Publications

Chang, W-L & Vasilakos, AV 2021, Fundamentals of Quantum Programming in IBM's Quantum Computers, Springer International Publishing.
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Lu, J, Zhang, Q & Zhang, G 2021, Recommender Systems Advanced Developments, World Scintific, USA.
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Recommender systems provide users (businesses or individuals) with personalized online recommendations of products or information, to address the problem of information overload and improve personalized services. Recent successful applications of recommender systems are providing solutions to transform online services for e-government, e-business, e-commerce, e-shopping, e-library, e-learning, e-tourism, and more. This unique compendium not only describes theoretical research but also reports on new application developments, prototypes, and real-world case studies of recommender systems. The comprehensive volume provides readers with a timely snapshot of how new recommendation methods and algorithms can overcome challenging issues. Furthermore, the monograph systematically presents three dimensions of recommender systems — basic recommender system concepts, advanced recommender system methods, and real-world recommender system applications. By providing state-of-the-art knowledge, this excellent reference text will immensely benefit researchers, managers, and professionals in business, government, and education to understand the concepts, methods, algorithms and application developments in recommender systems.

Doan, HS, Fatahi, B & Khabbaz, H 2021, 'Exact Series Solution for Plane Strain Consolidation of Stone Column Improved Soft Soil Accounting for Space-Dependent Total Stresses' in Barla, M, Di Donna, A & Sterpi, D (eds), Challenges and Innovations in Geomechanics, Springer, Switzerland, pp. 794-802.
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This paper provides an analytical solution to predict the free strain consolidation of a stone column supported soft soil under plane strain configuration. The external load on the ground surface was assumed to be applied instantly, which results in time-independent but space-dependent total stresses in the composite ground. A rigorous analytical solution to evaluate the changes of excess pore water pressure with time at any point in the model was derived as a double series, using the method of separation of variables. The obtained solution can capture any distribution patterns of total stresses caused by the external load, where the total stresses are described as separable functions against spatial coordinates. The validation of the proposed solution was exhibited through an example evaluating the effect of change patterns of the total stresses with depth on consolidation of the composite ground. The calculation results were presented graphically in terms of average degree of consolidation for column and for soft soil, and average differential settlement between the column and soil regions. The more diminution of the total stresses with depth led to accelerated consolidation of the composite ground and more significant reduction in the average differential settlement between the column and the soil.

El-Hawat, O, Fatahi, B & Mostafa, M 2021, 'Effect of Near-Fault Vertical Ground Motions on the Seismic Response of Bridges with Rocking Foundations', pp. 95-107.
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Rocking foundations can be used in bridges to reduce the seismic demand of the structure and prevent inelastic behaviour from developing at the piers during large earthquake events. Several studies have proven that rocking is an effective seismic isolation technique under lateral earthquake loading. However, limited research has been conducted on the effect of the vertical component of earthquakes on the rocking behaviour of bridge piers. This paper aims to numerically investigate the effect of the vertical component of near-fault earthquakes on the seismic performance of bridges with rocking pile foundations. Two identical bridge configurations with different foundation systems (conventional fixed base foundation and rocking foundation) are subjected to two loading cases: (1) horizontal ground motions only, (2) combined horizontal and vertical ground motions. Three-dimensional models of the bridges are developed with the appropriate material models to capture possible inelastic behaviour, as well as to model the soil–structure interaction. Four near-fault ground motions with three components are selected and scaled to the appropriate seismic hazard and applied to the bridges using nonlinear dynamic time history analyses. The dynamic responses of the bridges are compared in terms of deck displacements, deck bending moments, and pier axial and bending moments. The results show that the vertical component of ground motions can considerably increase the dynamic response of the bridge with the rocking foundation when compared to the fixed base foundation, leading to increased deck displacements and inertial actions on the bridge structure.

Guo, T, Zhu, X, Wang, Y & Chen, F 2021, 'Weak Supervision Network Embedding for Constrained Graph Learning' in Advances in Knowledge Discovery and Data Mining, Springer International Publishing, pp. 488-500.
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Jia, M, Gabrys, B & Musial, K 2021, 'Closure Coefficient in Complex Directed Networks', pp. 62-74.
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The 3-clique formation, a natural phenomenon in real-world networks, is typically measured by the local clustering coefficient, where the focal node serves as the centre-node in an open triad. The local closure coefficient provides a novel perspective, with the focal node serving as the end-node. It has shown some interesting properties in network analysis, yet it cannot be applied to complex directed networks. Here, we propose the directed closure coefficient as an extension of the closure coefficient in directed networks, and we extend it to weighted and signed networks. In order to better use it in network analysis, we introduce further the source closure coefficient and the target closure coefficient. Our experiments show that the proposed directed closure coefficient provides complementary information to the classic directed clustering coefficient. We also demonstrate that adding closure coefficients leads to better performance in link prediction task in most directed networks.

Kashani, AR, Akhani, M, Camp, CV & Gandomi, AH 2021, 'A neural network to predict spectral acceleration' in Basics of Computational Geophysics, Elsevier, pp. 335-349.
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Li, B, Guo, T, Wang, Y, Gandomi, AH & Chen, F 2021, 'Adaptive Graph Co-Attention Networks for Traffic Forecasting' in Advances in Knowledge Discovery and Data Mining, Springer International Publishing, pp. 263-276.
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Ma, M, Tam, VWY, Le, KN, Zhu, Y & Li, W 2021, 'Comparative Analysis of National Policies on Construction and Demolition Waste Management in China and Japan' in Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate, Springer Singapore, pp. 1543-1558.
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Malisetty, RS, Indraratna, B & Vinod, JS 2021, 'Constitutive Modelling of the Deformation and Degradation of Railway Ballast Using Multi-laminate Framework' in Challenges and Innovations in Geomechanics, Springer, Switzerland, pp. 474-481.
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Coarse granular materials such as railway ballast used in the shallow layers of railway tracks are often subjected to moving loads, which cause complex stress conditions involving the rotation of principal stress axes. Further, these materials are involved with particle breakage, which is also affected by the stress changes in the track. Computer modelling incorporating constitutive relationships is an effective technique for assessing the deformation behaviour of coarse granular materials under such conditions. This paper presents a constitutive model for coarse granular materials in multilaminate framework, considering classical plasticity and critical state concepts. A criterion for particle breakage in multi-laminate framework and its effect on the stress-strain behaviour has been incorporated using constitutive relationships in multilaminate framework. The influence of minor principal stress and principal stress rotation on the stress-strain, volumetric strain and particle breakage of these materials have been discussed.

Martin, R, Oberst, S & Stender, M 2021, 'Numerical analysis of dynamic hysteresis in tape springs for space applications' in Vibration Engineering for a Sustainable Future, Numerical and Analytical Methods to Study Dynamical Systems, Springer International Publishing, Switzerland.
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Antennas for small satellites are often simple tape springs, which have great advantages in terms of cost and space- and energy consumption. However, they are liable to unwanted vibrations, which can jeopardize a satellites' mission. Vibrations of thin elastic structures in space lead to energy dissipation, which is able to change the attitude behavior of a satellite in orbit. The present study investigates the dynamical system of a metal tape spring using a nite element model based on Oberst et al. (2018) and time domain simulations. Mesh convergence studies for element and method selection have been conducted. It is found that linear shell elements S4 provide good enough results for the dynamic analysis. The Hilber- Hughes-Taylor implementation and ABAQUS/Implicit are employed. In a previous study of Oberst and Tuttle (2018) hystereses in the system's response during an excitation amplitude sweep have been experimentally observed, which are repeated here for the numerical model. Similar to the experimental study, hysteresis curves can be generated (Oberst and Tuttle, 2018). Whether the dynamics as well as the static hysteresis curves in opposite and equal sense bending direction obtained from the numerical model are similar to those determined experimentally needs to be studied in the near future.

Meena, NK, Nimbalkar, S & Fatahi, B 2021, 'Finite Element Analysis of Soil Arching in Piled Embankment' in Challenges and Innovationsin Geomechanics, Springer, Switzerland, pp. 817-824.
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Rigid piles are used to alleviate the detrimental characteristics of soft soil. Recently, the use of piled embankments has increased by many folds, as it facilitates rapid construction without compromising on serviceability. In the piled embankment, soil arching mechanism between adjacent piles improves the load-transfer to the piles and reduces the stress applied to the soft soil. In this study, a two-dimensional plane strain finite element model is adopted to investigate the mechanism of soil arching in a piled embankment. An idealized unit cell model is used to simulate the pile-supported embankment. The effect of different characteristics of piles and embankment soil are assessed. The outcome shows that friction angle, and embankment modulus significantly affects soil arching. Inconsistency among existing design approaches in the literature is highlighted.

Ngo, T, Indraratna, B & Rujikiatkamjorn, C 2021, 'Application of Recycled Rubber Mats for Improved Performance of Ballasted Tracks' in Advances in Geotechnical Engineering & Geoenvironmental Engineering, Springer International Publishing, pp. 37-47.
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Ngo, T, Indraratna, B & Rujikiatkamjorn, C 2021, 'Combined Discrete-Continuum Analysis for Ballasted Rail Tracks', Springer, Switzerland, pp. 266-273.
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A study on the load-deformation behaviour of railway ballast aggregates subjected to cyclic loadings using a combined discrete-continuum modelling approach is presented. Discrete ballast particles are simulated in the DEM and the continuum-based subgrade is simulated by the FDM. Interface elements are generated to transmit contact forces and displacements between the two domains (i.e. discrete and continuum) whereby the DEM exchanges contact forces to the FDM, and then the FDM transfers the displacement back to the DEM. Distributions of contact forces, coordination number, stress contours on the subgrade and corresponding number of broken bonds (representing ballast breakage) are analysed.

Nguyen, TT & Indraratna, B 2021, 'Fluidization of Subgrade Soil Under Rail Tracks Through CFD-DEM Coupling', pp. 274-281.
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The increasing excess pore pressure under rail tracks especially in coastal soft soil regions has been indicated as one of the major reasons causing numerous track issues such as mud pumping. When the excess pore pressure develops to a certain degree, it causes considerable increase in hydraulic gradient associated with hydraulic forces over the soil depth that results in upward migration of fines, i.e., mud pumping followed by severe loss of soil mass and excessive deformation of soil matrix. This issue is simulated in this paper by using the computational fluid dynamics (CFD) coupled with the discrete element method (DEM). Subgrade soil is built in DEM with respect to realistic soil profiles while an increasing hydraulic gradient is generated to model upward fluid flows in CFD. The study indicates soil fluidization develops from localized scale via soil piping to overall scale where all particles become suspended and migrate upwards (i.e., heave formation). The overall fluidization results in a severe degradation in the contact network of soil particles (i.e., soil fabric) and effective stress. The study also shows reasonable predictions of the CFD-DEM coupling on the hydraulic response of subgrade soils.

Omar, KR, Fatahi, B & Nguyen, LD 2021, 'Investigation on the Mechanical Properties of Low Plasticity Clay Contaminated with Engine Oil' in Infrastructure Sustainability Through New Developments in Material, Design, Construction, Maintenance, and Testing of Pavements, Springer International Publishing, pp. 21-32.
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Punetha, P & Nimbalkar, S 2021, 'Performance Improvement of Ballasted Railway Tracks for High-Speed Rail Operations' in Challenges and Innovations in Geomechanics, Springer International Publishing, pp. 841-849.
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Punetha, P & Nimbalkar, S 2021, 'Prediction of Extra Confinement Offered by Cellular Inclusion Under Three-Dimensional Stress State' in Challenges and Innovations in Geomechanics, Springer International Publishing, pp. 850-858.
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Rasouli, H & Fatahi, B 2021, 'Effect of Burial Depth on Pipeline-Fault Rupture Interaction Mechanism and Mitigation Technique Using Geofoam Blocks' in Barla, M, Di Donna, A & Sterpi, D (eds), Challenges and Innovations in Geomechanics, Springer, Switzerland, pp. 966-974.
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This paper presents the effect of burial depth on the response of conventional buried pipelines under strike-slip fault rupture and also proposes a mitigation method using geofoam blocks to safeguard buried pipelines. The performance of the buried pipelines is assessed using three-dimensional numerical simulation using ABAQUS. The configuration of geofoam blocks for protection consists of two blocks at each side of the pipeline and one block on the top of pipeline. The results shows that although the conventional buried pipeline failed due to fault rupture as a result of excessive compressive strain as well as local buckling within the pipe wall, geofoam blocks could successfully reduce the compressive and tensile strains along the pipeline satisfying the design requirements.

Rasouli, H & Fatahi, B 2021, 'Effect of Strike-Slip Fault Rupture on Piled Raft Foundation' in Barla, M, Di Donna, A & Sterpi, D (eds), Challenges and Innovations in Geomechanics, Springer, Switzerland, pp. 653-660.
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This paper presents the interaction mechanism of a 12-story building sitting on a piled raft foundation with a strike-slip fault rupture. The mechanical response of foundation including both structural and geotechnical response of the foundation are evaluated through three-dimensional numerical modelling using ABAQUS. The obtained results showed that the raft significantly suffers from rotation about the vertical axpipelinis and horizontal displacement. Both bending moment and shear forces in piles due to fault rupture exceeded the capacity values of piles. The maximum bending moment and shear forces within piles took place at the connection of piles to the raft and exceeded allowable values when the fault slipped more than 0.3 m.

Salik, T & Gandomi, AH 2021, 'Improvement of shear strength of cohesive soils by additives: A review' in Basics of Computational Geophysics, Elsevier, pp. 189-211.
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Shan, F, Teng, J, Yan, X, Zhang, S & Sheng, D 2021, 'Numerical Modeling of Water-Vapor Migration and Phase Transformation in Unsaturated Freezing Soils' in Challenges and Innovations in Geomechanics, Springer, Switzerland, pp. 925-931.
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Because of ignoring the vapor migration in freezing soils, coarse-grained soils have long been deemed not susceptible to frost heave. However, recent studies reveal that vapor migration can increase the total water content and lead to remarkable frost heave hazards in coarse-grained soils. In this paper, a new numerical model is developed based on the coupled thermal and hydrological processes. The soil water characteristic curve and the soil freezing characteristic curve are taken into account in this model. The model is established by using COMSOL Multiphysics which contains 5 equations and 5 variables. In order to validate the numerical model, laboratory experiments were performed in coarse-grained soils. The result shows that there are sharp increases in water content at the top of samples and the freezing front. A good match between measured and computed results indicates that the new model can make a good explanation for the coupled movement of heat and moisture in coarse-grained soils.

Shdifat, B, Cetindamar Kozanoglu, D & Erfani, S 2021, 'Achieving the Triple Bottom Line through Big Data Analytics' in Park, SH, Gonzalez-Perez, MA & Floriani, DE (eds), The Palgrave Handbook of Corporate Sustainability in the Digital Era, Palgrave Macmillan, Switzerland, pp. 631-649.
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Sustaining growth and maximizing profitability over the long term are the main goals for businesses to survive in today’s’ competitive markets. However, the current sustainability agenda is pushing firms to extend their focus beyond traditional economic goals to include environmental and social goals. Achieving sustainability outcomes is based on the ability of a firm to deal with the conflicting relationships between the triple bottom line (TBL) pillars (economic, social, and environmental performance). While the rapid evolution of big data technologies has provided different opportunities for organizations, such as improving economic, social, and environmental performance, there is relatively little research on the managerial and academic understanding of how employing big data analytics could help to establish sustainable development outcomes based on the TBL approach. This book focuses on finding ways of improving corporate sustainability in the digital era. Hence, we hope that this chapter might contribute to this investigation by pointing out how big data analytics could be utilized to achieve all three dimensions of TBL sustainability.

Thalhammer, S, Patten, T & Vincze, M 2021, 'Usability Study of Learning-Based Pose Estimation of Industrial Objects from Synthetic Depth Data' in Ratchev, S (ed), Smart Technologies for Precision Assembly, Springer, Switzerland, pp. 285-296.
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For visual assistance systems deployed in an industrial setting, precise object pose estimation is an important task in order to support scene understanding and to enable subsequent grasping and manipulation. Industrial environments are especially challenging since mesh-models are usually available while physical objects are not or are expensive to model. Manufactured objects are often similar in appearance, have limited to no textural cues and exhibit symmetries. Thus, these are especially challenging for recognizers that are meant to provide detection, classification and pose estimation on instance level. A usability study of a recent synthetically trained learning-based recognizer for these particular challenges is conducted. Experiments are performed on the challenging T-LESS dataset due to its relevance for industry.

Yang, S, Yu, K, Lammers, T & Chen, F 2021, 'Artificial Intelligence in Pilot Training and Education – Towards a Machine Learning Aided Instructor Assistant for Flight Simulators' in HCI International 2021 - Posters, Springer International Publishing, pp. 581-587.
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Zhang, Z, Oberst, S & Lai, JCS 2021, 'Towards Overcoming the Challenges of the Prediction of Brake Squeal Propensity' in Vibration Engineering for a Sustainable Future, Active and Passive Noise and Vibration Control, Vol. 1, Springer International Publishing, Switzerland.
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Despite significant efforts made in the last two decades for the analysis and prediction of brake squeal propensity, brake squeal remains a major source of customer dissatisfaction and warranty-related costs. Brake squeal is a fugitive nonlinear self excitation phenomenon induced by friction. Traditional linear complex eigenvalue analysis (CEA) combined with noise dynamometer tests has achieved some success in the analysis of brake squeal and in designing countermeasures in suppressing brake squeal. However, prediction of brake squeal propensity without experimental testings is generally unreliable because of three main challenges. Firstly, most conventional analysis methods used are linear but brake squeal could be caused by nonlinearities. Secondly, while non-linear time-domain simulations have achieved some success, they require substantial high-performance computing resources and are too time consuming for practical applications. Thirdly, even if the second challenge can be overcome, there are many determining and interacting factors that are not known exactly such as material properties of pad and disc, operating conditions (brake pad pressure, temperature, speed), contact conditions between pad and disc, non-linear boundary conditions and modelling of friction. In this paper, examples are given to illustrate the success and limitations of the CEA. To address difficulties in predicting brakes squeal posed by nonlinearity, uncertain contact conditions and friction modelling, examples will be given to illustrate how a stochastic approach using the traditional linear eigenvalue analysis could be used to improve the prediction of brake squeal by identifying unstable vibration modes that are not too sensitive to these uncertainties.

