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 (Switzerland), vol. 19, no. 5.
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This study deals with the use of remote sensing (RS), geographic information systems (GISs), hydrologic modeling (water modeling system, WMS), and hydraulic modeling (Hydrologic Engineering Center River Analysis System, HEC-RAS) to evaluate the impact of flash flood hazards on the sustainable urban development of Tabuk City, Kingdom of Saudi Arabia (KSA). Determining the impact of flood hazards on the urban area and developing alternatives for protection and prevention measures were the main aims of this work. Tabuk City is exposed to frequent flash flooding due to its location along the outlets of five major wadis. These wadis frequently carry flash floods, seriously impacting the urban areas of the city. WMS and HEC-HMS models and RS data were used to determine the paths and morphological characteristics of the wadis, the hydrographic flow of different drainage basins, flow rates and volumes, and the expansion of agricultural and urban areas from 1998 to 2018. Finally, hydraulic modeling of the HEC-RAS program was applied to delineate the urban areas that could be inundated with floodwater. Ultimately, the most suitable remedial measures are proposed to protect the future sustainable urban development of Tabuk City from flood hazards. This approach is rarely used in the KSA. We propose a novel method that could help decision-makers and planners in determining inundated flood zones before planning future urban and agricultural development in the KSA.
Abdo, P, Huynh, BP, Irga, PJ & Torpy, FR 2019, 'Evaluation of air flow through an active green wall biofilter', Urban Forestry 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, pp. 1-15.
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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.
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.
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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Ahmed, AA & Pradhan, B 2019, 'Vehicular traffic noise prediction and propagation modelling using neural networks and geospatial information system', Environmental Monitoring and Assessment, vol. 191, no. 3.
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© 2019, Springer Nature Switzerland AG. This study proposes a neural network (NN) model to predict and simulate the propagation of vehicular traffic noise in a dense residential area at the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia. The proposed model comprises of two main simulation steps: that is, the prediction of vehicular traffic noise using NN and the simulation of the propagation of traffic noise emission using a mathematical model. First, the NN model was developed with the following selected noise predictors: the number of motorbikes, the sum of vehicles, car ratio, heavy vehicle ratio (e.g. truck, lorry and bus), highway density and a light detection and ranging (LiDAR)-derived digital surface model (DSM). Subsequently, NN and its hyperparameters were optimised by a systematic optimisation procedure based on a grid search approach. The noise propagation model was then developed in a geographic information system (GIS) using five variables, namely road geometry, barriers, distance, interaction of air particles and weather parameters. The noise measurement was conducted continuously at 15-min intervals and the data were analysed by taking the minimum, maximum and average values recorded during the day. The measurement was performed four times a day (i.e. morning, afternoon, evening, and midnight) over two days of the week (i.e. Sunday and Monday). An optimal radial basis function NN was used with 17 hidden layers. The learning rate and momentum values were 0.05 and 0.9, respectively. Finally, the accuracy of the proposed method achieved 78.4% with less than 4.02 dB (A) error in noise prediction. Overall, the proposed models were found to be promising tools for traffic noise assessment in dense urban areas.
Ahmed, MB, Hasan Johir, MA, Zhou, JL, Ngo, HH, Nghiem, LD, Richardson, C, Moni, MA & Bryant, MR 2019, 'Activated carbon preparation from biomass feedstock: Clean production and carbon dioxide adsorption', Journal of Cleaner Production, vol. 225, pp. 405-413.
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© 2019 Elsevier Ltd The current methods used for the production of activated carbon (AC) are often chemical and energy intensive and produce significant amount of chemical waste. Thus, clean production of AC is important to reduce its overall production cost and to limit the adverse effect on the environment. Therefore, the main aim of this study is to develop a clean method for AC production from woody biomass with low chemical consumption. Herein, this study reports a facile strategy for reducing chemical usages in the production of high-performance AC, by introducing a crucial pre-pyrolysis step before chemical activation of biomass. The ACs prepared were characterised using scanning electron microscopy, Fourier transform infrared spectroscopy, nitrogen and carbon dioxide gas adsorption measurements. All these characterisations indicated that produced ACs have similar physicochemical properties. The strategy reduced chemical use by 70% and produced high-performance ultra-microporous ACs with excellent carbon dioxide adsorption capacity (4.22–5.44 mmol m −2 ). The facile pre-pyrolysis method is recommended for further research as a cleaner activated carbon preparation method from biomass feedstock.
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%.
Akther, N, Lim, S, Tran, VH, Phuntsho, S, Yang, Y, Bae, TH, 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, pp. 284-293.
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© 2019 Elsevier B.V. In this study, Schiff base network-1 (SNW-1) nanoparticles, which are covalent organic frameworks (COFs), were used as fillers in the polyamide (PA) active layer to elevate the performance of thin-film nanocomposite (TFN) forward osmosis (FO) membranes. The TFN membranes were prepared by interfacial polymerization (IP) of m-phenylenediamine (MPD) and trimesoyl chloride (TMC), and the SNW-1 nanoparticles were dispersed in the MPD aqueous solution at various concentrations. The secondary amine groups of SNW-1 nanoparticles reacted with the acyl chloride groups of TMC during the IP reaction to form strong covalent/amide bonds, which facilitated better interface integration of SNW-1 nanoparticles in the PA layer. Additionally, the incorporation of amine-rich SNW-1 nanoparticles into the TFN membranes improved their surface hydrophilicity, and the porous structure of SNW-1 nanoparticles offered additional channels for transport of water molecules. The TFN0.005 membrane with a SNW-1 nanoparticle loading of 0.005 wt% demonstrated a higher water flux than that of pristine TFC membrane in both AL-FS (12.0 vs. 9.3 L m −2 h −1 ) and AL-DS (25.2 vs. 19.4 L m −2 h −1 ) orientations when they were tested with deionized water and 0.5 M NaCl as feed and draw solution, respectively.
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, Nguyen, LDK, 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-AN INTERNATIONAL JOURNAL, vol. 18, no. 1, pp. 35-48.
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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.
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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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, 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.
Alzahran, 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.
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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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.
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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%.
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...
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.
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© 2019, The Association of Korean Geoscience Societies and Springer-Verlag GmbH Germany, part of Springer Nature. 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...
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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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 TiO 2 P-25 and Al (NO 3 ) 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 TiO 2 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-TiO 2 . The results showed that Al(NO 3 ) 3 solution with a concentration of 0.25% and volume of 100 cm/ml, and calcined at 300 ๐ C for 4 h, was the optimum condition of Al-doped nano-TiO 2. Furthermore, the highest pseudo-first-order kinetic rate was 0.096 where the doped Al(NO 3 ) 3 of 0.75 w/v was used in the batch reactor. The Al-doped nano-TiO 2 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 TiO 2 P-25 alone at 2 g/L dosage. In addition, the removal of organic pollutant by TiO 2 /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.
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.
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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.
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.
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.
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.
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.
Azizivahed, A, Arefi, A, Ghavidel Jirsaraie, S, Shafie-khah, M, Li, L, Zhang, J & Catalao, JPS 2019, 'Energy Management Strategy in Dynamic Distribution Network Reconfiguration considering Renewable Energy Resources and Storage', IEEE Transactions on Sustainable Energy, pp. 1-1.
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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, P-Y, Ziolkowski, RW, Guo, YJ & Bird, TS 2019, 'A Wideband Low-Profile Tightly Coupled Antenna Array With a Very High Figure of Merit', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 67, no. 4, pp. 2332-2343.
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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.
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-Fe 3 O 4 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 Fe 3 O 4 nanoparticle/rectorite (Fe 3 O 4 /rectorite) catalysts are developed via in-situ precipitation oxidation reaction. Various physicochemical characterizations of Fe 3 O 4 /rectorite show that Fe 3 O 4 nanoparticles (nano-Fe 3 O 4 ) 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 Fe 3 O 4 /rectorite on degradation of P-chlorophenol by heterogeneous Fenton method is studied. Results of degradation experiments show that Fe 3 O 4 /rectorite has higher degradation efficiency of P-chlorophenol than bare nano-Fe 3 O 4 . Regeneration studies also show that Fe 3 O 4 /rectorite maintains 100% of its maximum P-chlorophenol degradation capacity after seven consecutive cycles. Fe 3 O 4 /rectorite can be easily separated by magnetic separation, and thus has good stability and reusability. The degradation mechanism of Fe 3 O 4 /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.
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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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.
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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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.
Bennett, J, Froggett, L, Kenning, G, Manley, J & Muller, L 2019, 'Memory Loss and Scenic Experience: An Arts Based Investigation', Forum : Qualitative Social Research, vol. 20, no. 1.
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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.
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 & 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 & Xu, Z 2019, 'Bonferroni means with induced ordered weighted average operators', International Journal of Intelligent Systems, vol. 34, no. 1, pp. 3-23.
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© 2018 Wiley Periodicals, Inc. The induced ordered weighted average is an averaging aggregation operator that provides a parameterized family of aggregation operators between the minimum and the maximum. This paper presents some new generalizations by using Bonferroni means (BM) forming induced BM. The main advantage of this approach is the possibility of reordering the results according to complex ranking processes based on order-inducing variables. The work also presents some additional extensions by using the weighted ordered weighted average, immediate weights, and hybrid averages. Some further generalizations with generalized and quasi-arithmetic means are also developed to consider a wide range of particular cases including quadratic and geometric aggregations. The article also considers the applicability of the new approach in-group decision-making developing an application in sales forecasting.
Bliuc, D, Tran, T, van Geel, T, Adachi, JD, Berger, C, van den Bergh, J, Eisman, JA, Geusens, P, Goltzman, D, Hanley, DA, Josse, RG, Kaiser, S, Kovacs, CS, Langsetmo, L, Prior, JC, Nguyen, TV & Center, JR 2019, 'Mortality risk reduction differs according to bisphosphonate class: a 15-year observational study', Osteoporosis International, vol. 30, no. 4, pp. 817-828.
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© 2019, International Osteoporosis Foundation and National Osteoporosis Foundation. Summary: In this prospective cohort of 6120 participants aged 50+, nitrogen-bisphosphonates but not non-nitrogen bisphosphonates were associated with a significant 34% mortality risk reduction compared to non-treated propensity score matched controls. These findings open new avenues for research into mechanistic pathways. Introduction: Emerging evidence suggests that bisphosphonates (BP), first-line treatment of osteoporosis, are associated with reduced risks for all-cause mortality. This study aimed to determine the association between different BP types and mortality risk in participants with or without a fracture. Methods: A prospective cohort study of users of different BPs matched to non-users by propensity score (age, gender, co-morbidities, fragility fracture status) and time to starting the BP medication from the population-based Canadian Multicentre Osteoporosis Study from nine Canadian centres followed from 1995 to 2013. Mortality risk for bisphosphonate users vs matched non-users was assessed using pairwise multivariable Cox proportional hazards models. Results: There were 2048 women and 308 men on BP and 1970 women and 1794 men who did not receive medication for osteoporosis. The relationship between BP and mortality risk was explored in three separate 1:1 propensity score-matched cohorts of BP users and no treatment (etidronate, n = 599, alendronate, n = 498, and risedronate n = 213). Nitrogen BP (n-BP) (alendronate and risedronate) was associated with lower mortality risks [pairwise HR, 0.66 (95% CI, 0.48–0.91)] while the less potent non-n-BP, etidronate, was not [pairwise HR: 0.89 (95% CI, 0.66–1.20)]. A direct comparison between n-BP and etidronate (n = 340 pairs) also suggested a better survival for n-BP [paired HR, 0.47 (95%CI, (95% CI, 031–0.70)] for n-BP vs. etidronate]. Conclusion: Compared to no treatment, nitrogen but not non-nitrogen bisphosphonates appear ...
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.
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
Bradbury, P, Rumzhum, NN & Ammit, AJ 2019, 'EP2 and EP4 receptor antagonists: Impact on cytokine production and β2 -adrenergic receptor desensitization in human airway smooth muscle.', Journal of cellular physiology.
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Prostaglandin E2 (PGE2 ) is a key prostanoid known to have both proinflammatory and anti-inflammatory impact in the context of chronic respiratory diseases. We hypothesize that these opposing effects may be the result of different prostanoid E (EP) receptor-mediated signaling pathways. In this study, we focus on two of the four EP receptors, EP2 and EP4 , as they are known to induce cyclic adenosine monophosphate (cAMP)-dependent signaling pathways. Using primary human airway smooth muscle (ASM) cells, we first focussed on the PGE2 -induced production of two cAMP-dependent proinflammatory mediators: interleukin 6 (IL-6) and cyclo-oxygenase 2 production. We show that PGE2 -induced IL-6 protein secretion occurs via an EP2 -mediated pathway, in a manner independent of receptor-mediated effects on messenger RNA (mRNA) expression and temporal activation kinetics of the transcription factor cAMP response element binding. Moreover, stimulation of ASM with PGE2 did not establish a positive, receptor-mediated, feedback loop, as mRNA expression for EP2 and EP4 receptors were not upregulated and receptor antagonists were without effect. Our studies revealed that the EP2 , but not the EP4 , receptor is responsible for β2 -adrenergic desensitization induced by PGE2 . We demonstrate that PGE2 -induced heterologous receptor desensitization responsible for tachyphylaxis to short- (salbutamol) or long- (formoterol) β2 -agonists (measured by cAMP release) can be reversed by the EP2 receptor antagonist PF-04418948. Importantly, this study highlights that inhibiting the EP2 receptor restores β2 -adrenergic receptor function in vitro and offers an attractive novel therapeutic target for treating infectious exacerbations in people suffering from chronic respiratory diseases in the future.
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.
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This study explores the challenges that have emerged from the outdated and inflexible workplace culture of the civil construction industry, and how it is affecting female engineers and women in other functional site roles. The study primarily explores issues such as the strong culture of long work hours, the perception of staff who pursue work-life balance, and the perception of part time and flexible working options within the industry. The study was conducted over three phases where members of the industry answered questionnaires on the workplace issues listed above. The first phase aimed to document a female perspective on the construction workplace culture while the second phase focused on the perceptions of the same respondents regarding the industry’s attitude to work-life balance and whether any of the aspects of workplace culture has discouraged them from staying in the industry. The final phase included men and women in management and employer roles to determine their views on part time and flexible working options within the industry. The results of the study are presented and discussed before recommendations are offered for contractor organisations, their employers and staff. The recommendations have been addressed in a way that a gradual culture change can be accepted and acted on throughout the entire workplace.
Bui, HH, Ha, NH, Nguyen, TND, Nguyen, AT, Pham, TTH, Kandasamy, J & Nguyen, TV 2019, 'Integration of SWAT and QUAL2K for water quality modeling in a data scarce basin of Cau River basin in Vietnam', Ecohydrology and Hydrobiology.
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© 2019 European Regional Centre for Ecohydrology of the Polish Academy of Sciences Water quality modeling in a river basin often faces the problem of having a large number of parameters yet limited available data. The important inputs to the water quality model are pollution concentrations and discharge from river tributaries, lateral inflows and related pollution load from different sources along the river. In general, such an extensive data set is rarely available, especially for data scarce basins. This makes water quality modeling more challenging. However, integration of models may be able to fill this data gap. Selection of models should be made based on the data that is available for the river basin. For the case of Cau River basin, the SWAT and QUAL2K models were selected. The outputs of SWAT model for lateral inflows and discharges of ungauged tributaries, and the observed pollutant concentrations data and estimated pollution loads of sub-watersheds were used as inputs to the water quality model QUAL2K. The resulting QUAL2K model was calibrated and validated using recent water quality data for two periods in 2014. Four model performance ratings PBIAS, NSE, RSR and R 2 were used to evaluate the model results. PBIAS index was chosen for water quality model evaluation because it more adequately accounted for the large uncertainty inherent in water quality data. In term of PBIAS, the calibration and validation results for Cau River water quality model were in the “very good” performance range with ǀPBIASǀ < 15%. The obtained results could be used to support water quality management and control in the Cau River basin.
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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Cagno, E, Moschetta, D & Trianni, A 2019, 'Only non-energy benefits from the adoption of energy efficiency measures? A novel framework', Journal of Cleaner Production, vol. 212, pp. 1319-1333.
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© 2018 Elsevier Ltd Industrial energy efficiency has been widely recognized as a major contributor to the reduction of greenhouse gases emissions and improvement of industrial competitiveness. Nevertheless, a broad set of studies have pointed out the existence of barriers limiting the adoption of promising Energy Efficiency Measures (EEMs). Recently, scholars have shown the relevance of the so-called “non-energy benefits” (NEBs) coming from the adoption of EEMs for overcoming those barriers. Still, the existence of such benefits has been pointed out from specific studies and manuals for practitioners, but an overall framework describing them in terms of savings and benefits, as well as technical and management implications, is missing yet. Moreover, a considerable part of the scholars and of the practitioners just focuses on the identification and definition of the positive benefits deriving from these measures after they have been completely adopted, thus neglecting to describe the full set of both positive and negative effects occurring also during the implementation phase. Thus, starting from a literature review of scientific as well as practitioners’ studies, we have proposed a novel framework and characterization of the relevant items to be considered by an industrial decision-maker when deciding whether to adopt an EEM considering both the implementation and service phases. Hence, by taking this perspective, we have tested and validated the framework and the characterization in a two-step process: firstly, considering a set of EEMs well diffused and adopted in industry; secondly, investigating benefits and losses in ad-hoc selected manufacturing companies. Finally, considerations and implications are drawn from the preliminary validation and suggestion for further research are proposed, for both industrial decision-making as well as policy-making purposes.
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.
Cao, X, Qiu, B & Xu, G 2019, 'BorderShift: toward optimal MeanShift vector for cluster boundary detection in high-dimensional data', Pattern Analysis and Applications, pp. 1-13.
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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, 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.
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, 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, 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.
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, G, Jin, Z, Liu, Y, Hu, Y, Zhang, J & Qing, X 2019, 'Programmable Topology Derivation of Integrated Three-Port DC-DC Converters with Reduced Switches for Low-Cost Applications', IEEE Transactions on Industrial Electronics.
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IEEE Thanks to the favorable advantage of low cost, integrated three-port dc-dc converters with reduced switches have attracted extensive attention. In order to provide more new topologies, this paper aims to propose a programmable topology derivation method, which effectively simplifies the cumbersome process of the conventional combination method. Instead of the manual connection and examination, the proposed alternative can quickly and rigorously derive multiple viable integrated three-port dc-dc topologies from a great number of possible connections with the aid of computer program. Besides, generalized analysis is also accomplished, with which performance characteristics of all derived converters are simultaneously obtained and then a comprehensive comparison can be easily conducted to select a preferred one for the practical application. Finally, an example specific application with one input and two outputs is given, with topology selection, design and experimental results demonstrated in detail.
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, 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, 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.
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© 2019, International Association for Mathematical Geosciences. Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of Fisher’s linear discriminant function (FLDA) with rotation forest (RFLDA) and bagging (BFLDA) ensembles was used for groundwater potential assessment at the Ningtiaota area in Shaanxi, China. A spatial database with 66 groundwater spring locations and 14 groundwater spring contributing factors was prepared; these factors were elevation, aspect, slope, plan and profile curvatures, sediment transport index, stream power index, topographic wetness index, distance to roads and streams, land use, lithology, soil and normalized difference vegetation index. The classifier attribute evaluation method based on the FLDA model was implemented to test the predictive competence of the mentioned contributing factors. The area under curve, confidence interval at 95%, standard error, Friedman test and Wilcoxon signed-rank test were used to compare and validate the success and prediction competence of the three applied models. According to the achieved results, the BFLDA model showed the most prediction competence, followed by the RFLDA and FLDA models, respectively. The resulting groundwater spring potential maps can be used for groundwater development plans and land use planning.
Chen, W, Yan, X, Zhao, Z, Hong, H, Bui, DT & Pradhan, B 2019, 'Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China)', Bulletin of Engineering Geology and the Environment, vol. 78, no. 1, pp. 247-266.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results s...
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, 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, 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, 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, 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, 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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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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Choi, J, Dorji, P, Shon, HK & Hong, S 2019, 'Applications of capacitive deionization: Desalination, softening, selective removal, and energy efficiency', Desalination, vol. 449, pp. 118-130.
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© 2018 Elsevier B.V. Capacitive deionization (CDI) has attracted a great attention as a promising desalination technology, and studies on CDI have increased significantly in the last ten years. However, there have been no guidelines for developing strategies involving CDI technology for specific applications. Therefore, our work presents a critical review of the recent advances in CDI to meet the technical requirements of various applicable areas, with an emphasis on hybrid systems. This paper first summarizes the major developments made on novel electrode materials for CDI for brackish water desalination. Then, CDI and reverse osmosis (RO) integrated systems are critically reviewed for both ultrapure water production and wastewater treatment. Additionally, the applicability of CDI on various industrial processes is discussed, covering two distinct topics: (1) water softening and (2) selective removal of valuable heavy metals and nutrients (nitrate/phosphate). Lastly, recent improvements on the energy efficiency of CDI processes are delineated, specifically focusing on energy recovery and hybridization with energy producing technology, such as reverse electrodialysis (RED) and microbial fuel cells (MFC). This review paper is expected to share the practical experience of CDI applications as well as to provide guidelines for electrode material development for each specific application.
Choi, Y, Naidu, G, Lee, S & Vigneswaran, S 2019, 'Effect of inorganic and organic compounds on the performance of fractional-submerged membrane distillation-crystallizer', Journal of Membrane Science, vol. 582, pp. 9-19.
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© 2019 Elsevier B.V. A novel approach - fractional-submerged membrane distillation crystallizer (F-SMDC) was evaluated for treating brine. F-SMDC is based on creating concentration gradient (CG) and temperature gradient (TG) in a reactor containing submerged hollow-fiber membrane. This enables water and salt recovery to occur simultaneously in a single reactor. The influence of inorganic and organic compounds present in brine solutions on the development and stability of CG/TG in F-SMDC was evaluated in detail in this study. The results of the study showed that properties of inorganic compounds - molecular weight and electronegativity played a significant role in influencing CG/TG in F-SMDC. A high CG ratio (between 1.51 and 1.83 after crystallization) was observed when using feed solutions with inorganic compounds such as KCl, MgSO 4 , and Na 2 SO 4 . However, only low CG ratio (between 0.94 and 1.46) was achieved in the case of feed solutions containing lower molecular weight compounds, NH 4 Cl and NaCl. The high CG ratio with KCl resulted in the occurrence of salt crystallization at a faster rate (from VCF 2.4 onwards) compared to the predicted theoretical salt saturation point of VCF 3.0. On the other hands, Na 2 SO 4 showed lower flux decline (12.56% flux decline) compared to MgSO 4 (55.93% flux decline) This was attributed to lower cation electronegativity of Na + . The presence of CG in F-SMDC by concentrated inorganic compounds also enhanced organic compounds to gravitate downwards to the bottom of the reactor, potentially mitigating organic deposition on the membrane.
Choi, Y, Ryu, S, Naidu, G, Lee, S & Vigneswaran, S 2019, 'Integrated submerged membrane distillation-adsorption system for rubidium recovery', Separation and Purification Technology, pp. 146-155.
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© 2019 Elsevier B.V. Seawater reverse osmosis (SWRO) brine management is essential for desalination. Improving brine recovery rate with resource recovery can enhance the overall desalination process. In this study, an integrated submerged membrane distillation (S-MD) with adsorption (granular potassium copper hexacyanoferrate (KCuFC)) was evaluated for improving water recovery from brine while extracting valuable Rb. The thermal S-MD process (55 °C) with a continuous supply of Rb-rich SWRO brine enabled Rb to be concentrated (99% rejection) while producing fresh water. Concentrated Rb in thermal condition enhanced Rb extraction by granular KCuFC. An optimum dose (0.24 g/L) KCuFC was identified based on 98% Rb mass adsorption (9.78 mg as Rb). The integrated submerged MD-adsorption system was able to achieve more than 85% water recovery and Rb extraction in continuous feed supply (in two cycles). Ca in SWRO brine resulted in CaSO 4 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.
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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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.
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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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© 2019 Elsevier Masson SAS Free convection studies in a section-triangular prismatic enclosure with different aspect ratios (depth-width ratios, A) is conducted using three-dimensional numerical modeling approach. The Rayleigh number (Ra) covers a broad range from 10 0 to 10 7 . Transient free convection is characterized under top cooled and bottom heated boundary conditions. The flow structure of transverse rolls and longitudinal rolls is described. The critical Rayleigh numbers for the transition of the flow from driven by the baroclinic to Rayleigh-Bénard instability and from a steady to an unsteady state have been obtained for different aspect ratios. Free convection in the section-triangular prismatic enclosure could be divided into three regimes, which are presented in a Ra-A space. The quantitative relationship between heat transfer and the aspect ratio as well as the Rayleigh number has been obtained numerically.
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, 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.
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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.
Dackermann, U, Smith, WA, Alamdari, MM, Li, J & Randall, RB 2019, 'Cepstrum-based damage identification in structures with progressive damage', Structural Health Monitoring, vol. 18, no. 1, pp. 87-102.
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© The Author(s) 2018. This article aims at developing a new framework to identify and assess progressive structural damage. The method relies solely on output measurements to establish the frequency response functions of a structure using cepstrum-based operational modal analysis. Two different damage indicative features are constructed using the established frequency response functions. The first damage feature takes the residual frequency response function, defined as the difference in frequency response function between evolving states of the structure, and then reduces its dimension using principle component analysis; while in the second damage indicator, a new feature based on the area under the residual frequency response function curve is proposed. The rationale behind this feature lies in the fact that damage often affects a number of modes of the system, that is, it affects the frequency response function over a wide range of frequencies; as a result, this quantity has higher sensitivity to any structural change by combining all contributions from different frequencies. The obtained feature vectors serve as inputs to a novel multi-stage neural network ensemble designed to assess the severity of damage in the structure. The proposed method is validated using extensive experimental data from a laboratory four-girder timber bridge structure subjected to gradually progressing damage at various locations with different severities. In total, 13 different states of the structure are considered, and it is demonstrated that the new damage feature outperforms the conventional principle component analysis–based feature. The contribution of the work is threefold: first, the application of cepstrum-based operational modal analysis in structural health monitoring is further validated, which has potential for real-life applications where only limited knowledge of the input is available; second, a new damage feature is proposed and its superior performance is demonstrat...
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.
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, 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.
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 (Basel), vol. 19, no. 9.
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In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu' al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.
Darabi, H, Choubin, B, Rahmati, O, Torabi Haghighi, A, Pradhan, B & Kløve, B 2019, 'Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques', Journal of Hydrology, vol. 569, pp. 142-154.
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© 2018 Elsevier B.V. Flood risk mapping and modeling is important to prevent urban flood damage. In this study, a flood risk map was produced with limited hydrological and hydraulic data using two state-of-the-art machine learning models: Genetic Algorithm Rule-Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST). The flood conditioning factors used in modeling were: precipitation, slope, curve number, distance to river, distance to channel, depth to groundwater, land use, and elevation. Based on available reports and field surveys for Sari city (Iran), 113 points were identified as flooded areas (with each flooded zone assigned a value of 1). Different conditioning factors, including urban density, quality of buildings, age of buildings, population density, and socio-economic conditions, were taken into account to analyze flood vulnerability. In addition, the weight of these conditioning factors was determined based on expert knowledge and Fuzzy Analytical Network Process (FANP). An urban flood risk map was then produced using flood hazard and flood vulnerability maps. The area under the receiver-operator characteristic curve (AUC-ROC) and Kappa statistic were applied to evaluate model performance. The results demonstrated that the GARP model (AUC-ROC = 93.5%, Kappa = 0.86) had higher performance accuracy than the QUEST model (AUC-ROC = 89.2%, Kappa = 0.79). The results also indicated that distance to channel, land use, and elevation played major roles in flood hazard determination, whereas population density, quality of buildings, and urban density were the most important factors in terms of vulnerability. These findings demonstrate that machine learning models can help in flood risk mapping, especially in areas where detailed hydraulic and hydrological data are not available.
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, 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.
Dekhtyar, A, Huffman Hayes, J, Hadar, I, Combs, E, Ferrari, A, Gregory, S, Horkoff, J, Levy, M, Nayebi, M, Paech, B, Payne, J, Primrose, M, Spoletini, P, Clarke, S, Brophy, C, Amyot, D, Maalej, W, Ruhe, G, Cleland-Huang, J & Zowghi, D 2019, 'Requirements engineering (RE) for social good: RE cares [requirements]', IEEE Software, vol. 36, no. 1.
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© 1984-2012 IEEE. As researchers and teachers and practitioners, we software types excel at multitasking. This, in part, led us to ask the question: Can one attend a software engineering conference and do something good for society? We found the answer to be a resounding yes. In this article, we present our first experience of running RE Cares, a conference collocated event. This event included a workshop, conference sessions, and a hackathon for developing an application to support emergency field activity for Mutual Aid Alberta, a nonprofit organization coordinating natural disaster responses in the Canadian province.
Dimuro, GP, Lucca, G, Bedregal, B, Mesiar, R, Sanz, JA, Lin, CT & Bustince, H 2019, 'Generalized C F 1 F 2 -integrals: From Choquet-like aggregation to ordered directionally monotone functions', Fuzzy Sets and Systems.
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© 2019 Elsevier B.V. This paper introduces the theoretical framework for a generalization of C F 1 F 2 -integrals, a family of Choquet-like integrals used successfully in the aggregation process of the fuzzy reasoning mechanisms of fuzzy rule based classification systems. The proposed generalization, called by gC F 1 F 2 -integrals, is based on the so-called pseudo pre-aggregation function pairs (F 1 ,F 2 ), which are pairs of fusion functions satisfying a minimal set of requirements in order to guarantee that the gC F 1 F 2 -integrals to be either an aggregation function or just an ordered directionally increasing function satisfying the appropriate boundary conditions. We propose a dimension reduction of the input space, in order to deal with repeated elements in the input, avoiding ambiguities in the definition of gC F 1 F 2 -integrals. We study several properties of gC F 1 F 2 -integrals, considering different constraints for the functions F 1 and F 2 , and state under which conditions gC F 1 F 2 -integrals present or not averaging behaviors. Several examples of gC F 1 F 2 -integrals are presented, considering different pseudo pre-aggregation function pairs, defined on, e.g., t-norms, overlap functions, copulas that are neither t-norms nor overlap functions and other functions that are not even pre-aggregation functions.
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, 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.
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.
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, 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.
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As an important tool for behavior informatics, negative sequential patterns (NSPs) (such as missing a medical treatment) are sometimes much more informative than positive sequential patterns (PSPs) (e.g., attending a medical treatment) in many applications. However, NSP mining is at an early stage and faces many challenging problems, including 1) how to mine an expected number of NSPs; 2) how to select useful NSPs; and 3) how to reduce high time consumption. To solve the first problem, we propose an algorithm Topk-NSP to mine the k most frequent negative patterns. In Topk-NSP, we first mine the top-k PSPs using the existing methods, and then we use an idea which is similar to top-k PSPs mining to mine the top-k NSPs from these PSPs. To solve the remaining two problems, we propose three optimization strategies for Topk-NSP. The first optimization strategy is that, in order to consider the influence of PSPs when selecting useful top-k NSPs, we introduce two weights, wP and wN, to express the user preference degree for NSPs and PSPs, respectively, and select useful NSPs by a weighted support wsup. The second optimization strategy is to merge wsup and an interestingness metric to select more useful NSPs. The third optimization strategy is to introduce a pruning strategy to reduce the high computational costs of Topk-NSP. Finally, we propose an optimization algorithm Topk-NSP⁺. To the best of our knowledge, Topk-NSP⁺ is the first algorithm that can mine the top-k useful NSPs. The experimental results on four synthetic and two real-life data sets show that the Topk-NSP⁺ is very efficient in mining the top-k NSPs in the sense of computational cost and scalability.
Dong, X, Yan, Y, Tan, M, Yang, Y & Tsang, IW 2019, 'Late Fusion via Subspace Search With Consistency Preservation', IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 28, no. 1, pp. 518-528.
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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, vol. 247, pp. 474-481.
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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, 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.
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.
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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, 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.
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.
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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Fahmideh, M & Beydoun, G 2019, 'Big data analytics architecture design—An application in manufacturing systems', Computers and Industrial Engineering.
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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 ...
Fahmideh, M, Daneshgar, F, Rabhi, F & Beydoun, G 2019, 'A generic cloud migration process model', European Journal of Information Systems.
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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.
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.
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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Far, C & Far, H 2019, 'Improving Energy Efficiency of Existing Residential Buildings Using Effective Thermal Retrofit of Building Envelope', Indoor and Built Environment, pp. 1-17.
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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. 1, pp. 1-6.
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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.
Farhadian, F, Kashani, MMR, Rezazadeh, J, Farahbakhsh, R & Sandrasegaran, K 2019, 'An efficient IoT cloud energy consumption based on genetic algorithm', Digital Communications and Networks.
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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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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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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, pp. 424-435.
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© 2019 There is growing interest worldwide in more effective policies to promote industrial energy efficiency and mitigate climate change. The White Certificates Scheme is a market-based mechanism aimed at stimulating the adoption of Energy Efficiency Measures. The Italian White Certificates scheme - one of the most long-standing and articulated - is a successful example of industrial energy efficiency policies, considered an interesting and remarkable case by other countries, especially due to its robustness in terms of the volume of certificates traded. Despite the considerable interest in White Certificates, an in-depth analysis of the economic efficiency of the mechanism from the perspective of different stakeholders is still lacking. To address this gap, this study develops a cost-benefit evaluation framework and a multi-stakeholder economic efficiency analysis of the Italian White Certificates scheme focusing on the Italian State, utilities, players in the energy efficiency value chain, and energy users. Our findings (also corroborated with sensitivity analyses) show that the White Certificates Scheme has led to several positive impacts for almost all stakeholders involved, with the exception of energy utilities that have suffered a major economic loss mainly due to a reduction of energy sold to end users. Such loss is likely to promote a deep change in the role of utilities in the energy market in terms of the services they offer and their business models. Our findings, in addition to providing useful directions for future research, offer interesting insights and implications for policymakers who may take inspiration from the pros and cons of the Italian White Certificates scheme when promoting energy efficiency through incentive mechanisms.
Fu, J, Liu, Q, Liufu, K, Deng, Y, Fang, J & Li, Q 2019, 'Design of bionic-bamboo thin-walled structures for energy absorption', THIN-WALLED STRUCTURES, vol. 135, pp. 400-413.
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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.
Ganesan, B, Yip, J, Al-Jumaily, A, Fong, SSM, Balasankar, SK, Batlle, AME, 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.
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, 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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© IMechE 2019. Aiming at improving regenerative braking ability in electric vehicles without compromising any safety, two different regenerative braking strategies are proposed in this study. The impact of continuously varying braking force distributions between front/rear wheel and electric/friction corresponding in two different strategies on braking noise are investigated. Based on the closed-loop coupling disk brake model, the relationship between the contact coupling stiffness and the braking force is established by considering the stationary modal test, the nonlinear optimization, and the relationship between brake-line pressure and braking force. The continuously varying braking force is initially transformed to continuously varying contact coupling stiffness, then, the brake noise tendency at each frequency band is calculated in closed-loop coupled model. The predicted result shows good consistency with the result recorded in bench test, verifying the reliability and effectivity of the presented method. The comparison of the two different electric braking strategies shows that the second braking strategy is superior to the first braking strategy in terms of suppressing the brake noise tendency.
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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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.
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.
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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 & SUSTAINABLE ENERGY REVIEWS, vol. 104, pp. 38-53.
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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.
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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.
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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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, 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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Goh, BHH, Ong, HC, Cheah, MY, Chen, WH, Yu, KL & Mahlia, TMI 2019, 'Sustainability of direct biodiesel synthesis from microalgae biomass: A critical review', Renewable and Sustainable Energy Reviews, pp. 59-74.
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© 2019 Elsevier Ltd Microalgae has been identified as a potential feedstock for biodiesel production since its cultivation requires less cropland compared to conventional oil crops and the high growth rate of microalgae. Research on microalgae oils often are focused on microalgae oil extraction and biomass harvesting techniques. However, energy intensive and costly lipid extraction methods are the major obstacles hampering microalgae biodiesel commercialisation. Direct biodiesel synthesis avoids such problems as it combines lipid extraction techniques and transesterification into a single step. In this review, the potential of direct biodiesel synthesis from microalgae biomass was comprehensively analysed. The various species of microalgae commonly used as biodiesel feedstock was critically assessed, particularly on high lipid content species. The production of microalgae biodiesel via direct conversion from biomass was systematically discussed, covering major enhancements such as heterogeneous catalysts, the use of ultrasonic and microwave- techniques and supercritical alcohols that focus on the overall improvement of biodiesel production. In addition, this review illustrates the cultivation conditions for biomass growth and lipid productivity improvement, the available harvesting and lipid extraction technologies, as well as the key challenges and future prospect of microalgae biodiesel production. This review serves as a basis for future research on direct biodiesel synthesis from modified microalgae biomass to improve profitability of microalgae biodiesel.
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.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Orange Spotting (OS) is an emerging disease in oil palm (Elaeis guineensis Jacq) that has been associated with Coconut cadang-cadang viroid (CCCVd; Cocadviroid, Pospiviroidae). This study was aimed at selecting efficient wavebands in the Visible/near infrared (VNIR) spectrum (400–1050 nm) for estimation of chlorophyll content in CCCVd-inoculated oil palm seedlings. The VNIR spectrum is most sensitive to chlorophyll stress and able to differentiate between healthy and diseased plants. Therefore, a greenhouse experiment was conducted to remotely sense the chlorophyll content using a hyperspectral hand-held spectroradiometer (Analytical Spectral Device (ASD) FieldSpec–II) and a portable chlorophyll meter (Minolta Soil-Plant Analysis Development (SPAD)−502). FieldSpec–II provides reflectance spectra with narrow and contiguous wavebands while SPAD-502 churns out SPAD readings corresponding to chlorophyll content. Interval Partial Least Squares (iPLS) model was used to assess the relationship between independent variable (spectra) and dependent variable (SPAD readings). Five different spectral datasets (i.e. raw spectra, first-order derivatives, Savitzky-Golay smoothing, Multiplicative Scatter Correction (MSC), and standard normal transformation) were investigated. Results showed that the MSC dataset yielded the most accurate estimation with a root mean square error of prediction of 3.70% and a correlation coefficient for prediction of 0.72. A total of thirty wavebands within a range between 601 nm and 630 nm was selected. This work showed that VNIR spectra has the potential to diagnose OS disease in oil palm using the iPLS regression method. This is completely a new application of hyperspectral remote sensing for the oil palm industry, particularly in the domain of plant protection.
Golhani, K, Balasundram, SK, Vadamalai, G & Pradhan, B 2019, 'Selection of a Spectral Index for Detection of Orange Spotting Disease in Oil Palm (Elaeis guineensis Jacq.) Using Red Edge and Neural Network Techniques', Journal of the Indian Society of Remote Sensing, vol. 47, no. 4, pp. 639-646.
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© 2019, Indian Society of Remote Sensing. Spectral screening can play an important role in successful detection of viroid-infected oil palm seedlings from nursery stage prior to transplanting into the field. Coconut cadang–cadang viroid (CCCVd) is the main causal agent of orange spotting (OS) disease. OS disease is an emerging disease in Malaysian plantation. In this study, a glasshouse experiment was conducted with fifteen CCCVd-inoculated and five healthy oil palm seedlings in the growing season of 2015. Spectral screening was performed using a hyperspectral spectroradiometer, Analytic Spectral Device HandHeld 2 (325–1075 nm). The red edge, a steep gradient in reflectance between red and near-infrared bands (680–780 nm), was used for selection of red edge bands. A maximum point (i.e., 700 nm) and minimum point (i.e., 768 nm) of red edge were selected from healthy and inoculated spectra. Shifts of red edge inflection point from healthy to inoculated spectra were also studied. Four well-known spectral indices, namely simple ratio, red edge normalized difference vegetation index, two-band enhanced vegetation index 2 (EVI2), and chlorophyll index red edge, were evaluated using selected red edge bands. The multilayer perceptron neural network model was used to establish a nonlinear relationship between selected spectral bands and each spectral index. EVI2 was selected as a best spectral index which resulted in zero errors at the training, testing, and validation datasets. The highest coefficient of correlation (r = 1) was recorded between spectral bands (input values) and EVI2 (target values).
Golsorkhi 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, 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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Gonzales, RR, Park, MJ, Bae, TH, 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, pp. 10-19.
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© 2019 A melamine-based covalent organic framework (COF) nanomaterial, Schiff base network-1 (SNW-1), was incorporated into the polyamide layer of a novel thin film nanocomposite (TFN) pressure retarded osmosis (PRO) membrane. The deposition of SNW-1 was made on an open mesh fabric-reinforced polyamide-imide (PAI) support substrate through interfacial polymerization (IP). SNW-1 loading influence on the water permeability and osmotic power density during PRO operation was investigated. The porous and highly hydrophilic SNW-1 nanomaterial facilitated the flow of water molecules across the membranes, while maintaining satisfactory salt rejection ability of the polyamide selective layer. The membranes exhibited significantly enhanced surface hydrophilicity, water permeability, and power density. The mode of incorporation of SNW-1 during IP was also investigated and it was observed that the secondary amine groups of SNW-1 react with the carbonyl groups of 1,3,5-benzenetricarbonyl trichloride, the acyl halide precursor in polyamide formation; thus, SNW-1 was incorporated through the amine precursor, 1,3-phenylenediamine. Testing with 1.0 M NaCl as the draw solution, the TFN membrane with a loading of 0.02 wt% SNW-1 exhibited the highest water flux of 42.5 Lm −2 h −1 and power density of 12.1 Wm −2 , while withstanding hydraulic pressure over 24 bar. This study suggests that COF-incorporation can be a promising method in PRO membrane fabrication to improve both osmotic performance and energy harvesting capability for the PRO process.
