Abas, AEP, Yong, JED, Mahlia, TM & 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.
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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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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.
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Abdo, P, Huynh, BP, Irga, PJ & Torpy, FR 2019, 'Evaluation of air flow through an active green wall biofilter', Urban Forestry and 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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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 where a moder...
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, SA, Mustafa, YT, Augustijn, EW & Filatova, T 2019, 'Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models', GeoInformatica, vol. 23, no. 2, pp. 243-268.
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© 2019, The Author(s). Machine learning (ML) algorithms steer agent decisions in agent-based models (ABMs), serving as a vehicle for implementing behaviour changes during simulation runs. However, when training an ML algorithm, obtaining large sets of micro-level human behaviour data is often problematic. Information on human behaviour is often collected via surveys of relatively small sample sizes. This paper presents a methodology for training a learning algorithm to guide agent behaviour in a spatial ABM using a limited survey data sample. We apply different implementation strategies using survey data and Bayesian networks (BNs). By being grounded in probabilistic directed graphical models, BNs stand out among other learning algorithms in that they can be based on expert knowledge and/or known datasets. This paper presents four alternative implementations of data-driven BNs to support agent decisions in a spatial ABM. We differentiate between training BNs prior to, or during the simulation runs, using only survey data or a combination of survey data and expert knowledge. The four different implementations are then illustrated using a spatial ABM of cholera diffusion for Kumasi, Ghana. The results indicate that a balance between expert knowledge and survey data provides the best control over the learning process of the agents and produces the most realistic agent behaviour.
Abidi, S, Piccardi, M, Tsang, WH & Williams, M-A 2019, 'Well-M³N: A Maximum-Margin Approach to Unsupervised Structured Prediction', IEEE Transactions on Emerging Topics in Computational Intelligence.
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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.
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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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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Adak, C, Chaudhuri, BB & Blumenstein, M 2019, 'An Empirical Study on Writer Identification and Verification From Intra-Variable Individual Handwriting', IEEE ACCESS, vol. 7, pp. 24738-24758.
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Adil Abboud, S, Al-Wais, S, Abdullah, SH, Alnajjar, F & Al-Jumaily, A 2019, 'Label Self-Advised Support Vector Machine (LSA-SVM)-Automated Classification of Foot Drop Rehabilitation Case Study', Biosensors, vol. 9, no. 4.
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Stroke represents a major health problem in our society. One of the effects of stroke is foot drop. Foot drop (FD) is a weakness that occurs in specific muscles in the ankle and foot such as the anterior tibialis, gastrocnemius, plantaris and soleus muscles. Foot flexion and extension are normally generated by lower motor neurons (LMN). The affected muscles impact the ankle and foot in both downward and upward motions. One possible solution for FD is to investigate the movement based on the bio signal (myoelectric signal) of the muscles. Bio signal control systems like electromyography (EMG) are used for rehabilitation devices that include foot drop. One of these systems is function electrical stimulation (FES). This paper proposes new methods and algorithms to develop the performance of myoelectric pattern recognition (M-PR), to improve automated rehabilitation devices, to test these methodologies in offline and real-time experimental datasets. Label classifying is a predictive data mining application with multiple applications in the world, including automatic labeling of resources such as videos, music, images and texts. We combine the label classification method with the self-advised support vector machine (SA-SVM) to create an adapted and altered label classification method, named the label self-advised support vector machine (LSA-SVM). For the experimental data, we collected data from foot drop patients using the sEMG device, in the Metro Rehabilitation Hospital in Sydney, Australia using Ethical Approval (UTS HREC NO. ETH15-0152). The experimental results for the EMG dataset and benchmark datasets exhibit its benefits. Furthermore, the experimental results on UCI datasets indicate that LSA-SVM achieves the best performance when working together with SA-SVM and SVM. This paper describes the state-of-the-art procedures for M-PR and studies all the conceivable structures.
Afzal, MK, Khan, WZ, Umer, T, Kim, BS & 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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Ahmad, A, Kashif, SAR, Saqib, MA, Ashraf, A & Shami, UT 2019, 'Tariff for reactive energy consumption in household appliances', Energy, vol. 186.
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© 2019 Elsevier Ltd The use of inefficient household appliances and their poor power quality results in energy wastage in residential buildings. These appliances also force the power system to operate at low power factor which results in an ineffective energy utilization. This paper reports the energy consumption pattern of mostly used household appliances individually and collectively over a year. Their power quality parameters are measured through experimentation to calculate the reactive energy consumed by household appliances. This paper also proposes the reactive energy tariffs to enhance the awareness among domestic consumers to make efficient use of household appliances. Currently the reactive power management is being dealt for only the industrial consumer by imposing low power factor penalty. This research estimated that Lahore Electric Supply Company (LESCO) can generate a revenue of almost 150 million US dollars in one year from household consumers by applying three part tariff scheme on reactive energy. By improving the power factor it is estimated that an energy conservation of 1.1 × 109 kWh per annum is also possible. Thus the proposed tariff for reactive energy encourages the domestic consumers to get involved actively in energy conservation while enabling the energy utilities to transfer more active energy to consumers without the expansion of the distribution network.
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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Ahmed, JB & Pradhan, B 2019, 'Spatial assessment of termites interaction with groundwater potential conditioning parameters in Keffi, Nigeria', Journal of Hydrology, vol. 578.
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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, 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.
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Ahmed, JBII, 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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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.
Ajuyah, P, Hill, M, Ahadi, A, Lu, J, Hutvagner, G & Nham, T 2019, 'MicroRNA (miRNA)-to-miRNA Regulation of Programmed Cell Death 4 (PDCD4)', MOLECULAR AND CELLULAR BIOLOGY, vol. 39, no. 18.
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Akgun, B, Krunz, M & Ozan Koyluoglu, O 2019, 'Vulnerabilities of massive MIMO systems to pilot contamination attacks', IEEE Transactions on Information Forensics and Security, vol. 14, no. 5, pp. 1251-1263.
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© 2018 IEEE. We consider a single-cell massive multiple-input multiple-output (MIMO) system in which a base station (BS) with a large number of antennas transmits simultaneously to several single-antenna users. The BS acquires the channel state information (CSI) for various receivers using uplink pilot transmissions. We demonstrate the vulnerability of the CSI estimation process to pilot-contamination (PC) attacks. In our attack model, the attacker aims at minimizing the sum rate of downlink transmissions by contaminating the uplink pilots. We first study these attacks for two downlink power allocation strategies under the assumption that the attacker knows the locations of the BS and its users. Later on, we relax this assumption and consider the case when such knowledge is probabilistic. The formulated problems are solved using stochastic optimization, Lagrangian minimization, and game-theoretic methods. A closed-form solution for a special case of the problem is obtained. Furthermore, we analyze the achievable individual secrecy rates under PC attacks and provide an upper bound on these rates. We also study this scenario without a priori knowledge of user locations at the attacker by introducing chance constraints. Our results indicate that such attacks can degrade the throughput of a massive MIMO system by more than 50%.
Akhani, M, Kashani, AR, Mousavi, M & Gandomi, AH 2019, 'A hybrid computational intelligence approach to predict spectral acceleration', MEASUREMENT, vol. 138, pp. 578-589.
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Akther, N, Lim, S, Van, HT, Phuntsho, S, Yang, Y, Tae-Hyun, B, 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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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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al zahrani, S, Islam, M & Saha, S 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.
Alaei, F, Alaei, A, Pal, U & Blumenstein, M 2019, 'A comparative study of different texture features for document image retrieval', Expert Systems with Applications, vol. 121, pp. 97-114.
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© 2018 Elsevier Ltd Due to the rapid increase of different digitised documents, there has been significant attention dedicated to document image retrieval over the past two decades. Finding discriminative and effective features is a fundamental task for providing a fast and more accurate retrieval system. Texture features are generally fast to compute and are suitable for large volume data. Thus, in this study, the effectiveness of texture features widely used in the literature of content-based image retrieval is investigated on document images. Twenty-six different texture feature extraction methods from four main categories of texture features, statistical, transform, model, and structural-based approaches, are considered in this research work to compare their performance on the problem of document image retrieval. Three document image datasets, MTDB, ITESOFT, and CLEF_IP with various content and page layouts are used to evaluate the twenty-six texture-based features on document image retrieval systems. The retrieval results are computed in terms of precision, recall and F-score, and a comparative analysis of the results is also provided. Feature dimensions and time complexity of the texture-based feature methods are further compared. Finally, some conclusions are drawn and suggestions are made about future research directions.
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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© The Author(s) 2018. 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.
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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.
Alderighi, T, Malomo, L, Giorgi, D, Bickel, B, Cignoni, P & Pietroni, N 2019, 'Volume-Aware Design of Composite Molds', ACM TRANSACTIONS ON GRAPHICS, vol. 38, no. 4.
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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 from Trametes 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, 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.
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Alilou, H, Rahmati, O, Singh, VP, Choubin, B, Pradhan, B, Keesstra, S, Ghiasi, SS & Sadeghi, SH 2019, 'Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria.', Journal of environmental management, vol. 232, pp. 22-36.
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Assessment of watershed health and prioritization of sub-watersheds are needed to allocate natural resources and efficiently manage watersheds. Characterization of health and spatial prioritization of sub-watersheds in data scarce regions helps better comprehend real watershed conditions and design and implement management strategies. Previous studies on the assessment of health and prioritization of sub-watersheds in ungauged regions have not considered environmental factors and their inter-relationship. In this regard, fuzzy logic theory can be employed to improve the assessment of watershed health. The present study considered a combination of climate vulnerability (Climate Water Balance), relative erosion rate of surficial rocks, slope weighted K-factor, topographic indices, thirteen morphometric characteristics (linear, areal, and relief aspects), and potential non-point source pollution to assess watershed health, using a new framework which considers the complex linkage between human activities and natural resources. The new framework, focusing on watershed health score (WHS), was employed for the spatial prioritization of 31 sub-watersheds in the Khoy watershed, West Azerbaijan Province, Iran. In this framework, an analytical network process (ANP) and fuzzy theory were used to investigate the inter-relationships between the above mentioned geo-environmental factors and to classify and rank the health of each sub-watershed in four classes. Results demonstrated that only one sub-watershed (C15) fell into the class that was defined as 'a potentially critical zone'. This article provides a new framework and practical recommendations for watershed management agencies with a high level of assurance when there is a lack of reliable hydrometric gauge data.
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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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 in
Portland 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 content
increased in mortars. Only in the case of vegetable oil and refined mineral oil could strength loss be attributed
in part to cement hydration inhibition, as evidenced by reduced total evolved heat. It is likely that
microstructural effects were also a key factor in strength loss for all mortars particularly for those containing
crude 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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Alnaghy, S, Kyme, A, Caillet, V, Doan, TN, O'Brien, R, Booth, JT & Keall, PJ 2019, 'A six-degree-of-freedom robotic motion system for quality assurance of real-time image-guided radiotherapy', PHYSICS IN MEDICINE AND BIOLOGY, vol. 64, no. 10.
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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.
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© 2019 by the authors. 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 plant...
Alnefaie, A, Fiaidhi, J & Mohammed, S 2019, 'Ontology-based food recommendation system for seniors: A peer to peer networking approach', International Journal of Advanced Science and Technology, vol. 123, pp. 41-46.
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© 2019 NADIA. This research is focused on the development of a state of art food recommendation system web platform for seniors with physical disability. The recommendation of food items is important for many reasons. Researchers have been studying these kinds of systems for many years, suggesting not only that can they be a means to help people find food they might want to eat, but also help them nourish themselves more healthily and avoids intolerances and food allergies. This paper created an ontology based Canada food allergies and intolerances databases 1 to guide the recommender according to the personalized information provided by the elder. The recommender incorporates other useful features such as the selections of different food items, their description and a peer to peer food delivery service. The delivery service is an added-value feature to connect seniors with the nearby couriers or online suppliers who are able to purchase the recommended items from stores and deliver it to the senior's home.
Alshehri, MD & Hussain, FK 2019, 'A fuzzy security protocol for trust management in the internet of things (Fuzzy-IoT)', Computing.
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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, Lu, DDC & Zhu, J 2019, 'An Analog BJT-Tuned Maximum Power Point Tracking Technique for PV Systems', IEEE Transactions on Circuits and Systems II: Express Briefs.
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IEEE In this paper, an analog, BJT-tuned voltage reference maximum power point tracking (MPPT) method for photovoltaic (PV) modules is proposed. 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 bipolar junction transistor (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, easy to implement and it can track the maximum power point very quickly without the need for a digital controller or PID controller. Hence, the circuit’s 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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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.
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Altaee, A, Hafez, MA & Hawari, AH 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. 1-7.
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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 kWh/m3 and 2.18 kWh/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 0.1M of diammonium phosphate in the draw solution.
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.
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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.
Altulyan, M, Yao, L, Kanhere, SS, Wang, X & Huang, C 2019, 'A unified framework for data integrity protection in people-centric smart cities', Multimedia Tools and Applications.
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With the rapid increase in urbanisation, the concept of smart cities has attracted considerable attention. By leveraging emerging technologies such as the Internet of Things (IoT), artificial intelligence and cloud computing, smart cities have the potential to improve various indicators of residents’ quality of life. However, threats to data integrity may affect the delivery of such benefits, especially in the IoT environment where most devices are inherently dynamic and have limited resources. Prior work has focused on ensuring integrity of data in a piecemeal manner and covering only some parts of the smart city ecosystem. In this paper, we address integrity of data from an end-to-end perspective, i.e., from the data source to the data consumer. We propose a holistic framework for ensuring integrity of data in smart cities that covers the entire data lifecycle. Our framework is founded on three fundamental concepts, namely, secret sharing, fog computing and blockchain. We provide a detailed description of various components of the framework and also utilize smart healthcare as use case.
Alturki, R & Gay, V 2019, 'The Development of An Arabic Weight Loss App ‘Akser Waznk’', JMIR Formative Research.
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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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Al-Zubi, MM & Sanagavarapu, A 2019, 'Modelling of Implantable Drug Delivery System in Tumor Microenvironment using Molecular Communication Paradigm', IEEE Access, vol. 7, pp. 141929-141940.
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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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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, DDC 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.
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IEEE This paper presents a new approach to controlling and optimizing single-stage boost-integrated full-bridge DC/DC converter for a stand-alone PV-battery powered DC motor system by combining pulse frequency modulation (PFM), pulse width modulation (PWM) and phase angle shift (PAS). Unlike most 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, namely, maximum power point tracking (MPPT), battery charging/discharging and driving the DC motor at variable speeds including bi-directional and stall motions. To achieve these control objectives, the boost inductor and motor inductance operate in different modes such that pulse-frequency modulation (PFM) and pulse-width modulation (PWM) can be used to achieve MPPT and wide motor voltage range respectively. By properly adjusting the phase angle shift (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 26W laboratory prototype converter confirmed the proposed design and operation, and efficiency improvement by 2-6%.
Anaissi, A & Goyal, M 2019, 'Blending Association Rules for Knowledge Discovery in Big Data', Enabling Technologies and Architectures for Next-Generation Networking Capabilities, pp. 254-271.
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Andrade-Valbuena, NA, Merigó-Lindahl, JM, Fernández, LV & Alarcón, CN 2019, 'Mapping leading universities in strategy research: Three decades of collaborative networks', Cogent Business and Management, 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. 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.
Anis, Z, Wissem, G, Riheb, H, Biswajeet, P & Mohamed Essghaier, G 2019, 'Effects of clay properties in the landslides genesis in flysch massif: Case study of Aïn Draham, North Western Tunisia', Journal of African Earth Sciences, vol. 151, pp. 146-152.
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© 2018 Elsevier Ltd Heavy rainfall in Aïn Draham province in the North-Western of Tunisia lead to the formation of some landslides which could poses danger to lives and properties. The geological outcrops of the region mainly consist of Numidian flysch rocks. In this study, field based 15 undisturbed samples were taken, from 11 boreholes drilled in 4 landslide points, to understand the real behaviour of soils when landslides occur. For this purpose, the geotechnical characterization of all samples was carried out. The grain size distribution shows that the clay and silt fractions prevail. The clay fraction ranges between 4% and 64% with an average of 40.4%, the silt fraction ranging from 19% to 71% averaging 39.8% and the sand fraction was between 6% and 44% with an average of 19.8%. The Casagrande plasticity chart indicates that 33.3% of samples were in the high plasticity group (CH group) and 66.6% having a medium to low plasticity. The water content varies between 12% and 31%. The direct shear strength test shows that the cohesions values range from 41 KPa to 77 KPa and the internal friction angle values range widely from 12° to 27°. A statistical approach was taken to determine the most important factors responsible for the decrease of the cohesion and friction angle which are in charge of slope failure. For this, a correlation matrix of all soil properties was done. The coefficients of correlation show that the clay fraction is the most correlated parameter to the cohesion with an index of −0.872. Unfortunately, the internal friction angle is very low correlated to all geotechnical parameters. The clay fraction, as the most correlated to the cohesion, and the water content, which depends on rainfall (landslide triggering factor), were considered as two independent parameters for the establishment of a multiple linear and non-linear regression models of the cohesion. The multiple linear model showed that the cohesion decrease with the increase of water conten...
Ansari, M, Zhu, H, Shariati, N & Guo, YJ 2019, 'Compact Planar Beamforming Array with End-Fire Radiating Elements for 5G Applications', IEEE Transactions on Antennas and Propagation.
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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 GHz 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 6-element linear antenna array with end-fire 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 5.6λ×4.6λ 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.
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. 17-25.
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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, 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.
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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 (96.7%) than t...
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, CW 2019, 'Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR-RF integrated model and multiresolution DEMs', Remote Sensing, vol. 11, no. 9.
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© 2019 by the authors. 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 ...
Arabameri, A, Pradhan, B, Rezaei, K, Lee, S & Sohrabi, M 2019, 'An ensemble model for landslide susceptibility mapping in a forested area', GEOCARTO INTERNATIONAL.
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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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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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© 2019 Elsevier B.V. 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.
Aravanis, T, Peppas, P & Williams, MA 2019, 'An investigation of parametrized difference revision operators', Annals of Mathematics and Artificial Intelligence.
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© 2019, Springer Nature Switzerland AG. In this article, we provide the epistemic-entrenchment and partial-meet characterizations of a new, important class of concrete revision operators (all of which satisfy the AGM postulates for revision), called Parametrized Difference revision operators (PD operators, for short). PD operators are natural generalizations of Dalal’s revision operator, with a much greater range of applicability, hence, the epistemic-entrenchment and partial-meet characterizations of the latter are also provided, as a by-product. Lastly, we prove that PD operators satisfy the strong version of Parikh’s relevance-sensitive axiom for belief revision, showing that they are fully compatible with the notion of relevance.
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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© 2018 Elsevier Ltd The objective of this research was to study the performance of batch and continuous recirculating reactor to photo-degrade dye and synthetic wastewater. Here, Aluminium (Al) was used as the doped metal. The commercially available TiO2 P-25 and Al (NO3)3 was used as a Ti-precursor and doping agent, respectively, via the impregnation method. Various parameters such as the concentration of the doping agent, and calcination temperature were studied. The TiO2 nanocrystal doped with Al was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), and thermogravimetric analyzer (TGA). The photocatalytic performance of Al-doped nanoparticle was quantified by the degradation of methylene blue (MB) solution under a visible light irradiation condition. Its performance was compared against undoped-nano-TiO2. The results showed that Al(NO3)3 solution with a concentration of 0.25% and volume of 100 cm/ml, and calcined at 300 ๐C for 4 h, was the optimum condition of Al-doped nano-TiO2. Furthermore, the highest pseudo-first-order kinetic rate was 0.096 where the doped Al(NO3)3 of 0.75 w/v was used in the batch reactor. The Al-doped nano-TiO2 that was obtained has the potential for use as a photocatalyst for degradation organics pollutant from wastewater under the visible light irradiation. The highest removal of organic pollutants from synthetic wastewater was 75% using TiO2 P-25 alone at 2 g/L dosage. In addition, the removal of organic pollutant by TiO2/doped with Al was 80% at a dosage of 0.5 g/L and was 85% at a dosage of 1 g/L.
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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© 2018 John Wiley & Sons, Ltd. This 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 H 2 -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, 'A framework for optimal actuator/sensor selection in a control system', International Journal of Control, vol. 92, no. 2, pp. 242-260.
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© 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group. When dealing with large-scale systems, manual selection of a subset of components (sensors/actuators), or equivalently identification of a favourable structure for the controller, that guarantees a certain closed-loop performance, is not very feasible. This paper is dedicated to the problem of concurrent optimal selection of actuators/sensors which can equivalently be considered as the structure identification for the controller. In the context of a multi-channel H 2 dynamic output feedback controller synthesis, we formulate and analyse a framework in which we incorporate two extra terms for penalising the number of actuators and sensors into the variational formulations of controller synthesis problems in order to induce a favourable controller structure. We then develop an explicit scheme as well as an iterative process for the purpose of dealing with the multi-objective problem of controller structure and control law co-design. It is also stressed that the immediate application of the proposed approach lies within the fault accommodation stage of a fault tolerant control scheme. By two numerical examples, we demonstrate the remarkable performance of the proposed approach.
Argha, A, Su, SW, Savkin, A & Celler, B 2019, 'Design of optimal sliding-mode control using partial eigenstructure assignment', International Journal of Control, pp. 1-13.
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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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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.
Armstrong, N, Sutton, GJ & Hibbert, DB 2019, 'Estimating probability density functions using a combined maximum entropy moments and Bayesian method. Theory and numerical examples', Metrologia, vol. 56, no. 1.
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© 2019 BIPM & IOP Publishing Ltd. Estimating the probability density function (pdf) from a limited sample of data is a challenging data analysis problem. Furthermore, determining which pdf best describes the available data involves an extra layer of complexity to the analysis, which if ignored, can have considerable consequences. We propose a combined maximum entropy (MaxEnt) moments and Bayesian model selection method to address this problem. The MaxEnt moments component is used to formulate a set of possible pdf models, each constrained by a different set of moments and parameterised by a set of Lagrangian multipliers. The Bayesian model selection component makes an inference about the most probable model, from the set of MaxEnt moment models. The structure of the prior pdf for the Lagrangian multipliers is determined from an expansion of the free energy functional for each MaxEnt model, and corresponding hyperparameters are calculated empirically. Numerical experiments were used to test the proposed method on samples taken from Gaussian and (more complex) non-Gaussian distributions, over a range of sample sizes. The results clearly demonstrate that the method can discriminate between simple and complex MaxEnt models for sample sizes approximately greater than 60. Our results demonstrate that MaxEnt and Bayesian methods are complementary. More critically, Bayesian inference is necessary when a set of competing MaxEnt models can be derived for a single dataset from a range of assumptions.
Arora, A, Furlong, PM, Fitch, R, Sukkarieh, S & Fong, T 2019, 'Multi-modal active perception for information gathering in science missions', Autonomous Robots, vol. 43, no. 7, pp. 1827-1853.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Robotic science missions in remote environments, such as deep ocean and outer space, can involve studying phenomena that cannot directly be observed using on-board sensors but must be deduced by combining measurements of correlated variables with domain knowledge. Traditionally, in such missions, robots passively gather data along prescribed paths, while inference, path planning, and other high level decision making is largely performed by a supervisory science team located at a different location, often at a great distance. However, communication constraints hinder these processes, and hence the rate of scientific progress. This paper presents an active perception approach that aims to reduce robots’ reliance on human supervision and improve science productivity by encoding scientists’ domain knowledge and decision making process on-board. We present a Bayesian network architecture to compactly model critical aspects of scientific knowledge while remaining robust to observation and modeling uncertainty. We then formulate path planning and sensor scheduling as an information gain maximization problem, and propose a sampling-based solution based on Monte Carlo tree search to plan informative sensing actions which exploit the knowledge encoded in the network. The computational complexity of our framework does not grow with the number of observations taken and allows long horizon planning in an anytime manner, making it highly applicable to field robotics with constrained computing. Simulation results show statistically significant performance improvements over baseline methods, and we validate the practicality of our approach through both hardware experiments and simulated experiments with field data gathered during the NASA Mojave Volatiles Prospector science expedition.
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.
Asadabadi, MR, Chang, E, Zwikael, O, Saberi, M & Sharpe, K 2019, 'Hidden fuzzy information: Requirement specification and measurement of project provider performance using the best worst method', Fuzzy Sets and Systems.
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© 2019 Elsevier B.V. The requirement specification process is an important part of a project and has the potential to prevent problems that may last for years after a project is delivered. Previous studies on the requirement specification process have focused on clarifying stated fuzzy terms in software requirement engineering. However, in many projects there is information that is not stated, but it is implied and can be inferred. This hidden information is usually ignored due to the assumption that ‘the provider understands what they mean/need’. This assumption is not always true. Such information, if extracted, may include fuzzy terms, namely hidden fuzzy terms (HFTs), which need specification. Therefore, these fuzzy terms have to be identified and then specified to avoid potential future consequences. This study proposes an algorithm to extract the hidden fuzzy terms, utilises a fuzzy inference system (FIS) to specify them, and applies the best worst multi-criteria decision making method (BWM) to evaluate the delivered product and measure the performance of the provider. The model is then used to examine a case from Defence Housing Australia. Such evaluation and measurement enable the project owner/manager to have a transparent basis to support decisions later in different phases of the project, and to ultimately reduce the likelihood of conflict and the receipt of an unsatisfactory product.
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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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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© 2019, Saudi Society for Geosciences. The popular use of geographical information system (GIS) technology in landslide assessment ensures good, accurate and fast results with less cost and manages mass movements and identifies safe areas for the development of new settlements. In the literature, different methods have been applied in landslide susceptibility assessment using various models. In these studies, the methods are either expert knowledge-based or data-driven approaches. These two approaches produce different outcomes based on their specific characteristics. Thus, the simultaneous use of a set of methods using fusion techniques at the decision-level improves the results. This study aims to investigate and identify a landslide zonation model by combining different methods in GIS and integrate the results (incorporated in the decision level) to achieve optimum accuracy. In this study, landslide hazard mapping was performed using knowledge-based models such as simple additive weighting (SAW) and fuzzy gamma operator and data-driven models namely radial basis function link network (RBFLN) and probabilistic neural network (PNN). Evaluation results indicate that data-driven methods have better performance than knowledge-based methods based on expert opinion. The SAW method also has a better result than other knowledge-based methods, such as fuzzy gamma. The assessment results of the randomly selected landslide control points indicate that PNN, SAW, RBFLN and fuzzy gamma have accuracies of 82.3, 69.62, 65.39 and 61.26% in landslide zoning, respectively. Meanwhile, weighted mean, maximum, median and minimum were used to incorporate the results in decision-level fusion; results show improved accuracies of 70.53, 83.5, 67.39 and 60.27% for each criterion, respectively.
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, SS, 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.
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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.
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.
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© 2019 fib. International Federation for Structural Concrete Self-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.
Atov, I, Chen, KC & Yu, S 2019, 'Data Science and Artificial Intelligence for Communications', IEEE Communications Magazine, vol. 57, no. 5, p. 56.
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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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Awan, I, Younas, M & Hussain, F 2019, 'Emerging challenges and frontiers in cloud computing', Concurrency Computation.