Abdo, P & Huynh, BP 2021, 'An experimental investigation of green wall bio-filter towards air temperature and humidity variation', Journal of Building Engineering, vol. 39.
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Green walls show promise in providing thermal comfort. Their benefits include the reduction of the temperature of air layers around them. They are classified as passive and active systems. Active systems are designed with ventilators that force air through the substrate and plant rooting system of the green wall. With a passive system, air is simply diffused through the green wall substrate and the plant foliage. The current work investigates the effect of green walls on the air temperature and humidity. Temperature and humidity are measured at different locations inside an acrylic chamber where different modules with different plant species are placed. The effect of changing the surrounding ambient conditions is also investigated. Experiments lasted at least 24 h to cover day and night time conditions. For the active modules, lower temperatures in the range of 1–3 °C, along with increased humidity levels have been observed when modules are saturated wet. Passive modules have also provided lower temperatures in the range of 0.5–2 °C. None of the plant species studied showed any preference, indicating that the moisture content of the substrate plays the major role affecting the temperature and humidity variations.

Abdollahi, A & Pradhan, B 2021, 'Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images', Expert Systems with Applications, vol. 176, pp. 114908-114908.
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Abdollahi, A, Pradhan, B & Alamri, AM 2021, 'An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images', Geocarto International.
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Abdollahi, A, Pradhan, B & Shukla, N 2021, 'Road Extraction from High-Resolution Orthophoto Images Using Convolutional Neural Network', Journal of the Indian Society of Remote Sensing.
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© 2020, Indian Society of Remote Sensing. Abstract: Two of the major applications in geospatial information system (GIS) and remote sensing fields are object detection and man-made feature extraction (e.g., road sections) from high-resolution remote sensing imagery. Extracting roads from high-resolution remotely sensed imagery plays a crucial role in multiple applications, such as navigation, emergency tasks, land cover change detection, and updating GIS maps. This study presents a deep learning technique based on a convolutional neural network (CNN) to classify and extract roads from orthophoto images. We applied the model on five orthophoto images to specify the superiority of the method for road extraction. First, we used principal component analysis and object-based image analysis for pre-processing to not only obtain spectral information but also add spatial and textural information for enhancing the classification accuracy. Then, the obtained results from the previous step were used as input for the CNN model to classify the images into road and non-road parts and trivial opening and closing operation are applied to extract connected road components from the images and remove holes inside the road parts. For the accuracy assessment of the proposed method, we used measurement factors such as precision, recall, F1 score, overall accuracy, and IOU. Achieved results showed that the average percentages of these factors were 91.09%, 95.32%, 93.15%, 94.44%, and 87.21%. The results were also compared with those of other existing methods. The comparison ascertained the reliability and superior performance of the suggested model architecture for extracting road regions from orthophoto images. Graphic Abstract: [Figure not available: see fulltext.]

Abraham, MT, Satyam, N, Reddy, SKP & Pradhan, B 2021, 'Runout modeling and calibration of friction parameters of Kurichermala debris flow, India', Landslides.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Debris flows account for a substantial economy and property loss in Western Ghats of Kerala, India, especially during monsoon seasons. Wayanad district is an active erosion zone in the plateau margins of Western Ghats, and there is a remarkable rise in the number of debris flows since 2018, due to very high-intensity rainfalls in this region. This study comprises geotechnical investigation, runout modeling, and calibration of friction parameters of Kurichermala debris flow, one of the devastating debris flow events that happened in Wayanad, during the 2018 monsoon. The detailed investigation and back analysis of such events are substantial in calibrating the flow parameters for the region. These parameters can be used for predicting the flow paths of possible debris flows and quantitative risk assessment in the future. The geotechnical investigation provided vital information regarding the soil type and shear strength parameters of the debris flow and has helped in understanding the flow behavior. A dynamic numerical model, rapid mass movements (RAMMS), was used for the back analysis of the debris flow, using the shape information of the flow. For precise calibration using statistical comparison, an image processing tool has been developed, to compare the structural similarity of simulated results with the original shape of debris flow. The dry-Coulomb friction coefficient (μ) was calibrated as 0.01 and turbulent friction coefficient (ξ) as 100 m/s2 for the event, using Voellmy-Salm rheology. The shape predicted by the model had a similarity index of 0.626 with the actual shape of debris flow. The results were found to be in accordance with the field and geotechnical observations. Hence, the results can be used to predict the shape of possible debris flows in the study area. The study is the first of its kind for the region and has significant influence in risk assessment for this highly susceptible l...

Abraham, MT, Satyam, N, Rosi, A, Pradhan, B & Segoni, S 2021, 'Usage of antecedent soil moisture for improving the performance of rainfall thresholds for landslide early warning', CATENA, vol. 200.
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Abraham, MT, Satyam, N, Shreyas, N, Pradhan, B, Segoni, S, Maulud, KNA & Alamri, AM 2021, 'Forecasting landslides using SIGMA model: a case study from Idukki, India', Geomatics Natural Hazards and Risk.
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Abu El-Magd, SA, Pradhan, B & Alamri, A 2021, 'Machine learning algorithm for flash flood prediction mapping in Wadi El-Laqeita and surroundings, Central Eastern Desert, Egypt', ARABIAN JOURNAL OF GEOSCIENCES, vol. 14, no. 4.
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Abu Ul Fazal, M, Ferguson, S & Johnston, A 2021, 'Evaluation of Information Comprehension in Concurrent Speech-based Designs', ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, vol. 16, no. 4.
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Abualigah, L, Elaziz, MA, Hussien, AG, Alsalibi, B, Jalali, SMJ & Gandomi, AH 2021, 'Lightning search algorithm: a comprehensive survey', Applied Intelligence.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem. Meta-heuristics have grown the focus of researches in the optimization domain, because of the foundation of decision-making and assessment in addressing various optimization problems. A review of LSA variants is displayed in this paper, such as the basic, binary, modification, hybridization, improved, and others. Moreover, the classes of the LSA’s applications include the benchmark functions, machine learning applications, network applications, engineering applications, and others. Finally, the results of the LSA is compared with other optimization algorithms published in the literature. Presenting a survey and reviewing the LSA applications is the chief aim of this survey paper.

Aburas, MM, Ho, YM, Pradhan, B, Salleh, AH & Alazaiza, MYD 2021, 'Spatio-temporal simulation of future urban growth trends using an integrated CA-Markov model', ARABIAN JOURNAL OF GEOSCIENCES, vol. 14, no. 2.
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Abuzied, SM & Pradhan, B 2021, 'Hydro-geomorphic assessment of erosion intensity and sediment yield initiated debris-flow hazards at Wadi Dahab Watershed, Egypt', Georisk: assessment and management of risk for engineered systems and geohazards.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. This study attempts to assess slope and channel erosion for modelling their implications on debris-flow occurrences in Wadi Dahab Watershed (WDW). Remote sensing and Geographic Information System (GIS) were integrated to appraise erosion rates from a hillslope and channel storage throughout WDW. A mass-wasting database was built initially for modelling hazard zones and validating the final map using a bivariate statistical analysis. An Erosion Hazard Model (EHM) was developed to evaluate the erosion intensity and sediment yield throughout WDW and to prognosticate the hazard zones due to debris-flows. The EHM was developed based on hydrological and geomorphic controls which are responsible for disintegrating bedrocks, delivering detritus downslopes, and accelerating debris through channels. Multi-source datasets, including topographic and geologic maps, climatic, satellite images, aerial photographs, and field-based datasets, were used to derive factors associated with the hydro-geomorphic processes. A spatial prediction of erosion intensity was obtained by the integration of both static and dynamic factors generated hazards in GIS platform. The erosion intensity map classifies WDW relatively to five intensity zones in which the most hazardous zones are distributed in steep sloping terrains and structurally controlled channels covered by metamorphic and clastic rocks. The erosion intensity map was correlated and tested against the debris-flows dataset which was not used during the spatial modelling process. The statistical correlation analysis has confirmed that the debris-flow locations increase exponentially in the high erosion intensity zones. The holistic integration approach provides the promising model for forecasting critical zones prone to erosion intensity and their associated hazards in WDW.

Afsari, M, Shon, HK & Tijing, LD 2021, 'Janus membranes for membrane distillation: Recent advances and challenges', Advances in Colloid and Interface Science.
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Aftab, MU, Oluwasanmi, A, Alharbi, A, Sohaib, O, Nie, X, Qin, Z & Ngo, ST 2021, 'Secure and dynamic access control for the Internet of Things (IoT) based traffic system', PeerJ Computer Science, vol. 7, pp. 1-26.
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Afzal, MU, Lalbakhsh, A & Esselle, KP 2021, 'Method to Enhance Directional Propagation of Circularly Polarized Antennas by Making Near-Electric Field Phase More Uniform', IEEE Transactions on Antennas and Propagation.
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A new approach to significantly increase the uniformity in aperture phase distribution, through time synchronization in near-electric field, of circularly polarized (CP) antennas is presented. The method uses the phase of the CP electric field vectors, obtained through full-wave numerical simulations, and does not rely on any approximation such as ray tracing. The near-field data is post-processed to extract the relative phase difference that exist due to the unsynchronized rotations of the electric field vectors in a plane parallel to the antenna aperture. The phase delay is compensated with a thin time-synchronizing metasurface (TSM) that has a 2D array of time-delay cells. The method is demonstrated with a prototype made of two-port patch antenna, which is fed through a hybrid junction, and a TSM that is placed at one wavelength spacing above the patch. When TSM is used with patch antenna, its uniform phase area increases manyfold thus increasing far-field directivity from 6.8 dBic to 22 dBic.

Ahmad, S, Alnowibet, K, Alqasem, L, Merigo, JM & Zaindin, M 2021, 'Generalized OWA operators for uncertain queuing modeling with application in healthcare', Soft Computing.
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© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature. The weighted averaging operators are one of the popular methods for aggregating information. In recent years, ordered weighted averaging operators (OWA) have attained a great attention by researchers. These OWA operators due to their versatility are very useful to model many real world situations. Several extensions of OWA operators are presented in the literature which can handle a situation with uncertainty. Although many queuing models have been proposed in numerous healthcare studies, the inclusion of OWA operators is still rare. In this research study, we propose a novel method using the uncertain generalized ordered weighted average and illustrate its application to the uncertain queue modeling in a hospital emergency room; where incoming flux of patients and the required level of service for each patient is unknown and uncertain. The model with multilateral decision making process has been described which will provide several alternatives to decision makers to select the best alternative for their challenging situations. The proposed method has resulted an improved performance of the queuing system, increased customer satisfaction as well as a significant reduction in the operational cost. This study will enable decision makers to operate a flexible and cost-effective system in the event of uncertainty, uncontrollable and unpredicted situations.

Ahmadianfar, I, Heidari, AA, Gandomi, AH, Chu, X & Chen, H 2021, 'RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method', Expert Systems with Applications, vol. 181.
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The optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers. Most of these cliché methods mimic animals' searching trends and possess a small contribution to the optimization process itself. Most of these cliché methods suffer from the locally efficient performance, biased verification methods on easy problems, and high similarity between their components' interactions. This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization method based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics. The proposed RUNge Kutta optimizer (RUN) was developed to deal with various types of optimization problems in the future. The RUN utilizes the logic of slope variations computed by the RK method as a promising and logical searching mechanism for global optimization. This search mechanism benefits from two active exploration and exploitation phases for exploring the promising regions in the feature space and constructive movement toward the global best solution. Furthermore, an enhanced solution quality (ESQ) mechanism is employed to avoid the local optimal solutions and increase convergence speed. The RUN algorithm's efficiency was evaluated by comparing with other metaheuristic algorithms in 50 mathematical test functions and four real-world engineering problems. The RUN provided very promising and competitive results, showing superior exploration and exploitation tendencies, fast convergence rate, and local optima avoidance. In optimizing the constrained engineering problems, the metaphor-free RUN demonstrated its suitable performance as well. The authors invite the community for extensive evaluations of this deep-rooted optimizer as a promising tool for real-world optimization. The source codes, supplementary materials, and guidance for the developed method will be publicly available at different hubs at http://imanahmadianfa...

Ahmed, AA, Pradhan, B, Chakraborty, S & Alamri, A 2021, 'Developing vehicular traffic noise prediction model through ensemble machine learning algorithms with GIS', Arabian Journal of Geosciences, vol. 14, no. 16.
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Ahmed, N, Howlader, N, Hoque, MA-A & Pradhan, B 2021, 'Coastal erosion vulnerability assessment along the eastern coast of Bangladesh using geospatial techniques', Ocean & Coastal Management.
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Ahmed, SF, Liu, G, Mofijur, M, Azad, AK, Hazrat, MA & Chu, YM 2021, 'Physical and hybrid modelling techniques for earth-air heat exchangers in reducing building energy consumption: Performance, applications, progress, and challenges', Solar Energy, vol. 216, pp. 274-294.
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© 2021 The Author(s) Noteworthy advancements are seen in developing the earth-air heat exchanger (EAHE) models in the past several decades to reduce building energy consumption. However, it is still an ongoing challenge in selecting and implementing the most suitable and appropriate EAHE modelling technique in buildings based on the climates, performance, and limitations of the techniques. Therefore, this paper aims to review the published research related to the physical, and hybrid EAHE modelling techniques used in buildings, and highlight the prospects, benefits, progress, and challenges of these techniques. This is the first study that comprehensively evidences the prospects and technical challenges caused by unmeasured disturbances, assumptions, or the uncertainties generated in experimental and numerical works of all EAHE modelling techniques. Nevertheless, this study found that hybrid modelling is more effective than physical models for accurate prediction. On the contrary, the hybrid models suffer from high complexity if EAHE operating conditions and all key parameters are considered during the model development. Regarding the generalization capability, the physical models offer improved performance followed by the hybrid models. A minimum number of training data is needed for developing physical models, whereas medium training data is required for the hybrid models. The outcome of this study also provides valuable information regarding the physical and hybrid EAHE modelling techniques to the scientists, researchers, and so on in adopting the most appropriate EAHE modelling technique for their climates.

Akella, A, Singh, AK, Leong, D, Lal, S, Newton, P, Clifton-Bligh, R, McLachlan, CS, Gustin, SM, Maharaj, S, Lees, T, Cao, Z & Lin, C-T 2021, 'Classifying multi-level stress responses from brain cortical EEG in Nurses and Non-health professionals using Machine Learning Auto Encoder', IEEE Journal of Translational Engineering in Health and Medicine.
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Akther, N, Kawabata, Y, Lim, S, Yoshioka, T, Phuntsho, S, Matsuyama, H & Shon, HK 2021, 'Effect of graphene oxide quantum dots on the interfacial polymerization of a thin-film nanocomposite forward osmosis membrane: An experimental and molecular dynamics study', JOURNAL OF MEMBRANE SCIENCE, vol. 630.
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Akther, N, Sanahuja-Embuena, V, Gorecki, R, Phuntsho, S, Helix-Nielsen, C & Shon, HK 2021, 'Employing the synergistic effect between aquaporin nanostructures and graphene oxide for enhanced separation performance of thin-film nanocomposite forward osmosis membranes', DESALINATION, vol. 498.
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Alanazi, F, Gay, V, Alanazi, MN & Alturki, R 2021, 'Modelling Health Process and System Requirements Engineering for Better e-Health Services in Saudi Arabia', International Journal of Advanced Computer Science and Applications, vol. 12, no. 1, pp. 549-559.
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This systematic review aimed to examine the published works on e-health modelling system requirements and suggest one applicable to Saudi Arabia. PRISMA method was adopted to search, screen and select the papers to be included in this review. Google Scholar was used as the search engine to collect relevant works. From an initial 74 works, 20 were selected after all screening procedures as per PRISMA flow diagram. The 20 selected works were discussed under various sections. The review revealed that goal setting is the first step. Using the goals, a model can be created based on which system requirements can be elicited. Different research used different approaches within this broad framework and applied the procedures to varying healthcare contexts. Based on the findings, an attempt has been made to set the goals and elicit the system requirements for a diabetes self-management model for the entire country in Saudi Arabian context. This is a preliminary model which needs to be tested, improved and then implemented.

Alempijevic, A, Vidal-Calleja, T, Falque, R, Quin, P, Toohey, E, Walmsley, B & McPhee, M 2021, 'Lean meat yield estimation using a prototype 3D imaging approach', MEAT SCIENCE, vol. 181.
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Alharbi, AI, Gay, V, Alghamdi, MJ, Alturki, R & Alyamani, HJ 2021, 'Towards an Application Helping to Minimize Medication Error Rate', Mobile Information Systems, vol. 2021.
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Medication errors related to medication administration done by both doctors and nurses can be considered a vital issue around the world. It is believed that systematisation and the introduction of main documents are done manually, which might increase the opportunities to have inaccuracies and errors because of unexpected wrong actions done by medical practitioners. Experts stated that the lack of pharmacological knowledge is one of the key factors, which play an important role in causing such errors. Doctors and nurses may face problems when they move from one unit to another and the medication administration list has changed. However, promoting public health activities and recent AI-enabled applications can provide general information about medication that helps both doctors and nurses administer the right medication. However, such an application can require a lot of time and effort to search and then find a medication. Therefore, this article aims to investigate whether AI-enabled applications can help avoid or at least minimize medication error rates.