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.
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, 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, 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, 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, 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.
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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© 2019, The Author(s). 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.
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, 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.
Handayani, K, Krozer, Y & Filatova, T 2019, 'From fossil fuels to renewables: An analysis of long-term scenarios considering technological learning', Energy Policy, pp. 134-146.
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© 2018 The Author(s) This study analyses a diffusion of renewable energy in an electricity system accounting for technological learning. We explore long-term scenarios for capacity expansion of the Java-Bali electricity system in Indonesia, considering the country's renewable energy targets. We apply the Long-range Energy Alternative Planning (LEAP) model with an integration of technological learning. Our results reveal that, at the medium and high pace of technological learning, the total costs of electricity production to achieve the long-term renewable energy target are 4–10% lower than the scenario without considering technological learning. With respect to technology, solar PV and wind become competitive with other types of renewables and nuclear. Moreover, the fulfilment of the renewable energy targets decreases CO2 emissions by 25% compared to the reference scenario. Implications of our results indicate that energy policies should focus on the early deployment of renewables, upgrading the grid capacity to accommodate variable renewable energy, and enabling faster local learning.
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.
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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He, L, Lu, Z, Pan, L, Zhao, H, Li, X & Zhang, J 2019, 'Optimal economic and emission dispatch of a microgrid with a combined heat and power system', Energies, vol. 12, no. 4.
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© 2019 by the authors. With the rapid development of the new concept of energy internet, electric power systems often need to be investigated together with thermal energy systems. Additionally, to reduce pollution from gas emissions, it is very important to study the economic and emission dispatch of integrated electrical and heating systems. Hence, this paper proposes a multi-objective optimization dispatch model for a microgrid (MG) with a combined heat and power (CHP) system. This CHP-based MG system consists of a CHP unit, a wind turbine, a PV system, a fuel cell, an electric boiler, an electric storage, and a heat storage. It can exchange electricity with the distribution network and exchange heat with the district heating network. Minimum economic cost and minimum environmental cost are considered as the two objectives for the operation of this CHP-based MG system. To solve the two objective optimization problem, the multi-objective bacterial colony chemotaxis algorithm is utilized to obtain the Pareto optimal solution set, and the optimal solution is chosen by the Technique for Order of Preference by Similarity to Ideal Solution method. Finally, numerical case studies demonstrate the effectiveness of proposed model and method for the optimal economic and emission dispatch of the CHP-based MG system.
He, LX, Wu, C & Li, J 2019, 'Post-earthquake evaluation of damage and residual performance of UHPSFRC piers based on nonlinear model updating', Journal of Sound and Vibration, vol. 448, pp. 53-72.
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© 2019 Elsevier Ltd This paper presents an innovative approach for damage and residual performance evaluation of ultra-high performance steel fiber reinforced concrete (UHPSFRC) piers after earthquakes utilizing low-level vibration tests. A nonlinear fiber section element model is constructed in OpenSees to simulate the hysteretic behavior of a UHPSFRC bridge pier. Experimental data from a UHPSFRC column is utilized to verify the accuracy of the nonlinear numerical model. Based on the nonlinear fiber section element model, a new technique of nonlinear finite element model updating involving two updating stages is developed. This new method is designed to incorporate the maximum and minimum strains of section fibers as the updating parameters. By forming the objective function from the modal information, the damage parameters related to the nonlinear material model can be updated by solving the constrained optimization problem. To validate the efficiency of this updating approach, it has been applied to a numerically simulated UHPSFRC pier. With using the updated nonlinear finite element model, the residual axial loading capacity and post-seismic performance of the UHPSFRC pier are examined. The numerical results indicate that the updated nonlinear finite element model can be used not only to assess the current damage state of the UHPSFRC pier but also to predict its future performance after an earthquake. Finally, the noise effect on the proposed method is also investigated. The results reveal that the post-earthquake evaluation approach for UHPSFRC piers based on this study's updating algorithm is robust to noise.
He, T, 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.
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.
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Hoang, NPV, Huu, HN, Guo, W, Thi, MHN, Liu, Y, Liu, Y, Dinh, DN & Chang, SW 2019, 'A critical review on designs and applications of microalgae-based photobioreactors for pollutants treatment', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 651, pp. 1549-1568.
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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.
Hoque, MAA, Tasfia, S, Ahmed, N & Pradhan, B 2019, 'Assessing spatial flood vulnerability at kalapara upazila in Bangladesh using an analytic hierarchy process', Sensors (Switzerland), vol. 19, no. 6.
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Floods are common natural disasters worldwide, frequently causing loss of lives and huge economic and environmental damages. A spatial vulnerability mapping approach incorporating multi-criteria at the local scale is essential for deriving detailed vulnerability information for supporting flood mitigation strategies. This study developed a spatial multi-criteria-integrated approach of flood vulnerability mapping by using geospatial techniques at the local scale. The developed approach was applied on Kalapara Upazila in Bangladesh. This study incorporated 16 relevant criteria under three vulnerability components: physical vulnerability, social vulnerability and coping capacity. Criteria were converted into spatial layers, weighted and standardised to support the analytic hierarchy process. Individual vulnerability component maps were created using a weighted overlay technique, and then final vulnerability maps were produced from them. The spatial extents and levels of vulnerability were successfully identified from the produced maps. Results showed that the areas located within the eastern and south-western portions of the study area are highly vulnerable to floods due to low elevation, closeness to the active channel and more social components than other parts. However, with the integrated coping capacity, western and south-western parts are highly vulnerable because the eastern part demonstrated particularly high coping capacity compared with other parts. The approach provided was validated by qualitative judgement acquired from the field. The findings suggested the capability of this approach to assess the spatial vulnerability of flood effects in flood-affected areas for developing effective mitigation plans and strategies.
Hossain, N, Mahlia, TMI, Zaini, J & Saidur, R 2019, 'Techno-economics and Sensitivity Analysis of Microalgae as Commercial Feedstock for Bioethanol Production', Environmental Progress and Sustainable Energy.
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© 2019 American Institute of Chemical Engineers The foremost purpose of this techno-economic analysis (TEA) modeling was to predict a harmonized figure of comprehensive cost analysis for commercial bioethanol generation from microalgae species in Brunei Darussalam based on the conventional market scenario. This model was simulated to set out economic feasibility and probabilistic assumption for large-scale implementations of a tropical microalgae species, Chlorella vulgaris, for a bioethanol plant located in the coastal area of Brunei Darussalam. Two types of cultivation systems such as closed system (photobioreactor—PBR) and open pond approaches were anticipated for a total approximate biomass of 220 t year−1 on 6 ha coastal areas. The biomass productivity was 56 t ha−1 for PBR and 28 t ha−1 for pond annually. The plant output was 58.90 m3 ha−1 for PBR and 24.9 m3 ha−1 for pond annually. The total bioethanol output of the plant was 57,087.58 gal year−1 along with the value added by-products (crude bio-liquid and slurry cake). The total production cost of this project was US$2.22 million for bioethanol from microalgae and total bioethanol selling price was US$2.87 million along with the by-product sale price of US$1.6 million. A sensitivity analysis was conducted to forecast the uncertainty of this conclusive modeling. Different data sets through sensitivity analysis also presented positive impacts of economical and environmental views. This TEA model is expected to be initialized to determine an alternative energy and also minimize environmental pollution. With this current modeling, microalgal-bioethanol utilization mandated with gasoline as well as microalgae cultivation, biofuel production integrated with existing complementary industries, are strongly recommended for future applications. © 2019 American Institute of Chemical Engineers Environ Prog, 2019.
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.
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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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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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© The Author(s) 2019. Forced vibrations of a Timoshenko beam covered with a piezoelectric actuator on its top surface were investigated in this article. As the proposed beam model complied with Timoshenko beam theory, the effects of both rotary inertia and shear deformation were considered. Hamilton principle in conjunction with the Galerkin procedure were applied to derive the governing equation of motion resulting in a second-order ordinary differential equation in time. A sinusoidal electric voltage was applied to the piezoelectric actuator, and a spatially distributed harmonic mechanical force was exerted to the beam. The response of the system to the force stimulation gave an analytical relation between natural frequency and amplitude of the vibration. Using the obtained analytical relation, the effects of different factors and material properties including the modulus of elasticity of the piezoelectric layer and the piezoelectric coefficient on the vibrational response of the beam were examined. The results indicated that the piezoelectric layer as an actuator provided an effective tool for active control of vibration. Increasing the piezoelectric coefficient as well as the electric voltage applied on the piezoelectric actuator increased the amplitude of vibration, while the amplitude decreased by increasing the modulus of elasticity of the piezoelectric actuator. The results were also verified by finite element analysis.
Howard, D, Macsween, K, Edwards, GC, Desservettaz, M, Guérette, EA, Paton-Walsh, C, Surawski, NC, Sullivan, AL, Weston, C, Volkova, L, Powell, J, Keywood, MD, Reisen, F & (Mick) Meyer, CP 2019, 'Investigation of mercury emissions from burning of Australian eucalypt forest surface fuels using a combustion wind tunnel and field observations', Atmospheric Environment, pp. 17-27.
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© 2018 Environmental cycling of the toxic metal mercury (Hg) is ubiquitous, and still not completely understood. Volatilisation and emission of mercury from vegetation, litter and soil during burning represents a significant return pathway for previously-deposited atmospheric mercury. Rates of such emission vary widely across ecosystems as they are dependent on species-specific uptake of atmospheric mercury as well as fire return frequencies. Wildfire burning in Australia is currently thought to contribute between 1 and 5% of the global total of mercury emissions, yet no modelling efforts to date have utilised local mercury emission factors (mass of emitted mercury per mass of dry fuel) or local mercury emission ratios (ratio of emitted mercury to another emitted species, typically carbon monoxide). Here we present laboratory and field investigations into mercury emission from burning of surface fuels in dry sclerophyll forests, native to the temperate south-eastern region of Australia. From laboratory data we found that fire behaviour — in particular combustion phase — has a large influence on mercury emission and hence emission ratios. Further, emission of mercury was predominantly in gaseous form with particulate-bound mercury representing <1% of total mercury emission. Importantly, emission factors and emission ratios with respect to carbon monoxide and carbon dioxide, from both laboratory and field data all show that gaseous mercury emission from biomass burning in Australian dry sclerophyll forests is currently overestimated by around 60%. Based on these results, we recommend a mercury emission factor of 28.7 ± 8.1 μg Hg kg −1 dry fuel, and emission ratio of gaseous elemental mercury relative to carbon monoxide of 0.58 ± 0.01 × 10 −7 , for estimation of mercury release from the combustion of Australian dry sclerophyll litter.
Hu, 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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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.
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, L, Liu, Z, Wu, C & Liang, J 2019, 'The scattering of plane P, SV waves by twin lining tunnels with imperfect interfaces embedded in an elastic half-space', Tunnelling and Underground Space Technology, vol. 85, pp. 319-330.
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© 2018 Elsevier Ltd A viscous-slip interface model is employed to simulate the contact between the tunnels lining and the surrounding rock, and the scattering of P, SV waves by twin shallowly buried lining tunnels is investigated with the indirect boundary integral equation method (IBIEM). The amplification effect of the dynamic stress concentration of the lining and the surface displacement near the tunnels is examined. It is evident that the slipping-stiffness coefficient and viscosity coefficient at the lining-surrounding rock interface have a significant influence on the dynamic stress distribution and the nearby surface displacement response of the lining tunnel, while the influence characteristics strongly depend on the incident wave type, frequency and angle. Under the incidence of low frequency wave, as a whole, with the increase of the sliding stiffness, the hoop stress increases gradually for plane P and SV waves; while in the resonance frequency (the incident wave frequency is consistent with the natural frequency of the soil column above the tunnels), specially for high-frequency band, the dynamic stress concentration effect is more significant for smaller sliding stiffness. With the increase of viscosity coefficient, the dynamic stress concentration factor inside the lining gradually decreases. Also, the tunnels with viscous-slip interfaces have a more significant amplification effect on the nearby surface displacement amplitude. Moreover, the hoop stress of the twin tunnels may be obviously larger than that of single tunnel in most cases. The dynamic analysis of the underground structure under the actual strong dynamic loading should consider the influence of the slip effect between the lining and surrounding rock interface.
Huang, L, 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, S 2019, 'A review of optimisation strategies used in simultaneous localisation and mapping', Journal of Control and Decision, vol. 6, no. 1, pp. 61-74.
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© 2018, © 2018 Northeastern University, China. This paper provides a brief review of the different optimisation strategies used in mobile robot simultaneous localisation and mapping (SLAM) problem. The focus is on the optimisation-based SLAM back end. The strategies are classified based on their purposes such as reducing the computational complexity, improving the convergence and improving the robustness. It is clearly pointed out that some approximations are made in some of the methods and there is always a trade-off between the computational complexity and the accuracy of the solution. The local submap joining is a strategy that has been used to address both the computational complexity and the convergence and is a flexible tool to be used in the SLAM back end. Although more research is needed to further improve the SLAM back end, nowadays there are quite a few relatively mature SLAM back end algorithms that can be used by SLAM researchers and users.
Huang, S, Kang, Z, Tsang, IW & Xu, Z 2019, 'Auto-weighted multi-view clustering via kernelized graph learning', Pattern Recognition, vol. 88, pp. 174-184.
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© 2018 Datasets are often collected from different resources or comprised of multiple representations (i.e., views). Multi-view clustering aims to analyze the multi-view data in an unsupervised way. Owing to the efficiency of uncovering the hidden structures of data, graph-based approaches have been investigated widely for various multi-view learning tasks. However, similarity measurement in these methods is challenging since the construction of similarity graph is impacted by several factors such as the scale of data, neighborhood size, choice of similarity metric, noise and outliers. Moreover, nonlinear relationships usually exist in real-world datasets, which have not been considered by most existing methods. In order to address these challenges, a novel model which simultaneously performs multi-view clustering task and learns similarity relationships in kernel spaces is proposed in this paper. The target optimal graph can be directly partitioned into exact c connected components if there are c clusters. Furthermore, our model can assign ideal weight for each view automatically without additional parameters as previous methods do. Since the performance is often sensitive to the input kernel matrix, the proposed model is further extended with multiple kernel learning ability. With the proposed joint model, three subtasks including construct the most accurate similarity graph, automatically allocate optimal weight for each view and find the cluster indicator matrix can be simultaneously accomplished. By this joint learning, each subtask can be mutually enhanced. Experimental results on benchmark datasets demonstrate that our model outperforms other state-of-the-art multi-view clustering algorithms.
Huang, 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, 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, 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.
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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.
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.
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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© 2019 Elsevier Ltd Recent advances in magnetic material characteristics and solid-state semiconductors have provided the feasibility of replacing the electrical buses with the high-frequency multi-winding magnetic links in small-scale renewable energy systems. This effectively reduces the number of conversion stages and improves the system's efficiency, cost, and size. Other advantages are galvanic isolation between the ports, bidirectional power flow capability and flexibility in energy management and control. Despite the advantages, design and development of the multi-winding magnetic links is relatively complex and based on computationally expensive numerical methods. Furthermore, the non-sinusoidal nature of voltage and currents, high-frequency parasitic effects and nonlinearity of magnetic material characteristics increase the design complexity. In this paper, the reluctance network modeling as a fast analytical method is used to design a three winding magnetic link. The core and copper losses of the designed component are evaluated taking into account duty ratio, amplitude and phase shift of the non-sinusoidal excitation voltage and currents. The thermal analysis is carried out using an accurate thermal-electric model. A prototype of the magnetic link was developed for application in a residential renewable energy system using amorphous magnetic materials. A set of experimental tests are conducted to measure the electrical parameters, magnetic characteristics, core loss, copper loss and temperature rise of the designed component and the results are compared to the specifications to validate the design procedure.
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.
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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.
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...
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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Jamilu Bala Ahmed, II, Pradhan, B, Mansor, S, Tongjura, JDC & Yusuf, B 2019, 'Multi-criteria evaluation of suitable sites for termite mounds construction in a tropical lowland', Catena, vol. 178, pp. 359-371.
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© 2019 Elsevier B.V. Termite mounds influence ecosystem heterogeneity and contribute to the stabilization of the system under global change. A number of environmental factors influence the distribution, height, diameter and designs of termite mounds but these factors are not only poorly understood, they cannot be extrapolated for everywhere. In this study, we employed a ground based survey and Geographical Information System (GIS) technique to map 156 km 2 study area in Keffi, Nigeria. The aims were to (1) estimate the density and area covered by termite mounds, (2) sample and identify species types and how they are distributed, and (3) use five environmental factors (elevation, geology, surface water drainage, land use/land cover and static water level) to model suitable sites for mounds construction. A total of 361 mounds were mapped representing a density of about 0.8 mounds ha −1 and covering only about 0.31% of the studied area. Next, the effect of the five chosen environmental factors on the geographic distribution, life status, height and diameter of mounds and species diversity were analysed and their relationships plotted in pairwise comparison matrices using the Saaty's Analytical Hierarchy Process. Normalized rates for classes in each factor and corresponding weights were computed and aggregated using the Weighted Linear Combination method. The result depicted that moderate to low elevation (270–330 m amsl), rock cover types that are more susceptible to weathering (schist), cultivated areas and shallow water table zones are most favourable for termites to build mounds. The result obtained in this study shows a promising correlation between the environmental factors and termite mounds distribution. The proposed model can easily be replicated in a different but similar multi-land use and rock cover types.
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.
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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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.
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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Jiang, J, Kim, DI, Dorji, P, Phuntsho, S, Hong, S & Shon, HK 2019, 'Phosphorus removal mechanisms from domestic wastewater by membrane capacitive deionization and system optimization for enhanced phosphate removal', Process Safety and Environmental Protection, vol. 126, pp. 44-52.
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© 2019 Institution of Chemical Engineers Membrane capacitive deionization (MCDI) is an emerging technology for effective removal of charged pollutants from the water sources including domestic wastewater. In this work, a lab-scale MCDI system was employed to investigate its feasibility for effective phosphorus removal from domestic wastewater. The effect of phosphate equilibrium reactions on the ion sorption behaviour was studied in sodium phosphate buffer solution at typical pH range maintained in a real domestic raw wastewater effluent (between 6.5 and 8.5). The results demonstrated that phosphate equilibrium system has positive impact on the degree of inorganic phosphorus (P) adsorption capacity in aqueous solution. In addition, the ion selectivity of P over other co-existing anions (Cl - , SO 42- ) were experimentally studied using a synthetic wastewater solution. And it was found that the preferential electrosorption sequence of the competitive anions is: Cl - > SO 42- > P, while the initial ion concentration order in the synthetic feed solution is: Cl - 1.90 mM> P (0.40 mM) > SO 42- (0.32 mM). The experiments with diverse operating conditions revealed that the optimal adsorption of inorganic phosphorus over chloride and sulphate can be achieved in some extent with slower flow rates and higher applied potentials (less than 1.23 V).
Jiang, J, Zhang, H, Pi, D & Dai, C 2019, 'A novel multi-module neural network system for imbalanced heartbeats classification', Expert Systems with Applications: X, 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, 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.
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).
Jo, Y, Johir, MAH, Cho, Y, Naidu, G, Rice, SA, McDougald, D, Kandasamy, J, Vigneswaran, S & Sun, S 2019, 'A comparative study on nitric oxide and hypochlorite as a membrane cleaning agent to minimise biofilm growth in a membrane bioreactor (MBR) process', Biochemical Engineering Journal, vol. 148, pp. 9-15.
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© 2019 Elsevier B.V. Reverse osmosis concentrates (ROC) produced from water reclamation plants can threaten the environment if it is not appropriately treated before discharge. A membrane bioreactor (MBR) process to treat ROC was used in this project. In an MBR, fouling is an essential and inevitable phenomenon which leads to higher operational and capital costs. A comparative study on chemical cleaning, such as sodium hypochlorite (NaOCl) and nitric oxide (NO), was experimentally evaluated together with the influence of filtration flux. Exposure to a low concentration of NO reduced biofilms in an MBR system. NO treatment delayed the formation of new biofilm biomass on the membrane. NO also showed good performance in reducing membrane fouling and had no adverse effect on activated sludge and the environment. In MBR, the bacterial community was dominated by Proteobacteria (61%), with Alpha and Beta-proteobacteria representing approximately 54% of the community. After NO treatment, the relative abundance of the Proteobacteria decreased to 44%, and this was also reflected in a reduction in Alpha and Beta-proteobacteria, to 30% and 5% respectively. Thus, NO treatment resulted in the decrease of the relative biofilms associated with reduced MBR performance.
Ju, M, Ding, C, Guo, YJ & Zhang, D 2019, 'Remote Sensing Image Haze Removal Using Gamma-Correction-Based Dehazing Model', IEEE Access, vol. 7, pp. 5250-5261.
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© 2013 IEEE. Haze is evident in most remote sensing (RS) images, particularly for the RS scenes captured in inclement weather, which severely hinders image interpretation. In this paper, two simple yet effective visibility restoration formulas are proposed for RGB-channel RS (RRS) images and multi-spectral RS (MSRS) images, respectively. More specifically, a robust gamma-correction-based dehazing model (RGDM) is first defined, which can better address the non-uniform illumination problem in hazy images. Then, the scene albedo restoration formula (SARF) used for the RRS images is obtained by imposing the existing prior knowledge on this RGDM, which enables us to simultaneously eliminate the interferences of haze and non-uniform illumination. In subsequence, according to Rayleigh's law, an expanded restoration formula (E-SARF) is further developed for MSRS data. Using the proposed E-SARF, the spatially varying haze in each band can be thoroughly removed without using any extra information. The experiments are conducted on the challenging RRS and MSRS images, including images with non-uniform illumination, non-uniform haze distribution, and heavy haze. The results reveal that the SARF and the E-SARF are superior to most other state-of-the-art techniques in terms of both the recover quality and the implementation efficiency.
Ju, M, Ding, C, Zhang, D & Guo, YJ 2019, 'BDPK: Bayesian Dehazing Using Prior Knowledge', IEEE Transactions on Circuits and Systems for Video Technology.
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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.
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, Nguyen, TV, Nur, T, Hasan Johir, MA, Nguyen, TH, Trinh, MV & Vigneswaran, S 2019, 'Iron-impregnated granular activated carbon for arsenic removal: Application to practical column filters', Journal of Environmental Management, vol. 239, pp. 235-243.
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© 2019 Elsevier Ltd Arsenic is a major drinking water contaminant in many countries causing serious health hazards, and therefore, attempts are being made to remove it so that people have safe drinking water supplies. The effectiveness of arsenic removal from As(V) solutions using granular activated carbon (GAC) (zero point of charge (ZPC) pH 3.2) and iron incorporated GAC (GAC-Fe) (ZPC pH 8.0) was studied at 25 ± 1 °C. The batch study confirmed that GAC-Fe had higher Langmuir adsorption capacity at pH 6 (1.43 mg As/g) than GAC (1.01 mg As/g). Adsorption data of GAC-Fe fitted the Freundlich model better than the Langmuir model, thus indicating the presence of heterogeneous adsorption sites. Weber and Morris plots of the kinetic adsorption data suggested intra-particle diffusion into meso and micro pores in GAC. The column adsorption study revealed that 2–4 times larger water volumes can be treated by GAC-Fe than GAC, reducing the arsenic concentration from 100 μg/L to the WHO guideline of 10 μg/L. The volume of water treated increased with a decrease in flow velocity and influent arsenic concentration. The study indicates the high potential of GAC-Fe to remove arsenic from contaminated drinking waters in practical column filters.
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, pp. 1-28.
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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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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%.
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.
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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Kenning, G 2019, 'Chronic health: if not us then who? A review of an exhibition exploring the assistive, speculative, provocative and challenging role of design for health — Embassy of Health, Dutch Design Week', Design for Health, pp. 1-18.
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Khabbaz, H, Gibson, R & Fatahi, B 2019, 'Effect of Constructing Twin Tunnels under a Building Supported by Pile Foundations in the Sydney Central Business District', Underground Space.
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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, 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, 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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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, 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.
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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 co-culture 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 co-regulated genes in human cohorts showed that healthy human controls and patients with monoclonal gammopathy of uncertain significance (MGUS) 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 to a two-fold 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 are 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.
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, pp. 241-250.
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© 2019 Elsevier B.V. This study investigated membrane capacitive deionization (MCDI) at a pilot-scale using ion-selective polymer-coated carbon electrodes for wastewater reuse. Several issues have been addressed to verify the suitability of MCDI for wastewater reclamation: electrosorption performance, removal efficiency and selectivity of ions present in wastewater, optimization of operating conditions, and performance degradation in long-term caused by the accumulation of organic contaminants. The coated electrodes had better adsorption capacities and charge efficiencies than the conventional MCDI system, which was attributed to their low electrical resistance induced by the thin coated layer. The pilot-scale MCDI test cell involved 50 pairs of anion- and cation-selective electrodes and achieved good removal efficiency of ions from the wastewater effluent, particularly for problematic charged impurities, such as nitrate (NO 3− ) (up to 91.08% of NO 3− was removed). Increasing the flow rate and reducing the applied potential were shown to be efficient for achieving better water quality by enhancing the NO 3− selectivity. Last, the 15 d operation showed good reproducibility in electrosorption and regeneration for the coated electrodes, despite the fact that high concentrations of organics were contained in the wastewater feed solution (12.4 mg/L of dissolved organic carbon).
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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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, p. 110403.
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We identify and explore the intriguing property of resource resonance arising within resource theories of entanglement, coherence, and thermodynamics. While the theories considered are reversible asymptotically, the same is generally not true in realistic scenarios where the available resources are bounded. The finite-size effects responsible for this irreversibility could potentially prohibit small quantum information processors or thermal machines from achieving their full potential. Nevertheless, we show here that by carefully engineering the resource interconversion process any such losses can be greatly suppressed. Our results are predicted by higher order expansions of the trade-off between the rate of resource interconversion and the achieved fidelity, and are verified by exact numerical optimizations of the appropriate underlying approximate majorization conditions.
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 CD34+ cells. Conversely, overexpression of HMGA2 in CB CD34+ cells led to overall enhanced reconstitution in serial transplantation assays accompanied by a skewing toward the myeloerythroid lineages. RNA-sequencing analysis showed that enforced HMGA2 expression in CD34+ cells induced gene-expression signatures associated with differentiation toward megakaryocyte-erythroid and myeloid lineages, as well as signatures associated with growth and survival, which at the protein level were coupled with strong activation of AKT. Taken together, our findings demonstrate a key role of HMGA2 in regulation of both proliferation and differentiation of human HSPCs.
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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Kutay, C 2019, 'Locating knowledge', Archives and Manuscripts, vol. 47, no. 1, pp. 72-93.
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© 2019 Cat Kutay. 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.
Kutay, CM 2019, 'Language Located', Archives and Manuscripts.
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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, 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.
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.
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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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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.
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, 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.
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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.
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, 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.
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, 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, 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 & 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, pp. 1-1.
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Li, J, Zhu, X, Law, SS & Samali, B 2019, 'Indirect bridge modal parameters identification with one stationary and one moving sensors and stochastic subspace identification', Journal of Sound and Vibration, vol. 446, pp. 1-21.
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© 2019 Elsevier Ltd A new indirect strategy is proposed to estimate the bridge modal parameters from the dynamic responses of two vehicles using stochastic subspace identification technique. The effect of ambient excitation, such as ongoing traffic, is simulated as white-noise excitation at the bridge supports. The state-space model of the vehicle-bridge interaction system is derived for a single-degree-of-freedom quarter-car model and the bridge deck modeled as a simply-supported Euler-Bernoulli beam. Bridge modal frequencies can be estimated accurately from the vehicle responses. Two instrumented vehicles are required to estimate the bridge mode shapes, with one serving as a fixed reference sensor and the other as a moving sensor. The measured accelerations from the vehicles are divided into segments and each pair of signal segments forms a state-space identification problem. Local mode shape value from each signal segment can be estimated using the reference-based SSI method. A rescaling on the local mode shape values is applied to construct the global mode shapes. Effects of the bridge surface roughness, measurement noise and vehicle properties on the mode shape identification are also numerically studied. A vehicle-bridge interaction model in the laboratory serves for the experimental validation of the proposed strategy. Both numerical and experimental results show that the proposed method can estimate the bridge modal parameters with acceptable accuracy.
Li, M, Sun, Y, Su, S, Tian, Z, Wang, Y & Wang, X 2019, 'DPIF: A framework for distinguishing unintentional quality problems from potential shilling attacks', Computers, Materials and Continua, vol. 59, no. 1, pp. 331-344.
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Copyright © 2019 Tech Science Press. Maliciously manufactured user profiles are often generated in batch for shilling attacks. These profiles may bring in a lot of quality problems but not worthy to be repaired. Since repairing data always be expensive, we need to scrutinize the data and pick out the data that really deserves to be repaired. In this paper, we focus on how to distinguish the unintentional data quality problems from the batch generated fake users for shilling attacks. A two-steps framework named DPIF is proposed for the distinguishment. Based on the framework, the metrics of homology and suspicious degree are proposed. The homology can be used to represent both the similarities of text and the data quality problems contained by different profiles. The suspicious degree can be used to identify potential attacks. The experiments on real-life data verified that the proposed framework and the corresponding metrics are effective.
Li, N, Fu, X, Zhu, J, Lin, M, Yang, G, Kong, Y & Hao, L 2019, 'Hybrid-Excited Series Permanent Magnet Axial Field Flux Switching Memory Machine', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2002-2011 IEEE. This paper presents a novel axial field flux switching memory (AFFSM) machine, which integrates series permanent magnet (SPM) and an additional dc field winding for improving the flux regulation capability. The configuration and the operation principle of the SPM-AFFSM machine are presented. The design considerations for the SPM and the dc field winding are discussed. The voltage regulation capability and torque-speed characteristics of the SPM-AFFSM machine are investigated by 3-D FEA. Finally, a prototype is developed for experimental verification. The measured results verify structural validity and the finite-element predictions.
Li, Q, Zhong, J, Cao, Z & Li, X 2019, 'Optimizing streaming graph partitioning via a heuristic greedy method and caching strategy', Optimization Methods and Software.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Graph partitioning is an important method for accelerating large distributed graph computation. Streaming graph partitioning is more efficient than offline partitioning, and it has been developed continuously in the application of graph partitioning in recent years. In this work, we first introduce a heuristic greedy streaming partitioning method and show that it outperforms the state-of-the-art streaming partitioning methods, leading to exact balance and fewer cut edges. Second, we propose a cache structure for streaming partitioning, called an adjacent edge structure, which can improve the partition efficiency several times on a single commodity type computer without affecting the partition quality. Regardless as to whether the memory capacity is limited (local cache) or not (global cache), our strategy can also improve the partition quality by restreaming partitioning. Taking linear weight greedy streaming algorithm as an example, the experimental results on 19 real-world graphs show that the average partitioning time of the new method is 4.9 times faster than that of the original method, which proves the effectiveness and superiority of the cache structure mentioned in this paper.
Li, Q, Zhong, J, Li, Q, Wang, C & Cao, Z 2019, 'A community merger of optimization algorithm to extract overlapping communities in networks', IEEE Access, vol. 7, pp. 3994-4005.
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© 2018 IEEE. A community in networks is a subset of vertices primarily connecting internal components, yet less connecting to the external vertices. The existing algorithms aim to extract communities of the topological features in networks. However, the edges of practical complex networks involving a weight that represents the tightness degree of connection and robustness, which leads a significant influence on the accuracy of community detection. In our study, we propose an overlapping community detection method based on the seed expansion strategy applying to both the unweighted and the weighted networks, called OCSE. First, it redefines the edge weight and the vertex weight depending on the influence of the network topology and the original edge weight, and then selects the seed vertices and updates the edges weight. Comparisons between OCSE approach and existing community detection methods on synthetic and real-world networks, the results of the experiment show that our proposed approach has the significantly better performance in terms of the accuracy.
Li, W, Ma, H & Gao, W 2019, 'A higher-order shear deformable mixed beam element model for accurate analysis of functionally graded sandwich beams', Composite Structures, vol. 221, pp. 110830-110830.
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Li, X, Liu, Y, Xu, Q, Liu, X, Huang, X, Yang, J, Wang, D, Wang, Q, Liu, Y & Yang, Q 2019, 'Enhanced methane production from waste activated sludge by combining calcium peroxide with ultrasonic: Performance, mechanism, and implication', BIORESOURCE TECHNOLOGY, vol. 279, pp. 108-116.
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Li, X, Mei, Q, Chen, L, Zhang, H, Dong, B, Dai, X, He, C & Zhou, J 2019, 'Enhancement in adsorption potential of microplastics in sewage sludge for metal pollutants after the wastewater treatment process', Water Research, pp. 228-237.
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© 2019 Elsevier Ltd Microplastics (MPs) as new pollutants of environmental concern have been widely detected in sewage sludge, and may act as significant vectors for metal pollutants due to their adsorption property. Our findings show that Cd, Pb, and Co, but not Ni, contents in sewage sludge are lower than that of corresponding metal irons adsorbed on sludge-based MPs, indicating that the MPs accumulate such metal pollutants as Cd in the sludge samples. In contrast to virgin MPs, sludge-based MPs are one order of magnitude higher adsorption capacity for Cd, which reaches up to 2.523 mg g −1 , implying that there is a considerable enhancement in adsorption potential of the MPs for metals after the wastewater treatment process. SEM analysis shows that sludge-based MPs have rougher and more porous surface than virgin MPs, and FTIR spectra reveal that functional groups such as C–O and O–H are found on sludge-based MPs. Further, two-dimensional FTIR correlation spectroscopy indicates that C–O and N–H functional groups play a vital role in the process that sludge-based MPs adsorb Cd, which are not found in virgin MPs. The results imply that increased adsorption potentials of the sludge-based MPs to Cd are attributed to changes in the MP physicochemical properties during wastewater treatment process. In addition, such factors as pH value, and sludge inorganic and organic components also have an effect on the MP adsorption to Cd. Principal component analysis shows that the MPs could be divided into three categories, i.e. polyamide, rubbery MPs (polyethylene and polypropylene) and glassy MPs (polyvinyl chloride and polystyrene). Their adsorption potentials to Cd follow the decreasing order: polyamide > rubbery MPs > glassy MPs. In summary, these findings indicate that MPs may exert an important influence on fate and transport of metal pollutants during sewage sludge treatment process, which deserves to be further concerned.
Li, X, Zhao, L, Ji, W, Wu, Y, Wu, F, Yang, M-H, Tao, D & Reid, I 2019, 'Multi-Task Structure-Aware Context Modeling for Robust Keypoint-Based Object Tracking.', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 4, pp. 915-927.
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In the fields of computer vision and graphics, keypoint-based object tracking is a fundamental and challenging problem, which is typically formulated in a spatio-temporal context modeling framework. However, many existing keypoint trackers are incapable of effectively modeling and balancing the following three aspects in a simultaneous manner: temporal model coherence across frames, spatial model consistency within frames, and discriminative feature construction. To address this problem, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning. Consequently, temporal model coherence is characterized by multi-task structured keypoint model learning over several adjacent frames; spatial model consistency is modeled by solving a geometric verification based structured learning problem; discriminative feature construction is enabled by metric learning to ensure the intra-class compactness and inter-class separability. To achieve the goal of effective object tracking, we jointly optimize the above three modules in a spatio-temporal multi-task learning scheme. Furthermore, we incorporate this joint learning scheme into both single-object and multi-object tracking scenarios, resulting in robust tracking results. Experiments over several challenging datasets have justified the effectiveness of our single-object and multi-object trackers against the state-of-the-art.
Li, Y, Huang, C, Ngo, HH, Pang, J, Zha, X, Liu, T & Guo, W 2019, 'In situ reconstruction of long-term extreme flooding magnitudes and frequencies based on geological archives', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 670, pp. 8-17.
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Li, Y, Lei, L & Li, S 2019, 'Computation tree logic model checking based on multi-valued possibility measures', Information Sciences, vol. 485, pp. 87-113.
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© 2019 Elsevier Inc. Multi-valued model checking has been studied extensively recently, but important uncertain information contained in systems of multi-valued logics has not been considered in previous work and, as a consequence, some serious deficiencies arise. To make up for these deficiencies, this paper considers the possibility information implied in multi-valued systems. Precisely, we investigate computation tree logic model checking based on multi-valued possibility measures. We model multi-valued logic systems by multi-valued Kripke structures (MvKSs) and specify their verification properties by multi-valued computation tree logic (MvCTL) formulae. Based on generalized possibility measures and generalized necessity measures, an MvCTL model checking method is proposed, the pseudocode of the corresponding model checking algorithm is presented, and its time complexity is analyzed in detail. Furthermore, after detailed comparisons with χCTL (introduced in Chechik et al. [10]) and the classical CTL, we show that MvCTL is more general than χCTL, but cannot be reduced to the classical CTL. The conditions on lattice and MvKS under which MvCTL is equivalent to χCTL are given. Finally, some examples and a case study are given to illustrate the MvCTL model-checking method.
Liang, H, Wang, H, Li, Q, Wang, J, Xu, G, Chen, J, Wei, JM & Yang, Z 2019, 'A general framework for learning prosodic-enhanced representation of rap lyrics', World Wide Web.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Learning and analyzing rap lyrics is a significant basis for many Web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web. Although numerous studies have explored the topic, knowledge in this field is far from satisfactory, because critical issues, such as prosodic information and its effective representation, as well as appropriate integration of various features, are usually ignored. In this paper, we propose a hierarchical attention variational a utoe ncoder framework (HAVAE), which simultaneously considers semantic and prosodic features for rap lyrics representation learning. Specifically, the representation of the prosodic features is encoded by phonetic transcriptions with a novel and effective strategy (i.e., rhyme2vec). Moreover, a feature aggregation strategy is proposed to appropriately integrate various features and generate prosodic-enhanced representation. A comprehensive empirical evaluation demonstrates that the proposed framework outperforms the state-of-the-art approaches under various metrics in different rap lyrics learning tasks.
Liang, J, Mondal, AK, Wang, DW & Iacopi, F 2019, 'Graphene-Based Planar Microsupercapacitors: Recent Advances and Future Challenges', Advanced Materials Technologies, vol. 4, no. 1.
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© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim The continuous development of integrated electronics such as maintenance-free biosensors, remote and mobile environmental sensors, wearable personal electronics, nanorobotics etc. and their continued miniaturization has led to an increasing demand for miniaturized energy storage units. Microsupercapacitors with graphene electrodes hold great promise as miniaturized, integrated power sources thanks to their fast charge/discharge rates, superior power performance, and long cycling stability. In addition, planar interdigitated electrodes also have the capability to reduce ion diffusion distances leading to a greatly improved electrochemical performance. Either as standalone power sources or complementing energy harvesting units, it is expected that graphene-based microsupercapacitors will play a key role as miniaturized power sources in electronic microsystems. This review highlights the recent development, challenges, and perspectives in this area, with an emphasis on the link between material and geometry design of planar graphene-based electrodes and their electrochemical performance and integrability.
Liang, J, Walker, PD, Ruan, J, Yang, H, Wu, J & Zhang, N 2019, 'Gearshift and brake distribution control for regenerative braking in electric vehicles with dual clutch transmission', MECHANISM AND MACHINE THEORY, vol. 133, pp. 1-22.
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Liang, T, Chen, L, Wu, J, Xu, G & Wu, Z 2019, 'SMS: A Framework for Service Discovery by Incorporating Social Media Information', IEEE Transactions on Services Computing, pp. 1-1.
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With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches.
Liang, X, Wu, C, Yang, Y & Li, Z 2019, 'Experimental study on ultra-high performance concrete with high fire resistance under simultaneous effect of elevated temperature and impact loading', Cement and Concrete Composites, vol. 98, pp. 29-38.
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© 2019 Fire is a big risk to buildings and structures, posing a great threat to human lives. In this study, a newly developed ultra-high performance concrete (UHPC) was investigated experimentally. Quasi-static compression tests were conducted after the UHPC was first exposed to a high temperature, i.e. 200, 400, 600, 800 or 1000 °C, and then cooled down to room temperature, while dynamic tests were carried out under combined effect of a high temperature, i.e. 200, 400, 600, or 800 °C, and impact loading. The dynamic tests were done both at high temperatures and after cooling down and comparisons were made between these two scenarios. Based on the tests on this UHPC, mechanical and physical characteristics under the combined effect were studied. Besides, explosive spalling was analysed. It was interesting to find polypropylene (PP) fibre could play a negative role in preventing explosive spalling between 320 and 380 °C.
Liberal, I, Ederra, I & Ziolkowski, RW 2019, 'Grating Lobes in Higher-Order Correlation Functions of Arrays of Quantum Emitters: Directional Photon Bunching Versus Correlated Directions', PHOTONICS, vol. 6, no. 1.
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Lin, C, Sun, S, Walker, P & Zhang, N 2019, 'Accelerated adaptive second order super-twisting sliding mode observer', IEEE Access.
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OAPA An accelerated second order super twisting sliding mode observer with an adaptive gain is proposed for a typical nonlinear system. The key contribution of this algorithm is that the rate of convergence of observation error is accelerated remarkably by introducing "system damping". Chattering issue is attenuated with satisfactory performance compared to conventional sliding mode observer. Furthermore adaptive gain can vary with deviation between the trajectory and sliding mode switching manifold dynamically so that overshoot can be reduced. The novel observer is proven mathematically to be convergent in a finite time. Finally, an example of nonlinear system is given to verify the performance.
Lin, C, Sun, S, Yi, J, Walker, P & Zhang, N 2019, 'Accelerated adaptive super twisting sliding mode observer-based drive shaft torque estimation for electric vehicle with automated manual transmission'.
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Lin, CT, Chiu, C-Y, Singh, AK, King, J-T & Wang, YK 2019, 'A Wireless Multifunctional SSVEP-Based Brain Computer Interface Assistive System', IEEE Transactions on Cognitive and Developmental Systems.