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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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(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.Observation of simulated hand hygiene encounters between a healthcare worker and a patient.Computer laboratory in a university.Healthy volunteers.Sensitivity and specificity of automatic detection of the first moment of hand hygiene.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.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%).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.
Aydogdu, MO, Chou, J, Altun, E, Ekren, N, Cakmak, S, Eroglu, M, Osman, AA, Kutlu, O, Oner, ET, Avsar, G, Oktar, FN, Yilmaz, I & Gunduz, O 2019, 'Production of the biomimetic small diameter blood vessels for cardiovascular tissue engineering', International Journal of Polymeric Materials and Polymeric Biomaterials, pp. 1-13.
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© 2018 Taylor & Francis A novel biomimetic vascular graft scaffolds were produced by electrospinning method with the most superior characteristics to be a proper biomimetic small diameter blood vessel using Polycaprolactone(PCL), Ethyl Cellulose(EC) and Collagen Type-1 were used to create the most convenient synergy of a natural and synthetic polymer to achieve similarity to native small diameter blood vessels. Scanning Electron Microscopy(SEM), Fourier Transform Infrared Spectroscopy(FTIR), Differential Scanning Calorimetry Analysis(DSC), tensile measurement tests, and in-vitro and in-vivo applications were performed. Results indicated significant properties such as having 39.33 nm minimum, 104.98 nm average fiber diameter, 3.2 MPa young modulus and 135% relative cell viability.
Azadi, M, Emrouznejad, A, Ramezani, F & Hussain, FK 2019, 'Efficiency measurement of cloud service providers using network data envelopment analysis', IEEE Transactions on Cloud Computing.
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IEEE An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements (Duan, 2017). To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, variable returns to scale (VRS), the non-oriented network slacks-based measure (SBM) model and input-oriented and output-oriented SBM models are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models.
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 - Molecular Cell Research, vol. 1866, no. 12.
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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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© 2019 Wiley Periodicals, Inc. To 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.
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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Azeez, OS, Pradhan, B & Jena, R 2019, 'Urban tree classification using discrete-return LiDAR and an object-level local binary pattern algorithm', GEOCARTO INTERNATIONAL.
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Azeez, OS, Pradhan, B, Shafri, HZM, Shukla, N, Lee, C & Rizeei, HM 2019, 'Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks', Applied Sciences (Bucureşti), vol. 9, no. 2.
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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.
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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Bah, AO, Qin, PY, 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.
Baier-Fuentes, H, Merigó, JM, Amorós, JE & Gaviria-Marín, M 2019, 'International entrepreneurship: a bibliometric overview', International Entrepreneurship and Management Journal, pp. 1-45.
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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.
Banaei, M, Ahmadi, A, Gramann, K & Hatami, J 2019, 'Emotional evaluation of architectural interior forms based on personality differences using virtual reality', Frontiers of Architectural Research.
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© 2019 The Authors Recent studies have shown that architectural interior forms could impact the affective state of inhabitants. However, the direct relation of specific forms with specific affective states is difficult to determine. In addition, no systematic categorization of architectural forms and their relation to emotional states exists. The investigation of the impact of architectural features on inhabitants' emotions is further complicated by the use of two-dimensional images of forms in laboratory investigations, which cannot perceive real-world architecture. Furthermore, the interior form consists of a combination of different forms rather than only pure forms, which was considered in previous studies. This study aimed to fill these gaps by evaluating interior forms on the basis of clustering different images of built living rooms throughout history as well as their impact on emotions. This study used pleasure, arousal, and dominance ratings with an emphasis on individual differences in personality. Virtual sample rooms were created based on formal clusters of architectural forms. Results showed a relationship between forms and emotional states for different personality traits. This work provided a novel approach on the influence of architecture on emotion by considering systematic form categorization and combinations, personality differences, and a virtual reality setup.
Bao, T, Damtie, MM, Wu, K, Wei, XL, Zhang, Y, Chen, J, Deng, CX, Jin, J, Yu, ZM, Wang, L & Frost, RL 2019, 'Rectorite-supported nano-Fe3O4 composite materials as catalyst for P-chlorophenol degradation: Preparation, characterization, and mechanism', Applied Clay Science, vol. 176, pp. 66-77.
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© 2019 Elsevier B.V. Clay minerals, as abundant natural resources, are among the most suitable supporting materials for nano metal. In this manuscript, new Fe3O4 nanoparticle/rectorite (Fe3O4/rectorite) catalysts are developed via in-situ precipitation oxidation reaction. Various physicochemical characterizations of Fe3O4/rectorite show that Fe3O4 nanoparticles (nano-Fe3O4) with an average particle diameter of approximately 10–20 nm are effectively loaded on the surface of acid leached rectorite (Al-rectorite) and have low coaggregation and improved dispersion. Moreover, the catalytic activity of Fe3O4/rectorite on degradation of P-chlorophenol by heterogeneous Fenton method is studied. Results of degradation experiments show that Fe3O4/rectorite has higher degradation efficiency of P-chlorophenol than bare nano-Fe3O4. Regeneration studies also show that Fe3O4/rectorite maintains 100% of its maximum P-chlorophenol degradation capacity after seven consecutive cycles. Fe3O4/rectorite can be easily separated by magnetic separation, and thus has good stability and reusability. The degradation mechanism of Fe3O4/rectorite is adsorption coupled with a Fenton-like reaction, which accounts for P-chlorophenol degradation of up to 625 mg/g. This work demonstrates a new composite material for the effective remediation of refractory organic compounds from wastewater.
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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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.
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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.
Barker, RA, Eager, D & Sharwood, LN 2019, 'Ensuring safety in public playgrounds is everybody's business.', The Medical journal of Australia, vol. 210, no. 1, pp. 9-11.
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When an injury occurs in a children’s playground, who is responsible? Media headlines1,2 have highlighted risks associated with a recently opened playground in north-western Sydney. The playground’s giant tube slide was closed following a spate of injuries to both adult and child patrons. With injuries described as “horrific”, the media questioned “how the 30 m long, 14 m tall slide passed safety rules”.1 While playground injuries are fairly common,3 there are specific multilevel responsibilities required to balance the importance of play and physical activity with mitigation of injury risk. In this article, we review the role of standards in industry governance and injury prevention.
Barker, RA, Eager, D & Sharwood, LN 2019, 'Ensuring safety in public playgrounds is everybody's business.', The Medical journal of Australia, vol. 210, no. 1, pp. 9-11.e1.
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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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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-um (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.
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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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.
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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.
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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.
Ben, X, Gong, C, Zhang, P, Yan, R, Wu, Q & Meng, W 2019, 'Coupled Bilinear Discriminant Projection for Cross-view Gait Recognition', IEEE Transactions on Circuits and Systems for Video Technology.
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IEEE A problem that hinders good performance of general gait recognition systems is that the appearance features of gaits are more affected-prone by views than identities, especially when the walking direction of the probe gait is different from the register gait. This problem cannot be solved by traditional projection learning methods, because these methods can learn only one projection matrix, thus for the same subject, it cannot transfer cross-view gait features into similar ones. This paper presents an innovative method to overcome this problem by aligning gait energy images (GEIs) across views with the Coupled bilinear discriminant projection (CBDP). Specifically, CBDP generates the aligned gait matrix features for two views with two sets of bilinear transformation matrices, so that the original GEIs’ spatial structure information can be preserved. By iteratively maximizing the ratio of inter-class distance metric to intra-class distance metric, CBDP can learn the optimal matrix subspace where GEIs across views are aligned in both horizontal and vertical coordinates. Therefore, CBDP is also able to avoid the under-sample problem. We also theoretically prove that the upper and lower bounds of the objective function sequence of CBDP are both monotonically increasing, so the convergence of CBDP is demonstrated. In terms of accuracy, the comparative experiments on the CASIA(B) and OU-ISIR gait databases show that our method is superior to the state-of-the-art cross-view gait recognition methods. More impressively, the encouraging performance is obtained by our method even in matching a lateral-view gait with a frontal-view gait.
Best, G, Cliff, OM, Patten, T, Mettu, RR & Fitch, R 2019, 'Dec-MCTS: Decentralized planning for multi-robot active perception', International Journal of Robotics Research, vol. 38, no. 2-3, pp. 316-337.
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© The Author(s) 2018. 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 & merigo, J 2019, 'Twenty Years of Information Systems Frontiers', Information Systems Frontiers, vol. Volume 21, Issue 2, 1 April 2019, Pages 485-494, no. 2.
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Beydoun, G, Abedin, B, Merigó, JM & Vera, M 2019, 'Twenty Years of Information Systems Frontiers', Information Systems 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.
Beydoun, G, Hoffmann, A, Valencia-García, R, Shen, J & Gill, A 2019, 'Towards an assessment framework of reuse: a knowledge level analysis approach', Complex and Intelligent Systems.
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Bharill, N, Patel, OP, Tiwari, A, Mu, L, Li, DL, 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.
Bharill, N, Tiwari, A, Malviya, A, Patel, OP, Gupta, A, Puthal, D, Saxena, A & Prasad, M 2019, 'Fuzzy knowledge based performance analysis on big data', Neurocomputing.
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© 2019 Elsevier B.V. Due to the various emerging technologies, an enormous amount of data, termed as Big Data, gets collected every day and can be of great use in various domains. Clustering algorithms that store the entire data into memory for analysis become unfeasible when the dataset is too large. Many clustering algorithms present in the literature deal with the analysis of huge amount of data. The paper discusses a new clustering approach called an Incremental Random Sampling with Iterative Optimization Fuzzy c-Means (IRSIO-FCM)algorithm. It is implemented on Apache Spark, a framework for Big Data processing. Sparks works really well for iterative algorithms by supporting in-memory computations, scalability, etc. IRSIO-FCM not only facilitates effective clustering of Big Data but also performs storage space optimization during clustering. To establish a fair comparison of IRSIO-FCM, we propose an incremental version of the Literal Fuzzy c-Means (LFCM)called ILFCM implemented in Apache Spark framework. The experimental results are analyzed in terms of time and space complexity, NMI, ARI, speedup, sizeup, and scaleup measures. The reported results show that IRSIO-FCM achieves a significant reduction in run-time in comparison with ILFCM.
Bhowmick, S, Saha, SC, Qiao, M & Xu, F 2019, 'Transition to a chaotic flow in a V-shaped triangular cavity heated from below', INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, vol. 128, pp. 76-86.
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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. e1801307-e1801307.
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Considerable 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, IT 2019, 'Factors Impacting Technology Business Incubator Performance', International Journal of Innovation Management, vol. 23, no. 1, pp. 1-30.
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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 well recognized. 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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Bishop, AN & Del Moral, P 2019, 'Stability Properties of Systems of Linear Stochastic Differential Equations with Random Coefficients', SIAM Journal on Control and Optimization, vol. 57, no. 2, pp. 1023-1042.
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This work is concerned with the stability properties of linear stochastic
differential equations with random (drift and diffusion) coefficient matrices,
and the stability of a corresponding random exponential semigroup. We consider
a class of random matrix drift coefficients that involves random perturbations
of an exponentially stable flow of deterministic (time-varying) drift matrices.
In contrast with more conventional studies, our analysis is not based on the
existence of Lyapunov functions and it does not draw on any ergodic properties.
These approaches are often difficult to apply in practice when the
drift/diffusion coefficients are random. We present rather weak and easily
checked perturbation-type conditions for the asymptotic stability properties of
time-varying and random linear stochastic differential equations. We provide
new log-Lyapunov estimates and exponential contraction inequalities on any time
horizon as soon as the fluctuation parameter is sufficiently small. This study
yields what seems to be the first result of this type for this class of linear
stochastic differential equations with random coefficient matrices.
Bishop, AN & Moral, PD 2019, 'An Explicit Floquet-Type Representation of Riccati Aperiodic Exponential Semigroups'.
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The article presents a rather surprising Floquet-type representation of
time-varying transition matrices associated with a class of nonlinear matrix
differential Riccati equations. The main difference with conventional Floquet
theory comes from the fact that the underlying flow of the solution matrix is
aperiodic. The monodromy matrix associated with this Floquet representation
coincides with the exponential (fundamental) matrix associated with the
stabilizing fixed point of the Riccati equation. The second part of this
article is dedicated to the application of this representation to the stability
of matrix differential Riccati equations. We provide refined global and local
contraction inequalities for the Riccati exponential semigroup that depend
linearly on the spectral norm of the initial condition. These refinements
improve upon existing results and are a direct consequence of the Floquet-type
representation, yielding what seems to be the first results of this type for
this class of models.
Blanco-Mesa, F, León-Castro, E & Merigó, JM 2019, 'A bibliometric analysis of aggregation operators', Applied Soft Computing Journal, vol. 81.
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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 & 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.
Blazevski, A, Scheltema, MJ, Yuen, B, Masand, N, Nguyen, TV, Delprado, W, Shnier, R, Haynes, A-M, Cusick, T, Thompson, J & Stricker, P 2019, 'Oncological and Quality-of-life Outcomes Following Focal Irreversible Electroporation as Primary Treatment for Localised Prostate Cancer: A Biopsy-monitored Prospective Cohort.', European urology oncology.
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BACKGROUND:Focal irreversible electroporation (IRE) can be used to treat men with localised prostate cancer (PCa) with reduced impact on quality of life (QoL). OBJECTIVE:To assess oncological and functional outcomes. DESIGN, SETTING, AND PARTICIPANTS:To report on a prospective database of patients undergoing primary IRE between February 2013 and August 2018. A minimum of 12-mo follow-up was available for 123 patients. Median follow-up was 36 mo (interquartile range [IQR] 24-52 mo). A total of 112 (91%) patients had National Comprehensive Cancer Network intermediate risk and 11 (9%) had low risk. A total of 12 (9.8%) had International Society of Urological Pathology (ISUP) grade 1, 88 (71.5%) had ISUP 2, and 23 (18.7%) had ISUP 3. INTERVENTION:Focal IRE ablation of PCa lesions. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:Follow-up involved serial prostate-specific antigen (PSA), multiparametric magnetic resonance imaging (mpMRI), and transperineal template mapping biopsy (TTMB) at 12 mo. Failure-free survival (FFS) was defined as progression to whole-gland or systemic treatment or metastasis/death. Functional outcomes were assessed. RESULTS AND LIMITATIONS:Median age was 68yr (IQR 62-73yr). Median preoperative PSA was 5.7ng/ml (IQR 3.8-8.0ng/ml). On post-treatment TTMB, in-field recurrence was present in 2.7-9.8% of patients. FFS at 3yr was 96.75%, metastasis-free survival 99%, and overall survival 100%. A total of 18 patients required salvage treatment (12 had repeat IRE; six had whole-gland treatment). The negative predictive value of mpMRI was 94% and sensitivity 40% for detecting in-field residual disease 6 mo after treatment. Among patients who returned questionnaires, 80/81 (98.8%) remained pad free and 40/53 (76%) had no change in erectile function. CONCLUSIONS:Focal IRE in select patients with localised clinically significant PCa has satisfactory short-term oncological outcomes with a minimal impact on patient QoL. PATIENT SUMMARY:In this study, 123 pati...
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, T & 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.
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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, T & 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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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, (2017), 7, 1, (10192), 10.1038/s41598-017-10141-3)', Scientific Reports, vol. 9, no. 1.
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© 2019, The Author(s). In the original version of this Article, Arti Agrawal was listed as corresponding author. Both authors now agree that Swetha S. Bobba should be considered the corresponding author. The PDF and HTML versions of the article have been modified to reflect this change.
Bou Ghantous, G & Gill, A 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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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.
Bracci, M, Tarini, M, Pietroni, N, Livesu, M & Cignoni, P 2019, 'HexaLab. net: an online viewer for hexahedral meshes', Computer-Aided Design, vol. 1110, pp. 24-36.
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We introduce HexaLab: a WebGL application for real time visualization, exploration and assessment of hexahedral meshes. HexaLab can be used by simply opening www.hexalab.net. Our visualization tool targets both users and scholars. Practitioners who employ hexmeshes for Finite Element Analysis, can readily check mesh quality and assess its usability for simulation. Researchers involved in mesh generation may use HexaLab to perform a detailed analysis of the mesh structure, isolating weak points and testing new solutions to improve on the state of the art and generate high quality images. To this end, we support a wide variety of visualization and volume inspection tools. Our system offers also immediate access to a repository containing all the publicly available meshes produced with the most recent techniques for hexmesh generation. We believe HexaLab, providing a common tool for visualizing, assessing and distributing results, will push forward the recent strive for replicability in our scientific community
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 (TIST), vol. 10, pp. 56-56.
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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.
Buchlak, QD, Esmaili, N, Leveque, JC, Farrokhi, F, Bennett, C, Piccardi, M & Sethi, RK 2019, 'Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review', Neurosurgical Review.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied. Our systematic search strategy yielded 6866 results, 70 of which met inclusion criteria. Performance statistics analyzed included area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Natural language processing (NLP) was used to model topics across the corpus and to identify keywords within surgical subspecialties. ML applications were heterogeneous. The densest cluster of studies focused on preoperative evaluation, planning, and outcome prediction in spine surgery. The main algorithms applied were NN, LR, and SVM. Input and output features varied widely and were listed to facilitate future research. The accuracy (F(2,19) = 6.56, p < 0.01) and specificity (F(2,16) = 5.57, p < 0.01) of NN, LR, and SVM differed significantly. NN algorithms demonstrated significantly higher accuracy than LR. SVM demonstrated significantly higher specificity than LR. We found no significant difference between NN, LR, and SVM AUC and sensitivity. NLP topic modeling reached maximum coherence at seven topics, which were defined by modeling approach, surgery type, and pathology themes. Keywords captured research foci within surgical domains. ML technology accurately predicts outcomes and facilitates clinical decision-making in neurosurgery. NNs frequently outperformed other algorithms on supervised learning tasks. This study identified gaps in the literature and opportunities for future neurosurgical ML research.
Bui, DT, Khosravi, K, Shahabi, H, Daggupati, P, Adamowski, JF, M.Melesse, A, Pham, BT, Pourghasemi, HR, 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.
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© 2019 by the authors. 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, TopographicWetness 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 ...
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.
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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, DT, Shahabi, H, Omidvar, E, Shirzadi, A, Geertsema, M, Clague, JJ, Khosravi, K, Pradhan, B, Pham, BT, 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.
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© 2019 by the authors. 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.
Bui, DT, Shirzadi, A, Chapi, K, Shahabi, H, Pradhan, B, Pham, BT, Singh, VP, Chen, W, Khosravi, K, Ahmad, BB & Lee, S 2019, 'A hybrid computational intelligence approach to groundwater spring potential mapping', Water (Switzerland), vol. 11, no. 10.
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© 2019 by the authors. 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 singleADTree, 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.
Bui, HH, Ha, NH, Nguyen, TND, Nguyen, AT, Pham, TTH, Kandasamy, J & Tien, VN 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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Buyya, R, Srirama, SN, Casale, G, Calheiros, R, Simmhan, Y, Varghese, B, Gelenbe, E, Javadi, B, Vaquero, LM, Netto, MAS, Toosi, AN, Rodriguez, MA, Llorente, IM, De Capitani Di Vimercati, S, Samarati, P, Milojicic, D, Varela, C, Bahsoon, R, Dias De Assuncao, M, Rana, O, Zhou, W, Jin, H, Gentzsch, W, Zomaya, AY & Shen, H 2019, 'A manifesto for future generation cloud computing: Research directions for the next decade', ACM Computing Surveys, vol. 51, no. 5.
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© 2018 Association for Computing Machinery. The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high-performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing.
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.
C. A. Tirado Cortes, H. Chen, D. Sturnieks, J. Garcia Marin, S. Lord & C. Lin 2019, 'Evaluating Balance Recovery Techniques for Users Wearing Head-Mounted Display in VR', IEEE Transactions on Visualization and Computer Graphics, pp. 1-1.
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Room-scale 3D position tracking allows users to explore the virtual environment by physically walking. However, since the eyesight of the user is blocked by a Head-Mounted Display (HMD), the user might lose her balance because of bumping into real-world obstacles or shifting the body weight onto virtual objects that are inexistent in the real-world. This paper investigates assistive fall recovery methods under the assumption that the onset of the fall is given. Our experiment simulated the forward loss-of-balance with a tether-release protocol. A magnetic lock attached to a counter-weight was released while the subject was in a static leaning posture and engaged in a secondary 3D object selection task. The experiment uses a two by two design that examines two assistive techniques, i.e. video-see-through and auditory warning, at two different timing, i.e. fall onset and 500ms prior to onset. The data from 17 subjects show that the video-see-through triggered 500ms before the onset of fall can effectively help users recover from falls. Surprisingly, the video-see-through at the fall onset has a significant negative impact on the fall recovery providing similar results to the baseline condition (no intervention).
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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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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Cai, Y, Pan, S, Wang, X, Chen, H, Cai, X & Zuo, M 2019, 'Measuring distance-based semantic similarity using meronymy and hyponymy relations', Neural Computing and Applications.
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© 2018, The Natural Computing Applications Forum. The assessment of semantic similarity between lexical terms plays a critical part in semantic-oriented applications for natural language processing and cognitive science. The optimization of calculation models is still a challenging issue for improving the performance of similarity measurement. In this paper, we investigate WordNet-based measures including distance-based, information-based, feature-based and hybrid. Among them, the distance-based measures are considered to have the lowest computational complexity due to simple distance calculation. However, most of existing works ignore the meronymy relation between concepts and the non-uniformity of path distances caused by various semantic relations, in which path distances are simply determined by conceptual hyponymy relation. To solve this problem, we propose a novel model to calculate the path distance between concepts, and also propose a similarity measure which nonlinearly transforms the distance to semantic similarity. In the proposed model, we assign different weights in accordance with various relations to edges that link different concepts. On basis of the distance model, we use five structure properties of WordNet for similarity measurement, which consist of multiple meanings, multiple inheritance, link type, depth and local density. Our similarity measure is compared against state-of-the-art WordNet-based measures on M&C dataset, R&G dataset and WS-353 dataset. According to experiment results, the proposed measure in this work outperforms others in terms of both Pearson and Spearman correlation coefficients, which indicates the effectiveness of our distance model. Besides, we construct six additional benchmarks to prove that the proposed measure maintains stable performance.
Cai, Y, Xu, KD, Ma, Z & Liu, Y 2019, 'Compact bandstop filters using coupled lines and open/short stubs with multiple transmission poles', IET Microwaves, Antennas and Propagation, vol. 13, no. 9, pp. 1368-1372.
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© The Institution of Engineering and Technology 2019 A compact bandstop filter (BSF) using parallel-coupled lines and open stubs with four transmission poles (TPs) is proposed. Through theoretical equation deduction, the positions of these four TPs can be obtained. Good roll-off skirts and stopband suppression characteristics of the BSF can be realised with the help of multiple TPs. To improve transition band roll-off rates, another BSF with five TPs is designed, where a short-circuited T-stub is loaded on the middle of the above BSF circuit structure. For demonstration, two filter examples with centre frequencies at 2 and 1.8 GHz are implemented with generation of four and five TPs, respectively, for theoretically calculated certification. Good agreement between simulated and measured results validates the design method.
Cancino, CA, Núñez, A & Merigó, JM 2019, 'Influence of a seed capital program for supporting high growth firms in Chile', Contaduria y Administracion, vol. 64, no. 1.
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© 2019 Universidad Nacional Autónoma de México, Facultad de Contaduría y Administración. 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.
Canning, J 2019, 'Optical hoovering on plasmonic rinks', MRS COMMUNICATIONS, vol. 9, no. 3, pp. 1072-1078.
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Cao, M, De Mel, N, Shannon, A, Prophet, M, Wang, C, Xu, W, Niu, B, Kim, J, Albarghouthi, M, Liu, D, Meinke, E, Lin, S, Wang, X & Wang, J 2019, 'Charge variants characterization and release assay development for co-formulated antibodies as a combination therapy', MABS, vol. 11, no. 3, pp. 489-499.
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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.
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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 (kNNs) 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, Z, Chuang, CH, King, JK & Lin, CT 2019, 'Multi-channel EEG recordings during a sustained-attention driving task', Scientific data, vol. 6, no. 1, p. 19.
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We 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, Ding, W, Wang, YK, Hussain, FK, Al-Jumaily, A & Lin, CT 2019, 'Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy', Neurocomputing.
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© 2019 Elsevier B.V. Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generally believed to reflect the ability of the brain to adapt to a visual stimulus environment. In this study, we explored repetitive steady-state visual evoked potential (SSVEP)-based EEG complexity by assessing multiscale inherent fuzzy entropy with relative measurements. We used a wearable EEG device with Oz and Fpz electrodes to collect EEG signals from 40 participants under the following three conditions: a resting state (closed-eyes (CE) and open-eyes (OE) stimulation with five 15-Hz CE SSVEPs and stimulation with five 20-Hz OE SSVEPs. We noted monotonic enhancement of occipital EEG relative complexity with increasing stimulus times in CE and OE conditions. The occipital EEG relative complexity was significantly higher for the fifth SSVEP than for the first SSEVP (FDR-adjusted p < 0.05). Similarly, the prefrontal EEG relative complexity tended to be significantly higher in the OE condition compared to that in the CE condition (FDR-adjusted p < 0.05). The results also indicate that multiscale inherent fuzzy entropy is superior to other competing multiscale-based entropy methods. In conclusion, EEG relative complexity increases with stimulus times, a finding that reflects the strong habituation of brain systems. These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.
Cao, Z, Lin, CT, Ding, W, Chen, MH, Li, CT & Su, TP 2019, 'Identifying Ketamine Responses in Treatment-Resistant Depression Using a Wearable Forehead EEG', IEEE Transactions on Biomedical Engineering.
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IEEE This study explores the responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited 55 outpatients with TRD who were randomized 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 (HDRS) scores. At baseline, responders showed a significantly weaker EEG theta power than did non- responders (p < 0.05). Responders exhibited a higher EEG alpha power but lower EEG alpha asymmetry and theta cordance at post-treatment than at baseline (p < 0.05). Furthermore, our baseline EEG predictor classified responders and non-responders with 81.3 $\pm$ 9.5% accuracy, 82.1 $\pm$ 8.6% sensitivity and 91.9 $\pm$ 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. The prefrontal EEG patterns at baseline may account for recognizing ketamine effects in advance. Our randomized, double- blind, placebo-controlled study provides information regarding clinical impacts on the potential targets underlying baseline identification and early changes from the effects of ketamine in patients with TRD.
Casanovas, M, Torres-Martínez, A & Merigó, JM 2019, 'Multi-person and multi-criteria decision making with the induced probabilistic ordered weighted average distance', Soft Computing.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents a new approach for selecting suppliers of products or services, specifically with respect to complex decisions that require evaluating different business characteristics to ensure their suitability and to meet the conditions defined in the recruitment process. To address this type of problem, this study presents the multi-person multi-criteria induced ordered weighted average distance (MP-MC-IOWAD) operator, which is an extension of the OWA operators that includes the notion of distances to multiple criteria and expert valuations. Thus, this work introduces new distance measures that can aggregate the information with probabilistic information and consider the attitudinal character of the decision maker. Further extensions are developed using probabilities to form the induced probabilistic ordered weighted average distance (IPOWAD) operator. An example in the management of insurance policies is presented, where the selection of insurance companies is very complex and requires the consideration of subjective criteria by experts in decision making.
