthetics to separate the layers in a track structure. On the other hand, the use of geosynthetics has proven to act as a separator between the track layers; their effectiveness is highly dependent on the type of subgrade soil. The comprehensive study reveals the probable causes of mud pumping and a better understanding of the phenomenon.
Sirivivatnanon, V, Hocking, D, Cheney, K & Rocker, P 1970, 'Reliability of extending AS1141.60.1 and 60.2 test methods to determine ASR mitigation', Concrete 2019 app, 29th Biennial National Conference of the Concrete Institute of Australia, Concrete Institute of Australia, Sydney, Australia, pp. 1-8.
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The Australian Standards AS 1141.60.1 and AS 1141.60.2 were published in 2014 as Accelerated Mortar Bar Test (AMBT) and Concrete Prism Test (CPT) to determine the potential alkali-silica reactivity (ASR) of aggregates. Both methods were extended to evaluate the effectiveness of supplementary cementitious material (SCM) in mitigating ASR, similar to ASTM C1567 and CSA A23.2-14A, in a research program undertaken by the Cement, Concrete and Aggregates Australia (CCAA). Eight aggregates were tested with various dosages of either fly ash or slag and expansions measured up to 35 days and 2 years for AMBT and CPT respectively. In addition, the efficacy of SCMs to mitigate ASR was determined for four additional reactive aggregates based on the AMBT. The results were evaluated based on the corresponding reactivity criteria in the two Australian Standards. They showed that fly ash or slag can effectively be used to mitigate ASR and that the AMBT provided a more conservative dosage of SCM in mitigation ASR than the CPT. The required fly ash or slag dosages are also found to be consistent with recommendations given in HB79. Most importantly, there are findings from many exposure sites around the world that showed the reliability of AMBT and CPT in predicting the effectiveness of SCM-mitigated solution in long-term field-exposed large concrete blocks.
Sirivivatnanon, V, Moghaddam, F & Vessalas, K 1970, 'Effect of fineness and dosage of fly ash on selected properties of mortars', 29th Biennial National Conference of the Concrete Institute of Australia,, 29th Biennial National Conference of the Concrete Institute of Australia,, Concrete Institute of Australia, Sydney, Australia.
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In this paper, a laboratory investigation was carried out to evaluate the effect of fineness and levels of fly ash on the selected fresh, hardened and durability properties of mortars such as flow, compressive strength, drying shrinkage, strength activity index and alkali-silica reactivity. Portland cement was partially replaced by 20%, 30% and 40% of three kinds of fly ashes with different fineness (classified, run-of-station and ground run-of-station fly ashes). Fixed water to binder ratio of 0.40 and sand to binder ratio of 2.5 with a fixed dosage of water reducer were maintained for these mixes. In addition, some mixes containing classified and run-of-station fly ash with 50%, 60% and 70% cement replacement with fixed water to binder ratio of 0.55 and sand to binder ratio of 5 with a fixed dosage of water reducer were cast to evaluate the effect of fineness of fly ash in low strength mortar. Moreover, the effectiveness and required level of classified and run-of-station fly ash on mitigating alkali-silica reactivity are evaluated using accelerated mortar bar test method, and the results are reported in this paper. The results showed that all kinds of fly ashes improved the flowability of the mix with superior performance for the finer fly ash. X-ray diffraction and compressive strength test results demonstrated the effect of fineness of fly ash in decreasing the crystalline phase, increasing reactivity and improving the strength development. Drying shrinkage was decreased considerably with the inclusion of all kinds of fly ashes at all replacement levels. Incorporation of 25% classified and run-of-station fly ash is needed to control the expansion of mortar bars due to alkali-silica reactivity by the reducing the alkalinity of the mix.
Sirivivatnanon, V, Moghaddam, F & Vessalas, K 1970, 'Investigation on the influence of run of station fly ash in concrete pavement construction', 29th Biennial National Conference of the Concrete Institute of Australia, 29th Biennial National Conference of the Concrete Institute of Australia, Sydney, Australia.
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The achievement of sustainable development has been a major challenge facing the concrete industry for years. In recent years there have been changes, both technical and policy driven, that have the potential to affect the availability of classified fly ash. The possible shortage of classified fly ash (CFA) supply has prompted researchers at UTS to examine the possible use of run-of-station fly ash (RFA) for use in concrete applications. In this paper, an experimental study was carried out to evaluate the influence of partially replacing cement with 20% RFA on the heat of hydration, and microstructure of blended cement pastes compared to the paste containing 20% CFA. In addition, the effects of RFA on fresh and hardened concrete properties of pavement mixes were examined and compared to CFA concrete mix. Only two lots of RFA from one single source were examined, and hence the variability and effectiveness of RFA from other sources cannot be generalised. Properties critical to the use of fly ash in pavement concrete are examined according to the R83 specification.
Siwakoti, YP, Long, T, Barzegarkhoo, R & Blaabjerg, F 1970, 'A Dual Mode 5-Level Inverter with Wide Input Voltage Range', 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 2019 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, Baltimore, MD, USA, pp. 3609-3615.
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© 2019 IEEE. This paper presents a novel dual mode six- switch five-level boost-ANPC inverter (5L-DM-ABNPC) topology with wide input voltage range (400 V - 800 V). It consists of one flying-capacitor and six semiconductor switches forming a similar structure to that of conventional 5L-ANPC or 5L-ABNPC inverter. Depending on the magnitude of the input voltage, the converter can operate in buck or boost mode to produce the same ac voltage out. Further, the number and the size of the active and passive components are also reduced with simple PWM control. Consequently, this make the overall system appealing for various industrial applications. The analysis shows that the proposed topology is suitable for wide range of power conversion applications (for example, rolling mills, fans, pumps, marine appliances, mining, tractions, and most prominently grid-connected renewable energy systems). Simulation and experimental prove the concept of the proposed inverter. The principle of operation and theoretical analysis supported by key simulation and preliminary experimental waveforms are presented.
Smithers, J, Burdon, S & Clay, J 1970, 'Setting Up Project's For Success-Research Insights WSP/UTS 2019', World Engineers Convention 2019, World Engineers Convention 2019, Melbourne.
Son, HH, Pham, CP, Franklin, DR, Walsum, TV & Luu, HM 1970, 'An Evaluation of CNN-based Liver Segmentation Methods using Multi-types of CT Abdominal Images from Multiple Medical Centers.', ISCIT, 2019 19th International Symposium on Communications and Information Technologies (ISCIT), IEEE, Ho Chi Minh City, Vietnam, pp. 20-25.
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Automatic segmentation of CT images has recently been applied in several clinical liver applications. Convolutional Neural Networks (CNNs) have shown their effectiveness in medical image segmentation in general and also in liver segmentation. However, liver image quality may vary between medical centers due to differences in the use of CT scanners, protocols, radiation dose, and contrast enhancement. In this paper, we investigate three wells known CNNs, FCN-CRF, DRIU, and V-net, for liver segmentation using data from several medical centers. We perform qualitative evaluation of the CNNs based on Dice score, Hausdorff distance, mean surface distance and false positive rate. The results show that all three CNNs achieved a mean Dice score of over 90% in liver segmentation with typical contrast enhanced CT images of the liver. p-values from paired T-test on Dice score of the three networks using Mayo dataset are larger than 0.05 suggesting that no statistical significant difference in their performance. DRIU performs the best in term of processing time. The results also demonstrate that those CNNs have reduced performance in liver segmentation in the case of low-dose and non-contrast enhanced CT images. In conclusion, these promising results enable further investigation of alternative deep learning based approaches to liver segmentation using CT images.
Song, LZ, Qin, PY & Guo, YJ 1970, 'Conformal Transmitarray and Its Beam Scanning', 2019 International Symposium on Antennas and Propagation, ISAP 2019 - Proceedings, International Symposium on Antennas and Propagation, IEEE, Xi'an, China, pp. 1-3.
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A mechanically beam-scanning conformal transmitarray is developed in this paper. Firstly, a transmitarray element with three layers of identical square ring slots is proposed and its performance for different element thickness is studied. A transmission phase range of 330° with a maximum 3.6 dB loss can be achieved when the thickness is 0.508mm (only 0.04 wavelength at 25GHz). Secondly, a cylindrically conformal transmitarray is designed using the above antenna elements, realizing a 45.3% simulated antenna efficiency. Finally, the above fixed-beam conformal transmitarray is expanded to a beam scanning one. By rotating the feed horn to different positions, the main beam of the array can be switched to ±15°, ±10°, ±5° and 0°, while the whole size of this array is only 2.5 times larger than the fixed beam one. A prototype is fabricated and measured with a stable gain of about 18.7 dBi at all beam angles.
Song, Y, Zhang, G, Lu, H & Lu, J 1970, 'A Noise-tolerant Fuzzy c-Means based Drift Adaptation Method for Data Stream Regression', 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, New Orleans, LA, USA, pp. 1-6.
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© 2019 IEEE. Concept drift referring to the changes of data distributions has been one critical challenge typically associated with mining data streams. Current drift detection and adaptation methods focus on how to immediately detect the distribution changes once the concept drift occurs and swiftly update the model to be applicable to the newly arrived data instances. Most of those methods assume the data does not have noise or the noise is too weak to affect the modeling procedure. However, realworld data are normally contaminated, and denoise techniques are highly preferred as a necessary preprocess. This issue is more complex for a data stream with concept drift because the noise is very likely to be confused with drift. Motivated by that, this paper proposes a Noise-tolerant Fuzzy c-means based drift Adaptation method (NFA) which can adapt to the changing distributions and is suitable for noisy data streams. The concept drift problem is solved by using a fuzzy c-means based regression model to continuously include the most relevant data instances to the latest pattern in the training set. In addition, a denoise technique is designed in NFA to remove noise, and the ability of incremental updating enables it to be embedded in the incremental drift adaptation process, and therefore NFA can solve concept drift and noise problems at the same time. Experimental evaluation results also show good performance of our method on handling data streams with concept drift and noise.
Sood, K, Pokhrel, SR, Karmakar, K, Vardharajan, V & Yu, S 1970, 'SDN-Capable IoT Last-Miles: Design Challenges', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA, pp. 1-6.
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We propose to redesign SDN control in IoT last-miles so as to extend the capability from edge routers to devices (\emph{end-node things enabled with SDN capabilities}). Our approach put forward existing and new challenges that are impossible to be resolved using the seminal approaches directly. The main challenges we identify are: scalability of sensor nodes/things, maintaining the security of the system, and fulfilling the Quality of Service (QoS) requirement of all IoT applications. Firstly, we elaborate and discuss the aforementioned critical and fundamental challenges that require immediate investigations. Secondly, we propose a policy-driven framework for secure routing and conduct performance modeling and analysis. Further, in the QoS context, we have proposed an intent-based flow offloading scheme to meet the flow-specific QoS requirements. More importantly, we have developed an analysis by modeling TCP-based flows over WiFi, thus forming the required SDN-IoT network, by using mathematics as a tool for reasoning our challenges. With new insights from our analysis, the feasibility of the proposed approach is validated using factors such as path set-up time in SDN-IoT networks, SDN controller/devices throughputs, packets losses and response time of the controller.
Soomro, AM, Paryani, S, Rehman, J, Echeverria, RA, Biloria, N & Prasad, M 1970, 'Influencing Human Behaviour to Optimise Energy in Commercial Buildings', ACIS 2019 Proceedings - 30th Australasian Conference on Information Systems, Australian Conference on Information Systems, ACIS 2019, Perth, Australia, pp. 901-907.
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This paper discusses the impact of user energy choices on building energy demand, and how energy choices could be influenced to minimise building energy consumption using information systems. Accordingly, a socio-technical framework is designed and presented, which draws upon the use of energy interventions. A novel Social-Economic-Environmental (SEE) model is presented within the socio-technical framework which is aimed at nudging inhabitants enabling them to conserve energy in the university buildings, thereby making the world a sustainable place to live. The framework takes into account the Agent-based Modelling (ABM) approach to model user energy choices and their willingness to conserve energy in buildings. This research intends to test the socio-technical framework in the next stage of this study. Finally, this paper highlights gaps and the significance of understanding how user behaviour and their energy consumption can be influenced to optimise energy in university buildings, thereby reducing global greenhouse emissions.
Soudagar, MEM, Nik-Ghazali, N-N, Badruddin, IA, Kalam, MA, Kittur, MDI, Akram, N, Ullah, MA, Khan, TMY & Mokashi, I 1970, 'Production of honge oil methyl ester (HOME) and its performance test on four stroke single cylinder VCR engine', AIP Conference Proceedings, ADVANCES IN BASIC SCIENCE (ICABS 2019), AIP Publishing, pp. 200006-200006.
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Sreevallabh Chivukula, A, Yang, X & Liu, W 1970, 'Adversarial Deep Learning with Stackelberg Games', Communications in Computer and Information Science, Springer International Publishing, pp. 3-12.
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© Springer Nature Switzerland AG 2019. Deep networks are vulnerable to adversarial attacks from malicious adversaries. Currently, many adversarial learning algorithms are designed to exploit such vulnerabilities in deep networks. These methods focus on attacking and retraining deep networks with adversarial examples to do either feature manipulation or label manipulation or both. In this paper, we propose a new adversarial learning algorithm for finding adversarial manipulations to deep networks. We formulate adversaries who optimize game-theoretic payoff functions on deep networks doing multi-label classifications. We model the interactions between a classifier and an adversary from a game-theoretic perspective and formulate their strategies into a Stackelberg game associated with a two-player problem. Then we design algorithms to solve for the Nash equilibrium, which is a pair of strategies from which there is no incentive for either the classifier or the adversary to deviate. In designing attack scenarios, the adversary’s objective is to deliberately make small changes to test data such that attacked samples are undetected. Our results illustrate that game-theoretic modelling is significantly effective in securing deep learning models against performance vulnerabilities attached by intelligent adversaries.
Stewart, MG 1970, 'Airblast variability and reliability-based design for protective structures', 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019.
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An understanding of airblast uncertainty allows reliability-based load factors to be calculated. Reliability-based load factors are influenced by the variability of model error, explosive mass and range distance, and are estimated for reliability levels of 0.05 to 0.99 for military, civilian and terrorist munitions. Structural reliabilities are then calculated for reinforced concrete columns, and compared to target values. It was found that RC columns designed to existing standards have a significant margin of safety conditional on successful detonation of the explosive and assuming a relatively low variability of range or explosive mass.
Suarez-Rodriguez, C, He, Y, Jayawickrama, BA & Dutkiewicz, E 1970, 'Low-Overhead Handover-Skipping Technique for 5G Networks.', WCNC, IEEE, pp. 1-6.
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Network densification has been one of the principal causes of performance gain in cellular networks, and 5G networks will not be any different. As cell sizes shrink, handovers become more frequent incurring extra delays that bury all the prospective gains. Mobility in multi-tier dense cellular networks calls for a change in the way it has been traditionally handled in an always-on world, where users take universal data access for granted. Invisible to them, mobile network operators need to provision backhauling to include advanced interference mitigation techniques. In this paper, we propose a spectrum database-aided handover management technique that aims to mitigate the number of disconnections without overloading the backhaul unnecessarily. The proposed technique exploits a spectrum database that stores reception information along with geolocation data, commercially available on any handheld device. Moreover, we have benchmarked several state-of-the-art handover schemes for 5G networks against ours in a realistic urban environment with user mobility trace data. The results highlight that our method can deliver the same downstream traffic with 33% decrease in disconnections when compared to the conventional approach. At the same time, backhaul traffic is reduced up to 68% against our counterparts.
Suchi, M, Patten, T, Fischinger, D & Vincze, M 1970, 'EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasets', 2019 International Conference on Robotics and Automation (ICRA), 2019 International Conference on Robotics and Automation (ICRA), IEEE.
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Developing robot perception systems for recognizing objects in the real-worldrequires computer vision algorithms to be carefully scrutinized with respect tothe expected operating domain. This demands large quantities of ground truthdata to rigorously evaluate the performance of algorithms. This paper presentsthe EasyLabel tool for easily acquiring high quality ground truth annotation ofobjects at the pixel-level in densely cluttered scenes. In a semi-automaticprocess, complex scenes are incrementally built and EasyLabel exploits depthchange to extract precise object masks at each step. We use this tool togenerate the Object Cluttered Indoor Dataset (OCID) that captures diversesettings of objects, background, context, sensor to scene distance, viewpointangle and lighting conditions. OCID is used to perform a systematic comparisonof existing object segmentation methods. The baseline comparison supports theneed for pixel- and object-wise annotation to progress robot vision towardsrealistic applications. This insight reveals the usefulness of EasyLabel andOCID to better understand the challenges that robots face in the real-world. Copyright 20XX IEEE. Personal use of this material is permitted. Permissionfrom IEEE must be obtained for all other uses, in any current or future media,including reprinting/republishing this material for advertising or promotionalpurposes, creating new collective works, for resale or redistribution toservers or lists, or reuse of any copyrighted component of this work in otherworks.
Sui, Y, Zhang, Y, Zheng, W, Zhang, M & Xue, J 1970, 'Event trace reduction for effective bug replay of Android apps via differential GUI state analysis', Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE '19: 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ACM, Tallinn, Estonia, pp. 1095-1099.
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© 2019 ACM. Existing Android testing tools, such as Monkey, generate a large quantity and a wide variety of user events to expose latent GUI bugs in Android apps. However, even if a bug is found, a majority of the events thus generated are often redundant and bug-irrelevant. In addition, it is also time-consuming for developers to localize and replay the bug given a long and tedious event sequence (trace). This paper presents ECHO, an event trace reduction tool for effective bug replay by using a new differential GUI state analysis. Given a sequence of events (trace), ECHO aims at removing bug-irrelevant events by exploiting the differential behavior between the GUI states collected when their corresponding events are triggered. During dynamic testing, ECHO injects at most one lightweight inspection event after every event to collect its corresponding GUI state. A new adaptive model is proposed to selectively inject inspection events based on sliding windows to differentiate the GUI states on-the-fly in a single testing process. The experimental results show that ECHO improves the effectiveness of bug replay by removing 85.11% redundant events on average while also revealing the same bugs as those detected when full event sequences are used.
Sukkar, F, Best, G, Yoo, C & Fitch, R 1970, 'Multi-Robot Region-of-Interest Reconstruction with Dec-MCTS', 2019 International Conference on Robotics and Automation (ICRA), 2019 International Conference on Robotics and Automation (ICRA), IEEE, Montreal, QC, Canada, Canada, pp. 9101-9107.
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© 2019 IEEE. We consider the problem of reconstructing regions of interest of a scene using multiple robot arms and RGB-D sensors. This problem is motivated by a variety of applications, such as precision agriculture and infrastructure inspection. A viewpoint evaluation function is presented that exploits predicted observations and the geometry of the scene. A recently proposed non-myopic planning algorithm, Decentralised Monte Carlo tree search, is used to coordinate the actions of the robot arms. Motion planning is performed over a navigation graph that considers the high-dimensional configuration space of the robot arms. Extensive simulated experiments are carried out using real sensor data and then validated on hardware with two robot arms. Our proposed targeted information gain planner is compared to state-of-the-art baselines and outperforms them in every measured metric. The robots quickly observe and accurately detect fruit in a trellis structure, demonstrating the viability of the approach for real-world applications.
Sun, F, Zhu, H, Zhu, X, Yang, Y, Sun, Y & Xue, Q 1970, 'Design of Ultra-Wideband On-Chip Millimter-Wave Bandpass Filter in 0.13-μm (Bi)-CMOS Technology', 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, Sapporo, Japan, pp. 1-4.
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© 2019 IEEE In this work, an on-chip bandpass filter (BPF) with ultra-wideband, low insertion loss, sharp selectivity and excellent in-band flatness is achieved using a novel design approach based on a quasi-lumped-element method. This approach simply utilizes folded metal strip lines with metal-insulator-metal (MIM) capacitors. To understand the principle of the presented design approach, theoretical analysis is given by means of a simplified equivalent LC-circuit model. Using the analyzed results with a full-wave electromagnetic (EM) simulator to guide the design, a BPF is implemented and fabricated in a standard 0.13-µm (Bi)-CMOS technology. The measurements show that a return loss of better than 10 dB is obtained from 13.5 to 32 GHz. Furthermore, the insertion loss of less than 2.3 dB is achieved with less than 0.1 dB in-band magnitude ripple. The BPF size without measurement pads is only 0.148 mm2 (0.37 × 0.4 mm2).
Sun, F, Zhu, X, Zhu, H, Yang, Y & Gomez-Garcia, R 1970, 'On-Chip Millimeter-Wave Bandpass Filter Design Using Multi-Layer Modified-Ground-Ring Structure', 2019 IEEE MTT-S International Microwave Symposium (IMS), 2019 IEEE/MTT-S International Microwave Symposium - IMS 2019, IEEE, Boston, MA, USA, pp. 853-856.
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© 2019 IEEE. A design approach for compact on-chip bandpass filters (BPFs) is presented in this work. It exploits a novel multi-layer modified-ground-ring structure (ML-MGRS) with additional ground plates that allows to generate a transmission zero for selectivity increase without extra occupied area. A simplified LC-equivalent circuit model is provided to understand the operational principles of this ML-MGRS concept. Moreover, to validate the experimental feasibility of this transmission-zero-creation technique for on-chip BPFs, a millimeter-wave compact BPF is designed and fabricated in a standard 0.13- CMOS technology. The measured results show that the filter exhibits out-of-band power-suppression levels above 40 dB beyond 40 GHz. The center frequency of this filter is 23.5 GHz with a power-insertion-loss level of 3.8 dB, while the input power-matching levels are higher than 10 dB from 19 GHz to 28 GHz. The size of the BPF, excluding the pads, is only 0.06 × 0.284 mm.
Sun, H-H, Ding, C, Zhu, H, Jones, B & Guo, YJ 1970, 'Cross-Band Scattering Suppression for MultiBand Base Station Antenna Arrays', 2019 8th Asia-Pacific Conference on Antennas and Propagation (APCAP), 2019 8th Asia-Pacific Conference on Antennas and Propagation (APCAP), IEEE, pp. 579-580.
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This paper presents a dual-band dual-polarized interleaved base station antenna array unit based on a filtering antenna for cross-band de-scattering. The array is configured as two columns of antenna arrays operating at a higher band from 1.71 GHz to 2.30 GHz interleaved with one column of antenna array operating at a lower band from 0.80 GHz to 0.96 GHz. By inserting low-pass high-stop filters into the low-band dipole arms, a filtering antenna that can efficiently suppress the radiation at the higher band is achieved. On one hand, the obtained filtering antenna has a slightly reduced gain and narrower bandwidth, which is attributed to the filters. On the other hand, the obtained filtering antenna working at the low band has minimum negative effect on the high band antenna performance.
Sun, K, Qian, T, Yin, H, Chen, T, Chen, Y & Chen, L 1970, 'What Can History Tell Us?', Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM '19: The 28th ACM International Conference on Information and Knowledge Management, ACM, pp. 1593-1602.
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© 2019 Association for Computing Machinery. Recommendation systems have been widely applied to many E-commerce and online social media platforms. Recently, sequential item recommendation, especially session-based recommendation, has aroused wide research interests. However, existing sequential recommendation approaches either ignore the historical sessions or consider all historical sessions without any distinction that whether the historical sessions are relevant or not to the current session, which motivates us to distinguish the effect of each historical session and identify relevant historical sessions for recommendation. In light of this, we propose a novel deep learning based sequential recommender framework for session-based recommendation, which takes Nonlocal Neural Network and Recurrent Neural Network as the main building blocks. Specifically, we design a two-layer nonlocal architecture to identify historical sessions that are relevant to the current session and learn the long-term user preferences mostly from these relevant sessions. Besides, we also design a gated recurrent unit (GRU) enhanced by the nonlocal structure to learn the short-term user preferences from the current session. Finally, we propose a novel approach to integrate both long-term and short-term user preferences in a unified way to facilitate training the whole recommender model in an end-to-end manner. We conduct extensive experiments on two widely used real-world datasets, and the experimental results show that our model achieves significant improvements over the state-of-the-art methods.
Suraweera, N, Li, S, Johnson, M, Collings, IB, Hanly, SV, Ni, W & Hedley, M 1970, 'Passive Target Localization by Asynchronous Self-Locating Receivers in Multipath Environments', 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE.
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Suraweera, N, Winter, A, Sorensen, J, Johnson, M, Li, S, Collings, IB, Hanly, SV, Ni, W & Hedley, M 1970, 'Stand-off Detection of Human Presence and Movement Using IEEE 802.11ac Beamforming Reports', 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, pp. 1-7.
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Suresh, A, Mak, KL, Benserhir, J, Lee, JE-Y & Rufer, L 1970, 'Air-coupled Ultrasonic Rangefinder with Meter-long Detection Range Based on a Dual-electrode PMUT Fabricated Using a Multi-user MEMS Process', 2019 IEEE SENSORS, 2019 IEEE SENSORS, IEEE, pp. 1-4.
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Suryanto, H, Guan, C, Voumard, A & Beydoun, G 1970, 'Transfer Learning in Credit Risk.', ECML/PKDD (3), Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, Germany, pp. 483-498.
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In the credit risk domain, lenders frequently face situations where there is no, or limited historical lending outcome data. This generally results in limited or unaffordable credit for some individuals and small businesses. Transfer learning can potentially reduce this limitation, by leveraging knowledge from related domains, with sufficient outcome data. We investigated the potential for applying transfer learning across various credit domains, for example, from the credit card lending and debt consolidation domain into the small business lending domain.
Sutjipto, S, Tish, D, Paul, G, Vidal-Calleja, T & Schork, T 1970, 'Towards Visual Feedback Loops for Robot-Controlled Additive Manufacturing', Robotic Fabrication in Architecture, Art and Design 2018, Robotic Fabrication in Architecture, Art and Design, Springer International Publishing, Zurich, pp. 85-97.
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Robotic additive manufacturing methods have enabled the design and fabrication of novel forms and material systems that represent an important step forward for architectural fabrication. However, a common problem in additive manufacturing is to predict and incorporate the dynamic behavior of the material that is the result of the complex confluence of forces and material properties that occur during fabrication. While there have been some approaches towards verification systems, to date most robotic additive manufacturing processes lack verification to ensure deposition accuracy. Inaccuracies, or in some instances critical errors, can occur due to robot dynamics, material self-deflection, material coiling, or timing shifts in the case of multi-material prints. This paper addresses that gap by presenting an approach that uses vision-based sensing systems to assist robotic additive manufacturing processes. Using online image analysis techniques, occupancy maps can be created and updated during the fabrication process to document the actual position of the previously deposited material. This development is an intermediary step towards closed-loop robotic control systems that combine workspace sensing capabilities with decision-making algorithms to adjust toolpaths to correct for errors or inaccuracies if necessary. The occupancy grid map provides a complete representation of the print that can be analyzed to determine various key aspects, such as, print quality, extrusion diameter, adhesion between printed parts, and intersections within the meshes. This valuable quantitative information regarding system robustness can be used to influence the system’s future actions. This approach will help ensure consistent print quality and sound tectonics in robotic additive manufacturing processes, improving on current techniques and extending the possibilities of robotic fabrication in architecture.
Taghikhah, F, Raffe, WL, Mitri, G, Du Toit, S, Voinov, A & Garcia, JA 1970, 'Last Island', Proceedings of the Australasian Computer Science Week Multiconference, ACSW 2019: Australasian Computer Science Week 2019, ACM, pp. 1-7.
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© 2019 Association for Computing Machinery. A serious game was designed and developed with the goal of exploring potential sustainable futures and the transitions towards them. This computer-assisted board game, Last Island, which incorporates a system dynamics model into a board game's core mechanics, attempts to impart knowledge and understanding on sustainability and how an isolated society may transition to various futures to a non-expert community of players. To this end, this collaborativecompetitive game utilizes the Miniworld model which simulates three variables important for the sustainability of a society: Human population, economic production and the state of the environment. The resulting player interaction offers possibilities to collectively discover and validate potential scenarios for transitioning to a sustainable future, encouraging players to work together to balance the model output while also competing on individual objectives to be the individual winner of the game.
Tahmassebi, A, Ehtemami, A, Mohebali, B, Gandomi, AH, Pinker, K & Meyer-Baese, A 1970, 'Big data analytics in medical imaging using deep learning', Big Data: Learning, Analytics, and Applications, Big Data: Learning, Analytics, and Applications, SPIE, Baltimore, MD, pp. 13-13.
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Taib, R, Yu, K, Berkovsky, S, Wiggins, M & Bayl-Smith, P 1970, 'Social Engineering and Organisational Dependencies in Phishing Attacks', Human-Computer Interaction – INTERACT 2019, IFIP Conference on Human-Computer Interaction, Springer International Publishing, Cyprus, pp. 564-584.