Ali, SMN, Sharma, V, Hossain, MJ, Mukhopadhyay, SC & Wang, D 2021, 'Optimized Energy Control Scheme for Electric Drive of EV Powertrain Using Genetic Algorithms', Energies.
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Alibeikloo, M, Khabbaz, H, Fatahi, B & Thu, ML 2021, 'Reliability Assessment for Time-Dependent Behaviour of Soft Soils Considering Cross Correlation between Visco-Plastic Model Parameters', RELIABILITY ENGINEERING & SYSTEM SAFETY, vol. 213.
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Aljarajreh, H, Lu, DD-C, Siwakoti, YP, Aguilera, RP & Tse, CK 2021, 'A Method of Seamless Transitions Between Different Operating Modes for Three-Port DC-DC Converters', IEEE Access.
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Almuntashiri, A, Hosseinzadeh, A, Volpin, F, Ali, SM, Dorji, U, Shon, H & Phuntsho, S 2021, 'Removal of pharmaceuticals from nitrified urine', CHEMOSPHERE, vol. 280.
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Alsalah, A, Holloway, D, Mousavi, M & Lavroff, J 2021, 'Identification of wave impacts and separation of responses using EMD', Mechanical Systems and Signal Processing, vol. 151.
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© 2020 Elsevier Ltd Various marine and off-shore structures experience rare extreme, and frequent substantial wave impacts, and these impacts have significant implications respectively for the ultimate strength and fatigue life of these structures. Likewise, many other structures experience forms of impact among their environmental loads that are critical for their design. One way of exploring these impacts is to study the vibration signal of the structure. However, due to the complexity of the non-stationary signal, the wave impacts on marine and off-shore structures are difficult to analyse. Using signals acquired during trials of a high-speed catamaran as a case study, this paper proposes employing the Empirical Mode Decomposition (EMD) to detect, identify and characterise significant wave impacts (known as ‘slams’), which is shown to be significantly better at classifying events than traditional methods of slam detection. With the application of EMD, the vibration signal is decomposed into many components that may be grouped into three categories: (1) the rigid-body response comprising two parts: the quasi-static response to the underlying wave spectrum (hydrostatic) and rigid-body resonance (hydrodynamic); (2) the elastic structural response (hydroelastic); and (3) local high frequency vibrations and/or noise. It is the second category that responds significantly to wave impact loads. By identifying the gap between the rigid body and structural resonant frequencies, a threshold is established to automate the physically rational separation of this hydroelastic response. Wave impact detection methods are then applied to this separated component, showing 94% true positive classification, compared with 64% for a recently published slam detection method that uses traditional filtering applied to the whole signal. Further, it allows for more targeted subsequent characterisation of the impact response. Thus, this paper concludes that EMD is an effective method to d...

Altaee, A, AlZainati, N, Saleem, H, Zaidi, SJ, Mohsen, M, Hawari, A & millar, G 2021, 'Pressure Retarded Osmosis: advancement, Challenges and Potential', Journal of Water Process Engineering, vol. 40, pp. 1-26.
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An excessive amount of renewable energy could be possibly produced when solutions of dissimilar salinities are combined simultaneously in a semipermeable membrane. The aforestated energy harnessing for transformation into power could be achieved through the pressure retarded osmosis (PRO) process. The PRO system utilizes a semipermeable membrane for separating a low concentration solution from a pressurized-high concentrated solution. This work examines the recent developments and applications of the PRO process and potential energy that could be conceivably harvested from salinity gradient resources in a single-stage and multi-stage PRO processes. One of the existing challenges for this process is finding a commercial membrane that combines characteristics of the forward osmosis membrane (for reducing the phenomenon of concentration polarization) and the reverse osmosis membrane (to withstand high hydraulic pressure). For addressing this challenge, details about the commercial PRO membranes and the innovative laboratory fabricated PRO membranes are introduced. The potential of the PRO process is presented by elucidating salinity gradient resources, the energy of Pretreatment, the process design, PRO-desalination systems, and dual-stage PRO (DSPRO). It is anticipated that this paper can assist in widely understanding the PRO process and thus deliver important data for activating additional research and development.

Altaee, A, Hafiz, MA, Hawari, AH, Alfahel, R & Hassan, MK 2021, 'Comparison of Nanofiltration with Reverse Osmosis in Reclaiming Tertiary Treated Municipal Wastewater for Irrigation Purposes', Membranes, vol. 11, pp. 1-13.
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This study investigates the performance of reverse osmosis (RO) and nanofiltration (NF) for the reclamation of ultra-filtered treated sewage effluent (TSE) for irrigation of food crops. RO and NF technologies were evaluated at different applied pressures, the performance of each technology was evaluated in terms of water flux, recovery rate, specific energy consumption and quality of permeate. It was found that the permeate from the reverse osmosis (RO) process complied with the Food and Agriculture Organization (FAO) standards at applied pressures between 10 bar and 18 bar. At an applied pressure of 20 bar the permeate quality did not comply with irrigation water standards in terms of chloride, sodium and calcium concentration. It was found that the nanofiltration process was not suitable for the reclamation of wastewater as the concentration of chloride, sodium and calcium exceeded the allowable limits at all applied pressures. In the reverse osmosis process, the highest recovery rate was 36% achieved at an applied pressure of 16 bar. The specific energy consumption at this applied pressure was 0.56 kWh/m3. The lowest specific energy of 0.46 kWh/m3 was achieved at an applied pressure of 12 bar with a water recovery rate of 32.7%.

Altaee, A, Samadi Samadiboroujeni, H & Naghshbandi, SA 2021, 'Time Variations of Sediment Floc Size and Density by using Settling Column Data', Civil Engineering Research Journal, vol. 11, no. 3, pp. 1-12.
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Amirgholipour, S, Jia, W, Liu, L, Fan, X, Wang, D & He, X 2021, 'PDANet: Pyramid density-aware attention based network for accurate crowd counting', Neurocomputing, vol. 451, pp. 215-230.
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Crowd counting, i.e., estimating the number of people in crowded areas, has attracted much interest in the research community. Although many attempts have been reported, crowd counting remains an open real-world problem due to the vast density variations and severe occlusion within the interested crowd area. In this paper, we propose a novel Pyramid Density-Aware Attention based network, abbreviated as PDANet, which leverages the attention, pyramid scale feature, and two branch decoder modules for density-aware crowd counting. The PDANet utilizes these modules to extract features of different scales while focusing on the relevant information and suppressing the misleading information. We also address the variation of crowdedness levels among different images with a Density-Aware Decoder (DAD) modules. For this purpose, a classifier is constructed to evaluate the density level of the input features and then passes them to the corresponding high and low density DAD modules. Finally, we generate an overall density map by considering the summation of low and high crowdedness density maps. Meanwhile, we employ different losses aiming to achieve a precise density map for the input scene. Extensive evaluations conducted on the challenging benchmark datasets well demonstrate the superior performance of the proposed PDANet in terms of the accuracy of counting and generated density maps over the well-known state-of-the-art approaches.

Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2021, 'Soil moisture remote sensing using SIW cavity based metamaterial perfect absorber', Scientific Reports.
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Amiri, M, Tofigh, F, Shariati, N, Lipman, J & Abolhasan, M 2021, 'Review on Metamaterial Perfect Absorbers and Their Applications to IoT', IEEE INTERNET OF THINGS JOURNAL, vol. 8, no. 6, pp. 4105-4131.
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Amjadipour, M, Bradford, J, Zebardastan, N, Motta, N & Iacopi, F 2021, 'MoS2/Epitaxial graphene layered electrodes for solid-state supercapacitors', NANOTECHNOLOGY, vol. 32, no. 19.
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An, N, Zhang, H, Zhu, X & Xu, F 2021, 'A Hybrid Approach for the Dynamic Instability Analysis of Single-Layer Latticed Domes with Uncertainties', INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS, vol. 21, no. 06.
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Anjum, M, Voinov, A, Taghikhah, F & Pileggi, SF 2021, 'Discussoo: Towards an intelligent tool for multi-scale participatory modeling', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 140.
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Ansari, M, Jones, B, Zhu, H, Shariati, N & Guo, YJ 2021, 'A Highly Efficient Spherical Luneburg Lens for Low Microwave Frequencies Realized with a Metal-based Artificial Medium', IEEE Transactions on Antennas and Propagation.
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IEEE This paper describes a novel spherical lens antenna constructed of planar layers of light-weight foam with equally spaced conducting inclusions of varying sizes on an orthogonal grid. This construction largely overcomes the problems of weight and cost that have tended to make larger low frequency Luneburg lenses impractical. A penalty for this type of design is that some anisotropy exists in the lens’s dielectric. This effect is examined using both ray tracing techniques and full-wave simulation and it is found that the principal consequence is that the focal length of the lens varies in different directions. Methods for mitigating the effect are proposed. A prototype lens antenna intended for cellular use in the band 3.3 – 3.8 GHz with dual linear slant polarized feeds was designed and constructed to confirm the findings. Measured results show a peak gain of 23 dBi which is less than 1 dB lower than the maximum possible directivity from the lens’s cross section area. Scanning loss is less than 0.8 dB over the whole sphere. Simulated and measured performance show excellent agreement over the whole sphere. The overall performance of the prototype lens antenna demonstrates that this type of lens should be very suitable for use in high-gain multibeam antennas at lower microwave frequencies.

Anwar, MJ, Gill, AQ, Hussain, FK & Imran, M 2021, 'Secure big data ecosystem architecture: challenges and solutions', EURASIP Journal on Wireless Communications and Networking.
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Arabameri, A, Chandra Pal, S, Costache, R, Saha, A, Rezaie, F, Seyed Danesh, A, Pradhan, B, Lee, S & Hoang, N-D 2021, 'Perdition of gully erosion susceptibility mapping using novel ensemble machine learning algorithms', Geomatics Natural Hazards and Risk.
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Arachchige, CMK, Indraratna, B, Qi, Y, Vinod, J & Rujikiatkamjorn, C 2021, 'Geotechnical characteristics of a Rubber Intermixed Ballast System', Acta Geotechnica.

Argha, A, Celler, BG & Lovell, NH 2021, 'A Novel Automated Blood Pressure Estimation Algorithm Using Sequences of Korotkoff Sounds', IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, vol. 25, no. 4, pp. 1257-1264.
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Asad, M, Yang, J, Tu, E, Chen, L & He, X 2021, 'Anomaly3D: Video anomaly detection based on 3D-normality clusters', Journal of Visual Communication and Image Representation, vol. 75.
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Abnormal behavior detection in surveillance videos is necessary for public monitoring and safety. In human-based surveillance systems, it requires continuous human attention and observation, which is a difficult task. The autonomous detection of such events is of essential significance. However, due to the scarcity of labeled data and the low occurrence probability of these events, abnormal event detection is a challenging vision problem. In this paper, we introduce a novel two-stage architecture for detecting anomalous behavior in videos. In the first stage, we propose a 3D Convolutional Autoencoder (3D-CAE) architecture to extract spatio-temporal features from normal event training videos. In 3D-CAE, the encoder and decoder architectures are based on 3D convolutions, which can learn both appearance and the motion features effectively in an unsupervised manner. In the second stage, we group the 3D spatio-temporal features into different normality clusters, and then remove the sparse clusters to represent a stronger pattern of normality. From these clusters, one-class SVM classifier is used to distinguish between normal and abnormal events based on the normality scores. Experimental results on four different benchmark datasets show significant performance improvement compared to state-of-the-art approaches while providing results in real-time.

Augustine, R, Dan, P, Hasan, A, Khalaf, IM, Prasad, P, Ghosal, K, Gentile, C, McClements, L & Maureira, P 2021, 'Stem cell-based approaches in cardiac tissue engineering: controlling the microenvironment for autologous cells', Biomedicine & Pharmacotherapy.
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Azeez, OS, Pradhan, B & Jena, R 2021, 'Urban tree classification using discrete-return LiDAR and an object-level local binary pattern algorithm', GEOCARTO INTERNATIONAL.
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Urban trees have the potential to mitigate some of the harm brought about by rapid urbanization and population growth, as well as serious environmental degradation (e.g. soil erosion, carbon pollution and species extirpation), in cities. This paper presents a novel urban tree extraction modelling approach that uses discrete laser scanning point clouds and object-based textural analysis to (1) develop a model characterised by four sub-models, including (a) height-based split segmentation, (b) feature extraction, (c) texture analysis and (d) classification, and (2) apply this model to classify urban trees. The canopy height model is integrated with the object-level local binary pattern algorithm (LBP) to achieve high classification accuracy. The results of each sub-model reveal that the classification of urban trees based on the height at 47.14 (high) and 2.12 m (low), respectively, while based on crown widths were highest and lowest at 22.5 and 2.55 m, respectively. Results also indicate that the proposed algorithm of urban tree modelling is effective for practical use.

Azzam, R, Alkendi, Y, Taha, T, Huang, S & Zweiri, Y 2021, 'AS acked LSTM-Based Approach for Reducing Semantic Pose Estimation Error', IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, vol. 70.
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Bagheri, S, Huang, Y, Walker, PD, Zhou, JL & Surawski, NC 2021, 'Strategies for improving the emission performance of hybrid electric vehicles', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 771.
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Balogun, A-L, Yekeen, ST, Pradhan, B & Yusof, KBW 2021, 'Oil spill trajectory modelling and environmental vulnerability mapping using GNOME model and GIS', ENVIRONMENTAL POLLUTION, vol. 268.
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Bano, M, Zowghi, D & Arora, C 2021, 'Requirements, Politics, or Individualism: What Drives the Success of COVID-19 Contact-Tracing Apps?', IEEE Software, vol. 38, no. 1, pp. 7-12.
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Bao, G, Wen, S, Lin, G, Yuan, J, Lin, J, Wong, K-L, Bunzli, J-CG & Jin, D 2021, 'Learning from lanthanide complexes: The development of dye-lanthanide nanoparticles and their biomedical applications', COORDINATION CHEMISTRY REVIEWS, vol. 429.
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Bao, T, Damtie, MM, Wei, W, Vo, HNP, Nguyen, KH, Hosseinzadeh, A, Cho, K, Yu, ZM, Jin, J, Wei, XL, Wu, K, Frost, RL & Ni, B-J 2021, 'Simultaneous adsorption and degradation of bisphenol A on magnetic illite clay composite: Eco-friendly preparation, characterizations, and catalytic mechanism', JOURNAL OF CLEANER PRODUCTION, vol. 287.
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Baral, P, Indraratna, B, Rujikiatkamjorn, C, Kelly, R & Vincent, P 2021, 'Consolidation by Vertical Drains beneath a Circular Embankment under Surcharge and Vacuum Preloading', Journal of Geotechnical and Geoenvironmental Engineering, vol. 147, no. 8.
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A membrane-type vacuum consolidation system, including surcharge loading and prefabricated vertical drains, was applied to rapidly consolidate soft clay beneath a circular embankment located at the National Field Testing Facility (NFTF) at Ballina, New South Wales (NSW), Australia. Most previous studies were devoted to multidrain systems corresponding to an embankment strip loading in two-dimensional (2D) plane strain. So far, no case study has been investigated using vacuum consolidation via prefabricated vertical drains (PVDs) beneath a circular loaded area, where the system conforms to an axisymmetric problem. This paper outlines the site investigation, construction technique, and installation of a suite of instrumentation on this circular embankment. It also describes and discusses consolidation during and after the construction of this embankment in terms of settlement, excess pore water pressure, lateral deformation, and water flow relationships as they pertain to prediction embankment with vertical drains and surcharge only. The case study demonstrates that a loss of vacuum pressure can be prevented using the proposed approach in a membrane system. Treatment of water extracted using the vacuum consolidation technique, especially in acid-sulfate terrain, is also presented.

Barbieri, DM, Lou, B, Passavanti, M, Hui, C, Hoff, I, Lessa, DA, Sikka, G, Chang, K, Gupta, A, Fang, K, Banerjee, A, Maharaj, B, Lam, L, Ghasemi, N, Naik, B, Wang, F, Foroutan Mirhosseini, A, Naseri, S, Liu, Z, Qiao, Y, Tucker, A, Wijayaratna, K, Peprah, P, Adomako, S, Yu, L, Goswami, S, Chen, H, Shu, B, Hessami, A, Abbas, M, Agarwal, N & Rashidi, TH 2021, 'Impact of COVID-19 pandemic on mobility in ten countries and associated perceived risk for all transport modes', PLOS ONE.
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Barzegarkhoo, R, Lee, SS, Khan, SA, Siwakoti, Y & Lu, DDC 2021, 'A Novel Generalized Common-Ground Switched-Capacitor Multilevel Inverter Suitable for Transformerless Grid-Connected Applications', IEEE Transactions on Power Electronics.
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Recent research on Common-Ground Switched-Capacitor Transformerless (CGSC-TL) inverters shows some intriguing features such as integrated voltage boosting ability, possible multilevel output voltage generation and nullification of the leakage current issue. However, the number of output voltage levels and also the overall voltage boosting ratio of the existed CGSC-TL inverters are limited to five and two, respectively. This paper presents a generalized circuit configuration of such converters capable of higher voltage gain and output voltage levels generation. A basic five-level (5L) CGSC-TL inverter is first proposed using eight power switches and two self-balanced DC-link capacitors. A generalized extension of the circuit for any output voltage levels and voltage gain is then presented, whilst keeping all the traits of the proposed basic 5L-CGSC-TL inverter. The circuit descriptions, control strategy, design guidelines, comparative study, and the relevant simulation and experimental results for the proposed 5L-CGSC-TL inverters and its seven-level derived topology are presented to validate the effectiveness and feasibility of this proposal.