Castilla-Rho, JC, Rojas, R, Andersen, MS, Holley, C & Mariethoz, G 2019, 'Sustainable groundwater management: How long and what will it take?', Global Environmental Change, vol. 58.
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© 2019 Elsevier Ltd Groundwater depletion is arguably one of humanity's greatest sustainability challenges of the 21st century. With Sustainable Development Goals only a decade away, water authorities around the world are in the urgent need for concrete and targeted measures to ensure that communities adhere to groundwater management policies as rapidly and as effectively as possible. In this paper, we combine computational social science, groundwater modelling and empirical data from the World Values Survey to generate future ensembles of hydro-social trajectories under alternative courses of management and social action or inaction. Our simulations shed new light on the role that cultural values can play in shaping the societal trajectories and norms that emerge when resources are either allocated or not sufficiently allocated to monitor compliance, issue fines, engage community leaders, and deter rule-breakers. This study presents a new approach to explore and evaluate the capacity of existing and future management actions to steer groundwater systems towards sustainable trajectories, to forecast the celerity and timing of social transformations at the inter-decadal scale, and to help nations identify the most pertinent management options under institutional, political, social, and/or cultural constraints. The methods presented here are broadly applicable to support strategic decisions that rely on the monitoring, enforcement, and compliance of environmental regulations.
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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© 2019 Elsevier B.V. 3D printing offers the flexibility to achieve favorable spacer geometrical modification. The role of 3D printed spacers for organic fouling mitigation in direct contact membrane distillation (DCMD) is evaluated. Compared to a commercial spacer, the design of 3D printed triply periodic minimal surfaces spacers (Gyroid and tCLP) - varying filament thickness and smaller hydraulic diameter enhanced DCMD fluxes by 50–65%. The highest DCMD flux was obtained with the 3D tCLP spacer due to its specific geometrical design feature. However, its design characteristics resulted in higher channel pressure drop compared to 3D Gyroid spacer. Moreover, 3D Gyroid spacer exhibited superior fouling mitigation (lower membrane organic mass deposition and reversible membrane hydrophobicity with humic acid solution), attributed to its tortuous design that repelled foulants. 3D Gyroid spacer was effective in achieving high water recovery (85%) while maintaining good quality distillate (10–15 μS/cm, 99% ion rejection) in DCMD with wastewater concentrate that contained high organics, mixed with inorganics. In MD, high organic contents minimally affected MD fluxes but reduced membrane hydrophobicity. Repeated DCMD cycles showed that organic pre-treatment as well as cleaning-in-place of membrane and spacer are essential for achieving high recovery rate while maintaining a stable long-term DCMD operation with wastewater concentrate.
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 AND BIOLOGY, vol. 64, no. 15.
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Chacon, A, Safavi-Naeini, M, Bolst, D, Guatelli, S, Franklin, D, Iwao, Y, Akamatsu, G, Tashima, H, Yoshida, E, Nishikido, F, Kitagawa, A, Mohammadi, A, Gregoire, M-C, Yamaya, T & Rosenfeld, A 2019, 'Monte Carlo investigation of the characteristics of radioactive beams for heavy ion therapy', Scientific Reports, vol. 9, no. 1.
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This 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.
Chai, J, Tsang, IW & Chen, W 2019, 'Large Margin Partial Label Machine', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Chapple, A, Nguyen, LN, Hai, FI, Dosseto, A, Rashid, MHO, Oh, S, Price, WE & Nghiem, LD 2019, 'Impact of inorganic salts on degradation of bisphenol A and diclofenac by crude extracellular enzyme from Pleurotus ostreatus', Biocatalysis and Biotransformation, vol. 37, no. 1, pp. 10-17.
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© 2017 Informa UK Limited, trading as Taylor & Francis Group This study investigated the influence of inorganic salts on enzymatic activity and the removal of trace organic contaminants (TrOCs) by crude laccase from the white-rot fungus Pleurotus ostreatus. A systematic analysis of 15 cations and anions from common inorganic salts was presented. Laccase activity was not inhibited by monovalent cations (i.e. Na + , NH 4 + , K + ), while the presence of divalent and trivalent cations showed variable impact – from negligible to complete inhibition – of both laccase activity and its TrOC removal performance. Of interest was the observation of discrepancy between residual laccase activity and TrOC removal in the presence of some ions. Mg 2+ had negligible impact on residual laccase activity but significant impact on TrOC removal. Conversely, F − showed greater impact on residual laccase activity than on TrOC removal. This observation indicated different impacts of the interfering ions on the interaction between laccase and TrOCs as compared to that between laccase and the reagent used to measure its activity, implicating that residual laccase activity may not always be an accurate indicator of TrOC removal. The degree of impact of halides was in the order of F − > I − > Br − > Cl − . Particularly, the tolerance of the tested laccase to Cl − has important implications for a range of industrial applications.
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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Chen, B, Zhong, C-H, Zhao, X, Ma, H, Qin, L & Liao, W-H 2019, 'Reference Joint Trajectories Generation of CUHK-EXO Exoskeleton for System Balance in Walking Assistance', IEEE ACCESS, vol. 7, pp. 33809-33821.
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Chen, D, Luo, Q, Meng, M & Sun, G 2019, 'Low velocity impact behavior of interlayer hybrid composite laminates with carbon/glass/basalt fibres', Composites Part B: Engineering, vol. 176.
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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, WT 2019, 'Introduction to the special issue on highlights of ACM intelligent user interface (IUI) 2017', ACM Transactions on Interactive Intelligent Systems, vol. 9, no. 2-3.
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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, 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.
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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. 102, pp. 133-146.
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Chen, K, Yao, L, Zhang, D, Wang, X, Chang, X & Nie, F 2019, 'A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition.', IEEE transactions on neural networks and learning systems.
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Recent years have witnessed the success of deep learning methods in human activity recognition (HAR). The longstanding shortage of labeled activity data inherently calls for a plethora of semisupervised learning methods, and one of the most challenging and common issues with semisupervised learning is the imbalanced distribution of labeled data over classes. Although the problem has long existed in broad real-world HAR applications, it is rarely explored in the literature. In this paper, we propose a semisupervised deep model for imbalanced activity recognition from multimodal wearable sensory data. We aim to address not only the challenges of multimodal sensor data (e.g., interperson variability and interclass similarity) but also the limited labeled data and class-imbalance issues simultaneously. In particular, we propose a pattern-balanced semisupervised framework to extract and preserve diverse latent patterns of activities. Furthermore, we exploit the independence of multi-modalities of sensory data and attentively identify salient regions that are indicative of human activities from inputs by our recurrent convolutional attention networks. Our experimental results demonstrate that the proposed model achieves a competitive performance compared to a multitude of state-of-the-art methods, both semisupervised and supervised ones, with 10% labeled training data. The results also show the robustness of our method over imbalanced, small training data sets.
Chen, S, Wang, Y, Lin, CT, 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, SL, Karmokar, DK, Li, Z, Qin, PY, 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, W, Li, Y, Xue, W, Shahabi, H, Li, S, Hong, H, Wang, X, Bian, H, Zhang, S, Pradhan, B & Bin Ahmad, B 2019, 'Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods', Science of The Total Environment, pp. 134979-134979.
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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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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 results s...
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.
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Chen, WH, Lin, YY, Liu, HC, Chen, TC, Hung, CH, Chen, CH & 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.
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Chen, X, Chamoli, U, Lapkin, S, Castillo, JV & Diwan, AD 2019, 'Complication rates of different discectomy techniques for the treatment of lumbar disc herniation: a network meta-analysis', EUROPEAN SPINE JOURNAL, vol. 28, no. 11, pp. 2588-2601.
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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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Chen, X, Ni, BJ & 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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© 2019 Wiley Periodicals, Inc. The 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 (N 2 O). A systematic analysis of the pathways and affecting parameters is, therefore, required for developing N 2 O mitigation strategies. To this end, a mathematical model capable of describing different N 2 O 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 N 2 O production in the granular structure as well as the impacts of operating conditions on N 2 O production. The results show that: (a) in the aerobic zone close to the granule surface where AOB contribute to N 2 O production through both the AOB denitrification pathway and the NH 2 OH pathway, the co-occurring HB consume N 2 O produced by AOB but indirectly enhance the N 2 O production by providing NO from NO 2− reduction for the NH 2 OH pathway, (b) the inner anoxic zone of granules with the dominance of anammox bacteria acts as a sink for NO 2− diffusing from the outer aerobic zone and, therefore, reduces N 2 O production from the AOB denitrification pathway, (c) operating parameters including bulk DO, influent NH 4+ , and granule size affect the N 2 O production in the granules mainly by regulating the NH 2 OH pathway of AOB, accounting for 34–58% of N 2 O turnover, and (d) the competition between the NH 2 OH pathway and heterotrophic denitrification for nitric oxide leads to the positive role of HB in reducing N 2 O production in the autotrophic nitrogen removal granules, which could b...
Chen, X, Ni, W, Chen, T, Collings, I, Wang, X, Liu, RP & Giannakis, GB 2019, 'Multi-Timescale Online Optimization of Network Function Virtualization for Service Chaining', IEEE Transactions on Mobile Computing.
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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, Sin, G & Ni, BJ 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, 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.
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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, Z, Duan, X, Wei, W, Wang, S & Ni, BJ 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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© 2019 The Royal Society of Chemistry. With the increasing demands in energy consumption and increasing environmental concerns, it is of vital significance for developing renewable and clean energy sources to substitute traditional fossil fuels. As an outstanding candidate, hydrogen is recognized as a green energy carrier due to its high gravimetric energy density, zero carbon footprints, and earth-abundance. Currently, water splitting in alkaline electrolytes represents one of the most promising methods for sustainable hydrogen production, and the key challenge lies in the development of high-performance electrocatalysts for the hydrogen evolution reaction (HER). Given the rapid advances in the design and development of efficient catalysts towards the alkaline HER, especially capable transition metal (TM)-based materials, this review aims to summarise recent progress in the theoretical understanding of the alkaline HER and TM-based electrocatalysts. TM-based catalysts classified by their different anionic compositions (metals, alloys, oxides, hydroxides, sulfides, selenides, tellurides, nitrides, phosphides, carbides, and borides) are comprehensively showcased. Special attention is given to mainstream strategies that can improve the catalytic properties of each category, as well as the underlying structure-activity regimes. Additionally, the challenges for the future development of novel catalysts are also analyzed.
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, BJ 2019, 'Recent advances in electrocatalysts for halogenated organic pollutant degradation', Environmental Science: Nano, vol. 6, no. 8, pp. 2332-2366.
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© The Royal Society of Chemistry 2019. Electrocatalysis has recently been extensively employed for the degradation of halogenated organic pollutants (HOPs) that normally act as persistent, toxic, and bioaccumulative substances in the environment and pose threats to aquatic species as well as human beings. This review article broadly gives the up-to-date status on promising electrocatalysts for the degradation of HOPs, with particular emphasis on the strategies for promoting the activities of catalysts. Firstly, the catalysts for the oxidative mineralization process including metallic oxide- and carbon-based anodes, as well as the oxidative dehalogenation mechanism of these catalysts, are comprehensively presented. Secondly, the catalysts for the reductive degradation process, which involves metal- and metal complex-based cathodes, together with their applications and organic transformation pathways, are fully analyzed. Thirdly, recent advances in the integrated techniques are introduced, and the integration of membrane techniques, biological methods, Fenton processes, and photocatalysis with electrocatalysis is discussed. Finally, several key directions for further research are exploited, which include catalyst design, experimental optimization, scientific exploration, and effective coupling techniques.
Chen, Z, Ren, Z, Gao, H, Zheng, R, Jin, Y & Niu, C 2019, 'Flotation studies of fluorite and barite with sodium petroleum sulfonate and sodium hexametaphosphate', JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T, vol. 8, no. 1, pp. 1267-1273.
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Chen, Z, Wong, H & Liu, Y 2019, 'A Polarizer Integrated Dielectric Resonator Antenna for Polarization Reconfigurability', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 67, no. 4, pp. 2723-2728.
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Chen, Z, Zhang, B, Zhang, N, Du, H & Kong, G 2019, 'Optimal preview position control for shifting actuators of automated manual transmission', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 233, no. 2, pp. 440-452.
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© IMechE 2018. This paper is concerned with the problem of position tracking control for the motor-driven gear-shift actuating mechanism for electro-mechanical automated manual transmissions (AMT). It is well known that torque interruption is an inherent flaw of AMT. As shift duration directly affects the torque interruption interval, shift quality can be improved by minimizing the duration of the shift. To realize rapid and precise gear-shift control, an optimal discrete-time preview position control scheme is proposed. The proposed control scheme consists of state-feedback control, a discrete integrator, and preview feed-forward control. Instead of the traditional difference method, the state transformation method is utilized to construct the augmented error system such that the augmented system can be kept in a simple form. The H∞ performance index is employed to reduce the effect of the shifting load disturbance during the gear-shifting process. The controller gains are obtained by solving a linear matrix inequality (LMI). Simulation and test bench results all show that the proposed preview control algorithm has better dynamic response and adaptability under different loads compared to the optimal linear quadratic regulator (LQR) controller and the standard PID controller.
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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Cheng, C, Xiao, F & Cao, Z 2019, 'A New Distance for Intuitionistic Fuzzy Sets Based on Similarity Matrix', IEEE Access, vol. 7, pp. 70436-70446.
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© 2013 IEEE. Measuring the distance between two intuitionistic fuzzy sets (IFSs) is an open issue. Many types of distances for the IFSs have been proposed in previous studies. Some existing methods cannot satisfy the axioms of similarity or provide counterintuitive cases. Others ignore the relationship between three parameters characterizing the information carried by the IFS. To address these issues, a new distance is proposed by analyzing the similarity among the three parameters of the IFS. The comparison with some existing distances illustrates that the new distance has a higher sensitivity and can effectively measure the similarity between the IFSs. The results of the application of pattern recognition are also shown that the proposed method has better recognition ability.
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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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, EJ, Chou, KP, Rajora, S, Jin, BH, Tanveer, M, Lin, CT, Young, KY, Lin, WC & 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, 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, HC & Hsieh, MH 2019, 'Matrix Poincaré, Φ -Sobolev inequalities, and quantum ensembles', Journal of Mathematical Physics, vol. 60, no. 3.
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© 2019 Author(s). Sobolev-type inequalities have been extensively studied in the frameworks of real-valued functions and non-commutative Lp spaces, and have proven useful in bounding the time evolution of classical/quantum Markov processes, among many other applications. In this paper, we consider yet another fundamental setting - matrix-valued functions - and prove new Sobolev-type inequalities for them. Our technical contributions are two-fold: (i) we establish a series of matrix Poincaré inequalities for separably convex functions and general functions with Gaussian unitary ensembles inputs; and (ii) we derive Φ-Sobolev inequalities for matrix-valued functions defined on Boolean hypercubes and for those with Gaussian distributions. Our results recover the corresponding classical inequalities (i.e., real-valued functions) when the matrix has one dimension. Finally, as an application of our technical outcomes, we derive the upper bounds for a fundamental entropic quantity - the Holevo quantity - in quantum information science since classical-quantum channels are a special instance of matrix-valued functions. This is obtained through the equivalence between the constants in the strong data processing inequality and the Φ-Sobolev inequality.
Cheng, HC, Gao, L & Hsieh, MH 2019, 'Properties of Scaled Noncommutative Rényi and Augustin Information', IEEE International Symposium on Information Theory - Proceedings, vol. 2019-July, pp. 2219-2223.
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© 2019 IEEE. The scaled Rényi information plays a significant role in evaluating the performance of information processing tasks by virtue of its connection to the error exponent analysis. In quantum information theory, there are three generalizations of the classical Rényi divergence - the Petz's, sandwiched, and log-Euclidean versions, that possess meaningful operational interpretation. The goal of this paper is thus to analyze fundamental properties of scaled Rényi information from a noncommutative measure-theoretic perspective. Firstly, we prove the uniform equicontinuity for all three quantum versions of Rényi information, hence it yields the joint continuity of these quantities in the orders and priors. Secondly, we establish the concavity in the region of s ∈ (-1, 0) for both Petz's and the sandwiched versions. This completes the open questions raised by Holevo, Mosonyi and Ogawa. For the applications, we show that the strong converse exponent in classical-quantum channel coding satisfies a minimax identity. The established concavity is further employed to prove an entropic duality between classical data compression with quantum side information and classical-quantum channel coding, and a Fenchel duality in joint source-channel coding with quantum side information.
Cheng, HC, Hanson, EP, Datta, N & Hsieh, MH 2019, 'Duality between source coding with quantum side information and c-q channel coding', IEEE International Symposium on Information Theory - Proceedings, vol. 2019-July, pp. 1142-1146.
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© 2019 IEEE. In this paper, we establish an interesting duality between two different quantum information-processing tasks, namely, classical source coding with quantum side information, and channel coding over classical-quantum channels. The duality relates the optimal error exponents of these two tasks, generalizing the classical results of Ahlswede and Dueck. We establish duality both at the operational level and at the level of the entropic quantities characterizing these exponents. For the latter, the duality is given by an exact relation, whereas for the former, duality manifests itself in the following sense: an optimal coding strategy for one task can be used to construct an optimal coding strategy for the other task. Along the way, we derive a bound on the error exponent for classical-quantum channel coding with constant composition codes which might be of independent interest.
Cheng, Q, Nguyen, D, Dutkiewicz, E & Mueck, M 2019, 'Preserving Honest/Dishonest Users' Operational Privacy with Blind Interference Calculation in Spectrum Sharing System', IEEE Transactions on Mobile Computing (TMC).
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Cheng, Q, Shi, Z, Nguyen, D & Dutkiewicz, E 2019, 'Sensing OFDM Signal: A Deep Learning Approach', IEEE Transactions on Communications (TCOM).
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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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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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, pp. 1212-1221.
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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 and Propagation, vol. 13, no. 7, pp. 926-929.
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© The Institution of Engineering and Technology 2019. 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 predistortion, 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.
Chepurin, D, Chamoli, U, Sheldrick, K, Lapkin, S, Scott, D, Kuan, J & Diwan, AD 2019, 'Bony stress in the lumbar spine is associated with intervertebral disc degeneration and low back pain: a retrospective case-control MRI study of patients under 25 years of age', EUROPEAN SPINE JOURNAL, vol. 28, no. 11, pp. 2470-2477.
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Cheuk, KY, Hu, Y, Tam, EMS, Shi, L, Yu, FWP, Hung, VWY, Lai, KCY, Cheng, WHW, Yip, BHK, Qin, L, Ng, BKW, Chu, WCW, Griffith, J, Guo, XE, Cheng, JCY & Lam, TP 2019, 'Bone measurements at multiple skeletal sites in adolescent idiopathic scoliosisan in vivo correlation study using DXA, HR-pQCT and QCT', ARCHIVES OF OSTEOPOROSIS, vol. 14, no. 1.
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Chiniforush, AA, Akbarnezhad, A, Valipour, H & Malekmohammadi, S 2019, 'Moisture and temperature induced swelling/shrinkage of softwood and hardwood glulam and LVL: An experimental study', Construction and Building Materials, vol. 207, pp. 70-83.
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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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© 2019 by The American Society of Hematology. 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.
Chivukula, A & Liu, W 2019, 'Adversarial Deep Learning Models with Multiple Adversaries', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 6, pp. 1066-1079.
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IEEE We develop an adversarial learning algorithm for supervised classification in general and Convolutional Neural Networks (CNN) in particular. The algorithm's objective is to produce small changes to data distribution defined over positive and negative class labels so that the resulting data distribution is misclassified by the CNN. Then we propose a CNN secure to such unforseen changes in data. The algorithm generates adversarial manipulations by formulating a multiplayer stochastic game targeting the classification performance of the CNN. The multiplayer stochastic game is expressed in terms of multiple two player sequential games. Each game consists of interactions between two players -- an intelligent adversary and the learner CNN -- such that player's payoff function increases with interactions. Following the convergence of a sequential noncooperative Stackelberg game, each two player game is solved for the Nash equilibrium. The Nash equilibrium finds a pair of strategies (learner weights and evolutionary operations) from which there is no incentive for either learner or adversary to deviate. We then retrain the learner over all the adversarial manipulations generated by multiple players to propose a secure CNN robust to subsequent adversarial data manipulations. The adversarial data and corresponding CNN performance is evaluated on MNIST handwritten digits data. The results suggest that game theory and evolutionary algorithms are very effective in securing deep learning models against performance vulnerabilities simulated as attack scenarios from multiple adversaries.
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, J, Dorji, P, Shon, HK & Hong, S 2019, 'Corrigendum to “Applications of capacitive deionization: Desalination, softening, selective removal, and energy efficiency” [Desalination 449 (2019) 118–130](S0011916418306386)(10.1016/j.desal.2018.10.013)', Desalination, vol. 468.
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© 2019 The authors regret that the Acknowledgements should be revised due to the change of funder.Previous: This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA), grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18IFIP-B116952-03). Update: This work was supported by Korea Environmental Industry and Technology Institute (KEITI) through Industrial Facilities & Infrastructure Research Program, funded by Korea Ministry of Environment (MOE) (1485016424).The authors would like to apologise for any inconvenience caused.
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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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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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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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.
Chomsiri, T, He, XS, Nanda, P & Tan, Z 2019, 'Hybrid Tree-rule Firewall for High Speed Data Transmission', IEEE Transactions on Cloud Computing.
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Traditional firewalls employ listed rules in both configuration and process phases to regulate network traffic. However, configuring a firewall with listed rules may create rule conflicts, and slows down the firewall. To overcome this problem, we have proposed a Tree-rule firewall in our previous study. Although the Tree-rule firewall guarantees no conflicts within its rule set and operates faster than traditional firewalls, keeping track of the state of network connections using hashing functions incurs extra computational overhead. In order to reduce this overhead, we propose a hybrid Tree-rule firewall in this paper. This hybrid scheme takes advantages of both Tree-rule firewalls and traditional listed-rule firewalls. The GUIs of our Tree-rule firewalls are utilized to provide a means for users to create conflict-free firewall rules, which are organized in a tree structure and called 'tree rules'. These tree rules are later converted into listed rules that share the merit of being conflict-free. Finally, in decision making, the listed rules are used to verify against packet header information. The rules which have matched with most packets are moved up to the top positions by the core firewall. The mechanism applied in this hybrid scheme can significantly improve the functional speed of a firewall.
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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Chow, DH, Zheng, L, Tian, L, Ho, K-S, Qin, L & Guo, X 2019, 'Application of ultrasound accelerates the decalcification process of bone matrix without affecting histological and immunohistochemical analysis', JOURNAL OF ORTHOPAEDIC TRANSLATION, vol. 17, pp. 112-120.
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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 (Barking, Essex : 1987), vol. 255, no. Pt 2, p. 113260.
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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 & NANOSTRUCTURES, vol. 110, pp. 115-122.
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Chu, Y, Fu, X, Luo, Y, Canning, J, Tian, Y, Cook, K, Zhang, J & Peng, G-D 2019, 'Silica optical fiber drawn from 3D printed preforms', Optics Letters, vol. 44, no. 21, pp. 5358-5358.
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Chubb, CT, Tomamichel, M & Korzekwa, K 2019, 'Moderate deviation analysis of majorization-based resource interconversion', PHYSICAL REVIEW A, vol. 99, no. 3.
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Coh, 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 & SUSTAINABLE ENERGY REVIEWS, vol. 107, pp. 59-74.
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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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© 2019 The Royal Society of Chemistry. Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS), in combination with Biotyper software, is a rapid, high-throughput, and accurate method for the identification of microbes. Microbial outbreaks in a brewery present a major risk for companies as it can lead to cost-intensive recalls and damage to the brand reputation. MALDI-TOF MS has been implemented into a brewery setting for quality control practices and the identification of beer spoilage microorganisms. However, the applicability of this approach is hindered by compatibility issues associated with mixed cultures, requiring the use of time-consuming selective cultivation techniques prior to identification. We propose a novel, low-cost approach based on the combination of inertial microfluidics and secondary flows in a spiral microchannel for high-throughput and efficient separation of yeasts (Saccharomyces pastorianus and Saccharomyces cerevisiae) from beer spoilage microorganisms (Lactobacillus brevis and Pediococcus damnosus). Flow rates were optimised using S. pastorianus and L. brevis, leading to separation of more than 90% of the L. brevis cells from yeast. The microorganisms were then identified to the species level using the MALDI-TOF MS platform using standard sample preparation protocols. This study shows the high-throughput and rapid separation of spoilage microorganisms (0.3-3 μm) from background yeast (5 μm) from beer, subsequent identification using MALDI Biotyper, and the potential applicability of the approach for biological control in the brewing industry.
Cui, H, Xu, F, Saha, SC & Liu, Q 2019, 'Transient free convection heat transfer in a section-triangular prismatic enclosure with different aspect ratios', INTERNATIONAL JOURNAL OF THERMAL SCIENCES, vol. 139, pp. 282-291.
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Cui, L, Qu, Y, Nosouhi, MR, Yu, S, Niu, JW & 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, L, Wu, J, Pi, D, Zhang, P & Kennedy, P 2019, 'Dual Implicit Mining-Based Latent Friend Recommendation', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.
Cui, P-F, Zhang, JA, Lu, W-J, Guo, YJ & Zhu, H-B 2019, 'Statistical Sparse Channel Modeling for Measured and Simulated Wireless Temporal Channels', IEEE Transactions on Wireless Communications, pp. 1-1.
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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, 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.
Curiskis, SA, Drake, B, Osborn, TR & Kennedy, PJ 2019, 'An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit', Information Processing and Management.
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© 2019 Methods for document clustering and topic modelling in online social networks (OSNs) offer a means of categorising, annotating and making sense of large volumes of user generated content. Many techniques have been developed over the years, ranging from text mining and clustering methods to latent topic models and neural embedding approaches. However, many of these methods deliver poor results when applied to OSN data as such text is notoriously short and noisy, and often results are not comparable across studies. In this study we evaluate several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit. We benchmark four different feature representations derived from term-frequency inverse-document-frequency (tf-idf) matrices and word embedding models combined with four clustering methods, and we include a Latent Dirichlet Allocation topic model for comparison. Several different evaluation measures are used in the literature, so we provide a discussion and recommendation for the most appropriate extrinsic measures for this task. We also demonstrate the performance of the methods over data sets with different document lengths. Our results show that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures. We also demonstrate a method for interpreting the clusters with a top-words based approach using tf-idf weights combined with embedding distance measures.
Cutler, RL, Torres-Robles, A, Wiecek, E, Drake, B, Van der Linden, N, Benrimoj, SIC & Garcia-Cardenas, V 2019, 'Pharmacist-led medication non-adherence intervention: reducing the economic burden placed on the Australian health care system.', Patient Preference and Adherence, vol. 13, pp. 853-862.
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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-AN INTERNATIONAL JOURNAL, vol. 18, no. 1, pp. 87-102.