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Phishing emails are a widespread cybersecurity attack method. Their breadth and depth have been on the rise as they target individuals and organisations with increased sophistication. In particular, social engineering in phishing focuses on human vulnerabilities by exploiting established psychological and behavioural cues to increase the credibility of phishing emails. This work presents the results of a 56,000-participant phishing attack simulation carried out within a multi-national financial organisation. The overarching hypothesis was that strong cultural and contextual factors impact employee vulnerability. Thus, five phishing emails were crafted, based on three of Cialdini’s persuasion principles used in isolation and in combination. Our results showed that Social proof was the most effective attack vector, followed by Authority and Scarcity. Furthermore, we examined these results in the light of a set of demographic and organisational features. Finally, both click-through rates and reporting rates were examined, to provide rich insights to developers of cybersecurity educational solutions.
Takalkar, MA, Zhang, H & Xu, M 1970, 'Improving Micro-expression Recognition Accuracy Using Twofold Feature Extraction', MultiMedia Modeling (LNCS), International Conference on Multimedia Modeling, Springer International Publishing, Thessaloniki, Greece, pp. 652-664.
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© 2019, Springer Nature Switzerland AG. Micro-expressions are generated involuntarily on a person’s face and are usually a manifestation of repressed feelings of the person. Micro-expressions are characterised by short duration, involuntariness and low intensity. Because of these characteristics, micro-expressions are difficult to perceive and interpret correctly, and they are profoundly challenging to identify and categorise automatically. Previous work for micro-expression recognition has used hand-crafted features like LBP-TOP, Gabor filter, HOG and optical flow. Recent work also has demonstrated the possible use of deep learning for micro-expression recognition. This paper is the first work to explore the use of hand-craft feature descriptor and deep feature descriptor for micro-expression recognition task. The aim is to use the hand-craft and deep learning feature descriptor to extract features and integrate them together to construct a large feature vector to describe a video. Through experiments on CASME, CASME II and CASME+2 databases, we demonstrate our proposed method can achieve promising results for micro-expression recognition accuracy with larger training samples.
Tan, ES, P, K, Indra, TMI, Tokimatsu, K & Yoshikawa, K 1970, 'Impact of biodiesel application on fuel savings and emission reduction for power generation in Malaysia', Energy Procedia, 10th International Conference on Applied Energy (ICAE), Elsevier BV, Hong Kong, HONG KONG, pp. 3325-3330.
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Tan, Z, Xiong, J, Liu, B & Gui, L 1970, 'A Novel Random Access Mechanism based on Real-time Access Intensity Detection', 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2019 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, Jeju, Korea (South), pp. 1-6.
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© 2019 IEEE. The Random Access (RA) procedure in 4G LTE serves for the uplink synchronization between UE and eNB and the allocation of the channel resource for data transmission. The existing 4G LTE RA Channel (RACH) lacks the adjustment to the real-time RA traffic and suffers from preamble collision and system congestion caused by the RA request flooding, which will not meet the requirement of ubiquitous and massive connection in 5G. To better improve the system throughput and the RA success probability of RACH in the dense device network, we propose a novel congestion-aware RA mechanism via a two-phase process in which concurrent devices carry out a Real-time Access Intensity Detection (RAID) prior to RA preamble message. In this paper, we develop an analytical model based on stochastic geometry and derive the RA success probability of our proposed model for typical device in single RA slot. The analytical results demonstrate the improvement of our proposed RA mechanism in terms of RA success probability under heavy RA traffic. Furthermore, a large amount of simulations under homogeneous and cluster Poisson Point Process (PPP) is carried out to analyze the performance of the proposed RA mechanism in terms of the system throughput, the RA success probability, the number of retransmission and the RA delay.
Tang, Z & Li, W 1970, 'Rate-dependent behaviour of fly ash-slag geopolymer concrete with recycled aggregate', 13th International Conference on Shock and Impact Loads on Structures, SILOS 2019, pp. 413-420.
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Geopolymer concrete incorporating recycled aggregates is featured in prolonging the life cycle of construction materials and conserving the natural resource, coupled with embracing the sustainable binder-geopolymer. The aim of this study is to investigate the rate-dependent behaviour of geopolymer recycled aggregate concrete in comparison with that of geopolymer normal aggregate concrete. In this study, the recycled coarse aggregate (RCA), sourced from construction and demolition waste, was used as a full replacement for normal coarse aggregate (NCA) in geopolymer concrete. Additionally, ground granulated blast furnace slag (GGBFS), acting as a strength modifier, was used to substitute fly ash at the levels of 0%, 10%, 20%, and 30% of total binder. A 5000kN high-force servo-hydraulic test system was used for quasi-static compressive tests at a constant strain rate of 10-5 s-1, whereas dynamic compressive tests were carried out by using a Ø80-mm split Hopkinson pressure bar (SHPB) apparatus at strain rates ranging from 33 s-1 to 200 s-1. The compressive properties, including stress-strain curve, compressive strength, and failure mode, are obtained and analysed. The test results show that the compressive properties of geopolymer concrete exhibit strong strain rate sensitivity in terms of compressive strength and failure patterns. Although the RCA replacement worsened the quasi-static compressive strength of geopolymer concrete, it had insignificant effects on the compressive strength at high strain rates. Furthermore, the inclusion of slag could improve both the quasi-static and dynamic compressive strength.
Tao, Q, Luo, X, Wang, H & Xu, R 1970, 'Enhancing Relation Extraction Using Syntactic Indicators and Sentential Contexts', 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, USA, pp. 1574-1580.
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© 2019 IEEE. State-of-the-art methods for relation extraction consider the sentential context by modeling the entire sentence. However, syntactic indicators, certain phrases or words like prepositions that are more informative than other words and may be beneficial for identifying semantic relations. Other approaches using fixed text triggers capture such information but ignore the lexical diversity. To leverage both syntactic indicators and sentential contexts, we propose an indicator-aware approach for relation extraction. Firstly, we extract syntactic indicators under the guidance of syntactic knowledge. Then we construct a neural network to incorporate both syntactic indicators and the entire sentences into better relation representations. By this way, the proposed model alleviates the impact of noisy information from entire sentences and breaks the limit of text triggers. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model significantly outperforms the state-of-the-art methods.
Tapas, M, Vessalas, K, Thomas, P & Sirivivatnanon, V 1970, 'An AMBT Study on the Effect of Limestone on ASR Mitigation: Ground Limestone Vs. Interground Limestone in Cements', Proceedings of the International Conference on Sustainable Materials, Systems and Structures (SMSS2019) Durability, Monitoring and Repair of Structures, International Conference on Sustainable Materials, Systems and Structures, RILEM Publications S.A.R.L., Rovinj, Croatia, pp. 201-207.
Tapas, M, Vessalas, K, Thomas, P, Sirivivatnanon, V & Kidd, P 1970, 'Mechanistic Role of Supplementary Cementitious Materials (SCMs) in Alkali-Silica Reaction (ASR) Mitigation', Concrete in Practice-Progress Through Knowledge, Concrete in Practice-Progress Through Knowledge, Sydney, Australia.
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Alkali-silica reaction (ASR) can cause premature failure of concrete structures and therefore is a major concrete durability issue. The use of commonly available supplementary cementitious materials (SCMs) such as fly ash and slag is generally regarded as the most optimal and economical solution in mitigating ASR. However, the eminent closure of coal fired power stations in favour of greener technologies for producing energy and increasing demand in steel recycling threaten the future supply of SCMs that are currently available. Hence, the need to better understand the ASR mitigation process in order to be able to identify potential alternatives. This experimental study aims to provide a better understanding of ASR mitigation by studying the influence of various SCMs on ASR expansion, portlandite consumption and pore solution alkalinity. Results show that the efficacy of the SCMs in reducing ASR expansion can be correlated to their ability to consume portlandite and bind alkalis. Further, results suggest that any material that has high content of soluble Al2O3 and/or SiO2 is a potential SCM for ASR mitigation.
Tawk, M, Indraratna, B, Rujikiatkamjorn, C & Heitor, A 1970, 'Review on Compaction and Shearing-Induced Breakage of Granular Material', Geotechnics for Transportation Infrastructure, International Symposium on Geotechnics for Transportation Infrastructure, Springer Singapore, Delhi, India, pp. 259-270.
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© Springer Nature Singapore Pte Ltd. 2019. With ongoing expansion of the transport infrastructure to accommodate the need of growing population, the stress on natural construction resources, such as quarried aggregates, has been increasing. Hence, the use of alternative non-traditional waste material is becoming more popular. Coal wash, a by-product of coal mining, has been recently suggested as a substitute to traditional quarried materials. However, recent research showed that these waste aggregates have a weaker structure than conventional materials, which translates into significant potential for breakage upon compaction and loading. Therefore, it is important to quantify breakage and evaluate its influence on the final structure of the soil body and the associated geotechnical properties. This paper presents a critical literature review on compaction and shearing-induced breakage of granular material. The review addresses the available breakage indices developed in the literature to quantify breakage and their limitations. The factors affecting the degree of breakage and the influence of the latter on the different geotechnical properties of compacted granular materials is also discussed. The findings of this review could be extrapolated to waste materials and corresponding treatment methods could be developed to reduce their breakage potential, so they can be more confidently accepted as substitutes to traditional materials in transport infrastructure.
Tayab, UB, Lu, J, Yang, F, Islam, M, Zia, A & Hossain, J 1970, 'Microgrid Energy Management System for Academic Building', 2019 29th Australasian Universities Power Engineering Conference (AUPEC), 2019 29th Australasian Universities Power Engineering Conference (AUPEC), IEEE, Nadi, Fiji, pp. 1-5.
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© 2019 IEEE. In this paper, an optimal energy management system (EMS) for grid-connected microgrid is proposed. The gridconnected microgrid system comprises of photovoltaic (PV) panel, and battery as an energy storage unit. The optimal EMS is aimed to minimize the total operating cost of grid-connected microgrid for academic building. The feedforward neural network with improved salp swarm alogrithm based on weight factor is used to determine the 24-hours ahead data forecasting of load demand and PV power, while improved salp swarm alogrithm based on weight factor (WSSA) is used to perform the day-ahead optimal scheduling to control the power flow between PV, energy storage unit, load and main grid. The proposed microgrid EMS (MGEMS) is simulated using MATLAB/Simulink. The simulation result shows the effectiveness and validity of presented EMS with academic load.
Tello, AMD & Abolhasan, M 1970, 'SDN Controllers Scalability and Performance Study', 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, Malaysia, pp. 1-10.
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Software Defined Networks (SDN) is a networking approach that decouples the intelligent control plane from networking devices and establishes a separate entity called ”controller” that rule the behaviour of the data plane on physical networking devices. Due to the rapid evolution and growth of SDN controllers in the market, this paper aims to present an extensive study on performance and scalability of different open source SDN controllers available in the existing literature. This work covers previous studies and expands them with updated information and official benchmarking methodologies. The study provides a framework based on the standards recommended by the IETF (Internet Engineering Task Force) and it will serve as a guideline to the SDN community to benchmark different SDN controllers.
Teng, J, Zhong, Y, Zhang, S & Sheng, D 1970, 'An interpretation of soil freezing characteristic curve of unsaturated freezing soils', 7th Asia-Pacific Conference on Unsaturated Soils, AP-UNSAT 2019.
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The Soil Freezing Characteristic Curve (SFCC) plays a crucial role in describing the behavior of frozen soils, which is as important as the Soil Water Characteristic Curve (SWCC) in unsaturated soil mechanics. SFCC describes the relationship between freezing temperature and liquid water content in soil, which is of great significance to reveal the mechanism of frost heave. In the 1960s, some researchers have studied the similarity between SFCC and SWCC, and found some explanations in theory. But a reasonable theoretical description model for SFCC is still not available. It is noted that Dash et al. (2006) put forward a theory of pre-melting film, which gives the function between the thickness of water film on ice surface and temperature and curvature. Cahn et al. (1992) considered the pre-melting film and the Gibbs-Thomson effect (G-T effect) of the bending interface. They measured and compared the freezing characteristic curves of polystyrene microspheres, and found a good agreement between each other. However, the polystyrene particle size is about 10 μm, which is far different from soil particles. This study extends Cahn's study and applies it to soil mechanics. The pre-melting phenomenon and the G-T effect are considered in this study. A theoretical framework for SFCC is then derived. Under the assumption of uniform particle radius and particle contact, two kinds of arrangement modes that are saturated by liquid water and ice are considered firstly. As for the first case, the most loose simple cube (SC) arrangement. The expression of the volumetric water content can be determined by considering the pre-melting phenomenon and G-T effect: (equation presented) where fSC(α)=(1+α)3-α2(4.5+3α), α=d/r, β=rbend/r, r is the soil particle radius, d is the thickness of pre-melting film, d=3.5×10-3/(Tm-T)×[1-0.0158/(r×(Tm-T))], and Tm=273.15 K, rbend is the bending interface radius of ice surface, rbend=0.0516/(Tm-T). The second case is the closest compact tetr...
Thalhammer, S, Patten, T & Vincze, M 1970, 'SyDPose: Object Detection and Pose Estimation in Cluttered Real-World Depth Images Trained using Only Synthetic Data', 2019 International Conference on 3D Vision (3DV), 2019 International Conference on 3D Vision (3DV), IEEE, pp. 106-115.
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Thomas, P, Ha Hau, V, Vessalas, K, Sirivivatnanon, V & South, W 1970, 'Assessment of Aggregate Reactivity Using Slurry Tests', https://concrete2019.com.au/mobile/content.html, 29th Biennial National Conference of the Concrete Institute of Australia, Sydney.
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The testing and screening of aggregates for their alkali-silica reactivity (ASR) is generally carried out initially by petrographic analysis. If reactive aggregates are identified by petrographic analysis then a rapid screening of the aggregate’s potential to cause expansion using the accelerated mortar bar test (AMBT, AS-1141.60.1) is carried out to determine further reactivity potential. Aggregates that are found to be reactive in the AMBT method may be further screened using the concrete prism test (CPT, AS-1141.60.2). Both AMBT and CPT methods are a compromise between introducing accelerated and reactive conditions and monitoring the expansion over short and long periods of time but with conditions that are more closely aligned with field conditions. Given that these tests are empirical estimates of reactivity potential, alternate testing may be developed for the screening of aggregates. Alternate laboratory tests are rapidly carried out using slurry tests on small samples of ground aggregate (e.g. ASTM C289). Simulating storage temperatures used in the AMBT (80°C) and CPT (38°C) in 1 M NaOH (1.25% Na2Oe) is an alternate approach to the development of new rapid screening tests. To assess the degree of aggregate reactivity a co-reactant, calcium hydroxide (CH), may be added to the reaction mixture aiding reactivity assessment through the consumption of CH. The results of a laboratory trial into the reactivity of aggregates using a ground aggregate slurry test of this nature are reported in this paper. The results are correlated with standard test method data using AMBT and CPT (AS-1141.60.1 and 2) with a view to assessing this method (or methods of this type) as an alternative rapid screening approach in the identification of aggregate reactivity for ASR potential.
Thomas, P, Roboredo, C, Boyd-Weetman, B, Vessalas, K, Farah, D & Sirivivatnanon, V 1970, 'Investigation of ASR Reactivity through Slurry Dissolution Tests', 29th Biennial National Conference of the Concrete Institute of Australia, Sydney.
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The potential for alkali silica reaction (ASR) has been investigated through dissolution tests and the determination of the concentration of elemental species, Na, K, Ca and Si in the supernatant fluid of GP cement, aggregate and fly ash slurries. The aggregates selected for investigation were a reactive greywacke and a non-reactive micro-diorite both of which contain quartz. Alkali ions were delivered to the solution by the cement, although lower concentrations were released by both the aggregates and fly ash. Silica was released into solution according to aggregate reactivity. Rapid and local release of silica can yield an expansive ASR gel for reactive aggregate. Fly ash was observed to release silica rapidly indicating that the primary action of fly ash is through a competitive reaction for the formation of silica gel thus mitigating deleterious ASR. Quartz content as determined by X-ray diffraction analysis indicated that this phase was the main source of solution silica for the reactive aggregate.
Tian, H, Khoa, NLD, Anaissi, A, Wang, Y & Chen, F 1970, 'Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring', Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM '19: The 28th ACM International Conference on Information and Knowledge Management, ACM, Beijing, China, pp. 2813-2821.
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© 2019 Association for Computing Machinery. Despite its success for anomaly detection in the scenario where only data representing normal behavior are available, one-class support vector machine (OCSVM) still has challenge in dealing with non-stationary data stream, where the underlying distributions of data are time-varying. Existing OCSVM-based online learning methods incrementally update the model to address the challenge, however, they solely rely on the location relationship between a test sample and error support vectors. To better accommodate normal behavior evolution, online anomaly detection in non-stationary data stream is formulated as a concept drift adaptation problem in this paper. It is proposed that OCSVM-based incremental learning is only performed in the case of a normal drift. For an incoming sample, its relative relationship with three sets of vectors in OCSVM, namely margin support vectors, error support vectors, and reserve vectors is fully utilized to estimate whether a normal drift is emerging. Extensive experiments in the field of structural health monitoring have been conducted and the results have shown that the proposed simple approach outperforms the existing OCSVM-based online learning algorithms for anomaly detection.
Tian, Y, Yu, X, Fan, B, Wu, F, Heijnen, H & Balntas, V 1970, 'SOSNet: Second Order Similarity Regularization for Local Descriptor Learning', 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 11008-11017.
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Tianling Shi, Fei Wang, Hui Guo, Lijun Zhang & Li Li 1970, 'Distributed Generations Interconnection Based on the Clustering Algorithm and Graphy Theory', 8th Renewable Power Generation Conference (RPG 2019), 8th Renewable Power Generation Conference (RPG 2019), Institution of Engineering and Technology, Shanghai, China, pp. 320 (6 pp.)-320 (6 pp.).
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© 2019 Institution of Engineering and Technology. All rights reserved. Because the renewable energy generation has obvious characteristics of spatiotemporal distribution and intermittence, the interconnection of various distributed generations and loads becomes an effective solution to achieve the reliable energy supply. Considering the type and distance of distributed generations, a weight-based clustering algorithm is proposed in this paper to divide the large-scale distributed generation into sub-areas and to further form structured microgrids. Between microgrids, the topology structure is achieved and optimized using the minimum spanning tree algorithm on the basis of clustering centers. In this way, the layout optimization of large-scale distributed energy can be solved. Finally, the effectiveness of the above optimization scheme is verified by simulations, which can provide a novel idea for building an economical and reliable distributed energy interconnection network.
Tirado Cortes, CA, Chen, H-T & Lin, C-T 1970, 'Analysis of VR Sickness and Gait Parameters During Non-Isometric Virtual Walking with Large Translational Gain', Proceedings of the 17th International Conference on Virtual-Reality Continuum and its Applications in Industry, VRCAI '19: The 17th International Conference on Virtual-Reality Continuum and its Applications in Industry, ACM, Brisbane, Australia, pp. 1-10.
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© 2019 Association for Computing Machinery. The combination of room-scale virtual reality and non-isometric virtual walking techniques is promising-the former provides a comfortable and natural VR experience, while the latter relaxes the constraint of the physical space surrounding the user. In the last few decades, many non-isometric virtual walking techniques have been proposed to enable unconstrained walking without disrupting the sense of presence in the VR environment. Nevertheless, many works reported the occurrence of VR sickness near the detection threshold or after prolonged use. There exists a knowledge gap on the level of VR sickness and gait performance for amplified nonisometric virtual walking at well beyond the detection threshold. This paper presents an experiment with 17 participants that investigated VR sickness and gait parameters during non-isometric virtual walking at large and detectable translational gain levels. The result showed that the translational gain level had a significant effect on the reported sickness score, gait parameters, and center of mass displacements. Surprisingly, participants who did not experience motion sickness symptoms at the end of the experiment adapted to the non-isometric virtual walking well and even showed improved performance at a large gain level of 10x.
To, KYC, Lee, KMB, Yoo, C, Anstee, S & Fitch, R 1970, 'Streamlines for Motion Planning in Underwater Currents', Proceedings - IEEE International Conference on Robotics and Automation, International Conference on Robotics and Automation, Montreal, QC, Canada, pp. 4619-4625.
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Motion planning for underwater vehicles must consider the effect of oceancurrents. We present an efficient method to compute reachability and costbetween sample points in sampling-based motion planning that supportslong-range planning over hundreds of kilometres in complicated flows. The ideais to search a reduced space of control inputs that consists of streamfunctions whose level sets, or streamlines, optimally connect two given points.Such stream functions are generated by superimposing a control input onto theunderlying current flow. A streamline represents the resulting path that avehicle would follow as it is carried along by the current given that controlinput. We provide rigorous analysis that shows how our method avoids exhaustivesearch of the control space, and demonstrate simulated examples in complicatedflows including a traversal along the east coast of Australia, using actualcurrent predictions, between Sydney and Brisbane.
Tonkin, M, Vitale, J, Herse, S, Raza, SA, Madhisetty, S, Kang, L, Vu, TD, Johnston, B & Williams, M-A 1970, 'Privacy First: Designing Responsible and Inclusive Social Robot Applications for in the Wild Studies', 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE, New Delhi, India, pp. 1-8.
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Deploying social robots applications in public spaces for conducting in the wild studies is a significant challenge but critical to the advancement of social robotics. Real world environments are complex, dynamic, and uncertain. Human-Robot interactions can be unstructured and unanticipated. In addition, when the robot is intended to be a shared public resource, management issues such as user access and user privacy arise, leading to design choices that can impact on users' trust and the adoption of the designed system. In this paper we propose a user registration and login system for a social robot and report on people's preferences when registering their personal details with the robot to access services. This study is the first iteration of a larger body of work investigating potential use cases for the Pepper social robot at a government managed centre for startups and innovation. We prototyped and deployed a system for user registration with the robot, which gives users control over registering and accessing services with either face recognition technology or a QR code. The QR code played a critical role in increasing the number of users adopting the technology. We discuss the need to develop social robot applications that responsibly adhere to privacy principles, are inclusive, and cater for a broad spectrum of people.
Trad, SP, Hadgraft, RG & Gardner, AP 1970, 'Sustainability invisibility: Are we hooked on technical rationality?', Proceedings of the 46th SEFI Annual Conference 2018: Creativity, Innovation and Entrepreneurship for Engineering Education Excellence, Annual Conference of the European Society for Engineering Education, SEFI, Copenhagen, Denmark, pp. 479-486.
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Education for Sustainable Development (ESD) is regarded as a key enabler for all the 17 Sustainable Development Goals. Higher Education Institutes have been slow in adopting a holistic approach to ESD in undergraduate engineering curricula. The aim of this research is to explore how tertiary subject coordinators understand and envision sustainability and how that subsequently manifests in their teaching curriculum design. A qualitative inquiry approach was adopted to explore the rationalities of ten academics within the School of Civil and Environmental Engineering at an Australian university. In a previous study in the School, the researchers identified a low percentage of ESD integration across the curriculum. The interviews showed that these academics perceive sustainability as a technical concept, presumably taught by someone else in the curriculum. As a result, sustainability is mostly invisible within undergraduate engineering curricula. Results elsewhere show that for ESD to be effectively implemented at a tertiary level, academics must come to understand and accept what ESD aims to achieve, which is to educate engineering students to encourage them to integrate sustainability decision making in their future engineering practice. Engineers Australia's Code of Ethics requires: Balance the needs of the present with the needs of future generations. The difficulty is that these behaviours are difficult to detect in engineering curricula, which are strongly focused on technical problem solving. This research will identify and disseminate good practice in curriculum design for sustainability at both unit level and program level. This paper represents an early part of the research program.
Tran, TS, Bliuc, D, Hansen, L, Abrahamsen, B, Van den Bergh, J, Eisman, JA, van Geel, T, Geusens, P, Vestergaard, P, Nguyen, TV & Center, JR 1970, 'Multimorbidity and long-term mortality following a specific fragility fracture: Latent class analysis of a nationwide population-based cohort', JOURNAL OF BONE AND MINERAL RESEARCH, Annual Meeting of the American-Society-for-Bone-and Mineral Research, WILEY, Orlando, FL, pp. 385-386.
Tran, TS, Bliuc, D, O'Donoghue, S, Hansen, L, Abrahamsen, B, van den Bergh, J, van Geel, T, Geusens, P, Vestergaard, P, Nguyen, TV & Center, JR 1970, 'Trajectories to subsequent admissions and mortality following a specific fragility fracture: A nationwide population-based follow-up study', JOURNAL OF BONE AND MINERAL RESEARCH, Annual Meeting of the American-Society-for-Bone-and Mineral Research, WILEY, Orlando, FL, pp. 386-386.
Tran, TT, Regan, B, Ekimov, EA, Mu, Z, Yu, Z, Gao, W, Narang, P, Solntsev, AS, Toth, M, Aharonovich, I & Bradac, C 1970, 'Anti-Stokes Excitation of Solid-State Quantum Emitters for Nanoscale Thermometry', Conference on Lasers and Electro-Optics, CLEO: Science and Innovations, OSA, San Jose, California, United States, pp. SM2F.5-SM2F.5.
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© 2019 The Author(s) We report the first demonstration of Anti-Stokes excitation on a single solid-state quantum emitter-namely the germanium-vacancy center in diamond and its application as a high-sensitive nanoscale thermal sensor.
Trede, F, Braun, R & Brookes, W 1970, 'Studio-based learning in a first year engineering curriculum: Exploring students' learning experiences and reflections using the rich picture method.', ITHET, International Conference on Information Technology Based Higher Education and Training, IEEE, Magdeburg, Germany, pp. 1-5.
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© 2019 IEEE. We have described engineering students in their first year participating in a 'studio' based experience. We used a rich picture method imbedded in research interviews to explore student's attitudes to, and understandings of their studio experience. Our findings demonstrate that this research method produces an enriched understanding of and deep insights into student experiences in the studio.
Trinh, CD, Kien, VC, Bac, DH, Dutkiewicz, E, Hanh, T & Trung, NL 1970, 'ISCIT 2019 Message', 2019 19th International Symposium on Communications and Information Technologies (ISCIT), 2019 19th International Symposium on Communications and Information Technologies (ISCIT), IEEE, pp. xxi-xxiv.
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Tsai, T-Y, Lin, C-T & Prasad, M 1970, 'An Intelligent Customer Churn Prediction and Response Framework', 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), IEEE, pp. 928-935.
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Customer retention is one of the most important issues for companies. Companies always seek to reduce customer churn in order to increase the customer lifetime value and reduce the cost of acquisition of new customers. By focusing on customer churn prediction and identification, companies can predict in advance which customers are going to churn and therefore decrease customers churn rate through related personalized actions. The key issue here is how to predict customer churn at an early stage. This paper identifies related issues in customer churn prediction and provides new definitions and classifications on customer churn identification and strategies. This paper also establishes a customer churn prediction and response framework consists of three main stages: customer churn prediction, customer churn understanding and customer churn response. The framework presents the characteristics and challenges of related stages of customer churn as well. These outcomes can be used for customized or personalized product and service developments, to improve customer service efficiency and related decision-making more effective and more particularly enabling strategic promotion campaigns to customers with high churn risk.
Tuyen Le, A, Tran, LC, Huang, X & Guo, J 1970, 'Analog Least Mean Square Loops for Self-Interference Cancellation in In-Band Full-Duplex Systems', 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, pp. 1-1.
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Ulapane, N, Piyathilaka, L & Kodagoda, S 1970, 'Some Convolution and Scale Transformation Techniques to Enhance GPR Images', 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, Xi'an, China, pp. 1453-1458.
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© 2019 IEEE. Locating reinforcement rods embedded inside concrete wall-like structures, as well as locating subsurface features such as voids, cracks, and interfaces is an essential part of structural health monitoring of concrete infrastructure. The Ground Penetrating Radar (GPR) technique has been commonly used as a means of Non-destructive Testing and Evaluation (NDT E) which suits the purpose. In the recent past, the interest of using GPR to assess the crowns (i.e., top) of concrete sewers has been rising. Moisture is well known to be a challenge for GPR imaging as moisture tends to influence GPR waves. This challenge becomes more common and persistent inside sewers since sewer walls contain considerable surface and subsurface moisture as a result of the humid environment created by the waste water flowing through sewers as well as the bacteria and gas induced acid attacks. Forming a part of sewer condition assessment-related research with the objective of assessing moist concrete, this paper presents some preliminary results which demonstrate how some simple scale transformations and convolution can help in enhancing GPR images in grey-scale. A set of raw GPR signals captured on a moist concrete block inside a laboratory environment is considered. The effect of enhancement is demonstrated against a benchmark image constructed by mapping the raw signals directly onto grey-scale.
Ulapane, N, Wickramanayake, S & Kodagoda, S 1970, 'Pulsed Eddy Current Sensing for Condition Assessment of Reinforced Concrete', 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), IEEE, China, pp. 1-6.
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© 2019 IEEE. Reinforced concrete (i.e., concrete wall-like structures having steel reinforcement rods embedded within) are commonly available as civil infrastructures. Such concrete structures, especially the walls of sewers, are vulnerable to bacteria and gas induced acid attacks which contribute to deterioration of the concrete and subsequent concrete wall loss. Therefore, quantification of concrete wall loss becomes important in determining the health and structural integrity of concrete walls. An effective strategy that can be formulated to quantify concrete wall loss is, locating a reinforcement rod and determining the thickness of concrete overlaying the rod via Non-destructive Testing and Evaluation (NDT E). Pulsed Eddy Current (PEC) sensing is commonly used for NDT E of metallic structures, including ferromagnetic materials. Since steel reinforcement rods that are commonly embedded in concrete also are ferromagnetic, this paper contributes by presenting experimental results, which suggest the usability of PEC sensing for reinforced concrete assessment, via executing the aforementioned strategy.