Barzegarkhoo, R, Lee, SS, Siwakoti, YP, Khan, SA & Blaabjerg, F 2021, 'Design, Control, and Analysis of a Novel Grid-Interfaced Switched-Boost Dual T-Type Five-Level Inverter With Common-Ground Concept.', IEEE Trans. Ind. Electron., vol. 68, pp. 8193-8206.
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Barzegarkhoo, R, Mojallali, H, Shahalami, SH & Siwakoti, YP 2021, 'A novel common‐ground switched‐capacitor five‐level inverter with adaptive hysteresis current control for grid‐connected applications', IET Power Electronics.
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Barzegarkhoo, R, Siwakoti, YP, Vosoughi, N & Blaabjerg, F 2021, 'Six-Switch Step-Up Common-Grounded Five-Level Inverter With Switched-Capacitor Cell for Transformerless Grid-Tied PV Applications', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 68, no. 2, pp. 1374-1387.
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Basaglia, BM, Li, J, Shrestha, R & Crews, K 2021, 'Response Prediction to Walking-Induced Vibrations of a Long-Span Timber Floor', JOURNAL OF STRUCTURAL ENGINEERING, vol. 147, no. 2.
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Begum, M, Eskandari, M, Abuhilaleh, M, Li, L & Zhu, J 2021, 'Fuzzy-Based Distributed Cooperative Secondary Control with Stability Analysis for Microgrids', ELECTRONICS, vol. 10, no. 4.
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Behera, A, Panigrahi, TK, Pati, SS, Ghatak, S, Ramasubbareddy, S & Gandomi, AH 2021, 'A hybrid evolutionary algorithm for stability analysis of 2-area multi-non-conventional system with communication delay and energy storage', INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, vol. 130.
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Behmanesh, R, Rahimi, I & Gandomi, AH 2021, 'Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study', Archives of Computational Methods in Engineering, vol. 28, pp. 678-688.
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© 2020, CIMNE, Barcelona, Spain. Many optimization problems encountered in the real-world have more than two objectives. To address such optimization problems, a number of evolutionary many-objective optimization algorithms were developed recently. In this paper, we tested 18 evolutionary many-objective algorithms against well-known combinatorial optimization problems, including knapsack problem (MOKP), traveling salesman problem (MOTSP), and quadratic assignment problem (mQAP), all up to 10 objectives. Results show that some of the dominance and reference-based algorithms such as non-dominated sort genetic algorithm (NSGA-III), strength Pareto-based evolutionary algorithm based on reference direction (SPEA/R), and Grid-based evolutionary algorithm (GrEA) are promising algorithms to tackle MOKP and MOTSP with 5 and 10 while increasing the number of objectives. Also, the dominance-based algorithms such as MaOEA-DDFC as well as the indicator-based algorithms such as HypE are promising to solve mQAP with 5 and 10 objectives. In contrast, decomposition based algorithms present the best on almost problems at saving time. For example, t-DEA displayed superior performance on MOTSP for up to 10 objectives.

Bera, B, Saha, S, Das, AK & Vasilakos, AV 2021, 'Designing Blockchain-Based Access Control Protocol in IoT-Enabled Smart-Grid System', IEEE INTERNET OF THINGS JOURNAL, vol. 8, no. 7, pp. 5744-5761.
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Bhuiyan, RA, Tarek, S & Tian, H 2021, 'Enhanced bag-of-words representation for human activity recognition using mobile sensor data', SIGNAL IMAGE AND VIDEO PROCESSING.
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Bishop, AN & Moral, PD 2021, 'An Explicit Floquet-Type Representation of Riccati Aperiodic Exponential Semigroups', International Journal of Control.
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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 stabilising 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.

Boroon, L, Abedin, B & Erfani, E 2021, 'The Dark Side of Using Online Social Networks: A Review of Individuals' Negative Experiences', Journal of Global Information Management.
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Brzozowska, MM, Tran, T, Bliuc, D, Jorgensen, J, Talbot, M, Fenton-Lee, D, Chen, W, Hong, A, Viardot, A, White, CP, Nguyen, TV, Pocock, N, Eisman, JA, Baldock, PA & Center, JR 2021, 'Roux-en-Y gastric bypass and gastric sleeve surgery result in long term bone loss.', International Journal of Obesity, vol. 45, no. 1, pp. 235-246.
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Objectives

Little is known about the long-term skeletal impact of bariatric procedures, particularly the increasingly commonly performed gastric sleeve surgery (GS). We examined bone density (BMD) change following three types of bariatric surgery Roux-en-Y gastric bypass (RYGB), GS and laparoscopic adjustable gastric banding (LAGB), compared with diet, over 36 months.

Methods

Non-randomized, prospective study of participants with severe obesity (n = 52), undergoing weight-loss interventions: RYGB (n = 7), GS (n = 21), LAGB (n = 11) and diet (n = 13). Measurements of calciotropic indices, gut hormones (fasting and post prandial) peptide YY (PYY), glucagon-like peptide 1 (GLP1) and adiponectin together with dual-X-ray absorptiometry and quantitative computed tomography scans were performed thorough the study.

Results

All groups lost weight during the first 12 months. Despite weight stability from 12 to 36 months and supplementation of calcium and vitamin D, there was progressive bone loss at the total hip (TH) over 36 months in RYGB -14% (95% CI: -12, -17) and GS -9% (95% CI: -7, -10). In RYGB forearm BMD also declined over 36 months -9% (95% CI: -6, -12) and LS BMD declined over the first 12 months -7% (95% CI: -3, -12). RYGB and GS groups experienced significantly greater bone loss until 36 months than LAGB and diet groups, which experienced no significant BMD loss. These bone losses remained significant after adjustment for weight loss and age. RYGB and GS procedures resulted in elevated postprandial PYY, adiponectin and bone turnover markers up to 36 months without such changes among LAGB and diet participants.

Conclusions

RYGB and GS but not LAGB resulted in ongoing TH bone loss for three postoperative years. For RYGB, bone loss was also observed at LS and non-weight-bearing forearms. These BMD changes were independent of weight and age differences. We, therefore, recommend close monitoring of bone health following RYGB and GS surg...

Bui, XT, Fujioka, T & Nghiem, LD 2021, 'Green Technologies for Sustainable Water (GTSW)', Environmental Technology and Innovation.
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Cao, X & Tsang, IW 2021, 'Shattering Distribution for Active Learning.', IEEE transactions on neural networks and learning systems, vol. PP.
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Active learning (AL) aims to maximize the learning performance of the current hypothesis by drawing as few labels as possible from an input distribution. Generally, most existing AL algorithms prune the hypothesis set via querying labels of unlabeled samples and could be deemed as a hypothesis-pruning strategy. However, this process critically depends on the initial hypothesis and its subsequent updates. This article presents a distribution-shattering strategy without an estimation of hypotheses by shattering the number density of the input distribution. For any hypothesis class, we halve the number density of an input distribution to obtain a shattered distribution, which characterizes any hypothesis with a lower bound on VC dimension. Our analysis shows that sampling in a shattered distribution reduces label complexity and error disagreement. With this paradigm guarantee, in an input distribution, a Shattered Distribution-based AL (SDAL) algorithm is derived to continuously split the shattered distribution into a number of representative samples. An empirical evaluation of benchmark data sets further verifies the effectiveness of the halving and querying abilities of SDAL in real-world AL tasks with limited labels. Experiments on active querying with adversarial examples and noisy labels further verify our theoretical insights on the performance disagreement of the hypothesis-pruning and distribution-shattering strategies. Our code is available at https://github.com/XiaofengCao-MachineLearning/Shattering-Distribution-for-Active-Learning.

Cao, X, Tsang, IW & Xu, J 2021, 'Cold-Start Active Sampling via ɣ-Tube', IEEE Transactions on Cybernetics.
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Active learning (AL) improves the generalization performance for the current classification hypothesis by querying labels from a pool of unlabeled data. The sampling process is typically assessed by an informative, representative, or diverse evaluation policy. However, the policy, which needs an initial labeled set to start, may degenerate its performance in a cold-start hypothesis. In this article, we first show that typical AL sampling can be equivalently formulated as geometric sampling over minimum enclosing balls1 (MEBs) of clusters. Following the ɣ-tube structure in geometric clustering, we then divide one MEB covering a cluster into two parts: 1) a ɣ-tube and 2) a ɣ-ball. By estimating the error disagreement between sampling in MEB and ɣ-ball, our theoretical insight reveals that ɣ-tube can effectively measure the disagreement of hypotheses in original space over MEB and sampling space over ɣ-ball. To tighten our insight, we present generalization analysis, and the results show that sampling in ɣ-tube can derive higher probability bound to achieve a nearly zero generalization error. With these analyses, we finally apply the informative sampling policy of AL over ɣ-tube to present a tube AL (TAL) algorithm against the cold-start sampling issue. As a result, the dependency between the querying process and the evaluation policy of active sampling can be alleviated. Experimental results show that by using the ɣ-tube structure to deal with cold-start sampling, TAL achieves the superior performance than standard AL evaluation baselines by presenting substantial accuracy improvements. Image edge recognition extends our theoretical results.

Cao, Y, Lv, T, Lin, Z & Ni, W 2021, 'Delay-Constrained Joint Power Control, User Detection and Passive Beamforming in Intelligent Reflecting Surface-Assisted Uplink mmWave System', IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, vol. 7, no. 2, pp. 482-495.
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Cetindamar Kozanoglu, D, Lammers, T, Kocaoglu, D & Zhang, Y 2021, 'The Anniversary Tribute of PICMET: 1989-2018', IEEE Transactions on Engineering Management, vol. 68, no. 2, pp. 612-627.
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The Portland International Conference for Management of Engineering and Technology (PICMET) has become a world-leading organization in the field of management of engineering and technology management (MET) since its inception in 1989. PICMET provides a strong platform for academics, industry professionals, and government representatives to exchange new knowledge in the field. To celebrate its 30-year journey, this article examines 20 conferences organized by PICMET covering 6601 accepted papers in order to show the trends in MET research and implementation through topics, authors, journals, and countries. In addition to the analysis of the PICMET data, the article delves into the past ten years (2009–2018) to carry out an in-depth bibliometric analysis of the citations of more than 3000 PICMET papers available at Scopus. The detailed analysis sheds light on how PICMET has developed a rich network of researchers and practitioners through its conferences over time. PICMET contributes to the interdisciplinary nature of the MET field and is also affected by the changes of the field. The article ends with key observations and a few suggestions for further studies.

Cetindamar Kozanoglu, D, Shdifat, B & Erfani, S 2021, 'Understanding Big Data Analytics Capability and Sustainable Supply Chains', Information Systems Management, pp. 1-13.
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This paper presents the knowledge available in the literature regarding big data analytics capability (BDAC) and sustainable supply chain performance (SSCP). A detailed analysis of systematic literature reviews points out the lack of studies bridging these two separate streams of work. The paper puts forward a research agenda for researchers interested in understanding the impact of big data on sustainability.

Chai, J & Tsang, IW 2021, 'Learning With Label Proportions by Incorporating Unmarked Data', IEEE Transactions on Neural Networks and Learning Systems.
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Learning with label proportions (LLP) deals with the problem that the training data are provided as bags, where the label proportions of training bags rather than the labels of individual training instances are accessible. Existing LLP studies assume that the label proportions of all training bags are accessible. However, in many applications, it is time-consuming to mark all training bags with label proportions, which leads to the problem of learning with both marked and unmarked bags, namely, semisupervised LLP (SLLP). In this work, we propose semisupervised proportional support vector machine (SS-∝SVM), which extends the proportional SVM (∝SVM) model to its semisupervised version. To the best of our knowledge, SS-∝SVM is the first attempt to cope with the SLLP problem. Two realizations, alter-SS-∝SVM and conv-SS-∝SVM, which are based on alternating optimization and convex relaxation, respectively, are developed to solve the proposed SS-∝SVM model. Moreover, we design a cutting plane (CP) method to optimize conv-SS-∝SVM with a guaranteed convergence rate and present a fast accelerated proximal gradient method to solve the multiple kernel learning subproblem in conv-SS-∝SVM efficiently. Empirical experiments not only justify the superiority of SS-∝SVM over its supervised counterpart in classification accuracy but also demonstrate the high competitive computational efficiency of the CP optimization of conv-SS-∝SVM.

Chakraborty, S, Milner, LE, Zhu, X, Sevimli, O, Parker, AE & Heimlich, MC 2021, 'An Edge-Coupled Marchand Balun with Partial Ground for Excellent Balance in 0.13 μm SiGe Technology', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 1, pp. 226-230.
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© 2004-2012 IEEE. An edge-coupled meandered three-coupled-line Marchand balun with a partial ground plane implemented in 0.13 μ {m} SiGe Bi-CMOS technology is presented in this brief. The balance performance of the designed balun is significantly improved by creating a 'no ground plane' beneath the coupled-line structure, which is demonstrated by simulating two baluns: one with a partial ground and the other with a solid ground underneath. The measured amplitude and phase imbalances are less than 0.4 dB and 2.5°, across the 3-dB bandwidth from 21.5 to 95 GHz, surpassing previously reported results of edge-coupled Marchand baluns. The balun occupies 230 μ \text{m}\,\,×370\,\,μ {m}.

Chan, Y, Mehta, M, Paudel, KR, Madheswaran, T, Panneerselvam, J, Gupta, G, Su, QP, Hansbro, PM, McLoughlin, R, Dua, K & Chellappan, DK 2021, 'Versatility of liquid crystalline nanoparticles in inflammatory lung diseases', NANOMEDICINE, vol. 16, no. 18, pp. 1545-1548.
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Chandran, M, Mitchell, PJ, Amphansap, T, Bhadada, SK, Chadha, M, Chan, D-C, Chung, Y-S, Ebeling, P, Gilchrist, N, Habib Khan, A, Halbout, P, Hew, FL, Lan, H-PT, Lau, TC, Lee, JK, Lekamwasam, S, Lyubomirsky, G, Mercado-Asis, LB, Mithal, A, Nguyen, TV, Pandey, D, Reid, IR, Suzuki, A, Chit, TT, Tiu, KL, Valleenukul, T, Yung, CK & Zhao, YL 2021, 'Development of the Asia Pacific Consortium on Osteoporosis (APCO) Framework: clinical standards of care for the screening, diagnosis, and management of osteoporosis in the Asia-Pacific region', OSTEOPOROSIS INTERNATIONAL, vol. 32, no. 7, pp. 1249-1275.
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Chang, W, Zhang, Q, Fu, C, Liu, W, Zhang, G & Lu, J 2021, 'A cross-domain recommender system through information transfer for medical diagnosis', Decision Support Systems, pp. 113489-113489.
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Cha-umpong, W, Li, Q, Razmjou, A & Chen, V 2021, 'Concentrating brine for lithium recovery using GO composite pervaporation membranes', DESALINATION, vol. 500.
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Chen, C, Liu, B, Liu, Y, Liao, J, Shan, X, Wang, F & Jin, D 2021, 'Heterochromatic Nonlinear Optical Responses in Upconversion Nanoparticles for Super‐Resolution Nanoscopy', Advanced Materials.
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Chen, C-S, Chen, S-K, Lai, C-C & Lin, C-T 2021, 'Sequential Motion Primitives Recognition of Robotic Arm Task via Human Demonstration Using Hierarchical BiLSTM Classifier', IEEE ROBOTICS AND AUTOMATION LETTERS, vol. 6, no. 2, pp. 502-509.
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Chen, J, Nelson, C, Wong, M, Tee, AE, Liu, PY, La, T, Fletcher, JI, Kamili, A, Mayoh, C, Bartenhagen, C, Trahair, TN, Xu, N, Jayatilleke, N, Wong, M, Peng, H, Atmadibrata, B, Cheung, BB, Lan, Q, Bryan, TM, Mestdagh, P, Vandesompele, J, Combaret, V, Boeva, V, Wang, JY, Janoueix-Lerosey, I, Cowley, MJ, MacKenzie, KL, Dolnikov, A, Li, J, Polly, P, Marshall, GM, Reddel, RR, Norris, MD, Haber, M, Fischer, M, Zhang, XD, Pickett, HA & Liu, T 2021, 'Targeted therapy of TERT-rearranged neuroblastoma with BET bromodomain inhibitor and proteasome inhibitor combination therapy', Clinical Cancer Research, vol. 27, no. 5, pp. 1438-1451.
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Purpose: TERT gene rearrangement with transcriptional superenhancers leads to TERT overexpression and neuroblastoma. No targeted therapy is available for clinical trials in patients with TERT-rearranged neuroblastoma. Experimental Design: Anticancer agents exerting the best synergistic anticancer effects with BET bromodomain inhibitors were identified by screening an FDA-approved oncology drug library. The synergistic effects of the BET bromodomain inhibitor OTX015 and the proteasome inhibitor carfilzomib were examined by immunoblot and flow cytometry analysis. The anticancer efficacy of OTX015 and carfilzomib combination therapy was investigated in mice xenografted with TERT-rearranged neuroblastoma cell lines or patient-derived xenograft (PDX) tumor cells, and the role of TERT reduction in the anticancer efficacy was examined through rescue experiments in mice. Results: The BET bromodomain protein BRD4 promoted TERT-rearranged neuroblastoma cell proliferation through upregulating TERT expression. Screening of an approved oncology drug library identified the proteasome inhibitor carfilzomib as the agent exerting the best synergistic anticancer effects with BET bromodomain inhibitors including OTX015. OTX015 and carfilzomib synergistically reduced TERT protein expression, induced endoplasmic reticulum stress, and induced TERT-rearranged neuroblastoma cell apoptosis which was blocked by TERT overexpression and endoplasmic reticulum stress antagonists. In mice xenografted with TERT-rearranged neuroblastoma cell lines or PDX tumor cells, OTX015 and carfilzomib synergistically blocked TERT expression, induced tumor cell apoptosis, suppressed tumor progression, and improved mouse survival, which was largely reversed by forced TERT overexpression. Conclusions: OTX015 and carfilzomib combination therapy is likely to be translated into the first clinical trial of a targeted therapy in patients with TERT-rearranged neuroblastoma.