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Dai, M, Cheng, S, He, X & Wang, D 2019, 'Object tracking in the presence of shaking motions', Neural Computing and Applications, pp. 1-18.
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© 2018 The Natural Computing Applications Forum Visual tracking can be particularly interpreted as a process of searching for targets and optimizing the searching. In this paper, we present a novel tracker framework for tracking shaking targets. We formulate the underlying geometrical relevance between a search scope and a target displacement. A uniform sampling among the search scopes is implemented by sliding windows. To alleviate any possible redundant matching, we propose a double-template structure comprising of initial and previous tracking results. The element-wise similarities between a template and its candidates are calculated by jointly using kernel functions which provide a better outlier rejection property. The STC algorithm is used to improve the tracking results by maximizing a confidence map incorporating temporal and spatial context cues about the tracked targets. For better adaptation to appearance variations, we employ a linear interpolation to update the context prior probability of the STC method. Both qualitative and quantitative evaluations are performed on all sequences that contain shaking motions and are selected from the OTB-50 challenging benchmark. The proposed approach is compared with and outperforms 12 state-of-the-art tracking methods on the selected sequences while running on MATLAB without code optimization. We have also performed further experiments on the whole OTB-50 and VOT 2015 datasets. Although the most of sequences in these two datasets do not contain motion blur that this paper is focusing on, the results of our method are still favorable compared with all of the state-of-the-art approaches.
Damanik, N, Ong, HC, Mofijur, M, Tong, CW, Silitonga, AS, Shamsuddin, AH, Sebayang, AH, Mahlia, TMI, Wang, CT & Jang, JH 2019, 'The performance and exhaust emissions of a diesel engine fuelled with Calophyllum inophyllum- palm biodiesel', Processes, vol. 7, no. 9.
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© 2019 by the authors. 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) alkalinecatalysed 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, p. 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.
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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.
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Dang, D, Hoang, D & Nguyen, D 2019, 'Trust-based Scheduling Framework for Big Data Processing with MapReduce', IEEE Transactions on Services Computing (TSC).
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Dang, LC, Dang, CC & Khabbaz, H 2019, 'Modelling of columns and fibre reinforced load transfer platform supported embankments', Proceedings of the Institution of Civil Engineers - Ground Improvement.
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This study proposes a novel ground modification technique utilising fibre reinforced load transfer platform (FRLTP) and deep cement mixing (DCM) columns supported (CS) embankment constructed over soft soils. An equivalent two-dimensional finite element model was developed to simulate the full geometry of a CS embankment reinforced without or with an FRLTP. A series of numerical analyses was firstly conducted on the proposed model for different improvement depths to assess the effectiveness of the introduction of FRLTP into the CS embankment system in terms of total and differential settlements, stress transfer mechanism and lateral displacement with depth. Subsequently, another extensive parametric study was conducted to further investigate the influence of the FRLTP key parameters including elastic deformation modulus, shear strength properties, and tensile strength on the embankment performance during construction and consolidation time. The numerical results show that the embankment with FRLTP can effectively diminish the total settlement and the lateral deformation of the embankment, meanwhile improve the stress concentration ratio and the embankment stability to a great extent. The findings of the extensive parametric study indicate that the FRLTP shear strength properties appear to be the most influence factors to be considered in the design procedure of a target CS-FRLTP-embankment system.
Daniel, J, Naderpour, M & Lin, CT 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, UL, Balogun, A-L, Matori, A-N, Yusouf, KW, Abubakar, IR, Mohamed, MAS, Aina, YA & Pradhan, B 2019, 'Flood Susceptibility Mapping Using GIS-Based Analytic Network Process: A Case Study of Perlis, Malaysia', WATER, vol. 11, no. 3.
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Daqamseh, ST, 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 (Switzerland), vol. 19, no. 9.
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© 2019 by the authors. 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.
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 Koning, K, Filatova, T & Bin, O 2019, 'Capitalization of Flood Insurance and Risk Perceptions in Housing Prices: An Empirical Agent-Based Model Approach', Southern Economic Journal, vol. 85, no. 4, pp. 1159-1179.
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© 2019 by the Southern Economic Association Federally regulated or insured lenders in the United States are mandated to require flood insurance on properties that are located in areas at high risk of flooding. Despite the existence of this mandatory flood insurance requirement, take-up rates for flood insurance have been low, and the federal government's exposure to uninsured property losses from flooding remains substantial. Meanwhile, the value of capital at risk varies significantly with flood events and changing risk perceptions, which necessitates mechanisms that stabilize these dynamics. In this article we discuss how a scenario of complete insurance uptake, under various risk attitudes, affects the value of properties in the 100-year and 500-year flood zones. Our results indicate that an increase in flood insurance uptake may provide such a mechanism by lowering the value of capital at risk in the flood zone consistently, independent of homeowners' risk attitudes. We apply an empirical adaptive agent-based model to examine the capitalization of insurance costs, risk premiums, and their interaction in housing prices. Our approach combines widely-used empirical hedonic analysis with the computational economic framework. We highlight the usefulness of our method in capturing the marginal implicit price of homeowners' preferences that may change over time and separately assess the effect of various factors and policies on property values, illustrating the agent-based modeling as a valuable complement to traditional hedonic analysis.
de Koning, K, Filatova, T, Need, A & Bin, O 2019, 'Avoiding or mitigating flooding: Bottom-up drivers of urban resilience to climate change in the USA', Global Environmental Change, vol. 59.
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© 2019 Elsevier Ltd Coastal areas around the world are urbanizing rapidly, despite the threat of sea level rise and intensifying floods. Such development places an increasing number of people and capital at risk, which calls for public flood management as well as household level adaptation measures that reduce social vulnerability to flooding and climate change. This study explores several private adaptation responses to flood risk, that are driven by various behavioral triggers. We conduct a survey among households in hazard-prone areas in eight coastal states in the USA, of which, some have recently experienced major flooding. While numerous empirical studies have investigated household-level flood damage mitigation, little attention has been given to examining the decision to retreat from flood zones. We examine what behavioral motives drive the choices for flood damage mitigation and relocation separately among property buyers and sellers. Hence, we focus on the drivers that shape demand for future development in flood-prone cities. We find that households’ choices to retreat from or to avoid flood zones (1) are highly sensitive to information that provokes people's feelings of fear, and (2) rely on hazardous events to trigger a protective action, which ideally would take place well before these events occur. We highlight that major flooding may cause a potential risk of large-scale outmigration and demographic changes in flood-prone areas, putting more low-income households at risk. Therefore, coordinated policies that integrate bottom-up drivers of individual climate adaptation are needed to increase urban resilience to floods.
Deng, L, Huu-Hao, N, 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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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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© 2019 by ASME. 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.
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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.
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.
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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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Dieu, TB, Shahabi, H, Omidvar, E, Shirzadi, A, Geertsema, M, Clague, JJ, Khosravi, K, Pradhan, B, Binh, TP, Chapi, K, Barati, Z, Bin Ahmad, B, Rahmani, H, Grof, G & Lee, S 2019, 'Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm', REMOTE SENSING, vol. 11, no. 8.
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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.
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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.', The Science of the total environment, vol. 650, no. Pt 2, pp. 1882-1888.
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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, H, Ji, J & Chen, L-Q 2019, 'Nonlinear vibration isolation for fluid-conveying pipes using quasi-zero stiffness characteristics', MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 121, pp. 675-688.
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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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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.
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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.
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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, W, Lin, C-T & Pedrycz, W 2019, 'Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce.', IEEE Transactions on Cybernetics.
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Although feature selection for large data has been intensively investigated in data mining, machine learning, and pattern recognition, the challenges are not just to invent new algorithms to handle noisy and uncertain large data in applications, but rather to link the multiple relevant feature sources, structured, or unstructured, to develop an effective feature reduction method. In this paper, we propose a multiple relevant feature ensemble selection (MRFES) algorithm based on multilayer co-evolutionary consensus MapReduce (MCCM). We construct an effective MCCM model to handle feature ensemble selection of large-scale datasets with multiple relevant feature sources, and explore the unified consistency aggregation between the local solutions and global dominance solutions achieved by the co-evolutionary memeplexes, which participate in the cooperative feature ensemble selection process. This model attempts to reach a mutual decision agreement among co-evolutionary memeplexes, which calls for the need for mechanisms to detect some noncooperative co-evolutionary behaviors and achieve better Nash equilibrium resolutions. Extensive experimental comparative studies substantiate the effectiveness of MRFES to solve large-scale dataset problems with the complex noise and multiple relevant feature sources on some well-known benchmark datasets. The algorithm can greatly facilitate the selection of relevant feature subsets coming from the original feature space with better accuracy, efficiency, and interpretability. Moreover, we apply MRFES to human cerebral cortex-based classification prediction. Such successful applications are expected to significantly scale up classification prediction for large-scale and complex brain data in terms of efficiency and feasibility.
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.
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Dinh, DN, Byong-Hun, J, Jeung, JH, Rene, ER, Banu, JR, Ravindran, B, Cuong, MV, Huu, HN, 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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Djebbara, Z, Fich, LB, Petrini, L & Gramann, K 2019, 'Sensorimotor brain dynamics reflect architectural affordances', PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, vol. 116, no. 29, pp. 14769-14778.
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Djebbara, Z, Fich, LB, Petrini, L & Gramann, K 2019, 'Sensory-motor brain dynamics reflect architectural affordances'.
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Anticipating meaningful actions in the environment is an essential function of the brain. Such predictive mechanisms originate from the motor system and allow for inferring actions from environmental affordances, the potential to act within a specific environment. Using architecture, we provide a unique perspective to the abiding debate in cognitive neuroscience and philosophy on whether cognition depends on movement or is decoupled from our physical structure. To investigate cognitive processes associated with architectural affordances, we used a Mobile Brain/Body Imaging approach recording brain activity synchronized to head-mounted virtual reality. Participants perceived and acted upon virtual transitions ranging from non-passable to easily passable. We demonstrate that early sensory brain activity, upon revealing the environment and before actual movement, differed as a function of affordances. Additionally, movement through transitions was preceded by a motor-related negative component also depended on affordances. Our results suggest that potential actions afforded by an environment influence perception.
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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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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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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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.
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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.
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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 and Design, vol. 182.
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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, W, Li, W, Tao, Z & Wang, K 2019, 'Piezoresistive properties of cement-based sensors: Review and perspective', Construction and Building Materials, vol. 203, pp. 146-163.
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© 2019 Elsevier Ltd Cement-based sensors are increasingly used in smart concrete to self-sense and monitor the damages and cracks through the measurements of concrete electrical resistivity. The fundamental concepts, key components, manufacturing process, piezoresistivity measurements, and primary applications of cement-based sensors are reviewed in this paper. Various materials, mechanical and environmental factors affecting concrete piezoresistive properties are explicated. Some contradictory results from different studies are reported and discussed. Future perspectives of piezoresistive cement-based sensors are also delineated. The review reveals that there is an optimal conductor content, below which the sensor would perform more like plain concrete with high resistivity and low sensitivity, while excessively higher than which, the dispersion of the conductive phase could become difficult, thus increasing resistivity. The manufacturing process, such as the dispersion method of conductors and curing condition, plays a significant role in conductor distribution, matrix density and pore structure of the sensors, which, together with rheology of the sensor composite, consequently alters the piezoresistive properties of the sensors. In addition to responding to mechanical loading, cement-based piezoresistive sensor also has a great potential for monitoring behaviour of concrete under freeze-thaw cycling. It is expected that this review will provide not only an orientation for new researchers to explore and engage in related studies but also an insight for experienced researchers to perform transformational examinations into cement-based piezoresistive sensors.
Dong, X, Gong, Y & Cao, L 2019, 'e-RNSP: An Efficient Method for Mining Repetition Negative Sequential Patterns.', IEEE transactions on cybernetics.
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Negative sequential patterns (NSPs), which capture both frequent occurring and nonoccurring behaviors, become increasingly important and sometimes play a role irreplaceable by analyzing occurring behaviors only. Repetition sequential patterns capture repetitions of patterns in different sequences as well as within a sequence and are very important to understand the repetition relations between behaviors. Though some methods are available for mining NSP and repetition positive sequential patterns (RPSPs), we have not found any methods for mining repetition NSP (RNSP). RNSP can help the analysts to further understand the repetition relationships between items and capture more comprehensive information with repetition properties. However, mining RNSP is much more difficult than mining NSP due to the intrinsic challenges of nonoccurring items. To address the above issues, we first propose a formal definition of repetition negative containment. Then, we propose a method to convert repetition negative containment to repetition positive containment, which fast calculates the repetition supports by only using the corresponding RPSP's information without rescanning databases. Finally, we propose an efficient algorithm, called e-RNSP, to mine RNSP efficiently. To the best of our knowledge, e-RNSP is the first algorithm to efficiently mine RNSP. Intensive experimental results on the first four real and synthetic datasets clearly show that e-RNSP can efficiently discover the repetition negative patterns; results on the fifth dataset prove the effectiveness of RNSP which are captured by the proposed method; and the results on the rest 16 datasets analyze the impacts of data characteristics on mining process.
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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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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Dong, X, Zheng, L, Ma, F, Yang, Y & Meng, D 2019, 'Few-Example Object Detection with Model Communication', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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IEEE In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named "few-example object detection". The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, easy samples are generated first and, then the poorly initialized model undergoes improvement. As the model becomes more discriminative, challenging but reliable samples are selected. After that, another round of model improvement takes place. To further improve the precision and recall of the generated training samples, we embed multiple detection models in our framework, which has proven to outperform the single model baseline and the model ensemble method. Experiments on PASCAL VOC'07, MS COCO'14, and ILSVRC'13 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels.
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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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, KK 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 (Barking, Essex : 1987), vol. 247, pp. 474-481.
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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, B, Xinyao, T, Wang, Z, Zhang, L & Tao, D 2019, 'Robust Graph-Based Semisupervised Learning for Noisy Labeled Data via Maximum Correntropy Criterion.', IEEE Transactions on Cybernetics, vol. 49, no. 4, pp. 1440-1453.
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Semisupervised learning (SSL) methods have been proved to be effective at solving the labeled samples shortage problem by using a large number of unlabeled samples together with a small number of labeled samples. However, many traditional SSL methods may not be robust with too much labeling noisy data. To address this issue, in this paper, we propose a robust graph-based SSL method based on maximum correntropy criterion to learn a robust and strong generalization model. In detail, the graph-based SSL framework is improved by imposing supervised information on the regularizer, which can strengthen the constraint on labels, thus ensuring that the predicted labels of each cluster are close to the true labels. Furthermore, the maximum correntropy criterion is introduced into the graph-based SSL framework to suppress labeling noise. Extensive image classification experiments prove the generalization and robustness of the proposed SSL method.
Du, X, Nie, F, Wang, W, Yang, Y & Zhou, X 2019, 'Exploiting Combination Effect for Unsupervised Feature Selection by l2,0 Norm.', IEEE transactions on neural networks and learning systems, vol. 30, no. 1, pp. 201-214.
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In learning applications, exploring the cluster structures of the high dimensional data is an important task. It requires projecting or visualizing the cluster structures into a low dimensional space. The challenges are: 1) how to perform the projection or visualization with less information loss and 2) how to preserve the interpretability of the original data. Recent methods address these challenges simultaneously by unsupervised feature selection. They learn the cluster indicators based on the k nearest neighbor similarity graph, then select the features highly correlated with these indicators. Under this direction, many techniques, such as local discriminative analysis, nonnegative spectral analysis, nonnegative matrix factorization, etc., have been successfully introduced to make the selection more accurate. In this paper, we focus on enhancing the unsupervised feature selection in another perspective, namely, making the selection exploit the combination effect of the features. Given the expected feature amount, previous works operate on the whole features then select those of high coefficients one by one as the output. Our proposed method, instead, operates on a group of features initially then update the selection when a better group appears. Compared to the previous methods, the proposed method exploits the combination effect of the features by l2,0 norm. It improves the selection accuracy where the cluster structures are strongly related to a group of features. We conduct the experiments on six open access data sets from different domains. The experimental results show that our proposed method is more accurate than the recent methods which do not specially consider the combination effect of the features.
Du, X, Yin, H, Chen, L, Wang, Y, Yang, Y & Zhou, X 2019, 'Personalized Video Recommendation Using Rich Contents from Videos', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods.
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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Duan, N, Xu, W, Wang, S & Zhu, J 2019, 'Quasi-3-D Cylindrical Coordinate XFEM Model of HTS Cable', IEEE TRANSACTIONS ON MAGNETICS, vol. 55, no. 6.
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Duan, P, Mao, G, Kang, J & Huang, B 2019, 'Estimation of Link Travel Time Distribution With Limited Traffic Detectors', IEEE Transactions on Intelligent Transportation Systems, pp. 1-14.
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Duan, P, Mao, G, Liang, W & Zhang, D 2019, 'A Unified Spatio-Temporal Model for Short-Term Traffic Flow Prediction', IEEE Transactions on Intelligent Transportation Systems.
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IEEE This paper proposes a unified spatio-temporal model for short-term road traffic prediction. The contributions of this paper are as follows. First, we develop a physically intuitive approach to traffic prediction that captures the time-varying spatio-temporal correlation between traffic at different measurement points. The spatio-temporal correlation is affected by the road network topology, time-varying speed, and time-varying trip distribution. Distinctly different from previous black-box approaches to road traffic modeling and prediction, parameters of the proposed approach have physically intuitive meanings which make them readily amendable to suit changing road and traffic conditions. Second, unlike some existing techniques that capture the variation of spatio-temporal correlation by a complete re-design and calibration of the model, the proposed approach uses a unified model that incorporates the physical factors potentially affecting the variation of spatio-temporal correlation into a series of parameters. These parameters are relatively easy to control and adjust when road and traffic conditions change, thereby greatly reducing the computational complexity. Experiments using two sets of real traffic traces demonstrate that the proposed approach has superior accuracy compared with the widely used space-time autoregressive integrated moving average (STARIMA) and the back propagation neural network approaches, and is only marginally inferior to that obtained by constructing multiple STARIMA models for different times of the day, however, with a much reduced computational and implementation complexity.
Duan, W & Kirby, R 2019, 'Guided wave propagation in buried and immersed fluid-filled pipes: Application of the semi analytic finite element method', Computers and Structures, vol. 212, pp. 236-247.
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© 2018 Elsevier Ltd Developing an understanding of guided wave propagation in multi-layered systems has important applications in non-destructive evaluation. This article presents a general weighted residual formulation for guided wave propagation in fluid-filled pipes buried in an elastic solid, or immersed in a quiescent fluid. A one-dimensional semi-analytic finite element (SAFE) approach is combined with a perfectly matched layer (PML), to compute dispersion curves for different pipe applications. The speed and accuracy of this approach are compared against the scaled boundary finite element method (SBFEM) and it is shown that for a rod immersed in a liquid the two methods provide very similar computational speeds. The speed and accuracy of the model is then investigated for immersed and buried fluid-filled pipes, and it is shown that when discretising the PML no advantage in computational speed is found when using either quadratic or higher order spectral elements, provided the number of degrees of freedom in the PML is equivalent. Accordingly, it is shown that the SAFE-PML method is capable of obtaining accurately the modal characteristics of buried and immersed fluid-filled pipes, with computational speeds comparable to the SBFEM approach.
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.
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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.
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.
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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. 586-586.
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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.
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, Johir, MAH, 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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Ernesto Solanes, J, Gracia, L, Muñoz-Benavent, P, Valls Miro, J, Perez-Vidal, C & Tornero, J 2019, 'Robust Hybrid Position-Force Control for Robotic Surface Polishing', Journal of Manufacturing Science and Engineering, Transactions of the ASME, vol. 141, no. 1.
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© 2018 HMP Communications. All rights reserved. This work presents a hybrid position-force control of robots for surface polishing using task priority. The robot force control is designed using sliding mode ideas in order to benefit from its inherent robustness and low computational cost. In order to avoid the chattering drawback typically present in sliding mode control, several chattering-free controllers are evaluated and tested. A distinctive feature of the method is that the sliding mode force task is defined using not only equality constraints but also inequality constraints, which are satisfied using conventional and nonconventional sliding mode control, respectively. Moreover, a lower priority tracking controller is defined to follow the desired reference trajectory on the surface being polished. The applicability and the effectiveness of the proposed approach considering the mentioned chattering-free controllers are substantiated by experimental results using a redundant 7R manipulator.
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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© The Institution of Engineering and Technology 2018.. 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 2019, 'A nodal approach based state-space model of droop-based autonomous networked microgrids', Sustainable Energy, Grids and Networks, vol. 18.
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© 2019 Elsevier Ltd As the requirement of expensive and unreliable high band-width communication infrastructure is obviated, decentralized droop-like control method has been considered for power sharing implementation in autonomous microgrids (MGs). To this end, the power network is regarded as a communication link and voltage variables (magnitude and frequency) as control signals. This, however, reduces the stability margin of islanded MGs due to the interaction of droop controllers through the power network. Lack of inertia of droop-controlled power converters and low X/R ratio of interconnecting power lines intensify this interaction which may lead to the instability of Networked MGs (NMG). On the other hand, the existing parallel-based small signal model of MGs is inadequate to represent this interaction, as the adopted common-based reference frame (RF) is not applicable in islanded NMGs. This issue is investigated in this work in details and, inspired from power flow equations, a local RF is proposed to improve the small-signal model accuracy. Droop controllers are also correlated through the power flow equations to properly model their interaction through the power network. Moreover, the state-space model is developed in a fully decentralized approach which does not rely on any converter for any specific role. Eigenvalue analysis and simulation in MATLAB\SIMULINK platforms are executed to evaluate the effectiveness and accuracy of the proposed model.
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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Esmaili, N, Norman, BA & Rajgopal, J 2019, 'Exact analysis of (R, s, S) inventory control systems with lost sales and zero lead time', NAVAL RESEARCH LOGISTICS, vol. 66, no. 2, pp. 123-132.
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Esmaili, N, Piccardi, M, Kruger, B & Girosi, F 2019, 'Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models (vol 13, e0206274, 2018)', PLOS ONE, vol. 14, no. 4.
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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. 108-130.
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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 & Low, G 2019, 'Experiential probabilistic assessment of cloud services', INFORMATION SCIENCES, vol. 502, pp. 510-524.
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Fahmideh, M, Daneshgar, F, Rabhi, F & Beydoun, G 2019, 'A generic cloud migration process model', European Journal of Information Systems, 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.
Fahmidehgholami, M & Beydoun, G 2019, 'Big data analytics architecture design—An application in manufacturing systems', Computers and Industrial Engineering, 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 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. 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 manufacturing 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 ...
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, 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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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.
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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.
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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.
Fanos, AM & Pradhan, B 2019, 'A novel rockfall hazard assessment using laser scanning data and 3D modelling in GIS', CATENA, vol. 172, pp. 435-450.
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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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© 2013 IEEE. Rockfall source identification is the most challenging task in rockfall hazard and risk assessment. This difficulty rises in the areas where there is a presence of other types of the landslide, such as shallow landslide and debris flow. The aim of this paper is to develop and test a hybrid model that can accurately identify the source areas. High-resolution light detection and ranging data (LiDAR) was employed to derive the digital terrain model (DTM), from which several conditioning factors were extracted. These conditioning factors were optimized utilizing the ant colony optimization (ACO). Different machine learning algorithms, namely, logistic regression (LR), random tree (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN), in addition to their ensemble models (stacking, bagging, and voting), were examined. This is based on the selected best subset of conditioning factors and inventory dataset. Stacking LR-RT (the best fit model) was then utilized to produce the probabilities of different landslide types. On the other hand, the Gaussian mixture model (GMM) was optimized and applied for automatically identifying the slope threshold of the occurrence of the different landslide types. In order to reduce the model sensitivity to the alteration in various conditioning factors and to improve the model computations performance, land use probability area was formed. The rockfall sources were identified by integrating the probability maps and the reclassified slope raster based on the GMM results. The accuracy assessment reveals that the developed hybrid model can identify the probable rockfall regions with an accuracy of 0.95 based on the validation dataset and 0.94 on based the training dataset. The slope thresholds calculated by GMM were found to be > 58°, 22°-58°, and 9°-22° for rockfall, shallow landslide, and debris flow, respectively. This indicates that the model can be generalized and replicated in differen...
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.
Far, H 2019, 'Advanced Computation Methods for Soil Structure Interaction Analysis of Structures Resting on Soft Soils', International Journal of Geotechnical Engineering, vol. 13, no. 4, pp. 352-359.
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Adopting the most accurate and realistic modelling technique and computation method for treatment of dynamic soil-structure interaction effects in seismic analysis and design of structures resting on soft soil deposits is one of the most discussed and challenging issues in the field of seismic design and re-qualification of different structures. In this study, a comprehensive critical review has been carried out on available and well-known modelling techniques and computation methods for dynamic soil-structure interaction analysis. Discussing and comparing the advantages and disadvantages of employing each method, in this study, the most precise and reliable modelling technique as well as computation method have been identified and proposed to be employed in studying dynamic soil-structure interaction analysis of structures resting on soft soil deposits.
Far, H 2019, 'Dynamic Behaviour of Unbraced Steel Frames Resting on Soft Ground', Steel Construction, vol. 12, no. 2, pp. 135-140.
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Far, H & Far, C 2019, 'Experimental Investigation on Creep Behaviour of Composite Sandwich Panels Constructed from Polystyrene/Cement-Mixed Cores and Thin Cement Sheet Facings', Australian Journal of Structural Engineering, vol. 20, no. 1, pp. 63-73.
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Composite sandwich panels have gradually become more popular due to their typical benefits including strength, weight, ease of handling, durability, versatility, thermal and acoustic properties. Many researchers are aware of these benefits and have undertaken detailed research and publicised large amounts of scientific papers on composite sandwich panels. With the variety of loads that could possibly be applied to a structure, often the in service life behaviour is significant. Reviewing whether it will function correctly and without excessive deformations is a factor that can sometimes impact the performance. Polystyrene/cement mixed cores and thin cement sheet facings composite sandwich panels are Australian products made of cement-polystyrene beaded mixture encapsulated between two thick cement board sheets. The creep and creep recovery properties of these sandwich panels are relatively unknown. Therefore, in this study, in order to understand the creep and creep recovery behaviour of those sandwich panels, a series of experimental tests have been performed and the outcomes have been explained and discussed. Based on the results of this study, values for immediate recovery, creep recovery and irrecoverable creep strain are determined and proposed. In addition, typical creep and creep recovery design curves have been developed and presented for practical applications in structural engineering.
Far, H & Far, C 2019, 'Timber Portal Frames vs Timber Truss-Based Systems for Residential Buildings', Advances in Civil Engineering, vol. 2019, pp. 1-7.
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A large number of structures have been built during or after the construction of a house or residential-zoned building, which are not built at the same time and/or integrally with the structural integrity of the residential dwelling. These include carports, pergolas, sheds, and barns. The typical method of constructing these structures is a general timber truss and column system. The aim of this study is to look at the feasibility and economic incentive that may be gained from using a timber portal frame system, similar to the steel or timber portal frames used for larger industrial constructions, over the traditional timber truss and column arrangement. In this study, designs for three cases of timber truss and timber portals were carried out using industry appropriate methods and standards. Using the design information and data gathered through talks with industry professionals, both methods of construction were compared on cost and overall time duration. From the comparison of the truss and portal designs, the use of timber portal frames over timber truss systems proved to have advantage in relation to overall cost and man power involved. This could certainly affect the current attitude towards the construction of small residential buildings in the future.