Ullah Siddiqi, MW & Lee, JE-Y 1970, 'Quality Factor Enhancement of AlN-on-Si Lamb Wave Resonators Using a Hybrid of Phononic Crystal Shapes in Anchoring Boundaries', 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII), 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII), IEEE.
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Ullah, S, Asif, M, Ahmad, S, Imdad, U & Sohaib, O 1970, 'Application of Data Science for Controlling Energy Crises: A Case Study of Pakistan.', ICSCA, 2019 8th International Conference on Software and Computer Applications, ACM, Penang, Malaysia, pp. 60-64.
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© 2019 Association for Computing Machinery. Today Pakistan is facing numerous challenges for the interconnection of local energy resources and for balanced energy policies. Data Science, Big Data, Artificial Intelligence (AI), IoT and Cloud computing draws our focus towards controlling energy crises in terms of smart energy generation, consumption and to overcome causes of energy crises. To make a conclusion valuable we have to extract significant value from a large amount of data that‟s why data management plays a significant role. This Paper presents a review of energy sectors, energy resources, energy crises in Pakistan. It also presents the possible solution of energy crises with the help of data science application and the involvement of Big Data, Cloud computing, IoT and AI.
Unanue, IJ, Arratibel, LG, Borzeshi, EZ & Piccardi, M 1970, 'English-Basque statistical and neural machine translation', LREC 2018 - 11th International Conference on Language Resources and Evaluation, Language Resources and Evaluation Conference, European Language Resource Association, Miyazaki, Japan, pp. 880-885.
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Neural Machine Translation (NMT) has attracted increasing attention in the recent years. However, it tends to require very large training corpora which could prove problematic for languages with low resources. For this reason, Statistical Machine Translation (SMT) continues to be a popular approach for low-resource language pairs. In this work, we address English-Basque translation and compare the performance of three contemporary statistical and neural machine translation systems: OpenNMT, Moses SMT and Google Translate. For evaluation, we employ an open-domain and an IT-domain corpora from the WMT16 resources for machine translation. In addition, we release a small dataset (Berriak) of 500 highly-accurate English-Basque translations of complex sentences useful for a thorough testing of the translation systems.
Unanue, IJ, Borzeshi, EZ, Esmaili, N & Piccardi, M 1970, 'ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems', NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, pp. 430-436.
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Regularization of neural machine translation is still a significant problem,especially in low-resource settings. To mollify this problem, we proposeregressing word embeddings (ReWE) as a new regularization technique in a systemthat is jointly trained to predict the next word in the translation(categorical value) and its word embedding (continuous value). Such a jointtraining allows the proposed system to learn the distributional propertiesrepresented by the word embeddings, empirically improving the generalization tounseen sentences. Experiments over three translation datasets have showed aconsistent improvement over a strong baseline, ranging between 0.91 and 2.54BLEU points, and also a marked improvement over a state-of-the-art system.
Uzair, M, Li, L, Zhu, JG & Eskandari, M 1970, 'A protection scheme for AC microgrids based on multi-agent system combined with machine learning', 2019 29th Australasian Universities Power Engineering Conference (AUPEC), 2019 29th Australasian Universities Power Engineering Conference (AUPEC), IEEE, Nadi, Fiji, pp. 1-6.
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© 2019 IEEE. Traditional protection schemes at the distribution level designed for unidirectional power flow will be compromised due to bi-directional flow of power with the increased penetration of distributed generation (DG) sources, resulting in miscoordination between protection devices. This paper proposes a new microgrid protection method based on the multi-agent system (MAS) combined with machine learning (ML) for fault detection in autonomous and grid-connected modes, protection coordination and updating relay settings to achieve adaptive protection. MAS framework with various layers and roles of each agent are described in detail. A meshed microgrid model is developed in Simulink to collect fault data for training and testing ML algorithms, while the behaviour of individual agents and interactions between them are validated in AnyLogic simulation software. The simulation results confirmed that the proposed MAS algorithm could provide primary and backup protection in both modes of microgrid.
Vahl, A, Huizingh, KRE & Sick, N 1970, 'Addressing wicked problems: Implementing innovations when facing stakeholder resistance', 33rd ANZAM Conference: Wicked Solutions to Wicked Problems, Cairns, Australia.
van den Hoven, E 1970, 'Materialising Memories', Proceedings of the 5th International ACM In-Cooperation HCI and UX Conference, CHIuXiD'19: The 5th International HCI and UX Conference, ACM, INDONESIA, pp. 188-189.
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Van Huynh, N, Hoang, DT, Nguyen, DN & Dutkiewicz, E 1970, 'Real-Time Network Slicing with Uncertain Demand: A Deep Learning Approach', ICC 2019 - 2019 IEEE International Conference on Communications (ICC), ICC 2019 - 2019 IEEE International Conference on Communications (ICC), IEEE, Shanghai, pp. 1-6.
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© 2019 IEEE. Practical and efficient network slicing often faces real-time dynamics of network resources and uncertain customer demands. This work provides an optimal and fast resource slicing solution under such dynamics by leveraging the latest advances in deep learning. Specifically, we first introduce a novel system model which allows the network provider to effectively allocate its combinatorial resources, i.e., spectrum, computing, and storage, to various classes of users. To allocate resources to users while taking into account the dynamic demands of users and resources constraints of the network provider, we employ a semi-Markov decision process framework. To obtain the optimal resource allocation policy for the network provider without requiring environment parameters, e.g., uncertain service time and resource demands, a Q-learning algorithm is adopted. Although this algorithm can maximize the revenue of the network provider, its convergence to the optimal policy is particularly slow, especially for problems with large state/action spaces. To overcome this challenge, we propose a novel approach using an advanced deep Q-learning technique, called deep dueling that can achieve the optimal policy at few thousand times faster than that of the conventional Q-learning algorithm. Simulation results show that our proposed framework can improve the long-term average return of the network provider up to 40% compared with other current approaches.
Verma, R & Merigo, JM 1970, 'On Generalized Intuitionistic Fuzzy Interaction Partitioned Bonferroni Mean Operators', 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, pp. 1-6.
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Verma, S, Wang, C, Zhu, L & Liu, W 1970, 'DeepCU: Integrating both Common and Unique Latent Information for Multimodal Sentiment Analysis', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, China, pp. 3627-3634.
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Multimodal sentiment analysis combines information available from visual, textual, and acoustic representations for sentiment prediction. The recent multimodal fusion schemes combine multiple modalities as a tensor and obtain either; the common information by utilizing neural networks, or the unique information by modeling low-rank representation of the tensor. However, both of these information are essential as they render inter-modal and intra-modal relationships of the data. In this research, we first propose a novel deep architecture to extract the common information from the multi-mode representations. Furthermore, we propose unique networks to obtain the modality-specific information that enhances the generalization performance of our multimodal system. Finally, we integrate these two aspects of information via a fusion layer and propose a novel multimodal data fusion architecture, which we call DeepCU (Deep network with both Common and Unique latent information). The proposed DeepCU consolidates the two networks for joint utilization and discovery of all-important latent information. Comprehensive experiments are conducted to demonstrate the effectiveness of utilizing both common and unique information discovered by DeepCU on multiple real-world datasets. The source code of proposed DeepCU is available at https://github.com/sverma88/DeepCU-IJCAI19.
Vranken, L, Wyers, CE, Van der Velde, RY, De Bruin, IJA, Van den Bergh, JPW, Janzing, HMJ, Kaarsemakers, S, Eisman, JA, Center, JR, Nguyen, TV, Bliuc, D, Tran, T & Geusens, PPMM 1970, 'The imminent subsequent fracture risk in patients with a recent fracture at the FLS is mainly associated with falls - a 3 year prospective cohort study.', JOURNAL OF BONE AND MINERAL RESEARCH, Annual Meeting of the American-Society-for-Bone-and Mineral Research, WILEY, Orlando, FL, pp. 181-182.
Vu, TH, Gowripalan, N, De Silva, P, Kidd, P & Sirivivatnanon, V 1970, 'Carbonation and chloride induced steel corrosion related aspects in fly ash/slag based geopolymers - A critical review', FIB 2018 - Proceedings for the 2018 fib Congress: Better, Smarter, Stronger, International fib Congress, Fédération internationale du béton, Melbourne, Australia, pp. 3061-3076.
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Carbonation and the presence of chloride ions are considered as two important factors affecting steel reinforcement corrosion in conventional ordinary Portland cement (OPC) concrete. Particularly, large OPC pre-cast pipes and culverts are expected to have a longer design life due to lower water/cement ratios and higher cement contents (hence higher strength and lower porosity). Although most of the time they are buried underground and corrosion conditions may not be present, the aggressive nature of fluids (highly acidic or salty) they carry internally and the aggressive ground water in which they are located have resulted in deterioration of these elements due to corrosion of steel. Nowadays, attempts are made to replace OPC concrete pipes or culverts with fly ash/slag based geopolymer pipes and culverts. In this paper, a comparison of the corrosion aspects of reinforced concrete elements, particularly, pre-cast pipes and culverts, manufactured of OPC or blended cements and fly ash/slag based geopolymers is made. Carbonation rate in OPC concrete is different to that of geopolymer concrete mainly due to different pore structure and reaction products. The chloride ion penetration will also be different mainly due to different binding capacity, chemical products and pore structure. The threshold concentration of chloride ions required to initiate corrosion of steel reinforcement is also different. These aspects are critically reviewed which includes diffusion rates and cover requirements for long-term performance.
Vu, TH, Gowripalan, N, Sirivivatnanon, V, De Silva, P & Kidd, P 1970, 'Assessing Corrosion Resistance of Powder form of Geopolymer Concrete', 29 Biennial Conference of the Concrete Institute of Australia, Sydney Australia.
Vu, TL, Liu, L, Paul, G & Vidal-Calleja, T 1970, 'Rectangular-shaped object recognition and pose estimation', Australasian Conference on Robotics and Automation, ACRA, Australian Conference on Robotics and Automation, ARAA, Adelaide, pp. 1-9.
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This paper presents a novel solution for rectangular-shaped object pose estimation in the robotic bin-picking problem, using data from a single RGB-D camera collecting point cloud data from a fixed position. The key benefit of the presented framework is its ability to accurately and robustly locate an object position and orientation, which allows for high-precision robotic grasping and placing of such objects in an open-loop motion execution system. Firstly, intelligent grasping surface selection is performed, then Principal Component Analysis is used for pose estimation and finally, rotation averaging is integrated to significantly improve noise-reduction over time. Comparisons between the resulting poses and ones estimated by a traditional Iterative Closest Point technique, have demonstrated the framework’s advantages for pose estimation tasks.
Vu, TT, Nguyen, DN, Hoang, DT & Dutkiewicz, E 1970, 'QoS-Aware Fog Computing Resource Allocation Using Feasibility-Finding Benders Decomposition', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Hawaii.
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We investigate a joint offloading and resource allocation under a multi-layer cooperative fog and cloud computing architecture, aiming to minimize the total energy consumption of mobile devices while meeting users' QoS requirements, e.g., delay, security, and application compatibility. Due to the mutual coupling amongst offloading decision and resource allocation variables, the resulting optimization is a mixed integer non- linear programming problem that is NP-hard. Such problem often requires exponential time to find the optimal solution. In this work, we propose a distributed approach, namely feasibility-finding Benders decomposition (FFBD), that decomposes the original problem into a master problem for the offloading decision and subproblems for resource allocation. These (simpler) subproblems can be solved in parallel at fog nodes, thereby reducing both the complexity and the computational time. The numerical results show that the FFBD always returns the optimal solution of the problem with significantly less computation time (e.g., in comparing with the branch-and-bound method).
Wadley, G, Krause, A, Liang, J, Wang, Z & Leong, TW 1970, 'Use of music streaming platforms for emotion regulation by international students', Proceedings of the 31st Australian Conference on Human-Computer-Interaction, OZCHI'19: 31ST AUSTRALIAN CONFERENCE ON HUMAN-COMPUTER-INTERACTION, ACM, Fremantle, Australia, pp. 337-341.
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© 2019 Association for Computing Machinery. Listening to music has always been an emotion-laden experience. Early research involving analog platforms showed that people use recorded music as a resource to manage their emotions, enhancing desired affective states and attenuating unwanted states. More recently, technological advances such as streaming services have made an almost-unlimited selection of music ubiquitously available. This paper examines whether this intensified access to recorded music has afforded new ways of shaping emotion. We studied the practices of international university students, a cohort who face significant stresses and make significant use of digital technology. We found that students actively and routinely use music streaming services to manage their emotional responses to the challenges of studying abroad.
Wan, Y, Shu, J, Sui, Y, Xu, G, Zhao, Z, Wu, J & Yu, P 1970, 'Multi-modal Attention Network Learning for Semantic Source Code Retrieval', 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), IEEE, San Diego, CA, USA, pp. 13-25.
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© 2019 IEEE. Code retrieval techniques and tools have been playing a key role in facilitating software developers to retrieve existing code fragments from available open-source repositories given a user query (e.g., a short natural language text describing the functionality for retrieving a particular code snippet). Despite the existing efforts in improving the effectiveness of code retrieval, there are still two main issues hindering them from being used to accurately retrieve satisfiable code fragments from large-scale repositories when answering complicated queries. First, the existing approaches only consider shallow features of source code such as method names and code tokens, but ignoring structured features such as abstract syntax trees (ASTs) and control-flow graphs (CFGs) of source code, which contains rich and well-defined semantics of source code. Second, although the deep learning-based approach performs well on the representation of source code, it lacks the explainability, making it hard to interpret the retrieval results and almost impossible to understand which features of source code contribute more to the final results. To tackle the two aforementioned issues, this paper proposes MMAN, a novel Multi-Modal Attention Network for semantic source code retrieval. A comprehensive multi-modal representation is developed for representing unstructured and structured features of source code, with one LSTM for the sequential tokens of code, a Tree-LSTM for the AST of code and a GGNN (Gated Graph Neural Network) for the CFG of code. Furthermore, a multi-modal attention fusion layer is applied to assign weights to different parts of each modality of source code and then integrate them into a single hybrid representation. Comprehensive experiments and analysis on a large-scale real-world dataset show that our proposed model can accurately retrieve code snippets and outperforms the state-of-the-art methods.
Wang, B, Lu, J, Yan, Z, Luo, H, Li, T, Zheng, Y & Zhang, G 1970, 'Deep Uncertainty Quantification', Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, Anchorage, USA, pp. 2087-2095.
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© 2019 Association for Computing Machinery. Weather forecasting is usually solved through numerical weather prediction (NWP), which can sometimes lead to unsatisfactory performance due to inappropriate setting of the initial states. In this paper, we design a data-driven method augmented by an effective information fusion mechanism to learn from historical data that incorporates prior knowledge from NWP. We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function. A notable advantage of our proposed method is that it simultaneously implements single-value forecasting and uncertainty quantification, which we refer to as deep uncertainty quantification (DUQ). Efficient deep ensemble strategies are also explored to further improve performance. This new approach was evaluated on a public dataset collected from weather stations in Beijing, China. Experimental results demonstrate that the proposed NLE loss significantly improves generalization compared to mean squared error (MSE) loss and mean absolute error (MAE) loss. Compared with NWP, this approach significantly improves accuracy by 47.76%, which is a state-of-the-art result on this benchmark dataset. The preliminary version of the proposed method won 2nd place in an online competition for daily weather forecasting1
Wang, C, Pan, S, Hu, R, Long, G, Jiang, J & Zhang, C 1970, 'Attributed Graph Clustering: A Deep Attentional Embedding Approach', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, Macao, China, pp. 3670-3676.
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Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step frameworks are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in a graph to a compact representation, on which an inner product decoder is trained to reconstruct the graph structure. Furthermore, soft labels from the graph embedding itself are generated to supervise a self-training graph clustering process, which iteratively refines the clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to mutually benefit both components. Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.
Wang, D, Zhang, W, Yu, S & He, H 1970, 'RLS-VNE: Repeatable Large-Scale Virtual Network Embedding over Substrate Nodes', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA, pp. 1-6.
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Embedding multiple virtual networks (VNs) on a shared substrate network (SN), known as virtual network embedding (VNE), is a challenging problem in cloud platforms. VNE methods can provide strategies to deploy VNs onto SN resources. However, as the scale of VN greatly increases, traditional VNE methods are time-consuming and waste link resource. Meanwhile, traditional VNE methods assign each virtual node of the same VN to different substrate nodes, whereas it is hard to provide larger scale SN to provision the VN. In order to efficiently embed large-scale VNs, multiple virtual nodes from the same VN need to share the same substrate node. We therefore model a repeatable large-scale virtual network embedding (RLSVNE) problem in this study, provisioning large-scale VNs, and propose a heuristic method (Rlsvne) to handle RLS-VNE. Rlsvne pre-processes the VN topology before embedding. In the pre-processing stage, the VN topology is processed through graph coarsening, partitioning, and uncoarsening. After the pre-processing, Rlsvne accomplishes an embedding stage with a topology- aware repeatable embedding solution. 1,000 and 10,000-scale VNE experiments are conducted to demonstrate our Rlsvne. The evaluation results demonstrate that our Rlsvne outperforms three modified heuristics. Rlsvne shows improved performance in reducing substrate cost and fully utilizing substrate resources, achieving high acceptance ratio and revenue values.
Wang, H, Li, Y, Zhang, G, Wang, J & Li, J 1970, 'Behaviours of lithium-based magnetorheological grease under triangular quasi-static test', Proceedings of 30th International Conference on Adaptive Structures and Technologies, ICAST 2019, pp. 131-132.
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This paper investigates the behaviour of lithium-based magnetorheological (MR) grease under the triangular quasi-static test. Three types of MR grease are prepared with weight fractions of carbon iron particles (CIP) as 30%, 50% and 70%, respectively. Quasi-static test of periodical triangular inputs, with various shear strain and strain rates, are employed to evaluate the performance of the MR greases, figure 1 and 2. Further evaluations are conducted by cross-checking the behaviour of the MR grease under various strain rate at a given max strain and the cases under various shear strains at a fixed strain rate.
Wang, J & Zhang, Q 1970, 'Intelligent Detection Method for Maximum Color Difference of Image Based on Machine Learning', ADVANCED HYBRID INFORMATION PROCESSING, ADHIP 2019, PT II, 3rd European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIP), Springer International Publishing, PEOPLES R CHINA, Nanjing, pp. 171-180.
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Wang, Q, Jia, W, He, X, Lu, Y, Blumenstein, M, Huang, Y & Lyu, S 1970, 'DeepText: Detecting Text from the Wild with Multi-ASPP-Assembled DeepLab', 2019 International Conference on Document Analysis and Recognition (ICDAR), 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE, Sydney, Australia, pp. 208-213.
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© 2019 IEEE. In this paper, we address the issue of scene text detection in the way of direct regression and successfully adapt an effective semantic segmentation model, DeepLab v3+ [1], for this application. In order to handle texts with arbitrary orientations and sizes and improve the recall of small texts, we propose to extract features of multiple scales by inserting multiple Atrous Spatial Pyramid Pooling (ASPP) layers to the DeepLab after the feature maps with different resolutions. Then, we set multiple auxiliary IoU losses at the decoding stage and make auxiliary connections from the intermediate encoding layers to the decoder to assist network training and enhance the discrimination ability of lower encoding layers. Experiments conducted on the benchmark scene text dataset ICDAR2015 demonstrate the superior performance of our proposed network, named as DeepText, over the state-of-the-art approaches.
Wang, Q, Jia, W, He, X, Lu, Y, Blumenstein, M, Huang, Y & Lyu, S 1970, 'ReELFA: A Scene Text Recognizer with Encoded Location and Focused Attention', 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), IEEE, Australia, pp. 71-76.
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Wang, S, Hu, L, Wang, Y, Cao, L, Sheng, QZ & Orgun, M 1970, 'Sequential Recommender Systems: Challenges, Progress and Prospects', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, Macao, pp. 6332-6338.
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The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users’ preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations. In this paper, we provide a systematic review on SRSs. We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area.
Wang, S, Hu, L, Wang, Y, Sheng, QZ, Orgun, M & Cao, L 1970, 'Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, Macao, pp. 3771-3777.
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A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this may not always be true in reality, and a session may often consist of multiple subsets of items for different purposes (e.g., breakfast and decoration). Specifically, items (e.g., bread and milk) in a subsethave strong purpose-specific dependencies whereas items (e.g., bread and vase) from different subsets have much weaker or even no dependencies due to the difference of purposes. Therefore, we propose a mixture-channel model to accommodate the multi-purpose item subsets for more precisely representing a session. Filling gaps in existing SBRSs, this model recommends more diverse items to satisfy different purposes. Accordingly, we design effective mixture-channel purpose routing networks (MCPRN) with a purpose routing network to detect the purposes of each item and assign it into the corresponding channels. Moreover, a purpose specific recurrent network is devised to model the dependencies between items within each channel for a specific purpose. The experimental results show the superiority of MCPRN over the state-of-the-art methods in terms of both recommendation accuracy and diversity.
Wang, X, Jin, D, Liu, M, He, D, Musial, K & Dang, J 1970, 'Emotional Contagion-Based Social Sentiment Mining in Social Networks by Introducing Network Communities', Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM '19: The 28th ACM International Conference on Information and Knowledge Management, ACM, Beijing, China, pp. 1763-1772.
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© 2019 Association for Computing Machinery. The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs, instant-messages, and so on. This article concentrates on the mining of readers' social sentiments evoked by social media materials. Existing methods are only applicable to a minority of social media like news portals with emotional voting information, while ignore the emotional contagion between writers and readers. However, incorporating such factors is challenging since the learned hidden variables would be very fuzzy (because of the short and noisy text in social networks). In this paper, we try to solve this problem by introducing a high-order network structure, i.e. communities. We first propose a new generative model called Community-Enhanced Social Sentiment Mining (CESSM), which 1) considers the emotional contagion between writers and readers to capture precise social sentiment, and 2) incorporates network communities to capture coherent topics. We then derive an inference algorithm based on Gibbs sampling. Empirical results show that, CESSM achieves significantly superior performance against the state-of-the-art techniques for text sentiment classification and interestingness in social sentiment mining.
Wang, X, Qi, W & Ghanbarikarekani, M 1970, 'Estimation of Heavy Vehicle Passenger Car Equivalents for On-Ramp Adjacent Zones Under Different Traffic Volumes: A Case Study', Smart Innovation, Systems and Technologies, International Conference on Intelligent Interactive Multimedia Systems and Services, Springer International Publishing, Gold Coast, Australia, pp. 338-346.
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© Springer International Publishing AG, part of Springer Nature 2019. Due to the difference in operational characteristic, heavy vehicles have been viewed as a hindrance in traffic flow and capacity analysis. The emergence of passenger car equivalents (PCE) can assist traffic agencies in better understanding the impact of heavy vehicles on passenger vehicles in the mixed traffic stream, by converting a heavy vehicle of a subject class into the equivalent number of passenger cars. However, according to existing literature, most researchers have devoted to the estimation of PCE for basic freeway sections. Therefore, in this study, we explore the variation of heavy vehicle PCE for on-ramp adjacent zones under varying traffic volume. A one-lane on-ramp in Queensland, Australia, is selected for a case study and four existing PCE approaches are applied in the calculation of PCE. They are homogenization based method, time headway based method, traffic flow based method, and multiple regression method, respectively. The final PCE values are compared to those derived from VISSIM simulation model. The following conclusions are drawn: (1) homogenization based method cannot reveal the variation trend of PCE factors over traffic volume; (2) the results obtained through time headway and traffic flow based methods are more consistent with outcome from simulation model.
Wang, X, Yu, P, Yu, G, Zha, X, Ni, W, Liu, RP & Guo, YJ 1970, 'A High-Performance Hybrid Blockchain System for Traceable IoT Applications', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Network and System Security, Springer International Publishing, Sapporo, Japan, pp. 721-728.
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© 2019, Springer Nature Switzerland AG. Blockchain, as an immutable distributed ledger, can be the key to realize secure and trustworthy IoT applications. However, existing blockchains can hardly achieve high-performance and high-security for large-scale IoT applications simultaneously. In this paper, we propose a hyper blockchain architecture combining the security of public blockchains with the efficiency of private blockchains. An IoT anchoring smart contract is proposed to anchor private IoT blockchains into a public blockchain. An IoT device management smart contract is also designed to trace sensory data. A comprehensive analysis reveals that the proposed hybrid blockchain system can achieve the performance of private blockchains and resist tampering.
Wang, Y, Jin, D, Musial, K & Dang, J 1970, 'Community Detection in Social Networks Considering Topic Correlations', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Hawaii, USA, pp. 321-328.
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Network contents including node contents and edge contents can be utilized for community detection in social networks. Thus, the topic of each community can be extracted as its semantic information. A plethora of models integrating topic model and network topologies have been proposed. However, a key problem has not been resolved that is the semantic division of a community. Since the definition of community is based on topology, a community might involve several topics. To ach
Wang, Y, Long, G, Peng, X, Clarke, A, Stevenson, R & Gerrard, L 1970, 'Interactive Deep Metric Learning for Healthcare Cohort Discovery', Communications in Computer and Information Science, Australasian Conference on Data Mining, Springer Singapore, Adelaide, Australia, pp. 208-221.
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© Springer Nature Singapore Pte Ltd. 2019. Given the continuous growth of large-scale complex electronic healthcare data, a data-driven healthcare cohort discovery facilitated by machine learning tools with domain expert knowledge is required to gain further insights of the healthcare system. Specifically, clustering plays a crucial role in healthcare cohort discovery, and metric learning is able to incorporate expert feedback to generate more fit-for-purpose clustering outputs. However, most of the existing metric learning methods assume all labelled instances already pre-exists, which is not always true in real-world applications. In addition, big data in healthcare also brings new challenges to metric learning on handling complex structured data. In this paper, we propose a novel systematic method, namely Interactive Deep Metric Learning (IDML), which uses an interactive process to iteratively incorporate feedback from domain experts to identify cohorts that are more relevant to a particular pre-defined purpose. Moreover, the proposed method leverages powerful deep learning-based embedding techniques to incrementally gain effective representations for the complex structures inherit in patient journey data. We experimentally evaluate the effectiveness of the proposed IDML using two public healthcare datasets. The proposed method has also been implemented into an interactive cohort discovery tool for a real-world application in healthcare.
Wang, Z, Li, Q, Li, G & Xu, G 1970, 'Polynomial Representation for Persistence Diagram', 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Long Beach, CA, USA, pp. 6116-6125.
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© 2019 IEEE. Persistence diagram (PD) has been considered as a compact descriptor for topological data analysis (TDA). Unfortunately, PD cannot be directly used in machine learning methods since it is a multiset of points. Recent efforts have been devoted to transforming PDs into vectors to accommodate machine learning methods. However, they share one common shortcoming: the mapping of PDs to a feature representation depends on a pre-defined polynomial. To address this limitation, this paper proposes an algebraic representation for PDs, i.e., polynomial representation. In this work, we discover a set of general polynomials that vanish on vectorized PDs and extract the task-adapted feature representation from these polynomials. We also prove two attractive properties of the proposed polynomial representation, i.e., stability and linear separability. Experiments also show that our method compares favorably with state-of-the-art TDA methods.
Warkiani, ME 1970, 'Label-free Cell Sorting Using Inertial Microfluidics', 2019 13TH IEEE INTERNATIONAL CONFERENCE ON NANO/MOLECULAR MEDICINE & ENGINEERING (IEEE-NANOMED 2019), 13th IEEE International Conference on Nano/Molecular Medicine and Engineering (IEEE NANOMED), IEEE, SOUTH KOREA, Gwangju, pp. 52-53.
Wei, J, Li, J & Wu, C 1970, 'Failure mechanisms of RC and UHPC columns under lateral impact', 13th International Conference on Shock and Impact Loads on Structures, SILOS 2019, pp. 449-457.
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Reinforced concrete (RC) columns are widely adopted in bridges, offices and car parks due to its low construction cost and easy formwork installation. Besides the service loads, RC column structures may experience accidental lateral impact loading during their service life, which could lead to structural failure. In this study, the impact response of axially loaded columns is investigated through low-velocity impact tests. Concrete materials adopted for column construction were ultra-high performance concrete (UHPC) and conventional concrete. The compressive strength for UHPC and conventional concrete were 136 MPa and 40 MPa, and the flexural strength for UHPC and conventional concrete were 21 MPa and 2.8 MPa, respectively. In total, 6 axially loaded columns, including 4 RC columns and 2 UHPC columns, were tested against 400 kg weight dropping from a height varying from 1 m to 1.5 m. UHPC columns outperformed the RC columns with marginal flexural damage. With the available material test data, a numerical model is built for RC and UHPC columns and validated against experimental testing results.