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IEEE Several kinds of brain-computer interface (BCI) systems have been proposed to compensate for the lack of medical technology for assisting patients who lose the ability to use motor functions to communicate with the outside world. However, most of the proposed systems are limited by their non-portability, impracticality and inconvenience because of the adoption of wired or invasive electroencephalography (EEG) acquisition devices. Another common limitation is the shortage of functions provided because of the difficulty of integrating multiple functions into one BCI system. In this study, we propose a wireless, non-invasive and multifunctional assistive system which integrates steady state visually evoked potential (SSVEP)-based BCI and a robotic arm to assist patients to feed themselves. Patients are able to control the robotic arm via the BCI to serve themselves food. Three other functions: video entertainment, video calling, and active interaction are also integrated. This is achieved by designing a functional menu and integrating multiple subsystems. A refinement decision-making mechanism is incorporated to ensure the accuracy and applicability of the system. Fifteen participants were recruited to validate the usability and performance of the system. The averaged accuracy and information transfer rate (ITR) achieved is 90.91% and 24.94 bit per min respectively. The feedback from the participants demonstrates that this assistive system is able to significantly improve the quality of daily life.
Lin, JY, Wong, SW, Wu, YM, Yang, Y, Zhu, L & He, Y 2019, 'Three-way multiple-mode cavity filtering crossover for narrowband and broadband applications', IEEE Transactions on Microwave Theory and Techniques, vol. 67, no. 3, pp. 896-905.
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© 1963-2012 IEEE. In this paper, the design of a cavity crossover with three intersecting channels is presented. The three fundamental modes of a cavity resonator, namely, TE 011 , TE 101 , and TM 110 modes, are adopted to resonate at each of three channels, respectively. Due to the modal orthogonality of these fundamental modes, high isolation among three channels can be achieved. Two kinds of crossovers, for narrowband and broadband applications, are presented. For the narrowband case, the proposed crossover resonates at 2.91 GHz with the fractional bandwidth of 1.4%. For the broadband case, the proposed crossover resonates at 3 GHz with the fractional bandwidth of 24%. The isolations of both designs reach more than 50 dB. For a proof of concept, the broadband example of the cavity crossover structures is fabricated and measured. A good agreement between the simulated and the measured results verifies the accuracy of the proposed design methodology.
Lin, L, Xu, L, Huang, Y, Xiang, Y & He, X 2019, 'On exploiting priority relation graph for reliable multi-path communication in mobile social networks', Information Sciences, vol. 477, pp. 490-507.
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© 2018 Elsevier Inc. A mobile social network (MSN) consists of certain amount of mobile users with social characteristics, and it provides data delivery concerning social relationships between mobile users. In MSN, ordinary people contact each other more frequently if they have more social features in common. In this paper, we apply a new topology structure–priority relation graph (PRG) to evaluate the data delivery routing in MSN on the system-level. By using the natural order of nodes’ representation, the diameter, the regular degree and the multi-path technology, we determine the priority relation graph-based social feature routing (PRG-SFR) algorithm to find disjointed multi-paths in MSN. Here, the multi-path technology can be exploited for ensuring that, between each pair of sender and receiver, the important information can be delivered through a highly reliable path. Then we calculate the tolerant ability of ‘faults’ and estimate the availability of MSN on the theoretical level. Finally, we analyze the efficiency of PRG-SFR algorithm from the numerical standpoint in terms of fault tolerance, forwarding number, transmission time and delivery rate. Moreover, we make comparisons between PRG-SFR algorithm and certain state-of-the-art technologies.
Lin, W & Ziolkowski, RW 2019, 'Electrically Small Huygens Antenna-Based Fully-Integrated Wireless Power Transfer and Communication System', IEEE ACCESS, vol. 7, pp. 39762-39769.
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Lin, W & Ziolkowski, RW 2019, 'Wirelessly Powered Light and Temperature Sensors Facilitated by Electrically Small Omnidirectional and Huygens Dipole Antennas', Sensors (Basel, Switzerland), vol. 19, no. 9.
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Wirelessly powered, very compact sensors are highly attractive for many emerging Internet-of-things (IoT) applications; they eliminate the need for on-board short-life and bulky batteries. In this study, two electrically small rectenna-based wirelessly powered light and temperature sensors were developed that operate at 915 MHz in the 902-928-MHz industrial, scientific, and medical (ISM) bands. First, a metamaterial-inspired near-field resonant parasitic (NFRP) Egyptian axe dipole (EAD) antenna was seamlessly integrated with a highly efficient sensor-augmented rectifier without any matching network. It was electrically small and very thin, and its omnidirectional property was ideal for capturing incident AC wireless power from any azimuthal direction and converting it into DC power. Both a photocell as the light sensor and a thermistor as the temperature sensor were demonstrated. The resistive properties of the photocell and thermistor changed the rectifier's output voltage level; an acoustic alarm was activated once a threshold value was attained. Second, an electrically small, low-profile NFRP Huygens antenna was similarly integrated with the same light- and temperature-sensor-augmented rectifiers. Their unidirectional nature was very suitable for surface-mounted wireless power transfer (WPT) applications (i.e., on-body and on-wall sensors). Measurements of the prototypes of both the light- and temperature-sensor-augmented omni- and unidirectional rectenna systems confirmed their predicted performance characteristics.
Lin, X, Far, H & Saleh, A 2019, 'Structural Behaviour and Mechanical Properties of Welded Steel I-Girders with Corrugated Webs', International Journal of Steel Structures.
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Steel I girders with corrugated webs are appropriate alternatives for normal flat-web girders in steel structures since they provide lighter and smaller beam features in steel design. Based on the existing literature, the corrugated web beams (CWBs) provide many advantages for structural applications. In this study, a series of numerical analyses have been performed in order to investigate the structural behaviour of steel I girders with corrugated web profile and to compare their mechanical performance with normal welded beams. Theory of Ultimate Limit State design has been adopted in accordance with AS4100 (1998) along with considering geometric and material non-linearity in the numerical analyses in SAP2000. Comparing the results of the numerical investigation, merits of using corrugated welded beams (CWBs) over normal welded beams (WBs) have become apparent. Moreover, investigations regarding force-displacement relationship and buckling analysis of the webs were carried out and presented to further validate the advantages of using corrugated web beams. CWBs have been used in some parts of Australia without detailed information about their mechanical properties. Thus, based on the outcomes of this study, CWB table for dimensions and cross sectional properties has been developed and proposed for practical applications.
Lin, Y, Zheng, Z, Zhang, H, Gao, C & Yang, Y 2019, 'Bayesian query expansion for multi-camera person re-identification', Pattern Recognition Letters.
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© 2018 Elsevier B.V. Person re-identification (re-ID) is challenging because pedestrians may exhibit distinct appearance under different cameras. Given a query image, previous methods usually output the person retrieval results directly, which may perform badly due to the limited information provided by the single query image. To mine more query information, we add an expansion step to post-process the initial ranking list. The intuition is that a true match in the gallery may be difficult to be found by the query alone, but it can be easily retrieved by other true matches in the initial ranking list. In this paper, we propose the Bayesian Query Expansion (BQE) method to generate a new query with information from the initial ranking list. The Bayesian model is used to predict true matches in the gallery. We apply pooling on the features of these “true matches” to get a single vector, i.e., the expanded new query, with which the retrieval process is performed again to obtain the final results. We evaluate BQE with various feature extraction methods and distance metric learning methods on four large-scale re-ID datasets. We observe consistent improvement over all the baselines and report competitive performances compared with the state-of-the-art results.
Lin, Z, Zhou, L & Qiu, X 2019, 'A composite objective measure on subjective evaluation of speech enhancement algorithms', APPLIED ACOUSTICS, vol. 145, pp. 144-148.
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Linares-Mustarós, S, Ferrer-Comalat, JC, Corominas-Coll, D & Merigó, JM 2019, 'The ordered weighted average in the theory of expertons', International Journal of Intelligent Systems, vol. 34, no. 3, pp. 345-365.
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© 2018 Wiley Periodicals, Inc. This work presents a data-fusion mathematical object that incorporates the optimism level of a decision-making agent. The new fusion object is constructed by extending the ordered weighted averaging (OWA) operator in the process of creating an experton. The main advantage of this approach is that it can represent the attitudinal character of the decision maker in the construction of the experton. Therefore, this approach represents a new method for addressing multiperson problems by using optimistic and pessimistic perspectives. The work presents different practical examples based on the absolute hierarchical relationships of the “minimum of the bottom end of the intervals,” “minimum of the top end of the intervals,” and “minimum size of the interval.” The work also considers a wide range of particular cases of the OWA-experton, including the minimum experton, the maximum experton, the average experton, and the olympic experton. In addition, the study presents software for the calculation of OWA-expertons. Finally, the paper ends with an application in business decision-making regarding the calculation of expected benefits.
Ling, Y, Wang, K, Li, W, Shi, G & Lu, P 2019, 'Effect of slag on the mechanical properties and bond strength of fly ash-based engineered geopolymer composites', Composites Part B: Engineering, vol. 164, pp. 747-757.
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© 2019 Recently, the concept of engineered cementitious composites (ECCs) has been extended to the creation of engineered geopolymer composites (EGCs). Although showing similar mechanical characteristics (e. g., strain hardening and multiple cracking) to conventional ECC, the strength of existing EGC is generally low, and this sometimes restrains its applications. In the present study, a low-calcium (Class F) fly ash-based, polyvinyl alcohol (PVA) fiber reinforced EGC was developed and further modified by a ground-granulated blast-furnace slag (slag). The slag was used to replace the fly ash at content of 0%, 10%, 20%, and 30% (by weight). The effects of the slag on the mechanical properties (e.g., compressive strength, modulus of elasticity, uniaxial tensile behavior, flexural bending strength, and pullout bond strength) of the EGCs were investigated. The results revealed that all EGCs studied exhibited a strain/deflection hardening behavior under tension/flexure, and all slag replacements for fly ash enhanced strength-related properties but reduced ductility-related properties of the EGCs. The EGC mix with 20% slag replacement for fly ash (FA-20%S) had 102.3 MPa compressive strength, 6.8 MPa tensile strength, and 6.2 MPa bond strength, while the EGC mix with no slag (FA-0%S) had 72.6 MPa compressive strength, 4.7 MPa tensile strength, and 3.5 MPa bond strength at 28 days. These strength enhancements were mainly attributed to the improved density of the EGC matrix and the bond between the matrix and fiber. There are close relationships between the bond strength and other strengths, especially the tensile and flexural strengths, of the EGCs.
Liu, B, Chen, L, Zhu, X & Qiu, W 2019, 'Encrypted data indexing for the secure outsourcing of spectral clustering', Knowledge and Information Systems.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. Spectral clustering is one of the most popular clustering methods and is particularly useful for pattern recognition and image analysis. When using spectral clustering for analysis, users are either required to implement their own platforms, which requires strong data analytics and machine learning skills, or allow a third party to access and analyze their data, which may compromise their data privacy or security. Traditionally, this problem is solved by privacy-preserving data mining using randomization perturbation or secure multi-party computation. However, the existing methods suffer from the problems of inaccurate results or high computational requirements on the data owner’s side. To address these problems, in this paper, we propose a new secure outsourcing data mining (SODM) paradigm, which allows data owners to encrypt their data to ensure maximum data security. After the encryption, data owners can outsource their encrypted data to data analytics service providers (i.e., data analytics agent) for knowledge discovery, with a guarantee that neither the data analytics agent nor the other parties can compromise data privacy. To allow data mining to be efficiently carried out on encrypted data, we design a secure KD-tree to index all the encrypted data. Based on the SODM framework, a secure spectral clustering algorithm is proposed. The experiments on real-world datasets demonstrate the effectiveness and the efficiency of the system for the secure outsourcing of data mining.
Liu, C, Wang, S, Wang, Y, Lei, G, Guo, O & Zhu, J 2019, 'Development of a New Flux Switching Transverse Flux Machine with the Ability of Linear Motion', China Electrotechnical Society Transactions on Electrical Machines and Systems, vol. 2, no. 4, pp. 384-391.
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Liu, C, Zhu, L, He, X & Chen, J 2019, 'Enabling Privacy-Preserving Shortest Distance Queries on Encrypted Graph Data', IEEE Transactions on Dependable and Secure Computing.
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IEEE When coming to perform shortest distance queries on encrypted graph data outsourced in external storage infrastructure such as cloud, a significant challenge is how to compute the shortest distance in an accurate, efficient and secure way. This issue is addressed by a recent work, which makes use of somewhat homomorphic encryption (SWHE) to encrypt distance values output by a 2-hop cover labeling (2HCL) scheme. However, it may import large errors and even yield negative results. Besides, SWHE would be too time-consuming for normal clients. In this paper, we propose GENOA, a novel Graph ENcryption scheme for shOrtest distAnce queries. GENOA employs only efficient symmetric-key primitives while significantly enhances the accuracy compared to the prior work. As a reasonable trade-off, it additionally reveals the order information among queried distance values in the 2HCL index. We theoretically prove the accuracy and security of GENOA under rigorous cryptographic model. Detailed experiments on eight real-world graphs demonstrate that GENOA is efficient and can produce almost exact results.
Liu, G, Quan, W, Cheng, N, Zhang, H & Yu, S 2019, 'Efficient DDoS attacks mitigation for stateful forwarding in Internet of Things', Journal of Network and Computer Applications, vol. 130, pp. 1-13.
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© 2019 Elsevier Ltd Stateful forwarding plane is fully considered as a novel forwarding paradigm, which is proven to be beneficial to delivery efficiency and resilient to certain types of attacks. However, this fresh attempt also introduces “varietal” Denial-of-Service attack due to complicated forwarding state operations, which may cause long-term memory exhaustion of forwarding nodes, especially for resource-limited IoT nodes. This new distributed exhaustion attack is extremely hidden and there is currently no effective defense against it. In this paper, we first establish a game model to analyze the attack benefit between attacker and defender. To further make the defender obtain more utility, it is significative to make the defender manage expired state-entries during stateful forwarding. To this end, we propose an enhanced distributed low-rate attack mitigating (eDLAM) mechanism. Particularly, eDLAM maintains a lightweight malicious request table (MRT), which is very small, to offload burden of practical forwarding state table. When a packet request is matched in MRT, it will be marked and dropped directly without any impact on forwarding state table. Based on this, eDLAM adopts an optimal threshold update method for MRT to achieve a maximum defender utility. We evaluate the eDLAM performance in terms of false negatives rate (FNR) and false positives rate (FPR). Extensive experimental results show that eDLAM can reduce FNR by 10.5% and FPR by 44% on average compared with state-of-the-art mechanisms.
Liu, J, Ma, J, Xiang, Y, Zhou, W & Huang, X 2019, 'Authenticated Medical Documents Releasing with Privacy Protection and Release Control', IEEE Transactions on Dependable and Secure Computing.
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IEEE In the context of Information Societies, a tremendous amount of information is daily exchanged or released. Among various information-release cases, medical document release has gained significant attention for its potential in improving healthcare service quality and efficacy. However, integrity and origin authentication of released medical documents is the priority in subsequent applications. Moreover, sensitive nature of much of this information also gives rise to a serious privacy threat when medical documents are uncontrollably made available to untrusted third parties. Redactable signatures allow any party to delete pieces of an authenticated document while guaranteeing the origin and integrity authentication of the resulting (released) subdocument. Nevertheless, most of existing redactable signature schemes (RSSs) are vulnerable to dishonest redactors or illegal redaction detection. To address the above issues, we propose two distinct RSSs with flexible release control (RSSs-FRC). We also analyse the performance of our constructions in terms of security, efficiency and functionality. The analysis results show that the performance of our construction has significant advantages over others, from the aspects of security and efficiency.
Liu, J, Wu, C, Li, C, Dong, W, Su, Y, Li, J, Cui, N, Zeng, F, Dai, L, Meng, Q & Pang, J 2019, 'Blast testing of high performance geopolymer composite walls reinforced with steel wire mesh and aluminium foam', CONSTRUCTION AND BUILDING MATERIALS, vol. 197, pp. 533-547.
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Liu, J, Wu, C, Li, J, Fang, J, Su, Y & Shao, R 2019, 'Ceramic balls protected ultra-high performance concrete structure against projectile impact-A numerical study', INTERNATIONAL JOURNAL OF IMPACT ENGINEERING, vol. 125, pp. 143-162.
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Liu, L, Zhang, T, Leighton, B, Zhao, L, Huang, S & Dissanayake, G 2019, 'Robust Global Structure From Motion Pipeline With Parallax on Manifold Bundle Adjustment and Initialization', IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 2164-2171.
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© 2016 IEEE. In this letter, we present a novel global structure from motion (SfM) pipeline that is particularly effective in dealing with low-parallax scenes and camera motion collinear with the features that represent the environment structure. It is therefore particularly suitable in Urban SLAM, in which frequent road-facing motion poses many challenges to conventional SLAM algorithms. Our pipeline includes a recently explored bundle adjustment (BA) method that exploits a feature parameterization using Parallax angle between on-Manifold observation rays (PMBA). It is demonstrated that this BA stage has a consistently stable optimization configuration for features with any parallax and therefore low-parallax features can stay in reconstruction without pre-filtering. To allow practical usage of PMBA, we provide a compatible initialization stage in the SfM to initialize all camera poses simultaneously, exhibiting friendliness to collinear motion. This is achieved by simplifying PMBA into a hybrid graph problem of high connectivity yet small node set size, solved using a robust linear programming technique. Using simulations and a series of publicly available real datasets including "KITTI" and "Bundle Adjustment in the Large," we demonstrate the robustness of the position initialization stage in handling collinear motion and outlier matches, superior convergence performance of the BA stage in the presence of low-parallax features, and effectiveness of our pipeline to handle many sequential or out-of-order urban scenes.
Liu, M, Luo, Y, Yang, C, Pang, S, Puthal, D, Ren, K & Zhang, X 2019, 'Privacy-preserving matrix product based static mutual exclusive roles constraints violation detection in interoperable role-based access control', Future Generation Computer Systems.
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© 2018 Elsevier B.V. Secure interoperation is an important technology to protect shared data in multi-domain environments. IRBAC (Interoperable Role-based Access Control) 2000 model has been proposed to achieve security interoperation between two or more RBAC administrative domains. Static Separation of Duties (SSoD) is an important security policy in RBAC, but it has not been enforced in the IRBAC 2000 model. As a result, some previous works have studied the problem of SMER (Statically Mutually Exclusive Roles) constraints violation between two RBAC domains in the IRBAC 2000 model. However all of them do not enforce how to preserve privacy of RBAC policies, such as roles, roles hierarchies and user-role assignment while detecting SMER constraints violation, if the two interoperable domains do not want to disclose them each other and to others. In order to enforce privacy-preserving detection of SMER constraints violation, we first introduce a solution without privacy-preserving mechanism using matrix product. Then a privacy-preserving solution is proposed to securely detect SMER constraints violation without disclosing any RBAC policy based on a secure three-party protocol to matrix product computation. By efficiency analysis and experimental results comparison, the secure three-party computation protocol to matrix product based on the Paillier cryptosystem is more efficient and practical.
Liu, M, Xue, Z, He, X & Chen, J 2019, 'Cyberthreat-Intelligence Information Sharing', IEEE CONSUMER ELECTRONICS MAGAZINE, vol. 8, no. 3, pp. 17-22.
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Liu, Q, Zhang, N, Feng, F & Zhou, M 2019, 'Handling performance of tractor-semitrailers equipped with hydraulically interconnected suspension', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering.
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© IMechE 2018. This paper focuses on the contribution of a hydraulically interconnected suspension on the handling performance of a tractor-semitrailer. A hydraulically interconnected suspension model is integrated with a traditional tractor-semitrailer suspension for the purpose of study, and the hydraulically interconnected suspension system is first investigated through the bench test simulation to study its nonlinear and damping characteristic. Then a static rollover test, a steady-state circular test, a pulse steer input test, a pulse road input test and a random road input test are simulated in order to fully investigate the performance of a tractor-semitrailer with hydraulically interconnected suspension. The comparison analyses are conducted among two vehicles which are a fully loaded tractor-semitrailer without hydraulically interconnected suspension, an unloaded tractor-semitrailer without hydraulically interconnected suspension, a fully loaded tractor-semitrailer with hydraulically interconnected suspension and an unloaded tractor-semitrailer with hydraulically interconnected suspension. The obtained results show that the fully loaded tractor-semitrailer has enhanced handling performance, an improved steady-state rollover threshold and unchanged ride performance with the assistance of hydraulically interconnected suspension.
Liu, T, Sun, G, Fang, J, Zhang, J & Li, Q 2019, 'Topographical design of stiffener layout for plates against blast loading using a modified ant colony optimization algorithm', STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, vol. 59, no. 2, pp. 335-350.
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Liu, W, Shen, X, Du, B, Tsang, IW, Zhang, W & Lin, X 2019, 'Hyperspectral Imagery Classification via Stochastic HHSVMs.', IEEE Transactions on Image Processing, vol. 28, no. 2, pp. 577-588.
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Hyperspectral imagery (HSI) has shown promising results in real-world applications. However, the technological evolution of optical sensors poses two main challenges in HSI classification: 1) the spectral band is usually redundant and noisy and 2) HSI with millions of pixels has become increasingly common in real-world applications. Motivated by the recent success of hybrid huberized support vector machines (HHSVMs), which inherit the benefits of both lasso and ridge regression, this paper first investigates the advantages of HHSVM for HSI applications. Unfortunately, the existing HHSVM solvers suffer from prohibitive computational costs on large-scale data sets. To solve this problem, this paper proposes simple and effective stochastic HHSVM algorithms for HSI classification. In the stochastic settings, we show that with a probability of at least , our algorithms find an -accurate solution using iterations. Since the convergence rate of our algorithms does not depend on the size of the training set, our algorithms are suitable for handling large-scale problems. We demonstrate the superiority of our algorithms by conducting experiments on large-scale binary and multiclass classification problems, comparing to the state-of-the-art HHSVM solvers. Finally, we apply our algorithms to real HSI classification and achieve promising results.
Liu, W, Xu, D, Tsang, IW & Zhang, W 2019, 'Metric Learning for Multi-Output Tasks.', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 2, pp. 408-422.
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Multi-output learning with the task of simultaneously predicting multiple outputs for an input has increasingly attracted interest from researchers due to its wide application. The k nearest neighbor ([Formula: see text]) algorithm is one of the most popular frameworks for handling multi-output problems. The performance of [Formula: see text] depends crucially on the metric used to compute the distance between different instances. However, our experiment results show that the existing advanced metric learning technique cannot provide an appropriate distance metric for multi-output tasks. This paper systematically studies how to efficiently learn an appropriate distance metric for multi-output problems with provable guarantee. In particular, we present a novel large margin metric learning paradigm for multi-output tasks, which projects both the input and output into the same embedding space and then learns a distance metric to discover output dependency such that instances with very different multiple outputs will be moved far away. Several strategies are then proposed to speed up the training and testing time. Moreover, we study the generalization error bound of our method for three learning tasks, which shows that our method converges to the optimal solutions. Experiments on three multi-output learning tasks (multi-label classification, multi-target regression, and multi-concept retrieval) validate the effectiveness and scalability of the proposed method.
Liu, X, Iftikhar, N, Huo, H, Li, R & Nielsen, PS 2019, 'Two approaches for synthesizing scalable residential energy consumption data', Future Generation Computer Systems, vol. 95, pp. 586-600.
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© 2019 Elsevier B.V. Many fields require scalable and detailed energy consumption data for different study purposes. However, due to privacy issues, it is often difficult to obtain sufficiently large datasets. This paper proposes two different methods for synthesizing fine-grained energy consumption data for residential households, namely a regression-based method and a probability-based method. They each use a supervised machine learning method, which trains models with a relatively small real-world dataset and then generates large-scale time series based on the models. This paper describes the two methods in details, including data generation process, optimization techniques, and parallel data generation. This paper evaluates the performance of the two methods, which compare the resulting consumption profiles with real-world data, including patterns, statistics, and parallel data generation in the cluster. The results demonstrate the effectiveness of the proposed methods and their efficiency in generating large-scale datasets.
Liu, X, Xu, Q, Wang, D, Wu, Y, Yang, Q, Liu, Y, Wang, Q, Li, X, Li, H, Zeng, G & Yang, G 2019, 'Unveiling the mechanisms of how cationic polyacrylamide affects short-chain fatty acids accumulation during long-term anaerobic fermentation of waste activated sludge', WATER RESEARCH, vol. 155, pp. 142-151.
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Liu, X, Xu, Q, Wang, D, Yang, Q, Wu, Y, Li, Y, Fu, Q, Yang, F, Liu, Y, Ni, B-J, Wang, Q & Li, X 2019, 'Thermal-alkaline pretreatment of polyacrylamide flocculated waste activated sludge: Process optimization and effects on anaerobic digestion and polyacrylamide degradation', BIORESOURCE TECHNOLOGY, vol. 281, pp. 158-167.
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Liu, X, Xu, Q, Wang, D, Yang, Q, Wu, Y, Yang, J, Liu, Y, Wang, Q, Ni, B-J, Li, X, Li, H & Yang, G 2019, 'Enhanced Short-Chain Fatty Acids from Waste Activated Sludge by Heat-CaO2 Advanced Thermal Hydrolysis Pretreatment: Parameter Optimization, Mechanisms, and Implications', ACS SUSTAINABLE CHEMISTRY & ENGINEERING, vol. 7, no. 3, pp. 3544-3555.
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Liu, XP, Zhang, GQ, Lu, J & Zhang, JQ 2019, 'Risk assessment using transfer learning for grassland fires', Agricultural and Forest Meteorology, vol. 269-270, pp. 102-111.
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© 2019 A new direction of risk assessment research in grassland fire management is data-driven prediction, in which data are collected from particular regions. Since some regions have rich datasets that can easily generate knowledge for risk prediction, and some have no data available, this study addresses how we can leverage the knowledge learned from one grassland risk assessment to assist with a current assessment task. In this paper, we first introduce the transfer learning methodology to map and update risk maps in grassland fire management, and we propose a new grassland fire risk analysis method. In this study, two major grassland areas (Xilingol and Hulunbuir) in northern China are selected as the study areas, and five representative indicators (features) are extracted from grassland fuel, fire climate, accessibility, human and social economy. Taking Xilingol as the source domain (where sufficient labelled data are available) and Hulunbuir as the target domain (which contains insufficient data but requires risk assessment/prediction), we then establish the mapping relationship between grassland fire indicators and the degrees of grassland fire risk by using a transfer learning method. Finally, the fire risk in the Hulunbuir grassland is assessed using the transfer learning method. Experiments show that the prediction accuracy reached 87.5% by using the transfer learning method, representing a significant increase over existing methods.
Liu, Y, Lan, C, Blumenstein, M & Li, J 2019, 'Bi-level error correction for PacBio long reads', IEEE/ACM Transactions on Computational Biology and Bioinformatics.
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IEEE The latest sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines can generate long reads at the length of thousands of nucleic bases which is much longer than the reads at the length of hundreds generated by Illumina machines. However, these long reads are prone to much higher error rates, for example 15%, making downstream analysis and applications very difficult. Error correction is a process to improve the quality of sequencing data. Hybrid correction strategies have been recently proposed to combine Illumina reads of low error rates to fix sequencing errors in the noisy long reads with good performance. In this paper, we propose a new method named Bicolor, a bi-level framework of hybrid error correction for further improving the quality of PacBio long reads. At the first level, our method uses a de Bruijn graph-based error correction idea to search paths in pairs of solid < formula > < tex > $k$ < /tex > < /formula > -mers iteratively with an increasing length of < formula > < tex > $k$ < /tex > < /formula > -mer. At the second level, we combine the processed results under different parameters from the first level. In particular, a multiple sequence alignment algorithm is used to align those similar long reads, followed by a voting algorithm which determines the final base at each position of the reads. We compare the superior performance of Bicolor with three state-of-the-art methods on three real data sets. Results demonstrate that Bicolor always achieves the highest identity ratio. Bicolor also achieves a higher alignment ratio ( < formula > < tex > $ & #x003E; 1.3\%$ < /tex > < /formula > ) and a higher number of aligned reads than the current methods on two data sets. On the third data set, our method is closely competitive to the current methods in terms of number of aligned reads and genome coverage. The C++ source codes of our algorithm are freely available at https://github.com/yuansliu/Bicolor.
Liu, Y, Ngo, HH, Guo, W, Peng, L, Wang, D & Ni, B 2019, 'The roles of free ammonia (FA) in biological wastewater treatment processes: A review.', Environment international, vol. 123, pp. 10-19.
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Free ammonia (FA) can pose inhibitory and/or biocidal effects on a variety of microorganisms involved in different biological wastewater treatment process, which is widely presented in wastewater treatment plants (WWTPs) due to the high levels of ammonium in the systems. This review article gives the up-to-date status on several essential roles of FA in biological wastewater treatment processes: the impacts of FA, mechanisms of FA roles, modeling of FA impacts, and implications of FA for wastewater treatment. Specifically, the impacts of FA on both wastewater and sludge treatment lines were firstly summarized, including nitrification, denitrification, anaerobic ammonium oxidation (Anammox), enhanced biological phosphorus removal and anaerobic processes. The involved mechanisms were then analyzed, which indicated FA inhibition can slow specific microbial activities or even reconfigure the microbial community structure, likely due to negative impacts of FA on intracellular pH, specific enzymes and extracellular polymeric substances (EPS), thus causing cell inactivation/lysis. Mathematical models describing the impact of FA on both wastewater and sludge treatment processes were also explored to facilitate process optimization. Finally, the key implications of FA were identified, that is FA can be leveraged to substantially enhance the biodegradability of secondary sludge, which would further improve biological nutrient removal and enhance renewable energy production.
Liu, Y, Xu, W, Zhu, J & Blaabjerg, F 2019, 'Sensorless Control of Standalone Brushless Doubly Fed Induction Generator Feeding Unbalanced Loads in a Ship Shaft Power Generation System', IEEE Transactions on Industrial Electronics, vol. 66, no. 1, pp. 739-749.
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© 1982-2012 IEEE. The standalone brushless doubly fed induction generator (BDFIG) has demonstrated excellent energy-saving performance in ship shaft power generation applications. As a standalone system, it exhibits unbalanced terminal voltages and poor performance under unbalanced loads. However, the existing control scheme of grid-connected BDFIGs cannot be directly applied to stabilize the amplitude and frequency of terminal voltage when the rotor speed and electrical load vary. This paper presents a new sensorless control scheme for the standalone BDFIG under unbalanced load conditions in the ship shaft power generation system. A second-order generalized integrator-based quadrature signal generator is introduced to realize the rotor speed observer for the standalone BDFIG feeding unbalanced loads. The compensation method of negative-sequence power winding voltage is proposed to eliminate the negative-sequence component of the unbalanced power winding voltage. Comprehensive experiments are carried out on a prototype BDFIG with and without the compensation of negative-sequence power winding voltage. The good performance of the proposed sensorless control scheme is verified by the experimental test results.
Liu, Y, Yu Zhang, L & Li, J 2019, 'Fast detection of maximal exact matches via fixed sampling of query k-mers and Bloom filtering of index k-mers.', Bioinformatics (Oxford, England).
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MOTIVATION:Detection of maximal exact matches (MEMs) between two long sequences is a fundamental problem in pairwise reference-query genome comparisons. To efficiently compare larger and larger genomes, reducing the number of indexed k-mers as well as the number of query k-mers has been adopted as a mainstream approach which saves the computational resources by avoiding a significant number of unnecessary matches. RESULTS:Under this framework, we proposed a new method to detect all MEMs from a pair of genomes. The method first performs a fixed sampling of k-mers on the query sequence, and add these selected k-mers to a Bloom filter. Then all the k-mers of the reference sequence are tested by the Bloom filter. If a k-mer passes the test, it is inserted into a hash table for indexing. Compared with the existing methods, much less number of query k-mers are generated and much less k-mers are inserted into the index to avoid unnecessary matches, leading to an efficient matching process and memory usage savings. Experiments on large genomes demonstrate that our method is at least 1.8 times faster than the best of the existing algorithms. This performance is mainly attributed to the key novelty of our method that the fixed k-mer sampling must be conducted on the query sequence and the index k-mers are filtered from the reference sequence via a Bloom filter. AVAILABILITY AND IMPLEMENTATION:https://github.com/yuansliu/bfMEM. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
Liu, Y, Yu, Y, Dinger, ME & Li, J 2019, 'Index suffix-prefix overlaps by (w, k)-minimizer to generate long contigs for reads compression.', Bioinformatics.
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Motivation:Advanced high-throughput sequencing technologies have produced massive amount of reads data, and algorithms have been specially designed to contract the size of these data sets for efficient storage and transmission. Reordering reads with regard to their positions in de novo assembled contigs or in explicit reference sequences has been proven to be one of the most effective reads compression approach. As there is usually no good prior knowledge about the reference sequence, current focus is on the novel construction of de novo assembled contigs. Results:We introduce a new de novo compression algorithm named minicom. This algorithm uses large k-minimizers to index the reads and subgroup those that have the same minimizer. Within each subgroup, a contig is constructed. Then some pairs of the contigs derived from the subgroups are merged into longer contigs according to a (w; k)-minimizer indexed suffix-prefix overlap similarity between two contigs. This merging process is repeated after the longer contigs are formed until no pair of contigs can be merged. We compare the performance of minicom with two reference-based methods and four de novo methods on 18 data sets (13 RNA-seq data sets and 5 whole genome sequencing data sets). In the compression of single-end reads, minicom obtained the smallest file size for 22 of 34 cases with significant improvement. In the compression of paired-end reads, minicom achieved 20-80% compression gain over the best state-of-the-art algorithm. Our method also achieved a 10% size reduction of compressed files in comparison with the best algorithm under the reads-order preserving mode. These excellent performances are mainly attributed to the exploit of the redundancy of the repetitive substrings in the long contigs. Availability and Implementation:https://github.com/yuansliu/minicom. Supplementary Information:Supplementary data are available at Bioinformatics online.
Liu, Z, Yang, X, Yang, Y, Wang, K & Mao, G 2019, 'DATS: Dispersive Stable Task Scheduling in Heterogeneous Fog Networks', IEEE Internet of Things Journal.
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IEEE Fog computing has risen as a promising architecture for future Internet of Things (IoT), 5G and embedded artificial intelligence (AI) applications with stringent service delay requirements along the cloud to things continuum. For a typical fog network consisting of heterogeneous fog nodes (FNs) with different computing resources and communication capabilities, how to effectively schedule complex computation tasks to multiple FNs in the neighborhood to achieve minimal service delay is a fundamental challenge. To tackle this problem, a new concept named processing efficiency (PE) is first defined to incorporate computing resources and communication capacities. Further, to minimize service delay in heterogeneous fog networks, a scalable, stable and decentralized algorithm, namely dispersive stable task scheduling (DATS), is proposed and evaluated, which consists of two key components: (i) a PE-based progressive computing resources competition (PCRC) and (ii) a QoE-oriented synchronized task scheduling (STS). Theoretical proofs and simulation results show that the proposed DATS algorithm can achieve effective tradeoff between computing resources and communication capabilities, thus significantly reducing service delay in heterogeneous fog networks.
Liu, Z, Zhang, H, Cheng, A, Wu, C & Yang, G 2019, 'Seismic Interaction between a Lined Tunnel and a Hill under Plane SV Waves by IBEM', International Journal of Structural Stability and Dynamics, vol. 19, no. 2.
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© 2019 World Scientific Publishing Company. This paper investigates the dynamic interaction between a lined tunnel and a hill under plane SV waves using the indirect boundary element method (IBEM), with the displacement and stress characteristics of the system presented in frequency domain. The IBEM has several unique advantages such as reducing calculation dimension, automatically satisfying the infinite radiation condition, etc. The numerical results indicated that the dynamic response of the tunnel-hill system is strongly dependent on incident wave characteristics, geometrical and material properties of the lined tunnel, as well as the topography of the hill. For a dimension ratio between the hill and tunnel of less than 10.0, the lined tunnel has large amplification or deamplification effect on the dynamic response of the hill. Correspondingly, the hill also greatly amplifies the displacement and stress concentration of the tunnel especially in the lower-frequency range, due to the complicated interference effect among the reflected waves and diffracted waves induced by the tunnel and hill. Also demonstrated is that the displacement and stress amplitude spectrums highly depend on the incident frequency and the space location, and there exist multiple peaks and troughs in the spectrum curve with the peaks usually appearing in the low-frequency range. Thus, for the seismic safety assessment of a hill slope or hill tunnel in practice, the dynamic interaction within the tunnel-hill system should be taken into consideration.
Llanos-Herrera, GR & Merigo, JM 2019, 'Overview of brand personality research with bibliometric indicators', Kybernetes.
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© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to present a global view of the research that has been conducted regarding brand personality by using the Core Collection of the Web of Science (WoS) as a reference. The main bibliometric indicators considered are number of articles, number of citations, main authors, principal journals, institutions, countries and keywords. Design/methodology/approach: Through a bibliometric investigation, this paper performs an analysis of investigations of brand personality that have been conducted to date. In particular, the analysis focuses on the papers that have generated the greatest impact in the scientific community, the journals that have given the most attention to this concept and the authors who have most strongly influenced the academic world in this field. The analysis reveals a series of relationships between the bases of knowledge considered for different authors and journals and the structure of those relationships based on the keywords considered in each contribution. Findings: This analysis allows to obtain a general and impartial view of brand personality research, and it reveals the most relevant contributions to the academic world in terms of authors, journals, institutions, countries and keywords. The analysis shows that the concept under study seems to still be in an early stage of development and there may well be an important amount of development ahead. Although there have been important contributions to this field, work is still required to consolidate this knowledge. Research limitations/implications: The information provided pertains to a relatively specific subject but is still general when considered within the context of this topic and thus leaves aside elements that could greatly enrich the analysis. However, this work presents some important guidelines for conducting in-depth academic research and publication. Practical implications: This work identifies the most product...
Lu, J, Liu, A, Dong, F, Gu, F, Gama, J & Zhang, G 2019, 'Learning under Concept Drift: A Review', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Concept drift describes unforeseeable changes in the underlying distribution of streaming data over time. Concept drift research involves the development of methodologies and techniques for drift detection, understanding and adaptation. Data analysis has revealed that machine learning in a concept drift environment will result in poor learning results if the drift is not addressed. To help researchers identify which research topics are significant and how to apply related techniques in data analysis tasks, it is necessary that a high quality, instructive review of current research developments and trends in the concept drift field is conducted. In addition, due to the rapid development of concept drift in recent years, the methodologies of learning under concept drift have become noticeably systematic, unveiling a framework which has not been mentioned in literature. This paper reviews over 130 high quality publications in concept drift related research areas, analyzes up-to-date developments in methodologies and techniques, and establishes a framework of learning under concept drift including three main components: concept drift detection, concept drift understanding, and concept drift adaptation. This paper lists and discusses 10 popular synthetic datasets and 14 publicly available benchmark datasets used for evaluating the performance of learning algorithms aiming at handling concept drift. Also, concept drift related research directions are covered and discussed. By providing state-of-the-art knowledge, this survey will directly support researchers in their understanding of research developments in the field of learning under concept drift.
Lu, P, Liu, T, Ni, BJ, Guo, J, Yuan, Z & Hu, S 2019, 'Growth kinetics of Candidatus ‘Methanoperedens nitroreducens’ enriched in a laboratory reactor', Science of the Total Environment, vol. 659, pp. 442-450.
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© 2018 Recently it has been shown that Candidatus ‘Methanoperedens nitroreducens’, an anaerobic methanotrophic archaea (ANME), can reduce nitrate to nitrite using electrons derived from anaerobic oxidation of methane. In this study, the growth kinetics of ‘M. nitroreducens’ enriched in a laboratory reactor were studied. In the experimental concentration range (up to 16 mg CH 4 L −1 ), anaerobic oxidation of methane by ‘M. nitroreducens’ was found to comply with first order kinetic model with a rate constant of 0.019 ± 0.006 h −1 and a biomass-specific rate constant of 0.04–0.14 L h −1 g −1 VSS. Meanwhile, the nitrate reduction to nitrite was well described by the Monod-type kinetic model with an affinity constant for nitrate of 2.1 ± 0.4 mg N L −1 , which is slightly higher than, but comparable to, that of most known denitrifying bacteria. This is the first time that the growth kinetics of ‘M. nitroreducens’ have been experimentally studied. The applicability of the kinetic model reported herein to this organism or similar organisms in natural or engineering systems requires further investigation.
Lu, Y, Fang, J, Guo, Z & Zhang, J 2019, 'Distributed Transmit Beamforming for UAV to Base Communications', China Communications, vol. 16, no. 1, pp. 15-25.
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Lu, Z, Zhao, B, He, L, Zhang, D & Zhang, J 2019, 'Security level classification based on power system partitioning', IET Generation, Transmission and Distribution.
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Lu, ZH, Lun, PY, Li, W, Luo, Z, Li, Y & Liu, P 2019, 'Empirical model of corrosion rate for steel reinforced concrete structures in chloride-laden environments', Advances in Structural Engineering, vol. 22, no. 1, pp. 223-239.
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© The Author(s) 2018. The corrosion rate of reinforcing steel is an important factor to determine the corrosion propagation of reinforced concrete structures in the chloride-laden environments. Since the corrosion rate of reinforcing steel is affected by several coupled parameters, the efficient prediction of which remains challenging. In this study, a total of 156 experimental data on corrosion rate from the literature were collected and compared. Seven empirical models for predicting the corrosion rate were reviewed and investigated using the collected experimental data. Based on the investigations, a new empirical model is proposed for predicting the corrosion rate in corrosion-affected reinforced concrete structures considering parameters including concrete resistivity, temperature, relative humidity, corrosion duration and concrete chloride content. The comparison between the experimental data and those predicted using the new empirical model demonstrates that the new model gives a good prediction of the corrosion rate. Furthermore, the uncertainty and probability characteristics of these empirical models are also investigated. It is found that the probability distributions of the model errors can be described as lognormal, normal, Weibull or Gumbel distributions. As a result, the new empirical model can provide an efficient prediction of the corrosion rate of reinforcing steel, and the model error analysis results can be utilized for reliability-based service life prediction of reinforced concrete structures under chloride-laden environments.