Farhadian, F, Kashani, MMR, Rezazadeh, J, Farahbakhsh, R & Sandrasegaran, K 2019, 'WITHDRAWN: An efficient IoT cloud energy consumption based on genetic algorithm', Digital Communications and Networks.
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Farrokhi, F, Buchlak, QD, Sikora, M, Esmaili, N, Marsans, M, McLeod, P, Mark, J, Cox, E, Bennett, C & Carlson, J 2019, 'Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms', World Neurosurgery.
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Farzadkhoo, M, Keshavarzi, A, Hamidifar, H & Ball, J 2019, 'Flow and longitudinal dispersion in channel with partly rigid floodplain vegetation', Proceedings of the Institution of Civil Engineers - Water Management, vol. 172, no. 5, pp. 229-240.
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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.
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Feng, Q, Xu, J, Zhang, K, Yao, H, Zheng, N, Zheng, L, Wang, J, Wei, K, Xiao, X, Qin, L & Bian, L 2019, 'Dynamic and Cell-Infiltratable Hydrogels as Injectable Carrier of Therapeutic Cells and Drugs for Treating Challenging Bone Defects', ACS CENTRAL SCIENCE, vol. 5, no. 3, pp. 440-450.
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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.
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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, Hormuth, DA & Yankeelov, TE 2019, 'An adjoint-based method for a linear mechanically-coupled tumor model: Application to estimate the spatial variation of murine glioma growth based on diffusion weighted magnetic resonance imaging.', Comput Mech, vol. 63, no. 2, pp. 159-180.
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We present an efficient numerical method to quantify the spatial variation of glioma growth based on subject-specific medical images using a mechanically-coupled tumor model. The method is illustrated in a murine model of glioma in which we consider the tumor as a growing elastic mass that continuously deforms the surrounding healthy-appearing brain tissue. As an inverse parameter identification problem, we quantify the volumetric growth of glioma and the growth component of deformation by fitting the model predicted cell density to the cell density estimated using the diffusion-weighted magnetic resonance imaging (DW-MRI) data. Numerically, we developed an adjoint-based approach to solve the optimization problem. Results on a set of experimentally measured, in vivo rat glioma data indicate good agreement between the fitted and measured tumor area and suggest a wide variation of in-plane glioma growth with the growth-induced Jacobian ranging from 1.0 to 6.0.
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.
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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.
Feng, Y, Zhao, Y, Jiang, B, Zhao, H, Wang, Q & Liu, S 2019, 'Discrepant gene functional potential and cross-feedings of anammox bacteria Ca. jettenia caeni and Ca. Brocadia sinica in response to acetate', WATER RESEARCH, vol. 165.
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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, C, Liu, XY, Yang, J, Yang, LT, Yu, S & Zhu, T 2019, 'Wormhole: The Hidden Virus Propagation Power of the Search Engine in Social Networks', IEEE Transactions on Dependable and Secure Computing, vol. 16, no. 4, pp. 693-710.
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© 2004-2012 IEEE. Today search engines are tightly coupled with social networks, and present users with a double-edged sword: They are able to acquire information interesting to users but are also capable of spreading viruses introduced by hackers. It is challenging to characterize how a search engine spreads viruses, since the search engine serves as a virtual virus pool and creates propagation paths over the underlying network structure. In this paper, we quantitatively analyze virus propagation effects and the stability of the virus propagation process in the presence of a search engine. First, although social networks have a community structure that impedes virus propagation, we find that a search engine generates a propagation wormhole. Second, we propose an epidemic feedback model and quantitatively analyze propagation effects based on a model employing four metrics: infection density, the propagation wormhole effect, the epidemic threshold, and the basic reproduction number. Third, we verify our analyses on four real-world data sets and two simulated data sets. Moreover, we prove that the proposed model has the property of partial stability. Evaluation results show that, compared the cases without a search engine, virus propagation with the search engine has a higher infection density, shorter network diameter, greater propagation velocity, lower epidemic threshold, and larger basic reproduction number.
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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Fujioka, T, Hoang, AT, Ueyama, T & Nghiem, LD 2019, 'Integrity of reverse osmosis membrane for removing bacteria: new insight into bacterial passage Electronic supplementary information (ESI) available. See DOI: 10.1039/c8ew00910d', ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY, vol. 5, no. 2, pp. 239-245.
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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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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, Giacomo, SD, 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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Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Amplification of the MYCN oncogene is associated with an aggressive phenotype and poor outcome in childhood neuroblastoma. Polyamines are highly regulated essential cations that are frequently elevated in cancer cells, and the rate-limiting enzyme in polyamine synthesis, ornithine decarboxylase 1 (ODC1), is a direct transcriptional target of MYCN. Treatment of neuroblastoma cells with the ODC1 inhibitor difluoromethylornithine (DFMO), although a promising therapeutic strategy, is only partially effective at impeding neuroblastoma cell growth due to activation of compensatory mechanisms resulting in increased polyamine uptake from the surrounding microenvironment. In this study, we identified solute carrier family 3 member 2 (SLC3A2) as the key transporter involved in polyamine uptake in neuroblastoma. Knockdown of SLC3A2 in neuroblastoma cells reduced the uptake of the radiolabeled polyamine spermidine, and DFMO treatment increased SLC3A2 protein. In addition, MYCN directly increased polyamine synthesis and promoted neuroblastoma cell proliferation by regulating SLC3A2 and other regulatory components of the polyamine pathway. Inhibiting polyamine uptake with the small-molecule drug AMXT 1501, in combination with DFMO, prevented or delayed tumor development in neuroblastoma-prone mice and extended survival in rodent models of established tumors. Our findings suggest that combining AMXT 1501 and DFMO with standard chemotherapy might be an effective strategy for treating neuroblastoma.
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, 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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Ganesan, B, Yip, J, Al-Jumaily, A, Fong, SSM, Kothe Balasankar, S, Ey Batlle, AM, Fong, KNK & Luximon, A 2019, 'A novel 3D evaluation method for assessing bone to bone relationships in clubfoot.', European Review for Medical and Pharmacological Sciences, vol. 23, no. 5, pp. 1882-1890.
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OBJECTIVE:Clubfoot is a complex congenital three-dimensional foot deformity, which affects 150,000-200,000 newborn babies annually around the world. A good understanding of the alignment of the two osseous columns and the lower leg of the ankle and foot complex is essential for evaluating the severity of clubfoot. The purposes of this study were to (1) develop an automated three-dimensional (3D) surface model of severe clubfoot based on two-dimensional (2D) slices of computed tomography (CT) images, (2) evaluate the alignment of foot bones relative to the ankle in severe clubfoot, and (3) examine the structural changes in the shape of the clubfoot. PATIENTS AND METHODS:Two-dimensional CT image was taken from a four-year-old child with a severe clubfoot. Subsequently, an automated and detailed 3D surface model of the severe clubfoot was developed from the 2D images by using MATLAB software programming. Then, the x, y, and z coordinate angles were automatically calculated for each bone in the foot relative to the ankle (lower end of the tibia) to determine the orientations and relationships among the bones. RESULTS:The relative position or orientation of each bone of the foot to the ankle of the severe clubfoot was objectively measured which was used to determine the orientation of each bone in the foot. Among the x, y, and z axes of the interested tarsal bones, the z axis represents the smallest moment of inertia, and the results showed that the bones in the x axis shifted medially with higher relative angle. CONCLUSIONS:This 3D objective measurement method for assessing clubfoot can be used to determine and classify the severity of clubfoot, as well as evaluate and monitor the progress of the clubfoot intervention based on the relative position of the tarsal bones. The method can also be used to quantify the relationship between the tarsal bones of the foot and lower end of the tibia. In addition, angular measurements can be used to assess other pathological cond...
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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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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© 2019 John Wiley & Sons, Ltd. This 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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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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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, 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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Gao, P, Du, Y & Walker, PD 2019, 'Vibration energy and repeated-root modes of disc rotor for high-frequency brake squeal', Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics.
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© IMechE 2018. In this paper, the generation mechanism of high-frequency brake squeal is revealed from the perspective of vibration energy. Based on a closed-loop coupling model, vibration energy transfer paths at the friction coupling interface between brake pads and disc are derived. Vibration energy equilibrium analysis is used to verify the reliability and accuracy of the derivation and the presented result demonstrates that vibration energy transferred from disc rotor to pads is the dominant transfer path. It is also demonstrated that the disc rotor is the key substructure affecting high-frequency brake squeal. As the disc rotor is axisymmetric, its repeated-root modes may lead to unreasonable calculated results by using the substructure modal composition method for analyzing the brake squeal. In this study, these repeated-root modes are processed by using a modified substructure modal composition method to obtain one unique integrated substructure modal composition coefficient of each disc repeated-root modes. Finally, the presented method is applied to analyze the brake squeal in the 13 kHz frequency band. The results easily identify the key vibration modes of the disc affecting high-frequency brake squeal, verifying the reliability of the presented method.
Gao, P, Ruan, J, Du, Y, Walker, PD & Zhang, N 2019, 'The prediction of braking noise in regenerative braking system using closed-loop coupling disk brake model', PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING.
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Gao, P, Walker, PD, Liu, H, Zhou, S & Xiang, C 2019, 'Application of an adaptive tuned vibration absorber on a dual lay-shaft dual clutch transmission powertrain for vibration reduction', Mechanical Systems and Signal Processing, vol. 121, pp. 725-744.
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© 2018 Elsevier Ltd This paper investigates the application of adaptive tuned vibration absorbers (ATVAs) to lightly damped automotive powertrains. To achieve this, a vibration characteristic analysis of a dual lay-shaft dual clutch transmission (DCT)-equipped powertrain system was performed, including a natural frequency evaluation and sensitivity studies. Transient responses due to gear changes and variations of both the gear ratio and engine speed were also evaluated. The results demonstrate that the impact of ATVAs on transient responses during gear changes is minimal, suggesting that these short-duration transients are outside the scope of application for ATVAs. However, as excitation forces originate from the engine, speed has a significant impact on excitation frequency. Furthermore, the gear ratios significantly influence the engine operating range and natural frequency in any particular gear. Therefore, variations of engine speeds and gear ratios are the main influencing factors affecting the dominant excitation frequency. Consequently, the relationships among the system natural frequencies, the dominant external excitation frequency and the inherent frequency of the ATVA are comprehensively studied. By investigating the relationships among the dominant external excitation frequency and system natural frequencies and the system sensitivity analysis results, the installation position, stiffness range and optimal frequency tuning scheme of the ATVA can be determined. The results were applied to a DCT powertrain with continuously changing engine speeds and an ATVA with corresponding variable stiffness, and a frequency tuning scheme is installed on the powertrain. By comparing the simulation results, the effectiveness and reliability of the presented method can be verified.
Gao, P, Wang, X, Huang, Z & Yu, H 2019, 'B-11 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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Gao, P, Xiang, C, Liu, H, Walker, P & Zhang, N 2019, 'Design of the frequency tuning scheme for a semi-active vibration absorber', MECHANISM AND MACHINE THEORY, vol. 140, pp. 641-653.
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Gao, P, Xiang, C, Liu, H, Walker, P & Zhou, H 2019, 'Vibration reduction performance parameters matching for adaptive tunable vibration absorber', Journal of Intelligent Material Systems and Structures, vol. 30, no. 2, pp. 198-212.
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© The Author(s) 2018. Based on the principle of vibration absorber, an adaptive tunable vibration absorber with the magnetorheological elastomer as the core intelligent component was designed to eliminate variable frequency vibration of the powertrain system. The simulation of the magnetic circuit of the adaptive tunable vibration absorber was carried out to ensure the magnetic field generated by the design with a closed magnetic circuit could meet the requirement. The transient dynamic simulation analysis of the adaptive tunable vibration absorber shows that the natural frequency of the vibration absorber could well follow the external excitation signal. The natural vibration sensitivity analysis of the 4-degree-of-freedom dynamic system of the powertrain system was performed to obtain the key moment of inertia that affects the natural frequencies of each stage and then the installation position of the adaptive tunable vibration absorber corresponding to external excitation near the resonant frequency band could be determined. Subsequently, taking the third natural vibration as an example, an adaptive tunable vibration absorber with corresponding variable-stiffness range was installed on the key moment of inertia affecting the vibration of this order. The transient changes of each natural vibration frequency of the powertrain system with the adaptive tunable vibration absorber were studied and then the best starting and stopping frequencies were determined. According to the optimal frequency tuning scheme, the variation range of the storage modulus required for the magnetorheological elastomer material in the limited magnetic field range was obtained. The relationship between the controllable current and the torsional stiffness of the vibration absorber is established. Aiming at the vibration problem near the third-order natural vibration, the best vibration reduction performance parameters were matched to the adaptive tunable vibration absorber. Finally, a time...
Gao, X, Du, J, Zhang, T & Guo, YJ 2019, 'High-T c 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-T c 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.
Garcia, MV, Luckett, T, Johnson, M, Hutchinson, A, Lal, S & Phillips, JL 2019, 'The roles of dispositional coping style and social support in helping people with respiratory disease cope with a breathlessness crisis.', Journal of advanced nursing, vol. 75, no. 9, pp. 1953-1965.
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AIM:To explore the role of coping moderators in self-management of breathlessness crises by people with advanced respiratory disease. DESIGN:A secondary analysis of semi-structured interview data. METHODS:Interviews with patients who had advanced respiratory disease, chronic breathlessness and at least one experience where they considered presenting to Emergency but self-managed instead (a "near miss"). Participants were recruited from New South Wales, Queensland, Victoria, South Australia or Tasmania. Eligible caregivers were those who contributed to Emergency-related decision-making. Interviews were coded inductively and then deductively against the coping moderators social support and dispositional coping style, defined by the Transactional Model of Stress and Coping. RESULTS:Interviews were conducted between October 2015 - April 2016 with 20 patients and three caregivers. Social networks offered emotional and practical support but also had potential for conflict with patients' 'hardy' coping style. Patient hardiness (characterized by a sense of 'commitment' and 'challenge') promoted a proactive approach to self-management but made some patients less willing to accept support. Information-seeking tendencies varied between patients and were sometimes shared with caregivers. An optimistic coping style appeared to be less equivocally beneficial. CONCLUSION:This study shows that social support and coping style may influence how people self-manage through their breathlessness crises and identified ways coping moderators can facilitate or hinder effective self-management. IMPACT:This study confers insights into how social-support and coping style can be supported and optimized to facilitate breathlessness self-management. Acknowledging coping moderator interactions is beneficial for developing resources and strategies that recognise patient mastery.
Gardner, AP & Willey, K 2019, 'The role of peer review in identity development for engineering education researchers', European Journal of Engineering Education.
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Garshasbi, S, Mohammadi, Y, Graf, S, Garshasbi, S & Shen, J 2019, 'Optimal learning group formation: A multi-objective heuristic search strategy for enhancing inter-group homogeneity and intra-group heterogeneity', EXPERT SYSTEMS WITH APPLICATIONS, vol. 118, pp. 506-521.
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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.
Ge, X, Tu, S, Mao, G, Lau, V & Pan, L 2019, 'Cost Efficiency Optimization of 5G Wireless Backhaul Networks', IEEE Transactions on Mobile Computing.
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IEEE The wireless backhaul network provides an attractive solution for the urban deployment of fifth generation (5G) wireless networks that enables future ultra dense small cell networks to meet the everincreasing user demands. Optimal deployment and management of 5G wireless backhaul networks is an interesting and challenging issue. In this paper we propose the optimal gateways deployment and wireless backhaul route schemes to maximize the cost efficiency of 5G wireless backhaul networks. In generally, the changes of gateways deployment and wireless backhaul route are presented in different time scales. Specifically, the number and locations of gateways are optimized in the long time scale of 5G wireless backhaul networks. The wireless backhaul routings are optimized in the short time scale of 5G wireless backhaul networks considering the time-variant over wireless channels. Numerical results show the gateways and wireless backhaul route optimization significantly increases the cost efficiency of 5G wireless backhaul networks. Moreover, the cost efficiency of proposed optimization algorithm is better than that of conventional and most widely used shortest path (SP) and Bellman-Ford (BF) algorithms in 5G wireless backhaul networks.
Geekiyanage, NM, Balanant, MA, Sauret, E, Saha, S, Flower, R, Lim, CT & Gu, Y 2019, 'A coarse-grained red blood cell membrane model to study stomatocyte-discocyte-echinocyte morphologies.', PLoS ONE, vol. 14, no. 4, pp. e0215447-e0215447.
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An improved red blood cell (RBC) membrane model is developed based on the bilayer coupling model (BCM) to accurately predict the complete sequence of stomatocyte-discocyte-echinocyte (SDE) transformation of a RBC. The coarse-grained (CG)-RBC membrane model is proposed to predict the minimum energy configuration of the RBC from the competition between lipid-bilayer bending resistance and cytoskeletal shear resistance under given reference constraints. In addition to the conventional membrane surface area, cell volume and bilayer-leaflet-area-difference constraints, a new constraint: total-membrane-curvature is proposed in the model to better predict RBC shapes in agreement with experimental observations. A quantitative evaluation of several cellular measurements including length, thickness and shape factor, is performed for the first time, between CG-RBC model predicted and three-dimensional (3D) confocal microscopy imaging generated RBC shapes at equivalent reference constraints. The validated CG-RBC membrane model is then employed to investigate the effect of reduced cell volume and elastic length scale on SDE transformation, to evaluate the RBC deformability during SDE transformation, and to identify the most probable RBC cytoskeletal reference state. The CG-RBC membrane model can predict the SDE shape behaviour under diverse shape-transforming scenarios, in-vitro RBC storage, microvascular circulation and flow through microfluidic devices.
Geng, J, Ziolkowski, RW, Wang, K, Zhao, X, Zhou, H, Chenhu, G, Liang, X & Jin, R 2019, 'Dual CP Polarization Diversity and Space Diversity Antennas Enabled by a Compact T-Shaped Feed Structure', IEEE Access, vol. 7, pp. 96284-96296.
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© 2013 IEEE. A compact T-shaped feed structure (TFS) is reported that enables the realization of two types of diversity antennas: a polarization diversity antenna (PDA) and a spatial diversity antenna (SDA). Both systems have a high potential for mobile wireless communication applications. The TFS includes four ports and two independent coaxial channels with effective isolation between them all. The PDA is a dual CP omnidirectional antenna. Its optimized prototype achieves measured impedance bandwidths of 16.4% and 15.28% in its LHCP and RHCP states, respectively, and realized gains in both between 4.8 and 6.46 dBic. The inner thin coaxial cable (ITCC) of the TFS directly drives its LHCP subsystem, facilitating its improved omnidirectional performance. This ITCC is also used to directly feed the SDA's low-profile directional planar equiangular spiral antenna and its side port drives its omnidirectional RHCP antenna. Good hemispherical coverage is realized with a measured common impedance bandwidth larger than 14.35% with more than 40-dB isolation between its two ports. The corresponding measured realized gain of the SDA is between 4 and 7.8 dBic. The measured results for both optimized prototypes confirm their simulated performance characteristics.
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.
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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.
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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', International Journal of Robotics Research, vol. 38, no. 6, pp. 658-685.
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© The Author(s) 2019. 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.
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.
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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, 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 Jirsaraie, S, Jabbari Ghadi, M, Vahed, AA, Aghaei, J, Li, L & Zhang, J 2019, 'Risk-Constrained Bidding Strategy for a Joint Operation of Wind Power and Compressed Air Energy Storage Aggregators', IEEE Transactions on Sustainable Energy, pp. 1-1.
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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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Ghorbani, S, Eyni, H, Khosrowpour, Z, Salari Asl, L, Shabani, R, Nazari, H, Mehdizadeh, M, Ebrahimi Warkiani, M & Amjadi, FS 2019, 'Spermatogenesis induction of spermatogonial stem cells using nanofibrous poly(l-lactic acid)/multi-walled carbon nanotube scaffolds and naringenin', Polymers for Advanced Technologies.
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© 2019 John Wiley & Sons, Ltd. 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, 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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Gill, AQ & Chew, E 2019, 'Configuration information system architecture: Insights from applied action design research', Information and Management.
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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.
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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 & Samali, B 2019, 'Debonding detection in a carbon fibre reinforced concrete structure using guided waves', Smart Materials and Structures, vol. 28, no. 4.
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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-AN INTERNATIONAL JOURNAL, vol. 18, no. 1, pp. 172-179.
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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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Golsorkhi Esfahani, MS, Shafiee, Q, Lu, DDC & Guerrero, JM 2019, 'Distributed Control of Low-Voltage Resistive AC Microgrids', IEEE Transactions on Energy Conversion.
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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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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-learn and learnin...
Gong, R-J, Ban, Y-L, Lian, J-W, Liu, Y & Nie, Z 2019, 'Circularly Polarized Multibeam Antenna Array of ME Dipole Fed by 5 x 6 Butler Matrix', IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 18, no. 4, pp. 712-716.
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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.
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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.
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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Govindarajan, P, Soundarapandian, RK, Gandomi, AH, Patan, R, Jayaraman, P & Manikandan, R 2019, 'Classification of stroke disease using machine learning algorithms', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the tracking of information with respect to semantic as well as syntactic perspectives. The proposed idea is to mine patients’ symptoms from the case sheets and train the system with the acquired data. In the data collection phase, the case sheets of 507 patients were collected from Sugam Multispecialty Hospital, Kumbakonam, Tamil Nadu, India. Next, the case sheets were mined using tagging and maximum entropy methodologies, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes. Then, the processed data were fed into various machine learning algorithms such as artificial neural networks, support vector machine, boosting and bagging and random forests. Among these algorithms, artificial neural networks trained with a stochastic gradient descent algorithm outperformed the other algorithms with a higher classification accuracy of 95% and a smaller standard deviation of 14.69.
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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An ultra accelerated multi-cycle autoclave test has been developed and evaluated for the assessment of expansion caused by alkali silica reaction in concrete.
Gowripalan, N, Nguyen, T, Yang, Y, Li, J & Sirivivatnanon, V 2019, 'Evaluation of elastic modulus reduction due to ASR', Concrete in Australia, vol. Volume 45, no. 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
Gowripalan, N, Vu, T, De Silva, P, Sirivivatnanon, V & Kidd, P 2019, 'Influence of curing and retarder on early-age properties of powder geopolymer concrete', Concrete in Australia, vol. Volume 45, no. No 2, pp. 41-46.
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The 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.
Gracia, L, Solanes, JE, Muñoz-Benavent, P, Miro, JV, Perez-Vidal, C & Tornero, J 2019, 'Human-robot collaboration for surface treatment tasks', Interaction Studies, vol. 20, no. 1, pp. 148-184.
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© John Benjamins Publishing Company. 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 nonconventional 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.
Gu, F, Niu, J, Jin, X & Yu, S 2019, 'FDFA: A fog computing assisted distributed analytics and detecting system for family activities', Peer-to-Peer Networking and Applications.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Researches have shown that taking parting in family activities could establish good relationships with family members. Fine-grained family activities detection is proven effective for increasing self-awareness and motivating people to modify their life styles for improved well being. Mobile health provides the possibility to solve this problem. However, with the increase of such applications, the requirements for computation, communication, and storage capability are becoming higher and higher. Fog computing, a new computing paradigm, utilizes a collaborative multitude of end-user clients or near-user edge devices to conduct a substantial amount of computing, communication, storage, and so on. In this paper, we propose FDFA, the first fog computing assisted distributed analytics and detecting system for family activities using smartphones and smart watches. Specifically, FDFA firstly uses the built-in sensors to obtain sensing data, such as the striding frequency and heart rate of the users, the sound of environment, and so forth. Then, a fog computing assisted resolution framework is proposed to efficiently detect family activities in an unobtrusive manner based on sensed data. Finally, considering the characteristics of different people, FDFA sets a personal plan for family members in doing some exercise and making continuous progress in the process of communicating. We have fully implemented FDFA on the Android platform and the extensive experimental results demonstrate that FDFA is easy to use, accurate, and appropriate for family activities with the accuracy of 79.1% and the user satisfaction degree of 82.4%. Moreover, the system can achieve more than 90% bandwidth efficiency and offer low-latency real time response with fog computing.
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, p. 105.
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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 rate to the right lung increases significantly by 2-30% (but there is no significant difference between each lobe), and the flow rate to the left lung drops accordingly. Many vortices are found in various places with severe distortions. CONCLUSIONS: The favorable and unfavorable adaptive alterations of tracheobronchial tree will occur after left upper pulmonary lobectomy, and these...
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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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, N, Liu, T, Zhang, Y, Tao, D & Davis, LS 2019, 'Truncated Cauchy Non-negative Matrix Factorization for Robust Subspace Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 1, pp. 1-14.
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IEEE Non-negative matrix factorization (NMF) minimizes the Euclidean distance between the data matrix and its low rank approximation, and it fails when applied to corrupted data because the loss function is sensitive to outliers. In this paper, we propose a Truncated Cauchy loss that handle outliers by truncating large errors, and develop a Truncated CauchyNMF to robustly learn the subspace on noisy datasets contaminated by outliers. We theoretically analyze the robustness of Truncated CauchyNMF comparing with the competing models and theoretically prove that Truncated CauchyNMF has a generalization bound which converges at a rate of order < formula > < tex > $O(\sqrt{\ln{n}/n})$ < /tex > < /formula > , where < formula > < tex > $n$ < /tex > < /formula > is the sample size. We evaluate Truncated CauchyNMF by image clustering on both simulated and real datasets. The experimental results on the datasets containing gross corruptions validate the effectiveness and robustness of Truncated CauchyNMF for learning robust subspaces.
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, 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.
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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-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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Guo, K & Guo, Y 2019, 'Key Parameter Design and Analysis of Flux Reversal Linear Rotary Permanent Magnet Actuator', IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, vol. 29, no. 2.
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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.
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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.
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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, yet 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. CFOND enjoys sound theoretical basis and proved convergence, and its performance is validated on real-world networks.
Guo, X, Yu, JX, Li, RH & Miao, X 2019, 'Direction-based multiple views on data', World Wide Web, vol. 22, no. 1, pp. 185-219.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. In this paper, we propose the direction heat maps to visualize the spreads of points w.r.t. different observers in the Euclidean space. It is a new way to look points. Traditionally, the spread of points is unique because it depends on the absolute coordinates of points w.r.t. the zero point. However, considering the relative directions of the points, the spread varies if the observers are different. To visualize the multiple views on the same set of points, we design a pie-shaped heat map. On a direction range the more points are, on the heat map the darker the corresponding sector is. Supporting by the heat map, we can visually know whether the points spread over evenly around or the points scatter only in small direction ranges. The heat map can be widely used in many decision-making applications where the tasks are direction sensitive. Typically, in bicycle sharing applications, the heat map can tell a user the general spread of the available bikes around, and the user may choose to walk on the deep coloured direction to meet more bikes. To generate the heat map, we propose the direction profile to store the direction intervals associated with the number of points in the intervals. We design an algorithm to build the direction profile incrementally. To measure a spread, we define the direction-based centrality which is a statistic value to reflect the uniform degree of the spread. The lower the centrality is, the more evenly the points spread. The observer with the minimum centrality is a direction-based center, while the observer with the maximum centrality is a direction-based edge. We design an algorithm to calculate the centralities. To find the center faster, we also design an algorithm to approximate the centralities. The experimental results show that the heat map can visualize different spreads effectively and the direction-based centralities can be calculated efficiently.