Weibel, J-B, Patten, T & Vincze, M 1970, 'Robust 3D Object Classification by Combining Point Pair Features and Graph Convolution', 2019 International Conference on Robotics and Automation (ICRA), 2019 International Conference on Robotics and Automation (ICRA), IEEE, pp. 7262-7268.
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Weisner, K, Knittel, M, Jaitner, T & Deuse, J 1970, 'Increasing Flexibility of Employees in Production Processes Using the Differential Learning Approach – Adaptation and Validation of Motor Learning Theories', Advances in Intelligent Systems and Computing, AHFE 2018 International Conference on Human Factors in Training, Education, and Learning Sciences, Springer International Publishing, USA, pp. 216-225.
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© Springer International Publishing AG, part of Springer Nature 2019. International expanding markets and continuous development of new customer oriented products lead to an increasing product and process variety and complexity as well as shortened product lifecycles. According to these challenges, manufacturing companies have to enhance their process flexibility to remain sustainable competitive. Due to that, employees have to deal with high flexible work processes including continuous change of constellations and objectives. These in turn require a high employee’s flexibility, adaptability and occupational competence as well as new training concepts to enable them. In the academic literature and industrial practice, exists a variety of concepts for employee’s qualification and training. However, these concepts do only partially focus the employee’s occupational competence. Therefore, an innovative learning concept based on motor learning theories was developed and empirically validated. The description of the examination design as well as the result presentation and discussion are subject of the present contribution.
Weßkamp, V, Seckelmann, T, Barthelmey, A, Kaiser, M, Lemmerz, K, Glogowski, P, Kuhlenkötter, B & Deuse, J 1970, 'Development of a sociotechnical planning system for human-robot interaction in assembly systems focusing on small and medium-sized enterprises', Procedia CIRP, CIRP Conference on Manufacturing Systems, Elsevier BV, Ljubljana, Slovenia, pp. 1284-1289.
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© 2019 The Authors. Published by Elsevier Ltd. As part of a research project a holistic planning system for human-robot interaction (HRI) is developed, focusing on the applicability for small and medium-sized enterprises (SMEs). This paper details several important aspects of the planning system, including an easy identification method of suitable processes for HRI. A key element is the work allocation between human workers and robots. The allocation considers e.g. technical feasibility, cycle time as well as personnel deployment and can easily be carried out by SMEs. Based on a simulation, a comprehensible scenario evaluation is developed, focusing primarily on economic aspects but also including ergonomic and safety considerations.
White, S, Wang, K, Tran, TT, Kianinia, M, Titchener, JG, Graefe, M, Fischbach, S, Rodt, S, Song, JD, Reitzenstein, S, Aharonovich, I, Sukhorukov, AA, Szameit, A & Solntsev, AS 1970, 'Tomography of quantum dots in a non-hermitian photonic chip', AOS Australian Conference on Optical Fibre Technology (ACOFT) and Australian Conference on Optics, Lasers, and Spectroscopy (ACOLS) 2019, AOS Australian Conference on Optical Fibre Technology (ACOFT) and Australian Conference on Optics, Lasers, and Spectroscopy (ACOLS) 2019, SPIE, Melbourne, Australia, pp. 62-62.
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© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. Quantum optical information systems offer the potential for secure communication and fast quantum computation. To fully characterise a quantum optical system one has to use quantum tomography.1 The integration of quantum optics onto photonic chips provides advantages such as miniaturisation and stability, significantly improving quantum tomography using both re-configurable, and more recently, simpler static designs. These on-chip designs have, so far, only used probabilistic single photon sources. Here we are working towards quantum tomography using a true deterministic source-an InGaAs quantum dot.
Wickramanayake, S, Thiyagarajan, K, Kodagoda, S & Piyathilaka, L 1970, 'Frequency Sweep Based Sensing Technology for Non-destructive Electrical Resistivity Measurement of Concrete', Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC), 36th International Symposium on Automation and Robotics in Construction, International Association for Automation and Robotics in Construction (IAARC), Canada, pp. 1290-1290.
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© 2019 International Association for Automation and Robotics in Construction I.A.A.R.C. All rights reserved. Electrical resistivity is an important parameter to be monitored for the conditional assessment and health monitoring of aging and new concrete infrastructure. In this paper, we report the design and development of a frequency sweep based sensing technology for non-destructive electrical resistivity measurement of concrete. Firstly, a sensing system prototype was developed based on the Wenner probe arrangement for the electrical resistivity measurements. This system operates by integrating three major units namely current injection unit, sensing unit and microcontroller unit. Those units govern the overall operations of the sensing system. Secondly, the measurements from the developed unit were compared with the measurements of the commercially available device at set conditions. This experimentation evaluated the measurement performance and demonstrated the effectiveness of the developed sensor prototype. Finally, the influence of rebar and the effect of frequency on the electrical measurements were studied through laboratory experimentation on a concrete sample. Experimental results indicated that the electrical resistivity measurements taken at a closer proximity to the rebar had its influence than the measurements taken away from the rebar in the ideal set condition. Also, the increase in electrical resistivity to the increase in frequency was observed, and then the measurements show lesser variations to higher frequency inputs.
Wijayaratna, K, Moylan, E, Jian, S, Jones, M & Waller, ST 1970, 'The unified reliability model', Australasian Transport Research Forum, ATRF 2019 - Proceedings, Australasian Transport Research Forum, Australia.
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Reliability of products and systems are fundamental to consumer choice. In the context of transport systems, journey time reliability is seen as a key determinant of traveller choices. Existing research has found that on-time arrival can be valued more than travel time savings. Thus, the quantification of reliability is paramount to monitoring and assessing the performance of transport systems, especially considering road transport systems. This paper presents the development of the Unified Reliability Model (URM), a supplementary tool for the simple and robust measurement of reliability on a road network. The URM unifies aspects of the UK Reliability Model and the New Zealand (NZ) model, both of which are currently applied as best practice. Applications of the URM using data from the Sydney road network present robust measurements of reliability that are comparable or exceed the accuracy of the existing approaches.
Willey, K 1970, 'Self and peer review to engage students with assessment criteria, develop their judgement and critical evaluation skills and extend the benefits of instructor feedback', Proceedings of the 46th SEFI Annual Conference 2018: Creativity, Innovation and Entrepreneurship for Engineering Education Excellence, pp. 512-519.
Willey, K & MacHet, T 1970, 'Assisting tutors to develop their student's competence when working with complexity', Proceedings of the 8th Research in Engineering Education Symposium, REES 2019 - Making Connections, Research in Engineering Education Symposium, South African Society of Engineering Education, Capetown, South Africa, pp. 501-509.
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Practising engineers are required to be independent learners, using their judgement and creativity to arrive at solutions to complex real-world problems. Research reports that these skills are currently underdeveloped in engineering students. This is not surprising given that most engineering students have undertaken mainly science and maths subjects in which they apply their mathematical knowledge to arrive at unique solutions. Conversely, in engineering practice, activities are rarely characterised by an ideal answer but rather are complex, requiring trade-offs and combining non-optimum solutions. Dealing with complex problems requires students to use judgement, subjectivity, and reasoning to make decisions instead of relying solely on the scientific evidence and facts. This challenges many students' feeling of competence and inhibits their learning motivation. In this paper, we report introducing student tutors to self-determination theory and a framework to provide a context and vocabulary to understand, reflect on and discuss learning when managing complexity to improve their students' learning and feelings of competence.
Williams, P, Kirby, R & Hill, J 1970, 'Numerical method for prediction of duct break out sound power', Proceedings of Meetings on Acoustics, 178th Meeting of the Acoustical Society of America, ASA, pp. 022001-022001.
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The acoustic design of duct systems requires consideration of both the noise propagating within a duct and also of the noise transmitted out through the duct walls into the environment. Control of this breakout noise can form a significant part of a noise control solution, and so understanding this phenomenon can lead to reduced material use. The breakout noise is investigated here using coupled structural-acoustic finite element models. Propagation along a waveguide with constant cross section is represented using a modal expansion of the acoustic pressure in the fluid and displacement in the duct walls. The free-field external environment requires an outer boundary condition, and for this purpose, a perfectly matched layer is applied at some distance from the elastic walls. The finite length of the waveguide is then enforced by coupling the fields to separate infinite length inlet and outlet ducts by the mode matching method. Transmitted power from the finite length duct is investigated when a noise source is placed in the inlet.
Wilson, KJ, Alabd, R, Abolhasan, M, Franklin, DR & Safavi-Naeini, M 1970, 'Localisation of the Lines of Response in a Continuous Cylindrical Shell PET Scanner.', EMBC, IEEE Engineering in Medicine and Biology Conference, IEEE, Berlin, Germany, pp. 4844-4850.
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This work presents a technique for localising the endpoints of the lines of response in a PET scanner based on a continuous cylindrical shell scintillator. The technique is demonstrated by applying it to a simulation of a sensitivity-optimised continuous cylindrical shell PET system using two novel scintillator materials - a transparent ceramic garnet, GLuGAG:Ce, and a LuF$_3$:Ce-polystyrene nanocomposite. Error distributions for the endpoints of the lines of response in the axial, tangential and radial dimension as well as overall endpoint spatial error are calculated for three source positions; the resultant distribution of error in the placement of the lines of response is also estimated.
Wu, A, Zhu, L, Han, Y & Yang, Y 1970, 'Connective cognition network for directional visual commonsense reasoning', Advances in Neural Information Processing Systems, pp. 5670-5680.
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Visual commonsense reasoning (VCR) has been introduced to boost research of cognition-level visual understanding, i.e., a thorough understanding of correlated details of the scene plus an inference with related commonsense knowledge. Recent studies on neuroscience have suggested that brain function or cognition can be described as a global and dynamic integration of local neuronal connectivity, which is context-sensitive to specific cognition tasks. Inspired by this idea, towards VCR, we propose a connective cognition network (CCN) to dynamically reorganize the visual neuron connectivity that is contextualized by the meaning of questions and answers. Concretely, we first develop visual neuron connectivity to fully model correlations of visual content. Then, a contextualization process is introduced to fuse the sentence representation with that of visual neurons. Finally, based on the output of contextualized connectivity, we propose directional connectivity to infer answers or rationales. Experimental results on the VCR dataset demonstrate the effectiveness of our method. Particularly, in Q ? AR mode, our method is around 4% higher than the state-of-the-art method.
Wu, B, Ma, Y, Zhang, Q & Qian, F 1970, 'Active checking buffer overflow vulnerability in binaries with symbolic execution', 2019 2ND INTERNATIONAL CONFERENCE ON MECHANICAL, ELECTRONIC AND ENGINEERING TECHNOLOGY (MEET 2019), 2nd International Conference on Mechanical, Electronic and Engineering Technology (MEET), CLAUSIUS SCIENTIFIC PR INC, PEOPLES R CHINA, Xian, pp. 137-144.
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Wu, D, Chen, J, Sharma, N, Pan, S, Long, G & Blumenstein, M 1970, 'Adversarial Action Data Augmentation for Similar Gesture Action Recognition', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-8.
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Human gestures are unique for recognizing and describing human actions, and video-based human action recognition techniques are effective solutions to varies real-world applications, such as surveillance, video indexing, and human-computer interaction. Most existing video human action recognition approaches either using handcraft features from the frames or deep learning models such as convolutional neural networks (CNN) and recurrent neural networks (RNN); however, they have mostly overlooked the similar gestures between different actions when processing the frames into the models. The classifiers suffer from similar features extracted from similar gestures, which are unable to classify the actions in the video streams. In this paper, we propose a novel framework with generative adversarial networks (GAN) to generate the data augmentation for similar gesture action recognition. The contribution of our work is tri-fold: 1) we proposed a novel action data augmentation framework (ADAF) to enlarge the differences between the actions with very similar gestures; 2) the framework can boost the classification performance either on similar gesture action pairs or the whole dataset; 3) experiments conducted on both KTH and UCF101 datasets show that our data augmentation framework boost the performance on both similar gestures actions as well as the whole dataset compared with baseline methods such as 2DCNN and 3DCNN.
Wu, D, Hu, R, Zheng, Y, Jiang, J, Sharma, N & Blumenstein, M 1970, 'Feature-Dependent Graph Convolutional Autoencoders with Adversarial Training Methods', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-8.
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© 2019 IEEE. Graphs are ubiquitous for describing and modeling complicated data structures, and graph embedding is an effective solution to learn a mapping from a graph to a low-dimensional vector space while preserving relevant graph characteristics. Most existing graph embedding approaches either embed the topological information and node features separately or learn one regularized embedding with both sources of information, however, they mostly overlook the interdependency between structural characteristics and node features when processing the graph data into the models. Moreover, existing methods only reconstruct the structural characteristics, which are unable to fully leverage the interaction between the topology and the features associated with its nodes during the encoding-decoding procedure. To address the problem, we propose a framework using autoencoder for graph embedding (GED) and its variational version (VEGD). The contribution of our work is two-fold: 1) the proposed frameworks exploit a feature-dependent graph matrix (FGM) to naturally merge the structural characteristics and node features according to their interdependency; and 2) the Graph Convolutional Network (GCN) decoder of the proposed framework reconstructs both structural characteristics and node features, which naturally possesses the interaction between these two sources of information while learning the embedding. We conducted the experiments on three real-world graph datasets such as Cora, Citeseer and PubMed to evaluate our framework and algorithms, and the results outperform baseline methods on both link prediction and graph clustering tasks.
Wu, D, Liu, J, Sui, Y, Chen, S & Xue, J 1970, 'Precise Static Happens-Before Analysis for Detecting UAF Order Violations in Android', 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST), 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST), IEEE, Xi'an, China, pp. 276-287.
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Unlike Java, Android provides a rich set of APIs to support a hybrid concurrency system, which consists of both Java threads and an event queue mechanism for dispatching asynchronous events. In this model, concurrency errors often manifest themselves in the form of order violations. An order violation occurs when two events access the same shared object in an incorrect order, causing unexpected program behaviors (e.g., null pointer dereferences). This paper presents SARD, a static analysis tool for detecting both intra-and inter-thread use-after-free (UAF) order violations, when a pointer is dereferenced (used) after it no longer points to any valid object, through systematic modeling of Android's concurrency mechanism. We propose a new flow-and context-sensitive static happens-before (HB) analysis to reason about the interleavings between two events to effectively identify precise HB relations and eliminate spurious event interleavings. We have evaluated SARD by comparing with NADROID, a state-of-the-art static order violation detection tool for Android. SARD outperforms NADROID in terms of both precision (by reporting three times fewer false alarms than NADROID given the same set of apps used by NADROID) and efficiency (by running two orders of magnitude faster than NADROID).
Wu, J, Xie, R, Song, L & Liu, B 1970, 'Deep Feature Guided Image Retargeting', 2019 IEEE Visual Communications and Image Processing (VCIP), 2019 IEEE Visual Communications and Image Processing (VCIP), IEEE, Sydney, Australia, pp. 1-4.
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© 2019 IEEE. Image retargeting is the technique to display images via devices with various aspect ratios and sizes. Traditional content-Aware retargeting methods rely on low-level features to predict pixel-wise importance and can hardly preserve both the structure lines and salient regions of the source image. To address this problem, we propose a novel adaptive image warping approach which integrates with deep convolutional neural network. In the proposed method, a visual importance map and a foreground mask map are generated by a pre-Trained network. The two maps and other constraints guide the warping process to yield retargeted results with less distortions. Extensive experiments in terms of visual quality and a user study are carried out on the widely used RetargetMe dataset. Experimental results show that our method outperforms current state-of-Art image retargeting methods.
Wu, J, Yao, L, Huang, Y, Xu, J, Wu, Q & Huang, L 1970, 'Improving Person Re-Identification Performance Using Body Mask Via Cross-Learning Strategy', 2019 IEEE Visual Communications and Image Processing (VCIP), 2019 IEEE Visual Communications and Image Processing (VCIP), IEEE, Sydney, Australia, pp. 1-4.
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© 2019 IEEE. The task of person re-identification (re-id) is to find the same pedestrian across non-overlapping cameras. Normally, the performance of person re-id can be affected by background clutters. However, existing segmentation algorithms are hard to obtain perfect foreground person images. To effectively leverage the body (foreground) cue, and in the meantime pay attention to discriminative information in the background (e.g., companion or vehicle), we propose to use a cross-learning strategy to take both foreground and other discriminative information into account. In addition, since currently existing foreground segmentation result always involves noise, we use Label Smoothing Regularization (LSR) to strengthen the generalization capability during our learning process. In experiments, we pick up two state-of-The-Art person re-id methods to verify the effectiveness of our proposed cross-learning strategy. Our experiments are carried out on two publicly available person re-id datasets. Obvious performance improvements can be observed on both datasets.
Wu, M, Pan, S, Du, L, Tsang, I, Zhu, X & Du, B 1970, 'Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning', Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM '19: The 28th ACM International Conference on Information and Knowledge Management, ACM, Beijing China, pp. 2157-2160.
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© 2019 Association for Computing Machinery. Graph neural nets are emerging tools to represent network nodes for classification. However, existing approaches typically suffer from two limitations: (1) they only aggregate information from short distance (e.g., 1-hop neighbors) each round and fail to capture long distance relationship in graphs; (2) they require users to label data from several classes to facilitate the learning of discriminative models; whereas in reality, users may only provide labels of a small number of nodes in a single class. To overcome these limitations, this paper presents a novel long-short distance aggregation networks (LSDAN) for positive unlabeled (PU) graph learning. Our theme is to generate multiple graphs at different distances based on the adjacency matrix, and further develop a long-short distance attention model for these graphs. The short-distance attention mechanism is used to capture the importance of neighbor nodes to a target node. The long-distance attention mechanism is used to capture the propagation of information within a localized area of each node and help model weights of different graphs for node representation learning. A non-negative risk estimator is further employed, to aggregate long- short-distance networks, for PU learning using back-propagated loss modeling. Experiments on real-world datasets validate the effectiveness of our approach.
Wu, P, Wu, C, Liu, Z & Xu, S 1970, 'Experimental study on ultra-high performance concrete with and without steel fiber reinforcement under triaxial compression', 13th International Conference on Shock and Impact Loads on Structures, SILOS 2019, pp. 467-471.
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In this study, ultra-high performance concrete (UHPC) specimens with and without steel fiber were tested to investigate their triaxial behavior. The triaxial compressive strength of UHPC under various confining pressures is discussed. The experimental results show that the triaxial compressive strength of UHPC increases with the confining pressure and steel fiber. Steel fiber and confining pressure have significant effect on the failure mode of the crack width and the angle between the oblique crack and the axial direction, respectively.
Wu, Y, Zhu, L, Yan, Y & Yang, Y 1970, 'Dual Attention Matching for Audio-Visual Event Localization', 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Seoul, Korea (South).
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In this paper, we investigate the audio-visual event localization problem. This task is to localize a visible and audible event in a video. Previous methods first divide a video into short segments, and then fuse visual and acoustic features at the segment level. The duration of these segments is usually short, making the visual and acoustic feature of each segment possibly not well aligned. Direct concatenation of the two features at the segment level can be vulnerable to a minor temporal misalignment of the two signals. We propose a Dual Attention Matching (DAM) module to cover a longer video duration for better high-level event information modeling, while the local temporal information is attained by the global cross-check mechanism. Our premise is that one should watch the whole video to understand the high-level event, while shorter segments should be checked in detail for localization. Specifically, the global feature of one modality queries the local feature in the other modality in a bi-directional way. With temporal co-occurrence encoded between auditory and visual signals, DAM can be readily applied in various audio-visual event localization tasks, e.g., cross-modality localization, supervised event localization. Experiments on the AVE dataset show our method outperforms the state-of-the-art by a large margin.
Wu, Z, Pan, S, Long, G, Jiang, J & Zhang, C 1970, 'Graph WaveNet for Deep Spatial-Temporal Graph Modeling', IJCAI International Joint Conference on Artificial Intelligence, The 28th International Joint Conference on Artificial Intelligence (IJCAI), International Joint Conferences on Artificial Intelligence Organization, Macao, China, pp. 1907-1913.
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Spatial-temporal graph modeling is an important task to analyze the spatialrelations and temporal trends of components in a system. Existing approachesmostly capture the spatial dependency on a fixed graph structure, assuming thatthe underlying relation between entities is pre-determined. However, theexplicit graph structure (relation) does not necessarily reflect the truedependency and genuine relation may be missing due to the incompleteconnections in the data. Furthermore, existing methods are ineffective tocapture the temporal trends as the RNNs or CNNs employed in these methodscannot capture long-range temporal sequences. To overcome these limitations, wepropose in this paper a novel graph neural network architecture, Graph WaveNet,for spatial-temporal graph modeling. By developing a novel adaptive dependencymatrix and learn it through node embedding, our model can precisely capture thehidden spatial dependency in the data. With a stacked dilated 1D convolutioncomponent whose receptive field grows exponentially as the number of layersincreases, Graph WaveNet is able to handle very long sequences. These twocomponents are integrated seamlessly in a unified framework and the wholeframework is learned in an end-to-end manner. Experimental results on twopublic traffic network datasets, METR-LA and PEMS-BAY, demonstrate the superiorperformance of our algorithm.
Wu, Z, Pan, S, Long, G, Jiang, J & Zhang, C 1970, 'Graph WaveNet for Deep Spatial-Temporal Graph Modeling', PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 28th International Joint Conference on Artificial Intelligence, IJCAI-INT JOINT CONF ARTIF INTELL, PEOPLES R CHINA, Macao, pp. 1907-1913.
Xiao, K, Zhao, J, He, Y & Yu, S 1970, 'Trajectory Prediction of UAV in Smart City using Recurrent Neural Networks', ICC 2019 - 2019 IEEE International Conference on Communications (ICC), ICC 2019 - 2019 IEEE International Conference on Communications (ICC), IEEE, Shanghai, China.
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© 2019 IEEE. The 5th generation (5G) wireless network with Unmanned aerial vehicle (UAV) is considered to be one of the most effective solutions for improving the communication coverage. However, UAV is easily affected by the wind, accompanied by a certain time delay during the air communication. Thus the inaccurate beamforming will be performed by the base station (BS), resulting in the unnecessary capacity loss. To address this issue, we propose a novel Recurrent Neural Networks (RNN)-based arrival angle predictor to predict the specific communication location of UAV under the 5G Internet of Things (IoT) networks in this paper. Specifically, a grid-based coordinate system is applied during the data preprocessing to make the training process easier and more effective. Moreover, the RNN model with the highest accuracy can be saved during the training process to ensure the real-time prediction. Simulation results reveal that the RNN-based predictor we proposed is of high prediction accuracy, which is 98% in average. Therefore, a more precise beamforming can be performed by BS to reduce the unnecessary capacity loss, resulting in a more effective and reliable communication system.
Xiao, Y, Xiao, L, Zhang, H, Yu, S & Poor, HV 1970, 'Privacy Aware Recommendation: Reinforcement Learning Based User Profile Perturbation', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA, pp. 1-6.
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User profile release in recommendation systems can apply the user profile perturbation technique to protect user privacy, in which each user sends a perturbed user profile such as the a list of clicked items to receive a recommendation service from a server. The perturbation policy such as the privacy budget determines the recommendation quality and the privacy level, while its optimization usually depends on the known attack model, which is rarely known by the users. In this paper, we propose a reinforcement learning based user profile perturbation scheme that applies differential privacy to protect user privacy for recommendation systems. According to reinforcement learning, the privacy budget to perturb the released user profile depends on the features of the actual user profiles and the released user profiles, and the estimated user privacy level. This scheme enables a user to optimize his or her perturbation policy in terms of both the user privacy level and the received recommendation quality without being aware of the attack model. We evaluate the computational complexity of this scheme and analyze a case study, a privacy aware movie recommendation system. Simulation results show that this scheme improves user privacy protection for a given level of recommendation quality compared with a benchmark profile perturbation scheme.
Xie, H-B, Li, C, Xu, RYD & Mengersen, K 1970, 'Robust Kernelized Bayesian Matrix Factorization for Video Background/Foreground Separation', Machine Learning, Optimization, and Data Science (LNCS), International Conference on Machine Learning, Optimization, and Data Science, Springer International Publishing, Siena, Italy, pp. 484-495.
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© Springer Nature Switzerland AG 2019. Development of effective and efficient techniques for video analysis is an important research area in machine learning and computer vision. Matrix factorization (MF) is a powerful tool to perform such tasks. In this contribution, we present a hierarchical robust kernelized Bayesian matrix factorization (RKBMF) model to decompose a data set into low rank and sparse components. The RKBMF model automatically infers the parameters and latent variables including the reduced rank using variational Bayesian inference. Moreover, the model integrates the side information of similarity between frames to improve information extraction from the video. We employ RKBMF to extract background and foreground information from a traffic video. Experimental results demonstrate that RKBMF outperforms state-of-the-art approaches for background/foreground separation, particularly where the video is contaminated.
Xie, Y, Xu, Z, Gong, S, Xu, J, Hoang, DT & Niyato, D 1970, 'Backscatter-Assisted Hybrid Relaying Strategy for Wireless Powered IoT Communications', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA, pp. 1-6.
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© 2019 IEEE. In this work, we consider multiple energy harvesting relays to assist information transmission from a hybrid access point (HAP) to a distant receiver. The multi-antenna HAP also beamforms RF power to the relays by using a power-splitting protocol. We aim to maximize the throughput by jointly optimizing the HAP's beamforming strategy as well as individual relays' energy harvesting and collaborative beamforming strategies. With dense user devices, the throughput maximization takes account of the direct links from the HAP to the receiver as they are short and contribute considerably to the overall throughput. Moreover, we introduce the concept of hybrid relaying communications which allows the energy harvesting relays to switch between two radio modes. In particular, the relays can operate either in RF communications or backscatter communications, depending on their channel conditions and energy status. This results in a non-convex and combinatorial throughput maximization problem. With the fixed relay mode, we can find a feasible lower performance bound via convex approximation, which further motivates our algorithm design to update the relay mode in an iterative manner. Simulation results verify that the proposed hybrid relaying strategy can achieve significant performance improvement compared to the conventional relaying strategy with all relays operating in the RF communications mode.
Xu, B, He, N & Li, D 1970, 'Study on the treatments and countermeasures for liquefiable foundation', MATEC Web of Conferences, EDP Sciences, pp. 01012-01012.
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This paper summarizes the current treatments and countermeasures for liquefiable foundations, and divides the existing anti-liquefaction countermeasures into two categories. One of the ideas is proceeding from the properties of liquefiable foundation soils, by the means of improvement for the soil’s qualities to enhance the capacity of soil’s anti-liquefaction in the early stage. The other idea is considering from the stress conditions of liquefiable foundation soils, and to reduce the liquefaction-induced disasters by changing the stress conditions of the soil. The advantages and disadvantages of various anti-liquefaction measures were analysed by verifying the effectiveness of field applications of anti-liquefaction measures against ground liquefaction hazards, and the applicable conditions of various anti-liquefaction measures were classified. This paper provides experience for resisting soil liquefaction disasters.
Xu, J-X, Zhang, XY & Yang, Y 1970, 'High-Q-Factor Dual-Band Bandpass Filter and Filtering Switch Using Stub-Loaded Coaxial Resonators', 2019 IEEE MTT-S International Wireless Symposium (IWS), 2019 IEEE MTT-S International Wireless Symposium (IWS), IEEE, Guangzhou, PEOPLES R CHINA, pp. 1-3.
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© 2019 IEEE. In this paper, a dual-band bandpass filter (BPF) and a dual-band filtering switch are designed based on high-Q-factor dual-mode stub-loaded coaxial resonators. A novel coaxial resonator is constructed in a 3-D cavity structure with two open studs and one centrally-loaded short stub. Resonant frequencies of the coaxial resonator are studied to obtain the desired passband frequencies. The required external quality factors and coupling coefficients of a dual-band BPF can be achieved by properly arranging the feeding probes and controlling the coupling windows between the two resonators. Accordingly, a high-Q-factor dual-band BPF with good filtering responses is realized. Moreover, a transmission zero can be generated to obtain high isolation between two passbands. Based on this dual-band combine resonator filter, two pieces of printed circuit boards mounted with PIN diodes are embedded into the cavity filter structure to switch on and off the two passbands, resulting in a dual-band filtering switch. The proposed dual-band BPF and filtering switch have the advantages of high Q factors, which are attractive in the multi-band wireless systems.
Xu, P, Deng, Z, Choi, K-S, Cao, L & Wang, S 1970, 'Multi-View Information-Theoretic Co-Clustering for Co-Occurrence Data', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu,Hawaii, USA, pp. 379-386.
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Multi-view clustering has received much attention recently. Most of the existing multi-view clustering methods only focus on one-sided clustering. As the co-occurring data elements involve the counts of sample-feature co-occurrences, it is more efficient to conduct two-sided clustering along the samples and features simultaneously. To take advantage of two-sided clustering for the co-occurrences in the scene of multi-view clustering, a two-sided multi-view clustering method is proposed, i.e., multi-view information-theoretic co-clustering (MV-ITCC). The proposed method realizes two-sided clustering for co-occurring multi-view data under the formulation of information theory. More specifically, it exploits the agreement and disagreement among views by sharing a common clustering results along the sample dimension and keeping the clustering results of each view specific along the feature dimension. In addition, the mechanism of maximum entropy is also adopted to control the importance of different views, which can give a right balance in leveraging the agreement and disagreement. Extensive experiments are conducted on text and image multiview datasets. The results clearly demonstrate the superiority of the proposed method.