Luo, F, Du, B, Zhang, L, Zhang, L & Tao, D 2019, 'Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image', IEEE Transactions on Cybernetics.
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IEEE Hyperspectral image (HSI) contains a large number of spatial-spectral information, which will make the traditional classification methods face an enormous challenge to discriminate the types of land-cover. Feature learning is very effective to improve the classification performances. However, the current feature learning approaches are most based on a simple intrinsic structure. To represent the complex intrinsic spatial-spectral of HSI, a novel feature learning algorithm, termed spatial-spectral hypergraph discriminant analysis (SSHGDA), has been proposed on the basis of spatial-spectral information, discriminant information, and hypergraph learning. SSHGDA constructs a reconstruction between-class scatter matrix, a weighted within-class scatter matrix, an intraclass spatial-spectral hypergraph, and an interclass spatial-spectral hypergraph to represent the intrinsic properties of HSI. Then, in low-dimensional space, a feature learning model is designed to compact the intraclass information and separate the interclass information. With this model, an optimal projection matrix can be obtained to extract the spatial-spectral features of HSI. SSHGDA can effectively reveal the complex spatial-spectral structures of HSI and enhance the discriminating power of features for land-cover classification. Experimental results on the Indian Pines and PaviaU HSI data sets show that SSHGDA can achieve better classification accuracies in comparison with some state-of-the-art methods.
Luo, Y, Wen, Y, Liu, T & Tao, D 2019, 'Transferring Knowledge Fragments for Learning Distance Metric from a Heterogeneous Domain.', IEEE transactions on pattern analysis and machine intelligence, vol. 41, no. 4, pp. 1013-1026.
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The goal of transfer learning is to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. In this paper, we examine the problem of transfer distance metric learning (DML), which usually aims to mitigate the label information deficiency issue in the target DML. Most of the current Transfer DML (TDML) methods are not applicable to the scenario where data are drawn from heterogeneous domains. Some existing heterogeneous transfer learning (HTL) approaches can learn target distance metric by usually transforming the samples of source and target domain into a common subspace. However, these approaches lack flexibility in real-world applications, and the learned transformations are often restricted to be linear. This motivates us to develop a general flexible heterogeneous TDML (HTDML) framework. In particular, any (linear/nonlinear) DML algorithms can be employed to learn the source metric beforehand. Then the pre-learned source metric is represented as a set of knowledge fragments to help target metric learning. We show how generalization error in the target domain could be reduced using the proposed transfer strategy, and develop novel algorithm to learn either linear or nonlinear target metric. Extensive experiments on various applications demonstrate the effectiveness of the proposed method.
Luo, Z, Li, W, Tam, VWY, Xiao, J & Shah, SP 2019, 'Current progress on nanotechnology application in recycled aggregate concrete', Journal of Sustainable Cement-Based Materials.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. As a very promising sustainable construction material, recycled aggregate concrete (RAC) has become a hot research topic and attracted wide attention. Although many investigations have been conducted, it is still a great challenge to product RAC with satisfactory and stable performance for practical engineering application. In recent years, nanotechnology has been introduced to RAC research and displayed distinct advantages over many traditional methods. This article is devoted to reviewing the current research on nanotechnology application in RAC, including nanoengineering and nanoscience. The corresponding results involving microstructure characteristics, mechanical properties, workability, and durability were summarized and discussed. It has been found that nanoscience has promoted better understanding of microstructure and various internal mechanism of RAC, while nanoengineering has helped RAC achieve dense microstructure, improved mechanical properties and durability. However, further efforts are required to realize the potential of nanotechnology and to bring breakthroughs in research and application of RAC.
Ly, BT & Far, H 2019, 'Investigation on Properties of Coarse Reclaimed Aggregates and Their Effects on Concrete Strength and Workability', Structural Concrete.
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Fresh concrete waste is constantly generated in concrete batch plants during the production of ready-mix concrete and this waste is generally dumped in landfills. Natural aggregates are a finite resource and therefore there is a need to recycle this fresh concrete waste. An on-site reclaimer can recycle fresh concrete waste to produce reclaimed aggregate and eliminate the need to dump fresh concrete waste in landfills. This study investigates the properties of coarse reclaimed aggregates sampled from an Australian concrete batch plant. It was observed that the particle size distribution was highly varied while particle density and water absorption were similar to their parent aggregate. It was also found that concrete containing coarse reclaimed aggregates exhibited a higher workability and lower compressive strength than concrete containing natural coarse aggregates. Utilisation of coarse reclaimed aggregates in concrete application is a viable option, however more research will need to be conducted in this area.
Lyu, B, Dinh, TH & Yang, Z 2019, 'User Cooperation in Wireless-Powered Backscatter Communication Networks', IEEE WIRELESS COMMUNICATIONS LETTERS, vol. 8, no. 2, pp. 632-635.
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Lyu, B, Qin, L, Lin, X, Chang, L & Yu, JX 2019, 'Supergraph Search in Graph Databases via Hierarchical Feature-Tree', IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 2, pp. 385-400.
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© 1989-2012 IEEE. Supergraph search is a fundamental problem in graph databases that is widely applied in many application scenarios. Given a graph database and a query-graph, supergraph search retrieves all data-graphs contained in the query-graph from the graph database. Most existing solutions for supergraph search follow the pruning-and-verification framework, which prune false answers based on features in the pruning phase and perform subgraph isomorphism testings on the remaining graphs in the verification phase. However, they are not scalable to handle large-sized data-graphs and query-graphs due to three drawbacks. First, they rely on a frequent subgraph mining algorithm to select features which is expensive and cannot generate large features. Second, they require a costly verification phase. Third, they process features in a fixed order without considering their relationships to the query-graph. In this paper, we address the three drawbacks and propose new indexing and query processing algorithms. In indexing, we select features directly from the data-graphs without expensive frequent subgraph mining. The features form a feature-tree that contains all-sized features and both the cost sharing and pruning power of the features are considered. In query processing, we propose a new algorithm, where the order to process features is query-dependent by considering both the cost sharing and the pruning power. We explore two optimization strategies to further improve the algorithm efficiency. The first strategy applies a lightweight graph compression technique and the second strategy optimizes the inclusion of answers. We further introduce how to efficiently maintain the index incrementally when the graph database is updated dynamically. Moreover, we propose an approximation approach to significantly reduce the computational cost for large data-graphs and/or query-graphs while preserving a high result quality. Finally, we conduct extensive performance studies on ...
Lyu, X, Xu, Y, Xu, Q & Yu, Y 2019, 'Axial Compression Performance of Square Thin Walled Concrete-Filled Steel Tube Stub Columns with Reinforcement Stiffener under Constant High-Temperature.', Materials (Basel, Switzerland), vol. 12, no. 7.
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This study investigated the axial compressive performance of six thin-walled concrete-filled steel tube (CFST) square column specimens with steel bar stiffeners and two non-stiffened specimens at constant temperatures of 20 °C, 100 °C, 200 °C, 400 °C, 600 °C and 800 °C. The mechanical properties of the specimens at different temperatures were analyzed in terms of the ultimate bearing capacity, failure mode, and load-displacement curve. The experiment results show that at high temperature, even though the mechanical properties of the specimens declined, leading to a decrease of the ultimate bearing capacity, the ductility and deformation capacity of the specimens improved inversely. Based on finite element software ABAQUS, numerical models were developed to calculate both temperature and mechanical fields, the results of which were in good agreement with experimental results. Then, the stress mechanism of eight specimens was analyzed using established numerical models. The analysis results show that with the increase of temperature, the longitudinal stress gradient of the concrete in the specimen column increases while the stress value decreases. The lateral restraint of the stiffeners is capable of restraining the steel outer buckling and enhancing the restraint effect on the concrete.
Ma, C, Tsang, IW, Shen, F & Liu, C 2019, 'Error Correcting Input and Output Hashing.', IEEE transactions on cybernetics, vol. 49, no. 3, pp. 781-791.
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Most learning-based hashing algorithms leverage sample-to-sample similarities, such as neighborhood structure, to generate binary codes, which achieve promising results for image retrieval. This type of methods are referred to as instance-level encoding. However, it is nontrivial to define a scalar to represent sample-to-sample similarity encoding the semantic labels and the data structure. To address this issue, in this paper, we seek to use a class-level encoding method, which encodes the class-to-class relationship, to take the semantic information of classes into consideration. Based on these two encodings, we propose a novel framework, error correcting input and output (EC-IO) coding, which does class-level and instance-level encoding under a unified mapping space. Our proposed model contains two major components, which are distribution preservation and error correction. With these two components, our model maps the input feature of samples and the output code of classes into a unified space to encode the intrinsic structure of data and semantic information of classes simultaneously. Under this framework, we present our hashing model, EC-IO hashing (EC-IOH), by approximating the mapping space with the Hamming space. Extensive experiments are conducted to evaluate the retrieval performance, and EC-IOH exhibits superior and competitive performances comparing with popular supervised and unsupervised hashing methods.
Ma, L, Pei, Q, Xiang, Y, Yao, L & Yu, S 2019, 'A reliable reputation computation framework for online items in E-commerce', Journal of Network and Computer Applications, vol. 134, pp. 13-25.
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© 2019 Elsevier Ltd Most of online trading platforms allow consumers to give personal ratings to online items. By computing the weighted mean of the ratings, the reputation values of online items can be derived to assist consumers to make purchasing decisions. However, it is never a simple task to derive a reliable reputation value of any given item and existing works fail to achieve this. Thus, in this paper, we propose a reliable reputation computation framework for online items which can be adopted by online trading platforms or run by a third party to provide reputation computation as a service. At first, a fine-grained two-phase detection method is proposed to detect malicious ratings. After filtering out the ratings detected as malicious, the weights of the remaining ratings are determined by computing the degrees to which the users giving these ratings are interested in a target item. Extensive experiments verify that the proposed reliable reputation computation framework is effective to detect different kinds of malicious ratings and determine the interest degrees of users.
Mahamedi, B, Eskandari, M, Fletcher, JE & Zhu, J 2019, 'Sequence-Based Control Strategy with Current Limiting for the Fault Ride-Through of Inverter-Interfaced Distributed Generators', IEEE Transactions on Sustainable Energy.
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IEEE Three-phase three-leg inverters comprise the majority of inverters that are integrated into power grids. They can develop positive- and negative-sequence components under unbalanced conditions including asymmetrical faults, but not zero sequence. This indicates that a sequence-based control strategy is required for a comprehensive control of inverter currents and voltages, hence positive- and negative-sequence components must be controlled separately with their own dedicated controllers. Given this critical need, a sequence-based control technique for grid-forming and grid-feeding inverter-interfaced distributed generators (IIDG) is proposed. As a necessary part of the fault ride-through (FRT) capability of IIDGs, a current limiter is developed which can limit inverter currents at a pre-defined threshold for both balanced and unbalanced faults/voltage sags. Whilst the current regulators work in the synchronous reference frame with simple PI controllers, the limiter works in natural reference frame and thus treats each phase current separately, which results in current limiting at the threshold for all phases. The advantage of the proposed limiter is that it virtually acts instantaneously such that the inverter current can be limited from the start of the disturbance. The simulation results in MATLAB/SIMULINK reveal promising performance of the proposed control strategy with the proposed current limiting.
Maharaj, S, Lees, T & Lal, S 2019, 'Negative Mental States and Their Association to the Cognitive Function of Nurses', Journal of Psychophysiology.
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© 2018 Hogrefe Publishing. Nurses' inherently stressful occupation leaves them at a higher risk of developing negative mental states (stress, anxiety, and depression). However, research examining the effect of negative mental states on these health professionals' cognitive performance is sparse. Thus, the present study aimed to assess the link between negative mental states and cognitive performance in nurses (n = 53). Negative mental state data was obtained using the Depression Anxiety Stress Scale, brain activity was measured using electroencephalography, and finally, cognitive performance was assessed using the Cognistat and the Mini-Mental State Examination. Significant negative correlations (p <.05) were observed between anxiety and attention, and all three negative mental states and memory performance. Electroencephalographic changes indicated that increases in anxiety were significantly associated (p <.05) with decreases in gamma reactivity at fronto-central sites. The current study suggests that higher levels of negative mental states are associated with domain-specific cognitive impairments, and variations in gamma reactivity; possibly reflecting less optimal cortical functioning.
Maharaj, S, Lees, T & Lal, S 2019, 'Prevalence and Risk Factors of Depression, Anxiety, and Stress in a Cohort of Australian Nurses', International Journal of Environmental Research and Public Health, vol. 16, no. 1, pp. 1-10.
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Nurses remain at the forefront of patient care. However, their heavy workload as a career can leave them overworked and stressed. The demanding nature of the occupation exposes nurses to a higher risk of developing negative mental states such as depression, anxiety, and stress. Hence, the current study aimed to assess the prevalence and risk factors of these mental states in a representative sample of Australian nurses. The Depression Anxiety Stress Scale was administered to 102 nurses. Information about demographic and work characteristics were obtained using lifestyle and in-house designed questionnaires. Prevalence rates of depression, anxiety, and stress were found to be 32.4%, 41.2%, and 41.2% respectively. Binominal logistic regressions for depression and stress were significant (p = 0.007, p = 0.009). Job dissatisfaction significantly predicted a higher risk of nurses developing symptoms of depression and stress respectively (p = 0.009, p = 0.011). Poor mental health among nurses may not only be detrimental to the individual but may also hinder professional performance and in turn, the quality of patient care provided. Further research in the area is required to identify support strategies and interventions that may improve the health and wellbeing of nursing professionals and hence the quality of care delivered.
Mahlia, TMI, Ismail, N, Hossain, N, Silitonga, AS & Shamsuddin, AH 2019, 'Palm oil and its wastes as bioenergy sources: a comprehensive review', Environmental Science and Pollution Research.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Due to global warming and increasing price of fossil fuel, scientists all over the world have been trying to find reliable alternative fuels. One of the most potential candidates is renewable energy from biomass. The race for renewable energy from biomass has long begun and focused on to combat the deteriorating condition of the environment. Palm oil has been in the spotlight as an alternative of bioenergy sources to resolve fossil fuel problem due to its environment-friendly nature. This review will look deep into the origins of palm oil and how it is processed, bioproducts from this biomass, and oil palm biomass-based power plant in Malaysia. Palm oil is usually processed from oil palm fruits and other parts of the oil palm plant are candidates for raw material of bioproduct generation. Oil palm biomass can be turned into three subcategories: bioproduct, biofuels, and biopower. Focusing on biofuel, the biodiesel from palm oil will be explored in detail and its implication in Malaysia as one of the biggest producers of oil palm in the world will also be emphasized comprehensively. The paper presents the detail of a schematic flow diagram of a palm oil mill process of transforming oil palm into crude palm oil and it wastes. This paper will also discuss the current oil palm biomass power plants in Malaysia. Palm oil has been proven itself as a potential alternative to reduce negative environmental impact of global warming.
Mahmudul, HM, Rasul, MG, Akbar, D & Mofijur, M 2019, 'Opportunities for solar assisted biogas plant in subtropical climate in Australia: A review', Energy Procedia, vol. 160, pp. 683-690.
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© 2019 The Authors. Published by Elsevier Ltd. Household waste generation has become a serious environmental issue in recent years. However, some technologies are available to convert household domestic waste into energy. One of such techniques is the biogas generation using household waste. The biogas generation technique is not a new method of energy generation, but its production efficiency is questionable. Biogas yield from domestic waste are influenced by pH level, temperature, HRT and C/N ratio. Moisture and the temperature levels in the biogas generation systems are very critical to its production efficiency, especially this is highly affected in the colder weather condition. Solar assisted biogas plant may provide better production efficiency compared to the traditionally designed biogas plant. In this paper, the scopes and opportunities of solar assisted biogas generation are reviewed. Possible benefits and challenges associated with the solar assisted biogas generation are highlighted.
Mai, HT, Tran, TS, Ho-Le, TP, Center, JR, Eisman, JA & Nguyen, TV 2019, 'Two-thirds of all fractures are not attributable to osteoporosis and advancing age: implication for fracture prevention.', The Journal of clinical endocrinology and metabolism.
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CONTEXT:Although bone mineral density (BMD) is strongly associated with fracture and post-fracture mortality, the burden of fractures attributable to low BMD has not been investigated. OBJECTIVES:We sought to estimate the population attributable fraction (PAF) of fractures and fracture-related mortality that can be attributed to low BMD. DESIGN AND SETTING:This study is a part of an ongoing population-based prospective cohort study, Dubbo Osteoporosis Epidemiology study. In total, 3700 participants aged from 50 years and older had participated in the study. Low-trauma fracture was ascertained by X-ray reports, and mortality was ascertain from the Birth, Death and Marriage Registry. RESULTS:Overall, 21% of women and 11% of men had osteoporotic BMD. In univariable analysis, 21% and 16% of total fractures in women and men, respectively, were attributable to osteoporosis. Osteoporosis combined with advancing age (>70 yrs) accounted for 34% and 35% of fractures in women and men, respectively. However, these two factors accounted for ∼60% of hip fractures. About 99% and 66% of postfracture mortality in women and men, respectively, were attributable to advancing age, osteoporosis and fracture; however, most of the attributable proportion was accounted for advancing age. CONCLUSIONS:A substantial health care burden of fracture is on individuals aged <70 years and/or non-osteoporosis, suggesting that treatment of individuals with osteoporosis is unlikely to reduce a large number of fractures in the general population.
Makhdoom, I, Abolhasan, M, Abbas, H & Ni, W 2019, 'Blockchain's adoption in IoT: The challenges, and a way forward', Journal of Network and Computer Applications.
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Makhdoom, I, Abolhasan, M, Lipman, J, Liu, RP & Ni, W 2019, 'Anatomy of Threats to The Internet of Things', Communications Surveys and Tutorials, IEEE Communications Society.
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Malek, S, Zobeiry, N, Dai, C & Vaziri, R 2019, 'Strain-Softening Response and Failure Prediction in Notched Oriented Strand Board', JOURNAL OF MATERIALS IN CIVIL ENGINEERING, vol. 31, no. 6.
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Malekjamshidi, Z, Jafari, M, Zhu, J & Xiao, D 2019, 'Bidirectional power flow control with stability analysis of the matrix converter for microgrid applications', International Journal of Electrical Power and Energy Systems, vol. 110, pp. 725-736.
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© 2019 Elsevier Ltd In this paper, the importance of the matrix converter stabilization in bidirectional power flow control is investigated. A stabilization technique for matrix converters based on a combination of the damping resistor and the input digital filter is proposed. The suggested technique is applied to a direct matrix converter employed as an interface between two power sources as the main grid and a microgrid to realize a stable four-quadrant power flow control process. The control method is based on the voltage oriented control strategy using anti-windup proportional-integral controllers. The stability analysis using the small-signal model of the converter is presented and simulated numerically. The proposed stabilization and bidirectional power flow control methods are validated by experimental tests on a prototype.
Man, X-F, Xia, B-Z, Luo, Z & Liu, J 2019, '3D Hilbert fractal acoustic metamaterials: low-frequency and multi-band sound insulation', JOURNAL OF PHYSICS D-APPLIED PHYSICS, vol. 52, no. 19.
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Mandal, R, Roy, PP, Pal, U & Blumenstein, M 2019, 'Bag-of-visual-words for signature-based multi-script document retrieval', Neural Computing and Applications, pp. 1-25.
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© 2018 The Natural Computing Applications Forum An end-to-end architecture for multi-script document retrieval using handwritten signatures is proposed in this paper. The user supplies a query signature sample, and the system exclusively returns a set of documents that contain the query signature. In the first stage, a component-wise classification technique separates the potential signature components from all other components. A bag-of-visual-words powered by SIFT descriptors in a patch-based framework is proposed to compute the features and a support vector machine (SVM)-based classifier was used to separate signatures from the documents. In the second stage, features from the foreground (i.e., signature strokes) and the background spatial information (i.e., background loops, reservoirs etc.) were combined to characterize the signature object to match with the query signature. Finally, three distance measures were used to match a query signature with the signature present in target documents for retrieval. The ‘Tobacco’ (The Legacy Tobacco Document Library (LTDL). University of California, San Francisco, 2007. http://legacy.library.ucsf.edu/) document database and an Indian script database containing 560 documents of Devanagari (Hindi) and Bangla scripts were used for the performance evaluation. The proposed system was also tested on noisy documents, and the promising results were obtained. A comparative study shows that the proposed method outperforms the state-of-the-art approaches.
Manzoor, M, Hussain, W, Sohaib, O, Hussain, FK & Alkhalaf, S 2019, 'Methodological investigation for enhancing the usability of university websites', Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 2, pp. 531-549.
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For university websites to be successful and to increase the chance of converting a prospective student into a current student, it is necessary to increase the visibility and accessibility of all related content so that a student can achieve their desired task in the fastest possible time. The criteria for evaluating university websites are very vague and are usually unknown to most developers, which adversely impacts the user-experience of the students visiting such websites. To solve this problem, we devised a usability metric and examined the leading university websites to analyze whether these websites were able to meet the requirements of students. In this research, we applied qualitative and quantitative approaches by considering 300 students and evaluating 86 university websites (26 from Canada, 30 from the United States, and 30 from Europe) based on a six-attribute metric comprising navigation, organization, ease of use (simplicity), design (layout), communication and content. From the evaluation results, we find that the 88% of the students are satisfied with our proposed usability attributes, but that most universities fail to meet basic standards of usability as desired by the students. The findings also show that the usability evaluation score for each usability feature varies from country to country, such as for (1) multiple language support − 23% of the Canadian websites, 63% of the European websites and none of the USA websites has the feature; for (2) Scholarships/Funding/Financial Aid link − 24% of the Canadian websites, 80% of the European and the USA websites has the feature; for (3) admission link − 88% of the Canadian websites, 20% of the European websites and 90% of the USA websites has the feature. In addition, from the evaluative result we find that our proposed approach will not only increase the usability of academic websites but will also provide an easiest way to covert prospective student inquiries into enrollment opportunities.
Mao, M, Lu, J, Han, J & Zhang, G 2019, 'Multiobjective e-commerce recommendations based on hypergraph ranking', INFORMATION SCIENCES, vol. 471, pp. 269-287.
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Mao, T, Mihăiţă, AS & Cai, C 2019, 'Traffic Signal Control Optimisation under Severe Incident Conditions using Genetic Algorithm', ITS World Congress 2019 (ITSWC2019), Singapore.
Mas-Tur, A, Modak, NM, Merigó, JM, Roig-Tierno, N, Geraci, M & Capecchi, V 2019, 'Half a century of Quality & Quantity: a bibliometric review', Quality and Quantity, vol. 53, no. 2, pp. 981-1020.
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© 2018, Springer Nature B.V. The Quality & Quantity was established in 1967 and in 2017 it completed its half century. The journal is interdisciplinary in nature and it mainly discusses methodological application of mathematics and statistics in the social sciences, particularly sociology, economics, and social psychology. It was created with the idea of advancing methodology of the various social studies. This study looks back journey of the journal from 1967 to 2017 aims to develop a bibliometric analysis of all the publications of the journal. Web of Science Core Collection database is used to collect data. The present study discovered the significant contributions of the journal in terms of impact, topics, authors, universities and countries. Utrecht University of Netherlands is the most productive university. Asian Universities are emerging and growing quickly in the recent years. Although USA leads among the countries but Europe leads among the six supranational regions. Finally, the visualization of similarities viewer software is used to present network visualization of the bibliographic coupling, co-citation, citation, co-authorship and co-occurrence of keywords.
Mehvar, S, Filatova, T, Sarker, MH, Dastgheib, A & Ranasinghe, R 2019, 'Climate change-driven losses in ecosystem services of coastal wetlands: A case study in the West coast of Bangladesh', Ocean and Coastal Management, vol. 169, pp. 273-283.
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© 2018 The Authors Climate change is globally recognized as one of the key drivers of degradation of coastal wetland ecosystems, causing considerable alteration of services provided by these habitats. Quantifying the physical impacts of climate change on these services is therefore of utmost importance. Yet, practical work in this field is fragmented and scarce in current literature, especially in developing countries which are likely to suffer most from the adverse climate change impacts. Using a coherent scenario-based approach that combines assessment of physical impacts with economic valuation techniques, here we quantify potential climate change driven losses in the value of wetland ecosystems services due to relative sea-level rise (RSLR)-induced inundation in the vulnerable Western coastal area of Bangladesh in 2100. The results show a small inundation area in 2100 under the three IPCC climate scenarios of RCP2.6 (with 0.25 m of RSLR), RCP6.0 (with 1.18 m of RSLR), and RCP8.5 (with 1.77 m of RSLR) for the coastal wetland ecosystems including the Sundarbans mangrove forest, neritic system and aquaculture ponds. In all scenarios, RSLR will drive a loss in the total value of ecosystem services such as provision of raw materials, and food provision, ranging from US$ 0–1 million to US$ 16.5–20 million, respectively. The outcomes of this study reveal that RSLR-induced inundation on its own, is unlikely to be a major threat to the wetland ecosystems in Western coast of Bangladesh. This would suggest that other climate change impacts such as coastal erosion, increase in frequency of cyclone events, and sea temperature rise might be the likely primary drivers of change in the value of wetland ecosystems services in this area.
Meng, Q, Wu, C, Su, Y, Li, J, Liu, J & Pang, J 2019, 'A study of steel wire mesh reinforced high performance geopolymer concrete slabs under blast loading', Journal of Cleaner Production, vol. 210, pp. 1150-1163.
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In this study, a novel green construction material, high performance alkali-activated geopolymer concrete is introduced. Both numerically and experimentally investigations were conducted on a new type of structural slabs made of steel wire mesh reinforced geopolymer concrete against close-in ground surface explosion. Steel rebar reinforced conventional concrete slabs are also studied to compare the results. The experimental investigation was conducted to study the slab damage mechanism. It is found that the steel wire mesh reinforced geopolymer concrete slab showed less damage and fragmentation under 50 kg Trinitrotoluene (TNT) blast load within 3 m, 5 m and 7 m distances as compared to the C30 concrete slab. Numerical analysis was then conducted to further investigate the slab dynamic responses. Combining the steel wire mesh reinforcement with geopolymer concrete can help increase the blast resistance capacity leading to promising and environmental friendly structural protective design.
Meng, Q, Wu, C, Su, Y, Li, J, Liu, J & Pang, J 2019, 'Experimental and numerical investigation of blast resistant capacity of high performance geopolymer concrete panels', Composites Part B: Engineering, vol. 171, pp. 9-19.
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Merigó, JM, Cobo, MJ, Laengle, S, Rivas, D & Herrera-Viedma, E 2019, 'Twenty years of Soft Computing: a bibliometric overview', Soft Computing, vol. 23, no. 5, pp. 1477-1497.
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© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. The journal Soft Computing was launched in 1997, and it is dedicated to promote advancements in soft computing theories, which includes fuzzy sets theory, neural networks, evolutionary computation, probabilistic reasoning and hybrid theories. 2017 marks the 20th anniversary of the journal. Motivated by this anniversary, this study presents a bibliometric analysis of the current publications in the journal in order to identify the leading trends ruling the journal. The paper also develops a mapping analysis of the bibliographic material by using the visualization of similarities viewer software. The results show that researchers from all over the world publish regularly in the journal. Soft Computing is growing significantly during the last years, becoming one of the leading journals in the field.
Miao, Z, Yu, J, Ji, J & Zhou, J 2019, 'Multi-objective region reaching control for a swarm of robots', AUTOMATICA, vol. 103, pp. 81-87.
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Mihăiţă, AS, Dupont, L, Chery, O, Camargo, M & Cai, C 2019, 'Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling', Journal of Cleaner Production, vol. 221, pp. 398-418.
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© 2019 Elsevier Ltd Air pollution impact assessment is a major objective for various community councils in large cities, which have lately redirected their attention towards using more low-cost sensing units supported by citizen involvement. However, there is a lack of research studies investigating real-time mobile air-quality measurement through smart sensing units and even more of any data-driven modelling techniques that could be deployed to predict air quality accurately from the generated data-sets. This paper addresses these challenges by: a) proposing a comparative and detailed investigation of various air quality monitoring devices (both fixed and mobile), tested through field measurements and citizen sensing in an eco-neighbourhood from Lorraine, France, and by b) proposing a machine learning approach to evaluate the accuracy and potential of such mobile generated data for air quality prediction. The air quality evaluation consists of three experimenting protocols: a) first, we installed fixed passive tubes for monitoring the nitrogen dioxide concentrations placed in strategic locations highly affected by traffic circulation in an eco-neighbourhood, b) second, we monitored the nitrogen dioxide registered by citizens using smart and mobile pollution units carried at breathing level; results revealed that mobile-captured concentrations were 3–5 times higher than the ones registered by passive-static monitoring tubes and c) third, we compared different mobile pollution stations working simultaneously, which revealed noticeable differences in terms of result variability and sensitivity. Finally, we applied a machine learning modelling by using decision trees and neural networks on the mobile-generated data and show that humidity and noise are the most important factors influencing the prediction of nitrogen dioxide concentrations of mobile stations.
Mihăiţă, AS, Liu, Z, Rizoiu, MA & Cai, C 2019, 'Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting', ITS World Congress 2019 (ITSWC2019), Singapore.
Mihăiţă, AS, Ortiz, MB, Camargo, M & Cai, C 2019, 'Predicting Air Quality by Integrating a Mesoscopic Traffic Simulation Model and Simplified Air Pollutant Estimation Models', International Journal of Intelligent Transportation Systems Research, vol. 17, no. 2, pp. 125-141.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Continuous growth in traffic demand has led to a decrease in the air quality in various urban areas. More than ever, local authorities for environmental protection and urban planners are interested in performing detailed investigations using traffic and air pollution simulations for testing various urban scenarios and raising citizen awareness where necessary. This article is focused on the traffic and air pollution in the eco-neighbourhood “Nancy Grand Cœur”, located in a medium-size city from north-eastern France. The main objective of this work is to build an integrated simulation model which would predict and visualize various environmental changes inside the neighbourhood such as: air pollution, traffic flow or meteorological information. Firstly, we conduct a data profiling analysis on the received data sets together with a discussion on the daily and hourly traffic patterns, average nitrogen dioxide concentrations and the regional background concentrations recorded in the eco-neighbourhood for the study period. Secondly, we build the 3D mesoscopic traffic simulation model using real data sets from the local traffic management centre. Thirdly, by using reliable data sets from the local air-quality management centre, we build a regression model to predict the evolution of nitrogen dioxide concentrations, as a function of the simulated traffic flow and meteorological data. We then validate the estimated results through comparisons with real data sets, with the purpose of supporting the traffic engineering decision-making and the smart city sustainability. The last section of the paper is reserved for further regression studies applied to other air pollutants monitored in the eco-neighbourhood, such as sulphur dioxide and particulate matter and a detailed discussion on benefit and challenges to conduct such studies.
Miller, HD, Akbarnezhad, A, Mesgari, S & Foster, SJ 2019, 'Performance of oxygen/argon plasma-treated steel fibres in cement mortar', Cement and Concrete Composites, vol. 97, pp. 24-32.
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© 2018 Elsevier Ltd Steel fibres are used widely to control initiation and growth of cracks in concrete. However, the bond between steel fibres and the cement matrix in steel fibre-reinforced concrete (SFRC) is almost always purely physical. A viable approach to reduce cracking in fibre-reinforced concrete is to supplement the physical bond between fibres and cement matrix with a relatively uniform chemical bond along the fibres’ surface. However, despite the promising results reported for other types of fibres, little attention has been paid to chemical surface treatment of steel fibres to improve their ability to bond with concrete. In this study, an oxygen/argon plasma treatment process is investigated as a potential technique to improve the bond between steel fibres and a cementitious matrix. Several variations in treatment parameters are made in order to identify the optimal treatment conditions. The results of X-ray Photoelectron Spectroscopy and surface energy measurements confirm that plasma treatment can significantly increase the fibres’ surface energy and consequently the strength of the fibre-cement bond. The improved bond between steel fibres and concrete is confirmed by the results of fibre pull-out tests, as well as the reduced average crack size observed in restrained drying shrinkage tests. Moreover, the results indicate considerable decrease in the volume of permeable voids in SFRC due to plasma treatment of steel fibres.
Milne, DN, McCabe, KL & Calvo, RA 2019, 'Improving Moderator Responsiveness in Online Peer Support Through Automated Triage.', Journal of medical Internet research, vol. 21, no. 4, p. e11410.
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BACKGROUND:Online peer support forums require oversight to ensure they remain safe and therapeutic. As online communities grow, they place a greater burden on their human moderators, which increases the likelihood that people at risk may be overlooked. This study evaluated the potential for machine learning to assist online peer support by directing moderators' attention where it is most needed. OBJECTIVE:This study aimed to evaluate the accuracy of an automated triage system and the extent to which it influences moderator behavior. METHODS:A machine learning classifier was trained to prioritize forum messages as green, amber, red, or crisis depending on how urgently they require attention from a moderator. This was then launched as a set of widgets injected into a popular online peer support forum hosted by ReachOut.com, an Australian Web-based youth mental health service that aims to intervene early in the onset of mental health problems in young people. The accuracy of the system was evaluated using a holdout test set of manually prioritized messages. The impact on moderator behavior was measured as response ratio and response latency, that is, the proportion of messages that receive at least one reply from a moderator and how long it took for these replies to be made. These measures were compared across 3 periods: before launch, after an informal launch, and after a formal launch accompanied by training. RESULTS:The algorithm achieved 84% f-measure in identifying content that required a moderator response. Between prelaunch and post-training periods, response ratios increased by 0.9, 4.4, and 10.5 percentage points for messages labelled as crisis, red, and green, respectively, but decreased by 5.0 percentage points for amber messages. Logistic regression indicated that the triage system was a significant contributor to response ratios for green, amber, and red messages, but not for crisis messages. Response latency was significantly reduced (P<.001), between ...
Mishra, AK, Tripathy, AK, Puthal, D & Yang, LT 2019, 'Analytical Model for Sybil Attack Phases in Internet of Things', IEEE Internet of Things Journal.
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IEEE The sybil attack in IoT commonly aims the sensing domain that may impose serious threat to the devices both in perception and communication layer. The singularity of the sybil attack is a sybil node that publish multiple identities of legitimate devices. It is highly essential to learn the behavior and predict possible actions of a sybil attacker while devising a defense mechanism for it. This paper provides a comprehensive characteristic analysis of sybil attack in IoT. Based on the nature of the task performed during this attack, it is classified into 3 phases as compromise, deployment, and launching phase. The compromise phase is modeled as an automaton with attacker state transition as a markov chain model. A heuristic is also proposed for selection criteria of an attacker to compromise a node. In the deployment phase of the attack, an algorithm based on K-mean clustering is proposed to group compromised identities and deploy the sybil node for corresponding identities without violating the set of adjacent nodes. In the launching phase, the process of replacing sybil identities either over time or on detection is modeled using age replacement policy. The results depict that the proposed model effectively visualize the behavior of a sybil attacker in challenging environments of Internet of Things.
Mo, W, Walker, PD & Zhang, N 2019, 'Dynamic analysis and control for an electric vehicle with harpoon-shift synchronizer', MECHANISM AND MACHINE THEORY, vol. 133, pp. 750-766.
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Moayedi, H, Mehrabi, M, Mosallanezhad, M, Rashid, ASA & Pradhan, B 2019, 'Modification of landslide susceptibility mapping using optimized PSO-ANN technique', Engineering with Computers.
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© 2018, Springer-Verlag London Ltd., part of Springer Nature. In the present study, we applied artificial neural network (ANN) optimized with particle swarm optimization (PSO) for the problem of landslide susceptibility mapping (LSM) prediction. Many studies have revealed that the ANN-based techniques are reliable methods for estimating the LSM. However, most ANN training models facing with major problems such as slow degree of learning system as well as being trapped in their local minima. Optimization algorithms (OA) such as PSO can improve performance results of ANN. Existing applications of PSO model to ANN training have not been used in area of landslide mapping, neither assess the optimal architecture of networks nor the influential factors affecting this problem. Hence, the present study focused on the application of a hybrid PSO-based ANN model (PSO-ANN) to the prediction of landslide susceptibility hazardous mapping. To prepare training and testing datasets for the ANN and PSO-ANN network models, large data collection (i.e., a database consists 168970 training datasets and 42243 testing datasets) were provided from an area of Layleh valley, located in Kermanshah, west of Iran. All the variables of PSO algorithm (e.g., in addition to the network parameter and network weights) were optimized to achieve the most reliable maps of landslide susceptibility. The input dataset includes elevation, slope aspect, slope degree, curvature, soil type, lithology, distance to road, distance to river, distance to fault, land use, stream power index (SPI) and topographic wetness index (TWI), where the output was taken landslide susceptibility value. The predicted results (e.g., from ANN, PSO-ANN) for both of datasets (e.g., training and testing) of the models were assessed based on two statistical indices namely, coefficient of determination (R2) and root-mean-squared error (RMSE). In this study, to evaluate the ability of all methods, color intensity rating (CER) based o...
Modak, NM, Merigó, JM, Weber, R, Manzor, F & Ortúzar, JDD 2019, 'Fifty years of Transportation Research journals: A bibliometric overview', Transportation Research Part A: Policy and Practice, vol. 120, pp. 188-223.
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© 2018 Elsevier Ltd Transportation Research (TR) was established in 1967 with the vision of promoting multi-disciplinary (economics, engineering, sociology, psychology) research on transport systems. The journal has continuously expanded its wings becoming a world-leading journal, now publishing research work through six parts, A to F, respectively addressing Policy and Practice, Methodological, Emerging Technologies, Transport and Environment, Logistics and Transportation Review, and Traffic Psychology and Behaviour. This study aims to celebrate the first half century of the journal through a bibliometric study of the publications on all six parts between 1967 and 2016. It uses the most reliable database for academic research, the Web of Science Core Collection, to identify the leading trends in all TR journals in terms of impact, topics, authors, universities, and countries. Moreover, it uses the Visualization of Similarities (VOS) viewer software to analyse bibliographic coupling, co-citation, citation, co-authorship, and co-occurrence of keywords.
Mofijur, M, Rasul, M, Hassan, NMS & Uddin, MN 2019, 'Investigation of exhaust emissions from a stationary diesel engine fuelled with biodiesel', Energy Procedia, vol. 160, pp. 791-797.
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© 2019 The Authors. Published by Elsevier Ltd. This paper studied the production of waste cooking biodiesel as an alternative fuel for diesel engine. The biodiesel was produced through conventional transesterification process using the base catalyst (KOH). A multi-cylinder diesel engine was used to evaluate the emission of 20% (B20) waste cooking biodiesel fuel at different engine speeds and full load condition. It was found that the characteristics of biodiesel are within the limit of specified standards (American Society for Testing and Materials, ASTM D6751) and comparable to diesel fuel. Engine emission results indicated that waste cooking biodiesel fuel sample reduces the average carbon monoxide (CO) and particulate matter (PM) emissions except nitrogen oxides (NO x ) than diesel fuel. Finally, it can be concluded that 20% of waste cooking biodiesel can significantly contribute to lower the harmful emission of an unmodified stationary diesel engine to the environment.
Mofijur, M, Rasul, MG, Hassan, NMS & Nabi, MN 2019, 'Recent development in the production of third generation biodiesel from microalgae', Energy Procedia, vol. 156, pp. 53-58.
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© 2019 The Authors. Published by Elsevier Ltd. Increasing global energy demand at a rate faster than the population growth has led the researcher to look for alternative fuel. Amongst the options, biodiesel is an environmentally sustainable substitute of diesel fuel being renewable, biodegradable and have similar properties of fossil diesel. Among the biodiesel sources, microalgae is a potential third generation biodiesel feedstock which can be produced throughout the year and its oil yield is higher than any other crops. This paper reviews recent development in microalgae biodiesel in terms of its oil extraction technics, challenges of oil extraction, production of biodiesel from microalgae oil and its fuel properties. Finally, the paper discusses the performance and combustion analysis of diesel engine fuelled with microalgae biodiesel. This paper provides a clear understanding of the potential use of microalgae biodiesel as an alternative source to fossil diesel for diesel engines.
Moghaddam, F, Sirivivatnanon, V & Vessalas, K 2019, 'The effect of fly ash fineness on heat of hydration, microstructure, flow and compressive strength of blended cement pastes', Case Studies in Construction Materials, vol. 10.
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© 2019 In this paper, an experimental study on the effect of fly ash fineness on the heat of hydration, microstructure, flow and compressive strength of blended cement pastes was carried out and evaluated against control cement paste. Fly ashes with different fineness: classified fly ash, run-of-station fly ash and grounded run-of-station fly ash; with a median particle size of 17.4, 11.3 and 5.7 μm, respectively, from the same power station source in Australia were used to partially replace Portland cement at 20% and 40% by weight of cement using a fixed water-to-binder ratio of 0.40. Results of this study showed that the cumulative heat of hydration of blended cement paste decreased as fly ash content in blended cement paste was increased. For a given cement replacement level, blended cement paste containing finer fly ash released more heat of hydration when compared to coarser fly ash. Moreover, increasing the fineness of fly ash resulted in a higher consumption of calcium hydroxide at 7 and 28 days reflecting pozzolanic reactivity and, thus, a denser microstructure than blended pastes containing coarser fly ash as revealed by the X-ray diffraction (XRD), scanning electron microscopy (SEM) and compressive strength results. In addition, the incorporation of fly ash in the blended pastes led to the introduction of an additional hydration peak in the heat evolution curve possibly due to the late activation of fly ash by calcium hydroxide renewing the C 3 A reaction and converting ettringite to monosulfate. The flow of the freshly blended cement pastes was also found to improve slightly with increasing fineness of the fly ash. In addition, the hardened blended cement pastes containing 20% ground run-of-station fly ash showed comparable compressive strength with the control cement pastes at both 7 and 28 days mainly due to the higher fineness of the ground run-of-station fly ash and increased reactivity compared to coarser grade fly ash.