Guo, Z, Yang, CT, Maritz, MF, Wu, H, Wilson, P, Warkiani, ME, Chien, CC, 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.
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© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim The 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, A, Agrawal, RK, Kirar, JS, Andreu-Perez, J, Ding, W-P, Lin, C-T & Prasad, M 2019, 'On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-13.
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Gupta, A, Agrawal, RK, Kirar, JS, Kaur, B, Ding, W, Lin, CT, Javier, AP & Prasad, M 2019, 'A hierarchical meta-model for multi-class mental task based brain-computer interfaces', Neurocomputing.
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© 2019 In the last few years, many research works have been suggested on Brain-Computer Interface (BCI), which assists severely physically disabled persons to communicate directly with the help of electroencephalogram (EEG)signal, generated by the thought process of the brain. Thought generation inside the brain is a dynamic process, and plenty thoughts occur within a small time window. Thus, there is a need for a BCI device that can distinguish these various ideas simultaneously. In this research work, our previous binary-class mental task classification has been extended to the multi-class mental task problem. The present work proposed a novel feature construction scheme for multi mental task classification. In the proposed method, features are extracted in two phases. In the first step, the wavelet transform is used to decompose EEG signal. In the second phase, each feature component obtained is represented compactly using eight parameters (statistical and uncertainty measures). After that, a set of relevant and non-redundant features is selected using linear regression, a multivariate feature selection approach. Finally, optimal decision tree based support vector machine (ODT-SVM)classifier is used for multi mental task classification. The performance of the proposed method is evaluated on the publicly available dataset for 3-class, 4-class, and 5-class mental task classification. Experimental results are compared with existing methods, and it is observed that the proposed plan provides better classification accuracy in comparison to the existing methods for 3-class, 4-class, and 5-class mental task classification. The efficacy of the proposed method encourages that the proposed method may be helpful in developing BCI devices for multi-class classification.
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.
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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.
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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.
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Ha, QP, Yen, L & Balaguer, C 2019, 'Robotic autonomous systems for earthmoving in military applications', Automation in Construction, vol. 107.
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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.
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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Most 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-miR430 and 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.
Hagihghi, R, Razmjou, A, Orooji, Y, Warkiani, ME & Asadnia, M 2019, 'A miniaturized piezoresistive flow sensor for real-time monitoring of intravenous infusion', Journal of Biomedical Materials Research - Part B Applied Biomaterials.
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© 2019 Wiley Periodicals, Inc. Drug overdose (DO) is considered one of the current issues of intravenous (IV) infusion particularly resulting in serious injuries and deaths. Malfunction of infusion pumps is reported as the main cause of the drug overdose. Live monitoring and flow rate calculation by health professionals have been practicing to avoid DO. However, human errors and miscalculations are inevitable. A secondary measurement tool is required to avoid the risk of OD when infusion pump malfunctions cannot be detected immediately. Here, inspired by nature, we developed a real-time monitoring device through which an administrator can review, evaluate, and modify the IV infusion process. Our flow sensor possesses an erected polymer hair cell on a multi-layered silicon base forming from a patterned gold strained gauge layer on a piezoresistive liquid crystal polymer (LCP) membrane. Gold strain gauges on an LCP membrane have been used instead of a piezoresistive silicon membrane as the sensing element. The combination of gold strain gauges and LCP membrane provides better sensitivity than a piezoresistive silicon membrane of the same dimensions and thickness. We also miniaturized our biocompatible sensor such that it can be possible to install it inside the IV tube in contact with the liquid providing an in-suite online flow monitoring. The proposed LCP membrane sensor is compared with two commercially available IV sensors to validate its flow sensing ability. The experimental results demonstrate that the proposed sensor provides a low threshold detection limit of 5 mL/hr, which betters the performance of other commercial sensors at low flow rates.
Hai, NT, Hoang, CN, Woo, SH, Tien, VN, Vigneswaran, S, Hosseini-Bandegharaei, A, Rinklebe, J, Sarmah, AK, Ivanets, A, Dotto, GL, Tho, TB, 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.
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Hai, TT, 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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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, CW & Fils, SCN 2019, 'A collaborative change detection approach on multi-sensor spatial imagery for desertwetland monitoring after a flash flood in Southern Morocco', Remote Sensing, vol. 11, no. 9.
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© 2019 by the authors. 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.
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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.).
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.
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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 la...
Han, B, Yao, Q, Pan, Y, Tsang, IW, Xiao, X, Yang, Q & Sugiyama, M 2019, 'Millionaire: a hint-guided approach for crowdsourcing', Machine Learning.
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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, F, Zhuo, J, Liu, N, Liu, Y, Liu, H & Liu, QH 2019, 'Fast solution of electromagnetic scattering for 3-D inhomogeneous anisotropic objects embedded in layered uniaxial media by the BCGS-FFT method', IEEE Transactions on Antennas and Propagation, vol. 67, no. 3, pp. 1748-1759.
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© 1963-2012 IEEE. In this paper, the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method is used to solve the electromagnetic scattering problem for 3-D inhomogeneous uniaxial anisotropic objects embedded in a layered uniaxial medium. The optical axis of the background medium is perpendicular to the layer boundaries. However, the optical axis of the scatterer can be rotated through any angles. The electric field integral equation (EFIE) is formulated in the layered uniaxial anisotropic background medium. The volumetric roof-top function is used for the basis function as well as the testing function. The dyadic Green's functions (DGFs) for both the electric field and the magnetic vector potential are used to solve the EFIE, and the results as well as the performance are compared. Several numerical simulations are carried out to validate the accuracy and efficiency of the proposed method. The major new contribution of this paper is to extend the BCGS-FFT method to accommodate the uniaxial anisotropy of the layered background medium. Therefore, the DGFs for the layered uniaxial medium are evaluated before solving the EFIE. In addition, the DGF for the electric field is also used to solve the 3-D scattering problem through BCGS-FFT iterations, which is different from the previous work.
Han, L, Han, XF, Ou, XP & Guo, Y 2019, 'Operating performances analysis of brushless doubly-fed machine using magnetic equivalent circuit', Dianji yu Kongzhi Xuebao/Electric Machines and Control, vol. 23, no. 1, pp. 27-34.
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© 2019, Harbin University of Science and Technology Publication. All right reserved. There are few effective tools to quickly and correctly analyze the operating performances of Brushless Doubly-Fed Machine (BDFM) due to its special structure and complex magnetic field modulation mechanism. Based on the basic concept of Magnetic Equivalent Circuit (MEC), this paper described the method for establishing the MEC model of BDFM with cage rotor, considering the effects of special structure of BDFM, core saturation and rotor revolution. Furthermore, a dynamic MEC model of a prototype with cage rotor was built up and solved by the mesh method. And then, the operating performances of the prototype under different operating conditions were obtained and analyzed. Finally, the results of dynamic MEC model were compared with those of Finite Element Analysis (FEA) and experiment data. The results show that the dynamic MEC model can correctly predict the operating performances of BDFM with cage rotor and is much faster than FEA. This work can broaden the research thought for analyzing the operating performances of BDFM of cage rotor.
Han, L, Zhou, M, Han, S, Jia, W, Sun, C & Fu, C 2019, 'Targeting malware discrimination based on reversed association task', Concurrency Computation.
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©2018 John Wiley & Sons, Ltd. Regarding 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, 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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Han, SF, Jin, W, Abomohra, AEF, Zhou, X, Tu, R, Chen, C, Chen, H, Gao, SH & Wang, Q 2019, 'Enhancement of Lipid Production of Scenedesmus obliquus Cultivated in Municipal Wastewater by Plant Growth Regulator Treatment', Waste and Biomass Valorization, pp. 1-7.
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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, Abomohra, AE-F, 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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Han, Y, Deng, Y, Cao, Z & Lin, CT 2019, 'An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Multicriteria decision-making process explicitly evaluates multiple conflicting criteria in decision making. The conventional decision-making approaches assumed that each agent is independent, but the reality is that each agent aims to maximize personal benefit which causes a negative influence on other agents’ behaviors in a real-world competitive environment. In our study, we proposed an interval-valued Pythagorean prioritized operator-based game theoretical framework to mitigate the cross-influence problem. The proposed framework considers both prioritized levels among various criteria and decision makers within five stages. Notably, the interval-valued Pythagorean fuzzy sets are supposed to express the uncertainty of experts, and the game theories are applied to optimize the combination of strategies in interactive situations. Additionally, we also provided illustrative examples to address the application of our proposed framework. In summary, we provided a human-inspired framework to represent the behavior of group decision making in the interactive environment, which is potential to simulate the process of realistic humans thinking.
Hanawal, M, Nguyen, D & Krunz, M 2019, 'Cognitive Networks with In-band Full-duplex Radios: Jamming Attacks and Countermeasures', IEEE Transactions on Cognitive Communications and Networking.
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Although in-band full-duplex (IBFD) radios promise to double the throughput of a wireless link, they are more vulnerable to jamming attacks than their out-of-band full-duplex (OBFD) counterparts. For two communicating OBFD nodes, a jammer needs to attack both the uplink and the downlink channels to completely break the communication link. In contrast, only one common channel needs to be jammed in the case of two IBFD nodes. Even worse, a jammer with self-interference suppression (SIS) capabilities (the underlying technique of IBFD radios) can learn the transmitters’ activity while injecting interference, allowing it to react instantly to the transmitter’s strategies. In this work, we consider a power-constrained IBFD “reactive-sweep” jammer that sweeps through the set of channels by jamming a subset of them simultaneously. We model the interactions between the IBFD radios and the jammer as a stochastic constrained zero-sum Markov game in which nodes adopt the frequency hopping (FH) technique as their strategies to counter jamming attacks. Beside the IBFD transmission-reception (TR) mode, we introduce an additional operation mode, called transmission-detection (TD), in which an IBFD radio transmits and leverages its SIS capability to detect jammers. The aim of the TD mode is to make IBFD radios more cognitive to jamming. The nodes’ optimal defense strategy that guides them when to hop and which operational mode (TD or TR) to use is then established from the equilibrium of the stochastic Markov game. We prove that this optimal policy has a threshold structure, in which IBFD nodes stay on the same channel up to a certain number of time slots before hopping. Simulation results show that our policy significantly improves the throughput of IBFD nodes under jamming attacks.
Handayani, K, Filatova, T & Krozer, Y 2019, 'The vulnerability of the power sector to climate variability and change: Evidence from Indonesia', Energies, vol. 12, no. 19.
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© 2019 by the authors. The power sector is a key target for reducing CO2 emissions. However, little attention has been paid to the sector’s vulnerability to climate change. This paper investigates the impacts of severe weather events and changes in climate variables on the power sector in developing countries, focusing on Indonesia as a country with growing electricity infrastructure, yet being vulnerable to natural hazards. We obtain empirical evidence concerning weather and climate impacts through interviews and focus group discussions with electric utilities along the electricity supply chain. These data are supplemented with reviews of utilities’ reports and published energy sector information. Our results indicate that severe weather events often cause disruptions in electricity supply—in the worst cases, even power outages. Weather-related power outages mainly occur due to failures in distribution networks. While severe weather events infrequently cause shutdowns of power plants, their impact magnitude is significant if it does occur. Meanwhile, transmission networks are susceptible to lightning strikes, which are the leading cause of the networks’ weather-related failures. We also present estimates of financial losses suffered by utilities due to weather-related power disruptions and highlights their adaptation responses to those disruptions.
Handayani, K, Krozer, Y & Filatova, T 2019, 'From fossil fuels to renewables: An analysis of long-term scenarios considering technological learning', ENERGY POLICY, vol. 127, pp. 134-146.
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Hao, P, Zhang, G, Martinez, L & Lu, J 2019, 'Regularizing Knowledge Transfer in Recommendation With Tag-Inferred Correlation', IEEE Transactions on Cybernetics.
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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.
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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.
Harper, R, Hincks, I, Ferrie, C, Flammia, ST & Wallman, JJ 2019, 'Statistical analysis of randomized benchmarking', Physical Review A, vol. 99, no. 5.
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© 2019 American Physical Society. Randomized benchmarking and variants thereof, which we collectively call RB+, are widely used to characterize the performance of quantum computers because they are simple, scalable, and robust to state-preparation and measurement errors. However, experimental implementations of RB+ allocate resources suboptimally and make ad-hoc assumptions that undermine the reliability of the data analysis. In this paper, we propose a simple modification of RB+ which rigorously eliminates a nuisance parameter and simplifies the experimental design. We then show that, with this modification and specific experimental choices, RB+ efficiently provides estimates of error rates with multiplicative precision. Finally, we provide a simplified rigorous method for obtaining credible regions for parameters of interest and a heuristic approximation for these intervals that performs well in currently relevant regimes.
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 and Robotic Systems.
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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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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.
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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.
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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Hayati, H, Mahdavi, F & Eager, D 2019, 'Analysis of agile canine gait characteristics using accelerometry', Sensors (Switzerland), vol. 19, no. 20.
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. 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 is 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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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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He, T, Zhu, J, Lu, DDC & Zheng, L 2019, 'Modified Model Predictive Control for Bidirectional Four-Quadrant EV Chargers with Extended Set of Voltage Vectors', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 7, no. 1, pp. 274-281.
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© 2013 IEEE. This paper presents a modified model predictive control (MMPC) for bidirectional power flow control between the electric vehicle (EV) chargers and the main grid. In contrast to the conventional finite control set MPC which selects an optimal switching state from eight possible voltage vectors, the proposed MMPC is based on the application of an optimal voltage vector chosen from an extended set of 20 modulated voltage vectors with a fixed duty ratio. To reduce the computational burden introduced by the increased number of voltage sets, a preselection algorithm is developed for the MMPC method. Six voltage vectors are preselected from the 20 sectors. Due to the increased number of the voltage space vectors, the grid currents and active and reactive power performance can be improved by using the proposed MMPC scheme. Both the conventional and proposed methods are compared through experimental test results of a two-level three-phase off-board EV charger.
He, X, Wu, W & Wang, S 2019, 'A constitutive model for granular materials with evolving contact structure and contact forcesPart 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.
Heidary, A, Radmanesh, H, Bakhshi, A, Rouzbehi, K & Pouresmaeil, E 2019, 'A compound current limiter and circuit breaker', Electronics, vol. 8, pp. 1-12.
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. The protection of sensitive loads against voltage drop is a concern for the power system. A fast fault current limiter and circuit breaker can be a solution for rapid voltage recovery of sensitive loads. This paper proposes a compound type of current limiter and circuit breaker (CLCB) which can limit fault current and fast break to adjust voltage sags at the protected buses. In addition, it can act as a circuit breaker to open the faulty line. The proposed CLCB is based on a series L-C resonance, which contains a resonant transformer and a series capacitor bank. Moreover, the CLCB includes two anti-parallel power electronic switches (a diode and an IGBT) connected in series with bus couplers. In order to perform an analysis of CLCB performance, the proposed structure was simulated using MATLAB. In addition, an experimental prototype was built, tested, and the experimental results were reported. Comparisons show that experimental results were in fair agreement with the simulation results and confirm CLCB’s ability to act as a fault current limiter and a circuit breaker.
Heidary, A, Radmanesh, H, Moghim, A, Ghorbanyan, K, Rouzbehi, K, Rodrigues, EMG & Pouresmaeil, E 2019, 'A Multi-Inductor H Bridge Fault Current Limiter', ELECTRONICS, vol. 8, no. 7.
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Heidary, A, Radmanesh, H, Rouzbehi, K & Pou, J 2019, 'A DC-Reactor-Based Solid-State Fault Current Limiter for HVdc Applications', IEEE TRANSACTIONS ON POWER DELIVERY, vol. 34, no. 2, pp. 720-728.
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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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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.
Hirzallah, M, Krunz, M & Xiao, Y 2019, 'Harmonious Cross-Technology Coexistence with Heterogeneous Traffic in Unlicensed Bands: Analysis and Approximations', IEEE Transactions on Cognitive Communications and Networking.
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IEEE The dramatic growth in demand for mobile data has prompted mobile network operators (MNOs) to explore spectrum sharing in unlicensed bands. MNOs have been allowed recently to operate their LTE services over the 5 GHz Unlicensed National Information Infrastructure (U-NII) bands, currently occupied by Wi-Fi. The unlicensed LTE operation has been standardized by 3GPP under the name Licensed Assisted Access (LAA). Unlicensed 5G New radio (NR) operation over the U-NII bands, a.k.a., NR-Unlicensed (NR-U), is also being explored. To support applications with diverse quality of service requirements, LAA, NR-U, and Wi-Fi technologies offer multiple priority classes with different contention parameters for accessing an unlicensed channel. How these different classes affect the interplay between coexisting MNOs and Wi-Fi systems is still a relatively under-explored topic. In this paper, we develop a simple yet efficient framework for fair coexistence between LTE MNOs and Wi-Fi systems, each with multiple priority classes. We derive approximate closed-form solutions for the probability of successful transmission (PST), average contention delay, and average throughput under different LAA and Wi-Fi priority classes. LTE and Wi-Fi operators can fit these solutions to offline and/or online measurements, and use them to further optimize their system throughput and latency. Our results reveal that PSTs computed with our approximate models are within 5% of these obtained via simulation under dense network deployments and high traffic loads.
Hoang, D, Luong, NT & Vo, TT 2019, 'FAPRP: A Machine Learning Approach to Flooding Attacks Prevention Routing Protocol in Mobile Ad Hoc Networks', Wireless Communications and Mobile Computing, vol. 2019.
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Hoang, VT, Phung, MD, Dinh, TH & Ha, QP 2019, 'System Architecture for Real-Time Surface Inspection Using Multiple UAVs', IEEE Systems Journal, pp. 1-12.
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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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© 2018 Wiley Periodicals, Inc. This 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, T 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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Ho-Pham, LT, Bich, T, 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.
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Hoque, MAA, 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 and Coastal Management, vol. 181.
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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, MAA, 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.
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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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Hossain, N, Mahlia, TMI & Saidur, R 2019, 'Latest development in microalgae-biofuel production with nano-additives', BIOTECHNOLOGY FOR BIOFUELS, vol. 12.
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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.
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Hossain, N, Zaini, J & Mahlia, TMI 2019, 'Experimental investigation of energy properties for Stigonematales sp. 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 CO2 capture and sequestration, water treatment, prolific growth rate and enormous energy content. Thus, energy research on microalgae is being harnessed to mitigate CO2 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/m3-673.07 kg/m3, respectively. Apart from experimental values, other physical bioenergy parameters were simulated and they were biomass characteristic index 61,822.29 kg/m3-62,341.3 kg/m3, energy density 5.27 GJ/m3-5.76G J/m3 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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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-BIOMEMBRANES, vol. 1861, no. 8, pp. 1458-1467.
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Hossain, SM, Park, MJ, Park, HJ, Tijing, L, Kim, JH & 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.
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© 2019 Elsevier Ltd 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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Howard, D, Macsween, K, Edwards, GC, Desservettaz, M, Guerette, E-A, Paton-Walsh, C, Surawski, NC, Sullivan, AL, Weston, C, Volkova, L, Powell, J, Keywood, MD, Reisen, F & Meyer, CPM 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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Hu, J, Li, Z, Zhu, J & Guerrero, JM 2019, 'Voltage Stabilization A Critical Step Toward High Photovoltaic Penetration', IEEE INDUSTRIAL ELECTRONICS MAGAZINE, vol. 13, no. 2, pp. 17-30.
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Hu, J, Liang, B & Qiu, X 2019, 'High-efficiency collimation of airborne sound through a single deep-subwavelength aperture in an ultra-thin planar plate', APPLIED PHYSICS EXPRESS, vol. 12, no. 2.
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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.
Hua, J, Huang, M & Huang, C 2019, 'Centrality Metrics’ Performance Comparisons on Stock Market Datasets', Symmetry, vol. 11, no. 7, pp. 916-916.
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The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks.
Hua, J, Huang, M, Huang, W & Zhao, C 2019, 'Applying Graph Centrality Metrics in Visual Analytics of Scientific Standard Datasets', Symmetry, vol. 11, no. 1, pp. 30-30.
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Graphs are often used to model data with a relational structure and graphs are usually visualised into node-link diagrams for a better understanding of the underlying data. Node-link diagrams represent not only data entries in a graph, but also the relations among the data entries. Further, many graph drawing algorithms and graph centrality metrics have been successfully applied in visual analytics of various graph datasets, yet little attention has been paid to analytics of scientific standard data. This study attempts to adopt graph drawing methods (force-directed algorithms) to visualise scientific standard data and provide information with importance ‘ranking’ based on graph centrality metrics such as Weighted Degree, PageRank, Eigenvector, Betweenness and Closeness factors. The outcomes show that our method can produce clear graph layouts of scientific standard for visual analytics, along with the importance ‘ranking’ factors (represent via node colour, size etc.). Our method may assist users with tracking various relationships while understanding scientific standards with fewer relation issues (missing/wrong connection etc.) through focusing on higher priority standards.
Hua, J, Huang, ML & Huang, C 2019, 'Centrality Metrics’ Performance Comparisons on Stock Market Datasets', Symmetry, vol. 11, no. 7, pp. 1-15.
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The stock market is an essential sub-sector in the financial area. Both understanding and evaluating the mountains of collected stock data has become a challenge in relevant fields. Data visualisation techniques can offer a practical and engaging method to show the processed data in a meaningful way, with centrality measurements representing the significant variables in a network, through exploring the aspects of the exact definition of the metric. Here, in this study, we conducted an approach that combines data processing, graph visualisation and social network analysis methods, to develop deeper insights of complex stock data, with the ultimate aim of drawing the correct conclusions with the finalised graph models. We addressed the performance of centrality metrics methods such as betweenness, closeness, eigenvector, PageRank and weighted degree measurements, drawing comparisons between the experiments’ results and the actual top 300 shares in the Australian Stock Market. The outcomes showed consistent results. Although, in our experiments, the results of the top 300 stocks from those five centrality measurements’ rankings did not match the top 300 shares given by the ASX (Australian Securities Exchange) entirely, in which the weighted degree and PageRank metrics performed better than other three measurements such as betweenness, closeness and eigenvector. Potential reasons may include that we did not take into account the factor of stock’s market capitalisation in the methodology. This study only considers the stock price’s changing rates among every two shares and provides a relevant static pattern at this stage. Further research will include looking at cycles and symmetry in the stock market over chosen trading days, and these may assist stakeholder in grasping deep insights of those stocks.
Huang, B, Xu, Z, Jia, B & Mao, G 2019, 'An Online Radio Map Update Scheme for WiFi Fingerprint-Based Localization', IEEE INTERNET OF THINGS JOURNAL, vol. 6, no. 4, pp. 6909-6918.
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Huang, C, Yao, L, Wang, X, Benatallah, B & Sheng, QZ 2019, 'Software Expert Discovery via Knowledge Domain Embeddings in a Collaborative Network', Pattern Recognition Letters.
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Huang, KC, Chuang, CH, Wang, YK, Hsieh, CY, King, JT & Lin, CT 2019, 'The effects of different fatigue levels on brain–behavior relationships in driving', Brain and Behavior.
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© 2019 The Authors. Brain and Behavior published by Wiley Periodicals, Inc. Background: In 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. Methods: A 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. Results: Results 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. Conclusion: Our 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 models to better predict the performance of human operators.
Huang, L, Kelly, S, Lv, K & Giurco, D 2019, 'A systematic review of empirical methods for modelling sectoral carbon emissions in China', JOURNAL OF CLEANER PRODUCTION, vol. 215, pp. 1382-1401.
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Huang, L, Liu, Z, Wu, C & Liang, J 2019, 'Interaction between a tunnel and alluvial valley under plane SV waves of earthquakes by IBIEM', European Journal of Environmental and Civil Engineering.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This paper investigates the dynamic interaction between a lined tunnel and an alluvial valley under plane SV waves with the indirect boundary integral equation method (IBIEM). The impact of different parameters on the displacement of sedimentary valley and the dynamic stress concentration factors (DSCF) of the lining inner and outer walls are studied. The dynamic response of a tunnel embedded in an alluvial valley is considerably different from that of a tunnel not embedded in an alluvial valley. Numerical results indicate that under the same frequency, different buried depths can change the distribution of displacement in the sedimentary area. The deeper the tunnel is buried, the more resonance points the response spectrum curve has. In general, the DSCF on the inner surfaces of the tunnels embedded in the sedimentary valley is obviously greater than that of the tunnels in the half space. However, the DSCF on the outer surfaces of the former is smaller than that of the latter. The results reported here will provide a quantitative basis to the security assessment and seismic design of lined tunnels.
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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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, QS, Wei, W, Sun, J, Mao, S & Ni, BJ 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, 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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Huang, W, Hua, W, Chen, F, Qi, J & Zhu, J 2019, 'Performance Improvement of Model Predictive Current Control of Fault-Tolerant Five-Phase Flux-Switching Permanent Magnet Motor Drive', IEEE Transactions on Industry Applications, vol. 55, no. 6, pp. 6001-6010.
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Huang, WY, Ngo, HH, Lin, C, Vu, CT, Kaewlaoyoong, A, Boonsong, T, Tran, HT, Bui, XT, Vo, TDH & Chen, JR 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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© 2018 Elsevier B.V. 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. 3557-3573.
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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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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, 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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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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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.
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, A, 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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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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Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for labelling 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 (GAN) 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 predefined training classes in the real data domain. Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work 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.
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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 performs 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 (DFS) 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 (AWGN) to the original target signals with different Signal to Noise Ratios (SNRs). 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.
Hung, S-H, Hietala, K, Zhu, S, Ying, M, Hicks, M & Wu, X 2019, 'Quantitative robustness analysis of quantum programs', Proceedings of the ACM on Programming Languages, vol. 3, no. POPL, pp. 1-29.
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Hussain, S, Al-Hitmi, M, Khaliq, S, Hussain, A & Saqib, MA 2019, 'Implementation and comparison of particle swarm optimization and genetic algorithm techniques in combined economic emission dispatch of an independent power plant', Energies, vol. 12, no. 11.
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license This paper presents the optimization of fuel cost, emission of NOX, COX, and SOX gases caused by the generators in a thermal power plant using penalty factor approach. Practical constraints such as generator limits and power balance were considered. Two contemporary metaheuristic techniques, particle swarm optimization (PSO) and genetic algorithm (GA), have were simultaneously implemented for combined economic emission dispatch (CEED) of an independent power plant (IPP) situated in Pakistan for different load demands. The results are of great significance as the real data of an IPP is used and imply that the performance of PSO is better than that of GA in case of CEED for finding the optimal solution concerning fuel cost, emission, convergence characteristics, and computational time. The novelty of this work is the parallel implementation of PSO and GA techniques in MATLAB environment employed for the same systems. They were then compared in terms of convergence characteristics using 3D plots corresponding to fuel cost and gas emissions. These results are further validated by comparing the performance of both algorithms for CEED on IEEE 30 bus test bed.