Chen, J, Wu, D, Zhao, Y, Sharma, N, Blumenstein, M & Yu, S 2021, 'Fooling intrusion detection systems using adversarially autoencoder', Digital Communications and Networks.
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Due to the increasing cyber-attacks, various Intrusion Detection Systems (IDSs) have been proposed to identify network anomalies. Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows, and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows. Although having been used in the real world widely, the above methods are vulnerable to some types of attacks. In this paper, we propose a novel attack framework, Anti-Intrusion Detection AutoEncoder (AIDAE), to generate features to disable the IDS. In the proposed framework, an encoder transforms features into a latent space, and multiple decoders reconstruct the continuous and discrete features, respectively. Additionally, a generative adversarial network is used to learn the flexible prior distribution of the latent space. The correlation between continuous and discrete features can be kept by using the proposed training scheme. Experiments conducted on NSL-KDD, UNSW-NB15, and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.

Chen, J, Wu, Y, Yang, Y, Wen, S, Shi, K, Bermak, A & Huang, T 2021, 'An Efficient Memristor-Based Circuit Implementation of Squeeze-and-Excitation Fully Convolutional Neural Networks', IEEE Transactions on Neural Networks and Learning Systems.
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Recently, there has been a surge of interest in applying memristors to hardware implementations of deep neural networks due to various desirable properties of the memristor, such as nonvolativity, multivalue, and nanosize. Most existing neural network circuit designs, however, are based on generic frameworks that are not optimized for memristors. Furthermore, to the best of our knowledge, there are no existing efficient memristor-based implementations of complex neural network operators, such as deconvolutions and squeeze-and-excitation (SE) blocks, which are critical for achieving high accuracy in common medical image analysis applications, such as semantic segmentation. This article proposes convolution-kernel first (CKF), an efficient scheme for designing memristor-based fully convolutional neural networks (FCNs). Compared with existing neural network circuits, CKF enables effective parameter pruning, which significantly reduces circuit power consumption. Furthermore, CKF includes the novel, memristor-optimized implementations of deconvolution layers and SE blocks. Simulation results on real medical image segmentation tasks confirm that CKF obtains up to 56.2% reduction in terms of computations and 33.62-W reduction in terms of power consumption in the circuit after weight pruning while retaining high accuracy on the test set. Moreover, the pruning results can be applied directly to existing circuits without any modification for the corresponding system.

Chen, L, Liu, H, Hora, J, Zhang, JA, Yeo, KS & Zhu, X 2021, 'A Monolithically Integrated Single-Input Load-Modulated Balanced Amplifier With Enhanced Efficiency at Power Back-Off', IEEE JOURNAL OF SOLID-STATE CIRCUITS, vol. 56, no. 5, pp. 1553-1564.
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Chen, L, Liu, Y, Yang, S & Guo, YJ 2021, 'Efficient Synthesis of Filter-and-Sum Array With Scanned Wideband Frequency-Invariant Beam Pattern and Space-Frequency Notching', IEEE Signal Processing Letters.
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IEEE This work generalizes the Fourier transform (FT)-based frequency-invariant beamforming (FIB) method to the synthesis of scanned frequency-invariant (FI) beam pattern with space-frequency notching for an array with non-isotropic elements. Wideband FI pattern characteristics are described by using multiple reference sub-band FI patterns that are obtained through an iterative single-frequency FT-based synthesis method. By applying fast Fourier transform (FFT) on the combination of these multiple reference sub-band FI patterns, a wideband excitation distribution can be generated. Based on this excitation distribution, we construct a new wideband excitation distribution that is conjugate-symmetric about zero frequency, so that real-valued finite-impulse-response (FIR) filter coefficients can be obtained by applying FFT on the constructed distribution. Two numerical examples are introduced to show the effectiveness and efficiency of the proposed method for synthesizing scanned FI beam patterns with complicated notching requirements.

Chen, M, Malook, T, Rehman, AU, Muhammad, Y, Alshehri, MD, Akbar, A, Bilal, M & Khan, MA 2021, 'Blockchain-Enabled healthcare system for detection of diabetes', JOURNAL OF INFORMATION SECURITY AND APPLICATIONS, vol. 58.
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Chen, QS, Peng, W, Tao, GL & Nimbalkar, S 2021, 'Strength and Deformation Characteristics of Calcareous Sands Improved by PFA', KSCE Journal of Civil Engineering, vol. 25, no. 1, pp. 60-69.
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© 2020, Korean Society of Civil Engineers. Calcareous sand is widely distributed in the islands of the South China Sea, which could be promisingly used as the construction materials. However, particle breakage commonly occurs in calcareous sands, which may significantly influence their mechanical characteristics. To address these issues, an eco-friendly agent, i.e., polyurethane foam adhesive (PFA) is proposed to improve the engineering properties of calcareous sands, compared to the commonly used alkaline stabilizing agents (e.g., lime, cement). The objective of this work is to examine the effectiveness of using PFA in improving the strength-deformation properties of calcareous sand. A series of laboratory tests including direct shear tests, unconfined compression tests, and oedometer tests were performed on the calcareous sands improved by PFA. In addition, A scanning electron microscope (SEM) was conducted to reveal microstructural analysis of using PFA for calcareous sand. The experimental results provided insights into the shear strength, deformation modulus, as well as the micro-structural characteristics of improved calcareous sands with various PFA contents and particle size distributions.

Chen, R, Yin, H, Jiao, Y, Dissanayake, G, Wang, Y & Xiong, R 2021, 'Deep Samplable Observation Model for Global Localization and Kidnapping', IEEE Robotics and Automation Letters.
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Chen, R, Zhu, L, Wong, S, Lin, J, Yang, Y, Li, Y & He, Y 2021, 'Miniaturized full‐metal bandpass filter and multiplexer using circular spiral resonator', IET Microwaves Antennas & Propagation.
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Chen, W-H, Chiu, G-L, Ong, HC, Lam, SS, Lim, S, Ok, YS & Kwon, EE 2021, 'Optimization and analysis of syngas production from methane and CO2 via Taguchi approach, response surface methodology (RSM) and analysis of variance (ANOVA)', FUEL, vol. 296.
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Chen, WH, Du, JT, Lee, KT, Ong, HC, Park, YK & Huang, CC 2021, 'Pore volume upgrade of biochar from spent coffee grounds by sodium bicarbonate during torrefaction', Chemosphere, vol. 275.
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A novel approach for upgrading the pore volume of biochar at low temperatures using a green additive of sodium bicarbonate (NaHCO3) is developed in this study. The biochar was produced from spent coffee grounds (SCGs) torrefied at different temperatures (200–300 °C) with different residence times (30–60 min) and NaHCO3 concentrations (0–8.3 wt%). The results reveal that the total pore volume of biochar increases with rising temperature, residence time, or NaHCO3 aqueous solution concentration, whereas the bulk density has an opposite trend. The specific surface area and total pore volume of pore-forming SCG from 300 °C torrefaction for 60 min with an 8.3 wt% NaHCO3 solution (300-TP-SCG) are 42.050 m2 g−1 and 0.1389 cm3·g−1, accounting for the improvements of 141% and 76%, respectively, compared to the parent SCG. The contact angle (126°) and water activity (0.48 aw) of 300-TP-SCG reveal that it has long storage time. The CO2 uptake capacity of 300-TP-SCG is 0.32 mmol g−1, rendering a 39% improvement relative to 300-TSCG, namely, SCG torrefied at 300 °C for 60 min. 300-TP-SCG has higher HHV (28.31 MJ·kg−1) and lower ignition temperature (252 °C). Overall, it indicates 300-TP-SCG is a potential fuel substitute for coal. This study has successfully produced mesoporous biochar at low temperatures to fulfill “3E”, namely, energy (biofuel), environment (biowaste reuse solid waste), and circular economy (bioadsorbent).

Chen, X, Gao, C, Li, C, Yang, Y & Meng, D 2021, 'Infrared Action Detection in the Dark via Cross-Stream Attention Mechanism', IEEE Transactions on Multimedia.
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Action detection plays an important role in the field of video understanding and attracts considerable attention in the last decade. However, current action detection methods are mainly based on visible videos, and few of them consider scenes with low-light, where actions are difficult to be detected by existing methods, or even by human eyes. Compared with visible videos, infrared videos are more suitable for the dark environment and resistant to background clutter. In this paper, we investigate the temporal action detection problem in the dark by using infrared videos, which is, to the best of our knowledge, the first attempt in the action detection community. Our model takes the whole video as input, a Flow Estimation Network (FEN) is employed to generate the optical flow for infrared data, and it is optimized with the whole network to obtain action-related motion representations. After feature extraction, the infrared stream and flow stream are fed into a Selective Cross-stream Attention (SCA) module to narrow the performance gap between infrared and visible videos. The SCA emphasizes informative snippets and focuses on the more discriminative stream automatically. Then we adopt a snippet-level classifier to obtain action scores for all snippets and link continuous snippets into final detection results. All these modules are trained in an end-to-end manner. We collect an Infrared action Detection (InfDet) dataset obtained in the dark and conduct extensive experiments to verify the effectiveness of the proposed method. Experimental results show that our proposed method surpasses the state-of-the-art temporal action detection methods designed for visible videos, and it also achieves the best performance compared with other infrared action recognition methods on both InfAR and Infrared-Visible datasets.

Chen, X, Huo, P, Liu, J, Li, F, Yang, L, Li, X, Wei, W, Liu, Y & Ni, BJ 2021, 'Model predicted N2O production from membrane-aerated biofilm reactor is greatly affected by biofilm property settings', Chemosphere, vol. 281.
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Even though modeling has been frequently used to understand the autotrophic deammonification-based membrane-aerated biofilm reactor (MABR), the relationships between system-specific biofilm property settings and model predicted N2O production have yet to be clarified. To this end, this study investigated the impacts of 4 key biofilm property settings (i.e., biofilm thickness/compactness, boundary layer thickness, diffusivity of soluble components in the biofilm structure, and biofilm discretization) on one-dimensional modeling of the MABR, with the focus on its N2O production. The results showed that biofilm thickness/compactness (200–1000 μm), diffusivity of soluble components in the biofilm structure (reduction factor of diffusivity: 0.2–0.9), and biofilm discretization (12–28 grid points) significantly influenced the simulated N2O production, while boundary layer thickness (0–300 μm) only played a marginal role. In the studied ranges of biofilm property settings, distinct upper and lower bounds of N2O production factor (i.e., the percentage ratio of N2O formed to NH4+ removed, 5.5% versus 2.3%) could be predicted. In addition to the microbial community structure, the N2O production pathway contribution differentiation was also subject to changes in biofilm property settings. Therefore, biofilm properties need to be quantified experimentally or set properly to model N2O production from the MABR correctly. As a good practice for one-dimensional modeling of N2O production from biofilm-based reactors, especially the MABR performing autotrophic deammonification, the essential information about those influential biofilm property settings identified in this study should be disclosed and clearly documented, thus ensuring both the reproducibility of modeling results and the reliable applications of N2O models.

Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Liu, Y, Deng, L & Chen, Z 2021, 'Evaluation of a continuous flow microbial fuel cell for treating synthetic swine wastewater containing antibiotics.', Science of the Total Environment, vol. 756, pp. 144133-144133.
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Microbial fuel cell (MFC) systems are promising technologies for wastewater treatment and renewable energy generation simultaneously. Performance of a double-chamber microbial fuel cell (MFC) to treat synthetic swine wastewater containing sulfonamide antibiotics (SMs) was evaluated in this study. The MFC was operated in continuous modes at different conditions. Results indicated that the current was successfully generated during the operation. The performance of MFC under the sequential anode-cathode operating mode is better than that under the single continuous running mode. Specifically, higher removal efficiency of chemical oxygen demand (>90%) was achieved under the sequential anode-cathode operating mode in comparison with that in the single continuous mode (>80%). Nutrients were also be removed in the MFC's cathode chamber with the maximum removal efficiency of 66.6 ± 1.4% for NH₄⁺-N and 32.1 ± 2.8% for PO₄^3--P. Meanwhile, SMs were partly removed in the sequential anode-cathode operating with the value in a range of 49.4%-59.4% for sulfamethoxazole, 16.8%-19.5% for sulfamethazine and 14.0%-16.3% for sulfadiazine, respectively. SMs' inhibition to remove other pollutants in both electrodes of MFC was observed after SMs exposure, suggesting that SMs exert toxic effects on the microorganisms. A positive correlation was found between the higher NH₄⁺-N concentration used in this study and the removal efficiency of SMs in the cathode chamber. In short, although the continuous flow MFC is feasible for treating swine wastewater containing antibiotics, its removal efficiency of antibiotics requires to be further improved.

Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Nguyen, QA, Zhang, J & Liang, S 2021, 'Improving sulfonamide antibiotics removal from swine wastewater by supplying a new pomelo peel derived biochar in an anaerobic membrane bioreactor.', Bioresource Technology, vol. 319, pp. 124160-124160.
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Sulfonamide antibiotics (SMs), as a class of antibiotics commonly used in swine industries, pose a serious threat to animal and human health. This study aims to evaluate the performance of an anaerobic membrane bioreactor (AnMBR) with and without supplying a new pomelo peel derived biochar to treat swine wastewater containing SMs. Results show that 0.5 g/L biochar addition could increase more than 30% of sulfadiazine (SDZ) and sulfamethazine (SMZ) removal in AnMBR. Approximately 95% of chemical oxygen demand (COD) was removed in the AnMBR at an influent organic loading rate (OLR) of 3.27 kg COD/(m3·d) while an average methane yield was 0.2 L/g CODremoved with slightly change at a small dose 0.5 g/L biochar addition. SMs inhibited the COD removal and methane production and increased membrane fouling. The addition of biochar could reduce the membrane fouling by reducing the concentration of SMP and EPS.

Cheng, H, Wu, J, Huang, D, Liu, Y & Wang, Q 2021, 'Robust adaptive boosted canonical correlation analysis for quality-relevant process monitoring of wastewater treatment', ISA Transactions.
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Quality-relevant process monitoring has attracted much attention for its ability to assist in maintaining efficient plant operation. However, when the process suffers from non-stationary and over-complex (with noise, multiplicative faults, etc.) characteristics, the traditional methods usually cannot be effectively applied. To this end, a novel method, termed as Robust adaptive boosted canonical correlation analysis (Rab-CCA), is proposed to monitor the wastewater treatment processes. First, a robust decomposition method is proposed to mitigate the defects of standard CCA by decomposing the corrupted matrix into a low-matrix and a sparse matrix. Second, to further improve the performance of the standard process monitoring method, a novel criterion function and control charts are reconstructed accordingly. Moreover, an adaptive statistical control limit is proposed that can adjust the thresholds according to the state of a system and can effectively reduce the missed alarms and false alarms simultaneously. The superiority of Rab-CCA is verified by Benchmark Simulation Model 1 (BSM1) and a real full-scale wastewater treatment plant (WWTP).

Cheng, T, Lu, DD-C & Siwakoti, YP 2021, 'A MOSFET SPICE Model With Integrated Electro-Thermal Averaged Modeling, Aging, and Lifetime Estimation', IEEE ACCESS, vol. 9, pp. 5545-5554.
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Cheng, X, Wang, H, Hua, J, Xu, G & Sui, Y 2021, 'DeepWukong: Statically Detecting Software Vulnerabilities Using Deep Graph Neural Network', ACM Transactions on Software Engineering and Methodology, vol. 30, no. 3.
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Static bug detection has shown its effectiveness in detecting well-defined memory errors, e.g., memory leaks, buffer overflows, and null dereference. However, modern software systems have a wide variety of vulnerabilities. These vulnerabilities are extremely complicated with sophisticated programming logic, and these bugs are often caused by different bad programming practices, challenging existing bug detection solutions. It is hard and labor-intensive to develop precise and efficient static analysis solutions for different types of vulnerabilities, particularly for those that may not have a clear specification as the traditional well-defined vulnerabilities. This article presents DeepWukong, a new deep-learning-based embedding approach to static detection of software vulnerabilities for C/C++ programs. Our approach makes a new attempt by leveraging advanced recent graph neural networks to embed code fragments in a compact and low-dimensional representation, producing a new code representation that preserves high-level programming logic (in the form of control-and data-flows) together with the natural language information of a program. Our evaluation studies the top 10 most common C/C++ vulnerabilities during the past 3 years. We have conducted our experiments using 105,428 real-world programs by comparing our approach with four well-known traditional static vulnerability detectors and three state-of-the-art deep-learning-based approaches. The experimental results demonstrate the effectiveness of our research and have shed light on the promising direction of combining program analysis with deep learning techniques to address the general static code analysis challenges.

Cheng, Z, Ye, D, Zhu, T, Zhou, W & Yu, PS 2021, 'Multi-agent reinforcement learning via knowledge transfer with differentially private noise', INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS.
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Cheung, BB, Kleynhans, A, Mittra, R, Kim, PY, Holien, JK, Nagy, Z, Ciampa, OC, Seneviratne, JA, Mayoh, C, Raipuria, M, Gadde, S, Massudi, H, Wong, IPL, Tan, O, Gong, A, Suryano, A, Diakiw, SM, Liu, B, Arndt, GM, Liu, T, Kumar, N, Sangfelt, O, Zhu, S, Norris, MD, Haber, M, Carter, DR, Parker, MW & Marshall, GM 2021, 'A novel combination therapy targeting ubiquitin-specific protease 5 in MYCN-driven neuroblastoma', Oncogene.
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Chivukula, A, Yang, X, Liu, W, Zhu, T & Zhou, W 2021, 'Game Theoretical Adversarial Deep Learning with Variational Adversaries', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Chowdhury, MA, Shuvho, MBA, Hossain, MI, Ali, MO, Kchaou, M, Rahman, A, Yeasmin, N, Khan, AS, Rahman, MA & Mofijur, M 2021, 'Multiphysical analysis of nanoparticles and their effects on plants.', Biotechnology and applied biochemistry.
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Nanoparticles are the magic bullets and at the leading edge in the field of nanotechnology, and their unique properties make these materials indispensable and superior in many areas, including the electronic field. Extensive applications of nanomaterials are incontrovertibly entering our living system. The increasing use of nanomaterials into the ecosystem is one of the crucial environmental factors that human being is facing. Nanomaterials raise noticeable toxicological concerns; particularly their accumulation in plants and the resultant toxicity may affect the food chain. Here, we analyzed the characterization of nanomaterials, such as graphene, Al₂ O₃ , TiO₂ , and semi-insulating or conducting nanoparticles. Quantitative evaluation of the nanomaterials was conducted and their commercialization aspects were discussed. Various characterization techniques, scanning electron microscopy, X-ray diffraction, and ultraviolet rays were utilized to identify the morphology, phase, absorbance, and crystallinity. In addition, we analyzed the effects of nanomaterials on plants. The toxicity of nanoparticles has severe effects on loss of morphology of the plants. Potential mechanisms including physical and physiological effects were analyzed. In future studies, it is indispensable to assess widely accepted toxicity evaluation for safe production and use of nanomaterials.