Xu, R & Fatahi, B 1970, 'Assessment of Soil Plasticity Effects on Seismic Response of Mid-Rise Buildings Resting on End-Bearing Pile Foundations', Springer International Publishing, pp. 146-159.
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Xu, W, Li, H, Zhang, J, Zhu, Y & Zhang, H 1970, 'Trajectory Tracking for Underwater Rescue Salvage Based on Backstepping Control', 2019 Chinese Control Conference (CCC), 2019 Chinese Control Conference (CCC), IEEE, pp. 1956-1961.
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Xu, W, Xiao, Y, Li, H, Zhang, J & Zhang, H 1970, 'Trajectory Tracking for Autonomous Underwater Vehicle Based on Model-Free Predictive Control', 2019 IEEE 20th International Conference on High Performance Switching and Routing (HPSR), 2019 IEEE 20th International Conference on High Performance Switching and Routing (HPSR), IEEE, pp. 1-6.
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Xu, X, Sui, Y, Yan, H & Xue, J 1970, 'VFix: Value-Flow-Guided Precise Program Repair for Null Pointer Dereferences', 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), IEEE, Montreal, QC, Canada,, pp. 512-523.
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Automated Program Repair (APR) faces a key challenge in efficiently generating correct patches from a potentially infinite solution space. Existing approaches, which attempt to reason about the entire solution space, can be ineffective (by often producing no plausible patches at all) and imprecise (by often producing plausible but incorrect patches). We present VFIX, a new value-flow-guided APR approach, to fix null pointer exception (NPE) bugs by considering a substantially reduced solution space in order to greatly increase the number of correct patches generated. By reasoning about the data and control dependences in the program, VFIX can identify bug-relevant repair statements more accurately and generate more correct repairs than before. VFIX outperforms a set of 8 state-of-the-art APR tools in fixing the NPE bugs in Defects4j in terms of both precision (by correctly fixing 3 times as many bugs as the most precise one and 50% more than all the bugs correctly fixed by these 8 tools altogether) and efficiency (by producing a correct patch in minutes instead of hours).
Xu, X, Tsang, IW, Cao, X, Zhang, R & Liu, C 1970, 'Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation', PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 28th International Joint Conference on Artificial Intelligence, IJCAI-INT JOINT CONF ARTIF INTELL, PEOPLES R CHINA, Macao, pp. 3989-3995.
Xu, Y, Afshar, S, Singh, RK, Wang, R, van Schaik, A & Hamilton, TJ 1970, 'A Binaural Sound Localization System using Deep Convolutional Neural Networks', 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, Sapporo, JAPAN.
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Xu, Y, Xu, D, Hong, X, Ouyang, W, Ji, R, Xu, M & Zhao, G 1970, 'Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection', 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Seoul, Korea, pp. 3788-3797.
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Recent saliency models extensively explore to incorporate multi-scale contextual information from Convolutional Neural Networks (CNNs). Besides direct fusion strategies, many approaches introduce message-passing to enhance CNN features or predictions. However, the messages are mainly transmitted in two ways, by feature-to-feature passing, and by prediction-to-prediction passing. In this paper, we add message-passing between features and predictions and propose a deep unified CRF saliency model . We design a novel cascade CRFs architecture with CNN to jointly refine deep features and predictions at each scale and progressively compute a final refined saliency map. We formulate the CRF graphical model that involves message-passing of feature-feature, feature-prediction, and prediction-prediction, from the coarse scale to the finer scale, to update the features and the corresponding predictions. Also, we formulate the mean-field updates for joint end-to-end model training with CNN through back propagation. The proposed deep unified CRF saliency model is evaluated over six datasets and shows highly competitive performance among the state of the arts.
Xu, Z, Ni, W, Li, L-G, Zhang, J-H, Wu, C-M, Li, C-Y & Zhang, Y-H 1970, 'The calculation and realization of the visibility between patches of complex 3D scene based on super-computation', Third International Conference on Photonics and Optical Engineering, The International Conference on Photonics and Optical Engineering, SPIE, pp. 118-118.
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Xue, M, Yuan, X, Lee, H & Ross, K 1970, 'Sensing the Chinese Diaspora: How Mobile Apps Can Provide Insights Into Global Migration Flows', 2019 International Conference on Data Mining Workshops (ICDMW), 2019 International Conference on Data Mining Workshops (ICDMW), IEEE, pp. 603-608.
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Yan, B, Zhao, Q, Zhang, JA, Li, Y & Wang, Z 1970, 'Convergence Acceleration for Multiobjective Sparse Reconstruction via Knowledge Transfer', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Evolutionary Multi-Criterion Optimization, Springer International Publishing, East Lansing, MI, USA, pp. 475-487.
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© Springer Nature Switzerland AG 2019. Multiobjective sparse reconstruction (MOSR) methods can potentially obtain superior reconstruction performance. However, they suffer from high computational cost, especially in high-dimensional reconstruction. Furthermore, they are generally implemented independently without reusing prior knowledge from past experiences, leading to unnecessary computational consumption due to the re-exploration of similar search spaces. To address these problems, we propose a sparse-constraint knowledge transfer operator to accelerate the convergence of MOSR solvers by reusing the knowledge from past problem-solving experiences. Firstly, we introduce the deep nonlinear feature coding method to extract the feature mapping between the search of the current problem and a previously solved MOSR problem. Through this mapping, we learn a set of knowledge-induced solutions which contain the search experience of the past problem. Thereafter, we develop and apply a sparse-constraint strategy to refine these learned solutions to guarantee their sparse characteristics. Finally, we inject the refined solutions into the iteration of the current problem to facilitate the convergence. To validate the efficiency of the proposed operator, comprehensive studies on extensive simulated signal reconstruction are conducted.
Yan, H, Chen, S, Sui, Y, Zhang, Y, Zou, C & Xue, J 1970, 'Per-Dereference Verification of Temporal Heap Safety via Adaptive Context-Sensitive Analysis', SAS 2019: Static Analysis, International Static Analysis Symposium, Springer International Publishing, Porto, Portugal, pp. 48-72.
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© Springer Nature Switzerland AG 2019. We address the problem of verifying the temporal safety of heap memory at each pointer dereference. Our whole-program analysis approach is undertaken from the perspective of pointer analysis, allowing us to leverage the advantages of and advances in pointer analysis to improve precision and scalability. A dereference (Forumala Presented)., say, via pointer q is unsafe iff there exists a deallocation (Forumala Presented)., say, via pointer p such that on a control-flow path (Forumala Presented).,p aliases with q (with both pointing to an object o representing an allocation), denoted, and (Forumala Presented). reaches (Forumala Presented). via control flow, denoted. Applying directly any existing pointer analysis, which is typically solved separately with an associated control-flow reachability analysis, will render such verification highly imprecise, since (i.e., (Forumala Presented). does not distribute over (Forumala Presented). ). For precision, we solve, with a control-flow path (Forumala Presented). containing an allocation o, a deallocation (Forumala Presented). and a dereference (Forumala Presented). abstracted by a tuple of three contexts. For scalability, a demand-driven full context-sensitive (modulo recursion) pointer analysis, which operates on pre-computed def-use chains with adaptive context-sensitivity, is used to infer, without losing soundness or precision. Our evaluation shows that our approach can successfully verify the safety of 81.3% (or (Forumala Presented).) of all the dereferences in a set of ten C programs totalling 1,166 KLOC.
Yan, H, Teng, J, Zhang, S & Sheng, D 1970, 'A mathematical model for tortuosity of soil with considering particles arrangement', 7th Asia-Pacific Conference on Unsaturated Soils, AP-UNSAT 2019.
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Tortuosity is an important variable to understand air or water permeability of soil. The existing studies have revealed that tortuosity is monotonously related to porosity. It has been recognized that tortuosity is only related to porosity, as shown in table 1. But the effect of particle redistribution on tortuosity that caused by penetration destroy or soil deformation has been less understood in literature. The previous formula of tortuosity was similar at high porosity, but different at low porosity. This study will conduct a theoretical study on tortuosity with considering the effect of particle arrangement on tortuosity By assuming that fluid passes through the squared particles in forms of laminar flow, a new mathematical model is developed in this study to compute the tortuosity of soil. The flow paths are shown in Fig.1, where the particle is assumed to be a square. There are some parameters should be defined, A is the side length of the square, B is the distance between the two particle center lines, C is the horizontal projection distance between the two particle center lines, and is the angle between the two particle center lines and the horizontal direction. According to the model, the relationship between A, B, C and θ can be determined as: (equation presented) We can then get the maximum flow path and the minimum flow path: (equation presented) Taking the average value of flow paths and considering the influence of overlap, we can obtain expression of tortuosity: (equation presented) This expression indicates that the tortuosity will change with the change of particle arrangement, but the range of this change has an interval, which corresponds to triangle arrangement (TA) and square arrangement (SA) (Fig. 2). By comparing this formula with the tortuosity expression proposed in literature, we can find that most data are within the range determined by this formula, indicating that the formula proposed in this paper has universality and accur...
Yan, W, Fu, A, Mu, Y, Zhe, X, Yu, S & Kuang, B 1970, 'EAPA', Proceedings of the 2nd International ACM Workshop on Security and Privacy for the Internet-of-Things, CCS '19: 2019 ACM SIGSAC Conference on Computer and Communications Security, ACM, United Kingdom, pp. 2-7.
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The wide deployment of devices in Internet of Things (IoT) not only brings many benefits, but also incurs some security challenges. Remote attestation becomes an attractive method to guarantee the security of IoT devices. Unfortunately, most current attestation schemes only focus on the software attacks, but cannot detect the physical attacks. Several remote attestation schemes resilient to physical attacks still have some drawbacks in energy consumption, runtime, and security. In this paper, we propose an Efficient Attestation scheme resilient to Physical Attacks (EAPA) for IoT devices. We exploit a distributed attestation mode to make the protocol be executed in parallel, which reduces the total runtime to $O(1)$. Besides, we introduce an accusation mechanism to report compromised devices and design a new key update method, ensuring the efficiency and the security of our scheme. Furthermore, we present the security analysis and the performance evaluation of EAPA. The results indicate that EAPA has the lowest energy and runtime consumption compared with related works. Particularly, it shows a constant value in terms of runtime consumption.
Yang, B, Wen, D, Qin, L, Zhang, Y, Chang, L & Li, R-H 1970, 'Index-Based Optimal Algorithm for Computing K-Cores in Large Uncertain Graphs.', ICDE, 2019 IEEE 35nd International Conference on Data Engineering (ICDE), IEEE, Macau SAR, China, pp. 64-75.
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© 2019 IEEE. Uncertainty in graph data occurs for a variety of reasons, such as noise and measurement errors. Recently, uncertain graph management and analysis have attracted many research attentions. Among them, computing k-cores in uncertain graphs (aka, (k, η)-cores) is an important problem and has emerged in many applications, for example, community detection, protein-protein interaction network analysis and influence maximization. Given an uncertain graph, the (k, η)-cores can be derived by iteratively removing the vertex with an η-degree of less than k and updating the η-degrees of its neighbors. However, the results heavily depend on the two input parameters k and η, and the settings for these parameters are unique to the specific graph structure and the user's subjective requirements. Additionally, computing and updating the η-degree for each vertex is the most costly component of the algorithm, and that cost is high. To overcome these drawbacks, we have developed an index-based solution for computing (k, η)-cores in this paper. The size of the index is well bounded by O(m), where m is the number of edges in the graph. Based on this index, queries for any k and η can be answered in optimal time. Further, the method is accompanied by several different optimizations to speed up construction of the index. We conduct extensive experiments on eight real-world datasets to practically evaluate the performance of all the proposed algorithms. The results demonstrate that this index-based approach is several orders of magnitude faster at processing queries than the traditional online approaches.?
Yang, H, Pan, S, Chen, L, Zhou, C & Zhang, P 1970, 'Low-Bit Quantization for Attributed Network Representation Learning', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, Macao, pp. 4047-4053.
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Attributed network embedding plays an important role in transferring network data into compact vectors for effective network analysis. Existing attributed network embedding models are designed either in continuous Euclidean spaces which introduce data redundancy or in binary coding spaces which incur significant loss of representation accuracy. To this end, we present a new Low-Bit Quantization for Attributed Network Representation Learning model (LQANR for short) that can learn compact node representations with low bitwidth values while preserving high representation accuracy. Specifically, we formulate a new representation learning function based on matrix factorization that can jointly learn the low-bit node representations and the layer aggregation weights under the low-bit quantization constraint. Because the new learning function falls into the category of mixed integer optimization, we propose an efficient mixed-integer based alternating direction method of multipliers (ADMM) algorithm as the solution. Experiments on real-world node classification and link prediction tasks validate the promising results of the proposed LQANR model.
Yang, T, Ding, C, Ziolkowski, RW & Guo, YJ 1970, 'A THz Single-Polarization-Single-Mode (SPSM) photonic crystal fiber based on epsilon-near-zero material', AOS Australian Conference on Optical Fibre Technology (ACOFT) and Australian Conference on Optics, Lasers, and Spectroscopy (ACOLS) 2019, AOS Australian Conference on Optical Fibre Technology (ACOFT) and Australian Conference on Optics, Lasers, and Spectroscopy (ACOLS) 2019, SPIE, Melbourne, AUSTRALIA, pp. 87-87.
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© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. To overcome the crosstalk happening between two degenerately fundamental modes of a fiber in Terahertz (THz) regime, a novel photonic crystal fiber (PCF) that yields a wide range of single-polarization-single-mode (SPSM) propagation with large loss differences (LDs) is designed. The method used to realize this SPSM PCF is to deposit an epsilon-near-zero (ENZ) material in four selected air holes in the cladding, which ends up with four ENZ rings. These ENZ rings introduce significant LDs between the wanted (X-polarized) and unwanted (Y-polarized and high order) modes. Extensive simulation results demonstrate that the LDs between the wanted and unwanted modes vary with the thickness of ENZ rings. With a very short length (4 cm) of the proposed PCF, pure SPSM propagation, i.e., the unwanted modes are 20 dB lower than the wanted mode, can be achieved from 1 to 1.2 THz.
Yang, Y & Hu, Z 1970, 'Advanced Multifunctional Antennas for 5G and Beyond', 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall), 2019 Photonics & Electromagnetics Research Symposium - Fall (PIERS - Fall), IEEE, Xiamen, China, pp. 1545-1550.
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Advanced multifunctional antennas are capable of providing full flexibilities of frequency, polarization and radiation patterns, with possibly steerable beams, to satisfy the desired specifications in modern intelligent systems for applications of 5G communication, wearable systems, Internet of Things (IoT) and future sensing technologies. 5G antennas designed with advanced scalability and new materials are highly expected to cater such emerging applications. Recent advancement in materials and manufacturing techniques has demonstrated new directions of a possible mechanism for diverse future 5G mobile antennas. This paper presents the expected performance of the next generation antennas, the potential design challenges to be tackled, as well as the application scenarios for the future wireless systems. In addition, recent advancement in multifunctional antennas for future communications are also introduced and discussed. For demonstration, microwave lens antennas are presented, which are believed to be the game changer for 5G and beyond.
Yang, Y & Zhu, X 1970, 'Overview of Millimeter-Wave On-Chip and Off-Chip Bandpass Filter Designs', 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT), 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT), IEEE, Guangzhou, China, pp. 1-2.
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© 2019 IEEE. In this paper, a brief review of recent advancement in on-chip and off-chip millimeter-wave bandpass filter designs are presented. First, the general background about the two solutions are provided, with a general discussion about their pros and cons. Second, the representative state-of-the-art works are presented. This short paper provides the researchers, in the relative fields, with some guidelines when deciding bandpass filters for a system-on-chip (SoC) solution.
Yang, Y, Hou, Z, Zhu, X, Che, W & Xue, Q 1970, 'A Millimeter-Wave Reconfigurable On-Chip Coupler with Tunable Power-Dividing Ratios', 2019 IEEE MTT-S International Wireless Symposium (IWS), 2019 IEEE MTT-S International Wireless Symposium (IWS), IEEE, pp. 1-3.
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© 2019 IEEE. This paper presents a millimeter-wave on-chip tunable coupler with tunable power dividing ratios and constant phase responses. Composed by two coupled-lines, two capacitors and two series-connected varactors, the proposed tunable coupler offers wideband frequency responses. Theoretical analysis for wideband operation is provided with design parameters. For demonstration, a millimeter-wave tunable coupler is implemented in a standard 0.13-μm SiGe (Bi) CMOS technology and measured through an on-wafer probing system. From 25 to 31 GHz, the proposed tunable coupler shows a power-dividing ratio tuned from 0 to 5 dB, while maintaining an in-band return loss of better than 10 dB and an output isolation of 20 dB, simultaneously. The phase imbalance is better than ±4° with a measured insertion loss of 1.6 dB across the entire tuning range.
Yang, Y, Wu, C, Liu, Z & Xu, S 1970, 'Splitting tensile properties of UHPFRC after exposure to high temperature', 13th International Conference on Shock and Impact Loads on Structures, SILOS 2019, pp. 513-517.
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In this research, the splitting tensile properties of ultra-high performance fiber-reinforced concrete (UHPFRC) after exposure to high temperature were investigated. Through the static splitting tensile test and dynamic splitting tensile test on the specimens with diameter of 50 mm and height of 25 mm after different temperatures, the strength variation of UHPFRC under static test, failure model and dynamic mechanical properties under dynamic test and dynamic increase factor (DIF) were obtained.
Yang, Y, Zhang, W, Zhang, Y, Lin, X & Wang, L 1970, 'Selectivity Estimation on Set Containment Search', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Database Systems for Advanced Applications, Springer International Publishing, Chiang Mai, Thailand, pp. 330-349.
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© Springer Nature Switzerland AG 2019. In this paper, we study the problem of selectivity estimation on set containment search. Given a query record Q and a record dataset S, we aim to accurately and efficiently estimate the selectivity of set containment search of query Q over S. The problem has many important applications in commercial fields and scientific studies. To the best of our knowledge, this is the first work to study this important problem. We first extend existing distinct value estimating techniques to solve this problem and develop an inverted list and G-KMV sketch based approach IL-GKMV. We analyse that the performance of IL-GKMV degrades with the increase of vocabulary size. Motivated by limitations of existing techniques and the inherent challenges of the problem, we resort to developing effective and efficient sampling approaches and propose an ordered trie structure based sampling approach named OT-Sampling. OT-Sampling partitions records based on element frequency and occurrence patterns and is significantly more accurate compared with simple random sampling method and IL-GKMV. To further enhance performance, a divide-and-conquer based sampling approach, DC-Sampling, is presented with an inclusion/exclusion prefix to explore the pruning opportunities. We theoretically analyse the proposed techniques regarding various accuracy estimators. Our comprehensive experiments on 6 real datasets verify the effectiveness and efficiency of our proposed techniques.
Yang, Y, Zhang, Y, Zhang, W & Huang, Z 1970, 'GB-KMV: An Augmented KMV Sketch for Approximate Containment Similarity Search', 2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019 IEEE 35th International Conference on Data Engineering (ICDE), IEEE, Macao, Macao, pp. 458-469.
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© 2019 IEEE. In this paper, we study the problem of approximate containment similarity search. Given two records Q and X, the containment similarity between Q and X with respect to Q is |Q intersect X|/ |Q|. Given a query record Q and a set of records S, the containment similarity search finds a set of records from S whose containment similarity regarding Q are not less than the given threshold. This problem has many important applications in commercial and scientific fields such as record matching and domain search. Existing solution relies on the asymmetric LSH method by transforming the containment similarity to well-studied Jaccard similarity. In this paper, we use a different framework by transforming the containment similarity to set intersection. We propose a novel augmented KMV sketch technique, namely GB-KMV, which is data-dependent and can achieve a good trade-off between the sketch size and the accuracy. We provide a set of theoretical analysis to underpin the proposed augmented KMV sketch technique, and show that it outperforms the state-of-the-art technique LSH-E in terms of estimation accuracy under practical assumption. Our comprehensive experiments on real-life datasets verify that GB-KMV is superior to LSH-E in terms of the space-accuracy trade-off, time-accuracy trade-off, and the sketch construction time. For instance, with similar estimation accuracy (F-1 score), GB-KMV is over 100 times faster than LSH-E on some real-life dataset.
Yang, Y, Zhu, X, Che, W & Xue, Q 1970, 'A Millimeter-Wave On-Chip Bandpass Filter with All-Pole Characteristics', 2019 IEEE MTT-S International Wireless Symposium (IWS), 2019 IEEE MTT-S International Wireless Symposium (IWS), IEEE, pp. 1-3.
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© 2019 IEEE. This paper presents a millimeter-wave on-chip bandpass filter (BPF) with all-pole characteristics. It is believed to be the first time that an all-pole frequency response has been successfully implemented by using a standard 0.13-μm Silicon-Germanium (SiGe) technology. To further demonstrate the feasibility of using this approach in practice, the designed resonator is fabricated. The measured results show that the BPF has an insertion loss of 2.2 dB at the center frequency of 31 GHz. The return loss is better than 10 dB from 26.5 GHz to 37.5 GHz. In addition, the out-of-band suppression of this filter is superior, which is better than 20 dB beyond 50.6 GHz. The chip size, excluding the pads, is only 0.091 × 0.268 mm2.
Yang, Z, Zhu, L, Wu, Y & Yang, Y 1970, 'Gated Channel Transformation for Visual Recognition', Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Seattle, WA, USA, pp. 11791-11800.
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In this work, we propose a generally applicable transformation unit forvisual recognition with deep convolutional neural networks. This transformationexplicitly models channel relationships with explainable control variables.These variables determine the neuron behaviors of competition or cooperation,and they are jointly optimized with the convolutional weight towards moreaccurate recognition. In Squeeze-and-Excitation (SE) Networks, the channelrelationships are implicitly learned by fully connected layers, and the SEblock is integrated at the block-level. We instead introduce a channelnormalization layer to reduce the number of parameters and computationalcomplexity. This lightweight layer incorporates a simple l2 normalization,enabling our transformation unit applicable to operator-level without muchincrease of additional parameters. Extensive experiments demonstrate theeffectiveness of our unit with clear margins on many vision tasks, i.e., imageclassification on ImageNet, object detection and instance segmentation on COCO,video classification on Kinetics.
Yao, H, Yuan, X, Zhang, P, Wang, J, Jiang, C & Guizani, M 1970, 'A Machine Learning Approach of Load Balance Routing to Support Next-Generation Wireless Networks', 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC), IEEE, pp. 1317-1322.
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Yao, J, Wu, H, Zhang, Y, Tsang, IW & Sun, J 1970, 'Safeguarded Dynamic Label Regression for Noisy Supervision', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, Hawaii USA, pp. 9103-9110.
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Learning with noisy labels is imperative in the Big Data era since it reduces expensive labor on accurate annotations. Previous method, learning with noise transition, has enjoyed theoretical guarantees when it is applied to the scenario with the class-conditional noise. However, this approach critically depends on an accurate pre-estimated noise transition, which is usually impractical. Subsequent improvement adapts the preestimation in the form of a Softmax layer along with the training progress. However, the parameters in the Softmax layer are highly tweaked for the fragile performance and easily get stuck into undesired local minimums. To overcome this issue, we propose a Latent Class-Conditional Noise model (LCCN) that models the noise transition in a Bayesian form. By projecting the noise transition into a Dirichlet-distributed space, the learning is constrained on a simplex instead of some adhoc parametric space. Furthermore, we specially deduce a dynamic label regression method for LCCN to iteratively infer the latent true labels and jointly train the classifier and model the noise. Our approach theoretically safeguards the bounded update of the noise transition, which avoids arbitrarily tuning via a batch of samples. Extensive experiments have been conducted on controllable noise data with CIFAR10 and CIFAR-100 datasets, and the agnostic noise data with Clothing1M and WebVision17 datasets. Experimental results have demonstrated that the proposed model outperforms several state-of-the-art methods.
Yao, L, Jia, Y, Zhang, H, Long, K, Pan, M & Yu, S 1970, 'A Decentralized Private Data Transaction Pricing and Quality Control Method', ICC 2019 - 2019 IEEE International Conference on Communications (ICC), ICC 2019 - 2019 IEEE International Conference on Communications (ICC), IEEE, Shanghai, China, pp. 1-5.
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© 2019 IEEE. In the past few years, it has become increasingly popular to analyze the information obtained to develop services by conducting a decentralized survey of private data for specific populations. Privacy security requirements for data providers force operators to implement reasonable privacy protections. But increasing the investment in privacy protection will also lead to a decline in operator revenue. In this case, operators need to ensure the privacy and security requirements of users while ensuring the sustainability of customized services. To this end, We study the relationship between collecting data quality and operator strategy, quantifying the price of private data, and building a model to maximize operator profitability. Specifically, closed-form solutions for best privacy data prices and subscription fees are designed to maximize the gross profit of service providers. Also includes the collection of data quality factors to ensure that the user perceived quality of service can be guaranteed to a certain extent. Finally, we explored the relationship between spending, subscription fees, and maximum gross profit of carriers during the data collection phase, based on the distribution of different user groups' privacy attitudes. In particular, we also explored the relationship between adding additional noise and collecting data utility in a decentralized privacy protection scenario. The simulation results show that compared with the existing methods, the algorithm can maximize the collected data quality while ensuring the provider's privacy security requirements. In addition, we demonstrate the benefits of our dynamic pricing approach and its applicability to other private data pricing algorithms.
Yao, Q, Lu, D & Lei, G 1970, 'Battery Impedance Measurement Using Fast Square Current Perturbation', 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), IEEE, Singapore, pp. 1-5.
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This paper proposes a simple but accurate battery impedance measurement method. Unlike other complicated and expensive strategies, the proposed method only need a MOSFET and a resistor. A square current perturbation will be generated by the proposed circuitry and a voltage response across the tested battery can be obtained. Bases on the voltage and current signals, Discrete Fourier transform is used to calculate the impedance at fundamental and odd harmonic frequencies. The feasibility of the proposed method has been verified by simulation and experiments. The error of the proposed method is less than 5% at 80% SoC (state of charge) compared with the conventional small AC (alternating current) signal injection method.
Yao, Y, Pan, Y, Tsang, IW & Yao, X 1970, 'Support Matching: A Novel Regularization to Escape from Mode Collapse in GANs', Communications in Computer and Information Science, Springer International Publishing, pp. 40-48.
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Generative adversarial network (GAN) is an implicit generative model known for its ability to generate sharp images. However, it is poor at generating diverse data, which refers to the mode collapse problem. It turns out that GAN is prone to emphasizing the quality of samples but ignoring their diversity. When mode collapse happens, the support of the generated data distribution is not aligned with that of the real data distribution. We thus propose Support Regularized-GAN (SR-GAN) to address such a mode collapse issue by matching their support. Our experiments on synthetic and real-world datasets show that our regularization can mitigate the mode collapse and also improve the data quality.
Ye, Y, Yan, L, Ren, S & Zhang, Q 1970, 'Research on Network Security Protection Strategy', 2019 International Conference on Robots & Intelligent System (ICRIS), 2019 International Conference on Robots & Intelligent System (ICRIS), IEEE, PEOPLES R CHINA, Haikou, pp. 152-154.
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Yeganeh, N, Fatahi, B & Mirlatifi, S 1970, 'Effects of hyperbolic hardening parameters on seismic response of high rise buildings considering soil-structure interaction', Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019, International Conference on Earthquake Geotechnical Engineering, Associazione Geotecnica Italiana, Roma, Italy, pp. 5754-5761.
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Seismic soil-structure interaction is referred to the process wherein the soil dynamic response is influenced by the structure motion whilst the latter is also affected by the soil motion. To assess the seismic response of the soil-structure systems, selecting the appropriate soil model parameters is of great importance and the predictions can be significantly impacted if the simply assumptive parameters, presented in the literature, are employed. In this study, the strain hardening soil constitutive model, named “hyperbolic hardening with hysteretic damping”, was employed in the 3D coupled soil-structure interaction numerical simulations using FLAC3D. Utilizing the numerical simulations, the impact of the choice of the soil model parameters, in the range, recommended in the literature, on the seismic response of a moment-resisting building was assessed. It was concluded that the relation between the hyperbolaand Mohr-Coulomb failurecriterion hasa major contribution to the prediction of the seismic response of a building considering the soil-structure interaction.
Yeung, J & McGregor, C 1970, 'Analyzing countermeasure effectiveness utilizing big data analytics for space medicine decision support: A case study', Proceedings of the International Astronautical Congress, IAC, International Astronautical Congress, Washington D.C.