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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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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Ibrar, I, Altaee, A, Zhou, JL, Naji, O & Khanafer, D 2019, 'Challenges and potentials of forward osmosis process in the treatment of wastewater', Critical Reviews in Environmental Science and Technology, pp. 1-45.
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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.
Imdad, U, Asif, M, Ahmad, M, Sohaib, O, Hanif, M & Chaudary, M 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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Irga, P, Pettit, T, Irga, R, Paull, N, Douglas, A & Torpy, F 2019, 'Does plant species selection in functional active green walls influence VOC phytoremediation efficiency?', Environmental Science and Pollution Research, vol. Volume 26,, no. 13, pp. 12851-12858.
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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) were compared 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, 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.
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Islam, MS, Saha, SC, Gemci, T, Yang, IA, Sauret, E, Ristovski, Z & Gu, T 2019, 'Euler-Lagrange Prediction of Diesel-Exhaust Polydisperse Particle Transport and Deposition in Lung: Anatomy and Turbulence Effects', SCIENTIFIC REPORTS, vol. 9.
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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 Annual IEEE Sy...
Jafari, M, Malekjamshidi, Z & Zhu, J 2019, 'A magnetically coupled multi-port, multi-operation-mode micro-grid with a predictive dynamic programming-based energy management for residential applications', International Journal of Electrical Power & Energy Systems, vol. 104, no. January 2019, pp. 784-796.
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This paper presents the development of a residential micro-grid topology based on a combination of common magnetic and electrical buses. The magnetic bus interfaces two low voltage dc buses linking a PV and a fuel cell to a high voltage dc bus connected to a grid-tied single-phase bidirectional inverter. A battery is used to store the surplus energy of the system and stabilise the dc voltage of the fuel cell bus. A synchronised bus voltage balance (SBVB) technique is used to reduce the conduction losses and increase the soft switching operation range of the converters. To improve the maximum power point tracking (MPPT) performance and system efficiency, appropriate control techniques and compensation blocks are designed. The proposed micro-grid is able to operate in multiple grid-connected and off-grid operation modes according to a predictive 2D dynamic programming-based energy management. A mode selection and transition strategy is developed to select the appropriate operation mode and smooth the mode transition. A detailed study of the micro-grid including steady-state operation, small signal modelling, controller design, and energy management is presented. A prototype of the system is developed, and experimental tests are conducted for an energy management scenario.
Jafari, M, Malekjamshidi, Z & Zhu, J 2019, 'Copper Loss Analysis of a Multi-winding High-frequency Transformer for a Magnetically-coupled Residential Micro-grid', IEEE Transactions on Industry Applications, vol. 55, no. 1, pp. 283-297.
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Improvements in characteristics of magnetic materials and switching devices have provided the feasibility of replacing the electrical buses with high-frequency magnetic links in small-scale micro-grids. This can effectively reduce the number of voltage conversion stages, size and cost of the micro-grid, and isolate the sources and loads. To optimally design the magnetic link, an accurate evaluation of copper loss of the windings considering both the current waveforms and parasitic effects are required. This paper studies the copper loss analysis of a three-winding high-frequency magnetic link for residential micro-grid applications. Due to the non-sinusoidal nature of the voltages and currents, the loss analysis is carried out on a harmonic basis taking into account the variations of phase shift, duty ratio and amplitude of the waveforms. The high-frequency parasitic phenomena including the skin and proximity effects are taken into account. The maximum and minimum copper loss operating conditions of the magnetic link and their dependency on the phase shift angle and the duty ratio of the connected waveforms are studied. A prototype of the micro-grid including the magnetic link is developed to validate the theoretical analysis, evaluate the micro-grid efficiency and perform the loss breakdown.
Jafari, M, Malekjamshidi, Z & Zhu, J 2019, 'Design and development of a multi-winding high-frequency magnetic link for grid integration of residential renewable energy systems', APPLIED ENERGY, vol. 242, pp. 1209-1225.
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Jafari, M, Malekjamshidi, Z, Lu, DDC & Zhu, J 2019, 'Development of a Fuzzy-Logic-Based Energy Management System for a Multi-Port Multi-Operation Mode Residential Smart Micro-grid', 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, Malekjamshidi, Z, Zhu, J & Khooban, MH 2019, 'Novel Predictive Fuzzy Logic-Based Energy Management System for Grid-connected and Off-grid Operation of Residential Smart Micro-grids', IEEE Journal of Emerging and Selected Topics in Power Electronics.
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IEEE In this paper, a novel energy management system with two operating horizons is proposed for a residential micro-grid application. The micro-grid utilises the energies of a photovoltaic (PV), a fuel cell and a battery bank to supply the local loads through a combination of electric and magnetic buses. The proposed micro-grid operates in a large number of grid-connected and off-grid operation modes. The energy management system includes a long-term data prediction unit based on a 2D dynamic programming and a short-term fuzzy controller. The long-term prediction unit is designed to determine the appropriate variation range of the battery state of charge and fuel cell state of hydrogen. The efficiency performance of the micro-grid components, predicted energy generation and demand, energy cost and the system constraints are taken into account. The resultant data then is sent to the short-term fuzzy controller which determines the operation mode of the micro-grid based on the real-time condition of the micro-grid elements. A prototype of the proposed micro-grid including the energy management system is developed, and experimental tests are conducted for three different energy management scenarios. The proposed management technique is validated through energy distribution and cost analysis.
Jafarzadeh, H, Abedin, B, Aurum, A & D'Ambra, J 2019, 'Search Engine Advertising Perceived Effectiveness: A Resource-Based Approach on the Role of Advertisers' Competencies', Journal of Organizational and End User Computing, vol. 31, no. 4, pp. 46-73.
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Search engine advertising (SEA), in which businesses (or advertisers) bid on keywords and pay search engine providers (e.g. Google) to get a secure place on the first page of search results, is a prominent source of revenue for search engine companies, and an effective method for businesses to promote their visibility on the Web. SEA is a complex and dynamic form of advertising and managing it effectively is still a challenge for many businesses, particularly smaller businesses. Building upon resource-based theory, this study has developed a context-specific model for understanding advertisers' competency factors that influence SEA effectiveness. Using structural equation modelling and survey data collected from 142 businesses, this research has found that SEA effectiveness is influenced by organizations' ability to manage keywords and bids, ability to analyse and monitor outcomes, advertising expertise, commitment to SEA task, and the use of external experts. Moderating relationships between the factors have been examined, and the implications of the findings for both academics and practitioners have been discussed.
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 strength of t...
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.
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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.
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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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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.
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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.
Jan, MA, Usman, M, He, X & Rehman, AU 2019, 'SAMS: A Seamless and Authorized Multimedia Streaming framework for WMSN-based IoMT', IEEE Internet of Things Journal.
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IEEE An Internet of Multimedia Things (IoMT) architecture aims to provide a support for real-time multimedia applications by using wireless multimedia sensor nodes that are deployed for long-term usage. These nodes are capable of capturing both multimedia and non-multimedia data, and form a network known as Wireless Multimedia Sensor Network (WMSN). In a WMSN, the underlying routing protocols need to provide an acceptable level of Quality-of-Service (QoS) support for the multimedia traffic. In this paper, we propose a seamless and authorized multimedia streaming framework (SAMS) for a cluster-based hierarchical WMSN. SAMS uses authentication at different levels to form secured clusters. The formation of these clusters allows only legitimate nodes to transmit captured data to their cluster heads. Each node senses the environment, stores captured data in its buffer, and waits for its turn to transmit to its cluster head. This waiting may result in an excessive packet loss and end-to-end delay for multimedia traffic. To address these issues, a channel allocation approach is proposed for inter-cluster communication. In case of buffer overflow, a member node in one cluster switches to a neighboring cluster head provided that the latter has an available channel for allocation. The experimental results show that SAMS provides an acceptable level of QoS and enhances the security of the underlying network.
Janeczko, C, Martelli, C, Canning, J & Dutra, G 2019, 'Assessment of Orchid Surfaces Using Top-Down Contact Angle Mapping', IEEE Access, vol. 7, pp. 31364-31375.
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© 2013 IEEE. Top-down contact angle (CA) measurements are used to characterize the green leaves and purple flowers of both old and young the Cattleya warneri orchids. The top-down CA allows the characterization of large surfaces away from the leaf edge, avoiding traditional cutting required for side view CA measurement. This allows large area mapping without damaging leaves making the method amenable to fieldwork and useful in environmental diagnostics. Young leaves are found to be hydrophobic whilst old leaves become practically hydrophilic across their entirety, mostly as a result of continued exposure to changes in the environment over time. The flowers are hydrophobic because of their visual and tactile attractor function for pollinating animals and the self-cleaning of dirt and pathogens. Real-time measurement and mapping of CA of surfaces open a new tool to assess the long-term impact of plant aging, pollution, and more of organisms in the field. The method has clear applications elsewhere such as in industrial probing of surfaces and products.
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.
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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.
Jay, B 2019, 'Intensional computation with higher-order functions', Theoretical Computer Science, vol. 768, pp. 76-90.
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© 2019 Elsevier B.V. Intensional computations are those that query the internal structure of their arguments. In a higher-order setting, such queries perform program analysis. This is beyond the expressive power of traditional term rewriting systems, such as lambda-calculus or combinatory logic, as they are extensional. In such settings it has been necessary to encode or quote the program before analysis. However, there are intensional calculi, specifically confluent term rewriting systems, that can analyse higher-order programs within the calculus proper, without quotation; there are even programs that produce the Goedel numbers of their program argument. This paper summarizes the current situation. Highlights include the following observations. We have known since 2011 that the simplest intensional calculus, SF-calculus, supports arbitrary queries of closed normal forms, including equality, pattern-matching, searching and self-interpretation. Recent work, verified using the Coq proof assistant, has shown that all recursive programs can be represented as closed normal forms in SF-calculus, and even in combinatory logic. Thus, we can here deduce that SF-calculus (but not combinatory logic) can define queries of programs. These results are compatible with direct support for lambda-abstraction. Although these results conflict with the traditional understanding of expressive power of combinatory logic and λ-calculus, as developed by Church and Kleene, our recent publication has shown that their approach is compromised by its reliance on encodings. To drive the point home, this paper uses a non-standard encoding to lambda-define a trivial solution of the Halting Problem.
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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Jena, R, Pradhan, B, Beydoun, G, Nizamuddin, Ardiansyah, Sofyan, H & Affan, M 2019, 'Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia', Geoscience Frontiers.
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© 2019 China University of Geosciences (Beijing) and Peking University Catastrophic natural hazards, such as earthquake, pose serious threats to properties and human lives in urban areas. Therefore, earthquake risk assessment (ERA) is indispensable in disaster management. ERA is an integration of the extent of probability and vulnerability of assets. This study develops an integrated model by using the artificial neural network–analytic hierarchy process (ANN–AHP) model for constructing the ERA map. The aim of the study is to quantify urban population risk that may be caused by impending earthquakes. The model is applied to the city of Banda Aceh in Indonesia, a seismically active zone of Aceh province frequently affected by devastating earthquakes. ANN is used for probability mapping, whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering. The risk map is subsequently created by combining the probability, hazard, and vulnerability maps. Then, the risk levels of various zones are obtained. The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%. Furthermore, results show that the central and southeastern regions of the city have moderate to very high risk classifications, whereas the other parts of the city fall under low to very low earthquake risk classifications. The findings of this research are useful for government agencies and decision makers, particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.
Jeong, SY, Chang, SW, Ngo, HH, Guo, W, Nghiem, LD, Banu, JR, Jeon, B-H & Dinh, DN 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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Ji, Z 2019, 'The complexity-theoretic Bell inequality', NATIONAL SCIENCE REVIEW, vol. 6, no. 1, pp. 21-21.
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Jia, H, Feng, F, Wang, J, Ngo, HH, 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.
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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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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. 14, no. 8, pp. 1-14.
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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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Jiang, J, Lin, F, Fan, J, Lv, H & Wu, J 2019, 'A Destination Prediction Network Based on Spatiotemporal Data for Bike-Sharing', Complexity, vol. 2019.
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© 2019 Jian Jiang et al. Bike-sharing is a new low-carbon and environment-friendly mode of public transport based on the "sharing economy". Since 2017, the bike-sharing market has boomed in China's major cities. Bikes equipped with GPS transmitters are docked along sidewalks that can be easily accessed through smartphone apps. However, this new form of transport has also led to problems, such as illegal parking, vandalism, and theft, each of which presents a major administrative challenge. Further, imbalances in user demand and bike availability need to be overcome to ensure a convenient, flexible service for customers. Hence, predicting a cyclist's destination could be of great importance to shared-bike operators. In this paper, we propose an innovative deep learning model to predict the most probable destination for each user. The model, called destination prediction network based on spatiotemporal data (DPNst), comprises three steps. First, the data is preprocessed and a pool of likely candidate destinations is generated based on frequent item mining. This candidate set is then used to build the DPNst model: a long short-term memory network learns the user's behavior; a convolutional neural network learns the spatial relationships between the origin and the candidate destinations; and a fully connected neural network learns the external features. In the final step, DPNst dynamically aggregates the output of the three neural networks based on the given data and generates the predictions. In a series of experiments on real-world stationless bike-sharing data, DPNst returned an F1 score of 42.71% and demonstrated better performance overall than the compared baselines.
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, pp. 100003-100003.
Jiang, P, Li, R, Lu, H & Zhang, X 2019, 'Modeling of electricity demand forecast for power system', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. The emerging complex circumstances caused by economy, technology, and government policy and the requirement of low-carbon development of power grid lead to many challenges in the power system coordination and operation. However, the real-time scheduling of electricity generation needs accurate modeling of electricity demand forecasting for a range of lead times. In order to better capture the nonlinear and non-stationary characteristics and the seasonal cycles of future electricity demand data, a new concept of the integrated model is developed and successfully applied to research the forecast of electricity demand in this paper. The proposed model combines adaptive Fourier decomposition method, a new signal preprocessing technology, for extracting useful element from the original electricity demand series through filtering the noise factors. Considering the seasonal term existing in the decomposed series, it should be eliminated through the seasonal adjustment method, in which the seasonal indexes are calculated and should multiply the forecasts back to restore the final forecast. Besides, a newly proposed moth-flame optimization algorithm is used to ensure the suitable parameters of the least square support vector machine which can generate the forecasts. Finally, the case studies of Australia demonstrated the efficacy and feasibility of the proposed integrated model. Simultaneously, it can provide a better concept of modeling for electricity demand prediction over different forecasting horizons.
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 & Xu, YDR 2019, 'Relative Pairwise Relationship Constrained Non-negative Matrix Factorisation', IEEE Transactions on Knowledge and Data Engineering.
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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, W, Qi, J, Yu, JX, Huang, J & Zhang, R 2019, 'HyperX: A Scalable Hypergraph Framework', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 5, pp. 909-922.
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IEEE Hypergraphs are generalizations of graphs where the hyperedges can connect any number of vertices. They are powerful tools for representing complex and non-pairwise relationships. However, existing graph frameworks cannot accommodate hypergraphs without converting them into graphs, because they do not offer APIs that support (hyper)edges directly. This graph conversion create excessive replicas and result in large graphs, causing difficulties in workload balancing. A few tools have been developed for hypergraph partitioning, but they are not general-purpose frameworks for hypergraph processing. In this paper, we propose HyperX, a general-purpose distributed hypergraph processing framework built on top of Spark. HyperX is based on the computation paradigm "Pregel", which is user-friendly and has been widely adopted by popular graph computation frameworks. To help create balanced workloads for distributed hypergraph processing, we investigate the hypergraph partitioning problem and propose a novel label propagation partitioning (LPP) algorithm. We conduct extensive experiments using both real and synthetic data. The result shows that HyperX achieves an order of magnitude improvement for running hypergraph learning algorithms compared with graph conversion based approaches in terms of running time, network communication costs, and memory consumption. For hypergraph partitioning, LPP outperforms the baseline algorithms significantly in these measures as well.
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.
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 (Switzerland), vol. 10, no. 6.
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© 2019 by the authors. 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.
Johnson, B, Chang, L, Afridi, K, Ali, MH, Von Appen, J, Chen, YM, Davoudi, A, Dhople, S, Enslin, JH, Flicker, J, Islam, MR, Koutroulis, E, Kim, KA, Li, Y, Liserre, M, Long, T, Lu, X, Mattavelli, P, Rodriguez, P, Ruan, X, Suntio, T, Wang, H, Xu, D, Xu, W, Yazdani, A, Zeineldin, H & Zhu, J 2019, 'Guest editorial joint special section on power conversion & control in photovoltaic power plants', IEEE Transactions on Energy Conversion, vol. 34, no. 1, pp. 159-160.
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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.
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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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Jupp, JR & Parkes, M 2019, 'Integrated Construction Enterprise Systems: A Strategic Approach to Model-based Data and Process Management', Automation in Construction, vol. (to appear).
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.
Kalaruban, M, Loganathan, P, Tien, VN, Nur, T, Johir, MAH, Thi, HN, Minh, VT & 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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Kaljahi, MA, Palaiahnakote, S, Anisi, MH, Idris, MYI, Blumenstein, M & Khan, MK 2019, 'A scene image classification technique for a ubiquitous visual surveillance system', Multimedia Tools and Applications, vol. 78, no. 5, pp. 5791-5818.
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© 2018 Springer Science+Business Media, LLC, part of Springer Nature The concept of smart cities has quickly evolved to improve the quality of life and provide public safety. Smart cities mitigate harmful environmental impacts and offences and bring energy-efficiency, cost saving and mechanisms for better use of resources based on ubiquitous monitoring systems. However, existing visual ubiquitous monitoring systems have only been developed for a specific purpose. As a result, they cannot be used for different scenarios. To overcome this challenge, this paper presents a new ubiquitous visual surveillance mechanism based on classification of scene images. The proposed mechanism supports different applications including Soil, Flood, Air, Plant growth and Garbage monitoring. To classify the scene images of the monitoring systems, we introduce a new technique, which combines edge strength and sharpness to detect focused edge components for Canny and Sobel edges of the input images. For each focused edge component, a patch that merges nearest neighbor components in Canny and Sobel edge images is defined. For each patch, the contribution of the pixels in a cluster given by k-means clustering on edge strength and sharpness is estimated in terms of the percentage of pixels. The same percentage values are considered as a feature vector for classification with the help of a Support Vector Machine (SVM) classifier. Experimental results show that the proposed technique outperforms the state-of-the-art scene categorization methods. Our experimental results demonstrate that the SVM classifier performs better than rule and template-based methods.
Kaljahi, MA, Shivakumara, P, Idris, MYI, Anisi, MH, Lu, T, Blumenstein, M & Noor, NM 2019, 'An automatic zone detection system for safe landing of UAVs', Expert Systems with Applications, vol. 122, pp. 319-333.
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© 2019 Elsevier Ltd As the demand increases for the use Unmanned Aerial Vehicles (UAVs) to monitor natural disasters, protecting territories, spraying, vigilance in urban areas, etc., detecting safe landing zones becomes a new area that has gained interest. This paper presents an intelligent system for detecting regions to navigate a UAV when it requires an emergency landing due to technical causes. The proposed system explores the fact that safe regions in images have flat surfaces, which are extracted using the Gabor Transform. This results in images of different orientations. The proposed system then performs histogram operations on different Gabor-oriented images to select pixels that contribute to the highest peak, as Candidate Pixels (CP), for the respective Gabor-oriented images. Next, to group candidate pixels as one region, we explore Markov Chain Codes (MCCs), which estimate the probability of pixels being classified as candidates with neighboring pixels. This process results in Candidate Regions (CRs) detection. For each image of the respective Gabor orientation, including CRs, the proposed system finds a candidate region that has the highest area and considers it as a reference. We then estimate the degree of similarity between the reference CR with corresponding CRs in the respective Gabor-oriented images using a Chi square distance measure. Furthermore, the proposed system chooses the CR which gives the highest similarity to the reference CR to fuse with that reference, which results in the establishment of safe landing zones for the UAV. Experimental results on images from different situations for safe landing detection show that the proposed system outperforms the existing systems. Furthermore, experimental results on relative success rates for different emergency conditions of UAVs show that the proposed intelligent system is effective and useful compared to the existing UAV safe landing systems.
Kang, Y, Xie, H, Li, B, Zhang, J, Ngo, HH, Guo, W, Gun, 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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Karacan, I, Ben-Nissan, B, Wang, HA, Juritza, A, Swain, MV, Müller, WH, Chou, J, Stamboulis, A, Macha, IJ & Taraschi, V 2019, 'Mechanical testing of antimicrobial biocomposite coating on metallic medical implants as drug delivery system', Materials Science and Engineering: C, vol. 104, no. 109757, pp. 109757-109757.
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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.
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© 2019 John Wiley & Sons, Ltd. An 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 Re c = 4.9 × 10 5 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, SL, 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%.
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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Katz, A, Shon, HK, Chekli, L & Kim, JH 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.
Keall, PJ, Nguyen, DT, O'Brien, R, Zhang, P, Bertholet, J & Poulsen, PR 2019, 'In Reply to Dahele and Verbakel', International Journal of Radiation Oncology Biology Physics, vol. 103, no. 1, pp. 283-284.
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Keerthisinghe, TP, Luong, NN, Kwon, EE & Oh, S 2019, 'Antiseptic chlorhexidine in activated sludge: Biosorption, antimicrobial susceptibility, and alteration of community structure', JOURNAL OF ENVIRONMENTAL MANAGEMENT, vol. 237, pp. 629-635.
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Keith, A, Brown, NJ & Zhou, JL 2019, 'The feasibility of a collapsible parabolic solar cooker incorporating phase change materials', Renewable Energy Focus, vol. 30, pp. 58-70.
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© 2019 Elsevier Ltd This paper presents a solar energy-based cooking solution for reducing the dependency of refugees on firewood for cooking food. The use of firewood is associated with a variety of problems such as deforestation, environmental degradation and household air pollution. This paper proposes that a collapsible parabolic solar cooker with 12 panels and a phase change material-incorporated cooking pot is a viable alternative to firewood. The phase change material allows food cooked during the day to be kept warm and subsequently consumed as an evening meal. Furthermore, the proposed solution considers, and fits within, the cultural aspect of the refugee context. The cultural aspect is highlighted as it is a factor in determining whether refugees will accept the proposed solution. This paper also presents a cost-benefit analysis of the proposed solution which shows that if used by a family unit of four members, the payback period is 52 weeks or less. Finally, this paper concludes with recommendations pertaining to the efficiency of the system to reduce cooking time and enable the system to keep food warm for subsequent meals. These recommendations are focused on maximising the chances of acceptance of the parabolic solar cooker by refugees during humanitarian crises.
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.
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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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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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Khan, HU, Aruya, JA & Gill, A 2019, 'Web 2.0 Technologies Adoption Barriers for External Contacts and Participation: A Case Study of Federal Establishment of Africa', International Journal of Business Information Systems.
Khan, MA, Huu, HN, Guo, W, Chang, SW, Dinh, DN, 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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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, MNH, Forouzesh, M, Siwakoti, YP, Li, L, Kerekes, T & Blaabjerg, F 2019, 'Transformerless Inverter Topologies for Single-Phase Photovoltaic Systems: A Comparative Review', IEEE Journal of Emerging and Selected Topics in Power Electronics.
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Khan, MNH, Siwakoti, YP, Li, L & Blaabjerg, F 2019, 'H-Bridge Zero-Voltage Switch Controlled Rectifier (HB-ZVSCR) Transformerless Mid-Point-Clamped Inverter for Photovoltaic Applications', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Khan, SA, Guo, Y & Zhu, J 2019, 'Model Predictive Observer Based Control for Single-Phase Asymmetrical T-Type AC/DC Power Converter', IEEE Transactions on Industry Applications, vol. 55, no. 2, pp. 2033-2044.
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© 1972-2012 IEEE. This paper presents a robust control strategy for the control of single-phase five-level asymmetrical T-type ac/dc power converter. A cascaded control scheme consisting of a finite control set model predictive control (FCS-MPC) with an extended state observer (ESO) is proposed to govern the converter. In this scheme, a proportional integral (PI) controller combined with an ESO-based disturbance observer is employed as an external control loop. This control loop dynamically modifies the active power reference to realize the desired operating point of the system state (converter output voltage). The proposed control system presents a high degree of disturbance rejection capability and robustness against the external disturbances to the converter, whereas the conventional PI control performance suffers in the presence of these disturbances. In this paper, the inner current tracking loop is accomplished by an FCS-MPC algorithm. This algorithm is derived to force the input currents to track the reference values while realizing a user-defined reactive power and maintaining balanced voltages in the series-connected capacitors. Theoretical analysis and the design procedure of the proposed control system are presented. Finally, experimental studies are conducted to verify the effectiveness of the proposed control scheme.
Khan, SA, Guo, Y, Siwakoti, YP, Lu, DD-C & Zhu, J 2019, 'A Disturbance Rejection Based Control Strategy for Five-Level T-Type Hybrid Power Converters with Ripple Voltage Estimation Capability', IEEE Transactions on Industrial Electronics, pp. 1-1.
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Khan, SA, Islam, MR, Guo, Y & Zhu, J 2019, 'An Amorphous Alloy Magnetic-Bus-Based SiC NPC Converter with Inherent Voltage Balancing for Grid-Connected Renewable Energy Systems', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2002-2011 IEEE. This paper presents an amorphous alloy magnetic-bus-based neutral point clamped (NPC) converter for grid-connected renewable generation systems. In the proposed system, the amorphous alloy high-frequency high-power density multi-winding magnetic bus generates balanced dc supplies for the five-level (5L) NPC converter for high-quality power conversion. Compared to the traditional NPC converter topologies, the proposed magnetic-bus-based architecture does not require any control circuit for voltage balancing of the series connected capacitors. The magnetic bus inherently overcomes galvanic isolation issues and may reduce the size of the boosting inductor. In this paper, a finite control set model predictive control algorithm is derived to control the grid-connected 5L-NPC inverter for multilevel voltage synthesizing, while achieving the user-defined active and reactive power values. To verify the proposed concept, a simulation model is developed and analyzed in MATLAB/Simulink environment. To validate the technology, a scale d-down prototype test platform is developed in the laboratory with silicon carbide switching devices, which achieves high blocking voltage, low power dissipation, high switching frequency, and high-Temperature operation. Based on the simulation and the experimental results, it is expected that the proposed converter will have a great potential for widespread application in renewable generation systems including superconducting generator-based wind turbines.
Khan, SA, Islam, R, Guo, Y & Zhu, J 2019, 'A New Isolated Multi-Port Converter With Multi-Directional Power Flow Capabilities for Smart Electric Vehicle Charging Stations', IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, vol. 29, no. 2.
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Khan, TA & Ling, SH 2019, 'Review on electrical impedance tomography: Artificial intelligence methods and its applications', Algorithms, vol. 12, no. 5.