Mojaddadi Rizeei, H, Pradhan, B & Saharkhiz, MA 2019, 'Urban object extraction using Dempster Shafer feature-based image analysis from worldview-3 satellite imagery', International Journal of Remote Sensing, vol. 40, no. 3, pp. 1092-1119.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. A detailed and up-to-date land use of the urban environment is essentially required in many applications. Very high-resolution (VHR), Multispectral Scanner System (MSS) Worldview-3 (WV-3) satellite imagery provides detailed information on urban characteristics, which should be professionally mined. In this research, WV-3 was processed by machine learning (ML) methods to extract the most accurate urban features. Fuze-Go panchromatic sharpening in conjunction with atmospheric and topographic correction was initially utilized to increase the image quality and colour contrast. Three image analysis approaches including, current pixel-based image analysis (PBIA), object-based image analysis (OBIA) and new feature-based image analysis (FBIA) were implemented on WV-3 image. The k-nearest neighbour (k-NN), Naive Bayes (NB), support vector machine (SVM) classifiers were represented by PBIA, the Decision Tree (DT) classifier was examined as OBIA and the Dempster–Shafer (DS) fusion classifier was manifested for the first time as FBIA. In order to engage DS as FBIA, four types of Belief Masses, namely, Precision, Recall, Overall Accuracy, and kappa coefficient (ĸ) were implemented and compared to assign the most likelihood urban features. All the applied classifiers were also trained on the first site and then tested on another site to examine the transferability. The accuracy, reliability, and computational time of all classifiers were examined by confusion matrix and McNemar assessment. Results show improvements on the detailed urban extraction obtained using the proposed FBIA with 92.2% overall accuracy in compared with PBIA and OBIA. The FBIA result of urban extraction is more consistent when transferred to another study area and consumes much lesser time than OBIA. Also, the precision mass belief measurement achieved highest efficiency regarding receiver operating characteristic (ROC) curve rate.
Moktadir, MA, Ali, SM, Paul, SK & Shukla, N 2019, 'Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh', Computers and Industrial Engineering, vol. 128, pp. 1063-1075.
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Recently, big data (BD) has attracted researchers and practitioners due to its potential usefulness in decision-making processes. Big data analytics (BDA) is becoming increasingly popular among manufacturing companies as it helps gain insights and make decisions based on BD. However, there many barriers to the adoption of BDA in manufacturing supply chains. It is therefore necessary for manufacturing companies to identify and examine the nature of each barrier. Previous studies have mostly built conceptual frameworks for BDA in a given situation and have ignored examining the nature of the barriers to BDA. Due to the significance of both BD and BDA, this research aims to identify and examine the critical barriers to the adoption of BDA in manufacturing supply chains in the context of Bangladesh. This research explores the existing body of knowledge by examining these barriers using a Delphi-based analytic hierarchy process (AHP). Data were obtained from five Bangladeshi manufacturing companies. The findings of this research are as follows: (i) data-related barriers are most important, (ii) technology-related barriers are second, and (iii) the five most important components of these barriers are (a) lack of infrastructure, (b) complexity of data integration, (c) data privacy, (d) lack of availability of BDA tools and (e) high cost of investment. The findings can assist industrial managers to understand the actual nature of the barriers and potential benefits of using BDA and to make policy regarding BDA adoption in manufacturing supply chains. A sensitivity analysis was carried out to justify the robustness of the barrier rankings.
Mukhtar, NM & Lu, DD-C 2019, 'A Bidirectional Two-Switch Flyback Converter With Cross-Coupled LCD Snubbers for Minimizing Circulating Current', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 66, no. 8, pp. 5948-5957.
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Mulet-Forteza, C, Genovart-Balaguer, J, Mauleon-Mendez, E & Merigó, JM 2019, 'A bibliometric research in the tourism, leisure and hospitality fields', Journal of Business Research.
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© 2018 Elsevier Inc. This paper presents a study of the most cited papers, the most productive and influential institutions and countries, and the most influential authors in the tourism, leisure, and hospitality fields. The number of publications in journals focused on these areas has increased exponentially over the past 40 years. This paper examines the fundamental contributions in these areas using a bibliometric approach. This paper also uses the visualization of similarities to graphically map the main topics and keywords. No study has examined all journals indexed in the Web of Science in these fields over a period as wide as the one considered in this study. This study is valuable for several reasons. It can help scholars and researchers to identify the countries and institutions with the most potential to develop and share research, as well as where it would be interesting to carry out their doctoral studies and develop their careers.
Naghibi, SA, Dolatkordestani, M, Rezaei, A, Amouzegari, P, Heravi, MT, Kalantar, B & Pradhan, B 2019, 'Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential', ENVIRONMENTAL MONITORING AND ASSESSMENT, vol. 191, no. 4.
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Naidu, G, Ryu, S, Thiruvenkatachari, R, Choi, Y, Jeong, S & Vigneswaran, S 2019, 'A critical review on remediation, reuse, and resource recovery from acid mine drainage', Environmental Pollution, pp. 1110-1124.
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© 2019 Elsevier Ltd Acid mine drainage (AMD) is a global environmental issue. Conventionally, a number of active and passive remediation approaches are applied to treat and manage AMD. Case studies on remediation approaches applied in actual mining sites such as lime neutralization, bioremediation, wetlands and permeable reactive barriers provide an outlook on actual long-term implications of AMD remediation. Hence, in spite of available remediation approaches, AMD treatment remains a challenge. The need for sustainable AMD treatment approaches has led to much focus on water reuse and resource recovery. This review underscores (i) characteristics and implication of AMD, (ii) remediation approaches in mining sites, (iii) alternative treatment technologies for water reuse, and (iv) resource recovery. Specifically, the role of membrane processes and alternative treatment technologies to produce water for reuse from AMD is highlighted. Although membrane processes are favorable for water reuse, they cannot achieve resource recovery, specifically selective valuable metal recovery. The approach of integrated membrane and conventional treatment processes are especially promising for attaining both water reuse and recovery of resources such as sulfuric acid, metals and rare earth elements. Overall, this review provides insights in establishing reuse and resource recovery as the holistic approach towards sustainable AMD treatment. Finally, integrated technologies that deserve in depth future exploration is highlighted. Challenges associated with AMD can be sustainability addressed through integrated treatment approaches that attain both water reuse and valuable resource recovery.
Nanda, A, Nanda, P, He, X, Jamdagni, A & Puthal, D 2019, 'A hybrid encryption technique for Secure-GLOR: The adaptive secure routing protocol for dynamic wireless mesh networks', Future Generation Computer Systems.
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As we progress in into a digital era where most aspects of our life depend upon a network of computers,it is essential to focus on digital security. Each component of a network, be it a physical network, virtualnetwork or social network requires security when transmitting data. Hence the dynamic wireless meshnetwork must also deploy high levels of security as found in current legacy networks. This paper presentsa secure Geo-Location Oriented Routing (Secure-GLOR) protocol for wireless mesh networks, whichincorporates a hybrid encryption scheme for its multilevel security framework. The hybrid encryptiontechnique improves the network’s overall performance compared to the basic encryption by using acombination of symmetric key as well as asymmetric key encryption. Using the combination of the twoencryption schemes, the performance of the network can be improved by reducing the transmitted datasize, reduced computational overhead and faster encryption–decryption cycles. In this paper discussedmultiple encryption schemes for both symmetric and asymmetric encryption, compare their performancein various experimental scenarios. Proposed security scheme achieves better performance based on theresults obtained with most viable options for our network model.
Nasir, AA, Tuan, HD, Duong, TQ & Poor, HV 2019, 'Improper Gaussian Signaling for Broadcast Interference Networks', IEEE Signal Processing Letters, vol. 26, no. 6, pp. 808-812.
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© 1994-2012 IEEE. For a multi-user multi-cell network, which suffers both intra-cell and inter-cell interference, this letter considers improper Gaussian signaling (IGS) as a means to improve the achievable rate. The problem of interest is designing of improper Gaussian signals' augmented covariance matrices to maximize the users' minimum rate subject to transmit power constraints. This problem is seen as a nonconvex matrix optimization problem, which cannot be solved by conventional techniques, such as weighted minimum mean square error minimization or alternating optimization. A path-following algorithm, which iterates a sequence of improved feasible points, is proposed for its computation. The provided simulation results for three cells serving 18 users show that IGS offers a much better max-min rate compared with that achieved by conventional proper Gaussian signaling. Another problem of maximizing the energy efficiency in IGS is also considered.
Natgunanathan, I, Mehmood, A, Xiang, Y, Gao, L & Yu, S 2019, 'Location Privacy Protection in Smart Health Care System', IEEE Internet of Things Journal.
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IEEE In a smart health system, patients’ location information is periodically sent to hospitals and this information helps hospitals to provide improved health care services. The location information together with time stamp alone can reveal a patient’s private information such as person’s life style, places frequently visited by the person and personal interests. Thus, it is important to protect the location privacy of a patient. In the existing privacy protection mechanisms, trusted third party (TTP) and location perturbation techniques are used. However, in TTP based mechanism, an adversary who illegally gets access to TTP server will have access to the private location information. On the other hand in location perturbation technique, utility of the location information is significantly compromised. In this paper, we propose a location privacy protection mechanism in which location privacy is protected while maintaining the utility of the location data. In the proposed mechanism, a main processing unit (MPU) attached to a patient’s body generates the perturbed location by considering the distance between the patient’s location and the pre-identified patient’s sensitive locations. This adaptive generation of perturbed location, removes the necessity to trust other parties while preserving the privacy and utility of the location data. The validity of the proposed mechanism is demonstrated by simulation results.
Nathan, KS, Ghosh, S, Siwakoti, YP & Long, T 2019, 'A New DC-DC Converter for Photovoltaic Systems: Coupled-Inductors Combined Cuk-SEPIC Converter', IEEE Transactions on Energy Conversion.
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IEEE An enhanced DC-DC converter is proposed in this paper, based on the combination of the Cuk and SEPIC converters, which is well-suited for solar photovoltaic (PV) applications. The converter uses only one switch (which is ground-referenced, so simple gate drive circuitry may be used), yet provides dual outputs in the form of a bipolar DC bus. The bipolar output from the DC-DC converter is able to send power to the grid via any inverter with a unipolar or bipolar DC input, and leakage currents can be eliminated if the latter type is used without using lossy DC capacitors in the load current loop. The proposed converter uses integrated magnetics cores to couple the input and output inductors, which significantly reduces the input current ripple and hence greatly improves the power extracted from the solar PV system. The design methodology along with simulation, experimental waveforms, and efficiency measurements of a 4 kW DC-DC converter are presented to prove the concept of the proposed converter. Further, a 1 kW inverter is also developed to demonstrate the converter's grid-connection potential.
Nawaz, F, Hussain, O, Hussain, FK, Janjua, NK, Saberi, M & Chang, E 2019, 'Proactive management of SLA violations by capturing relevant external events in a Cloud of Things environment', Future Generation Computer Systems, vol. 95, pp. 26-44.
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© 2018 Elsevier B.V. The cloud of things (CoT) is an emerging paradigm that has merged and combined cloud computing and the Internet of Things (IoT). Such a paradigm has enabled service providers to provide on-demand computing resources from devices spread across different locations for service users to be dynamically connected to them. While this benefits the CoT service providers and users in many ways, it also brings a key challenge of ensuring that the service is delivered according to the promised quality. Failure to ensure this will result in the service provider experiencing penalties of different types and the service user experiencing disruptions. The literature addresses this problem by proactively managing for SLA violations. However, given the geographically dispersed region of a formed CoT service, in this paper we argue that for proactive SLA violation identification, we need specialized techniques that also consider events that are outside the usual control of service providers and users, but will impact the CoT environment and the quality of service. We propose a framework that identifies such external events of interest and ascertains their impact on achieving the service according to the promised quality. We explain the working of our proposed framework in detail and demonstrate its superiority in proactively determining SLA violations as compared to existing approaches.
Nejadshafiee, V, Naeimi, H, Goliaei, B, Bigdeli, B, Sadighi, A, Dehghani, S, Lotfabadi, A, Hosseini, M, Nezamtaheri, MS, Amanlou, M, Sharifzadeh, M & Khoobi, M 2019, 'Magnetic bio-metal–organic framework nanocomposites decorated with folic acid conjugated chitosan as a promising biocompatible targeted theranostic system for cancer treatment', Materials Science and Engineering C, vol. 99, pp. 805-815.
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© 2019 Elsevier B.V. In this work, a multifunctional magnetic Bio-Metal-Organic Framework (Fe 3 O 4 @Bio-MOF) coated with folic acid-chitosan conjugate (FC) was successfully prepared for tumor-targeted delivery of curcumin (CUR) and 5-fluorouracil (5-FU) simultaneously. Bio-MOF nanocomposite based on CUR as organic linker and zinc as metal ion was prepared by hydrothermal method in the presence of amine-functionalized Fe 3 O 4 magnetic nanoparticles (Fe 3 O 4 @NH 2 MNPs). 5-FU was loaded in the magnetic Bio-MOF and the obtained nanocarrier was then coated with FC network. The prepared nanocomposite (NC) was fully characterized by high resolution-transmission electron microscope (HR-TEM), field emission scanning electron microscopy (FE-SEM), Dynamic light scattering (DLS), X-ray diffraction analysis (XRD), thermogravimetric analysis (TGA), vibrating sample magnetometry (VSM), nuclear magnetic resonance (NMR), and UV–vis analyses. In vitro release study showed controlled release of CUR and 5-FU in acidic pH confirming high selectivity and performance of the carrier in cancerous microenvironments. The selective uptake of 5-FU-loaded Fe 3 O 4 @Bio-MOF-FC by folate receptor-positive MDA-MB-231 cells was investigated and verified. The ultimate nanocarrier exhibited no significant toxicity, while drug loaded nanocarrier showed selective and higher toxicity against the cancerous cells than normal cells. SDS PAGE was also utilized to determine the protein pattern attached on the surface of the nanocarriers. In vitro and in vivo MRI studies showed negative signal enhancement in tumor confirming the ability of the nanocarrier to be applied as diagnostic agent. Owing to the selective anticancer release and cellular uptake, acceptable blood compatibility as well as suitable T 2 MRI contrast performance, the target nanocarrier could be considered as favorable theranostic in breast cancer.
Ngoc, TP, Fatahi, B & Khabbaz, H 2019, 'Impacts of Drying-Wetting and Loading-Unloading Cycles on Small Strain Shear Modulus of Unsaturated Soils', International Journal of Geomechanics, vol. 19, no. 8, pp. 04019090-04019090.
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Nguyen, AQ, Nguyen, LN, Phan, HV, Galway, B, Bustamante, H & Nghiem, LD 2019, 'Effects of operational disturbance and subsequent recovery process on microbial community during a pilot-scale anaerobic co-digestion', INTERNATIONAL BIODETERIORATION & BIODEGRADATION, vol. 138, pp. 70-77.
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Nguyen, H, Khabbaz, H & Fatahi, B 2019, 'A numerical comparison of installation sequences of plain concrete rigid inclusions', Computers and Geotechnics, vol. 105, pp. 1-26.
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Soil displacement induced when installing controlled modulus columns (CMC) as ground reinforcement could affect the columns installed close by. Realising numerical analyses may provide useful insights, this paper describes a numerical approach to investigate how groups of CMC installed in different sequences could affect columns installed previously. Coupled consolidation analyses in large strain mode and incorporating soil-CMC interaction were carried out using the three-dimensional finite difference software package FLAC3D. The CMCs were modelled using advanced non-linear Hoek-Brown material with a tensile yield criterion while soils with a typical profile were characterised using the modified Cam Clay and the elastic-perfectly plastic material with a Mohr-Coulomb yield criterion. Where possible, the predicted responses of ground surrounding the CMCs were compared to a number of existing analytical methods. Predictions revealed that lateral soil movement and soil heave near existing CMCs induced by installing new CMCs towards the existing CMCs were approximately 15% and 25% greater than corresponding predictions when a reverse installation sequence was adopted. The maximum excess pore water pressures, induced near existing columns due to installing new columns towards the existing ones, were almost twice more than those caused by the reverse sequence of installation. Moreover, the predicted bending moments generated in the existing columns induced by installing new columns towards the existing CMCs were almost 22% greater than the corresponding values when the reverse installation sequence was adopted. This shows the importance of selecting an appropriate installation sequence in the CMC construction process as well as considering the initial stress field and bending moments in the surrounding soil and CMCs, respectively when designing embankments on improved soft soils.
Nguyen, KT, Nguyen, HM, Truong, CK, Ahmed, MB, Huang, Y & Zhou, JL 2019, 'Chemical and microbiological risk assessment of urban river water quality in Vietnam', Environmental Geochemistry and Health.
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© 2019, Springer Nature B.V. Abstract: The contamination and risk by nutrients (NH 4+ , NO 2− , NO 3− and PO 43− ), COD, BOD 5 , coliform and potentially toxic elements (PTEs) of As, Cd, Ni, Hg, Cu, Pb, Zn and Cr were investigated in urban river (Nhue River), Vietnam during 2010–2017. The extensive results demonstrated that concentrations of these contaminants showed significant spatial and temporal variations. The Nhue River was seriously polluted by NH 4+ (0.025–11.28 mg/L), PO 43− (0.17–1.72 mg/L), BOD 5 (5.8–179.6 mg/L), COD (1.4–239.8 mg/L) and coliform (1540–326,470 CFU/100 mL); moderately polluted by As (0.2–131.15 μg/L) and Hg (0.11–4.1 μg/L); and slightly polluted by NO 2− (0.003–0.33 mg/L) and Cd (2.1–18.2 μg/L). The concentrations of NH 4+ , PO 43− , COD, BOD 5 and coliform frequently exceeded both drinking water guidelines and irrigation water standards. Regarding PTEs, As, Cd and Hg concentrations were frequently higher than the regulatory limits. Human health risks of PTEs were evaluated by estimating hazard index (HI) and cancer risk through ingestion and dermal contacts for adults and children. The findings indicated that As was the most important pollutant causing both non-carcinogenic and carcinogenic concerns. The non-carcinogenic risks of As were higher than 1.0 at all sites for both adults (HI = 1.83–7.4) and children (HI = 2.6–10.5), while As posed significant carcinogenic risks for adults (1 × 10 −4 −4.96 × 10 −4 ). A management strategy for controlling wastewater discharge and protecting human health is urgently needed. Graphical abstract: [Figure not available: see fulltext.].
Nguyen, L, Valls Miro, J & Qiu, X 2019, 'Multilevel B-Splines based Learning Approach for Sound Source Localization', IEEE Sensors Journal, vol. 10, no. 10, pp. 3871-3881.
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In this paper, a new learning approach for sound source localization is presented using ad hoc either synchronous or asynchronous distributed microphone networks based on the time differences of arrival (TDOA) estimation. It is first to propose a new concept in which the coordinates of a sound source location are defined as the functions of TDOAs, computing for each pair of microphone signals in the network. Then, given a set of pre-recorded sound measurements and their corresponding source locations, the multilevel B-splines-based learning model is proposed to be trained by the input of the known TDOAs and the output of the known coordinates of the sound source locations. For a new acoustic source, if its sound signals are recorded, the correspondingly computed TDOAs can be fed into the learned model to predict the location of the new source. Superiorities of the proposed method are to incorporate the acoustic characteristics of a targeted environment and even remaining uncertainty of TDOA estimations into the learning model before conducting its prediction and to be applicable for both synchronous or asynchronous distributed microphone sensor networks. The effectiveness of the proposed algorithm in terms of localization accuracy and computational cost in comparisons with the state-of-the-art methods was extensively validated on both synthetic simulation experiments as well as in three real-life environments.
Nguyen, LD, Tuan, HD, Duong, TQ & Poor, HV 2019, 'Multi-user regularized zero-forcing beamforming', IEEE Transactions on Signal Processing, vol. 67, no. 11, pp. 2839-2853.
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© 1991-2012 IEEE. Regularized zero-forcing beamforming (RZFB) is an interesting class of linear signal processing problems, which is very attractive for use in large-scale communication networks due its simple visualization as a straightforward extension of the well-accepted zero-forcing beamforming (ZFB). However, unlike ZFB, which is multi-user interference free, RZFB must manage multi-user interference to achieve its high throughput performance. Most existing works focus on the performance analysis of particular RZBF schemes such as the equip-power allocated RZBF under a fixed regularization parameter. This paper is the first work to consider the joint design of power allocation and regularization parameter for RZFB to maximize the worst users' throughput or the quality-of-service awarded energy efficiency under a fixed transmit power constraint. Such designs pose very computationally challenging optimization problems, for which the paper proposes two-stage optimization algorithms of low computational complexity. Their computational and performance efficiencies are substantiated through numerical examples.
Nguyen, LN & Oh, S 2019, 'Impacts of antiseptic cetylpyridinium chloride on microbiome and its removal efficiency in aerobic activated sludge', International Biodeterioration & Biodegradation, vol. 137, pp. 23-29.
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This study evaluated short- and long-term exposure of activated sludge (AS) microbiome to cetylpyridinium chloride (CPC), a quaternary ammonium compound widely used as biocidal additive or cationic surfactant. Toxicity assay in batch mode showed that CPC (50 μg L−1) inhibited cell growth. However, in a continuous reactor, CPC concentration in the range of 50–500 μg L−1 did not result in any observable impact on the biological activities of the AS microbiome. Similarly, 16S rRNA gene-based community profiling revealed that CPC had no observable impact on microbial diversity. At the phylogenetic structure, Rhodobacter (15 ± 7% of the total) and Asticcacaulis (9 ± 3%) were the only two phyla with increasing population in the 500 μg L−1-exposed reactors. This was also supported by an observation of no major change in the community structure. The reactors could remove >60% of CPC at initial concentrations of 50–500 μg L−1, primarily by adsorption and biodegradation. The enrichment of Rhodobacter and Asticcacaulis genus might contribute to CPC biodegradation and emerge as a potential microbial niche for the removal of CPC.
Nguyen, LN, Johir, MAH, Commault, A, Bustamante, H, Aurisch, R, Lowrie, R & Nghiem, LD 2019, 'Impacts of mixing on foaming, methane production, stratification and microbial community in full-scale anaerobic co-digestion process', BIORESOURCE TECHNOLOGY, vol. 281, pp. 226-233.
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Nguyen, LN, Nghiem, LD, Pramanik, BK & Oh, S 2019, 'Cometabolic biotransformation and impacts of the anti-inflammatory drug diclofenac on activated sludge microbial communities', Science of the Total Environment, vol. 657, pp. 739-745.
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© 2018 Elsevier B.V. This study evaluated the removal of diclofenac (DCF) in activated sludge and its long-term exposure effects on the function and structure of the microbial community. Activated sludge could remove <50% of 50 μg/L DCF. The removal decreased significantly to below 15% when DCF concentrations increased to 500 and 5000 μg/L. Quantitative assessment of the fate of DCF showed that its main removal routes were biodegradation (21%) and adsorption (7%), with other abiotic removals being insignificant (<5%). The biodegradation occurred through cometabolic mechanisms. DCF exposure in the range of 50–5000 μg/L did not disrupt the major functions of the activated sludge ecosystem (e.g. biomass yield and heterotrophic activity) over two months of DCF exposure. Consistently, 16S rRNA gene-based community analysis revealed that the overall community diversity (e.g. species richness and diversity) and structure of activated sludge underwent no significant alterations. The analysis did uncover a significant increase in several genera, Nitratireductor, Asticcacaulis, and Pseudacidovorax, which gained competitive advantages under DCF exposure. The enrichment of Nitratireductor, Asticcacaulis, and Pseudacidovorax genus might contribute to DCF biodegradation and emerge as a potential microbial niche for the removal of DCF.
Nguyen, LN, Nghiem, LD, Pramanik, BK & Oh, S 2019, 'Cometabolic biotransformation and impacts of the anti-inflammatory drug diclofenac on activated sludge microbial communities', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 657, pp. 739-745.
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Nguyen, LN, Nguyen, AQ, Johir, MAH, Guo, W, Ngo, HH, Chaves, AV & Nghiem, LD 2019, 'Application of rumen and anaerobic sludge microbes for bio harvesting from lignocellulosic biomass', Chemosphere, vol. 228, pp. 702-708.
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© 2019 Elsevier Ltd This study investigated the production of biogas, volatile fatty acids (VFAs), and other soluble organic from lignocellulosic biomass by two microbial communities (i.e. rumen fluid and anaerobic sludge). Four types of abundant lignocellulosic biomass (i.e. wheat straw, oaten hay, lurence hay and corn silage)found in Australia were used. The results show that rumen microbes produced four-time higher VFAs level than that of anaerobic sludge reactors, indicating the possible application of rumen microorganism for VFAs generation from lignocellulosic biomass. VFA production in the rumen fluid reactors was probably due to the presence of specific hydrolytic and acidogenic bacteria (e.g. Fibrobacter and Prevotella). VFA production corroborated from the observation of pH drop in the rumen fluid reactors indicated hydrolytic and acidogenic inhibition, suggesting the continuous extraction of VFAs from the reactor. Anaerobic sludge reactors on the other hand, produced more biogas than that of rumen fluid reactors. This observation was consistent with the abundance of methanogens in anaerobic sludge inoculum (3.98% of total microbes)compared to rumen fluid (0.11%). VFA production from lignocellulosic biomass is the building block chemical for bioplastic, biohydrogen and biofuel. The results from this study provide important foundation for the development of engineered systems to generate VFAs from lignocellulosic biomass.
Nguyen, LTN, Eager, D & Nguyen, H 2019, 'The relationship between compression garments and electrocardiogram signals during exercise and recovery phase', BioMedical Engineering Online, vol. 18, no. 1.
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© 2019 The Author(s). Background: The direction of the current research was to investigate whether electrocardiogram (ECG) signals have been impacted by using compression garments during exercise and recovery phase. Each subject is non-athletes, conducted two running tests, wearing either non-compression garments (NCGs) or compression garments (CGs) throughout experiments and 2-h of the recovery phase. Experiment 1 (number of participants (n) = 8; 61.4 ± 13.7 kg, 25.1 ± 3.8 years, 165.9 ± 8.3 cm) focused on the exercising phase while Experiment 2 (n = 14; 60.9 ± 12.0 kg, 24.7 ± 4.5 years, 166.0 ± 7.6 cm) concentrated on the recovery phase. Electrocardiogram (ECG) data were collected through wearable biosensors. Results: The results demonstrated a significant difference between compression garments and non-compression garments at the end of the tests and from 90 min onwards during the recovery phase (p < 0.05). Corrected QT (QTc), ST interval and heart rate (HR) indicated the significant difference between NCGs and CGs. Conclusion: Based on the findings, the utilization of compression garments showed a positive influence in non-athletes based on the quicker recovery in HR, ST, and QTc.
Nguyen, LV, Hu, G & Spanos, CJ 2019, 'Efficient Sensor Deployments for Spatio-Temporal Environmental Monitoring', IEEE Transactions on Systems Man and Cybernetics: Systems.
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This paper addresses the problem of efficiently deploying sensors in spatial environments, e.g., buildings, for the purposes of monitoring spatio-temporal environmental phenomena. By modeling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality-cost function of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is proven not to be affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment optimization problem is then solved by a practical and feasible polynomial algorithm, where its solutions are theoretically proven to be guaranteed. The proposed method is also theoretically and experimentally compared with the existing works. The effectiveness of the proposed algorithm is demonstrated by implementation in a real tested space in a university building, where the obtained results are highly promising.
Nguyen, MN, Nguyen, LD, Duong, TQ & Tuan, HD 2019, 'Real-time optimal resource allocation for embedded UAV communication systems', IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 225-238.
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© 2012 IEEE. We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.
Nguyen, T, Thai, N, Pham, H, Phan, T, Le, H, Nguyen, H & Nguyen, L 2019, 'Adaptive neural network based backstepping sliding mode control approach for dual arm robots', Journal of Control, Automation and Electrical Systems.
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The paper introduces an adaptive strategy to effectively control a nonlinear dual arm robot under external disturbances and uncertainties. By the use of the backstepping sliding mode control (BSSMC) method,
the proposed algorithm rst allows the manipulators to be able to robustly track the desired trajectories. Furthermore, due to the nonlinear, uncertain and unmodelled dynamics of the dual arm robot, it is proposed to employ the radial basis function network (RBFN) to adaptively estimate the robot's dynamic model. Though the estimation of the dynamics is approximate, the adaptation law is derived from the Lyapunov theory, which provides the controller with ability to guarantee stability of the whole system in spite of its nonlinearities, parameter uncertainties and external load variations. The effectiveness of the proposed RBFN-BSSMC approach is demonstrated by implementation in a simulation environment with realistic parameters, where the obtained results are highly promising.
Nguyen, TKL, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Nghiem, LD, Liu, Y, Ni, B & Hai, FI 2019, 'Insight into greenhouse gases emissions from the two popular treatment technologies in municipal wastewater treatment processes', Science of the Total Environment, vol. 671, pp. 1302-1313.
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© 2019 Elsevier B.V. Due to the impact of methane, carbon dioxide and nitrous oxide on global warming, the quantity of these greenhouse gases (GHG) emissions from municipal wastewater treatment plants (WWTPs) has attracted more and more attention. Consequently, GHG emissions from the two popular treatment technologies: anaerobic/anoxic/oxic (AAO) process and sequencing batch reactor (SBR) should be properly identified and discussed toward the current situation in developing countries. Direct and indirect carbon dioxide (with and/or without including in Intergovernmental Panel on Climate Change (IPCC) report) are all discussed in this article. This literature study observed that a quantity of total carbon dioxide emissions from SBR (374 g/m 3 of wastewater) was double that of AAO whilst 10% of these was direct carbon dioxide. Methane emitted from an SBR was 0.50 g/m 3 wastewater while 0.18 g CH 4 /m 3 wastewater was released from an AAO. The level of nitrous oxide from AAO and SBR accounted for 0.97 g/m 3 wastewater and 4.20 g/m 3 wastewater, respectively. Although these results were collected from different WWTPs and where influent was in various states, GHGs emitted from both biological units and other treatment units in various processes are significant. The results also revealed that aerated zone is the major contributing factor in a wastewater treatment plant to the large amount of GHG emissions.
Nguyen, TT, Ngo, HH, Guo, W, Wang, XC, Ren, N, Li, G, Ding, J & Liang, H 2019, 'Implementation of a specific urban water management - Sponge City.', The Science of the total environment, vol. 652, pp. 147-162.
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Climate change, rapid urbanization and inappropriate urban planning policies in many countries have resulted in urban water-related problems, such as flooding disasters, water pollution and water shortages. To tackle these issues, the specific urban water management strategy known as Sponge City has been implemented in China since 2013. This is a complex method and one involving many challenges. This paper critically assesses the approaches associated with conventional urban water management. The Sponge City concept and its adoption are then scrutinized to comprehensively assess the limitations and opportunities. It emerges that Sponge City has four main principles, these being: urban water resourcing, ecological water management, green infrastructures, and urban permeable pavement. The uncertainties in Sponge City design and planning, and financial insufficiencies are the most serious problems that can risk the failure of the Sponge City concept. While significant barriers exist, the opportunities for implementing a Sponge City are evident. To obtain multi-ecosystem services of Sponge City, it should be implemented at the watershed scales and be flexible, depending on different decision levels or catchment characteristics. It is essential to apply an intelligent decision-making mechanism and consider the need for close cooperation between various agencies with which the central government can work. A suitable sized and harmonious Sponge City, ensuring a good balance between socio-economic development and environmental conservation, is the ideal.
Ni, B-J, Huang, Q-S, Wang, C, Ni, T-Y, Sun, J & Wei, W 2019, 'Competitive adsorption of heavy metals in aqueous solution onto biochar derived from anaerobically digested sludge', CHEMOSPHERE, vol. 219, pp. 351-357.
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Ni, J, Bongers, A, Chamoli, U, Bucci, J, Graham, P & Li, Y 2019, 'In Vivo 3D MRI Measurement of Tumour Volume in an Orthotopic Mouse Model of Prostate Cancer', CANCER CONTROL, vol. 26, no. 1.
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Nimbalkar, SS, Punetha, P, Basack, S & Mirzababaei, M 2019, 'Piles subjected to torsional cyclic load: Numerical analysis', Frontiers in Built Environment, vol. 5.
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© 2019 Nimbalkar, Punetha, Basack and Mirzababaei. Pile foundations supporting large structures (such as high-rise buildings, oil drilling platforms, bridges etc). are often subjected to eccentric lateral load (in addition to the vertical loads) due to the action of wind, waves, high speed traffic, and ship impacts etc. The eccentric lateral load, which is usually cyclic (repetitive) in nature, induces torsion in the pile foundation. This paper presents a numerical model based on boundary element approach to study the performance of a single pile subjected to the torsional cyclic load. The model is initially validated by comparing it with the experimental data available from the literature. Thereafter, the model has been utilized to conduct a parametric study to understand the influence of the torsional cyclic loading parameters on the axial pile capacity. The results indicated that the model is able to capture the degradation in the axial pile capacity due to the torsional cyclic loading with a reasonable accuracy. Moreover, the parametric study showed that the frequency, amplitude and number of cycles play a significant role in the torsional cyclic response of the pile. The present study is essential for the development of design guidelines for pile foundations subjected to torsional cyclic load.
Ning, D, Sun, S, Yu, J, Zheng, M, Du, H, Zhang, N & Li, W 2019, 'A rotary variable admittance device and its application in vehicle seat suspension vibration control', Journal of the Franklin Institute.
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© 2019 The Franklin Institute In this paper, a novel semi-active variable admittance (VA) concept is proposed, and a seat suspension prototype with a magnetorheological fluid damper based rotary VA device is designed, manufactured, and experimentally validated. The conventional inerter with a single flywheel has a constant inertance, which can effectively improve the suspension performance by being integrated into a mechanical network with springs and dampers. The proposed rotary VA device comprises a gear reducer, two flywheels and a variable damping (VD) device which is used to connect the two flywheels. With carefully designing, the rotary VA device is compacted and is similar with a VD device in size. The rotary VA device is installed in the centre of a seat suspension's scissors structure to form a VA seat suspension. According to the test results, the equivalent inertance of the seat suspension can vary from 11.3 Kg–76.6 Kg with a 3 Hz frequency and 5 mm amplitude sinusoidal movement by changing the current from 0 A–1 A. By analysing the system characteristics, a hybrid controller with two acceleration feedbacks is proposed. Thereafter, the seat suspension and controller are validated in experiments by comparing the performance with a conventional passive seat suspension. The random vibration test shows the excellent performance of the proposed seat suspension; the frequency weighted root mean square acceleration of the seat is reduced by 43.6%, which indicates a great improvement of the ride comfort. The VA device shows great prospect in the suspension application.
Niu, Q, Xu, Q, Wang, Y, Wang, D, Liu, X, Liu, Y, Wang, Q, Ni, BJ, Yang, Q, Li, X & Li, H 2019, 'Enhanced hydrogen accumulation from waste activated sludge by combining ultrasonic and free nitrous acid pretreatment: Performance, mechanism, and implication', Bioresource Technology, vol. 285.
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© 2019 Elsevier Ltd This study presents a novel and effective method, i.e., adding nitrite into acidic fermentations after ultrasonic (US) pretreatment to form free nitrous acid (FNA), to further enhance hydrogen yield. Experimental results showed that when 180 mg/L nitrite was added into the US (2 W/mL, 15 min) pretreated waste activated sludge (WAS), the maximal hydrogen yield of 24.81 ± 1.24 mL/g VSS (volatile suspended solids) was obtained under acidic fermentation (1.0 mg/L FNA was initially formed under this condition), which was 2.21-folds (or 1.36-folds) of that from US pretreatment (or FNA treatment) alone. This combination approach caused a positive synergy on sludge disintegration and enhanced the transformation of the released organics from non-biodegradable to biodegradable. Further study showed that the inhibiting effect of this combination method on hydrogen consuming microorganism was severer. Considering its pollution free, this combination strategy is an attractive technology for hydrogen recovery from WAS.
Noori, L, Pour, AB, Askari, G, Taghipour, N, Pradhan, B, Lee, CW & Honarmand, M 2019, 'Comparison of different algorithms to map hydrothermal alteration zones using ASTER remote sensing data for polymetallic Vein-Type ore exploration: Toroud-Chahshirin Magmatic Belt (TCMB), north Iran', Remote Sensing, vol. 11, no. 5.
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© 2019 by the authors. Polymetallic vein-type ores are important sources of precious metal and a principal type of orebody for various base-metals. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping hydrothermal alteration zones associated with epithermal polymetallic vein-type mineralization in the Toroud-Chahshirin Magmatic Belt (TCMB), North of Iran. The TCMB is the largest known goldfield and base metals province in the central-north of Iran. Propylitic, phyllic, argillic, and advanced argillic alteration and silicification zones are typically associated with Au-Cu, Ag, and/or Pb-Zn mineralization in the TCMB. Specialized image processing techniques, namely Selective Principal Component Analysis (SPCA), Band Ratio Matrix Transformation (BRMT), Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) were implemented and compared to map hydrothermal alteration minerals at the pixel and sub-pixel levels. Subtle differences between altered and non-altered rocks and hydrothermal alteration mineral assemblages were detected and mapped in the study area. The SPCA and BRMT spectral transformation algorithms discriminated the propylitic, phyllic, argillic and advanced argillic alteration and silicification zones as well as lithological units. The SAM and MTMF spectral mapping algorithms detected spectrally dominated mineral groups such as muscovite/montmorillonite/illite, hematite/jarosite, and chlorite/epidote/calcite mineral assemblages, systematically. Comprehensive fieldwork and laboratory analysis, including X-ray diffraction (XRD), petrographic study, and spectroscopy were conducted in the study area for verifying the remote sensing outputs. Results indicate several high potential zones of epithermal polymetallic vein-type mineralization in the northeastern and southwestern parts of the study area, which can be considered for future systematic exploration programs. The...
Nur, T, Loganathan, P, Ahmed, MB, Johir, MAH, Nguyen, TV & Vigneswaran, S 2019, 'Removing arsenic from water by coprecipitation with iron: Effect of arsenic and iron concentrations and adsorbent incorporation', Chemosphere, pp. 431-438.
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© 2019 Elsevier Ltd Arsenic (As) contamination of drinking water is a major cause of As toxicity in many parts of the world. A study was conducted to evaluate As removal from water containing 100–700 μg/L of As and As to Fe concentration ratios of 1:5–1:1000 using the coprecipitation process with and without As/Fe adsorption onto granular activated carbon (GAC). Fe concentration required to reduce As concentrations in order to achieve the WHO standard level of 10 μg/L increased exponentially with the increase in initial As concentration. When small amounts of GAC were added to the As/Fe solutions the Fe required to remove these As concentrations reduced drastically. This decline was due to the GAC adsorption of Fe and As, enhancing the removal of these metals through coprecipitation. Predictive regression equations were developed relating the GAC dose requirement to the initial As and Fe concentrations. Zeta potential data revealed that As was adsorbed on the GAC by outer-sphere complexation whereas Fe was adsorbed by inner-sphere complexation reversing the negative charge on GAC to positive values. X-ray diffraction of the GAC samples in the presence of Fe had an additional peak characteristic of ferrihydrite (Fe oxide) compared to that of the GAC sample without Fe. The study showed that incorporating an adsorbent into the coprecipitation process has the advantage of removing As from waters at all concentrations of Fe and As compared to coprecipitation alone which does not remove As to the required levels if Fe concentration is low.
Ojeda, A, Klug, M, Kreutz-Delgado, K, Gramann, K & Mishra, J 2019, 'A Bayesian Framework for Unifying Data Cleaning, Source Separation and Imaging of Electroencephalographic Signals'.
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Abstract Electroencephalographic (EEG) source imaging depends upon sophisticated signal processing algorithms for data cleaning, source separation, and localization. Typically, these problems are addressed by independent heuristics, limiting the use of EEG images on a variety of applications. Here, we propose a unifying parametric empirical Bayes framework in which these dissimilar problems can be solved using a single algorithm (PEB+). We use sparsity constraints to adaptively segregate brain sources into maximally independent components with known anatomical support, while minimally overlapping artifactual activity. Of theoretical relevance, we demonstrate the connections between Infomax ICA and our framework. On real data, we show that PEB+ outperforms Infomax for source separation on short time-scales and, unlike the popular ASR algorithm, it can reduce artifacts without significantly distorting clean epochs. Finally, we analyze mobile brain/body imaging data to characterize the brain dynamics supporting heading computation during full-body rotations, replicating the main findings of previous experimental literature.
Ong, HC, Milano, J, Silitonga, AS, Hassan, MH, Shamsuddin, AH, Wang, CT, Indra Mahlia, TM, Siswantoro, J, Kusumo, F & Sutrisno, J 2019, 'Biodiesel production from Calophyllum inophyllum-Ceiba pentandra oil mixture: Optimization and characterization', Journal of Cleaner Production, vol. 219, pp. 183-198.