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The physiological health and wellbeing of every individual crew member is critical to the success of any long duration space mission. In the most predominant space travel in recent years that are the expeditions aboard the International Space Station (ISS), astronaut's physiological data, psychological surveyed data, and the spacecraft's habitable environmental data are periodically monitored by Mission Control, while also providing a range of data for retrospective research studies. This has led to the optimization of onboard environmental control and life support systems, countermeasure exercise programs, and preventive measures, extending human space travel capacities from 90 minutes in 1961 to 180 days or even a year for current day ISS expeditions. Although current methodologies help minimize health impacts for the astronauts pre-flight, during, and post-flight, these impacts are not detected in real-time and there is much that remain unknown for longer duration missions that will last 2-3 years, such as one to Mars. Acquired physiological data from existing onboard equipment is still monitored retrospectively and issues such as intracranial pressure resulting in vision changes for astronauts during spaceflight and post-flight still prevail. Behavioural health and psychological effects due to the isolation, confinement, and social impacts with other astronauts in the spacecraft for periods longer than current expeditions also still remain. Such health and well-being implications are critical for the astronauts themselves to comprehend given the autonomous nature of every mission into space, therefore Autonomous Medical Care development is critical. In recent research, advanced prognostic health management enabled by the online analytics platform, Artemis, has demonstrated its potential in determining health states of astronauts utilizing heart rate variability (HRV) data. However, this environment exists independent to the countermeasure exercise p...
Yin, J, Liu, S, Li, Q & Xu, G 1970, 'Prediction and Analysis of Rumour's Impact on Social Media', 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), IEEE, Beijing, China, pp. 1-6.
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© 2019 IEEE. Rumour, as an important form of social communication, has been run through the whole evolutionary history of mankind. People maliciously disseminate rumours in order to increase awareness, slander others or cause panic, etc. To eliminate this issue, many researchers resort to detecting rumours on social media. However, rumour detection is not sufficient to eliminate the negative impact, which also requires official institutions to provide the refutations. In practice, the number of rumours on social media is too large, there is no need to refute some rumours with little or no concern. Therefore, we need to evaluate the impact of the rumours in advance. In this paper, we devise a rumour influence prediction model RISM (Rumour Impact on Social Media) based on a popular rumour intensity formula to predict the impact of a newborn rumour. Extensive numerical experiments are carried out on the real rumour data that are collected from Toutiao.com, which demonstrate the effectiveness of our proposed RISM model.
Yin, Q, Niu, K, Li, N, Peng, X & Pan, Y 1970, 'ACO-RR: Ant Colony Optimization Ridge Regression in Reuse of Smart City System', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), The 18th International Conference on Software and Systems Reuse, Springer International Publishing, Cincinnati, OH, United States, pp. 204-219.
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© 2019, Springer Nature Switzerland AG. With the rapid development of artificial intelligence, governments of different countries have been focusing on building smart cities. To build a smart city is a system construction process which not only requires a lot of human and material resources, but also takes a long period of time. Due to the lack of enough human and material resources, it is a key challenge for lots of small and medium-sized cities to develop the intelligent construction, compared with the large cities with abundant resources. Reusing the existing smart city system to assist the intelligent construction of the small and medium-sizes cities is a reasonable way to solve this challenge. Following this idea, we propose a model of Ant Colony Optimization Ridge Regression (ACO-RR), which is a smart city evaluation method based on the ridge regression. The model helps small and medium-sized cities to select and reuse the existing smart city systems according to their personalized characteristics from different successful stories. Furthermore, the proposed model tackles the limitation of ridge parameters’ selection affecting the stability and generalization ability, because the parameters of the traditional ridge regression is manually random selected. To evaluate our model performance, we conduct experiments on real-world smart city data set. The experimental results demonstrate that our model outperforms the baseline methods, such as support vector machine and neural network.
Yin, R, Li, K, Lu, J & Zhang, G 1970, 'Enhancing Fashion Recommendation with Visual Compatibility Relationship', The World Wide Web Conference, WWW '19: The Web Conference, ACM, San Francisco CA USA, pp. 3434-3440.
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© 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License. With the increasing of online shopping services, fashion recommendation plays an important role in daily online shopping scenes. A lot of recommender systems have been developed with visual information. However, few works take into account compatibility relationship when they are generating recommendations. The challenge is that fashion concept is often subtle and subjective for different customers. In this paper, we propose a fashion compatibility knowledge learning method that incorporates visual compatibility relationships as well as style information. We also propose a fashion recommendation method with domain adaptation strategy to alleviate the distribution gap between the items in target domain and the items of external compatible outfits. Our results indicate that the proposed method is capable of learning visual compatibility knowledge and outperforms all the baselines.
Yin, R, Li, K, Lu, J & Zhang, G 1970, 'RsyGAN: Generative Adversarial Network for Recommender Systems', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-7.
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© 2019 IEEE. Many recommender systems rely on the information of user-item interactions to generate recommendations. In real applications, the interaction matrix is usually very sparse, as a result, the model cannot be optimised stably with different initial parameters and the recommendation performance is unsatisfactory. Many works attempted to solve this problem, however, the parameters in their models may not be trained effectively due to the sparse nature of the dataset which results in a lower quality local optimum. In this paper, we propose a generative network for making user recommendations and a discriminative network to guide the training process. An adversarial training strategy is also applied to train the model. Under the guidance of a discriminative network, the generative network converges to an optimal solution and achieves better recommendation performance on a sparse dataset. We also show that the proposed method significantly improves the precision of the recommendation performance on several datasets.
Yoo, C, Anstee, S & Fitch, R 1970, 'Stochastic Path Planning for Autonomous Underwater Gliders with Safety Constraints', 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Macau, China, pp. 3725-3732.
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© 2019 IEEE. Autonomous underwater gliders frequently execute extensive missions with high levels of uncertainty due to limitations of sensing, control and oceanic forecasting. Glider path planning seeks an optimal path with respect to conflicting objectives, such as travel cost and safety, that must be explicitly balanced subject to these uncertainties. In this paper, we derive a set of recursive equations for state probability and expected travel cost conditional on safety, and use them to implement a new stochastic variant of FMT-{ast } in the context of two types of objective functions that allow a glider to reach a destination region with minimum cost or maximum probability of arrival given a safety threshold. We demonstrate the framework using three simulated examples that illustrate how user-prescribed safety constraints affect the results.
Yu, E, Liu, W, Kang, G, Chang, X, Sun, J & Hauptmann, A 1970, 'Inf@TRECVID 2019: Instance search task', 2019 TREC Video Retrieval Evaluation, TRECVID 2019.
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We participated in one of the two types of Instance Search task in TRECVID 2019: Fully Automatic Search, without any human intervention. Firstly, the specific person and action are searched separately, and then we re-rank the two sorts of search results by ranking the one type scores according to the other type, as well as the score fusion. And thus, three kinds of final instance search results are submitted. Specifically, for the person search, our baseline consists of face detection, alignment and face feature selection. And for the action search, we integrate person detection, person tracking and feature selection into a framework to get the final 3D features for all tracklets in video shots. The official evaluations showed that our best search result gets the 4th place in the Automatic search.
Yu, E, Sun, J, Wang, L, Chang, X, Zhang, H & Hauptmann, AG 1970, 'Cross-Modal Transfer Hashing Based on Coherent Projection', 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), IEEE, Shanghai, PEOPLES R CHINA, pp. 477-482.
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Due to the low storage and high efficiency, cross-modal hashing drew more and more attention in recent years. However, most existing methods ignore the intrinsic distribution of raw features and the inheritance relationship between raw feature space and hash space. In this paper, we propose the transfer hashing based on coherent projection for large-scale cross-modal retrieval. It preserves the inherent correlation of intrinsic distribution among raw heterogeneous features via a linear cross-modal transfer. In addition, the coherent projection is applied to cooperate with the cross-modal transfer to inherit the correlation from raw features space to hash space straightly. Furthermore, the anchor graph with linear complexity is utilized to further explore the local structure of each modality. And the semantic information is also explored by regressing the semantic labels to hash space. Finally, the succinct iterative algorithm is used for discrete optimization, which avoids continuous relaxation and reduces quantization error. Extensive experiments on two large-scale datasets show that our method has superiority in retrieval performance.
Yu, H, Lu, J, Xu, J & Zhang, G 1970, 'A Hybrid Incremental Regression Neural Network for Uncertain Data Streams', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-8.
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© 2019 IEEE. The design of classical regression algorithms was based on the assumption that all the required data is obtained at one time. With the emergence of big data, however, data is increasingly displayed in sequence form, such as in data streams, and can be read only once in a specific order. Many incremental regression algorithms which process data in a sequential manner have been proposed, but the accuracy of these algorithms deteriorates when the value of the data is uncertain. This paper proposes a hybrid incremental regression neural network based on self-organizing incremental neural network and incremental fuzzy support vector regression. In our proposed network, the neurons of the regression neural network are obtained by an improved self-organized incremental neural network (SOINN). This enables the regression neural network structure to self-organize as the number of neurons increases. An incremental fuzzy support vector regression (IFSVR) algorithm is then used to modify the parameters of the regression neural network. By combining the improved SOINN and IFSVR algorithms, our proposed hybrid incremental regression neural network is able to learn an accurate regression model from large uncertain data. Experiments on both artificial and real-world datasets indicate that our proposed hybrid incremental regression neural network achieves superior performance compared to other incremental regression algorithms.
Yu, H, Lu, W & Liu, D 1970, 'A Unified Closed-Loop Motion Planning Approach For An I-AUV In Cluttered Environment With Localization Uncertainty', 2019 International Conference on Robotics and Automation (ICRA), 2019 International Conference on Robotics and Automation (ICRA), IEEE, Montreal, CA, pp. 4646-4652.
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© 2019 IEEE. This paper presents a unified motion planning approach for an Intervention Autonomous Underwater Vehicle (I-AUV) in a cluttered environment with localization uncertainty. With the uncertainty being propagated by an information filter, a trajectory optimization problem closed by a Linear-Quadratic-Gaussian controller is formulated for a coupled design of optimal trajectory, localization, and control. Due to the presence of obstacles or complexity of the cluttered environment, a set of feasible initial I-AUV trajectories covering multiple homotopy classes are required by optimization solvers. Parameterized through polynomials, the initial base trajectories are from solving quasi-quadratic optimization problems that are linearly constrained by waypoints from RRTconnect, while the initial trajectories of the manipulator are generated by a null space saturation controller. Simulations on an I-AUV with a 3 DOF manipulator in cluttered underwater environments demonstrated that initial trajectories are generated efficiently and that optimal and collision-free I-AUV trajectories with low state uncertainty are obtained.
Yu, J, Nerse, C, Lee, G, Wang, S & Kyoung-Jin, C 1970, 'Mass production applicable locally resonant metamaterials for NVH applications', Proceedings of the 26th International Congress on Sound and Vibration, ICSV 2019.
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Locally resonant metamaterials have emerged as effective solutions for several types of NVH problems over the last few years, since they offer better performance of noise and vibration reduction than other NVH solutions. However, there have not been many cases that extend to the industrial applications, due to limitations which are often linked with design requirements, such as robustness, lightness and durability. In this paper, the authors propose a novel structural design which is feasible for mass production of locally resonant metamaterial that improves the productivity for industrial application. The proposed structure is made of metal insert injection molding. It enables mass production with insert injection and effectively joins plastic with metal. The manufactured model is applied on a thin plate structure, and experimentally verified. It is observed that the proposed structure effectively reduces the noise and vibration at the target frequency band, and is relatively superior to other vibration treatments such as deadeners. So, the proposed structure of locally resonant metamaterial is expected to be applicable to various industrial fields such as automobiles and home appliances
Yu, K, Berkovsky, S, Taib, R, Zhou, J & Chen, F 1970, 'Do I trust my machine teammate?', Proceedings of the 24th International Conference on Intelligent User Interfaces, IUI '19: 24th International Conference on Intelligent User Interfaces, ACM, USA, pp. 460-468.
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© 2019 Copyright held by the owner/author(s). In the human-machine collaboration context, understanding the reason behind each human decision is critical for interpreting the performance of the human-machine team. Via an experimental study of a system with varied levels of accuracy, we describe how human trust interplays with system performance, human perception and decisions. It is revealed that humans are able to perceive the performance of automatic systems and themselves, and adjust their trust levels according to the accuracy of systems. The 70% system accuracy suggests to be a threshold between increasing and decreasing human trust and system usage. We have also shown that trust can be derived from a series of users' decisions rather than from a single one, and relates to the perceptions of users. A general framework depicting how trust and perception affect human decision making is proposed, which can be used as future guidelines for human-machine collaboration design.
Yu, K, Taib, R, Butavicius, MA, Parsons, K & Chen, F 1970, 'Mouse Behavior as an Index of Phishing Awareness', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, Cyprus, pp. 539-548.
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© IFIP International Federation for Information Processing 2019. Phishing attacks are one of the most common security challenges faced by individuals and organizations today. Although many techniques exist to filter out phishing emails, they are not always effective leaving humans as the most vulnerable links in the information security chain. This paper presents a study investigating how human behavior, especially mouse movements, may reflect cybersecurity awareness, in particular to phishing emails. Using an email sorting task, we examined three key mouse movement features: hover, slow movement, and response time. The results suggest that slow mouse movements indicate high awareness of phishing emails and could be used to determine the likelihood of users falling victim to phishing attacks. However, contrary to intuition, response time and mouse hovering behaviors do not correlate with phishing awareness.
Yu, X, Han, B, Yao, J, Niu, G, Tsang, IW & Sugiyama, M 1970, 'How does disagreement help generalization against label corruption?', 36th International Conference on Machine Learning, ICML 2019, International Conference on Machine Learning, Curran, Long Beach Convention Center, Long Beach, pp. 12407-12417.
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Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances becomes very promising for handling noisy labels. This fosters the state-of-the-art approach 'Co-teaching' that cross-trains two deep neural networks using the small-loss trick. However, with the increase of epochs, two networks converge to a consensus and Co-teaching reduces to the self-training MentorNet. To tackle this issue, we propose a robust learning paradigm called Co-teaching+, which bridges the 'Update by Disagreement' strategy with the original Co-teaching. First, two networks feed forward and predict all data, but keep prediction disagreement data only. Then, among such disagreement data, each network selects its small-loss data, but back propagates the small-loss data from its peer network and updates its own parameters. Empirical results on benchmark datasets demonstrate that Cotcaching+ is much superior to many statc-of-thcart methods in the robustness of trained models.
Yu, X, Tian, Y, Porikli, F, Hartley, R, Li, H, Heijnen, H & Balntas, V 1970, 'Unsupervised Extraction of Local Image Descriptors via Relative Distance Ranking Loss', 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), IEEE, pp. 2893-2902.
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Yu, Y, Li, Y, Li, J, Nguyen, TN, Li, S & Erkmen, E 1970, 'Vibration control of MRE isolator-embedded smart building using genetic algorithm', Proceedings of 30th International Conference on Adaptive Structures and Technologies, ICAST 2019, International Conference on Adaptive Structures and Technologies, Montreal, Canada, pp. 9-10.
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This study developed the adaptive genetic algorithm (GA) for vibration control of building structures subjected to ambient hazard excitations. An innovative smart building system was designed based on magnetorheological elastomer (MRE) isolators under each storey of the structure instead of being only installed beneath the entire structure. Such innovative system allows high authority semi-active control of storey responses by instantly changing the stiffness of the isolator, the control process of which can be considered as solving a global multi-objective optimization problem. Finally, a numerical investigation was conducted using a 5-storey international benchmark model under four benchmark earthquakes.
Yu, Y, Nguyen, TN, Li, J & Sirivivatnanon, V 1970, 'Soft computing techniques for evaluation of elastic modulus of ASR affected concrete', Concrete 2019, Concrete 2019, Sydney.
Yu, Y, Nguyen, TN, Li, J & Sirivivatnanon, V 1970, 'Soft computing techniques for evaluation of elastic modulus of ASR affected concrete', Concrete 2019: Concrete in Practice – Progress Through Knowledge, Concrete 2019: Concrete in Practice – Progress Through Knowledge, Sydney.
Yuan, Y, Lyu, Y, Shen, X, Tsang, IW & Yeung, DY 1970, 'Marginalized average attentional network for weakly-supervised learning', 7th International Conference on Learning Representations, ICLR 2019, International Conference on Learning Representations, OpenReview, New Orleans, Louisiana, United States, pp. 1-19.
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In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner. The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion. MAA samples multiple subsets from the video snippet features according to a set of latent discriminative probabilities and takes the expectation over all the averaged subset features. Theoretically, we prove that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others. Therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos. Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from O(2T) to O(T2). Extensive experiments on two large-scale video datasets show that our MAAN achieves a superior performance on weakly-supervised temporal action localization.
Zahra, H, Abbas, SM, Hashmi, RM, Matekovits, L & Esselle, KP 1970, 'Bending Analysis of Switchable Frequency Selective Surface Based on Flexible Composite Substrate', 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, IEEE, pp. 2033-2034.
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© 2019 IEEE. In this paper presents a switchable frequency selective surface (FSS) based on composite flexible substrate has been investigated. To make the FSS switchable, various combinations of switches are used. The design is bent E-field and H-field directions over various bending curvatures and the corresponding behavior is analyzed. It is observed that design has less variation when bending is applied along H-field direction. Whereas, slight variations are observed when bending is applied along the E-field direction. It is noted that the design exhibits stop band and pass band characteristics. Furthermore, in pass band it provides single wideband and dual band operations. These characteristics are preserved when bending is applied, thus making it suitable for wearable applications and modern communication systems.
Zakeri, A, Saberi, M, Aboutalebi, S, Hussain, OK & Chang, E 1970, 'Smart Farm', Proceedings of the Workshop on Interactive Data Mining, WSDM '19: The Twelfth ACM International Conference on Web Search and Data Mining, ACM, Melbourne, Australia, pp. 1-8.
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© 2019 Association for Computing Machinery. In recent past, there is a growing trend of interest among the downstream stakeholders of a dairy chain to receive milk (and dairy products) of high quality. Moreover, the rejection of milk and other dairy products by customers due to its poor quality has severe negative impacts on the dairy chain's upstream stakeholders. “Smart Farm” is a system for proactive management of raw milk quality in dairy farms. It aims to empower the dairy chain's stakeholders of milk farmers and logistics service providers with next-generation interactive artificial intelligence-based automated systems to be active participants in proactively managing raw milk quality and consequently, maximizing their expected benefits through maintaining higher quality for the milk they supply to the processor.
Zamani, R, Moghaddam, MP, Imani, M, Alhelou, HH, Golshan, MEH & Siano, P 1970, 'A Novel Improved Hilbert-Huang Transform Technique for Implementation of Power System Local Oscillation Monitoring', 2019 IEEE Milan PowerTech, 2019 IEEE Milan PowerTech, IEEE, pp. 1-6.
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Zdankowski, P, Trusiak, M, McGloin, D & Swedlow, JR 1970, 'Quasi-noise-free stimulated emission depletion microscopy imaging of thick samples using adaptive optics and block-matching 3D filtering', Imaging and Applied Optics 2019 (COSI, IS, MATH, pcAOP), Imaging Systems and Applications, OSA, pp. ITh1C.4-ITh1C.4.
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© 2019 The Author(s). We present a novel-method of increasing signal-to-noise-ratio and effective-resolution of STED microscope by combining aberration-correction and image-processing. We imaged 15μm thick mitotic cell and observed in-plane resolution of 118nm without filtering and 70nm with filtering.
Zhang, B, Lu, J & Zhang, G 1970, 'Drift Adaptation via Joint Distribution Alignment', 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), IEEE, pp. 498-504.
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© 2019 IEEE. Machine learning in evolving environment faces challenges due to concept drift. Most concept drift adaptation methods focus on modifying the model. In this paper, a method, Drift Adaptation via Joint Distribution Alignment (DAJDA), is proposed. DAJDA performs a linear transformation to the drift instances instead of modifying model. Instances are transformed into a common feature space, reducing the discrepancy of distributions before and after drift. Experimental studies show that DAJDA has abilities to improve the performance of learning model under concept drift.
Zhang, C, Zhang, W, Zhang, Y, Qin, L, Zhang, F & Lin, X 1970, 'Selecting the Optimal Groups: Efficiently Computing Skyline k-Cliques.', CIKM, CIKM '19: The 28th ACM International Conference on Information and Knowledge Management, ACM, Beijing, PEOPLES R CHINA, pp. 1211-1220.
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© 2019 Association for Computing Machinery. In many applications, graphs often involve the nodes with multi-dimensional numerical attributes, and it is desirable to retrieve a group of nodes that are both highly connected (e.g., clique) and optimal according to some ranking functions. It is well known that the skyline returns candidates for the optimal objects when ranking functions are not specified. Motivated by this, in this paper we formulate the novel model of skyline k-cliques over multi-valued attributed graphs and develop efficient algorithms to conduct the computation. To verify the group based dominance between two k-cliques, we make use of maximum bipartite matching and develop a set of optimization techniques to improve the verification efficiency. Then, a progressive computation algorithm is developed which enumerates the k-cliques in an order such that a k-clique is guaranteed not to be dominated by those generated after it. Novel pruning and early termination techniques are developed to exclude unpromising nodes or cliques by investigating the structural and attribute properties of the multi-valued attributed graph. Empirical studies on four real datasets demonstrate the effectiveness of the skyline k-clique model and the efficiency of the novel computing techniques.
Zhang, C, Zhang, Y, Zhang, W, Qin, L & Yang, J 1970, 'Efficient Maximal Spatial Clique Enumeration.', ICDE, International Conference on Data Engineering, IEEE, Macao, Macao, pp. 878-889.
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© 2019 IEEE. Maximal clique enumeration is a fundamental problem in graph database. In this paper, we investigate this problem in the context of spatial database. Given a set P of spatial objects in a 2-dimensional space (e.g., geo-locations of users or point of interests) and a distance threshold r, we can come up with a spatial neighbourhood graph Pr by connecting every pair of objects (vertices) in P within distance r. Given a clique S of Pr, namely a spatial clique, it is immediate that any pairwise distance among objects in S is bounded by r. As the maximal pairwise distance has been widely used to capture the spatial cohesiveness of a group of objects, the maximal spatial clique enumeration technique can identify groups of spatially close objects in a variety of location-based-service (LBS) applications. In addition, we show that the maximal spatial clique enumeration can also be used to identify maximal clique pattern instances in the co-location pattern mining applications. Given the existing techniques for maximal clique enumeration, which can be immediately applied on the spatial neighbourhood graph Pr, two questions naturally arise for the enumeration of maximal spatial cliques: (1) the maximal clique enumeration on general graph is NP hard, can we have a polynomial time solution on the spatial neighbourhood graph? and (2) can we exploit the geometric property of the spatial clique to speed up the computation? In this paper, we give a negative answer to the first question by an example where the number of maximal spatial cliques is exponential to the number of the objects. While the answer to the second question is rather positive: we indeed develop two pruning techniques based on geometric properties of the maximal spatial clique to significantly enhance the computing efficiency. Extensive experiments on real-life geolocation data demonstrate the superior performance of proposed methods compared with two baseline algorithms.
Zhang, D, Yao, L, Chen, K, Long, G & Wang, S 1970, 'Collective Protection: Preventing Sensitive Inferences via Integrative Transformation', 2019 IEEE International Conference on Data Mining (ICDM), 2019 IEEE International Conference on Data Mining (ICDM), IEEE, Beijing, China, pp. 1498-1503.
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© 2019 IEEE. Sharing ubiquitous mobile sensor data, especially physiological data, raises potential risks of leaking physical and demographic information that can be inferred from the time series sensor data. Existing sensitive information protection mechanisms that depend on data transformation are effective only on a particular sensitive attribute, together with usually requiring the labels of sensitive information for training. Considering this gap, we propose a novel user sensitive information protection framework without using a sensitive training dataset or being validated on protecting only one specific sensitive information. The presented approach transforms raw sensor data into a new format that has a 'style' (sensitive information) of random noise and a 'content' (desired information) of the raw sensor data, thus is free of user sensitive information for training and able to collectively protect all sensitive information at once. Our implementation and experiments on two real-world multisensor human activity datasets demonstrate that the proposed data transformation technique can achieve the protection for all sensitive information at once without requiring the knowledge of users' personal attributes for training, and simultaneously preserve the usability of the new transformed data with regard to inferring human activities with insignificant performance loss.
Zhang, D, Zhang, Q, Zhang, G & Lu, J 1970, 'FreshGraph: A Spam-Aware Recommender System for Cold Start Problem', 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), IEEE, pp. 1211-1218.
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Recommender systems provide personalized recommendation to help users levitating from information overload. Collaborative filtering based recommendation methods are playing a dominant role in the industry because of its versatility and simplicity. However, its performance suffers from sparse data, and being less effective in cold-start problem settings. In real world scenario, when users are recommended with items, it is very easy to overwhelm the target users with impersonalized information, which drives away valuable audience. In this paper, we propose a two-steps spam aware recommendation framework to effectively recommend new items to target users. By utilizing heterogeneous information graph structure, we first use item-user Meta-Path similarity measure for user candidate selection. Then we use entropy encoding measurement to identify false positive from candidate list to prevent possible spam from happening. The proposed method leverages the semantic information that persists inside the graph structure, which not only considers item content features, but also take user activeness into account for more effective audience targeting. The proposed method produces an explainable top-K user list for the new item, while K is a trailed number to each given item individually. Meanwhile, the proposed method is also adaptive to data change overtime, while capable of processing requests in a real-time fashion.
Zhang, H, Huang, X & Zhang, JA 1970, 'Comparison of OTFS Diversity Performance over Slow and Fast Fading Channels', 2019 IEEE/CIC International Conference on Communications in China (ICCC), 2019 IEEE/CIC International Conference on Communications in China (ICCC), IEEE, Changchun, China, pp. 828-833.
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© 2019 IEEE. Orthogonal time frequency space (OTFS) modulation shows great performance improvement over high-mobility wireless channels compared with traditional orthogonal frequency division multiplexing (OFDM). In this paper, we first derive the input and output relationship of OTFS signal in the delay-time domain, which shows that OTFS can be regarded as a combination of OFDM and single-carrier frequency-division multiple access (SC-FDMA). We then examine the diversity order of an OTFS system through received signal-to-noise ratio analysis and predict that this modulation technique can potentially achieve full diversity in both delay and Doppler domains. Finally, we simulate the OTFS performance based on 5G tapped-delay-line channel models under both slow and fast fading conditions. Extensive simulation results confirm that OTFS performs significantly better than other modulation techniques in fast fading channels.
Zhang, H, Huang, X, Zhang, JA, Guo, YJ, Song, R, Wang, C, Wu, W, Xu, X & Lu, Z 1970, 'A High-Speed Low-Cost Millimeter Wave System with Dual Pulse Shaping Transmission and Symbol Rate Equalization Techniques', 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, Sapporo, Japan, pp. 1-4.
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© 2019 IEEE A millimeter wave system with commercially available and affordable data conversion devices is presented in this paper for achieving high-speed and low-cost wireless communications. By adopting the proposed dual pulse shaping (DPS) transmission scheme, the system can achieve full Nyquist rate transmission with only half of the sampling rate required by conventional Nyquist pulse shaping. Structures of the DPS transmitter and receiver are described and effective symbol rate equalization techniques suitable for DPS transmission are presented. Simulation results with two sets of practical dual spectral shaping pulses are also provided to compare system performance with the conventional Nyquist pulse shaping system.
Zhang, H, Huang, X, Zhang, T, Zhang, JA & Jay Guo, Y 1970, 'A 30 Gbps Low-Complexity and Real-Time Digital Modem for Wireless Communications at 0.325 THz', 2019 19th International Symposium on Communications and Information Technologies (ISCIT), 2019 19th International Symposium on Communications and Information Technologies (ISCIT), IEEE, pp. 260-264.
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© 2019 IEEE. A high-speed wideband terahertz (THz) communication system with low-complexity and real-time digital signal processing (DSP) is presented in this paper. The architectures of baseband platform, intermediate frequency (IF) module and radio frequency (RF) frontend are described. For real-time DSP implementation with affordable field programmable gate array (FPGA) device, some effective strategies are discussed to reduce resource usage and ensure that the clock constraints are met. Adopting these strategies, all physical layer DSP modules are implemented in two FPGAs with more than 300 MHz system clock. The experimental test results using the developed real-time digital modem prototype demonstrate the superb performance for THz wireless communications.
Zhang, J, Chen, B, Yu, S & Deng, H 1970, 'PEFL: A Privacy-Enhanced Federated Learning Scheme for Big Data Analytics', 2019 IEEE Global Communications Conference (GLOBECOM), GLOBECOM 2019 - 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA, pp. 1-6.