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© 2019 by the authors. 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.
Khare, V, Shivakumara, P, Chan, CS, Lu, T, Meng, LK, Woon, HH & Blumenstein, M 2019, 'A novel character segmentation-reconstruction approach for license plate recognition', Expert Systems with Applications, vol. 131, pp. 219-239.
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© 2019 Elsevier Ltd Developing an automatic license plate recognition system that can cope with multiple factors is challenging and interesting in the current scenario. In this paper, we introduce a new concept called partial character reconstruction to segment characters of license plates to enhance the performance of license plate recognition systems. Partial character reconstruction is proposed based on the characteristics of stroke width in the Laplacian and gradient domain in a novel way. This results in character components with incomplete shapes. The angular information of character components determined by PCA and the major axis are then studied by considering regular spacing between characters and aspect ratios of character components in a new way for segmenting characters. Next, the same stroke width properties are used for reconstructing the complete shape of each character in the gray domain rather than in the gradient domain, which helps in improving the recognition rate. Experimental results on benchmark license plate databases, namely, MIMOS, Medialab, UCSD data, Uninsbria data Challenged data, as well as video databases, namely, ICDAR 2015, YVT video, and natural scene data, namely, ICDAR 2013, ICDAR 2015, SVT, MSRA, show that the proposed technique is effective and useful.
Khari, M, Garg, AK, Gandomi, AH, Gupta, R, Patan, R & Balusamy, B 2019, 'Securing Data in Internet of Things (IoT) Using Cryptography and Steganography Techniques', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-8.
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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, Ghajar, KA, 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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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.
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© 2019 Elsevier Ltd This work aimed to study the application of liquid biphasic flotation (LBF) for the efficient and rapid recovery of astaxanthin from H. pluvialis microalgae. The performance of LBF for the extraction of astaxanthin was studied comprehensively under different operating conditions, including types and concentrations of food-grade alcohol and salt, volume ratio, addition of neutral salt, flotation period, and mass of dried H. pluvialis biomass powder. The maximum recovery, extraction efficiency and partition coefficient of astaxanthin obtained from the optimum LBF system were 95.11 ± 1.35%, 99.84 ± 0.05% and 385.16 ± 3.87, respectively. A scaled-up LBF system was also performed, demonstrating the feasibility of extracting natural astaxanthin from microalgae at a larger scale. This exploration of LBF system opens a promising avenue to the extraction of astaxanthin at lower cost and shorter processing time.
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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The 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.
Khosoussi, K, Giamou, M, Sukhatme, GS, Huang, S, Dissanayake, G & How, JP 2019, 'Reliable Graphs for SLAM', International Journal of Robotics Research, vol. 38, no. 2-3, pp. 260-298.
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© The Author(s) 2019. 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, HB, 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.
Kieferova, 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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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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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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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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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, 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.
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King, JT, Prasad, M, Tsai, T, Ming, YR & Lin, CT 2019, 'Influence of Time Pressure on Inhibitory Brain Control During Emergency Driving', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE It is believed that failures of people's reaction to emergencies occurred during driving are closely related to the inhibitory mechanism of brain's operations. To investigate the role of this function in emergency driving, two virtual realistic driving conditions based on stop signal task were designed and time limitation was manipulated to increase the stress in one condition. Sixteen subjects with behavioral encephalography recordings were collected and analyzed. By comparing successful and unsuccessful stop trials with event-related spectral perturbation analysis, δ and θ band power increases in frontal and central areas are correlated with driving inhibitory control of the brain. Moreover, β and ɣ band power in frontal and central areas showed more increases upon stress condition. Time pressure in driving could adjust the operation of brain's inhibition control, to benefit the people's reactive ability upon emergency.
Kirby, R & Duan, W 2019, 'Guided wave propagation in cylindrical ducts with elastic walls enclosing a fluid moving with a uniform velocity', WAVE MOTION, vol. 85, pp. 1-9.
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Kishore Kumar, D, Hsu, MH, Ivaturi, A, Chen, B, Bennett, N & Upadhyaya, HM 2019, 'Optimizing room temperature binder free TiO2 paste for high efficiency flexible polymer dye sensitized solar cells', Flexible and Printed Electronics, vol. 4, no. 1.
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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.
Ko, LW, Lu, YC, Bustince, H, Chang, YC, Chang, Y, Ferandez, J, Wang, YK, Sanz, JA, Pereira Dimuro, G & Lin, CT 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.
Kordestani, MD, Naghibi, SA, Hashemi, H, Ahmadi, K, Kalantar, B & Pradhan, B 2019, 'Groundwater potential mapping using a novel data-mining ensemble model', Hydrogeology Journal, vol. 27, no. 1, pp. 211-224.
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© 2018, The Author(s). Freshwater scarcity is an ever-increasing problem throughout the arid and semi-arid countries, and it often results in poverty. Thus, it is necessary to enhance understanding of freshwater resources availability, particularly for groundwater, and to be able to implement functional water resources plans. This study introduces a novel statistical approach combined with a data-mining ensemble model, through implementing evidential belief function and boosted regression tree (EBF-BRT) algorithms for groundwater potential mapping of the Lordegan aquifer in central Iran. To do so, spring locations are determined and partitioned into two groups for training and validating the individual and ensemble methods. In the next step, 12 groundwater-conditioning factors (GCFs), including topographical and hydrogeological factors, are prepared for the modeling process. The mentioned factors are employed in the application of the EBF model. Then, the EBF values of the GCFs are implemented as input to the BRT algorithm. The results of the modeling process are plotted to produce spring (groundwater) potential maps. To verify the results, the receiver operating characteristics (ROC) test is applied to the model’s output. The findings of the test indicated that the areas under the ROC curves are 75 and 82% for the EBF and EBF-BRT models, respectively. Therefore, it can be inferred that the combination of the two techniques could increase the efficacy of these methods in groundwater potential mapping.
Korzekwa, K, Chubb, CT & Tomamichel, M 2019, 'Avoiding Irreversibility: Engineering Resonant Conversions of Quantum Resources', PHYSICAL REVIEW LETTERS, vol. 122, no. 11.
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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%.
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.
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Abstract: 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 Journal, vol. 82.
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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 & Nam-Trung, N 2019, 'Modeling of mass transfer enhancement in a magnetofluidic micromixer', PHYSICS OF FLUIDS, vol. 31, no. 6.
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Kumar, DK, 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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Kumar, DK, Suazo-Davila, D, Garcia-Torres, D, Cook, NP, Ivaturi, A, Hsu, M-H, Marti, 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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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.
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© 2019 Elsevier B.V. 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, N, Puthal, D, Theocharides, T & Mohanty, SP 2019, 'Unmanned Aerial Vehicles in Consumer Applications', IEEE CONSUMER ELECTRONICS MAGAZINE, vol. 8, no. 3, pp. 66-67.
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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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© 2019 by The American Society of Hematology. 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 CD341 cells. Conversely, overexpression of HMGA2 in CB CD341 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 CD341 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.
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.
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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.
Kuruneru, STW, Saha, SC, Sauret, E & Gu, YT 2019, 'Transient heat transfer and non-isothermal particle-laden gas flows through porous metal foams of differing structure', APPLIED THERMAL ENGINEERING, vol. 150, pp. 888-903.
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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.
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Kutay, CM 2019, 'Locating Knowledge', Archives and Manuscripts, vol. 47, no. 1, pp. 72-93.
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This paper arose from a discussion of the richness of languages used to describe different landscapes of Australia and how the landscape provides the affordances for the language created from this land. More importantly, each language embodies its place and associated world view. This paper looks at how information technology (IT) is supporting knowledge-sharing through approaches used in Indigenous community IT practice and projects to enhance multimedia repositories of knowledge. The origin of any archive is important, in terms of access and control of the use of this material, but also it is important in teaching, to provide the context and connectedness when presenting the material. A collection of knowledge resources can be used to recreate online flexible learning environments around engineering on country and traditional knowledge practices. IT can provide an interactive interface for people wishing to learn the material, through games or worksheet-style activities. Various case studies and their analysis illustrate the way IT can be used to share this knowledge in a legitimate manner across landscapes and cultures. In particular the aim is to understand how authentic this approach can be in view of concerns over appropriation or co-option of Aboriginal knowledge.
La Paz, A, Merigo, JM, Powell, P, Ramaprasad, A & Syn, T 2019, 'Twenty-five years of the Information Systems Journal: A bibliometric and ontological overview', INFORMATION SYSTEMS JOURNAL.
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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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Copyright © Cambridge University Press 2018 This 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.
Laccone, F, Malomo, L, Froli, M, Cignoni, P & Pietroni, N 2019, 'Automatic Design of Cable-Tensioned Glass Shells', COMPUTER GRAPHICS FORUM.
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Laengle, S, Merigó, JM, Modak, NM & Yang, JB 2019, 'Bibliometrics in operations research and management science: a university analysis', Annals of Operations Research.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Many universities around the World have made important contributions in the field of operations research and management science. This article presents the most productive and influential universities between 1991 and 2015. For doing so, we use the Web of Science database in order to search for the information which is usually regarded as the most relevant for scientific research. The results show the country of origin of the leading universities being mainly from North America and Asia and especially from USA and China. The Centre National de la Recherche Scientifique (CNRS) of France is the most productive university while the Massachusetts Institute of Technology (MIT) of USA is the most influential one. The temporal evolution shows that USA is trailing its dominancy while China progressing quickly. The evaluation also reveals that Asian universities outperform North American universities during the last 5 years.
Lai, Y, Li, Y, Cao, H, Long, J, Wang, X, Li, L, Li, C, Jia, Q, Teng, B, Tang, T, Peng, J, Eglin, D, Alini, M, Grijpma, DW, Richards, G & Qin, L 2019, 'Osteogenic magnesium incorporated into PLGA/TCP porous scaffold by 3D printing for repairing challenging bone defect', BIOMATERIALS, vol. 197, pp. 207-219.
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Lam, SC & Sandrasegaran, K 2019, 'A Model Based Poisson Point Process for Downlink Cellular Networks Using Joint Scheduling', Wireless Personal Communications, vol. 107, no. 44, pp. 1717-1725.
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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.
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© 2019, King Abdulaziz University and Springer Nature Switzerland AG. 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 km2 and 1.32 km2, respectively, compared with 20.10 km2 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.
Lan, T, Liu, N, Liu, Y, Han, F & Liu, QH 2019, '2-D electromagnetic scattering and inverse scattering from magnetodielectric objects based on integral equation method', IEEE Transactions on Antennas and Propagation, vol. 67, no. 2, pp. 1346-1351.
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© 1963-2012 IEEE. Efficient 2-D full-waveform forward and inverse electromagnetic scattering solvers are presented in this communication. The combined field volume integral equation (CFVIE) is formulated in a layered medium. Different from the previous work, the mixed-order basis function is adopted in the discretization of the 2-D CFVIE. Meanwhile, the permittivity and permeability are simultaneously reconstructed in the inversion through the variational Born iteration method, which is enhanced with a self-adaptive regularization factor and applied to the multifrequency data. The proposed forward solver is validated by comparisons with the finite-element simulations.
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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© 2018 by Yale University A 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.
Lashkari, B, Rezazadeh, J, Farahbakhsh, R & Sandrasegaran, K 2019, 'Crowdsourcing and Sensing for Indoor Localization in IoT - a Review', IEEE Sensors Journal, vol. 19, no. 7, pp. 2408-2434.
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IEEE Among the emerging concepts in the context of IoT, crowdsourcing and crowdsensing are known as two critical building blocks on the intersection point of things and humanbased techniques. Although contribution and involvement of people is a key factor in both crowdsensing and crowdsourcing systems which provides reliability and data quality, level of human contribution is what makes crowd-powered systems different. To scrutinize this difference it is considered that user intervention in crowd-based schemes can be either implicit or explicit. Since one of the most important applications of crowdsourcing and crowdsensing systems is providing location services in indoor environments, in this work we study the level of user contribution in available crowd-powered techniques and propose a classification for crowd-powered indoor localization solutions to clarify which crowds-based approach is utilized in each indoor localization solution because in many cases the distinction is not clear and often leads to misconception. Dependency on site survey process is considered as another distinction point in our proposed classification. Hence we consider indoor localization solutions with implicit user participation and latent mobile utilization as crowdsensing indoor localization systems. Respectively, indoor localization solutions with explicit user participation and manifest mobile utilization are classified as crowdsourcing indoor localization systems.
Law, Y, Matysik, A, Chen, X, Swa Thi, S, Ngoc Nguyen, TQ, Qiu, G, Natarajan, G, Williams, RBH, Ni, BJ, Seviour, TW & Wuertz, S 2019, 'High dissolved oxygen selection against nitrospira sublineage i in full-scale activated sludge', Environmental Science and 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, HJ 2019, 'Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR Data', Sensors (Switzerland), vol. 19, no. 16.
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. 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, A, Le, H, Nguyen, L & Phan, M 2019, 'An efficient adaptive hierarchical sliding mode control strategy using neural networks for 3D overhead cranes', International Journal of Automation and Computing.
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In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented by two actuated and un-actuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are highly difficult to determine. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and real-life systems, where the results obtained by our method are highly promising.
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, Jay Guo, Y & 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, HX, Le, AV & Nguyen, L 2019, 'Adaptive Fuzzy Observer based Hierarchical Sliding Mode Control for Uncertain 2D Overhead Cranes', Cyber-Physical Systems, vol. 5, no. 3, pp. 191-208.
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This paper proposes a new approach to robustly control a 2D under-actuated overhead crane system, where a payload is effectively transported to a destination in real time with small sway angles, given its inherent uncertainties such as actuator nonlinearities and external disturbances. The control law is proposed to be developed by the use of the robust hierarchical sliding mode control (HSMC) structure in which a second-level sliding surface is formulated by two first-level sliding surfaces drawn on both actuated and under-actuated outputs of the crane. The unknown and uncertain parameters of the proposed control scheme are then adaptively estimated by the fuzzy observer, where the adaptation mechanism is derived from the Lyapunov theory. More importantly, stability of the proposed strategy is theoretically proved. Effectiveness of the proposed adaptive fuzzy observer based HSMC (AFHSMC) approach was extensively validated by implementing the algorithm in both synthetic simulations and real-life experiments, where the results obtained by our method are highly promising.
Le, NT & Hoang, DB 2019, 'A Threat Computation Model using a Markov Chain and Common Vulnerability Scoring System and its Application to Cloud Security', Australian Journal of Telecommunications and the Digital Economy, vol. 7, no. 1, pp. 37-56.
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Copyright © 2019 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, H, Phillips, JB, Hall, RM & Tipper, JL 2019, 'Neural cell responses to wear debris from metal-on-metal total disc replacements.', European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society.
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PURPOSE:Total disc replacements, comprising all-metal articulations, are compromised by wear and particle production. Metallic wear debris and ions trigger a range of biological responses including inflammation, genotoxicity, cytotoxicity, hypersensitivity and pseudotumour formation, therefore we hypothesise that, due to proximity to the spinal cord, glial cells may be adversely affected. METHODS:Clinically relevant cobalt chrome (CoCr) and stainless steel (SS) wear particles were generated using a six-station pin-on-plate wear simulator. The effects of metallic particles (0.5-50 μm3 debris per cell) and metal ions on glial cell viability, cellular activity (glial fibrillary acidic protein (GFAP) expression) and DNA integrity were investigated in 2D and 3D culture using live/dead, immunocytochemistry and a comet assay, respectively. RESULTS:CoCr wear particles and ions caused significant reductions in glial cell viability in both 2D and 3D culture systems. Stainless steel particles did not affect glial cell viability or astrocyte activation. In contrast, ions released from SS caused significant reductions in glial cell viability, an effect that was especially noticeable when astrocytes were cultured in isolation without microglia. DNA damage was observed in both cell types and with both biomaterials tested. CoCr wear particles had a dose-dependent effect on astrocyte activation, measured through expression of GFAP. CONCLUSIONS:The results from this study suggest that microglia influence the effects that metal particles have on astrocytes, that SS ions and particles play a role in the adverse effects observed and that SS is a less toxic biomaterial than CoCr alloy for use in spinal devices. These slides can be retrieved under Electronic Supplementary Material.
Lee, J, Jung, HS, Zlatanoya, S & Pradhan, B 2019, 'Editorial', International Journal of Urban Sciences, vol. 23, no. 3, pp. 301-302.
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Lee, JS & Filatova, T 2019, 'Dimension reduction of multivariate outputs of socio-environmental agent-based models', Ecological Complexity.
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© 2018 Often, socio-environmental agent-based models (ABMs) are driven by a host of parameters, and their outputs are similarly multidimensional or vastly high-dimensional. While this complex data and its inter-relationships may be rendered tractable, the task is far from trivial. In this paper, we study the multidimensional outcome space of the socio-environmental, land-use RHEA ABM (Risks and Hedonics in an Empirical Agent-based Land Market), specifically the inter-distances among the outcome measures, and their reducibility using several well-known dimension reduction techniques, variants of multidimensional scalings. In testing the efficacy of several reduction algorithms, we temporally characterize the model's reducibility while exposing changes in behavior across a wide parameter space that can signal sudden or gradual shifts and possible critical transitions. Our findings reveal that the ABM's signature reducibility trends exhibit idiosyncrasies and unexpected non-linearity as well as discontinuities. These non-linearities and discontinuities are indicative of both gradual and sudden shifts, signaling potential propensity to internal perturbations induced by parameter settings. Additionally, we related the outcome space via their inter-distances to the multidimensional input parameter space, effectively assessing outcome “reducibility to model controls”. This analysis reveals that the model's sensitivity to parameters is not only temporally dependent, but also can be partitioned by them, some of which suppress variability in this reduction to model controls, raising questions regarding the extent and structure of endogeneity that yields the distinct temporal trends in the relationship between inputs and outputs and their connections to outcome reducibility.
Lee, S & Wong, SC 2019, 'Group-based approach to derivatives of delay models based on incremental queue accumulations for isolated signalized junctions', TRANSPORTMETRICA B-TRANSPORT DYNAMICS, vol. 7, no. 1, pp. 202-222.
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Lee, S, Choi, J, Park, YG, Shon, H, Ahn, CH & Kim, SH 2019, 'Hybrid desalination processes for beneficial use of reverse osmosis brine: Current status and future prospects', Desalination, 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.
Lee, S, Ngoduy, D & Keyvan-Ekbatani, M 2019, 'Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways', TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, vol. 106, pp. 360-377.
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Lee, S, Xie, K, Ngoduy, D & Keyvan-Ekbatani, M 2019, 'An advanced deep learning approach to real-time estimation of lane-based queue lengths at a signalized junction', Transportation Research Part C: Emerging Technologies, vol. 109, pp. 117-136.
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© 2019 Elsevier Ltd In this study, we develop a real-time and novel estimation method of lane-based queue lengths using two deep learning processes, which include of a Convolutional Neural Network (CNN) into a Long Short-Term Memory (LSTM). This approach not only outperforms the recently developed real-time estimation of lane-based queue lengths but also captures the spatiotemporal attributes of traffic. There are three primary challenges to design a deep learning based queue estimation model. First, the CNN and the LSTM are integrated to estimate lane-based queue lengths minimizing accumulative counting errors. Furthermore, short-term arrival patterns and long-term traffic demand trends are captured by the LSTM to improve the accuracy of estimates of cycle-based proportional lane-uses. In addition, imaged second-based occupancy rates and impulse memories are used to identify whether vehicular queues are remained at the end of each cycle by using the CNN. In numerical examples and case study, the integrated CNN – LSTM method shows excellent performance to estimate queue lengths in individual lanes in seconds compared to the other approaches applied in this paper. This work paves the way for the applicability of the deep learning to estimate traffic quantities in real-time for lane-based adaptive traffic control systems (ATCS). Furthermore, we will introduce offset in a signal plan and lane-based turning proportion on the proposed framework to explain vehicular spillbacks in an individual lane and a grid lock for pursuing coordinated traffic movements along arterials and in signalized urban networks.
Lee, SS, Lim, CS, Siwakoti, YP & Lee, K-B 2019, 'Hybrid 7-Level Boost Active-Neutral-Point-Clamped (H-7L-BANPC) Inverter', IEEE Transactions on Circuits and Systems II: Express Briefs, pp. 1-1.
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Lees, T, Elliott, JL, Gunning, S, Newton, PJ, Rai, T & Lal, S 2019, 'A systematic review of the current evidence regarding interventions for anxiety, PTSD, sleepiness and fatigue in the law enforcement workplace.', Industrial health.
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Law enforcement is inherently stressful, and police officers are particularly vulnerable to mental and physical disorders. As such, researchers are currently assessing intervention strategies that may combat or manage these psychological, physical and mental issues. To review most recent information regarding anxiety, PTSD, and sleepiness and fatigue and identify the interventions and treatments proposed to overcome work related stressors and associated mental illnesses inflicting law enforcement officers. The EMBASE, OVID MEDLINE and PsycINFO databases were canvassed for articles investigating anxiety, post-traumatic stress disorder, sleepiness, and fatigue. Initial article selections were made based on title, whilst final inclusion was informed by a full critical appraisal with respect to the primary and secondary effects. The systematic search returned 363 records, of which 183 were unique. Following screening, 43 records were included in the final review. The included literature assessed the efficacy of several interventions, and provided a number of recommendations regarding interventions, and policy. Moreover, literature indicates that police officers benefit from interventions targeting work-related stress and potential psychological disorders, if these interventions are continuous. Furthermore, larger controlled studies are required to further elucidate the benefits of psychosocial intervention in law enforcement.
Lei, G, Liu, C, Li, Y, Chen, D, Guo, Y & Zhu, J 2019, 'Robust Design Optimization of a High-Temperature Superconducting Linear Synchronous Motor Based on Taguchi Method', IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, vol. 29, no. 2.
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Lei, G, Wang, T, Zhu, J & Guo, Y 2019, 'Robust Multiobjective and Multidisciplinary Design Optimization of Electrical Drive Systems', China Electrotechnical Society Transactions on Electrical Machines and Systems, vol. 2, no. 4, pp. 409-416.
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Li, C, Luo, Q, Mao, G, Sheng, M & Li, J 2019, 'Vehicle-Mounted Base Station for Connected and Autonomous Vehicles: Opportunities and Challenges', IEEE Wireless Communications, vol. 26, no. 4, pp. 30-36.
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© 2002-2012 IEEE. The crown jewel of intelligent transportation is achieving autonomous driving that can ultimately become accident- and congestion-free through CAVs and the associated traffic management systems. Recently, this vision has sparked huge research interest, such as IoV, LTE-V2X, and 5G. However, the huge amount of traffic data generated by CAVs poses challenges for the current networks and even the upcoming 5G communication networks.In this article, we first analyze the communication requirements of CAVs and present the progress of vehicular communication networks, and on that basis propose the novel concept of VMBS and a VMBS-CCNA for CAVs. The VMBS plays multiple roles of a user node and edge computing node for external communication networks, and a base station and information caching node for CAVs, thus achieving the fusion of communication and computing for CAVs. To this end, we present both the VMBS-enabled wireless communication and computing for CAVs, and the VMBS-assisted wireless communication for other wireless devices. Several research challenges and some open research issues are highlighted and discussed. Finally, simulation results reveal that the proposed VMBS-CCNA can bring a significant improvement in terms of throughput, delay, and average number of links.
Li, CY, Chen, SJ, Li, WG, Li, XY, Ruan, D & Duan, WH 2019, 'Dynamic increased reinforcing effect of graphene oxide on cementitious nanocomposite', Construction and Building Materials, vol. 206, pp. 694-702.
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© 2019 The reinforcing effect of graphene oxide on cementitious materials under high strain rate remains largely unknown. Existing studies on microfiber reinforced cementitious composites showed that the dynamic increase factor (DIF) decreases due to these fibres. This study reports that the reinforcing effect of graphene oxide nanosheets can, in contrast, increase with strain rate. Tensile splitting and compression tests were conducted under both static and dynamic loadings. High strain rate, up to 1700 s −1 , is achieved using a split Hopkinson pressure bar apparatus. It is found that the DIF of graphene oxide nanocomposite only increase when the DIF is higher than a threshold which is about 780 and 30 s −1 for compression and tensile test respectively. The increased of DIF was correlated with the speed of crack development and pull-out of the graphene oxide nanosheets. Also, the pull-out or fracture of graphene oxide on fragmented sand was also found a possible contributing factor to the increased DIF. The findings of this study indicate the future potential of atomic-thin nanosheets for materials under extreme impact and blast loading.
Li, G & Yan, Z 2019, 'Reconstruction of sparse signals via neurodynamic optimization', International Journal of Machine Learning and Cybernetics.
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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.
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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 (CS) 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 ADMM-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, Hu, J, Li, Y & Zhu, J 2019, 'An Improved Model Predictive Direct Torque Control Strategy for Reducing Harmonic Currents and Torque Ripples of Five Phase Permanent Magnet Synchronous Motors', IEEE Transactions on Industrial Electronics.
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IEEE Five phase permanent magnet synchronous motors (PMSMs) offer merits of high fault tolerant capability and high torque per rms ampere, and thus, are suitable for applications like aerospace and electric vehicles (EVs). However, the complex machine model causes difficulties in controller design. Besides, having 32 voltage vectors with various effects on currents and torque, the selection of the optimal switching state becomes a challenge to achieve a performance trade-off. This paper proposes an improved model predictive direct torque control (MPDTC) strategy consisting of a quadratic evaluation method (QEM) and a harmonic voltage elimination method (HVEM). In QEM, the preliminary vector is firstly chosen from the vectors of the outer decagon according to a cost function for torque and flux regulation. This preliminary vector, composed of three sets of different amplitudes, is further synthesized according to the error between the actual toque/flux and the references. In this way, the optimal voltage vector can be obtained without significantly increasing the computational burden. In HVEM, by subtracting the harmonics voltage component from the vector determined previously in QEM, the final voltage vector is obtained for mitigating stator harmonic currents. The proposed control strategy is compared with the conventional MPDTC approach. The results confirm the effectiveness of the proposed methods with good steady-state performance while maintaining quick dynamic responses.
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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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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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 & Guo, YJ 2019, 'Adaptive AoA and Polarization Estimation for Receiving Polarized mmWave Signals', IEEE Wireless Communications Letters.
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IEEE This paper 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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Li, H, Zhang, N, Wang, S & Zhu, J 2019, 'An Analytical Loss Model of Litz-Wire Windings for Transformers Excited by Converters With Winding Configurations Considered', IEEE Transactions on Magnetics, vol. 55, no. 9.
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An accurate analytical model for litz-wire (LW) winding loss calculation of power electronic transformers (PETs) was deduced. First, the skin effect and proximity effect losses at bundle levels of a single straight LW were introduced. Then, the proximity loss of an air-core LW winding was studied by adopting the coordinate transformation technique (CTT) considering helical winding (HW) configurations and twisting configurations (TCs). Last, a 2-D analytical model for LW windings considering magnetic materials, LW configurations, and winding configurations (WCs) was proposed with the help of CTT, Dowell method, and Roth's method. This model is helpful for the design of LW windings considering more details such as the choices of WCs and LW configurations.
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.
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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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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, 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 preve