Chowdhury, MA, Shuvho, MBA, Shahid, MA, Haque, AKMM, Kashem, MA, Lam, SS, Ong, HC, Uddin, MA & Mofijur, M 2021, 'Prospect of biobased antiviral face mask to limit the coronavirus outbreak.', Environmental Research, vol. 192, pp. 110294-110294.
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The rapid spread of COVID-19 has led to nationwide lockdowns in many countries. The COVID-19 pandemic has played serious havoc on economic activities throughout the world. Researchers are immensely curious about how to give the best protection to people before a vaccine becomes available. The coronavirus spreads principally through saliva droplets. Thus, it would be a great opportunity if the virus spread could be controlled at an early stage. The face mask can limit virus spread from both inside and outside the mask. This is the first study that has endeavoured to explore the design and fabrication of an antiviral face mask using licorice root extract, which has antimicrobial properties due to glycyrrhetinic acid (GA) and glycyrrhizin (GL). An electrospinning process was utilized to fabricate nanofibrous membrane and virus deactivation mechanisms discussed. The nanofiber mask material was characterized by SEM and airflow rate testing. SEM results indicated that the nanofibers from electrospinning are about 15-30 μm in diameter with random porosity and orientation which have the potential to capture and kill the virus. Theoretical estimation signifies that an 85 L/min rate of airflow through the face mask is possible which ensures good breathability over an extensive range of pressure drops and pore sizes. Finally, it can be concluded that licorice root membrane may be used to produce a biobased face mask to control COVID-19 spread.

Choy, S-M, Cheng, E, Wilkinson, RH, Burnett, I & Austin, MW 2021, 'Quality of Experience Comparison of Stereoscopic 3D Videos in Different Projection Devices: Flat Screen, Panoramic Screen and Virtual Reality Headset', IEEE Access.
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Clegg, SR & Burdon, S 2021, 'Exploring creativity and innovation in broadcasting', Human Relations, pp. 001872671988800-001872671988800.
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We consider the emergence of design innovations in process, emerging around the form of polyarchy. This is done by using a case study of innovation conducted by a production organization’s project that was embedded in and hosted by a bureaucratic public institution, the Australian Broadcasting Corporation (ABC). The research reported here was part of a larger project comparing the BBC and ABC’s use of different modes of organization. It focused mainly on the organization designed to deliver a six-part television series, The Code. The innovative process of Scribe, the organization in question, in producing the story is a good example of idea work being instituted in a polyarchic design process. Scribe represents a new organizational design characterized by a polyarchic structure, which is soft and decentralized, with strict and relatively insuperable social and symbolic boundaries. This results in a project-based organization to coordinate collective innovation that is curated by making the writer also the creative director or showrunner. The research contributes further to exploring organizational idea work, through prioritizing creativity and innovation by an explicit positioning of a product and collaborative generative idea work.

Cu Thi, P, Ball, JE & Dao, NH 2021, 'Early stopping technique using a genetic algorithm for calibration of an urban runoff model', International Journal of River Basin Management.
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Identifying suitable parameter sets for use in catchment modelling remains a critical issue in hydrology. This paper describes an early stopping technique (EST) for use during calibration of a multi-parameter urban catchment modelling system. The proposed method takes advantage of MODE and lower confidence limit (LCL) functions in statistical analysis of spanning set of objective function values. The paper also introduces a monitoring process and regularization techniques to avoid under/overfitting during the calibration and to enhance generalisation performance. The methodology is assessed using SWMM and linked with a Genetic Algorithm for calibration of a Powells Creek catchment model in Sydney, Australia. Results demonstrate that the statistical spanning set analysis approach overcomes issues of poor interpretation and deterioration in the model’s generalisation properties. By stopping early, the calibration process avoided overfitting; this was indicated by too closely fitting to the calibration dataset and a failure to fit to the monitoring dataset.

Cullen, M, Zhao, S, Ji, J & Qiu, X 2021, 'Classification of transfer modes in gas metal arc welding using acoustic signal analysis', The International Journal of Advanced Manufacturing Technology.
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Cuzmar, RH, Pereda, J & Aguilera, RP 2021, 'Phase-Shifted Model Predictive Control to Achieve Power Balance of CHB Converters for Large-Scale Photovoltaic Integration', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 68, no. 10, pp. 9619-9629.
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Dang, CC, Dang, LC, Khabbaz, H & Sheng, D 2021, 'Numerical study on deformation characteristics of fibre-reinforced load-transfer platform and columns-supported embankments', CANADIAN GEOTECHNICAL JOURNAL, vol. 58, no. 3, pp. 328-350.
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Dang, D, Hoang, D & Nguyen, D 2021, 'Trust-based Scheduling Framework for Big Data Processing with MapReduce', IEEE Transactions on Services Computing (TSC).
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Security and privacy have become a great concern in cloud computing platforms in which users risk the leakage of their private data. The leakage can happen while the data is at rest (in storage), in processing, or on moving within a cloud or between different cloud infrastructures, e.g., from private to public clouds. This paper focuses on protecting data "in processing". For big data applications, the MapReduce framework has been proven as an efficient solution and has been widely deployed, e.g., in healthcare and business data analysis. In this article, we propose a trust-based framework for MapReduce in big data processing tasks. Specifically, we first quantify and propose to assign the sensitive values for data and trust values for map and reduce slots. We then compute the trust value of each resource employed in the big data processing tasks. Depending on the data's sensitivity level of a task, the task requires a given level of trust (i.e., higher sensitive data requires servers/slots with higher trust level). The MapReduce scheduling problem is then formulated as the maximum weighted matching problem of a bipartite graph that aims to maximize the total trust value over all possible assignments subject to various trust requirement of different tasks. The problem is known to be NP-hard. To tackle it, we observe that within a computing node (VM), slots share the same trust value granted from the secured transformation phase. This helps reduce the number of slot nodes of a weight bipartite graph. Leveraging this fact, we propose an efficient heuristic algorithm that achieves 94.7% of the optimal solution obtained via exhaustive search. Extensive simulations show that the trust-based scheduling scheme provides much higher protection for data sensitivity while ensuring good performance for big data applications.

Dansana, D, Kumar, R, Parida, A, Sharma, R, Adhikari, JD, Le, HV, Pham, BT, Singh, KK & Pradhan, B 2021, 'Using Susceptible-Exposed-Infectious-Recovered Model to Forecast Coronavirus Outbreak', CMC-COMPUTERS MATERIALS & CONTINUA, vol. 67, no. 2, pp. 1595-1612.
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Dao, N-N, Na, W, Tran, A-T, Nguyen, DN & Cho, S 2021, 'Energy-Efficient Spectrum Sensing for IoT Devices', IEEE Systems Journal, pp. 1-9.
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Darwish, A, Halkon, B, Rothberg, S, Oberst, S & Fitch, R 2021, 'A comparison of time and frequency domain-based approaches to laser Doppler vibrometer instrument vibration correction'.

Daviran, M, Maghsoudi, A, Ghezelbash, R & Pradhan, B 2021, 'A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach', COMPUTERS & GEOSCIENCES, vol. 148.
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Dawson, N, Molitorisz, S, Rizoiu, M-A & Fray, P 2021, 'Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to 2020', Journalism.
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Dawson, N, Williams, M-A & Rizoiu, M-A 2021, 'Skill-driven recommendations for job transition pathways.', PloS one, vol. 16, no. 8, p. e0254722.
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Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because new technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displaces labor en masse. Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work. Successful transitions typically occur when workers leverage their existing skills in the new occupation. Here, we propose a novel method to measure the similarity between occupations using their underlying skills. We then build a recommender system for identifying optimal transition pathways between occupations using job advertisements (ads) data and a longitudinal household survey. Our results show that not only can we accurately predict occupational transitions (Accuracy = 76%), but we account for the asymmetric difficulties of moving between jobs (it is easier to move in one direction than the other). We also build an early warning indicator for new technology adoption (showcasing Artificial Intelligence), a major driver of rising job transitions. By using real-time data, our systems can respond to labor demand shifts as they occur (such as those caused by COVID-19). They can be leveraged by policy-makers, educators, and job seekers who are forced to confront the often distressing challenges of finding new jobs.

Deady, M, Glozier, N, Calvo, R, Johnston, D, Mackinnon, A, Milne, D, Choi, I, Gayed, A, Peters, D, Bryant, R, Christensen, H & Harvey, SB 2021, 'Preventing depression using a smartphone app: a randomized controlled trial.', Psychological Medicine, pp. 1-10.
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BACKGROUND:There is evidence that depression can be prevented; however, traditional approaches face significant scalability issues. Digital technologies provide a potential solution, although this has not been adequately tested. The aim of this study was to evaluate the effectiveness of a new smartphone app designed to reduce depression symptoms and subsequent incident depression amongst a large group of Australian workers. METHODS:A randomized controlled trial was conducted with follow-up assessments at 5 weeks and 3 and 12 months post-baseline. Participants were employed Australians reporting no clinically significant depression. The intervention group (N = 1128) was allocated to use HeadGear, a smartphone app which included a 30-day behavioural activation and mindfulness intervention. The attention-control group (N = 1143) used an app which included a 30-day mood monitoring component. The primary outcome was the level of depressive symptomatology (PHQ-9) at 3-month follow-up. Analyses were conducted within an intention-to-treat framework using mixed modelling. RESULTS:Those assigned to the HeadGear arm had fewer depressive symptoms over the course of the trial compared to those assigned to the control (F3,734.7 = 2.98, p = 0.031). Prevalence of depression over the 12-month period was 8.0% and 3.5% for controls and HeadGear recipients, respectively, with odds of depression caseness amongst the intervention group of 0.43 (p = 0.001, 95% CI 0.26-0.70). CONCLUSIONS:This trial demonstrates that a smartphone app can reduce depression symptoms and potentially prevent incident depression caseness and such interventions may have a role in improving working population mental health. Some caution in interpretation is needed regarding the clinical significance due to small effect size and trial attrition.Trial Registration Australian and New Zealand Clinical Trials Registry (www.anzctr.org.au/) ACTRN12617000548336.

Deng, JY, Luo, RQ, Lin, W, Zhang, Y, Sun, D, Zhang, XM & Guo, LX 2021, 'Horn Antenna with Miniaturized Size and Increased Gain by Loading Slow Wave Periodic Metal Blocks', IEEE Transactions on Antennas and Propagation, vol. 69, no. 4, pp. 2365-2369.
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An H-plane horn antenna with miniaturized size and increased gain is reported, which is realized by loading slow wave structures (SWSs) inside the shortened horn cavity. The SWSs in the form of periodic metal blocks can decelerate the phase velocity of the electromagnetic wave traveling to the radiating aperture. So the phase difference between the center and the edges of the aperture enlarged by the shortened longitudinal dimension can be compensated, and the longitudinal length of the horn antenna is significantly reduced. Meanwhile, uniform magnitude distribution of electric fields on the aperture is achieved by cleverly designing the heights of embedded blocks. Thus, the realized gain and aperture efficiency are maximized. The proposed horn antenna is easily fabricated by standard mechanical manufacture process. The longitudinal length of the prototype is 33.3% less than the original horn antenna with the same aperture size. Moreover, the average gain is 1.7 dB higher than the original horn, while maximized aperture efficiency up to 80.7% is achieved.

Deng, S, Ji, J, Wen, G & Xu, H 2021, 'A comparative study of the dynamics of a three-disk dynamo system with and without time delay', APPLIED MATHEMATICS AND COMPUTATION, vol. 399.
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Deveci, Ö & Shannon, AG 2021, 'Matrix manipulations for properties of Fibonacci p-numbers and their generalizations', Analele Stiintifice ale Universitatii Al I Cuza din Iasi - Matematica, vol. 67, no. 1, pp. 85-95.
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In this paper, we define the Fibonacci-Fibonacci p-sequence and then we discuss the connection of the Fibonacci-Fibonacci p-sequence with Fibonacci and Fibonacci p-sequences. We also provide a new Binet formula and a new combinatorial representation of Fibonacci p-numbers by the aid of the nth power of the generating matrix the Fibonacci-Fibonacci p-sequence. We furthermore develop relationships between the Fibonacci-Fibonacci p-numbers and their permanent, determinant and sums of certain matrices.

Dharmapriya, S, Kiridena, S & Shukla, N 2021, 'Multiagent Optimization Approach to Supply Network Configuration Problems With Varied Product-Market Profiles', IEEE Transactions on Engineering Management.
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IEEE This article demonstrates the application of a novel multiagent modeling approach to support supply network configuration (SNC) decisions toward addressing several challenges reported in the literature. These challenges include: enhancing supply network (SN)-level performance in alignment with the goals of individual SN entities; addressing the issue of limited information sharing between SN entities; and sustaining competitiveness of SNs in dynamic business environments. To this end, a multistage, multiechelon SN consisting of geographically dispersed SN entities catering to distinct product-market profiles was modeled. In modeling the SNC decision problem, two types of agents, each having distinct attributes and functions, were used. The modeling approach incorporated a reverse-auctioning process to simulate the behavior of SN entities with differing individual goals collectively contributing to enhance SN-level performance, by means of setting reserve values generated through the application of a genetic algorithm. A set of Pareto-optimal SNCs catering to distinct product-market profiles was generated using Nondominated Sorting Genetic Algorithm II. Further evaluation of these SNCs against additional criteria, using a rule-based approach, allowed the selection of the most appropriate SNC to meet a broader set of conditions. The model was tested using a refrigerator SN case study drawn from the literature. The results reveal that a number of SNC decisions can be supported by the proposed model, in particular, identifying and evaluating robust SNs to suit varied product-market profiles, enhancing SC capabilities to withstand disruptions and developing contingencies to recover from disruptions.

Di, X, Wang, D, Zhou, J, Zhang, L, Stenzel, MH, Su, QP & Jin, D 2021, 'Quantitatively Monitoring In Situ Mitochondrial Thermal Dynamics by Upconversion Nanoparticles.', Nano Letters.
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Diao, K, Sun, X, Lei, G, Guo, Y & Zhu, J 2021, 'Multimode Optimization of Switched Reluctance Machines in Hybrid Electric Vehicles', IEEE Transactions on Energy Conversion.
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IEEE The belt-driven starter/generator (BSG), as a cost-effective solution, has been widely employed in hybrid electric vehicles (HEVs) to improve the stability and reduce the fuel consumption of the vehicles. It can provide more than 10% reduction in CO2. Electrical machine is the heart of the BSG system, which is functioned both as motor and generator. In order to optimize both aspects of motor and generator simultaneously, this paper presents a new multimode optimization method for the switched reluctance machines. First, the general multimode concept and optimization method are presented. The switched reluctance motor and the switched reluctance generator are the two operation modes. The optimization models are established based on motor and generator functions. Sensitivity analysis, surrogate models and genetic algorithms are employed to improve the efficiency of the multimode optimization. Then, a design example of a segmented-rotor switched reluctance machine (SSRM) is investigated. Seven design variables and four driving modes are considered in the multiobjective optimization model. The Kriging model is employed to approximate the finite element model (FEM) in the optimization. Finally, the optimization results are depicted, and an optimal solution is selected. The comparison between the initial and optimal designs shows that the proposed method can improve the foremost performance of the SSRM under all driving modes.

Dikshit, A, Pradhan, B & Alamri, AM 2021, 'Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 755.
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Dikshit, A, Pradhan, B & Alamri, AM 2021, 'Pathways and challenges of the application of artificial intelligence to geohazards modelling', Gondwana Research.
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© 2020 International Association for Gondwana Research The application of artificial intelligence (AI) and machine learning in geohazard modelling has been rapidly growing in recent years, a trend that is observed in several research and application areas thanks to recent advances in AI. As a result, the increasing dependence on data driven studies has made its practical applications towards geohazards (landslides, debris flows, earthquakes, droughts, floods, glacier studies) an interesting prospect. These aforementioned geohazards were responsible for roughly 80% of the economic loss in the past two decades caused by all natural hazards. The present study analyses the various domains of geohazards which have benefited from classical machine learning approaches and highlights the future course of direction in this field. The emergence of deep learning has fulfilled several gaps in: i) classification; ii) seasonal forecasting as well as forecasting at longer lead times; iii) temporal based change detection. Apart from the usual challenges of dataset availability, climate change and anthropogenic activities, this review paper emphasizes that the future studies should focus on consecutive events along with integration of physical models. The recent catastrophe in Japan and Australia makes a compelling argument to focus towards consecutive events. The availability of higher temporal resolution and multi-hazard dataset will prove to be essential, but the key would be to integrate it with physical models which would improve our understanding of the mechanism involved both in single and consecutive hazard scenario. Geohazards would eventually be a data problem, like geosciences, and therefore it is essential to develop models that would be capable of handling large voluminous data. The future works should also revolve towards interpretable models with the hope of providing a reasonable explanation of the results, thereby achieving the ultimate goal of Explainable AI.