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© 2019 Elsevier Ltd In this study, a novel modeling approach (artificial neural networks (ANN) and ant colony optimization (ACO)) was used to optimize the process variables for alkaline-catalyzed transesterification of CI40CP60 oil mixture (40 wt% of Calophyllum inophyllum oil mixed with 60 wt% of Ceiba pentandra oil) in order to maximize the biodiesel yield. The optimum values of the methanol-to-oil molar ratio, potassium hydroxide catalyst concentration, and reaction time predicted by the ANN-ACO model are 37%, 0.78 wt%, and 153 min, respectively, at a constant reaction temperature and stirring speed of 60 °C and 1000 rpm, respectively. The ANN-ACO model was validated by performing independent experiments to produce the CI40CP60 methyl ester (CICPME) using the optimum transesterification process variables predicted by the ANN-ACO model. There is very good agreement between the average CICPME yield determined from experiments (95.18%) and the maximum CICPME yield predicted by the ANN-ACO model (95.87%) for the same optimum values of process variables, which corresponds to a difference of 0.69%. Even though the ANN-ACO model is only implemented to optimize the transesterification of process variables in this study. It is believed that the model can be used to optimize other biodiesel production processes such as seed oil extraction and acid-catalyzed esterification for various types of biodiesels and biodiesel blends.
Pan, S, Hu, R, Fung, S-F, Long, G, Jiang, J & Zhang, C 2019, 'Learning Graph Embedding with Adversarial Training Methods', arXiv preprint arXiv:1901.01250.
Pan, Y, Liu, Y, Peng, L, Ngo, HH, Guo, W, Wei, W, Wang, D & Ni, B-J 2019, 'Substrate Diffusion within Biofilms Significantly Influencing the Electron Competition during Denitrification.', Environmental science & technology, vol. 53, no. 1, pp. 261-269.
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A common and long-existing operational issue of wastewater denitrification is the unexpected accumulation of nitrite (NO2-) that could suppress the activity of various microorganisms involved in biological wastewater treatment process and nitrous oxide (N2O) that could emit as a potent greenhouse gas. Recently, it has been confirmed that the accumulation of these denitrification intermediates in biological wastewater treatment process is greatly influenced by the electron competition between the four denitrification steps. However, little is known about this in biofilm systems. In this work, we applied a mathematical model that links carbon oxidation and nitrogen reduction processes through a pool of electron carriers, to assess electron competition in denitrifying biofilms. Simulations were performed comprehensively at seven combinations of electron acceptor addition scheme (i.e., simultaneous addition of one, two or three among nitrate (NO3-), NO2-, and N2O) to compare the effect of electron competition on NO3-, NO2- and N2O reduction. Overall, the effects of substrate loading, biofilm thickness and effective diffusion coefficients on electron competition are not always intuitive. Model simulations show that electron competition was intensified due to the substrate load limitation (from 120 to 20 mg COD/L) and increasing biofilm thicknesses (from 0.1 to 1.6 mm) in most cases, where electrons were prioritized to nitrite reductase because of the insufficient electron donor availability in the biofilm. In contrast, increasing effective diffusion coefficients did not pose a significant effect on electron competition and only increased electrons distributed to nitrite reductase when both NO2- and N2O are added.
Park, MJ, Lim, S, Gonzales, RR, Phuntsho, S, Han, DS, Abdel-Wahab, A, Adham, S & Shon, HK 2019, 'Thin-film composite hollow fiber membranes incorporated with graphene oxide in polyethersulfone support layers for enhanced osmotic power density', Desalination, vol. 464, pp. 63-75.
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© 2019 Elsevier B.V. This study focused on the development of pressure retarded osmosis (PRO) thin film composite (TFC) membranes for enhanced osmotic power using hollow fiber polyethersulfone (PES) support structure modified by incorporating hydrophilic graphene oxide (GO) nanosheets. The GO loadings in the hollow fiber substrates were varied to improve water flux performances without compromising the mechanical strength. GO embedded (≤0.2 wt%) PES hollow fiber supports revealed noticeable improvements in pure water permeability, improved structural morphologies, as well as the hydrophilicity within the support layer, without deteriorating the mechanical properties. The GO (0.2 wt%)-incorporated TFC-PRO membrane appeared to have an initial PRO flux (without any applied pressure) of 43.74 L m −2 h −1 , lower specific reverse salt flux of 0.04 g L −1 and structural parameter (S) of 522 μm, significantly better than the control membrane. The maximum power density of 14.6 W m −2 was achieved at an operating pressure of 16.5 bar under the condition of DI water and 1 M NaCl as feed and draw solutions, respectively. The results obtained in this study indicate that modification of PRO hollow fiber support layer by incorporating nanoparticles such as GO nanosheet can be a useful tool to improve the PRO performance.
Paryani, S & Ramadan-Jradi, R 2019, 'The Impact of Flipped Learning on Student Performance and Engagement: A Systematic Literature Review', International Journal for Learning and Teaching, vol. 5, no. 1.
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Patel, OP, Bharill, N, Tiwari, A & Prasad, M 2019, 'A Novel Quantum-inspired Fuzzy Based Neural Network for Data Classification', IEEE Transactions on Emerging Topics in Computing.
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IEEE The performance of the neural network (NN) depends on the various parameters such as structure, initial weight, number of hidden layer neurons, and learning rate. The improvement in classification performance of NN without changing its structure is a challenging issue. This paper proposes a novel learning model called Quantum-inspired Fuzzy Based Neural Network (Q-FNN) to solve two-class classification problems. In the proposed model, NN architecture is formed constructively by adding neurons in the hidden layer and learning is performed using the concept of Fuzzy c-Means (FCM) clustering, where the fuzziness parameter (m) is evolved using the quantum computing concept. The quantum computing concept provides a large search space for a selection of m, which helps in finding the optimal weights and also optimizes the network architecture. This paper also proposes a modified step activation function for the formation of hidden layer neurons, which handles the overlapping samples belong to different class regions. The performance of the proposed Q-FNN model is superior and competitive with the state-of-the-art methods in terms of accuracy, sensitivity, and specificity on 15 real-world benchmark datasets.
Patel, OP, Tiwari, A, Chaudhary, R, Nuthalapati, SV, Bharill, N, Prasad, M, Hussain, FK & Hussain, OK 2019, 'Enhanced quantum-based neural network learning and its application to signature verification', Soft Computing, pp. 1-14.
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© 2017 Springer-Verlag GmbH Germany, part of Springer Nature In this paper, an enhanced quantum-based neural network learning algorithm (EQNN-S) which constructs a neural network architecture using the quantum computing concept is proposed for signature verification. The quantum computing concept is used to decide the connection weights and threshold of neurons. A boundary threshold parameter is introduced to optimally determine the neuron threshold. This parameter uses min, max function to decide threshold, which assists efficient learning. A manually prepared signature dataset is used to test the performance of the proposed algorithm. To uniquely identify the signature, several novel features are selected such as the number of loops present in the signature, the boundary calculation, the number of vertical and horizontal dense patches, and the angle measurement. A total of 45 features are extracted from each signature. The performance of the proposed algorithm is evaluated by rigorous training and testing with these signatures using partitions of 60–40 and 70–30%, and a tenfold cross-validation. To compare the results derived from the proposed quantum neural network, the same dataset is tested on support vector machine, multilayer perceptron, back propagation neural network, and Naive Bayes. The performance of the proposed algorithm is found better when compared with the above methods, and the results verify the effectiveness of the proposed algorithm.
Peng, L, Ngo, HH, Song, S, Xu, Y, Guo, W, Liu, Y, Wei, W, Chen, X, Wang, D & Ni, BJ 2019, 'Heterotrophic denitrifiers growing on soluble microbial products contribute to nitrous oxide production in anammox biofilm: Model evaluation', Journal of Environmental Management, vol. 242, pp. 309-314.
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© 2019 Elsevier Ltd In this work, a model framework was constructed to assess and predict nitrous oxide (N 2 O) production, substrate and microbe interactions in an anammox biofilm bioreactor. The anammox kinetics were extended by including kinetics of autotrophic soluble microbial products (SMP) formation, which consisted of utilization-associated products (UAP) and biomass-associated products (BAP). Heterotrophic bacteria growing on UAP, BAP and decay released substance (SS) were modelled to perform four-step sequential reductions from nitrate to dinitrogen gas. N 2 O was modelled as an intermidiate of heterotrophic denitrification via three pathways with UAP, BAP and SS as the electron donors. The developed model framework was evaluated using long-term operational data from an anammox biofilm reactor and satisfactorily reproduced effluent nitrogen and SMP as well as N 2 O emission factors under different operational conditions. The modeling results revealed that N 2 O was mainly produced with UAP as the electron donor while BAP and SS play minor roles. Heterotrophic denitrifiers growing on UAP would significantly contribute to N 2 O emission from anammox biofilm reactor even though heterotrophs only account for a relatively small fraction of active biomass in the anammox biofilm. Comprehensive simulations were conducted to investigate the effects of N loading rate and biofilm thickness, which indicated that maintaining a low N loading rate and a thick biofilm thickness were essential for high total nitrogen removal efficiency and low N 2 O emission.
Pettit, T, Irga, PJ & Torpy, FR 2019, 'The in situ pilot-scale phytoremediation of airborne VOCs and particulate matter with an active green wall', Air Quality, Atmosphere and Health, pp. 33-44.
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© 2019, Springer Nature B.V. Atmospheric pollutant phytoremediation technologies, such as potted plants and green walls, have been thoroughly tested in lab-scale experiments for their potential to remove air pollutants. The functional value of these technologies, however, is yet to be adequately assessed in situ, in ‘high value’ environments, where pollutant removal will provide the greatest occupant health benefits. Air pollution in countries such as China is a significant public health issue, and efficient air pollution control technologies are needed. This work used pilot-scale trials to test the capacity of potted plants, a passive green wall and an active green wall (AGW) to remove particulate matter (PM) and total volatile organic compounds (TVOCs) from a room in a suburban residential house in Sydney, Australia, followed by an assessment of the AGW’s potential to remove these pollutants from a classroom in Beijing. In the residential room, compared to potted plants and the passive green wall, the AGW maintained TVOCs at significantly lower concentrations throughout the experimental period (average TVOC concentration 72.5% lower than the control), with a similar trend observed for PM. In the classroom, the AGW reduced the average TVOC concentration by ~ 28% over a 20-min testing period compared to levels with no green wall and a filtered HVAC system in operation. The average ambient PM concentration in the classroom with the HVAC system operating was 101.18 μg/m3, which was reduced by 42.6% by the AGW. With further empirical validation, AGWs may be implemented to efficiently clean indoor air through functional reductions in PM and TVOC concentrations.
Pou, J, Perez, MA & Aguilera, RP 2019, 'Modular multilevel converters', IEEE Transactions on Industrial Electronics, vol. 66, no. 3, pp. 2204-2206.
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Prokopenko, M, Harré, M, Lizier, J, Boschetti, F, Peppas, P & Kauffman, S 2019, 'Self-referential basis of undecidable dynamics: From the Liar paradox and the halting problem to the edge of chaos', Physics of Life Reviews.
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© 2019 In this paper we explore several fundamental relations between formal systems, algorithms, and dynamical systems, focussing on the roles of undecidability, universality, diagonalization, and self-reference in each of these computational frameworks. Some of these interconnections are well-known, while some are clarified in this study as a result of a fine-grained comparison between recursive formal systems, Turing machines, and Cellular Automata (CAs). In particular, we elaborate on the diagonalization argument applied to distributed computation carried out by CAs, illustrating the key elements of Gödel's proof for CAs. The comparative analysis emphasizes three factors which underlie the capacity to generate undecidable dynamics within the examined computational frameworks: (i) the program-data duality; (ii) the potential to access an infinite computational medium; and (iii) the ability to implement negation. The considered adaptations of Gödel's proof distinguish between computational universality and undecidability, and show how the diagonalization argument exploits, on several levels, the self-referential basis of undecidability.
Pu, L, Chen, X, Mao, G, Xie, Q & Xu, J 2019, 'Chimera: An Energy-efficient and Deadline-aware Hybrid Edge Computing Framework for Vehicular Crowdsensing Applications', IEEE Internet of Things Journal.
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IEEE In this paper, we propose Chimera, a novel hybrid edge computing framework, integrated with the emerging edge cloud radio access network, to augment network-wide vehicle resources for future large-scale vehicular crowdsensing applications, by leveraging a multitude of cooperative vehicles and the virtual machine (VM) pool in the edge cloud via the control of the application manager deployed in the edge cloud. We present a comprehensive framework model and formulate a novel multi-vehicle and multi-task offloading problem, aiming at minimizing the energy consumption of network-wide recruited vehicles serving heterogeneous crowdsensing applications, and meanwhile reconciling both application deadline and vehicle incentive. We invoke Lyapunov optimization framework to design TaskSche, an online task scheduling algorithm, which only utilizes the current system information. As the core components of the algorithm, we propose a task workload assignment policy based on graph transformation and a knapsack-based VM pool resource allocation policy. Rigorous theoretical analyses and extensive trace-driven simulations indicate that our framework achieves superior performance (e.g., 20% – 68% energy saving without overstepping application deadlines for network-wide vehicles compared with vehicle local processing) and scales well for a large number of vehicles and applications.
Puthal, D 2019, 'Lattice-modelled Information Flow Control of Big Sensing Data Streams for Smart Health Application', IEEE Internet of Things Journal.
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IEEE Internet of Things (IoT) provides a promising opportunity to build powerful data analytics systems with real time event detection for smart health, and therefore wearable IoT has become a rising source of big data streams for smart health, for which security needs to be assured by detecting real-time event to avoid malicious activities, and meanwhile to control the information leakage of big sensing data streams. I refer to this as an information flow control problem. To address this problem, this paper proposes a static lattice model for information flow control over big sensing data streams. I initialize two static lattices i.e. sensor lattice for wearable sensors and user lattice for users, and then static lattices aim to process the flow control model faster, because I am dealing with high volume and velocity of data streams. The experimental evaluation and results of the information flow model show that it can excellently handle the incoming big data streams with low latency and buffer requirement.
Puthal, D & Mohanty, SP 2019, 'Proof of Authentication: IoT-Friendly Blockchains', IEEE Potentials, vol. 38, no. 1, pp. 26-29.
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© 2018 IEEE. This article introduces the concept of proof of authentication (PoAh) for the lightweight implementation of blockchains in the Internet of Things (IoT). The PoAh can replace existing consensus algorithms, such as proof of work (PoW), proof of stake (PoS), and proof of activity (PoA), for resource- and energyconstrained infrastructures, such as the IoT.
Puthal, D, Mohanty, SP, Bhavake, SA, Morgan, G & Ranjan, R 2019, 'Fog Computing Security Challenges and Future Directions', IEEE CONSUMER ELECTRONICS MAGAZINE, vol. 8, no. 3, pp. 92-96.
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Puthal, D, Ranjan, R, Nanda, A, Nanda, P, Jayaraman, PP & Zomaya, AY 2019, 'Secure authentication and load balancing of distributed edge datacenters', JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, vol. 124, pp. 60-69.
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Putra, N, Hakim, II, Erwin, FP, Abdullah, NA, Ariantara, B, Amin, M, Mahlia, TMI & Kusrini, E 2019, 'Development of a novel thermoelectric module based device for thermal stability measurement of phase change materials', Journal of Energy Storage, vol. 22, pp. 331-335.
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© 2019 Elsevier Ltd A recently developed method for the thermal stability measurement of phase-change materials (PCMs) involves thermal cycling using a thermoelectric module as a heating and cooling element. However, the utility of this approach was found to have some limitations, mainly because the thermoelectric polarity is changed according to time rather than the actual sample temperature. A method for thermal cycling test, where the thermoelectric polarity is automatically changed according to the sample temperature was developed in this study. In addition, a new cartridge design in this device requires a small sample volume (1.53 cm 3 ) and can be easily assembled and disassembled. This proposed device was tested on beeswax as a PCM sample. This is very important for savings PCMs material which usually expensive. The results showed that the apparatus had automatically cycled between the melting and cooling temperatures of beeswax. The thermal data showed that beeswax retains consistent melting and freezing temperatures after 1000 cycles, however, its heat of fusion degrades over repeated thermal cycling. This apparatus can be readily applied to study a wide range of PCMs for such as thermal energy storage materials for energy conservation. To our best knowledge, yet no study has been performed on this kind of equipment so far.
Putra, N, Rawi, S, Amin, M, Kusrini, E, Kosasih, EA & Indra Mahlia, TM 2019, 'Preparation of beeswax/multi-walled carbon nanotubes as novel shape-stable nanocomposite phase-change material for thermal energy storage', Journal of Energy Storage, vol. 21, pp. 32-39.
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© 2018 Elsevier Ltd Development of phase-change material (PCM) as thermal energy storage for building envelopes is promising for energy utilization. However, there are two major drawbacks of PCM application, which are low thermal conductivity and high-volume reduction due to phase-change transition. One solution is to develop a shape-stabilized phase-change material (SSPCM) as a composite that is able to prevent leakage during the transition from solid to liquid. Therefore, the objective of this study is to prepare beeswax/multi-walled carbon nanotubes as form-stable nanocomposite phase-change material for thermal energy storage, based on previously unattempted methods. Beeswax was being used as PCM because of its high latent heat and multi-walled carbon nanotubes (MWCNTs) as supporting material with high thermal conductivity. There are three types of MWCNTs applied in this research: pristine MWCNTs, ball-milled MWCNTs and acid-treated MWCNTs. Beeswax/CNT composite samples were prepared with ratios of 5 and 20 wt%. Composite samples were tested from structure modification and thermal performance, including latent heat, sensible heat, melting point, solidifying point, thermal conductivity, and thermal-cycle testing for up to 300 cycles. Experimental results showed that thermal conductivity of novel shape-stable nanocomposite PCM increased by a factor of 2 and there was no significant phase transition in the melting or solidifying temperature. The high heat storage capability and thermal conductivity of nanocomposite PCM enable it to be a potential material for thermal energy storage in practical applications.
Qiao, M, Yu, J, Bian, W, Li, Q & Tao, D 2019, 'Adapting Stochastic Block Models to Power-Law Degree Distributions.', IEEE Transactions on Cybernetics, vol. 49, no. 2, pp. 626-637.
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Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by introducing degree decay variables to explicitly encode the varying degree distribution over all nodes. With an exponential prior, it is proved that PLD-SBM approximately preserves the scale-free feature in real networks. In addition, from the inference of variational E-Step, PLD-SBM is indeed to correct the bias inherited in SBM with the introduced degree decay factors. Furthermore, experiments conducted on both synthetic networks and two real-world datasets including Adolescent Health Data and the political blogs network verify the effectiveness of the proposed model in terms of cluster prediction accuracies.
Qin, C, Ni, W, Tian, H, Lu, L & Liu, RP 2019, 'Radio over Cloud (RoC): Cloud-Assisted Distributed Beamforming for Multi-class Traffic', IEEE Transactions on Mobile Computing.
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IEEE Cloud has yet to be applied to computationally intensive radio signal processing, due to closely coupled computing tasks resulting from interference. This paper presents a new cloud-assisted joint beamforming architecture, where computations are decoupled for individual wireless users and pipelined for cloud execution, using Difference of Convex (DC), l1-norm approximations, and dual decompositions. User-specific tasks are constructed and aligned with the cloud to leverage computation reuses and minimize overhead. The time-complexity is dramatically improved to support networks with tens to hundreds of base stations and users, without compromising the sum rate and quality-of-service. Further, the superiority of DC to the state-of-the-art Weighted Minimum Mean Square Error (WMMSE) in terms of convex relaxation is observed and discussed. Corroborated by simulations, the reason is revealed as WMMSE aggressively increases the data rate at interim stages, hence adversely interacting with l1-norm approximation and reducing the feasible solution regions at later stages.
Qin, Y, Wei, C, Tang, X, Zhang, N, Dong, M & Hu, C 2019, 'A novel nonlinear road profile classification approach for controllable suspension system: Simulation and experimental validation', Mechanical Systems and Signal Processing, vol. 125, pp. 79-98.
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© 2018 Elsevier Ltd Driven by the increasing requirement for road conditions in the field of the controllable suspension system, this paper presents a novel nonlinear road-excitation classification procedure for arbitrary suspension control strategy. The proposed procedure includes four steps: the definition of controller parameters, selection of insensitive frequency ranges, calculation of superior features, and generation of the classifier. To better illustrate the proposed procedure, the clipped optimal control strategy is taken as an example in the simulation part. Simulation results reveal that the proposed method can accurately estimate road excitation level for various controller parameters, vehicle speeds, and vehicle models. Three contributions have been made in this paper: (1) A road classification procedure that can be used for road adaptive suspension control with any control algorithm is developed; (2) In order to improve classification accuracy, the concept of insensitive index which is based on the time-frequency analysis is proposed; (3) Experimental validation with a quarter vehicle test rig is performed, which has verified the effectiveness of the proposed method for the adaptive controllable suspension system.
Qiu, W, Sun, J, Fang, M, Luo, S, Tian, Y, Dong, P, Xu, B & Zheng, C 2019, 'Occurrence of antibiotics in the main rivers of Shenzhen, China: Association with antibiotic resistance genes and microbial community.', The Science of the total environment, vol. 653, pp. 334-341.
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The occurrence and distribution of antibiotics were investigated in surface water and sediment collected from the main rivers of Shenzhen, China. Total concentrations of 20 selected antibiotics ranged from 36.510 to 1075.687 ng L-1 (mean 244.992 ng L-1) in 31 water samples and from 28.124 to 2728.810 ng g-1 (mean 680.169 ng g-1) in 31 sediment samples. Notably, STZ and SDZ were the dominant antibiotics in both water and sediment as their higher concentrations compared with the other compounds. Furthermore, comprehensive profiling of antibiotic resistance genes (ARGs) and microbial community was performed to gain an understanding of the evolution and dissemination of ARGs in microbial communities caused by the occurrence of antibiotics in sediment samples from Maozhou River. As a result, the sul1 gene was found to be the most abundant ARG and Proteobacteria was the most abundant microorganism in all the samples (37.4-51.7%), followed by Bacteroidetes (15.3-18.4%). Statistical analysis figured out the relations among antibiotics, ARGs and microbial community. A specific conclusion could be drawn from the positive correlations among the bla_d gene, Fusobacteria, and sulfamethoxazole. It suggests that antibiotics may be positively linked to the expression of ARGs in certain bacteria, and thus high reproduction would occur within the bacterial community. Overall, the widespread distribution of ARGs underscores the need for further research on the mechanism of antibiotics influence as emerging contaminants in the environment and the associated risks to microbial community.
Rahman, MA, Islam, MR, Muttaqi, KM, Guo, Y, Zhu, J, Sutanto, D & Lei, G 2019, 'A Modified Carrier-Based Advanced Modulation Technique for Improved Switching Performance of Magnetic-Linked Medium-Voltage Converters', IEEE Transactions on Industry Applications, vol. 55, no. 2, pp. 2088-2098.
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© 1972-2012 IEEE. The high-frequency magnetic link is gaining popularity due to its lightweight, small volume, and inherent voltage balancing capability. Those features can simplify the utilization of a multilevel converter (MLC) for the integration of renewable energy sources to the grid with compact size and exert economic feasibility. The modulation and control of the MLC are crucial issues, especially for grid-connected applications. To support the grid, the converter may need to operate in an overmodulation (OVM) region for short periods depending upon the loading conditions. This OVM operation of the converter causes increased harmonic losses and adverse effects on the overall system efficiency. On top of that, the size and cost of filtering circuitry become critical to eliminate the unwanted harmonics. In this regard, a modified OVM scheme with phase-disposed carriers for a grid-connected high-frequency magnetic-link-based cascaded H-bridge (CHB) MLC is proposed for the suppression of harmonics and the reduction of converter loss. Furthermore, with the proposed OVM technique, the voltage gain with the modulation index can be increased up to the range which is unlikely to be achieved using the classical ones. Extensive simulations are carried out with a 2.24 MVA permanent magnet synchronous generator based wind energy conversion system, which is connected to the 11 kV ac grid through a high-frequency magnetic-link and a five-level CHB MLC. A scaled down laboratory prototype is implemented to validate the performance of the converter.
Rana, HK, Akhtar, MR, Islam, MB, Ahmed, MB, Liò, P, Quinn, JMW, Huq, F & Moni, MA 2019, 'Genetic effects of welding fumes on the development of respiratory system diseases', Computers in Biology and Medicine, vol. 108, pp. 142-149.
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© 2019 Elsevier Ltd Background: The welding process releases potentially hazardous gases and fumes, mainly composed of metallic oxides, fluorides and silicates. Long term welding fume (WF) inhalation is a recognized health issue that carries a risk of developing chronic health problems, particularly respiratory system diseases (RSDs). Aside from general airway irritation, WF exposure may drive direct cellular responses in the respiratory system which increase risk of RSD, but these are not well understood. Methods: We developed a quantitative framework to identify gene expression effects of WF exposure that may affect RSD development. We analyzed gene expression microarray data from WF-exposed tissues and RSD-affected tissues, including chronic bronchitis (CB), asthma (AS), pulmonary edema (PE), lung cancer (LC) datasets. We built disease-gene (diseasome) association networks and identified dysregulated signaling and ontological pathways, and protein-protein interaction sub-network using neighborhood-based benchmarking and multilayer network topology. Results: We observed many genes with altered expression in WF-exposed tissues were also among differentially expressed genes (DEGs) in RSD tissues; for CB, AS, PE and LC there were 34, 27, 50 and 26 genes respectively. DEG analysis, using disease association networks, pathways, ontological analysis and protein-protein interaction sub-network suggest significant links between WF exposure and the development of CB, AS, PE and LC. Conclusions: Our network-based analysis and investigation of the genetic links of WFs and RSDs confirm a number of genes and gene products are plausible participants in RSD development. Our results are a significant resource to identify causal influences on the development of RSDs, particularly in the context of WF exposure.
Rana, HK, Akhtar, R, Ahmed, MB, Lio, P, Quinn, JMW, Huq, F & Moni, MA 2019, 'Genetic effects of welding fumes on the progression of neurodegenerative diseases', NEUROTOXICOLOGY, vol. 71, pp. 93-101.
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Rao, P, Zhao, L, Chen, Q & Nimbalkar, S 2019, 'Three-dimensional limit analysis of slopes reinforced with piles in soils exhibiting heterogeneity and anisotropy in cohesion', Soil Dynamics and Earthquake Engineering, vol. 121, pp. 194-199.
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© 2019 Elsevier Ltd Reinforcement of slopes by placing piles is one of the most common and effective techniques. Most of existing studies are limited to homogeneous and isotropic slopes, while in practice, the soil in the slope often exhibits heterogeneous and anisotropic characteristics. To address these issues, an innovative approach is introduced in this Technical Note to evaluate the stability of heterogeneous and anisotropic slopes incorporating the effect of anti-slide piles. Employing a three-dimensional upper-bound limit analysis, safety factor adopting the strength reduction technique is utilized herewith. The effects of soil heterogeneity and anisotropy with reference to cohesion on the optimal pile location and the slope stability in both cohesive-frictional and purely cohesive soils are investigated. The results amply demonstrate that the proposed limit analysis is appropriate for the stability assessment of reinforced slopes in heterogeneous and anisotropic soils. The safety factor increases with increase in heterogeneous factor and decrease in anisotropic factor. The optimal pile location is irrespective of these two factors, which should be carefully considered in engineering design.
Rao, P, Zhao, L, Chen, Q & Nimbalkar, S 2019, 'Three-dimensional slope stability analysis incorporating coupled effects of pile reinforcement and reservoir drawdown', International Journal of Geomechanics, vol. 19, no. 4.
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© 2019 American Society of Civil Engineers. In pile-reinforced dams and bank slopes, the antislide effect of piles and drawdown of reservoirs are two aspects that could significantly affect the slope stability. However, existing studies have incorporated these two factors separately, albeit not in tandem. Moreover, stability assessment of these earth structures is usually performed ignoring the three-dimensional (3D) effect. To address these issues, the kinematic approach of limit analysis is adopted in this technical note for evaluating slope stability based on the 3D rotational failure mechanism. In addition, the coupled effects of pile reinforcement and water drawdown are considered. The analysis is performed for four types of drawdown cases. The results demonstrate that the optimal pile location undergoes significant change during the external drawdown process, while the effect of the declining water level on slope stability follows the similar pattern for varying pile locations.
Rao, T, Li, X, Zhang, H & Xu, M 2019, 'Multi-level region-based Convolutional Neural Network for image emotion classification', NEUROCOMPUTING, vol. 333, pp. 429-439.
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Rasouli, H & Fatahi, B 2019, 'A novel cushioned piled raft foundation to protect buildings subjected to normal fault rupture', COMPUTERS AND GEOTECHNICS, vol. 106, pp. 228-248.
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Rehman, J, Sohaib, O, Asif, M & Pradhan, B 2019, 'Applying systems thinking to flood disaster management for a sustainable development', International Journal of Disaster Risk Reduction, vol. 36, pp. 1-10.
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Ren, C, Lyu, X, Ni, W, Tian, H & Liu, RP 2019, 'Distributed Online Learning of Fog Computing under Nonuniform Device Cardinality', IEEE Internet of Things Journal, vol. 6, no. 1, pp. 1147-1159.
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© 2014 IEEE. Processing data around the point of capture, fog computing can support computationally demanding Internet-of-Things (IoT) services. Distributed online optimization is important given the size of IoT, but challenging due to time variations of random traffic and nonuniform connectivity (or cardinality) of edge servers and IoT devices. This paper presents a distributed online learning approach to asymptotically minimizing the time-average cost of fog computing in the absence of the a-priori knowledge on traffic randomness, for light-weight, and delay-tolerant application scenarios. Stochastic gradient descent is exploited to decouple the optimizations between time slots. A graph matching problem is then formulated for every time slot by decoupling and unifying the nonuniform cardinalities, and solved in a distributed manner by developing a new linear (1/2)-approximation method. We prove that the optimality loss resulting from the distributed approximate graph matching method can be compensated and diminish by increasing the learning time. Corroborated by simulations, the proposed distributed online learning is asymptotically optimal and superior to the state of the art in terms of throughput and energy efficiency.
Rezaei, M, Winter, M, Zander-Fox, D, Whitehead, C, Liebelt, J, Warkiani, ME, Hardy, T & Thierry, B 2019, 'A Reappraisal of Circulating Fetal Cell Noninvasive Prenatal Testing', Trends in Biotechnology, vol. 37, no. 6, pp. 632-644.
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© 2018 Elsevier Ltd New tools for higher-resolution fetal genome analysis including microarray and next-generation sequencing have revolutionized prenatal screening. This article provides commentary on this rapidly advancing field and a future perspective emphasizing circulating fetal cell (CFC) utility. Despite the tremendous technological challenges associated with their reliable and cost-effective isolation from maternal blood, CFCs have a strong potential to bridge the gap between the diagnostic sensitivity of invasive procedures and the desirable noninvasive nature of cell-free fetal DNA (cffDNA). Considering the rapid advances in both rare cell isolation and low-input DNA analysis, we argue here that CFC-based noninvasive prenatal testing is poised to be implemented clinically in the near future.
Rezazadeh, J, Moradi, M, Sandrasegaran, K & Farahbakhsh, R 2019, 'Transmission Power Adjustment Scheme for Mobile Beacon-Assisted Sensor Localization', IEEE Transactions on Industrial Informatics, vol. 15, no. 5, pp. 2859-2869.
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Richards, D, Ranjbartabar, H, Bilgin, A & Kutay, C 2019, 'First Impressions Count! The Role of the Human’s Emotional State on Rapport Established with an Empathic versus Neutral Virtual Therapist', IEEE Transactions on Affective Computing, pp. 1-3.
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Intelligent virtual agents are being endowed with empathic behaviours to perform roles such as virtual therapists. Studies often evaluate the level of rapport established, but do not measure the therapeutic benefit and the relative advantage of empathic versus neutral behaviours. We have created two virtual (empathic/neutral) therapists. Our experiment with 63 participants consisted of one within-subjects (empathic/neutral) and one between-subjects (order) factors. Regardless of the virtual therapist used, improvements in baseline emotion were reported after the first interaction (time one) and further improvement after the second interaction (time two). Our study reveals that if the human initially expresses strong emotional feeling for a problem they are facing, rapport will be higher for the empathic therapist and the level of rapport established at the first meeting will persist regardless of whether the second encounter used empathic or neutral dialogue. Conversely, participants experiencing low emotional feeling reported greater rapport with the neutral therapist, and that level of rapport persisted in the second encounter with the alternative therapist. This study shows that an empathic agent will not necessarily build more rapport or deliver better emotional outcomes than a neutral agent. Further studies are needed to determine when tailoring and complex behaviours are justified.
Rizeei, HM, Pradhan, B & Saharkhiz, MA 2019, 'An integrated fluvial and flash pluvial model using 2D high-resolution sub-grid and particle swarm optimization-based random forest approaches in GIS', Complex and Intelligent Systems.
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Rohde, P & Tan, S-H 2019, 'The resurgence of the linear optics quantum interferometer - recent advances & applications', Review in Physics.
Roodposhti, MS, Aryal, J & Pradhan, B 2019, 'A Novel Rule-based Approach In Mapping Landslide Susceptibility.', Sensors (Basel, Switzerland), vol. 19, no. 10.
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Despite recent advances in developing landslide susceptibility mapping (LSM) techniques, resultant maps are often not transparent, and susceptibility rules are barely made explicit. This weakens the proper understanding of conditioning criteria involved in shaping landslide events at the local scale. Further, a high level of subjectivity in re-classifying susceptibility scores into various classes often downgrades the quality of those maps. Here, we apply a novel rule-based system as an alternative approach for LSM. Therein, the initially assembled rules relate landslide-conditioning factors within individual rule-sets. This is implemented without the complication of applying logical or relational operators. To achieve this, first, Shannon entropy was employed to assess the priority order of landslide-conditioning factors and the uncertainty of each rule within the corresponding rule-sets. Next, the rule-level uncertainties were mapped and used to asses the reliability of the susceptibility map at the local scale (i.e., at pixel-level). A set of If-Then rules were applied to convert susceptibility values to susceptibility classes, where less level of subjectivity is guaranteed. In a case study of Northwest Tasmania in Australia, the performance of the proposed method was assessed by receiver operating characteristics' area under the curve (AUC). Our method demonstrated promising performance with AUC of 0.934. This was a result of a transparent rule-based approach, where priorities and state/value of landslide-conditioning factors for each pixel were identified. In addition, the uncertainty of susceptibility rules can be readily accessed, interpreted, and replicated. The achieved results demonstrate that the proposed rule-based method is beneficial to derive insights into LSM processes.
Roselin, AG, Nanda, P, Nepal, S & He, X 2019, 'Testbed Evaluation of Lightweight Authentication Protocol(LAUP) fo r6LoWPAN wireless sensor networks', Concurrency and Computation: Practice and Experience.
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Rouzbehi, K, Miranian, A, Escaño, JM, Rakhshani, E, Shariati, N & Pouresmaeil, E 2019, 'A Data-Driven Based Voltage Control Strategy for DC-DC Converters: Application to DC Microgrid', Electronics, vol. 8, no. 5, pp. 493-493.
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This paper develops a data-driven strategy for identification and voltage control for DC-DC power converters. The proposed strategy does not require a pre-defined standard model of the power converters and only relies on power converter measurement data, including sampled output voltage and the duty ratio to identify a valid dynamic model for them over their operating regime. To derive the power converter model from the measurements, a local model network (LMN) is used, which is able to describe converter dynamics through some locally active linear sub-models, individually responsible for representing a particular operating regime of the power converters. Later, a local linear controller is established considering the identified LMN to generate the control signal (i.e., duty ratio) for the power converters. Simulation results for a stand-alone boost converter as well as a bidirectional converter in a test DC microgrid demonstrate merit and satisfactory performance of the proposed data-driven identification and control strategy. Moreover, comparisons to a conventional proportional-integral (PI) controllers demonstrate the merits of the proposed approach.
Roy, S 2019, 'Fundamental noisy multiparameter quantum bounds', SCIENTIFIC REPORTS, vol. 9.
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Ryu, S, Naidu, G, Johir, MAH, Choi, Y, Jeong, S & Vigneswaran, S 2019, 'Acid mine drainage treatment by integrated submerged membrane distillation-sorption system', CHEMOSPHERE, vol. 218, pp. 955-965.
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Salvador, MM, Budka, M & Gabrys, B 2019, 'Automatic Composition and Optimization of Multicomponent Predictive Systems With an Extended Auto-WEKA', IEEE Transactions on Automation Science and Engineering, vol. 16, no. 2, pp. 1-14.
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IEEE Composition and parameterization of multicomponent predictive systems (MCPSs) consisting of chains of data transformation steps are a challenging task. Auto-WEKA is a tool to automate the combined algorithm selection and hyperparameter (CASH) optimization problem. In this paper, we extend the CASH problem and Auto-WEKA to support the MCPS, including preprocessing steps for both classification and regression tasks. We define the optimization problem in which the search space consists of suitably parameterized Petri nets forming the sought MCPS solutions. In the experimental analysis, we focus on examining the impact of considerably extending the search space (from approximately 22,000 to 812 billion possible combinations of methods and categorical hyperparameters). In a range of extensive experiments, three different optimization strategies are used to automatically compose MCPSs for 21 publicly available data sets. The diversity of the composed MCPSs found is an indication that fully and automatically exploiting different combinations of data cleaning and preprocessing techniques is possible and highly beneficial for different predictive models. We also present the results on seven data sets from real chemical production processes. Our findings can have a major impact on the development of high-quality predictive models as well as their maintenance and scalability aspects needed in modern applications and deployment scenarios.
Sameen, MI, Pradhan, B & Lee, S 2019, 'Self-Learning Random Forests Model for Mapping Groundwater Yield in Data-Scarce Areas', Natural Resources Research.
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© 2018, International Association for Mathematical Geosciences. Globally, groundwater plays a major role in supplying drinking water for urban and rural population and is used for irrigation to grow crops and in many industrial processes. A novel self-learning random forest (SLRF) model is developed and validated for groundwater yield zonation within the Yeondong Province in South Korea. This study was conducted with an inventory data initially divided randomly into 70% for training and 30% for testing and 13 groundwater-conditioning factors. SLRF was optimized using Bayesian optimization method. We also compared our method to other machine learning methods including support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and voting ensemble models. Model validation was accomplished using several methods, including a confusion matrix, receiver operating characteristics, cross-validation, and McNemar’s test. Our proposed self-learning method improves random forest (RF) generalization performance by about 23%, with SLRF success rates of 0.76 and prediction rates of 0.83. In addition, the optimized SLRF performed better [according to a threefold cross-validated AUC (area under curve) of 0.75] than that using randomly initialized parameters (0.57). SLRF outperformed all of the other models for the testing dataset (RF, SVM, ANN, DT, and Voted ANN-RF) when the overall accuracy, prediction rate, and cross-validated AUC metrics were considered. The SLRF also estimated the contribution of individual groundwater conditioning factors and showed that the three most influential factors were geology (1.00), profile curvature (0.97), and TWI (0.95). Overall, SLRF effectively modeled groundwater potential, even within data-scarce regions.
Sánchez-Orgaz, EM, Denia, FD, Baeza, L & Kirby, R 2019, 'Numerical mode matching for sound propagation in silencers with granular material', Journal of Computational and Applied Mathematics, vol. 350, pp. 233-246.
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© 2018 Elsevier B.V. This work presents an efficient numerical approach based on the combination of the mode matching technique and the finite element method (FEM) to model the sound propagation in silencers containing granular material and to evaluate their acoustic performance through the computation of transmission loss (TL). The methodology takes into account the presence of three-dimensional (3D) waves and the corresponding higher order modes, while reducing the computational expenditure of a full 3D FEM calculation. First, the wavenumbers and transversal pressure modes associated with the silencer cross section are obtained by means of a two-dimensional FEM eigenvalue problem, which allows the consideration of arbitrary transversal geometries and material heterogeneities. The numerical approach considers the possibility of using different filling levels of granular material, giving rise to cross sections with abrupt changes of properties located not only in the usual central perforated passage, but also in the transition between air and material, that involves a significant change in porosity. After solving the eigenvalue problem, the acoustic fields (acoustic pressure and axial velocity) are coupled at geometric discontinuities between ducts through the compatibility conditions to obtain the complete solution of the wave equation and the acoustic performance (TL). The granular material is analysed as a potential alternative to the traditional dissipative silencers incorporating fibrous absorbent materials. Sound propagation in granular materials can be modelled through acoustic equivalent properties, such as complex and frequency dependent density and speed of sound. TL results computed by means of the numerical approach proposed here show good agreement with full 3D FEM calculations and experimental measurements. As expected, the numerical mode matching outperforms the computational expenditure of the full 3D FEM approach. Different configurations have be...
Sang, L, Xu, M, Qian, S & Wu, X 2019, 'Multi-modal multi-view Bayesian semantic embedding for community question answering', NEUROCOMPUTING, vol. 334, pp. 44-58.
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Sani, AS, Yuan, D, Jin, J, Gao, L, Yu, S & Dong, ZY 2019, 'Cyber security framework for Internet of Things-based Energy Internet', Future Generation Computer Systems, vol. 93, pp. 849-859.
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© 2018 Elsevier B.V. With the significant improvement in deployment of Internet of Things (IoT) into the smart grid infrastructure, the demand for cyber security is rapidly growing. The Energy Internet (EI) also known as the integrated internet-based smart grid and energy resources inherits all the security vulnerabilities of the existing smart grid. The security structure of the smart grid has become inadequate in meeting the security needs of energy domains in the 21st century. In this paper, we propose a cyber security framework capable of providing adequate security and privacy, and supporting efficient energy management in the EI. The proposed framework uses an identity-based security mechanism (I-ICAAAN), a secure communication protocol and an Intelligent Security System for Energy Management (ISSEM) to certify security and privacy in the EI. Nash Equilibrium solution of game theory is applied for the evaluation of our proposed ISSEM based on security events allocation. The formal verification and theoretical analysis show that our proposed framework provides security and privacy improvement for IoT-based EI.
Saqib, M, Khan, SD, Sharma, N & Blumenstein, M 2019, 'Crowd Counting in Low-Resolution Crowded Scenes Using Region-Based Deep Convolutional Neural Networks', IEEE Access, vol. 7, pp. 35317-35329.