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Federated learning has emerged as a promising solution for big data analytics, which jointly trains a global model across multiple mobile devices. However, participants' sensitive data information may be leaked to an untrusted server through uploaded gradient vectors. To address this problem, we propose a privacy-enhanced federated learning (PEFL) scheme to protect the gradients over an untrusted server. This is mainly enabled by encrypting participants' local gradients with Paillier homomorphic cryptosystem. In order to reduce the computation costs of the cryptosystem, we utilize the distributed selective stochastic gradient descent (DSSGD) method in the local training phase to achieve the distributed encryption. Moreover, the encrypted gradients can be further used for secure sum aggregation at the server side. In this way, the untrusted server can only learn the aggregated statistics for all the participants' updates, while each individual's private information will be well-protected. For the security analysis, we theoretically prove that our scheme is secure under several cryptographic hard problems. Exhaustive experimental results demonstrate that PEFL has low computation costs while reaching high accuracy in the settings of federated learning.
Zhang, J, Chen, J, Wu, D, Chen, B & Yu, S 1970, 'Poisoning Attack in Federated Learning using Generative Adversarial Nets', 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), IEEE, Rotorua, New Zealand, pp. 374-380.
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© 2019 IEEE. Federated learning is a novel distributed learning framework, where the deep learning model is trained in a collaborative manner among thousands of participants. The shares between server and participants are only model parameters, which prevent the server from direct access to the private training data. However, we notice that the federated learning architecture is vulnerable to an active attack from insider participants, called poisoning attack, where the attacker can act as a benign participant in federated learning to upload the poisoned update to the server so that he can easily affect the performance of the global model. In this work, we study and evaluate a poisoning attack in federated learning system based on generative adversarial nets (GAN). That is, an attacker first acts as a benign participant and stealthily trains a GAN to mimic prototypical samples of the other participants' training set which does not belong to the attacker. Then these generated samples will be fully controlled by the attacker to generate the poisoning updates, and the global model will be compromised by the attacker with uploading the scaled poisoning updates to the server. In our evaluation, we show that the attacker in our construction can successfully generate samples of other benign participants using GAN and the global model performs more than 80% accuracy on both poisoning tasks and main tasks.
Zhang, J, Wu, Q, Zhang, J, Shen, C & Lu, J 1970, 'Mind Your Neighbours: Image Annotation With Metadata Neighbourhood Graph Co-Attention Networks', 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.
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Zhang, J, Yang, L, Yu, S & Ma, J 1970, 'A DNS Tunneling Detection Method Based on Deep Learning Models to Prevent Data Exfiltration', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Network and System Security, Springer International Publishing, Sapporo, Japan, pp. 520-535.
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© 2019, Springer Nature Switzerland AG. DNS tunneling is a typical DNS attack that has been used for stealing information for many years. The stolen data is encoded and encapsulated into the DNS request to evade intrusion detection. The popular detection methods of machine learning use features, such as network traffic and DNS behavior. However, most features can only be extracted when data exfiltration occurs, like time-frequency related features. The key to prevent data exfiltration based on DNS tunneling is to detect the malicious query from single DNS request. Since we don’t use the network traffic features and DNS behavior features, our method can detect DNS tunneling before data exfiltration. In this paper, we propose a detection method based on deep learning models, which uses the DNS query payloads as predictive variables in the models. As the DNS tunneling data is a kind of text, our approach use word embedding as a part of fitting the neural networks, which is a feature extraction method in natural language processing (NLP). In order to achieve high performance, the detection decision is made by these common deep learning models, including dense neural network (DNN), one-dimensional convolutional neural network (1D-CNN) and recurrent neural network (RNN). We implement the DNS tunneling detection system in the real network environment. The results show that our approach achieves 99.90% accuracy and is more secure than existing methods.
Zhang, M, Li, H & Su, S 1970, 'High Dimensional Bayesian Optimization via Supervised Dimension Reduction', Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}, International Joint Conferences on Artificial Intelligence Organization, Macao, China, pp. 4292-4298.
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Bayesian optimization (BO) has been broadly applied to computational expensive problems, but it is still challenging to extend BO to high dimensions. Existing works are usually under strict assumption of an additive or a linear embedding structure for objective functions. This paper directly introduces a supervised dimension reduction method, Sliced Inverse Regression (SIR), to high dimensional Bayesian optimization, which could effectively learn the intrinsic sub-structure of objective function during the optimization. Furthermore, a kernel trick is developed to reduce computational complexity and learn nonlinear subset of the unknowing function when applying SIR to extremely high dimensional BO. We present several computational benefits and derive theoretical regret bounds of our algorithm. Extensive experiments on synthetic examples and two real applications demonstrate the superiority of our algorithms for high dimensional Bayesian optimization.
Zhang, P, Wu, Q & Xu, J 1970, 'VN-GAN: Identity-preserved Variation Normalizing GAN for Gait Recognition', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-8.
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© 2019 IEEE. Gait is recognized as a unique biometric characteristic to identify a walking person remotely across surveillance networks. However, the performance of gait recognition severely suffers challenges from view angle diversity. To address the problem, an identity-preserved Variation Normalizing Generative Adversarial Network (VN-GAN) is proposed for learning purely identity-related representations. It adopts a coarse-to-fine manner which firstly generates initial coarse images by normalizing view to an identical one and then refines the coarse images by injecting identity-related information. In specific, Siamese structure with discriminators for both camera view angles and human identities is utilized to achieve variation normalization and identity preservation of two stages, respectively. In addition to discriminators, reconstruction loss and identity-preserving loss are integrated, which forces the generated images to be the same in view and to be discriminative in identity. This ensures to generate identity-related images in an identical view of good visual effect for gait recognition. Extensive experiments on benchmark datasets demonstrate that the proposed VN-GAN can generate visually interpretable results and achieve promising performance for gait recognition.
Zhang, P, Wu, Q & Xu, J 1970, 'VT-GAN: View Transformation GAN for Gait Recognition Across Views', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-8.
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© 2019 IEEE. Recognizing gaits without human cooperation is of importance in surveillance and forensics because of the benefits that gait is unique and collected remotely. However, change of camera view angle severely degrades the performance of gait recognition. To address the problem, previous methods usually learn mappings for each pair of views which incurs abundant independently built models. In this paper, we proposed a View Transformation Generative Adversarial Networks (VT-GAN) to achieve view transformation of gaits across two arbitrary views using only one uniform model. In specific, we generated gaits in target view conditioned on input images from any views and the corresponding target view indicator. In addition to the classical discriminator in GAN which makes the generated images look realistic, a view classifier is imposed. This controls the consistency of generated images and conditioned target view indicator and ensures to generate gaits in the specified target view. On the other hand, retaining identity information while performing view transformation is another challenge. To solve the issue, an identity distilling module with triplet loss is integrated, which constrains the generated images inheriting identity information from inputs and yields discriminative feature embeddings. The proposed VT-GAN generates visually promising gaits and achieves promising performances for cross-view gait recognition, which exhibits great effectiveness of the proposed VT-GAN.
Zhang, Q, Cao, M, Wang, T & Chang, J 1970, 'Programming Error Repair Guidance Based on Historical Learning Behavior', 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), IEEE, PEOPLES R CHINA, Beijing, pp. 1-7.
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Zhang, Q, Hao, P, Lu, J & Zhang, G 1970, 'Cross-domain Recommendation with Semantic Correlation in Tagging Systems', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, Hungary, pp. 1-8.
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© 2019 IEEE. The tagging system provides users with a platform to express their preferences as they annotate terms or keywords to items. Tag information is a bridge between two domains for transferring knowledge and helping to alleviate the data sparsity problem, which is a crucial and challenging problem in most recommender systems. Existing methods incorporate correlations extracted from overlapping tags at a lexical level in cross-domain recommendation, but they neglect semantical relationships between different tags, which impairs prediction accuracy in the target domain. To solve this challenging problem, we propose a cross-domain recommendation method with semantic correlation in tagging systems. This method automatically captures the semantic relationships between non-identical tags and applies them to the recommendation. The word2vec technique is used to learn the latent representations of tags. Semantically equivalent tags are then grouped to form a joint embedding space comprised of tag clusters. This embedding space serves as the bridge between domains. By mapping users and items from both the source and target domains into the same embedding space, similar users or items across domains can be identified. Thus, the recommendation in a sparse target domain is improved by transferring knowledge through correlated users and items. Experimental results with three datasets on six cross-domain recommendation tasks demonstrate that the proposed method exploits the semantic links from tags in two domains and outperforms five benchmarks in prediction accuracy. The results indicate that transferring knowledge through tags semantics is feasible and effective.
Zhang, Q, Liu, X, Zhang, B, Tan, L & Zheng, P 1970, 'Synthesis of 8-(2-fluoro-4-nitrophenoxy)-[1,2,4] triazolo [4,3-a] pyrazine', IOP Conference Series: Earth and Environmental Science, 4th International Conference on Environmental Science and Material Application (ESMA), IOP Publishing, PEOPLES R CHINA, Xian, pp. 022088-022088.
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Zhang, Q, Zhang, D, Lu, J, Zhang, G, Qu, W & Cohen, M 1970, 'A Recommender System for Cold-start Items: A Case Study in the Real Estate Industry', 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), IEEE, pp. 1185-1192.
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The recommender systems provide users with what they prefer and filter unnecessary information. In the fierce marketing environment, it is crucial to recommend items to users in an early stage to keep user's interests and loyalty. With the fast product renewal, classical recommendation methods such as collaborative filtering cannot handle the cold-start item problem. In many real-world applications, content information of items or users is available and can be used to assist recommendation. Besides, user may interact with the items in different behaviors such as view, click or subscribe. How to use the complex content information and multiple user behaviors are real problems that are not well solved in applications. In this paper, we propose a content-based recommender system to deal with the practical problem. Boosting tree model also added to the system to avoid potential Spam. We applied our developed method to real estate application to recommend new property which just landed into the market to users. Experimental results with three data subsets and three recommendation scenarios demonstrate that the proposed method can outperform the baseline on recommendation accuracy. The results indicate that our method can effectively reduce potential Spam to users, so that user experience will be improved.
Zhang, R, Walder, C & Rizoiu, M-A 1970, 'Variational Inference for Sparse Gaussian Process Modulated Hawkes Process', Proceedings of the AAAI Conference on Artificial Intelligence., p. 04.
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The Hawkes process (HP) has been widely applied to modeling self-excitingevents including neuron spikes, earthquakes and tweets. To avoid designingparametric triggering kernel and to be able to quantify the predictionconfidence, the non-parametric Bayesian HP has been proposed. However, theinference of such models suffers from unscalability or slow convergence. Inthis paper, we aim to solve both problems. Specifically, first, we propose anew non-parametric Bayesian HP in which the triggering kernel is modeled as asquared sparse Gaussian process. Then, we propose a novel variational inferenceschema for model optimization. We employ the branching structure of the HP sothat maximization of evidence lower bound (ELBO) is tractable by theexpectation-maximization algorithm. We propose a tighter ELBO which improvesthe fitting performance. Further, we accelerate the novel variational inferenceschema to linear time complexity by leveraging the stationarity of thetriggering kernel. Different from prior acceleration methods, ours enjoyshigher efficiency. Finally, we exploit synthetic data and two large socialmedia datasets to evaluate our method. We show that our approach outperformsstate-of-the-art non-parametric frequentist and Bayesian methods. We validatethe efficiency of our accelerated variational inference schema and practicalutility of our tighter ELBO for model selection. We observe that the tighterELBO exceeds the common one in model selection.
Zhang, R, Walder, C, Rizoiu, M-A & Xie, L 1970, 'Efficient Non-parametric Bayesian Hawkes Processes', PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 28th International Joint Conference on Artificial Intelligence, IJCAI-INT JOINT CONF ARTIF INTELL, PEOPLES R CHINA, Macao, pp. 4299-4305.
Zhang, R, Walder, CJ, Bonilla, EV, Rizoiu, M-A & Xie, L 1970, 'Quantile Propagation for Wasserstein-Approximate Gaussian Processes', Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Virtual Conference.
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Approximate inference techniques are the cornerstone of probabilistic methodsbased on Gaussian process priors. Despite this, most work approximatelyoptimizes standard divergence measures such as the Kullback-Leibler (KL)divergence, which lack the basic desiderata for the task at hand, while chieflyoffering merely technical convenience. We develop a new approximate inferencemethod for Gaussian process models which overcomes the technical challengesarising from abandoning these convenient divergences. Our method---dubbedQuantile Propagation (QP)---is similar to expectation propagation (EP) butminimizes the $L_2$ Wasserstein distance (WD) instead of the KL divergence. TheWD exhibits all the required properties of a distance metric, while respectingthe geometry of the underlying sample space. We show that QP matches quantilefunctions rather than moments as in EP and has the same mean update but asmaller variance update than EP, thereby alleviating EP's tendency toover-estimate posterior variances. Crucially, despite the significantcomplexity of dealing with the WD, QP has the same favorable locality propertyas EP, and thereby admits an efficient algorithm. Experiments on classificationand Poisson regression show that QP outperforms both EP and variational Bayes.
Zhang, W, Zhang, F, Zhang, Y & Qin, L 1970, 'Database Systems for Advanced Applications', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Database Systems for Advanced Applications, Springer International Publishing, Thailand, pp. 587-589.
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Zhang, X, Fatahi, B & Khabbaz, H 1970, 'Investigating Effects of Individual Fracture Length on Behaviour of Weak Rock Using Discrete Element Method', Proceedings of the 5th GeoChina International Conference 2018 – Civil Infrastructures Confronting Severe Weathers and Climate Changes: From Failure to Sustainability, GeoChina International Conference, Springer International Publishing, Wuhan, pp. 46-56.
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In this paper weak rock specimens with different individual fracture lengths are numerically simulated using the discrete element method (DEM). Effects of micro or macro-mechanical responses of intact and fractured specimens subjected to triaxial test have been studied. Various individual fracture lengths with a given fracture density within the weak rock specimens were reproduced using the particle flow code in three-dimension software (PFC3D). Different lengths of fractures were simulated by altering the size of each fracture to give insight over the influence of continual fractures and non-persistent fractures within bonded assemblies. As expected, for a given fracture density the individual fracture length affected the strength and deformability of rock mass. For an individual fracture length to specimen width ratio (the normalized fracture length) less than a limiting value, the effects of the individual fracture length on the stress-strain behaviour of rock specimens were more evident. Indeed, the strength decreased with decreasing the normalized fracture length. However, with a ratio above the limiting value, the effects of the individual fracture length were minimal. It can be concluded that for a given fracture density, present of shorter mini-fractures could be potentially more detrimental to stiffness and strength of the rock mass in comparison to longer major fractures.
Zhang, X, Liu, J, Li, Y, Cui, Q, Tao, X & Liu, RP 1970, 'Blockchain Based Secure Package Delivery via Ridesharing', 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), IEEE, pp. 1-6.
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© 2019 IEEE. Delivery service via ridesharing is a promising service to share travel costs and improve vehicle occupancy. Existing ridesharing systems require participating vehicles to periodically report individual private information (e.g., identity and location) to a central controller, which is a potential central point of failure, resulting in possible data leakage or tampering in case of controller break down or under attack. In this paper, we propose a Blockchain secured ridesharing delivery system, where the immutability and distributed architecture of the Blockchain can effectively prevent data tampering. However, such tamper-resistance property comes at the cost of a long confirmation delay caused by the consensus process. A Hash-oriented Practical Byzantine Fault Tolerance (PBFT) based consensus algorithm is proposed to improve the Blockchain efficiency and reduce the transaction confirmation delay from 10 minutes to 15 seconds. The Hash-oriented PBFT effectively avoids the double-spending attack and Sybil attack. Security analysis and simulation results demonstrate that the proposed Blockchain secured ridesharing delivery system offers strong security guarantees and satisfies the quality of delivery service in terms of confirmation delay and transaction throughput.
Zhang, X, Shi, J, Zhu, X, Wang, Y, Chen, J, Ding, G & Yang, Z 1970, 'Heterogeneous Integrated MEMS Inertial Switch with Electrostatic Locking and Compliant Cantilever Stationary Electrode for Holding Stable ‘on’-State', 2019 IEEE 32nd International Conference on Micro Electro Mechanical Systems (MEMS), 2019 IEEE 32nd International Conference on Micro Electro Mechanical Systems (MEMS), IEEE, pp. 978-981.
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Zhang, X, Yao, L, Wang, X, Zhang, W, Zhang, S & Liu, Y 1970, 'Know Your Mind: Adaptive Cognitive Activity Recognition with Reinforced CNN', 2019 IEEE International Conference on Data Mining (ICDM), 2019 IEEE International Conference on Data Mining (ICDM), IEEE, Beijing, China, pp. 896-905.
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© 2019 IEEE. Electroencephalography (EEG) signals reflect and measure activities in certain brain areas. Its zero clinical risk and easy-to-use features make it a good choice of providing insights into the cognitive process. However, effective analysis of time-varying EEG signals remains challenging. First, EEG signal processing and feature engineering are time-consuming and highly rely on expert knowledge, and most existing studies focus on domain-specific classification algorithms, which may not apply to other domains. Second, EEG signals usually have low signal-to-noise ratios and are more chaotic than other sensor signals. In this regard, we propose a generic EEG-based cognitive activity recognition framework that can adaptively support a wide range of cognitive applications to address the above issues. The framework uses a reinforced selective attention model to choose the characteristic information among raw EEG signals automatically. It employs a convolutional mapping operation to dynamically transform the selected information into a feature space to uncover the implicit spatial dependency of EEG sample distribution. We demonstrate the effectiveness of the framework under three representative scenarios: intention recognition with motor imagery EEG, person identification, and neurological diagnosis, and further evaluate it on three widely used public datasets. The experimental results show our framework outperforms multiple state-of-the-art baselines and achieves competitive accuracy on all the datasets while achieving low latency and high resilience in handling complex EEG signals across various domains. The results confirm the suitability of the proposed generic approach for a range of problems in the realm of brain-computer Interface applications.
Zhang, X, Zhang, X, Verma, S, Liu, Y, Blumenstein, M & Li, J 1970, 'Detection of Anomalous Traffic Patterns and Insight Analysis from Bus Trajectory Data', PRICAI 2019: Trends in Artificial Intelligence, The 16th Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing, Cuvu, Fiji, pp. 307-321.
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Zhang, Y, Li, R, Guo, T, Li, Z, Wang, Y & Chen, F 1970, 'A conditional Bayesian delay propagation model for large-scale railway traffic networks', Australasian Transport Research Forum, ATRF 2019 - Proceedings, Canberra, Australia, pp. 1-12.
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Reliability is one of the critical success factors for both passenger and freight rail service delivery. One major factor that significantly impacts reliability performance is delays spanning over spatial and temporal dimensions. One way to increase reliability is to avoid systematic delay propagation through better timetable design to reduce the interdependencies between trains caused by route conflicts and train connections. In this paper, we aim to predict the propagation of delays on a railway network by developing a conditional Bayesian delay propagation model. In the model, the propagation satisfies the Markov property that determination of delay propagation for the future of the process is based solely on its present state, and that the history does not have an influence on the future. For the cases of delay caused by cross line conflicts and train connection, throughput estimation is considered in the model. The proposed model benefits from scalable computing time and complexity advantages over the Markov property. Implementation of actual operational data shows the feasibility and accuracy of the proposed model when compared to traditional probability models. The proposed model can be used for timetable evaluation and operations management decision support.
Zhang, Y, Qin, L, Zhang, F & Zhang, W 1970, 'Hierarchical Decomposition of Big Graphs.', ICDE, International Conference on Data Engineering, IEEE, Macao, Macao, pp. 2064-2067.
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© 2019 IEEE. Graph decomposition has been widely used to analyze real-life networks from different perspectives. Recent studies focus on the hierarchical graph decomposition methods to handle big graphs in many real-life applications such as community detection, network analysis, network visualization, internet topology analysis and protein function prediction. In this tutorial, we first highlight the importance of hierarchical graph decomposition in a variety of applications and the unique challenges that need to be addressed. Subsequently, we provide an overview of the existing models and the computation algorithms under different computing environments. Then we discuss the integration of existing models with other approaches to better capture the cohesiveness of subgraphs in real-life scenarios. Finally, we discuss the future research directions in this important and growing research area.
Zhang, Y, Saberi, M, Wang, M & Chang, E 1970, 'K3S: Knowledge-Driven Solution Support System', Proceedings of the AAAI Conference on Artificial Intelligence, Thirty-Third AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), USA, pp. 9873-9874.
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As the volume of scientific papers grows rapidly in size, knowledge management for scientific publications is greatly needed. Information extraction and knowledge fusion techniques have been proposed to obtain information from scholarly publications and build knowledge repositories. However, retrieving the knowledge of problem/solution from academic papers to support users on solving specific research problems is rarely seen in the state of the art. Therefore, to remedy this gap, a knowledge-driven solution support system (K3S) is proposed in this paper to extract the information of research problems and proposed solutions from academic papers, and integrate them into knowledge maps. With the bibliometric information of the papers, K3S is capable of providing recommended solutions for any extracted problems. The subject of intrusion detection is chosen for demonstration in which required information is extracted with high accuracy, a knowledge map is constructed properly, and solutions to address intrusion problems are recommended.
Zhang, Y, Yao, D, Tie, Y, Wang, Y, Zhang, X, Cui, Y, Hao, J, Wu, X, Su, S & Xu, P 1970, 'Identification of Neuromuscular Causal Relationship Between Brain and Muscles in Limb Movement by Using Ensemble Empirical Mode Decomposition based Causal Decomposition', 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Berlin, Germany, pp. 2667-2670.
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This paper proposes the potential extension of Ensemble Empirical Mode Decomposition based Causal Decomposition (EEMD-CD) to the physiological system. The neural basis of Volitional Motor Control (VMC), resulting in skilled motor behaviors through a connected interaction between limb biomechanical properties and Central Neural System (CNS), has been well documented. Specifically, the Primary Motor Cortex (M1) contributes volitional and goal-directed limb movements in terms of motor planning and motor behavior. The actual applications of causality detection approaches were still dominated by the prediction concept, i.e., Granger Causality (GC). This study concerns clearly some of components of M1 regulating motor properties of upper limbs, and holds the neuroscience finding from which the bi-directional causal interaction in brain and muscles has been concluded. The study performs an experiment by which Electromyography (EMG) of limb muscles and Electroencephalography (EEG) across from prefrontal cortex to M1, were synchronously acquired during wrist extensions. It also provides a valid example of how the casuality can be approached by EEMD-CD and offers a first step in the identification of casual relationship in mutual physiological systems.
Zhang, Y, Zhao, X, Li, X, Zhong, M, Curtis, C & Chen, C 1970, 'Enabling Privacy-Preserving Sharing of Genomic Data for GWASs in Decentralized Networks', Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM '19: The Twelfth ACM International Conference on Web Search and Data Mining, ACM, pp. 204-212.
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Zhang, Y, Zhu, Y, Huang, L, Zhang, G & Lu, J 1970, 'Characterizing the potential of being emerging generic technologies: A Bi-Layer Network Analytics-based Prediction Method', 17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings, International Conference on Scientometrics & Informetrics, Edizioni Efesto, Rome, Italy, pp. 1436-1447.
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Despite tremendous involvement of bibliometrics in profiling technological landscapes and identifying emerging topics, how to predict potential technological change is still unclear. This paper proposes a bi-layer network analytics-based prediction method to characterize the potential of being emerging generic technologies. Initially, based on the innovation literature, three technological characteristics are defined, and quantified by topological indicators in network analytics; a link prediction approach is applied for reconstructing the network with weighted missing links, and such reconstruction will also result in the change of related technological characteristics; the comparison between the two ranking lists of terms can help identify potential emerging generic technologies. A case study on predicting emerging generic technologies in information science demonstrates the feasibility and reliability of the proposed method.
Zhang, Z, Wang, Y, Wu, Q & Chen, F 1970, 'Visual Relationship Attention for Image Captioning', 2019 International Joint Conference on Neural Networks (IJCNN), 2019 International Joint Conference on Neural Networks (IJCNN), IEEE, Budapest, HUNGARY, pp. 1-8.
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© 2019 IEEE. Visual attention mechanisms have been broadly used by image captioning models to attend to related visual information dynamically, allowing fine-grained image understanding and reasoning. However, they are only designed to discover the region-level alignment between visual features and the language feature. The exploration of higher-level visual relationship information between image regions, which is rarely researched in recent works, is beyond their capabilities. To fill this gap, we propose a novel visual relationship attention model based on the parallel attention mechanism under the learnt spatial constraints. It can extract relationship information from visual regions and language and then achieve the relationship-level alignment between them. Using combined visual relationship attention and visual region attention to attend to related visual relationships and regions respectively, our image captioning model can achieve state-of-the-art performances on the MSCOCO dataset. Both quantitative analysis and qualitative analysis demonstrate that our novel visual relationship attention model can capture related visual relationship and further improve the caption quality.
Zhao, C, Wu, Y, Li, J, Nie, J, Meng, X & Yu, Y 1970, 'Sinusoidal Pressure Generator Contraction Section Simulation Analysis', 2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM), 2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM), IEEE, Shanghai, China, pp. 6-11.
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© 2019 IEEE. The key to dynamic pressure calibration is the generation of sinusoidal pressure signals. This paper uses a liquid-gas two-phase piston sinusoidal pressure generator for generating higher frequency pressure signals. The design of the contraction section has a large effect on the sinusoidal pressure generator. The flow field characteristics of the contraction section were simulated by Fluent simulation software. We analyze the frequency response, exit velocity distribution and axial pressure distribution of different contraction curves, then select the appropriate contraction curve and verify it by experiment. The simulation work in this paper provides a reference for the design of liquid-gas two-phase high-frequency sinusoidal pressure generator.
Zhao, M, Shu, Y, Liu, S & Xu, G 1970, 'Electricity Price Forecast using Meteorology data: A study in Australian Energy Market', 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), IEEE, Beijing, China, pp. 1-2.
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© 2019 IEEE. Electricity price as a fundamental cost for each family which is an essential segment in the electricity market. The adjustment of electricity price can present the change in electricity supply and demand relationship. For the electricity supply companies, an appropriate defined electricity price can eventually determine the level of profit. On the other hand, an accurate prediction can help to seize opportunities in the electricity market. In this paper, we aim to predict the electricity price with more confident accuracy by leveraging data mining techniques. Our experiment on 12 months of electricity prices as well as climate data in the New South Wales has achieved a promising prediction result.
Zhao, M, Zhang, J, Zhang, C & Zhang, W 1970, 'Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting', 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Long Beach, CA, pp. 12728-12737.
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Zhao, M, Zhang, J, Zhang, C & Zhang, W 1970, 'Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks', COMPUTER VISION - ACCV 2018, PT VI, Asian Conference on Computer Vision, Springer International Publishing, Perth, AUSTRALIA, pp. 247-261.
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High-density object counting in surveillance scenes is challenging mainly due to the drastic variation of object scales. The prevalence of deep learning has largely boosted the object counting accuracy on several benchmark datasets. However, does the global counts really count? Armed with this question we dive into the predicted density map whose summation over the whole regions reports the global counts for more in-depth analysis. We observe that the object density map generated by most existing methods usually lacks of local consistency, i.e., counting errors in local regions exist unexpectedly even though the global count seems to well match with the ground-truth. Towards this problem, in this paper we propose a constrained multi-stage Convolutional Neural Networks (CNNs) to jointly pursue locally consistent density map from two aspects. Different from most existing methods that mainly rely on the multi-column architectures of plain CNNs, we exploit a stacking formulation of plain CNNs. Benefited from the internal multi-stage learning process, the feature map could be repeatedly refined, allowing the density map to approach the ground-truth density distribution. For further refinement of the density map, we also propose a grid loss function. With finer local-region-based supervisions, the underlying model is constrained to generate locally consistent density values to minimize the training errors considering both the global and local counts accuracy. Experiments on two widely-tested object counting benchmarks with overall significant results compared with state-of-the-art methods demonstrate the effectiveness of our approach.
Zhao, S, Qiu, X, Burnett, I, Rigby, M & Lele, A 1970, 'GMAW metal transfer mode identification from welding sound', Australian Acoustical Society Annual Conference, AAS 2018, Australian Acoustical Society, Australian Acoustical Society, Adelaide, Australia,, pp. 482-491.
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Gas Metal Arc Welding (GMAW) is an arc welding process that forms an electric arc between a consumable electrode and the base metal with a shielding gas to protect the arc. In GMAW, there are various metal transfer modes such as the short circuit mode, the globular mode, the spray mode, and the rotational transfer mode, which show different arc stabilities, weld pool penetrations and spatter production. Identifying the metal transfer mode is critical for process monitoring and quality control of GMAW. In this paper, a m ethod for metal transfer mode identification from the welding sound is presented. A recorder mounted on the welder helmet is used to record the sound signals generated by GMAW under different metal transfer modes, which are analysed in both time and frequency domains. New psychoacoustic parameters based on the auditory perception of an expert welder are extracted to distinguish the metal transfer modes. The Gaussian Mixture Model (GMM) is utilised to identify the metal transfer mode from the welding sound signals and a 10-fold cross validation shows 90% recognition accuracy.
Zhao, S, Qiu, X, Burnett, I, Rigby, M & Lele, A 1970, 'Statistical characteristics of gas metal arc welding (GMAW) sound', Proceedings of the International Congress on Acoustics, Internatioanal Congress on Acoustics, EAA, Achen, Germany, pp. 7594-7601.