Dikshit, A, Pradhan, B & Huete, A 2021, 'An improved SPEI drought forecasting approach using the long short-term memory neural network', JOURNAL OF ENVIRONMENTAL MANAGEMENT, vol. 283.
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Ding, L, Zhou, J, Fu, Q, Bao, G, Liu, Y & Jin, D 2021, 'Triplet Fusion Upconversion with Oxygen Resistance in Aqueous Media', ANALYTICAL CHEMISTRY, vol. 93, no. 10, pp. 4641-4646.
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Ding, W, Jin, W, Zhou, X, Yang, Q, Chen, C & Wang, Q 2021, 'Role of extracellular polymeric substances in anaerobic granular sludge: Assessing dewaterability during Fe(II)-peroxydisulfate conditioning and granulation processes', Journal of Cleaner Production, vol. 286.
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© 2020 Elsevier Ltd In this study, Fe(II) activated peroxydisulfate (PDS) conditioning and sludge granulation were conducted to investigate the dewaterability of anaerobic granular sludge (AGS). After Fe(II)-PDS conditioning, the dewaterability of three AGS from different sources was enhanced. The specific resistance to filtration (SRF) reduction rates were achieved (98.30% ± 0.19%, 99.51% ± 0.17% and 96.47% ± 1.25%, respectively) under the optimal Fe(II) and PDS additions; And the optimal reductions of capillary suction time (CST) were 93.49% ± 2.49%, 95.33% ± 0.02% and 88.04% ± 2.95%, respectively. The mechanism of improving AGS dewaterability by Fe(II)-PDS conditioning was proposed. The radical SO4⋅−/OH⋅ destroyed the structure of extracellular polymeric substances (EPS) layers and microbial cells, resulting in the bound water released from AGS. Thereafter, the generated Fe(III) facilitated the sludge re-flocculation and decreased the electrostatic repulsion. During a 132-day granulation, the CST value showed a positive correlation with protein (S-EPS), polysaccharide and zeta potential, and a negative correlation with protein (LB-EPS), protein (TB-EPS), particle size and VSS. Collectively, the protein was the primary component in AGS and showed a strong correlation with dewaterability. The variations of protein in TB-EPS during the conditioning and the granulation were consistent with the changes of sludge dewaterability.

Ding, W, Ming, Y, Cao, Z & Lin, CT 2021, 'A Generalized Deep Neural Network Approach for Digital Watermarking Analysis', IEEE Transactions on Emerging Topics in Computational Intelligence.
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Technology advancement has facilitated digital content, such as images, being acquired in large volumes. However, requirement from the privacy or legislation perspective still demands the need for intellectual content protection. In this paper, we propose a deep neural network (DNN) based watermarking method to achieve this goal. Instead of training a neural network for protecting a specific image, we train the network on an image dataset and generalize the trained model to protect distinct test images in a bulk manner. Respective evaluations from both the subjective and objective aspects confirm the generality and practicality of our proposed method. To demonstrate the robustness of this general neural watermarking approach, commonly used attacks are applied to the watermarked images to examine the corresponding extracted watermarks, which still retain sufficient recognizable traits for some occasions. Testing on distinctive dataset shows the satisfying generalization of our proposed method, and practice such as loss function adjustment can cater to the capacity requirement of complicated watermark. We also discuss some traits of the trained model, which incur the vulnerability to JPEG compression attack. However, remedy seeking for this can potentially open a window to understand the underlying working principle of DNN in future work. Considering its performance and economy, it is concluded that subsequent studies that generalize our work on utilizing DNN for intellectual content protection might be a promising research trend.

Ding, W, Triguero, I & Lin, C-T 2021, 'Coevolutionary Fuzzy Attribute Order Reduction With Complete Attribute-Value Space Tree', IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, vol. 5, no. 1, pp. 130-142.
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Dingr, W, Pal, NR, Lin, C-T, Cheung, Y-M, Cao, Z & Luo, W 2021, 'Guest Editorial Special Issue on Emerging Computational Intelligence Techniques for Decision Making With Big Data in Uncertain Environments', IEEE Transactions on Emerging Topics in Computational Intelligence.
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Do, MH, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Sharma, P, Pandey, A, Bui, XT & Zhang, X 2021, 'Performance of a dual-chamber microbial fuel cell as biosensor for on-line measuring ammonium nitrogen in synthetic municipal wastewater', Science of the Total Environment, vol. 795.
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This study investigates the performance of microbial fuel cells (MFC) for on-line monitoring ammonium (NH4+-N) in municipal wastewater. A double chamber microbial fuel cell (MFC) was established in a continuous mode under different influent ammonium concentrations ranging from 5 to 40 mg L−1. Results indicated that excess ammonium would inhibit the activity of electrogenic bacteria in the anode chamber and consequently affect electricity production. An inversely linear relationship between concentration and voltage generation was obtained with coefficient R2 0.99 and the MFC could detect up to 40 mg L−1 of NH4+-N. Notably, no further decline was observed in voltage output and there was in fact a further increase in ammonia concentration (>40 mg L−1). The stability and high accuracy of ammonium-based MFC biosensors exposed competitive results compared to traditional analytical tools, confirming the biosensor's reliability. Furthermore, pH 7.0; R 1000 Ω and HRT of 24 h are the best possible conditions for the MFC biosensor for monitoring ammonium. The simplicity in design and operation makes the biosensor more realistic for practical application.

Do, TTN, Jung, TP & Lin, CT 2021, 'Retrosplenial Segregation Reflects the Navigation Load during Ambulatory Movement', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 488-496.
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Spatial navigation is a complex cognitive process based on vestibular, proprioceptive, and visualcues that are integrated and processed by an extensive network of brain areas. The retrosplenial complex (RSC) is an integral part of coordination and translation between spatial reference frames. Previous studies have demonstrated that the RSC is active during a spatial navigation tasks. The specifics of RSC activity under various navigation loads, however, are still not characterized. This study investigated the local information processed by the RSC under various navigation load conditions manipulated by the number of turns in the physical navigation setup. The results showed that the local information processed via the RSC, which was reflected by the segregation network, was higher when the number of turns increased, suggesting that RSC activity is associated with the navigation task load. The present findings shed light on how the brain processes spatial information in a physical navigation task.

Do, TTN, Wang, YK & Lin, CT 2021, 'Increase in brain effective connectivity in multitasking but not in a high-fatigue state', IEEE Transactions on Cognitive and Developmental Systems.
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IEEE Multitasking has become omnipresent in daily activities, and increased brain connectivity under high workload conditions has been reported. Moreover, the effect of fatigue on neural activity has been shown in participants performing cognitive tasks, but the effect of fatigue on different cognitive workload conditions is unclear. In this study, we investigated the effect of fatigue on changes in effective connectivity (EC) across the brain network under distinctive workload conditions. There were 133 electroencephalography (EEG) datasets collected from sixteen participants over a five-month study in which high-risk, reduced, and normal states of real-world fatigue were identified through a daily sampling system. The participants were required to perform a lane-keeping task (LKT) with/without multimodal dynamic attention-shifting (DAS) tasks. The results show that the EC magnitude is positively correlated with the increased workload in normal and reduced states. However, low EC was discovered in the high-risk state under high workload condition. To the best of our knowledge, this investigation is the first EEG-based longitudinal study of real-world fatigue under multitasking conditions. These results could be beneficial for real-life applications, and adaptive models are essential for monitoring important brain patterns under varying workload demands and fatigue states.

Doan, S & Fatahi, B 2021, 'Green's function analytical solution for free strain consolidation of soft soil improved by stone columns subjected to time-dependent loading', Computers and Geotechnics, vol. 136.
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This paper proposes an analytical solution in terms of Green's function formulations for axisymmetric consolidation of a stone column improved soft soil deposit subjected to time-dependent loading under free strain condition. The mathematical derivations incorporate the pore water flows in radial and vertical directions in stone column and soft soil synchronously. The capabilities of the proposed analytical solution are evaluated via worked examples investigating the influences of three common time-dependent external surcharges (namely step, ramp and sinusoidal loadings) on consolidation response of the composite ground. The examples show that a faster increase of load from an initial surcharge to an expected loading might generate more significant excess pore water pressure to be dissipated during the early stages of consolidation, but the dissipation rate in soft soil would speed up significantly afterwards. The column and soil settlements along with the differential settlement between them also proceed faster corresponding to the acceleration of loading – unloading processes. Finally, the proposed analytical solution is employed to evaluate the excess pore water pressure dissipation rate at an investigation point in soft clay of a case history foundation. The calculation results exhibit a reasonable agreement with field measurement data when various constant values of stress concentration ratio are substituted into the solution to reflect the increase of stress concentration ratio with consolidation time in real practice.

Dolmark, T, Sohaib, O, Beydoun, G & Wu, K 2021, 'The Effect of Individual’s Technological Belief and Usage on Their Absorptive Capacity towards Their Learning Behaviour in Learning Environment', Sustainability, vol. 13, no. 2, pp. 1-17.

Dong, F, Lu, J, Song, Y, Liu, F & Zhang, G 2021, 'A Drift Region-Based Data Sample Filtering Method.', IEEE transactions on cybernetics, vol. PP.
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Concept drift refers to changes in the underlying data distribution of data streams over time. A well-trained model will be outdated if concept drift occurs. Once concept drift is detected, it is necessary to understand where the drift occurs to support the drift adaptation strategy and effectively update the outdated models. This process, called drift understanding, has rarely been studied in this area. To fill this gap, this article develops a drift region-based data sample filtering method to update the obsolete model and track the new data pattern accurately. The proposed method can effectively identify the drift region and utilize information on the drift region to filter the data sample for training models. The theoretical proof guarantees the identified drift region converges uniformly to the real drift region as the sample size increases. Experimental evaluations based on four synthetic datasets and two real-world datasets demonstrate our method improves the learning accuracy when dealing with data streams involving concept drift.

Dong, W, Guo, Y, Sun, Z, Tao, Z & Li, W 2021, 'Development of piezoresistive cement-based sensor using recycled waste glass cullets coated with carbon nanotubes', Journal of Cleaner Production, vol. 314.
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Different from widely exploring the application of waste glass to replace natural aggregate or cement powder, this study firstly utilized waste glass cullets coated with carbon nanotubes (CNTs) as conductive fillers to develop novel self-sensing cement-based sensors. The coating efficiency of CNTs and self-sensing properties were also investigated in terms of workability, water absorption, mechanical properties, electrical resistivity and microstructure. The results show that CNTs are attached to the surfaces of waste glass particles, especially the small-size waste glass particles with high roughness. Workability decreased significantly with the increased waste glass. Cementitious mortar with sand replaced by CNTs-coated waste glass exhibited the highest flowability when the replacement ratio was 25%. Moreover, the water impermeability continuously increased with the content of waste glass. The compressive strength was higher than that of the control mortar, which reached the highest with 50% waste glass content. Additionally, an excellent piezoresistivity was achieved for cement-based sensors with CNTs-coated waste glass particles for the self-monitoring of stress magnitude and failure. The CNTs are uniformly distributed well in the cement matrix by attaching the surfaces of waste glass particles, thus the conductive passages are formed in cement-based sensors for structural health monitoring.

Dong, W, Li, W & Tao, Z 2021, 'A comprehensive review on performance of cementitious and geopolymeric concretes with recycled waste glass as powder, sand or cullet', RESOURCES CONSERVATION AND RECYCLING, vol. 172.
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Dong, W, Li, W, Shen, L, Zhang, S & Vessalas, K 2021, 'Integrated self-sensing and self-healing cementitious composite with microencapsulation of nano-carbon black and slaked lime', MATERIALS LETTERS, vol. 282.
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Dong, W, Li, W, Vessalas, K, He, X, Sun, Z & Sheng, D 2021, 'Piezoresistivity deterioration of smart graphene nanoplate/cement-based sensors subjected to sulphuric acid attack', COMPOSITES COMMUNICATIONS, vol. 23.
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Dong, W, Li, W, Wang, K & Shah, SP 2021, 'Physicochemical and Piezoresistive properties of smart cementitious composites with graphene nanoplates and graphite plates', Construction and Building Materials, vol. 286.
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Graphene nanoplate (GNP) and graphite plate (GP) are promising functional nanofillers for smart self-sensing cementitious composites. The effects of GNP and GP on physicochemical, mechanical and piezoresistive properties of cementitious composite were investigated in this paper. The results show that cement hydration was accelerated with the increased amounts of GNP and GP because of nucleation effect. The electrical resistivity of GNP-cementitious composites was always lower than the counterpart with GP with the same concentration. On the other hand, percolation occurred for the GNP/cementitious composites at the dosages from 2 to 3% (by weight), while it never happens for the GP/cementitious composites. Moreover, the GNP/cementitious composites reached the maximum mechanical strength when the GNP content was 1.0%, while for the GP/cementitious composites, only minor strength improvement was obtained with a dosage of 0.5% GP. As for the piezoresistivity, the cementitious composites with GNP exhibited higher fractional changes of resistivity. Irreversible resistivity happened for 2–3% GP/cementitious composites subjected to cyclic compression, due to the poor and loose microstructures. The outcomes are expected to provide an insight into the application of GNP/cementitious and GP/cement composites as cement-based sensors for the future structural health monitoring.

Dong, W, Li, W, Zhu, X, Sheng, D & Shah, SP 2021, 'Multifunctional cementitious composites with integrated self-sensing and hydrophobic capacities toward smart structural health monitoring', CEMENT & CONCRETE COMPOSITES, vol. 118.
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Dorji, U, Tenzin, U, Dorji, P, Pathak, N, Johir, MAH, Volpin, F, Dorji, C, Chernicharo, CAL, Tijing, L, Shon, H & Phuntsho, S 2021, 'Exploring shredded waste PET bottles as a biofilter media for improved on-site sanitation', Process Safety and Environmental Protection, vol. 148, pp. 370-381.
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© 2020 Institution of Chemical Engineers This study explores an improved alternative on-site treatment for unsewered urban Bhutan. The system combines up-flow anaerobic sludge blanket for blackwater treatment and anaerobic biofilter for a mixture of up-flow anaerobic sludge blanket effluent and greywater. Shredded waste plastic bottles are used as novel biofilter media that provides a large surface area for attached growth while addressing waste plastic problems. A bench-scale up-flow anaerobic sludge blanket (operated at hydraulic retention time or HRT of 1–10 days) and anaerobic biofilter (HRT of 0.25–3 days) study were conducted for 188 days. At 2-d HRT, up-flow anaerobic sludge blanket removed 70–80 % of chemical oxygen demand (COD) while anaerobic biofilter achieved 90–98 % COD removal at eight-hour HRT. Combined up-flow anaerobic sludge blanket and anaerobic biofilter achieved final effluent with COD less than 50 mg/L and turbidity of less than 3 NTU that meets the discharge standard of Bhutan. The study shows that shredded waste plastic bottles can be an effective biofilter support medium for low-cost on-site treatment while helping address waste plastic problems.

Du, M, Liu, X, Wang, D, Yang, Q, Duan, A, Chen, H, Liu, Y, Wang, Q & Ni, B-J 2021, 'Understanding the fate and impact of capsaicin in anaerobic co-digestion of food waste and waste activated sludge.', Water Research, vol. 188, pp. 116539-116539.
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Anaerobic co-digestion is an attractive option to treat food waste and waste activated sludge, which is increasingly applied in real-world situations. As an active component in Capsicum species being substantially present in food waste in many areas, capsaicin has been recently demonstrated to inhibit the anaerobic co-digestion. However, the interaction between capsaicin and anaerobic co-digestion are still poorly understood. This work therefore aims to deeply understand the fate and impact of capsaicin in the anaerobic co-digestion. Experiment results showed that capsaicin was completely degraded in anaerobic co-digestion by hydroxylation, O-demethylation, dehydrogenation and doubly oxidization, respectively. Although methane was proven to be produced from capsaicin degradation, the increase in capsaicin concentration resulted in decrease in methane yield from the anaerobic co-digestion. With an increase of capsaicin from 2 ± 0.7 to 68 ± 4 mg/g volatile solids (VS), the maximal methane yield decreased from 274.6 ± 9.7 to 188.9 ± 8.4 mL/g VS. The mechanic investigations demonstrated that the presence of capsaicin induced apoptosis, probably by either altering key kinases or decreasing the intracellular NAD⁺/NADH ratio, which led to significant inhibitions to hydrolysis, acidogenesis, and methanogenesis, especially acetotrophic methanogenesis. Illumina Miseq sequencing analysis exhibited that capsaicin promoted the populations of complex organic degradation microbes such as Escherichia-Shigella and Fonticella but decreased the numbers of anaerobes relevant to hydrolysis, acidogenesis, and methanogenesis such as Bacteroide and Methanobacterium.

Duan, N, Xu, W, Ma, X, Wang, S & Zhu, J 2021, 'Magnetic Characteristic Analysis of High Temperature Superconductors by the Elemental Operator Model', IEEE Transactions on Magnetics.
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In this paper, to accurately analysis the magnetic properties of high temperature superconductor, a superconductor hysteresis model is proposed. Based on the physical mechanism, the hysteresis loop of high temperature superconductor can be decomposed into an antimagnetic curve and a paramagnetic loop. The calculated and measured results of silver-sheathed Bi2223 superconducting tape samples under external magnetic field are compared to show the effectiveness and accuracy of the presented method.

Duan, W & Kirby, R 2021, 'A Numerical Approach for Calculation of Characteristics of Edge Waves in Three-Dimensional Plates', Journal of Theoretical and Computational Acoustics.
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Surface waves have been extensively studied in earthquake seismology. Surface waves are trapped near an infinitely large surface. The displacements decay exponentially with depth. These waves are also named Rayleigh and Love waves. Surface waves are also used for nondestructive testing of surface defects. Similar waves exist in finite width three-dimensional plates. In this case, displacements are no longer constant in the direction perpendicular to the wave propagation plane. Wave energy could still be trapped near the edge of the three-dimensional plate, and hence the term edge waves. These waves are thus different to the two-dimensional Rayleigh and Love waves. This paper presents a numerical model to study dispersion properties of edge waves in plates. A two-dimensional semi-analytical finite element method is developed, and the problem is closed by a perfectly matched layer adjacent to the edge. The numerical model is validated by comparing with available analytical and numerical solutions in the literature. On this basis, higher order edge waves and mode shapes are presented for a three-dimensional plate. The characteristics of the presented edge wave modes could be used in nondestructive testing applications.