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© 2013 IEEE. Crowd counting and density estimation is an important and challenging problem in the visual analysis of the crowd. Most of the existing approaches use regression on density maps for the crowd count from a single image. However, these methods cannot localize individual pedestrian and therefore cannot estimate the actual distribution of pedestrians in the environment. On the other hand, detection-based methods detect and localize pedestrians in the scene, but the performance of these methods degrades when applied in high-density situations. To overcome the limitations of pedestrian detectors, we proposed a motion-guided filter (MGF) that exploits spatial and temporal information between consecutive frames of the video to recover missed detections. Our framework is based on the deep convolution neural network (DCNN) for crowd counting in the low-to-medium density videos. We employ various state-of-the-art network architectures, namely, Visual Geometry Group (VGG16), Zeiler and Fergus (ZF), and VGGM in the framework of a region-based DCNN for detecting pedestrians. After pedestrian detection, the proposed motion guided filter is employed. We evaluate the performance of our approach on three publicly available datasets. The experimental results demonstrate the effectiveness of our approach, which significantly improves the performance of the state-of-the-art detectors.
Sarikaya, A & Erkmen, R 2019, 'A plastic-damage model for concrete under compression', International Journal of Mechanical Sciences, vol. 150, pp. 584-593.
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A phenomenological model for plain concrete under compression is formulated within the framework of the coupled elastoplastic-damage theory. Phenomenological elastoplastic-damage models have been widely used for concrete because of their capability of representing both the permanent inelastic deformations and the degra- dation of material moduli beyond the elastic range. The essential contribution introduced in this paper is the proposed partitioning of the strain tensor within the coupled elastoplastic-damage framework which simplifies the selection of the failure surface and the potential function. Proposed partitioning permits the use of single failure criterion and single potential surface that are effective for both damage and plasticity models during inelastic deformations. Therefore, the coupled elastoplastic-damage model can be easily calibrated to fit the ob- served concrete behaviour based on well-established non-associated plasticity rules for concrete. The proposed approach also simplifies the numerical procedure by eliminating iterations that is required to equilibrate the stresses in plastic and damage components of the model. The numerical implementation is explained, and the results predicted by the model are compared with experimental data provided in the literature.
Sarker, PC, Islam, MR, Guo, Y, Zhu, J & Lu, HY 2019, 'State-of-The-Art Technologies for Development of High Frequency Transformers with Advanced Magnetic Materials', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2002-2011 IEEE. With the development of advanced soft magnetic materials of high-saturation flux density and low specific core loss and semiconductor power devices, the high-frequency transformer (HFT) has received significant attention in recent years for its widespread emerging applications. The optimal design of high-power-density HFTs for high-performance energy conversion systems is, however, a multiphasic problem that needs special considerations on various aspects such as core material selection, minimization of parasitic components, and thermal management. This paper presents a comprehensive review on advancement of soft magnetic materials for high-power-density magnetic devices and advanced technologies for characterizations and optimal design of HFTs. The future research and development trends are also discussed.
Sattarian, M, Rezazadeh, J, Farahbakhsh, R & Bagheri, A 2019, 'Indoor navigation systems based on data mining techniques in internet of things: a survey', Wireless Networks, vol. 25, no. 3, pp. 1385-1402.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Internet of Things (IoT) is turning into an essential part of daily life, and numerous IoT-based scenarios will be seen in future of modern cities ranging from small indoor situations to huge outdoor environments. In this era, navigation continues to be a crucial element in both outdoor and indoor environments, and many solutions have been provided in both cases. On the other side, recent smart objects have produced a substantial amount of various data which demands sophisticated data mining solutions to cope with them. This paper presents a detailed review of previous studies on using data mining techniques in indoor navigation systems for the loT scenarios. We aim to understand what type of navigation problems exist in different IoT scenarios with a focus on indoor environments and later on we investigate how data mining solutions can provide solutions on those challenges.
Scott, M, Millar, GJ & Altaee, A 2019, 'Process design of a treatment system to reduce conductivity and ammoniacal nitrogen content of landfill leachate', Journal of Water Process Engineering, vol. 31, pp. 100806-100806.
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Seifollahi, S, Bagirov, A, Zare Borzeshi, E & Piccardi, M 2019, 'A simulated annealing‐based maximum‐margin clustering algorithm', Computational Intelligence, vol. 2018, pp. 1-19.
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Maximum‐margin clustering is an extension of the support vector machine (SVM) to clustering. It partitions a set of unlabeled data into multiple groups by finding hyperplanes with the largest margins. Although existing algorithms have shown promising results, there is no guarantee of convergence of these algorithms to global solutions due to the nonconvexity of the optimization problem. In this paper, we propose a simulated annealing‐based algorithm that is able to mitigate the issue of local minima in the maximum‐margin clustering problem. The novelty of our algorithm is twofold, ie, (i) it comprises a comprehensive cluster modification scheme based on simulated annealing, and (ii) it introduces a new approach based on the combination of k‐means++ and SVM at each step of the annealing process. More precisely, k‐means++ is initially applied to extract subsets of the data points. Then, an unsupervised SVM is applied to improve the clustering results. Experimental results on various benchmark data sets (of up to over a million points) give evidence that the proposed algorithm is more effective at solving the clustering problem than a number of popular clustering algorithms.
Shaffiei, S, Mihăiţă, AS & Cai, C 2019, 'Demand Estimation and Prediction for Short-term Traffic Forecasting in Existence of Non-recurrent Incidents', ITS World Congress 2019 (ITSWC2019), Singapore.
Shakibayifar, M, Hassannayebi, E, Mirzahossein, H, Taghikhah, F & Jafarpur, A 2019, 'An intelligent simulation platform for train traffic control under disturbance', International Journal of Modelling and Simulation.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. Railway disturbance management is inherently a multi-objective optimization problem that concerns both the operators’ cost and passenger’s service level. This study proposes a multi-objective simulation-based optimization framework to effectively manage the train conflicts after the occurrences of a disturbance caused by a temporary line blockage. The simulation model enhanced with a dynamic priority dispatching rule in order to speed up the optimization procedure. A multi-objective variable neighborhood search meta-heuristic is proposed to solve the train rescheduling model. The obtained Pareto optimal solutions for disturbance management model support the decision maker to find a trade-off between both user and operator viewpoints. The proposed approach has been validated on a set of disruption scenarios covering a large part of the Iranian rail network. The computational results prove that the proposed model can generate good-quality timetables with the minimum passenger delay and deviation from the initial timetable. The outcomes indicate that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly when compared to currently accepted solutions. Abbreviation: MOVNS: multi-objective variable neighbourhood search; DES: discrete-event simulation; SO: simulation-optimization; AG: Alternative Graph; FCFS: First Come First Served; MIP: mixed integer programming; MILP: mixed-integer linear programming; B&B: branch and bound algorithm; VND: Variable Neighborhood Descent; NSGA-II: Non-dominated Sorting Genetic Algorithm–II; CD: crowding distance; DP: dynamic priority; EDD: earliest due date first; SRTT: shortest remaining traveling time; LST: least slack time first.
Shakor, P, Nejadi, S, Paul, G & Malek, S 2019, 'Review of emerging additive manufacturing technologies in 3D printing of cementitious materials in the construction industry', Frontiers in Built Environment, vol. 4.
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Additive manufacturing is a fabrication technology that is rapidly revolutionizing the manufacturing and construction sectors. In this paper, a review of various prototyping technologies for printing cementitious materials and selected 3D printing techniques are presented in detail. Benchmark examples are provided to compare three well-known printing techniques; inkjet printing (binder jetting), selected laser sintering (SLS), and extrusion printing (extrusion based process). A comprehensive search in the literature was conducted to identify various mix designs that could be employed when printing cementitious materials. Aspects of concrete mix design are described, and some new experiments are conducted to analyse the printability of new mixes by the authors. Future research in the area of the rheology of cementitious materials and its relationship with the structural performance of finished concretes are highlighted.
Shakor, P, Nejadi, S, Paul, G & Malekmohammadi, S 2019, 'Review of emerging additive manufacturing technologies in 3D printing of cementitious materials in the construction industry', Frontiers in Built Environment, vol. 4, pp. 1-17.
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Shakor, P, Nejadi, S, Paul, G, Sanjayan, J & Nazari, A 2019, 'Mechanical Properties of Cement-Based Materials and Effect of Elevated Temperature on Three-Dimensional (3-D) Printed Mortar Specimens in Inkjet 3-D Printing', ACI Materials Journal, vol. 116, no. 2, pp. 55-67.
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Shalby, M, Dorrell, D & Walker, P 2019, 'Multi–chamber oscillating water column wave energyconverters and air turbines: A review', International Journal of Energy Research.
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Shalby, M, Elhanafi, A, Walker, P & Dorrell, DG 2019, 'CFD modelling of a small–scale fixed multi–chamber OWC device', Applied Ocean Research, vol. 88, pp. 37-47.
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Shao, R, Wu, C, Su, Y, Liu, Z, Liu, J & Xu, S 2019, 'Numerical analysis on impact response of ultra-high strength concrete protected with composite materials against steel ogive-nosed projectile penetration', Composite Structures, vol. 220, pp. 861-874.
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Shao, R, Wu, C, Su, Y, Liu, Z, Liu, J, Chen, G & Xu, S 2019, 'Experimental and numerical investigations of penetration resistance of ultra-high strength concrete protected with ceramic balls subjected to projectile impact', Ceramics International, vol. 45, no. 6, pp. 7961-7975.
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© 2019 Elsevier Ltd and Techna Group S.r.l. Ceramic materials characterized by high hardness, high inherent strength, low density and excellent dimensional stability have been extensively applied in the design of high-performance and lightweight protective structures to resist the high-speed projectile impact. In order to study the anti-penetration capability of ceramic balls protected ultra-high strength concrete (CB-UHSC), high-speed projectile impact tests were conducted at striking velocities of 545 m/s, 679 m/s, and 809 m/s to investigate the impact performance of ceramic balls, projectiles, and the protected UHSC. The experimental results indicated the effectiveness and economy of ceramic balls in resisting the high-speed projectile impact. Numerical studies were then conducted to reproduce the projectile penetration process within CB-UHSC targets with the assistance of LS-DYNA. Based on the validated numerical models, impact resistance and ballistic deviation of projectiles, as well as the energy evolution between projectiles and targets, were further investigated to comprehensively understand the impact performance of this newly designed protective structure under projectile penetration.
Sheikhrahimi, A, Pour, AB, Pradhan, B & Zoheir, B 2019, 'Mapping hydrothermal alteration zones and lineaments associated with orogenic gold mineralization using ASTER data: A case study from the Sanandaj-Sirjan Zone, Iran', Advances in Space Research, vol. 63, no. 10, pp. 3315-3332.
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© 2019 COSPAR The Sanandaj-Sirjan Zone (SSZ) is considered as an important region for gold exploration in the western sector of Iran. Its mountainous topography and unpaved routes make its study challenging for researchers and raise the costs for mining companies strating new exploration plans. Gold mineralization mainly occurs as irregular to lenticular sulfide-bearing quartz veins along shear zones in deformed mafic to intermediate metavolcanic and metasedimentary rocks. In this investigation, ASTER data are used for mapping hydrothermal alteration minerals and to better discriminate geological structural features associated with orogenic gold occurrences in the area. Image transformation techniques such as specialized band ratioing and Principal Component Analysis are used to delineate lithological units and alteration minerals. Supervised classification techniques, namely Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) are applied to detect subtle differences between indicator alteration minerals associated with ground-truth gold locations in the area. The directional filtering technique is applied to help in tracing along the strike the different linear structures. Results demonstrate that the integration of image transformation techniques and supervised classification of ASTER data with fieldwork and geochemical exploration studies has a great efficiency in targeting new prospects of gold mineralization in the SSZ. The approach used in this research provides a fast, cost-efficient means to start a comprehensive geological and geochemical exploration programs in the study area and elsewhere in similar regions.
Shen, J, Liang, Z, Liu, J, Sun, H, Shao, L & Tao, D 2019, 'Multiobject Tracking by Submodular Optimization', IEEE Transactions on Cybernetics.
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IEEE In this paper, we propose a new multiobject visual tracking algorithm by submodular optimization. The proposed algorithm is composed of two main stages. At the first stage, a new selecting strategy of tracklets is proposed to cope with occlusion problem. We generate low-level tracklets using overlap criteria and min-cost flow, respectively, and then integrate them into a candidate tracklets set. In the second stage, we formulate the multiobject tracking problem as the submodular maximization problem subject to related constraints. The submodular function selects the correct tracklets from the candidate set of tracklets to form the object trajectory. Then, we design a connecting process which connects the corresponding trajectories to overcome the occlusion problem. Experimental results demonstrate the effectiveness of our tracking algorithm. Our source code is available at https://github.com/shenjianbing/submodulartrack.
Shen, W, Wu, Y, Yuan, J, Duan, L, Zhang, J & Jia, Y 2019, 'Robust Distracter-Resistive Tracker via Learning a Multi-Component Discriminative Dictionary', IEEE Transactions on Circuits and Systems for Video Technology.
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IEEE Discriminative dictionary learning (DDL) provides an appealing paradigm for appearance modeling in visual tracking. However, most existing DDL based trackers cannot handle drastic appearance changes, especially for scenarios with background cluster and/or similar object interference. One reason is that they often suffer from the loss of subtle visual information which is critical to distinguish an object from distracters. In this paper, we explore the use of deep features extracted from the Convolutional Neural Networks (CNNs) to improve the object representation and propose a robust distracter-resistive tracker via learning a multi-component discriminative dictionary. The proposed method exploits both the intra-class and the interclass visual information to learn shared atoms and the classspecific atoms. By imposing several constraints into the objective function, the learned dictionary is reconstructive, compressive and discriminative, thus can better distinguish an object from the background. In addition, our convolutional features (deep features extracted from CNNs) have structural information for object localization and balance the discriminative power and semantic information of the object. Tracking is carried out within a Bayesian inference framework where a joint decision measure is used to construct the observation model. To alleviate the drift problem, the reliable tracking results obtained online are accumulated to update the dictionary. Both the qualitative and quantitative results on the CVPR2013 benchmark, the VOT2015 dataset and the SPOT dataset demonstrate that our tracker achieves better performance over the state-of-the-art approaches.
Shi, J, Chu, L & Braun, R 2019, 'A Kriging Surrogate Model for Uncertainty Analysis of Graphene Based on a Finite Element Method', International journal of molecular sciences, vol. 20, pp. 2355-2355.
Shi, T, Tang, M-C, Wu, Z, Xu, H-X & Ziolkowski, RW 2019, 'Improved Signal-to-Noise Ratio, Bandwidth-Enhanced Electrically Small Antenna Augmented With Internal Non-Foster Elements', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 67, no. 4, pp. 2763-2768.
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Shi, X, Zhu, J, Li, L & Lu, DDC 2019, 'Model-Predictive-Based Duty Cycle Control with Simplified Calculation and Mutual Influence Elimination for AC/DC Converter', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 7, no. 1, pp. 504-514.
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© 2013 IEEE. The single-vector-based model-predictive-based direct power control (MPDPC) is commonly used for control of three-phase full-bridge ac/dc converters, but it could only select the best switching vector to be implemented. Due to the limited number of voltage vectors in a two-level three-phase converter, the sampling frequency needs to be high to achieve an acceptable performance. Also, it bears variable switching frequency that causes spread spectrum nature of harmonics. In this paper, a three-vector-based simplified model predictive duty cycle control (SMPDCC) is proposed. The adjacent two nonzero vectors are selected by evaluating the effects of each vector pairs based on the revised cost function. The duration calculation is simplified compared with the conventional predictive duty cycle control (CPDCC) by allocating a control period in reciprocal proportion with the corresponding cost function value of the selected vectors. Besides, the negative duration issue of CPDCC could be completely avoided and the mutual influence elimination ability could be realized. A comparative study with MPDPC and CPDCC has been conducted to verify the superiority of the proposed scheme by the simulation and experimental results. It shows that the SMPDCC has the advantages of lower power ripple, fixed switching frequency, lower total harmonic distortion, and mutual influence elimination ability.
Shi, X, Zhu, J, Li, L, Lu, DDC, Zhang, J & Yang, H 2019, 'Predictive Duty Cycle Control with Reversible Vector Selection for Three-Phase AC/DC Converters', IEEE Transactions on Power Electronics.
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IEEE The conventional predictive duty cycle control (CPDCC) of three-phase full-bridge AC/DC converters selects adjacent non-zero vector pair based on the grid-voltage vector location, then the duration for each vector is calculated. Though the vector selection method is quite simple, it has a significant disadvantage that the values of calculated durations could be frequently less than zero due to non-optimal vector selection, which results in high current harmonics and power notches. It could be improved with improved predictive duty cycle control (IPDCC) by reselecting the non-zero vector pair when negative duration exists, however, the whole vector selection and calculation procedure are repeated. By theoretical verification that the power variation rates of reversible vector pair are symmetrical with respect to that of zero vector, this paper proposes the reversible predictive duty cycle control (RPDCC) simply by replacing the original vector with its opposite vector and the recalculation of vector duration is eliminated compared with IPDCC. Thus the calculation effort is almost not increased compared with CPDCC while system performance is significantly improved. The proposed control is theoretically derived and verified with the simulation and experimental results showing that RPDCC has better steady and dynamic performance than CPDCC and IPDCC methods.
Shi, Z, Sun, X, Cai, Y, Yang, Z, Lei, G, Guo, Y & Zhu, J 2019, 'Torque Analysis and Dynamic Performance Improvement of a PMSM for EVs by Skew Angle Optimization', IEEE Transactions on Applied Superconductivity, vol. 29, no. 2.
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© 2018 IEEE. In this paper, a permanent magnet synchronous machine (PMSM) for electric vehicles (EVs) is studied. Since EVs need to face some low speed road conditions, it is necessary to drive the machine to maintain a stable torque at low speed. The stator skew slot is often adopted to reduce torque ripple; however, it declines the output torque at same time. Besides, the difference between positive rotation performance and negative rotation performance, which caused by the skew slot are often ignored. Through the finite element analysis, the cogging torque and dynamic performance of the PMSM at different skew angle are studied. Moreover, the different influence of slot skew angle on positive and negative rotation performance is studied. Then, the optimum skew angle of the PMSM is studied through comprehensive consideration. Finally, the cogging torque of the prototype is verified to be less than 2 N·m through the experiment.
Shukla, N, Dunbar, M, Belieres, S, Amirghasemi, M, Perez, P & Mishra, N 2019, 'A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network', pp. 1-19.
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Shukla, N, Tiwari, MK & Beydoun, G 2019, 'Next generation smart manufacturing and service systems using big data analytics', Computers and Industrial Engineering.
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Si, L, Eisman, JA, Winzenberg, T, Sanders, KM, Center, JR, Nguyen, TV & Palmer, AJ 2019, 'Microsimulation model for the health economic evaluation of osteoporosis interventions: Study protocol', BMJ Open, vol. 9, no. 2.
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© Author(s) (or their employer(s)) 2019. Introduction Osteoporosis is a systemic skeletal disease that is characterised by reduced bone strength and increased fracture risk. Osteoporosis-related fractures impose enormous disease and economic burden to the society. Although many treatments and health interventions are proven effective to prevent fractures, health economic evaluation adds evidence to their economic merits. Computer simulation modelling is a useful approach to extrapolate clinical and economic outcomes from clinical trials and it is increasingly used in health economic evaluation. Many osteoporosis health economic models have been developed in the past decades; however, they are limited to academic use and there are no publicly accessible health economic models of osteoporosis. Methods and analysis We will develop the Australian osteoporosis health economic model based on our previously published microsimulation model of osteoporosis in the Chinese population. The development of the model will follow the recommendations for the conduct of economic evaluations in osteoporosis by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases and the US branch of the International Osteoporosis Foundation. The model will be a state-transition semi-Markov model with memory. Clinical parameters in the model will be mainly obtained from the Dubbo Osteoporosis Epidemiology Study and the health economic parameters will be collected from the Australian arm of the International Costs and Utilities Related to Osteoporotic Fractures Study. Model transparency and validates will be tested using the recommendations from Good Research Practices in Modelling Task Forces. The model will be used in economic evaluations of osteoporosis interventions including pharmaceutical treatments and primary care interventions. A user-friendly graphical user interface will be developed, which will connect the user to the calculat...
Sick, N, Preschitschek, N, Leker, J & Bröring, S 2019, 'A new framework to assess industry convergence in high technology environments', Technovation.
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© 2018 Elsevier Ltd The process of convergence, from science and technology convergence to that of markets as well as entire industries can be witnessed in a range of different high technology environments such as IT and NanoBiotech. Although this phenomenon has been subject of analysis in an increasing number of studies, the notion of industry convergence – the final step of a full convergence process - still lacks a common definition. The missing conceptual definition of what industry convergence really is and how it can be assessed impedes both analyses and monitoring - let alone its anticipation. To address the missing conceptual definition of the final step in convergence, this paper seeks to develop a framework based on novel indicators that enable identifying and monitoring trends of industry convergence in high technology environments. Building on indicators in the domain of collaboration, a framework, which distinguishes different stages and types of industry convergence is developed. Subsequently, the newly developed framework is empirically illustrated in the area of stationary energy storage based on publicly available data. To this end, the full text database Nexis is used to conduct a search in news reports on collaborations in the domain of stationary energy storage. The study contributes to the growing body of convergence literature by providing a novel framework allowing the identification of not only industry convergence as the final step of the convergence process but also the classification of its type. Practical implications include an orientation for companies in converging environments on when and how to close the resulting technology and market competence gaps.
Silitonga, AS, Mahlia, TMI, Kusumo, F, Dharma, S, Sebayang, AH, Sembiring, RW & Shamsuddin, AH 2019, 'Intensification of Reutealis trisperma biodiesel production using infrared radiation: Simulation, optimisation and validation', RENEWABLE ENERGY, vol. 133, pp. 520-527.
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Siwakoti, Y, Mahajan, A, Rogers, D & Blaabjerg, F 2019, 'A Novel Seven-Level Active Neutral Point Clamped Converter with Reduced Active Switching Devices and DC-link Voltage', IEEE Transactions on Power Electronics, pp. 1-1.
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Siwakoti, YP, Mostaan, A, Abdelhakim, A, Davari, P, Soltani, M, Khan, MNH, Li, L & Blaabjerg, F 2019, 'High Voltage Gain Quasi-SEPIC DC-DC Converter', IEEE Journal of Emerging and Selected Topics in Power Electronics.
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IEEE This paper proposes a modified coupled-inductor SEPIC dc-dc converter for high voltage gain (2 < G < 10) applications. It utilizes the same components as the conventional SEPIC converter with an additional diode. The voltage stress on the switch is minimal, which helps the designer to select a low voltage and low RDS-on MOSFET, resulting in a reduction of cost, conduction and turn ON losses of the switch. Compared to equivalent topologies with similar voltage gain expression, the proposed topology uses lower component-count to achieve the same or even higher voltage gain. This helps to design a very compact and lightweight converter with higher power density and reliability. Operating performance, steady-state analysis and mathematical derivations of the proposed dc-dc converter have been demonstrated in the paper. Moreover, extension of the circuit for higher gain (G>10) application is also introduced and discussed. Finally, the main features of the proposed converter have been verified through simulation and experimental results of a 400 W laboratory prototype. The efficiency is almost flat over a wide range of load with the highest measured efficiency of 96.2%, and the full-load efficiency is 95.2% at a voltage gain of 10.
Siyari, P, Krunz, MM & Nguyen, D 2019, 'Distributed Power Control in Single-stream MIMO Wiretap Interference Networks with Full-duplex Jamming Receivers', IEEE Transactions on Signal Processing.
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Skilodimou, HD, Bathrellos, GD, Chousianitis, K, Youssef, AM & Pradhan, B 2019, 'Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study', Environmental Earth Sciences, vol. 78, no. 2.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Multi-hazard assessment modeling comprises an essential tool in any plan that aims to mitigate the impact of future natural disasters. For a particular area they can be generated by combining assessment maps for different types of natural hazards. In the present study, the analytical hierarchy process (AHP) supported by a Geographical Information System (GIS) was utilized to initially produce assessment maps on hazards from landslides, floods and earthquakes and subsequently to combine them into a single multi-hazard map. Evaluation of the reliability of the proposed model predictions was performed through uncertainty analysis of the variables that we used for producing the final model. The drainage basin of Peneus (Pinios) River (Western Peloponnesus, Greece), an area that is prone to landslides, floods and seismic events, was selected for the implementation of the aforementioned approach. Our findings revealed that the high hazard zones are mainly distributed in the western and north-eastern part of the region under investigation. The calculated multi-hazard map, which corresponds to the potential urban development suitability map of the study area, was classified into five classes, namely of very low, low, moderate, high and very high suitability. The most suitable areas for urban development are distributed mostly in the eastern part, in agreement with the low and very low hazard level for the three considered natural hazards. In addition, by performing uncertainty analysis we showed that the spatial distribution of the suitability zones does not change significantly. Ultimately, the final map was verified using the actual inventory of landslides and floods that affected the study area. In this context, we showed that 80% of the landslide occurrences and all the recorded flood events fall within the boundaries of the moderate, low and very low suitability zones. Consequently, the predictive capaci...
Sohaib, O, Kang, K & Nurunnabi, M 2019, 'Gender-Based iTrust in E-Commerce: The Moderating Role of Cognitive Innovativeness', Sustainability, vol. 11, no. 1, pp. 1-16.
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Sohaib, O, Kang, K-S & Miliszewska, I 2019, 'Uncertainty Avoidance and Consumer Cognitive Innovativeness in E-Commerce', Journal of Global Information Management, vol. 27, no. 2, pp. 59-77.
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Sohaib, O, Naderpour, M, Hussain, W & Martinez, L 2019, 'Cloud Computing Model Selection for E-commerce Enterprises Using a New 2-tuple Fuzzy Linguistic Decision-Making Method', Computers & Industrial Engineering, vol. 132, pp. 47-47.
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Sohaib, O, Solanki, H, Dhaliwa, N, Hussain, W & Asif, M 2019, 'Integrating design thinking into extreme programming', Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 6, pp. 2485-2492.
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Song, K, Yeerken, S, Li, L, Sun, J & Wang, Q 2019, 'Improving Post-Anaerobic Digestion of Full-Scale Anaerobic Digestate Using Free Ammonia Treatment', ACS Sustainable Chemistry and Engineering, vol. 7, no. 7, pp. 7171-7176.
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© 2019 American Chemical Society. Post-anaerobic digestion of full-scale anaerobic digestate (AD) is used in enhancing sludge reduction in some sewage treatment plants (STPs). However, the AD degradation is usually inhibited due to its slow hydrolysis rate and low degradability. This study presents an innovative pretreatment method by using free ammonia (FA, i.e., NH 3 ) to improve post-anaerobic digestion full-scale AD degradation. The FA treatment at over 360 mg NH 3 -N/L for 24 h has improved AD degradation with the highest solubilization (0.1 mg chemical oxygen demand (COD)/mg volatile solids (VS), at 850 mg NH 3 -N/L), being 5.3 times that without pretreatment (0.019 mg COD/mg VS). After 8 days of post-anaerobic digestion, non-pretreated AD has degraded 8.5%, while the FA pretreated AD at 360-850 mg NH 3 -N/L has degraded 9.9-10.9%, representing a relative increase of 14-22%. The mathematical model captured the tested data well with R 2 > 0.994 in all cases, and the model revealed that AD degradation improvement was attributed to an increase in AD degradation percentage. Economic analysis shows that the FA pretreatment method could be economically favorable in enhancing full-scale AD post-anaerobic digestion.
Song, R, Clemon, L & Telenko, C 2019, 'Uncertainty and Variability of Energy and Material Use by Fused Deposition Modeling Printers in Makerspaces', Journal of Industrial Ecology.
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© 2018, Yale University. Desktop-grade fused deposition modeling (FDM) printers are popular because of compact sizes and affordable prices. If we are moving toward a future where desktop FDM printers are in every school and office, like conventional printers, then these machines will consume a large amount of energy and material. However, it is very difficult to evaluate the environmental impacts of FDM printers since there are so many different brands and types of printers using different raw materials under different scenarios. This study uses data from two different printing sites to evaluate the scenario and parameter uncertainty and variability in energy and material balances for FDM printers. Data from the two makerspaces provide insight into the material and energy consumption data using polylactic acid and acrylonitrile butadiene styrene (ABS) with four types of printers. The use of actual performance data allowed for the additional study of scrap ratio. Regressions provide insight into predictive factors for energy and material consumption. Monte Carlo simulations show the range of energy life cycle inventory values for the desktop-grade FDM printers. From the regressions, Type A Pro was the most energy-intensive machine. For material waste, an open-access makerspace using ABS was associated with higher scrap ratio. Regression analysis indicates that the rate of material usage is not a strong predictor of waste rates. The amount of waste generated across both sites indicates that more ubiquitous access to FDM printing may create a significant addition to the waste stream.
Song, Z, Zhang, X, Ngo, HH, Guo, W, Song, P, Zhang, Y, Wen, H & Guo, J 2019, 'Zeolite powder based polyurethane sponges as biocarriers in moving bed biofilm reactor for improving nitrogen removal of municipal wastewater', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 651, pp. 1078-1086.
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Song, Z, Zhang, X, Ngo, HH, Guo, W, Wen, H & Li, C 2019, 'Occurrence, fate and health risk assessment of 10 common antibiotics in two drinking water plants with different treatment processes', Science of the Total Environment, vol. 674, pp. 316-326.
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© 2019 Elsevier B.V. The occurrence of antibiotics in drinking water has become a serious problem worldwide as they are a potential and real threat to human health. In this study, the variability of 10 typical antibiotics in two drinking water plants was investigated in two seasons (n = 12). The total concentrations of target antibiotics in raw water were significantly higher in winter than in summer, which may be attributed to the more frequent occurrence of colds and respiratory diseases as well as less rainfall in winter. The efficiency in removing the antibiotics varied from −46.5% to 45.1% in water plant A (WP-A) using a conventional process and 40.3% to 70.3% in water plant B (WP-B) with an advanced treatment process. Results indicated that the antibiotics in WP-A were mainly removed via the coagulation process. However in WP-B, the ultraviolet + chlorination process played a key role in antibiotics removal, followed by the pre-ozone + coagulation process. According to the human health risk assessment, it was suggested that the risk of drinking water was significantly higher than that of skin contact. However, the risk of carcinogenesis and non-carcinogenesis caused by antibiotics was at an acceptable level.
Sood, K, Karmakar, KK, Varadharajan, V, Tupakula, U & Yu, S 2019, 'Analysis of Policy-Based Security Management System in Software-Defined Networks', IEEE Communications Letters, vol. 23, no. 4, pp. 612-615.
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© 1997-2012 IEEE. In software-defined networks, policy-based security management or architecture (PbSA) is an ideal way to dynamically control the network. We observe that on the one hand, this enables security capabilities intelligently and enhance fine-grained control over end user behavior. But, on the other hand, dynamic variations in network, rapid increases in security attacks, geographical distribution of nodes, complex heterogeneous networks, and so on have serious effects on the performance of PbSAs. These affect the flow specific quality of service requirements with further degradation of the performance of the security context. Hence, in this letter, PbSA's performance is evaluated. The key factors including a number of rules, rule-table size, position of rules, flow arrival rate, and CPU utilization are examined, and found to have considerable impact on the performance of PbSAs.
Stender, M, Oberst, S & Hoffmann, N 2019, 'Recovery of Differential Equations from Impulse Response Time Series Data for Model Identification and Feature Extraction', Vibration, vol. 2, no. 1, pp. 25-46.
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Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for (a) describing the dynamics and (b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and nonlinearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and how to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of nonlinearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.
Styrkarsdottir, U, Stefansson, OA, Gunnarsdottir, K, Thorleifsson, G, Lund, SH, Stefansdottir, L, Juliusson, K, Agustsdottir, AB, Zink, F, Halldorsson, GH, Ivarsdottir, EV, Benonisdottir, S, Jonsson, H, Gylfason, A, Norland, K, Trajanoska, K, Boer, CG, Southam, L, Leung, JCS, Tang, NLS, Kwok, TCY, Lee, JSW, Ho, SC, Byrjalsen, I, Center, JR, Lee, SH, Koh, J-M, Lohmander, LS, Ho-Pham, LT, Nguyen, TV, Eisman, JA, Woo, J, Leung, P-C, Loughlin, J, Zeggini, E, Christiansen, C, Rivadeneira, F, van Meurs, J, Uitterlinden, AG, Mogensen, B, Jonsson, H, Ingvarsson, T, Sigurdsson, G, Benediktsson, R, Sulem, P, Jonsdottir, I, Masson, G, Holm, H, Norddahl, GL, Thorsteinsdottir, U, Gudbjartsson, DF & Stefansson, K 2019, 'GWAS of bone size yields twelve loci that also affect height, BMD, osteoarthritis or fractures', NATURE COMMUNICATIONS, vol. 10.
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Sun, F, Zhu, H, Zhu, X, Yang, Y, Sun, Y & Zhang, X 2019, 'Design of millimeter-wave bandpass filters with broad bandwidth in Si-based technology', IEEE Transactions on Electron Devices, vol. 66, no. 3, pp. 1174-1181.
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© 2019 IEEE. In this paper, a novel design approach is proposed for on-chip bandpass filter (BPF) design with improved passband flatness and stopband suppression. The proposed approach simply uses a combination of meander-line structures with metal-insulator-metal (MIM) capacitors. To demonstrate the insight of this approach, a simplified equivalent LC-circuit model is used for theoretical analysis. Using the analyzed results as a guideline along with a full-wave electromagnetic (EM) simulator, two BPFs are designed and implemented in a standard 0.13-μm (Bi)-CMOS technology. The measured results show that good agreements between EM simulated and measured results are achieved. For the first BPF, the return loss is better than 10 dB from 13.5 to 32 GHz, which indicates a fractional bandwidth (FBW) of more than 78%. In addition, the minimum insertion loss of 2.3 dB is achieved within the frequency range from 17 to 27 GHz and the in-band magnitude ripple is less than 0.1 dB. The chip size of this design, excluding the pads, is 0.148 mm 2 . To demonstrate a miniaturized design, a second design example is given. The return loss is better than 10 dB from 17.3 to 35.9 GHz, which indicates an FBW of more than 70%. In addition, the minimum insertion loss of 2.6 dB is achieved within the frequency range from 21.4 to 27.7 GHz and the in-band magnitude ripple is less than 0.1 dB. The chip size of the second design, excluding the pads, is only 0.066 mm 2 .
Sun, HH, Ding, C, Zhu, H, Jones, B & Guo, YJ 2019, 'Suppression of Cross-Band Scattering in Multiband Antenna Arrays', IEEE Transactions on Antennas and Propagation, vol. 67, no. 4, pp. 2379-2389.
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© 1963-2012 IEEE. This paper presents a novel method of suppressing cross-band scattering in dual-band dual-polarized antenna arrays. The method involves introducing chokes into low-band (LB) elements to suppress high-band (HB) scattering currents. The experimental results show that by inserting LB-pass HB-stop chokes into LB radiators, suppression of induced HB currents on the LB elements is achieved. This greatly reduces the pattern distortion of the HB array caused by the presence of LB elements. The array considered is configured as two columns of HB antennas operating from 1.71 to 2.28 GHz interleaved with a single column of LB antennas operating from 0.82 to 1.0 GHz. The realized array with choked LB element has stable and symmetrical radiation in both HB and LB.
Sun, J, Dai, X, Wang, Q, van Loosdrecht, MCM & Ni, B-J 2019, 'Microplastics in wastewater treatment plants: Detection, occurrence and removal', WATER RESEARCH, vol. 152, pp. 21-37.
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Sun, L, Dong, H, Hussain, OK, Hussain, FK & Liu, AX 2019, 'A framework of cloud service selection with criteria interactions', Future Generation Computer Systems, vol. 94, pp. 749-764.
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© 2018 Elsevier B.V. Existing cloud service selection techniques assume that service evaluation criteria are independent. In reality, there are different types of interactions between criteria. These interactions influence the performance of a service selection system in different ways. In addition, a lack of measurement indices to validate the performance of service selection methods has hindered the development of decision making techniques in the service selection area. This paper addresses these critical issues of modeling the interactions between cloud service selection criteria, and designing indices to validate service selection methods. In this paper, we propose a Cloud Service Selection with Criteria Interactions framework (CSSCI) that applies a fuzzy measure and Choquet integral to measure and aggregate non-linear relations between criteria. We employ a non-linear constraint optimization model to estimate the Shapley importance and criteria interaction indices. In addition, we design a priority-based CSSCI (PCSSCI) to solve service selection problems in the situation where there is a lack of historical information to determine criteria relations and weights. Furthermore, we discuss an approximate solution for CSSCI to reduce its computing complexity. Finally, we design three indices to validate the cloud service selection methods. The experimental results preliminarily prove the technical advantage of the proposed models in contrast to several existing models.
Sun, X, Gui, G, Li, Y, Liu, RP & An, Y 2019, 'ResInNet: A Novel Deep Neural Network with Feature Reuse for Internet of Things', IEEE Internet of Things Journal, vol. 6, no. 1, pp. 679-691.
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© 2014 IEEE. Deep neural networks (DNNs) have widely used in various Internet-of-Things (IoT) applications. Pursuing superior performance is always a hot spot in the field of DNN modeling. Recently, feature reuse provides an effective means of achieving favorable nonlinear approximation performance in deep learning. Existing implementations utilizes a multilayer perception (MLP) to act as a functional unit for feature reuse. However, determining connection weight and bias of MLP is a rather intractable problem, since the conventional back-propagation learning approach encounters the limitations of slow convergence and local optimum. To address this issue, this paper develops a novel DNN considering a well-behaved alternative called reservoir computing, i.e., reservoir in network (ResInNet). In this structure, the built-in reservoir has two notable functions. First, it behaves as a bridge between any two restricted Boltzmann machines in the feature learning part of ResInNet, performing a feature abstraction once again. Such reservoir-based feature translation provides excellent starting points for the following nonlinear regression. Second, it serves as a nonlinear approximation, trained by a simple linear regression using the most representative (learned) features. Experimental results over various benchmark datasets show that ResInNet can achieve the superior nonlinear approximation performance in comparison to the baseline models, and produce the excellent dynamic characteristics and memory capacity. Meanwhile, the merits of our approach is further demonstrated in the network traffic prediction related to real-world IoT application.
Sun, Y, Nimbalkar, S & Chen, C 2019, 'Particle breakage of granular materials during sample preparation', Journal of Rock Mechanics and Geotechnical Engineering, vol. 11, no. 2, pp. 417-422.
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© 2019 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences Particle breakage is commonly observed in granular materials when subjected to external loads. It was found that particle breakage would occur during both sample preparation and loading stages. However, main attention was usually paid to the particle breakage behaviour of samples during loading stage. This study attempts to explore the breakage behaviour of granular materials during sample preparation. Triaxial samples of rockfill aggregates are prepared by layered compaction method to achieve different relative densities. Extents of particle breakage based on the gradings before and after test are presented and analysed. It is found that particle breakage during sample preparation cannot be ignored. Gradings after test are observed to shift away from the initial grading. Aggregates with larger size that appear to break are more than the smaller-sized ones. Irrespective of the initial gradings, an increase in the extent of particle breakage with the increasing relative density is observed during sample preparation.
Sun, Y, Tian, Z, Wang, Y, Li, M, Su, S, Wang, X & Fan, D 2019, 'Lightweight Anonymous Geometric Routing for Internet of Things', IEEE ACCESS, vol. 7, pp. 29754-29762.
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Sun, Y, Yue, H, Zhang, J & Booth, C 2019, 'Minimisation of residential energy cost considering energy storage system and EV with driving usage probabilities', IEEE Transactions on Sustainable Energy.
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Surindra, MD, Caesarendra, W, Prasetyo, T, Mahlia, TMI & Taufik 2019, 'Comparison of the utilization of 110 °C and 120 °C heat sources in a geothermal energy system using Organic Rankine Cycle (ORC) with R245fa, R123, and mixed-ratio fluids as working fluids', Processes, vol. 7, no. 2.
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© 2019 by the authors. Binary cycle experiment as one of the Organic Rankine Cycle (ORC) technologies has been known to provide an improved alternate scenario to utilize waste energy with low temperatures. As such, a binary geothermal power plant simulator was developed to demonstrate the geothermal energy potential in Dieng, Indonesia. To better understand the geothermal potential, the laboratory experiment to study the ORC heat source mechanism that can be set to operate at fixed temperatures of 110 °C and 120 °C is conducted. For further performance analysis, R245fa, R123, and mixed ratio working fluids with mass flow rate varied from 0.1 kg/s to 0.2 kg/s were introduced as key parameters in the study. Data from the simulator were measured and analyzed under steady-state condition with a 20 min interval per given mass flow rate. Results indicate that the ORC system has better thermodynamic performance when operating the heat source at 120 °C than those obtained from 110 °C. Moreover, the R123 fluid produces the highest ORC efficiency with values between 9.4% and 13.5%.
Sutton, G, Liu, RP & Guo, J 2019, 'Coexistence Performance and Limits of Frame-Based Listen-Before-Talk', IEEE Transactions on Mobile Computing, pp. 1-1.
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Sutton, GJ, Zeng, J, Liu, RP, Ni, W, Nguyen, DN, Jayawickrama, BA, Huang, X, Abolhasan, M, Zhang, Z, Dutkiewicz, E & Lv, T 2019, 'Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives', IEEE Communications Surveys & Tutorials, pp. 1-1.
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Taghikhah, F, Voinov, A & Shukla, N 2019, 'Extending the supply chain to address sustainability', Journal of Cleaner Production, vol. 229, pp. 652-666.