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Gas Metal Arc Welding (GMAW) is an arc welding process to join two or more metal materials through fusion, where an electric arc is formed between a consumable electrode and the base metal. It has been reported that expert GMAW welders can direct the welding arc type based on the welding sound, and psychoacoustic experiments show that the welding performance is significantly degraded without the acoustic feedback to the welders. In addition, identifying the metal transfer mode based on the welding sound is critical for automatic GMAW process monitoring, quality control and a training pathway for competency. However, the research on the generation and characteristics of the welding sound is still rare. In this paper, the welding sound is measured simultaneously with the welding current at different metal transfer modes to investigate the unique characteristics of welding sound. The welding sound consists of many impulses corresponding to the current leap. The envelope of the impulse responses is estimated based on the sound pressure signal for statistical analysis. It is found that the probability density function of the peak sound pressure, impulse interval and event duration can be well modelled by the Burr distribution. The findings can be used to classify the metal transfer mode from its welding sound.
Zhao, Y, Chen, J, Wu, D, Teng, J & Yu, S 1970, 'Multi-Task Network Anomaly Detection using Federated Learning', Proceedings of the Tenth International Symposium on Information and Communication Technology - SoICT 2019, the Tenth International Symposium, ACM Press, Hanoi Ha Long Bay, Vietnam, pp. 273-279.
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© 2019 Association for Computing Machinery. Because of the complexity of network traffic, there are various sig-nificant challenges in the network anomaly detection fields. One of the major challenges is the lack of labeled training data. In this paper, we use federated learning to tackle data scarcity problem and to preserve data privacy, where multiple participants collaboratively train a global model. Unlike the centralized training architecture, participants do not need to share their training to the server in federated learning, which can prevent the training data from being exploited by attackers. Moreover, most of the previous works focus on one specific task of anomaly detection, which restricts the application areas and can not provide more valuable information to network administrators. Therefore, we propose a multi-task deep neural network in federated learning (MT-DNN-FL) to perform network anomaly detection task, VPN (Tor) traffic recognition task, and traffic classification task, simultaneously. Compared with multiple single-task models, the multi-task method can reduce training time overhead. Experiments conducted on well-known CICIDS2017, ISCXVPN2016, and ISCXTor2016 datasets, show that the detection and classification performance achieved by the proposed method is better than the baseline methods in centralized training architecture.
Zhao, Y, Liang, B, Wang, Y, Dang, S, Taib, R, Chen, F, Hua, T, Vitanage, D & Doolan, C 1970, 'Optimising Pump Scheduling for Water Distribution Networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer International Publishing, pp. 433-444.
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© 2019, Springer Nature Switzerland AG. Energy costs can be a major component of operational costs for water utilities. Operational efficiencies including optimising energy costs while maintaining continuity of supply is one area to reduce overall operational costs. To address the challenge, we have proposed an effective optimisation model to minimise the energy cost for water distribution networks. A simulation of the model over a water distribution network in Sydney demonstrated that 15% saving in energy cost could be achieved using this approach, as compared with the existing rule-based method.
Zheng, C, Cai, Y, Xu, J, Leung, H-F & Xu, G 1970, 'A Boundary-aware Neural Model for Nested Named Entity Recognition', Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, pp. 357-366.
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© 2019 Association for Computational Linguistics In natural language processing, it is common that many entities contain other entities inside them. Most existing works on named entity recognition (NER) only deal with flat entities but ignore nested ones. We propose a boundary-aware neural model for nested NER which leverages entity boundaries to predict entity categorical labels. Our model can locate entities precisely by detecting boundaries using sequence labeling models. Based on the detected boundaries, our model utilizes the boundary-relevant regions to predict entity categorical labels, which can decrease computation cost and relieve error propagation problem in layered sequence labeling model. We introduce multitask learning to capture the dependencies of entity boundaries and their categorical labels, which helps to improve the performance of identifying entities. We conduct our experiments on nested NER datasets and the experimental results demonstrate that our model outperforms other state-of-the-art methods.
Zheng, H, Yao, J, Zhang, Y, Tsang, IW & Wang, J 1970, 'Understanding VAEs in Fisher-Shannon Plane', Proceedings of the AAAI Conference on Artificial Intelligence, 33rd AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, Hawaii USA, pp. 5917-5924.
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In information theory, Fisher information and Shannon information (entropy) are respectively used to quantify the uncertainty associated with the distribution modeling and the uncertainty in specifying the outcome of given variables. These two quantities are complementary and are jointly applied to information behavior analysis in most cases. The uncertainty property in information asserts a fundamental trade-off between Fisher information and Shannon information, which enlightens us the relationship between the encoder and the decoder in variational auto-encoders (VAEs). In this paper, we investigate VAEs in the Fisher-Shannon plane, and demonstrate that the representation learning and the log-likelihood estimation are intrinsically related to these two information quantities. Through extensive qualitative and quantitative experiments, we provide with a better comprehension of VAEs in tasks such as high-resolution reconstruction, and representation learning in the perspective of Fisher information and Shannon information. We further propose a variant of VAEs, termed as Fisher auto-encoder (FAE), for practical needs to balance Fisher information and Shannon information. Our experimental results have demonstrated its promise in improving the reconstruction accuracy and avoiding the noninformative latent code as occurred in previous works.
Zheng, T, Chen, W-J, Tsang, I & Yao, X 1970, 'Rectified Encoder Network for High-Dimensional Imbalanced Learning', PRICAI 2019: Trends in Artificial Intelligence, Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing, Cuvu, Yanuca Island, Fiji, pp. 684-697.
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© 2019, Springer Nature Switzerland AG. Many existing works have studied the learning on imbalanced data, however, it is still very challenging to handle high-dimensional imbalanced data. One key challenge of learning on imbalanced data is that most learning models usually have a bias towards the majority and its performance will deteriorate in the presence of underrepresented data and severe class distribution skews. One solution is to synthesize the minority data to balance the class distribution, but it may lead to more overlapping, especially in the high-dimensional setting. To alleviate the above challenges, in this paper, we present a novel Rectified Encoder Network (REN) for high-dimensional imbalanced learning tasks. The main contribution is that: (1) To deal with high-dimensionality, REN encodes high-dimensional imbalanced data into low dimensional latent codes as a latent representation. (2) To obtain a discriminative representation, we introduce a Rectifier to match the latent codes with our proposed Predefined Codes, which disentangles the overlapping among classes. (3) During rectification, in the Predefined Latent Distribution, we can efficiently identify and generate informative samples to maintain the balance of class distribution, so that the minority classes will not be neglected. The experimental results on several high-dimensional and image imbalanced data sets indicate that our REN obtains good representation code for classification and visualize the reason why REN gets better performance in high-dimensional imbalanced learning.
Zhi, Y, Yang, L, Yu, S & Ma, J 1970, 'BQSV: Protecting SDN Controller Cluster’s Network Topology View Based on Byzantine Quorum System with Verification Function', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Symposium on Cyberspace Safety and Security, Springer International Publishing, Guangzhou, China, pp. 73-88.
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© 2019, Springer Nature Switzerland AG. In Software-defined network (SDN), SDN applications and administrators rely on the logically centralized view of the network topology to make management decisions. Therefore, the correctness of SDN controller cluster’s network topology view becomes critical. However, the lack of security mechanism in SDN controller cluster makes the network topology view easy to be tampered with. In this paper, we argue that malicious controllers in a cluster can easily damage the network view of the cluster through the east-west bound interfaces. We present a scheme based on Byzantine Quorum System with verification function (BQSV) to prevent malicious controllers from manipulating the cluster’s network view through east-west bound interface and providing wrong topology information to SDN applications and administrators. Moreover, we implement the prototype of our scheme and extensive experiments to show that the proposed scheme can prevent malicious controllers from damaging the topology information of the cluster with trivial overheads.
Zhou, F, Zhang, Y, Li, Z, Fan, X, Wang, Y, Sowmya, A & Chen, F 1970, 'Hawkes Process with Stochastic Triggering Kernel', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Macau, China, pp. 319-330.
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© Springer Nature Switzerland AG 2019. The impact from past to future is a vital feature in modelling time series data, which has been described by many point processes, e.g. the Hawkes process. In classical Hawkes process, the triggering kernel is assumed to be a deterministic function. However, the triggering kernel can vary with time due to the system uncertainty in real applications. To model this kind of variance, we propose a Hawkes process variant with stochastic triggering kernel, which incorporates the variation of triggering kernel over time. In this model, the triggering kernel is considered to be an independent multivariate Gaussian distribution. We derive and implement a tractable inference algorithm based on variational auto-encoder. Results from synthetic and real data experiments show that the underlying mean triggering kernel and variance band can be recovered, and using the stochastic triggering kernel is more accurate than the vanilla Hawkes process in capacity planning.
Zhou, I, Makhdoom, I, Abolhasan, M, Lipman, J & Shariati, N 1970, 'A Blockchain-based File-sharing System for Academic Paper Review', 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, Australia, pp. 1-10.
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Zhou, J & Chen, F 1970, 'Towards Trustworthy Human-AI Teaming under Uncertainty', Proceedings of the IJCAI Workshop on Explainable Artificial Intelligence, IJCAI Workshop on Explainable AI, Macau, China, pp. 143-147.
Zhou, J, Hu, H, Li, Z, Yu, K & Chen, F 1970, 'Physiological Indicators for User Trust in Machine Learning with Influence Enhanced Fact-Checking', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Springer International Publishing, Canterbury, UK, pp. 94-113.
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© IFIP International Federation for Information Processing 2019. Trustworthy Machine Learning (ML) is one of significant challenges of “black-box” ML for its wide impact on practical applications. This paper investigates the effects of presentation of influence of training data points on machine learning predictions to boost user trust. A framework of fact-checking for boosting user trust is proposed in a predictive decision making scenario to allow users to interactively check the training data points with different influences on the prediction by using parallel coordinates based visualization. This work also investigates the feasibility of physiological signals such as Galvanic Skin Response (GSR) and Blood Volume Pulse (BVP) as indicators for user trust in predictive decision making. A user study found that the presentation of influences of training data points significantly increases the user trust in predictions, but only for training data points with higher influence values under the high model performance condition, where users can justify their actions with more similar facts to the testing data point. The physiological signal analysis showed that GSR and BVP features correlate to user trust under different influence and model performance conditions. These findings suggest that physiological indicators can be integrated into the user interface of AI applications to automatically communicate user trust variations in predictive decision making.
Zhou, J, Li, Z, Hu, H, Yu, K, Chen, F, Li, Z & Wang, Y 1970, 'Effects of Influence on User Trust in Predictive Decision Making', Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, CHI '19: CHI Conference on Human Factors in Computing Systems, ACM, Glasgow, SCOTLAND, pp. 1-6.
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© 2019 Copyright held by the owner/author(s). This paper introduces fact-checking into Machine Learning (ML) explanation by referring training data points as facts to users to boost user trust. We aim to investigate what influence of training data points, and how they affect user trust in order to enhance ML explanation and boost user trust. We tackle this question by allowing users check the training data points that have the higher influence and the lower influence on the prediction. A user study found that the presentation of influences significantly increases the user trust in predictions, but only for training data points with higher influence values under the high model performance condition, where users can justify their actions with more similar facts.
Zhou, K, Luo, X, Wang, H & Xu, R 1970, 'Multi-task Learning for Relation Extraction', 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), IEEE, USA, pp. 1480-1487.
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© 2019 IEEE. Distantly supervised relation extraction leverages knowledge bases to label training data automatically. However, distant supervision may introduce incorrect labels, which harm the performance. Many efforts have been devoted to tackling this problem, but most of them treat relation extraction as a simple classification task. As a result, they ignore useful information that comes from related tasks, i.e., dependency parsing and entity type classification. In this paper, we first propose a novel Multi-Task learning framework for Relation Extraction (MTRE). We employ dependency parsing and entity type classification as auxiliary tasks and relation extraction as the target task. We learn these tasks simultaneously from training instances to take advantage of inductive transfer between auxiliary tasks and the target task. Then we construct a hierarchical neural network, which incorporates dependency and entity representations from auxiliary tasks into a more robust relation representation against the noisy labels. The experimental results demonstrate that our model improves the predictive performance substantially over single-task learning baselines.
Zhou, L, Yu, N & Ying, M 1970, 'An applied quantum Hoare logic.', PLDI, the 40th ACM SIGPLAN Conference, ACM, Phoenix, AZ, pp. 1149-1162.
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© 2019 Association for Computing Machinery. We derive a variant of quantum Hoare logic (QHL), called applied quantum Hoare logic (aQHL for short), by: (1) restricting QHL to a special class of preconditions and postconditions, namely projections, which can significantly simplify verification of quantum programs and are much more convenient when used in debugging and testing; and (2) adding several rules for reasoning about robustness of quantum programs, i.e. error bounds of outputs. The effectiveness of aQHL is shown by its applications to verify two sophisticated quantum algorithms: HHL (Harrow-Hassidim-Lloyd) for solving systems of linear equations and qPCA (quantum Principal Component Analysis).
Zhou, Z, Liu, S, Xu, G & Zhang, W 1970, 'On Completing Sparse Knowledge Base with Transitive Relation Embedding', Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), Honolulu, Hawaii USA, pp. 3125-3132.
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Multi-relation embedding is a popular approach to knowledge base completion that learns embedding representations of entities and relations to compute the plausibility of missing triplet. The effectiveness of embedding approach depends on the sparsity of KB and falls for infrequent entities that only appeared a few times. This paper addresses this issue by proposing a new model exploiting the entity-independent transitive relation patterns, namely Transitive Relation Embedding (TRE). The TRE model alleviates the sparsity problem for predicting on infrequent entities while enjoys the generalisation power of embedding. Experiments on three public datasets against seven baselines showed the merits of TRE in terms of knowledge base completion accuracy as well as computational complexity.
Zhu, F, Zhu, L & Yang, Y 1970, 'Sim-Real Joint Reinforcement Transfer for 3D Indoor Navigation', 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Long Beach, CA, USA, pp. 11380-11389.
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There has been an increasing interest in 3D indoor navigation, where a robot in an environment moves to a target according to an instruction. To deploy a robot for navigation in the physical world, lots of training data is required to learn an effective policy. It is quite labour intensive to obtain sufficient real environment data for training robots while synthetic data is much easier to construct by render-ing. Though it is promising to utilize the synthetic environments to facilitate navigation training in the real world, real environment are heterogeneous from synthetic environment in two aspects. First, the visual representation of the two environments have significant variances. Second, the houseplans of these two environments are quite different. There-fore two types of information,i.e. visual representation and policy behavior, need to be adapted in the reinforce mentmodel. The learning procedure of visual representation and that of policy behavior are presumably reciprocal. We pro-pose to jointly adapt visual representation and policy behavior to leverage the mutual impacts of environment and policy. Specifically, our method employs an adversarial feature adaptation model for visual representation transfer anda policy mimic strategy for policy behavior imitation. Experiment shows that our method outperforms the baseline by 19.47% without any additional human annotations.
Zhu, H & Guo, YJ 1970, 'Design of Out-of-phase Filtering Power Dividers Using Flexible Coupling Schemes', 2019 IEEE Asia-Pacific Microwave Conference (APMC), 2019 IEEE Asia-Pacific Microwave Conference (APMC), IEEE, Singapore, Singapore, pp. 1023-1025.
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© 2019 IEEE. This paper presents based on flexible coupling schemes using multiple quarter-wavelength resonators. Microstrip-to-slotline transitions were are used to provide external coupling to the filters. A dual-mode and tri-mode filter are designed. For the dual-mode design, a pair of quarter-wavelength resonators is used with a gap between them providing a suitable coupling coefficient. For the tri-band design, an additional resonator is coupled to the previous quarter-wavelength resonators, leading to a three-pole response within the passband. A microstrip line with a grounded resistor is added at the middle point of two output ports, leading to excellent output matching and isolation between output ports. Two design examples with dual-mode and tri-mode responses are realized with the verification using full-wave simulations.
Zhu, H, Leighton, B, Chen, Y, Ke, X, Liu, S & Zhao, L 1970, 'Indoor Navigation System Using the Fetch Robot', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Intelligent Robotics and Applications, Springer International Publishing, Shenyang, China, pp. 686-696.
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© 2019, Springer Nature Switzerland AG. In this paper, we present a navigation system, including off-line mapping and on-line localization, for the Fetch robot in an indoor environment using Cartographer. This framework aims to build a practical, robust, and accurate Robot Operating System (ROS) package for the Fetch robot. Firstly, using Cartographer and the fusion of data from a laser scan and RGB-D camera, a two-dimensional (2D) off-line map is built. Then, the Adaptive Monte Carlo Localization (AMCL) ROS package is used to perform on-line localization. We use a simulation to validate this method of mapping and localization, then demonstrate our method live on the Fetch robot. A video about the simulation and experiment is shown in https://youtu.be/oOvxTOowe34.
Zhu, H, Lin, J-Y & Guo, YJ 1970, 'Wideband Filtering Out-of-Phase Power Dividers Using Slotline Resonators and Microstrip-to-Slotline Transitions', 2019 IEEE MTT-S International Microwave Symposium (IMS), 2019 IEEE/MTT-S International Microwave Symposium - IMS 2019, IEEE, Boston, MA, USA, pp. 919-922.
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© 2019 IEEE. Two wideband filtering out-of-phase power dividers are designed and analyzed. The initial design is composed of a quarter-wavelength slotline resonator and two microstrip-to-slotline transitions. Three transmission poles are produced to form the passband. Excellent matching and isolation are achieved using extra matching network. Based on the initial design, a pair of stepped-impedance open stubs are shunted at the output ports to improve the passband selectivity. The designs have been verified through experiment. The tested results show that the proposed devices have achieved 60% operating bandwidth, 1.8 dB insertion loss, 1.2° phase error and excellent in-band matching and isolation.
Zhu, H, Sun, H, Ding, C & Guo, YJ 1970, 'Butler Matrix Based Multi-Beam Base Station Antenna Array', 13th European Conference on Antennas and Propagation, EuCAP 2019, European Conference on Antennas and Propagation, IEEE, Krakow, Poland,.
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In this paper, a three-beam Butler matrix as well as its antenna arrays is presented for cellular base stations. The three-beam Butler matrix is able to generate three beams in the azimuth plane, which can increase the capacity of base stations. Striplines are used for developing the 3 and times; 3 Butler matrix, which is compose of directional couplers and phase shifters. To extend the 3 and times; 3 Butler matrix to a 3 and times; 5 one, unequal power dividers are also require. To verify the beam-forming network, 5-element dual-polarized antenna arrays covering LTE band are developed. Multiple beams are obtained by feeding the antenna array with the augmented 3 and times; 5 Butler matrix. The design is verified by both simulation and experiments.
Zhu, H, Zhu, X, Yang, Y, Sun, Y, Le, VH & Zhang, F 1970, 'Design of Miniaturized On-Chip Bandpass Filters using Inverting-Coupled Structure for Millimter-Wave Applications', 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE, Sapporo, Japan, pp. 1-5.
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© 2019 IEEE In this work, a new type of miniaturized on-chip resonator using an inductively-coupled structure is presented. The resonator is constructed by two spiral conductors that are implemented using two different metal layers. Since the two conductors are identical but placed in different rotating pattern, a kind of inductive coupling called inverting coupling will be introduced in addition to the broadside capacitive coupling. To fully understand the working mechanism of the resonator, simplified LC equivalent-circuit models and thorough analysis are provided. To further demonstrate the feasibility of the proposed miniaturized resonator in practice, two bandpass filters, namely a 1st-order and 2nd-order, are designed and fabricated in a standard 0.13-µm (Bi)-CMOS technology. Good agreements between simulation and measurement have obtained, which verify that the presented design approach is suitable for miniaturized on-chip passive design.
Zhu, J & Marjanovic, O 1970, 'Understanding Social Impact of Platform Co-ops', 14th International Cooperative Alliance Asia-Pacific Research Conference., Newcastle Australia.
Zhu, J, Yang, Y, Chu, C, Li, S, Liao, S & Xue, Q 1970, '60-GHz High Gain Planar Aperture Antenna Using Low-Temperature Cofired Ceramics (LTCC) Technology', 2019 IEEE MTT-S International Wireless Symposium (IWS), 2019 IEEE MTT-S International Wireless Symposium (IWS), IEEE, pp. 1-3.
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© 2019 IEEE. This paper presents a new single-ended-fed planar aperture antenna using low-temperature co-fired ceramics (LTCC) process technology. The proposed antenna not only inherits the merits of the aperture antennas including high gain, wide bandwidth, but also exhibits advantages of low profile and compact size. The aperture is excited by a cross-shaped patch and a loop-shaped balun structure placing below the patch. In this way, the energy can propagate on the patch in a traveling wave form and illuminate the aperture with uniform E-field distributions. Therefore, the antenna achieves good electrical and radiation performances, which are comparable to its balancedfed counterparts, while processing a simplified structure. Measured results demonstrate that the impedance bandwidth of the antenna covers the 60-GHz license-free band (57-64GHz) and the maximum gain can reach 11.5 dBi with a cavity size of only about 27 mm2.
Zhu, J, Yang, Y, Chu, C, Li, S, Liao, S & Xue, Q 1970, 'Mm-Wave Low-Profile Wideband Antenna Array Using Low-temperature Co-fired Ceramics (LTCC) Technique', 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT), 2019 International Conference on Microwave and Millimeter Wave Technology (ICMMT), IEEE, Guangzhou, China, pp. 1-2.
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© 2019 IEEE. A low-profile wideband and high gain patch antenna array using low-temperature co-fired ceramics (LTCC) fabrication technique is proposed for 60-GHz applications. Coupled feeding scheme is adopted so that the antenna achieves wide impedance bandwidth that covers the entire 60-GHz license-free band with the height of only 0.384 mm (four tape layers). The shorting pin connecting the upper patch and ground imitates the virtual ac ground plane of differential feeding. This enables the antenna element to achieve good radiation performances including stable gain, symmetrical beam with low cross-polarization, which are comparable to those of the differential-driven patch antenna while the complex differential feeding network is not required. Antenna array prototype is fabricated and measured to verify the idea.
Zhu, L, Arik, SO, Yang, Y & Pfister, T 1970, 'Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning', Computer Vision – ECCV 2020 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVII, European Conference on Computer Vision, Springer, Glasgow, UK, pp. 342-358.
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We propose a novel adaptive transfer learning framework, learning to transferlearn (L2TL), to improve performance on a target dataset by careful extractionof the related information from a source dataset. Our framework considerscooperative optimization of shared weights between models for source and targettasks, and adjusts the constituent loss weights adaptively. The adaptation ofthe weights is based on a reinforcement learning (RL) selection policy, guidedwith a performance metric on the target validation set. We demonstrate thatL2TL outperforms fine-tuning baselines and other adaptive transfer learningmethods on eight datasets. In the regimes of small-scale target datasets andsignificant label mismatch between source and target datasets, L2TL showsparticularly large benefits.
Zhu, L, Sevilla-Lara, L, Tran, D, Feiszli, M, Yang, Y & Wang, H 1970, 'FASTER Recurrent Networks for Efficient Video Classification', AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, AAAI, AAI, pp. 13098-13105.
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Typical video classification methods often divide a video into short clips,do inference on each clip independently, then aggregate the clip-levelpredictions to generate the video-level results. However, processing visuallysimilar clips independently ignores the temporal structure of the videosequence, and increases the computational cost at inference time. In thispaper, we propose a novel framework named FASTER, i.e., Feature Aggregation forSpatio-TEmporal Redundancy. FASTER aims to leverage the redundancy betweenneighboring clips and reduce the computational cost by learning to aggregatethe predictions from models of different complexities. The FASTER framework canintegrate high quality representations from expensive models to capture subtlemotion information and lightweight representations from cheap models to coverscene changes in the video. A new recurrent network (i.e., FAST-GRU) isdesigned to aggregate the mixture of different representations. Compared withexisting approaches, FASTER can reduce the FLOPs by over 10x? while maintainingthe state-of-the-art accuracy across popular datasets, such as Kinetics,UCF-101 and HMDB-51.
Zhu, Q, Phung, MD & Ha, QP 1970, 'Crack detection using enhanced hierarchical convolutional neural networks', Australasian Conference on Robotics and Automation, ACRA, Australasian Conference on Robotics and Automation, ARAA, Adelaide, Australia, pp. 1-8.
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Unmanned aerial vehicles (UAV) are expected to replace human in hazardous tasks of surface inspection due to their flexibility in operating space and capability of collecting high quality visual data. In this study, we propose enhanced hierarchical convolutional neural networks (HCNN) to detect cracks from image data collected by UAVs. Unlike traditional HCNN, here a set of branch networks is utilised to reduce the obscuration in the down-sampling process. Moreover, the feature preserving blocks combine the current and previous terms from the convolutional blocks to provide input to the loss functions. As a result, the weights of resized images can be reduced to minimise the information loss. Experiments on images of different crack datasets have been carried out to demonstrate the effectiveness of proposed HCNN.
Zhu, Q, Qiu, X, Coleman, P & Burnett, I 1970, 'Reducing number of transfer function measurement in local sound field reproduction using acoustic modeling', Proceedings of the International Congress on Acoustics, International Congress on Acoustics, EAA, Aachen, Germany, pp. 2684-2689.
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Broadband local sound field reproduction over an extended spatial region is a challenging problem when only limited transfer function measurements are available. An approach based on acoustics modeling is proposed in this paper to reduce the required number of transfer function measurements in local sound field reproduction. The proposed method only requires measuring the transfer functions from each source to a few samples over the boundary of the controlled region, and the transfer functions to the samples inside the controlled region are then estimated through efficient acoustic modelling. Simulations demonstrate that the proposed method requires fewer transfer function measurements than existing methods such as the least squares and the spatial harmonic decomposition methods.
Zhu, X & Wang, HL 1970, 'Impact factors for curved continuous CFST truss girder bridges', 13th International Conference on Shock and Impact Loads on Structures, SILOS 2019, pp. 635-639.
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A finite element model for curved continuous concrete-filled steel tubular (CFST) composite truss girder bridges has been built, and a new iterative process has been proposed for the analysis of the vehicle-bridge coupled system. The vibration modes and impact factors of the curved continuous CFST composite truss girder bridge have been obtained. The effects of parameters on the impact factors, such as the vehicle speed and deck unevenness, have been studied. The results show that the impact factor of the bridge is much larger than the value calculated from the current design code. The resonant critical vehicle speed and the resonant critical loading position are varied for different girder spans. The vehicle speed limit cannot effectively reduce the dynamic impact, and the bridge deck roughness excitation has an amplification effect on the impact factor causing by resonance. The results in this study are useful for design consideration and maintenance of CFST composite truss girder bridges.
Zou, C, Sui, Y, Yan, H & Xue, J 1970, 'TCD: Statically Detecting Type Confusion Errors in C++ Programs', 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE), 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE), IEEE, Berlin, Germany, pp. 292-302.
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© 2019 IEEE. For performance reasons, C++, albeit unsafe, is often the programming language of choice for developing software infrastructures. A serious type of security vulnerability in C++ programs is type confusion, which may lead to program crashes and control flow hijack attacks. While existing mitigation solutions almost exclusively rely on dynamic analysis techniques, which suffer from low code coverage and high overhead, static analysis has rarely been investigated. This paper presents TCD, a static type confusion detector built on top of a precise demand-driven field-, context-and flow-sensitive pointer analysis. Unlike existing pointer analyses, TCD is type-aware as it not only preserves the type information in the pointed-to objects but also handles complex language features of C++ such as multiple inheritance and placement new, making it therefore possible to reason about type casting in C++ programs. We have implemented TCD in LLVM and evaluated it using seven C++ applications (totaling 526,385 lines of C++ code) from Qt, a widely-adopted C++ toolkit for creating GUIs and cross-platform software. TCD has found five type confusion bugs, including one reported previously in prior work and four new ones, in under 7.3 hours, with a low false positive rate of 28.2%.
Zuo, H, Zhang, G, Pedrycz, W & Lu, J 1970, 'Domain Selection of Transfer Learning in Fuzzy Prediction Models', 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, pp. 1-6.
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© 2019 IEEE. Transfer learning has emerged as a solution for the cases where little or no labeled data are available in the training process. It leverages the previously acquired knowledge (a source domain with a large amount of labeled data) to facilitate solving the current tasks (a target domain with little labeled data). Many transfer learning methods have been proposed, and especially fuzzy transfer learning method, which is based on fuzzy systems, has been developed because of its capability to deal with the uncertainty in transfer learning. However, there is one issue with fuzzy transfer learning that has not yet been resolved: the domain selection problem, which is heavily depended on the knowledge transfer method and the applied prediction model. In this work, we explore the domain selection problem in TakagiSugeno fuzzy model when multiple source domains are accessible, and define the similarity between the source and target domains to provide guidance for the domain selection. The experiments on synthetic datasets are designed to simulate the situations of multiple sources in transfer learning, and demonstrate the rationality of the proposed similarity in selecting the source domain for the target domain. Further, the real-world datasets are used to validate the proposed domain adaptation method, and verify its capability in solving practical situations.