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© 2019 Elsevier Ltd In today's growing economy, overconsumption and overproduction have accelerated environmental deterioration worldwide. Consumers, through unsustainable consumption patterns, and producers, through production based on traditional resource depleting practices, have contributed significantly to the socio-environmental problems. Consumers and producers are linked by supply chains, and as sustainability became seen as a way to reverse socio-environmental degradation, it has also started to be introduced in research on supply chains. We look at the evolution of research on sustainable supply chains and show that it is still largely focused on the processes and networks that take place between the producer and the consumer, hardly taking into account consumer behavior and its influence on the performance of the producer and the supply chain itself. We conclude that we cannot be talking about sustainability, without extending the supply chains to account for consumers' behavior and their influence on the overall system performance. A conceptual framework is proposed to explain how supply chains can become sustainable and improve their economic and socio-environmental performance by motivating consumer behavior toward green consumption patterns, which, in turn, motivate producers and suppliers to change their operations.
Talaei-Khoei, A & Wilson, JM 2019, 'Using time-series analysis to predict disease counts with structural trend changes', INFORMATION PROCESSING & MANAGEMENT, vol. 56, no. 3, pp. 674-686.
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Talaei-Khoei, A, Wilson, JM & Kazemi, SF 2019, 'Period of measurement in time-series predictions of disease counts from 2007 to 2017 in northern Nevada: Analytics experiment', Journal of Medical Internet Research, vol. 21, no. 1.
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© Amir Talaei-Khoei, James M Wilson, Seyed-Farzan Kazemi. Background: The literature in statistics presents methods by which autocorrelation can identify the best period of measurement to improve the performance of a time-series prediction. The period of measurement plays an important role in improving the performance of disease-count predictions. However, from the operational perspective in public health surveillance, there is a limitation to the length of the measurement period that can offer meaningful and valuable predictions. Objective: This study aimed to establish a method that identifies the shortest period of measurement without significantly decreasing the prediction performance for time-series analysis of disease counts. Methods: The data used in this evaluation include disease counts from 2007 to 2017 in northern Nevada. The disease counts for chlamydia, salmonella, respiratory syncytial virus, gonorrhea, viral meningitis, and influenza A were predicted. Results: Our results showed that autocorrelation could not guarantee the best performance for prediction of disease counts. However, the proposed method with the change-point analysis suggests a period of measurement that is operationally acceptable and performance that is not significantly different from the best prediction. Conclusions: The use of change-point analysis with autocorrelation provides the best and most practical period of measurement.
Tan, X, Li, W, Zhao, M & Tam, VWY 2019, 'Numerical discrete-element method investigation on failure process of recycled aggregate concrete', Journal of Materials in Civil Engineering, vol. 31, no. 1.
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© 2018 American Society of Civil Engineers. This study numerically investigates the failure processes of recycled aggregate concrete (RAC) and natural aggregate concrete (NAC). A two-dimensional simulation based on a discrete-element method (DEM) is conducted with a universal distinct-element code (UDEC) program. RAC is modeled by a combination of rigid Voronoi blocks cemented to each other using contacts for interfaces. The determination procedure of contact microparameters is analyzed, and a series of microscopic contact parameters in different components of modeled recycled aggregate concrete (MRAC) is calibrated using nanoindentation results. The complete stress-strain curves, fracture process, and failure pattern of numerical model are verified by experimental results, proving its accuracy and validation. The initiation, propagation, and coalescence of microcracks and subsequent nonlinear deformation behaviors of cement mortar, modeled natural aggregate, and recycled aggregate concrete are captured through DEM numerical simulations and compared with digital image correlation (DIC) results. It is found that both the new interfacial transition zone and the old interfacial transition zone are the weak links in RAC, where most microcracks initiate and propagate into the cement mortar region. The failure behaviors of MRAC revealed by both experimental and numerical results can effectively provide insights into the failure mechanism and enhancement of RAC.
Tang, L, Wang, Y, Ding, X, Yin, H, Xiong, R & Huang, S 2019, 'Topological local-metric framework for mobile robots navigation: a long term perspective', Autonomous Robots, vol. 43, no. 1, pp. 197-211.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Long term mapping and localization are the primary components for mobile robots in real world application deployment, of which the crucial challenge is the robustness and stability. In this paper, we introduce a topological local-metric framework (TLF), aiming at dealing with environmental changes, erroneous measurements and achieving constant complexity. TLF organizes the sensor data collected by the robot in a topological graph, of which the geometry is only encoded in the edge, i.e. the relative poses between adjacent nodes, relaxing the global consistency to local consistency. Therefore the TLF is more robust to unavoidable erroneous measurements from sensor information matching since the error is constrained in the local. Based on TLF, as there is no global coordinate, we further propose the localization and navigation algorithms by switching across multiple local metric coordinates. Besides, a lifelong memorizing mechanism is presented to memorize the environmental changes in the TLF with constant complexity, as no global optimization is required. In experiments, the framework and algorithms are evaluated on 21-session data collected by stereo cameras, which are sensitive to illumination, and compared with the state-of-art global consistent framework. The results demonstrate that TLF can achieve similar localization accuracy with that from global consistent framework, but brings higher robustness with lower cost. The localization performance can also be improved from sessions because of the memorizing mechanism. Finally, equipped with TLF, the robot navigates itself in a 1 km session autonomously.
Tang, MC, Duan, Y, Wu, Z, Chen, X, Li, M & Ziolkowski, RW 2019, 'Pattern reconfigurable, vertically polarized, low-profile, compact, near-field resonant parasitic antenna', IEEE Transactions on Antennas and Propagation, vol. 67, no. 3, pp. 1467-1475.
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© 1963-2012 IEEE. A vertically polarized, low-profile, compact, near-field resonant parasitic antenna with pattern reconfigurability is demonstrated. The antenna has three dynamic end-fire states facilitated with only three p-i-n diodes. The radiation pattern in each state covers more than 120° in its azimuth plane and, hence, it achieves beam scanning that covers the entire azimuth plane. The antenna height and transverse size are, respectively, only 0.048λ 0 and 0.1λ 02 . Measured results, in good agreement with their simulated values, demonstrate that the antenna exhibits a 11% fractional impedance bandwidth, and a 6.6 dBi peak realized gain in all three of its pattern-reconfigurable states. Stable and high peak realized gain values are realized over its entire operational band surrounding 2.22 GHz.
Tang, MC, Wu, Z, Shi, T & Ziolkowski, RW 2019, 'Dual-Band, Linearly Polarized, Electrically Small Huygens Dipole Antennas', IEEE Transactions on Antennas and Propagation, vol. 67, no. 1, pp. 37-47.
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© 1963-2012 IEEE. Two electrically small, dual-band Huygens dipole antennas are reported. In both designs, two pairs of magnetic and electric near-field resonant parasitic (NFRP) elements are combined organically within an electrically small, low profile package. The NFRP elements are excited effectively using only one coaxial-fed-driven element. One dual-band Huygens system produces parallel, linearly polarized (LP) fields at its two operating frequencies. The other dual-band system produces two orthogonal LP fields. Additional parasitic elements are introduced to mitigate the mutual coupling effects between the pairs of NFRP elements. The measured values for prototypes of both antennas in the L-band demonstrate their electrically small size (ka< 1) and low profile (∼ 0.03λ0). They also confirm their broadside radiation and polarization performance characteristics, as well as the isolation between each operating frequency. Their fractional bandwidths, peak realized gains, front-to-back ratios, and radiation efficiencies are, respectively, 0.6%, > 2 dBi, > 10 dB, and > 60% at both frequencies. These dual-band systems would provide multifunctional performance in a variety of portable, compact wireless devices.
Tang, Z, Li, W, Hu, Y, Zhou, JL & Tam, VWY 2019, 'Review on designs and properties of multifunctional alkali-activated materials (AAMs)', CONSTRUCTION AND BUILDING MATERIALS, vol. 200, pp. 474-489.
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Tang, Z, Li, W, Ke, G, Zhou, JL & Tam, VWY 2019, 'Sulfate attack resistance of sustainable concrete incorporating various industrial solid wastes', JOURNAL OF CLEANER PRODUCTION, vol. 218, pp. 810-822.
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Tao, M, Li, Z, Cao, W, Li, X & Wu, C 2019, 'Stress redistribution of dynamic loading incident with arbitrary waveform through a circular cavity', International Journal for Numerical and Analytical Methods in Geomechanics, vol. 43, no. 6, pp. 1279-1299.
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© 2019 John Wiley & Sons, Ltd. In actual geotechnical and civil engineering, dynamic stress concentrations around cavities generated by wave sources widely exist. In this study, based on the complex variable theory and Fourier transform method, the expression of the dynamic stress concentration factor (DSCF) around a circular cavity in infinite homogeneous media subjected to transient waves with arbitrary waveform is obtained. The relationships between both steady-state and transient DSCF and their waveform parameters are investigated quantitatively. The results indicate that a relatively large tensile stress is generated with low Poisson's ratio under steady-state incidence. Under the condition of transient incidence, the position of the wave peak has a minor effect on the DSCF in the case of small wavenumber, but it has a significant effect in the opposite case. It is found that when the wavenumber is high, such as 0.5, the stress response lags behind the stress wave. In addition, the closer the wave peak to the center of the waveform, the greater the potential damage of the transient incidence.
Taylor, CN & Bishop, AN 2019, 'Homogeneous functionals and Bayesian data fusion with unknown correlation', Information Fusion, vol. 45, pp. 179-189.
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© 2018 Information or data fusion concerns the aggregation, or combination, of probability measures. For example, in machine learning, statistics and signal processing, one may seek to ‘combine’ posterior distributions, [e.g. 1) Bayes classifiers or 2) posteriors over target states etc], arising from distinct but not necessarily independent sources. For example, sources might include partially disjoint trainers, or spatially distinct sensors correlated via state dependent measurements, etc. Data fusion is common in risk analysis where one is broadly interested in pooling expert opinions described by probability measures, and where it is often hard to assess and account for correlation among experts. The contribution of this work is the introduction of a broad class of data fusion rules that seek the combination of two (or more) probability distributions in the presence of non-zero, but unknown, correlation. We introduce rules that are improved in the sense that they are ‘closer’ to the true Bayesian result that would be computed if one could exploit knowledge of the correlation between the input distributions. We introduce these rules under the common algorithmic constraint of avoiding the so-called ‘double-counting’ of correlated information. The general framework proposed is based on homogeneous functionals. We examine the fusion performance and computational properties when using these functionals. We also consider distributed data fusion on (possibly) time-varying and incomplete network topologies and related convergence properties.
Terechovs, AKE, Ansari, AJ, McDonald, JA, Khan, SJ, Hai, FI, Knott, NA, Zhou, J & Nghiem, LD 2019, 'Occurrence and bioconcentration of micropollutants in Silver Perch (Bidyanus bidyanus) in a reclaimed water reservoir.', The Science of the total environment, vol. 650, no. Pt 1, pp. 585-593.
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This study examined the occurrence of 49 micropollutants in reclaimed water and Silver Perch (Bidyanus bidyanus) living in a reclaimed water reservoir. The numbers of micropollutants detected in reclaimed water, Silver Perch liver, and Silver Perch flesh were 20, 23, and 19, respectively. Concentrations of all micropollutants in reclaimed water, except benzotriazole, were well below the Australian Guideline for Recycled Water (AGRW) values for potable purposes. The concentration of benzotriazole in reclaimed water was 675 ± 130 ng/L while the AGRW value for this compound was 7 ng/L. Not all micropollutants detected in the water phase were identified in the Silver Perch flesh and liver tissues. Likewise, not all micropollutants detected in the Silver Perch flesh and liver were identified in the reclaimed water. In general, micropollutant concentrations in the liver were higher than in the flesh. Perfluorooctane sulfonate (PFOS) was detected at a trace level in reclaimed water well below the AGRW guideline value for potable purposes, but showed a high and medium bioconcentration factor in Silver Perch liver and flesh, respectively. In addition, the risk quotient for PFOS was medium and high when considering its concentration in Silver Perch liver and flesh, respectively. Results reported here highlight the need to evaluate multiple parameters for a comprehensive risk assessment. The results also single out PFOS as a notable contaminant of concern for further investigation.
Thabit, MS, Hawari, AH, Ammar, MH, Zaidi, S, Zaragoza, G & Altaee, A 2019, 'Evaluation of forward osmosis as a pretreatment process for multi stage flash seawater desalination', Desalination, vol. 461, pp. 22-29.
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Thanh, HT, Li, J & Zhang, YX 2019, 'Numerical modelling of the flow of self-consolidating engineered cementitious composites using smoothed particle hydrodynamics', Construction and Building Materials, vol. 211, pp. 109-119.
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© 2019 Elsevier Ltd In this paper, a numerical method is developed to simulate the flow of self-consolidating engineered cementitious composites (SC-ECC) which can be described as a non-Newtonian viscous fluid in two dimensions. The Lagrangian form of the Navier-Stockes constitutive equations of the SC-ECC is solved using a mesh-free, weakly compressible smoothed particle hydrodynamics (WCSPH). Flexible synthetic fibres in the SC-ECC are modelled as separate particles in the computational domain, which possess the same continuum properties as mortar particles except for the drag force between two adjacent fibre particles. The numerical model is then validated using slump flow tests, and the numerical results are found to be consistent with the experimental data obtained from the literature. The correlations between the wall-effects, the distance of flow, the casting position with the orientation and the distribution of synthetic fibres are also quantitatively investigated in this work using the proposed method.
To, VHP, Nguyen, TV, Bustamante, H & Vigneswaran, S 2019, 'Deleterious effects of soluble extracellular polymeric substances on polyacrylamide demand for conditioning of anaerobically digested sludge', Journal of Environmental Chemical Engineering, vol. 7, no. 2.
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© 2019 Elsevier Ltd. All rights reserved. High polyacrylamide (polymer) demand for conditioning of sludge, especially anaerobically digested sludge (ADS), is a major issue for the water industry. Currently, this problem is being investigated and the reasons for doing so are varied. It has been demonstrated that excess amounts of soluble extracellular polymeric substances (EPS) can lead to high polymer demand for conditioning. This study developed a simple and unique yet effective method for quantifying the contribution of soluble EPS to conditioning polymer demand. It did this by measuring absorbance at 191.5 nm wavelength of the supernatant derived from conditioned ADS. Experimental results confirmed that approximately 87 wt% of soluble EPS interacted with polyacrylamides during the conditioning process. Furthermore, they revealed that a specified amount of soluble EPS could not be removed by polymer flocculation despite high polymer dosage. This study concluded that about 86 wt% of the polyacrylamide used for conditioning was consumed solely by soluble EPS. These results confirm the important role of reducing this EPS fraction in ADS in order to curtail significant chemical costs for sludge conditioning and dewatering.
Torres Robles, A, Wiecek, E, Cutler, R, Drake, B, Benrimoj, SI, Fernandez-Llimos, F & Garcia Cardenas, M 2019, 'Using dispensing data to evaluate adherence implementation rates in community pharmacy', Frontiers in Pharmacology.
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Tran, HN, Nguyen, DT, Le, GT, Tomul, F, Lima, EC, Woo, SH, Sarmah, AK, Nguyen, HQ, Nguyen, PT, Nguyen, DD, Nguyen, TV, Vigneswaran, S, Vo, DVN & Chao, HP 2019, 'Adsorption mechanism of hexavalent chromium onto layered double hydroxides-based adsorbents: A systematic in-depth review', Journal of Hazardous Materials, pp. 258-270.
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© 2019 Elsevier B.V. An attempt has been made in this review to provide some insights into the possible adsorption mechanisms of hexavalent chromium onto layered double hydroxides-based adsorbents by critically examining the past and present literature. Layered double hydroxides (LDH) nanomaterials are typical dual-electronic adsorbents because they exhibit positively charged external surfaces and abundant interlayer anions. A high positive zeta potential value indicates that LDH has a high affinity to Cr(VI) anions in solution through electrostatic attraction. The host interlayer anions (i.e., Cl − , NO 3− , SO 42− , and CO 32− ) provide a high anion exchange capacity (53–520 meq/100 g) which is expected to have an excellent exchangeable capacity to Cr(VI) oxyanions in water. Regarding the adsorption-coupled reduction mechanism, when Cr(VI) anions make contact with the electron-donor groups in the LDH, they are partly reduced to Cr(III) cations. The reduced Cr(III) cations are then adsorbed by LDH via numerous interactions, such as isomorphic substitution and complexation. Nonetheless, the adsorption-coupled reduction mechanism is greatly dependent on: (1) the nature of divalent and trivalent salts utilized in LDH preparation, and the types of interlayer anions (i.e., guest intercalated organic anions), and (3) the adsorption experiment conditions. The low Brunauer–Emmett–Teller specific surface area of LDH (1.80–179 m 2 /g) suggests that pore filling played an insignificant role in Cr(VI) adsorption. The Langmuir maximum adsorption capacity of LDH (Q omax ) toward Cr(VI) was significantly affected by the natures of used inorganic salts and synthetic methods of LDH. The Q omax values range from 16.3 mg/g to 726 mg/g. Almost all adsorption processes of Cr(VI) by LDH-based adsorbent occur spontaneously (ΔG° <0) and endothermically (ΔH° >0) and increase the randomness (ΔS° >0) in the system. Thus, LDH has much potential as a promising material that can effectively...
Tran, NT & Khoury, S 2019, 'The challenges of using blood-based miRNAs in the clinic'.
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This preprint addresses the enormous challenges confronting researchers invested in serum miRNAs and their potential use as clinical biomarkers. We discuss some of the inherent technical obstacles and suggest several possible solutions. We hope to encourage all researchers to be united and collaborative in the pursuit of delivering clinically relevant biomarkers in the near future.
Tran, VH, Lim, S, Han, DS, Pathak, N, Akther, N, Phuntsho, S, Park, H & Shon, HK 2019, 'Efficient fouling control using outer-selective hollow fiber thin-film composite membranes for osmotic membrane bioreactor applications', Bioresource Technology, pp. 9-17.
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© 2019 Elsevier Ltd This paper investigates the efficiency of fouling mitigation methods using a novel outer selective hollow fiber thin-film composite forward osmosis (OSHF TFC FO) membrane for osmosis membrane bioreactor (OMBR) system treating municipal wastewater. Two home-made membrane modules having similar transport properties were used. Two operation regimes with three different fouling mitigation strategies were utilized to test the easiness of membrane for fouling cleaning. These two membrane modules demonstrated high performance with high initial water flux of 14.4 LMH and 14.1 LMH and slow increase rate of mixed liquor's salinity in the bioreactor using 30 g/L NaCl as draw solution. OMBR system showed high removals of total organic carbon and NH4 + -N (>98%). High fouling cleaning efficiency was achieved using OSHF TFC FO membrane with different fouling control methods. These results showed that this membrane is suitable for OMBR applications due to its high performance and its simplicity for fouling mitigation.
Treadaway, C, Fennell, J, Taylor, A & Kenning, G 2019, 'Designing for playfulness through compassion: design for advanced dementia', Design for Health.
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Tri, DQ, Mai Linh, NT, Thai, TH & Kandasamy, J 2019, 'Application of 1D–2D coupled modeling in water quality assessment: A case study in Ca Mau Peninsula, Vietnam', Physics and Chemistry of the Earth.
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© 2018 Elsevier Ltd A few 1D and 2D models are used to simulate and calculate the water level and water quality regarding four state variable (DO, NH4, NO3, and BOD) observations in the main rivers and coastal estuaries on the Ca Mau peninsula. This study calibrates and validates 1D and 2D models during the dry and flood seasons for 2014 and 2015, as well as assesses water quality in coastal estuaries during the dry and flood season of 2016 by using a 2D model. The calibration and validation results of the hydrodynamic 1D and 2D models show that there is a high degree of conformity regarding the phase and amplitude of water level at observing stations with mean absolute error (MAE) ranges from 0.05 m to 0.37 m. The RMSE–observation standard deviation ratio (RSR) vary from 0.12 to 0.64, and the percent bias (PBIAS) is from −8.9% to 3.2%. Calibration and validation of water quality parameter (DO, NH4, NO3, and BOD5 concentration) results have a high correlation coefficient during both the dry and flood season of 2014 and 2015. The standards, originating from the National Technical Regulation on Surface Water quality and on Coastal Water Quality, are used to evaluate pollutant concentrations in estuaries in the study area during the dry and flood seasons of 2016. The water quality parameters contain DO (4.6 mg/l–7.9 mg/l) and BOD (4.6 mg/l–10.7 mg/l) concentrations over the National Technical Regulation on Surface and Coastal Water Quality and the A1 limit with DO (>4 mg/l) and BOD5 (4 mg/l) on surface water quality for domestic water use in the dry and flood seasons. The calculated results will help managers make better plans for aquaculture and aquatic conservation zones in coastal estuaries in the future.
Trianni, A, Cagno, E, Bertolotti, M, Thollander, P & Andersson, E 2019, 'Energy management: A practice-based assessment model', Applied Energy, pp. 1614-1636.
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© 2018 Elsevier Ltd Industrial energy efficiency is crucial for energy cost saving and sustainable competitiveness, but its potential is not exploited due to several barriers. Previous literature has pointed out that, among the most effective means, energy management in industrial companies could bring a valuable contribution. Therefore, it is crucial to assess and evaluate the energy management status in an organisation so to undertake the most appropriate improvement actions. So far, literature has neither described the fundamental characteristics of energy management practices, nor specifically developed an assessment model to support industrial decision-makers. Stemming from those research gaps, the present work presents and discusses an innovative energy management assessment model based on a novel characterization of energy management practices. We validated and applied the model through case studies among large Italian and Swedish manufacturing companies, both proving the model to be able to thoroughly describe the energy management status and benchmarking the adoption level of energy management practices with respect to specific baselines. The model highlights both strengths and critical areas in an industrial company's energy management, thus offering a valuable support to drive further improvement activities. The work concludes with interesting suggestions for industrial decision-makers and policy-makers, sketching also some further research avenues.
Tuan, HD, Nasir, AA, Nguyen, HH, Duong, TQ & Poor, HV 2019, 'Non-Orthogonal Multiple Access with Improper Gaussian Signaling', IEEE Journal on Selected Topics in Signal Processing.
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IEEE Improper Gaussian signaling (IGS) helps to improve the throughput of a wireless communication network by taking advantage of the additional degrees of freedom in signal processing at the transmitter. This paper exploits IGS in a general multiuser multi-cell network, which is subject to both intra-cell and inter-cell interference. With IGS under orthogonal multiple access (OMA) or non-orthogonal multiple access (NOMA), designs of transmit beamforming to maximize the users' minimum throughput subject to transmit power constraints are addressed. Such designs are mathematically formulated as nonconvex optimization problems of structured matrix variables, which cannot be solved by popular techniques such as weighted minimum mean square error or convex relaxation. By exploiting the lowest computational complexity of 2×2 linear matrix inequalities, lower concave approximations are developed for throughput functions, which are the main ingredients for devising efficient algorithms for finding solution of these difficult optimization problems. Numerical results obtained under practical scenarios reveal that (i) there is an almost two-fold gain in the throughput by employing IGS instead of the conventional proper Gaussian signaling (PGS) under both OMA and NOMA; and (ii) NOMA-IGS offers better throughput compared to that achieved by OMA-IGS.
Tuan, HD, Nasir, AA, Nguyen, MN & Masood, M 2019, 'Han-Kobayashi Signaling in MIMO Broadcasting', IEEE Communications Letters.
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IEEE This letter applies Han-Kobayashi (H-K) superposition signaling to a network of a multi-antenna transmitter serving two multi-antenna users. By making the rate of the common message for both users contribute to user individual rate, it shows that H-K superposition signaling clearly outperforms both stateof- the-art orthogonal multiaccess (OMA) and nonorthogonal multiaccess schemes (NOMA) in terms of the worst user rate. More importantly, unlike NOMA, H-K superposition signaling does not require the user channels to be differentiated for efficient implementation.
Uddin, MB, Su, SW, Chen, W & Chow, CM 2019, 'Dynamic changes in electroencephalogram spectral power with varying apnea duration in older adults', Journal of Sleep Research.
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© 2019 European Sleep Research Society Sleep apnea elicits brain and physiological changes and its duration varies across the night. This study investigates the changes in the relative powers in electroencephalogram (EEG) frequency bands before and at apnea termination and as a function of apnea duration. The analysis was performed on 30 sleep records (375 apnea events) of older adults diagnosed with sleep apnea. Power spectral analysis centered on two 10-s EEG epochs, before apnea termination (BAT) and after apnea termination (AAT), for each apnea event. The relative power changes in EEG frequency bands were compared with changes in apnea duration, defined as Short (between 10 and 20 s), Moderate (between 20 and 30 s) and Long (between 30 and 40 s). A significant reduction in EEG relative powers for lower frequency bands of alpha and sigma were observed for the Long compared to the Moderate and Short apnea duration groups at BAT, and reduction in relative theta, alpha and sigma powers for the Long compared to the Moderate and Short groups at AAT. The proportion of apnea events showed a significantly decreased trend with increased apnea duration for non-rapid eye movement sleep but not rapid eye movement sleep. The proportion of central apnea events decreased with increased apnea duration, but not obstructive episodes. The findings suggest EEG arousal occurred both before and at apnea termination and these transient arousals were associated with a reduction in relative EEG powers of the low-frequency bands: theta, alpha and sigma. The clinical implication is that these transient EEG arousals, without awakenings, are protective of sleep. Further studies with large datasets and different age groups are recommended.
Uddin, MN, Rahman, MA, Mofijur, M, Taweekun, J, Techato, K & Rasul, MG 2019, 'Renewable energy in Bangladesh: Status and prospects', Energy Procedia, vol. 160, pp. 655-661.
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© 2019 The Authors. Published by Elsevier Ltd. Global energy demand has risen sharply over the years with developing countries recording the greatest share in this trend. Biomass as an energy resource is mostly available locally and can easily be converted into secondary energy without huge capital investments. Nowadays, Bangladesh shares a percentage of renewable energy only 3% of total energy ratio, Bangladesh has already taken a master plan in the renewable energy sector. Whereas installed electricity generation installed capacity of Bangladesh rapidly increased to 13265 MW with captive generation capacity which is insufficient for fulfilling the demand of electricity of the nations. One-third of the power production of Bangladesh depends on expensive imported fossil fuel energy resources and 65% of power generation depends on a natural gas reserve of the country, though one day the reserve of current gas will be diminished. Moreover, inadequate electricity production leads the country in a un-industrialization. The present and future crucial energy crisis situation adapted by installing renewable power into electricity production. The current renewable energy agenda of Bangladeshi government force the specialization of renewable energy generation budget by decreasing global pollution with saving movement of biomass, solar, hydro, wind, and tidal power sector. This paper presents the currents national energy scenario of Bangladesh. According to this, the greater potentiality of renewable energy resources is also reviewed and presented in this paper.
Uddin, MN, Rahman, MA, Taweekun, J, Techato, K, Mofijur, M & Rasul, M 2019, 'Enhancement of biogas generation in up-flow sludge blanket (UASB) bioreactor from palm oil mill effluent (POME)', Energy Procedia, vol. 160, pp. 670-676.
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© 2019 The Authors. Published by Elsevier Ltd. There are numerous different sorts of pre-treatment technique have been utilized with a few impediments regarding supportable natural administration in anaerobic assimilation for improvement of biogas generation. Albeit corrosive and salt pretreatment have a huge impact on the corruption of biomass, these techniques have some negative effects on the condition because of their perilous nature, while the enzymatic pre-treatment is more environmentally friendly. In this investigation is to streamline the biogas generation by enzymatic pre-treatment from Palm Oil Mill Effluent (POME) with assessing the improved biogas creation in a pilot scale bioreactor. It is to concentrate coordinate utilization of protein as enzymatic pre-treatment on POME to the improved generation of biogas. Proficiency of privately manufactured chemical with Up-flow Sludge Blanket (UASB) has not been researched in pilot scale previously. In this examination proficiency of COD expulsion and aggregate methane emanation is explored through pilot scale UASB bioreactor from POME through the application of enzyme and reviewed a study with the discussion.
Uddin, MN, Taweekun, J, Techato, K, Rahman, MA, Mofijur, M & Rasul, MG 2019, 'Sustainable Biomass as an Alternative Energy Source: Bangladesh Perspective', Energy Procedia, vol. 160, pp. 648-654.
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© 2019 The Authors. Published by Elsevier Ltd. Presently, the crisis of energy has become a growing concern all over the world and a serious barrier for the developing nations. Conversely, the resources of fossil fuels are limited and depleting due to the exploration and higher production activities around the glove. The impact of fossil fuels exploration has become a serious threat for naturalism and creates environmental hazardous accidents. However, the demand for energy in Bangladesh is increasing day by day and fossil fuel reserves continue the countries demand almost only 50 years. So, it is the high time to utilize its natural biomass resources to fulfill energy demand. The climate condition and waste generation rate of Bangladesh is key factors for biomass energy production. Nowadays the Bangladeshi government has made an agenda "waste to electricity" thus the utilization of biomass resources has become a blessing for the static economic growth in the nation. Recently Bangladeshi government has made an extra effort for the commercialization and marketable of biomass energy engendering and production in the country. This paper explored the potential of biomass energy as a sustainable energy source and their future implementation challenges in Bangladesh.
Uddin, MN, Techato, K, Rasul, MG, Hassan, NMS & Mofijur, M 2019, 'Waste coffee Oil: A promising source for biodiesel production', Energy Procedia, vol. 160, pp. 677-682.
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© 2019 The Authors. Published by Elsevier Ltd. The oil from waste coffee possesses the potential as a feedstock for biodiesel production. In this study, waste coffee grounds were collected from Turkey. Then the oil was extracted from waste coffee grounds using N-hexane. The two-step acid-base catalyst transesterification process was used to produce biodiesel as the acid value of the crude oil was found higher (15.4 mgKOH/g).This was followed by an investigation of some physical and chemical properties. It was found that the properties of waste coffee biodiesel fell within the limit of ASTM standards. So the studied physicochemical properties of the waste coffee biodiesel suggest that this fuel can be used as a fuel in diesel engines.
Ulapane, N & Nguyen, L 2019, 'Review of Pulsed Eddy Current Signal Feature Extraction Methods for Conductive Ferromagnetic Material Thickness Quantification', Electronics (Basel).
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Thickness quantification of conductive ferromagnetic materials has become a common necessity in present-day structural health monitoring and infrastructure maintenance. Recent research has found Pulsed Eddy Current (PEC) sensing, especially the detector-coil-based PEC sensor architecture, to effectively serve as a nondestructive sensing technique for this purpose. As a result, several methods of varying complexity have been proposed in recent years to extract PEC signal features, against which conductive ferromagnetic material thickness behaves as a function, in return enabling thickness quantification owing to functional behaviours. It can be seen that almost all features specifically proposed in the literature for the purpose of conductive ferromagnetic material-thickness quantification are in some way related to the diffusion time constant of eddy currents. This paper examines the relevant feature-extraction methods through a controlled experiment in which the methods are applied to a single set of experimentally captured PEC signals, and provides a review by discussing the quality of the extractable features, and their functional behaviours for thickness quantification, along with computational time taken for feature extraction. Along with this paper, the set of PEC signals and some MATLAB codes for feature extraction are provided as supplementary materials for interested readers.
Usman, M, He, X, Lam, KKM, Xu, M, Chen, J, Bokhari, SMM & Jan, MA 2019, 'Error Concealment for Cloud-based and Scalable Video Coding of HD Videos', IEEE Transactions on Cloud Computing.
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IEEE The encoding of HD videos faces two challenges: requirements for a strong processing power and a large storage space. One time-efficient solution addressing these challenges is to use a cloud platform and to use a scalable video coding technique to generate multiple video streams with varying bit-rates. Packet-loss is very common during the transmission of these video streams over the Internet and becomes another challenge. One solution to address this challenge is to retransmit lost video packets, but this will create end-to-end delay. Therefore, it would be good if the problem of packet-loss can be dealt with at the user's side. In this paper, we present a novel system that encodes and stores the videos using the Amazon cloud computing platform, and recover lost video frames on user side using a new Error Concealment (EC) technique. To efficiently utilize the computation power of a user's mobile device, the EC is performed based on a multiple-thread and parallel process. The simulation results clearly show that, on average, our proposed EC technique outperforms the traditional Block Matching Algorithm (BMA) and the Frame Copy (FC) techniques.
Usman, M, Jan, MA, He, X & Chen, J 2019, 'A Mobile Multimedia Data Collection Scheme for Secured Wireless Multimedia Sensor Networks', IEEE Transactions on Network Science and Engineering.
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IEEE Wireless Multimedia Sensor Networks (WMSNs) produce an enormous amount of big multimedia data. Due to large size, Multimedia Sensor Nodes (MSNs) cannot store generated multimedia data for a long time. In this scenario, mobile sinks can be utilized for data collection. However, due to vulnerable nature of wireless networks, there is a need for an efficient security scheme to authenticate both MSNs and mobile sinks. In this paper, we propose a scheme to protect an underlying WMSN during mobile multimedia data collection. The proposed scheme is a two-layer scheme. At the first layer, all MSNs are distributed into small clusters, where each cluster is represented by a single Cluster Head (CH). At the second layer, all CHs verify identities of mobile sinks before sharing multimedia data. Authentication at both layers ensures a secure data exchange. We evaluate the performance of proposed scheme through extensive simulation results. The simulation results prove that the proposed scheme performs better as compared to existing state-of-the-art approaches in terms of resilience and handshake duration. The proposed scheme is also analyzed in terms of authentication rate, data freshness, and packet delivery ratio, and has shown a better performance.
Usman, M, Jan, MA, He, X & Chen, J 2019, 'P2DCA: A Privacy-Preserving based Data Collection and Analysis Framework for IoMT Applications', IEEE Journal on Selected Areas in Communications.
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IEEE The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, devices, e.g., Multimedia Sensor Nodes (MSNs) are capable of generating both multimedia and nonmultimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, e.g., revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient Privacy-Preserving based Data Collection and Analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, aggregated multimedia data are analyzed on the cloud server using a counterpropagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacypreserving schemes, and can be used to collect multimedia data in various IoMT applications.
Usman, M, Jan, MA, He, X & Nanda, P 2019, 'QASEC: A secured data communication scheme for mobile Ad-hoc networks', Future Generation Computer Systems.
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© 2018 Elsevier B.V. Mobile Adhoc NETworks (MANETs) are valuable for various applications due to an efficient, flexible, low-cost and dynamic infrastructure. In these networks, proper utilization of network resources is desirable to maintain Quality of Service (QoS). In multi-hop end-to-end communication, intermediate nodes may eavesdrop on data in transit. As a result, a secured and reliable data delivery from source to destination is required. In this paper, we propose a novel scheme, known as QASEC, to achieve better throughput by securing end-to-end communication in MANETs. The QoS is maintained through an optimal link selection from a queue of available transmission links. The end-to-end communication is secured by authentication. A simple secret-key based symmetric encryption is deployed for interacting nodes. Our proposed QASEC scheme prevents the malicious nodes from data exchange with legitimate intermediate nodes on any established path between the source and the destination. Experimental results show that QASEC performs better in terms of packet-loss rate, jitter and end-to-end delay. Furthermore, QASEC is efficient against various attacks and has a much better performance in terms of associated costs, such as key generation, encryption, and storage and communication.
Vahedian, A, Shrestha, R & Crews, K 2019, 'Experimental and analytical investigation on CFRP strengthened glulam laminated timber beams: Full-scale experiments', Composites Part B: Engineering, vol. 164, pp. 377-389.
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Vakhshouri, B & Nejadi, S 2019, 'Empirical models and design codes in prediction of modulus of elasticity of concrete', Frontiers of Structural and Civil Engineering, vol. 13, no. 1, pp. 38-48.
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© 2018, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature. Modulus of Elasticity (MOE) is a key parameter in reinforced concrete design. It represents the stress-strain relationship in the elastic range and is used in the prediction of concrete structures. Out of range estimation of MOE in the existing codes of practice strongly affect the design and performance of the concrete structures. This study includes: (a) evaluation and comparison of the existing analytical models to estimating the MOE in normal strength concrete, and (b) proposing and verifying a new model. In addition, a wide range of experimental databases and empirical models to estimate the MOE from compressive strength and density of concrete are evaluated to verification of the proposed model. The results show underestimation of MOE of conventional concrete in majority of the existing models. Also, considering the consistency between density and mechanical properties of concrete, the predicted MOE in the models including density effect, are more compatible with the experimental results.
Vakhshouri, B, Nejadi, S & Erkmen, E 2019, 'Advances in numerical analysis of creep effect in time-dependent deflection of light-weight concrete slabs', Mechanics of Advanced Materials and Structures.
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© 2018, © 2018 Taylor & Francis Group, LLC. Using a wide range of creep models, the experimental results of long-term deflection of lightweight concrete slabs subjected to two levels of early-age loading are investigated. Different creep models give considerably different estimation of the experimental deflection of slabs. The included factors in each creep model to simulate the experimental creep behavior of the concrete, and loading level on the slabs are the main causes of different results. Among the investigated models, the BP1 and FIBMC-2010 models including the aggregate type and concrete density is shown to be in good agreement with the experimental data in both loading levels.
Valenzuela Fernandez, LM, Nicolas, C, Merigó, JM & Arroyo-Cañada, FJ 2019, 'Industrial marketing research. A bibliometric analysis (1990-2015)', Journal of Business and Industrial Marketing.
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© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to determine the most influential countries and universities that have contributed to science in the field of industrial marketing research during the period from 1990 to2015. Design/methodology/approach: Bibliometric methodology is adopted, focusing on the most productive and influential countries and universities within this discipline, for the scientific community analyzing journals listed in the Web of Science (WoS) database from 1990 to 2015 and is supplemented by using VOS viewer to graph the existing bibliometric networks for each and every variable. Findings: Evidence that the USA and UK remain leaders in the investigation of industrial marketing research. Finland stands at the third place, leaving Australia and Germany behind. In reference to the universities, Michigan State University ranks as the leader. Research limitations/implications: The process of data classification originates from WoS. Moreover, to provide a comprehensive analytical scenario, other factors could have potentially been considered such as the editor’s commitment to leading journals, to partnerships and conferences, as well as other databases. Originality/value: This paper takes into account alternative variables that have not been previously considered in previous studies, such as universities and countries in which the transcendental contributions to this field have taken place, providing a closer look, which gives rise to further discussions and studies with more detail to the history of this science in the future.
Van Huynh, N, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2019, 'Optimal and Fast Real-time Resource Slicing with Deep Dueling Neural Networks', IEEE Journal on Selected Areas in Communications, pp. 1-1.
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van Rijmenam, M, Erekhinskaya, T, Schweitzer, J & Williams, MA 2019, 'Avoid being the turkey: How big data analytics changes the game of strategy in times of ambiguity and uncertainty', Long Range Planning.
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In order for organisations to remain competitive in times of ambiguity and uncertainty, there is a need to detect and anticipate unknown unknowns, also called ‘black swans’. When these are ignored they may lead to competitive struggles. In this paper, we build on this view and suggest that big data analytics can provide necessary insights to help change strategy making. Research suggests that ambidextrous organisations should focus on developing and maintaining their dynamic capabilities. Following on from this, we take a dynamic capabilities perspective and propose a theoretical framework to explain the intricacies of big data analytics. This framework explains the ability of organisations to detect, anticipate and respond strategically in ambiguous and uncertain business environments. For a meta- synthesis of 101 cases of big data analytics, we employ a multi-method approach that incorporates Natural Language Processing, semantic analysis and case analysis, allowing extraction and analysis of structured information from unstructured data. Overall, we find evidence of big data analytics helping to detect, anticipate and respond to industry disruption. We offer six propositions about the relationships between the levels of data analytics capabilities and strategic dynamic capabilities. We find that descriptive data analytics improves the capability of an organisation to understand the business context (sensing) and that predictive data analytics aids in the realisation of business opportunities (seizing). This study contributes to an understanding of big data analytics as a dynamic organisational capability that supports strategic decision-making in times of ambiguity and uncertainty. We conclude by suggesting areas for further investigation, particularly in regard to the strategic application of prescriptive data analytics.
Van Thanh, P, Tran, DT, Nguyen, DC, Duc Anh, N, Nhu Dinh, D, El-Rabaie, S & Sandrasegaran, K 2019, 'Development of a Real-Time, Simple and High-Accuracy Fall Detection System for Elderly Using 3-DOF Accelerometers', Arabian Journal for Science and Engineering, vol. 44, no. 4, pp. 3329-3342.
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© 2018, King Fahd University of Petroleum & Minerals. Falls represent a major problem for the elderly people aged 60 or above. There are many monitoring systems which are currently available to detect the fall. However, there is a great need to propose a system which is of optimal effectiveness. In this paper, we propose to develop a low-cost fall detection system to precisely detect an event when an elderly person accidentally falls. The fall detection algorithm compares the acceleration with lower fall threshold and upper fall threshold values to accurately detect a fall event. The post-fall recognition module is the combination of posture recognition and vertical velocity estimation that has been added to our proposed method to enhance the performance and accuracy. In case of a fall, our device will transmit the location information to the contacts instantly via SMS and voice call. A smartphone application will ensure that the notifications are delivered to the elderly person’s relatives so that medical attention can be provided with minimal delay. The system was tested by volunteers and achieved 100% sensitivity and accuracy. This was confirmed by testing with public datasets and it also achieved the same percentage in sensitivity and accuracy as in our recorded datasets.
Van, HT, Nguyen, LH, Nguyen, VD, Nguyen, XH, Nguyen, TH, Nguyen, TV, Vigneswaran, S, Rinklebe, J & Tran, HN 2019, 'Characteristics and mechanisms of cadmium adsorption onto biogenic aragonite shells-derived biosorbent: Batch and column studies.', Journal of environmental management.
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Calcium carbonate (CaCO3)-enriched biomaterial derived from freshwater mussel shells (FMS) was used as