Duong, HC, Tran, LTT, Vu, MT, Nguyen, D, Tran, NTV & Nghiem, LD 2021, 'A new perspective on small-scale treatment systems for arsenic affected groundwater', Environmental Technology & Innovation, vol. 23, pp. 101780-101780.
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Ejeian, M, Grant, A, Shon, HK & Razmjou, A 2021, 'Is lithium brine water?', Desalination.
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With the development of light and rechargeable batteries for electric vehicles, global demand for lithium has increased considerably in recent years. This has drawn more attention to how lithium is produced, especially on primary extraction operations such as those at the Salar de Atacama in Northern Chile. There are concerns that brine extraction at the Atacama could irreversibly damage the basin's complex hydrological system. However, differing opinions over the definition of water have frustrated basic action measures for minimizing impacts of operations like these. Some lithium industry stakeholders have historically described brine as a mineral, while others emphasize that brine is also a type of water in a complex network of different water resources. In this communication, we show that brines are undeniably a type of water. We support this position by investigating brine's water molecular structure using molecular dynamics simulations and comparing Gibbs formation energy of the brine using thermodynamic principles. Molecular dynamics show that the structure of water molecules in brine is similar to the structure of molecules in pure water at a pressure of 1.2 atm. The analysis of Gibbs formation energy shows that more than 99% of the brine's formation energy is directly from water, not dissolved minerals.

Ekanayake, D, Loganathan, P, Johir, MAH, Kandasamy, J & Vigneswaran, S 2021, 'Enhanced Removal of Nutrients, Heavy Metals, and PAH from Synthetic Stormwater by Incorporating Different Adsorbents into a Filter Media', Water, Air, & Soil Pollution.
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Ekanayake, UGM, Barclay, M, Seo, DH, Park, MJ, MacLeod, J, O'Mullane, AP, Motta, N, Shon, HK & Ostrikov, K 2021, 'Utilization of plasma in water desalination and purification', Desalination, vol. 500.
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Supplying fresh drinking water to the world population is a persistent global challenge. Therefore, effective and efficient desalination processes are becoming increasingly important. Oceans account for most of the water on Earth and the presence of salts and other contaminants in seawater prevents them from being used as a source of drinking water. Owing to this challenge, non-thermal plasma can be utilized in order to enhance the existing desalination processes via membrane or material modification while it can also be used as a direct tool for seawater desalination leading to significant process improvements. A direct non-thermal plasma-based desalination process is a new emerging area of research and recent efforts have shown its promise with many unexplored mechanisms, providing benefits that conventional desalination processes cannot offer. Here we critically review the use of plasma technologies in water desalination including membrane modification by plasma for pressure, thermal, photothermal processes and direct plasma-based desalination process. We also address the use of plasmas in water purification. Finally, the existing challenges and future prospects are outlined.

Elghamrawy, SM, Hassanien, AE & Vasilakos, AV 2021, 'Genetic‐based adaptive momentum estimation for predicting mortality risk factors for COVID‐19 patients using deep learning', International Journal of Imaging Systems and Technology.
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Fan, H, Zhuo, T, Yu, X, Yang, Y & Kankanhalli, M 2021, 'Understanding Atomic Hand-Object Interaction with Human Intention', IEEE Transactions on Circuits and Systems for Video Technology.
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Hand-object interaction plays a very important role when humans manipulate objects. While existing methods focus on improving hand-object recognition with fully automatic methods, human intention has been largely neglected in the recognition process, thus leading to undesirable interaction descriptions. To better interpret human-object interaction that is aligned to human intention, we argue that a reference specifying human intention should be taken into account. Thus, we propose a new approach to represent interactions while reflecting human purpose with three key factors, i.e., hand, object and reference. Specifically, we design a pattern of <hand-object, object-reference, hand, object, reference> (HOR) to recognize intention based atomic hand-object interactions. This pattern aims to model interactions with the states of hand, object, reference and their relationships. Furthermore, we design a simple yet effective Spatially Part-based (3+1)D convolutional neural network, namely SP(3+1)D, which leverages 3D and 1D convolutions to model visual dynamics and object position changes based on our HOR, respectively. With the help of our SP(3+1)D network, the recognition results are able to indicate human purposes accurately. To evaluate the proposed method, we annotate a Something-1.3k dataset, which contains 10 atomic hand-object interactions and about 130 videos for each interaction. Experimental results on Something-1.3k demonstrate the effectiveness of our SP(3+1)D network.

Fan, Z-B, LI, Y, Zhang, K, Yu, J & Huang, ML 2021, 'Measuring and Evaluating the Visual Complexity of Chinese Ink Paintings', The Computer Journal, pp. 1-13.
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Painters arrange white space in contrast with chromatic space composed of strokes. This research measures white space, color complexity, and stroke density in Chinese ink paintings and examines how these attributes influence the paintings’ perceived complexity. Empirical evidence from twenty-one well-known modern Chinese artists’ ink paintings shows that white space decreases paintings’ complexity, while chromatic space and stroke density increase complexity. We also reveal that a large rate of white space guides the viewers’ attention on chromatic space and enhances the impacts of color complexity and stroke density on perceived complexity. An eye-tracker measures viewers’ elaboration duration on each painting, which provides consistent evidence to validate our conclusion based on subjective reported visual complexity. Our research provides insights into the rhetorical role of white space in sensory information processing.

Fang, C, Liu, W, Zhang, P, Rajabzadeh, S, Kato, N, Sasaki, Y, Shon, HK & Matsuyama, H 2021, 'Hollow fiber membranes with hierarchical spherulite surface structure developed by thermally induced phase separation using triple-orifice spinneret for membrane distillation', Journal of Membrane Science, vol. 618.
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© 2020 Elsevier B.V. Polyvinylidene fluoride (PVDF) hollow fiber membranes were developed by the thermally induced phase separation (TIPS) process using a triple-orifice spinneret with solvent co-extrusion at the outermost channel for applications in membrane distillation (MD). The polymer surface concentration during membrane preparation was controlled by exploiting the interfacial interactions of the diluent and polymer at the extruded solvent surface. The membrane surface was controlled from a dense to a porous structure with a large pore size and a high porosity, which considerably enhanced the membrane water vapor permeability to 13.5 L m−2 h−1. Furthermore, the solvent co-extrusion was responsible for the formation of surface spherulites with different shapes, such as contacted spherulites, isolated spherulites, and isolated spherulites with humps. The spherulites with humps constructed a novel hierarchical structure, which created a superhydrophobic surface that conferred upon the PVDF membrane a remarkable wetting resistance in the MD process toward low-surface-tension saline water. More significantly, all the unique structures were achieved using the one-step membrane fabrication process of solvent co-extrusion without additional processes and materials. Thus, this work provides a new, simple, and useful alternative for the preparation of hollow fiber membranes with high performances for MD desalination.

Fang, C, Liu, W, Zhang, P, Yao, M, Rajabzadeh, S, Kato, N, Kyong Shon, H & Matsuyama, H 2021, 'Controlling the inner surface pore and spherulite structures of PVDF hollow fiber membranes in thermally induced phase separation using triple-orifice spinneret for membrane distillation', Separation and Purification Technology, vol. 258.
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© 2020 Elsevier B.V. In this study, we controlled the inner surface structures of polyvinylidene fluoride (PVDF) hollow fiber membranes via a thermally induced phase separation process using a triple-orifice spinneret for direct-contact membrane distillation (DCMD). The coextrusion of propylene carbonate (PC) through the outermost channel of the spinneret led to porous outer surfaces with similar pore sizes and spherulitic structures for all the PVDF hollow fiber membranes. In the innermost channel, the extrusion of solvents having different compatibilities with PVDF and the diluent (PC) as the bore liquids controlled the inner surface pore sizes and spherulite structures, and the effects of these inner surface structures on the DCMD performance were investigated in detail. Increasing the compatibility of the bore liquids toward the diluent led to an increase in the inner surface pore size because of the formation of loose, isolated spherulites, which remarkably enhanced the water vapor permeability from 4 to 8.3 L m−2 h−1, while reducing the membrane hydrophobicity, liquid entry pressure, and salt rejection. When increasing the bore liquid compatibility with the polymer, the surface pore size decreased because of the tight spherulite contact, enhancing membrane salt rejection and wetting resistance. Given the significance of bore liquid compatibility with the diluent and the polymer in controlling the inner surface structures, a useful guideline is presented for selecting the appropriate bore liquids to prepare hollow fiber membranes with the desired inner surface structures for high MD performance.

Fang, G, Lu, H, Aboulkheyr Es, H, Wang, D, Liu, Y, Warkiani, ME, Lin, G & Jin, D 2021, 'Unidirectional intercellular communication on a microfluidic chip', Biosensors and Bioelectronics, vol. 175.

Fang, L, Li, Y, Liu, Z, Yin, C, Li, M & Cao, ZJ 2021, 'A Practical Model Based on Anomaly Detection for Protecting Medical IoT Control Services Against External Attacks', IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 4260-4269.
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Fang, L, Yin, C, Zhu, J, Ge, C, Tanveer, M, Jolfaei, A & Cao, Z 2021, 'Privacy Protection for Medical Data Sharing in Smart Healthcare', ACM Transactions on Multimedia Computing, Communications and Applications, vol. 16, no. 3s.
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In virtue of advances in smart networks and the cloud computing paradigm, smart healthcare is transforming. However, there are still challenges, such as storing sensitive data in untrusted and controlled infrastructure and ensuring the secure transmission of medical data, among others. The rapid development of watermarking provides opportunities for smart healthcare. In this article, we propose a new data-sharing framework and a data access control mechanism. The applications are submitted by the doctors, and the data is processed in the medical data center of the hospital, stored in semi-trusted servers to support the selective sharing of electronic medical records from different medical institutions between different doctors. Our approach ensures that privacy concerns are taken into account when processing requests for access to patients' medical information. For accountability, after data is modified or leaked, both patients and doctors must add digital watermarks associated with their identification when uploading data. Extensive analytical and experimental results are presented that show the security and efficiency of our proposed scheme.

Fang, Z, Lu, J, Liu, F, Xuan, J & Zhang, G 2021, 'Open Set Domain Adaptation: Theoretical Bound and Algorithm.', IEEE Transactions on Neural Networks and Learning Systems.
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The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain--the basic strategy being to mitigate the effects of discrepancies between the two distributions. Most existing algorithms can only handle unsupervised closed set domain adaptation (UCSDA), i.e., where the source and target domains are assumed to share the same label set. In this article, we target a more challenging but realistic setting: unsupervised open set domain adaptation (UOSDA), where the target domain has unknown classes that are not found in the source domain. This is the first study to provide learning bound for open set domain adaptation, which we do by theoretically investigating the risk of the target classifier on unknown classes. The proposed learning bound has a special term, namely, open set difference, which reflects the risk of the target classifier on unknown classes. Furthermore, we present a novel and theoretically guided unsupervised algorithm for open set domain adaptation, called distribution alignment with open difference (DAOD), which is based on regularizing this open set difference bound. The experiments on several benchmark data sets show the superior performance of the proposed UOSDA method compared with the state-of-the-art methods in the literature.

Fani, A, Naseri, H, Golroo, A, Mirhassani, SA & Gandomi, AH 2021, 'A progressive hedging approach for large-scale pavement maintenance scheduling under uncertainty', The International Journal of Pavement Engineering.
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Farooq, MA, Nimbalkar, S & Fatahi, B 2021, 'Three-dimensional finite element analyses of tyre derived aggregates in ballasted and ballastless tracks', Computers and Geotechnics, vol. 136.
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Scrap tyres are a significant source of pollution and pose a grave threat to the environment and human health. The present study aims to examine the application of Tyre Derived Aggregate (TDA) in a concrete slab track and ballasted track and compare its performance in both track forms. In this study, long-term performance of slab track and ballasted track subjected to train induced loading is demonstrated based on the three-dimensional finite element modelling. The most suitable constitutive hyperelastic model for TDA has been identified. Subsequently, the most suitable position for the location of TDA is determined for both track types. A comparative analysis between slab track and ballasted track, with and without TDA, is presented in terms of stress transfer, vibration reduction and displacement (elastic and plastic). It is shown that TDA helps in reducing up to 50% vibration levels of both track types. The influence of train speed and axle load on the vertical and horizontal displacement and stress response of both track forms is shown for a large number of load cycles. Overall, it is observed that the long-term performance of TDA is better in slab track compared to ballasted track.

Feng, S, Hao Ngo, H, Guo, W, Woong Chang, S, Duc Nguyen, D, Cheng, D, Varjani, S, Lei, Z & Liu, Y 2021, 'Roles and applications of enzymes for resistant pollutants removal in wastewater treatment', Bioresource Technology, vol. 335.
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Resistant pollutants like oil, grease, pharmaceuticals, pesticides, and plastics in wastewater are difficult to be degraded by traditional activated sludge methods. These pollutants are prevalent, posing a great threat to aquatic environments and organisms since they are toxic, resistant to natural biodegradation, and create other serious problems. As a high-efficiency biocatalyst, enzymes are proposed for the treatment of these resistant pollutants. This review focused on the roles and applications of enzymes in wastewater treatment. It discusses the influence of enzyme types and their sources, enzymatic processes in resistant pollutants remediation, identification and ecotoxicity assay of enzymatic transformation products, and typically employed enzymatic wastewater treatment systems. Perspectives on the major challenges and feasible future research directions of enzyme-based wastewater treatment are also proposed.

Feng, S, Shi, H, Huang, L, Shen, S, Yu, S, Peng, H & Wu, C 2021, 'Unknown hostile environment-oriented autonomous WSN deployment using a mobile robot', JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, vol. 182.
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Feng, Y & Ying, M 2021, 'Quantum Hoare logic with classical variables.', CoRR, vol. abs/2008.06812.

Fernandez, E, Hossain, MJ, Mahmud, K, Nizami, MSH & Kashif, M 2021, 'A Bi-level optimization-based community energy management system for optimal energy sharing and trading among peers', Journal of Cleaner Production, vol. 279.
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© 2020 Elsevier Ltd The economic and environmental benefits of renewable energy have increased in significance over the past decade. Local energy markets can play a vital role in energy transition by facilitating the rapid proliferation of renewable-based energy resources, thereby increasing the renewable energy hosting capacity of the power grid. This paper proposes an energy management system for a smart locality that facilitates local energy trading involving consumers with renewable energy units, a central storage facility, and a power grid. Two optimization frameworks for sharing surplus onsite produced energy are developed here. The first framework maximizes the combined revenue of sellers and buyers, while the second, a game theoretical model, maximizes consumer utilization at the lower level and the revenue of the common storage facility at the higher level. An intensive study is carried out to investigate the benefits of energy sharing that maximizes overall revenue. The results indicate that the grid pricing scheme is a major factor that determines the revenue sharing between the central storage facility entity and the consumers. The first framework results in optimal resource allocation, while the second framework concentrates only on revenue generation. Results indicate that the energy seller profits are higher if the real-time grid prices are used and if the consumers are not charged according to their willingness to pay.

Fetanat, M, Keshtiara, M, Keyikoglu, R, Khataee, A, Daiyan, R & Razmjou, A 2021, 'Machine learning for design of thin-film nanocomposite membranes', Separation and Purification Technology, vol. 270.
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In this study, a novel machine learning approach is proposed for estimation of the permeate flux and foulant rejection in nanocomposite filtration membranes. Nine independent variables are fed to artificial neural networks (ANNs) including support, nanoparticles concentration, concentration of organic phase trimesoyl chloride (TMC) in-n-hexane (TMC in n-hexane), operation pressure, contact angle, thin layer thickness, location of the nanoparticles (NPs), post-treatment temperature and duration, and permeate flux and foulant rejection were derived as the outputs of the ANNs. The proposed method was evaluated on two datasets across training, validation and test datasets, and an unseen dataset. 2250 different initial weights and number of the neurons in the hidden layer for the proposed ANN models were considered and compared to find the optimized ANN models. The mean squared error (MSE) and coefficient of determination (R2) were employed to select the best 20 ANN models for further analysis. The proposed ANN models resulted in accurate estimates for both permeate flux and foulant rejection with R2 of 0.9958 and 0.9412 in all data included in the training, validation and test datasets and R2 of 0.9938 and 0.9811 in unseen dataset, respectively. In addition, results of sensitivity analysis revealed that post treatment temperature and contact angle were found the most important input variables for estimation of permeate flux and foulant rejection. The proposed method can provide valuable insights for formulating permeate flux and foulant rejection and considering the effects of each experimental condition on nanocomposite filtration membranes without doing real experiments, which is time-consuming and expensive.

Fis, AM & Cetindamar, D 2021, 'Unlocking the Relationship between Corporate Entrepreneurship and Firm Performance', ENTREPRENEURSHIP RESEARCH JOURNAL, vol. 11, no. 1.
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Fu, Q, Wang, D, Li, X, Yang, Q, Xu, Q, Ni, B-J, Wang, Q & Liu, X 2021, 'Towards hydrogen production from waste activated sludge: Principles, challenges and perspectives', Renewable and Sustainable Energy Reviews.
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Ganesan, B, Yip, J, Luximon, A, Gibbons, PJ, Chivers, A, Balasankar, SK, Tong, RK-Y, Chai, R & Al-Jumaily, A 2021, 'Infrared Thermal Imaging for Evaluation of Clubfoot After the Ponseti Casting Method—An Exploratory Study', Frontiers in Pediatrics.
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Gao, G, Yu, Y, Xie, J, Yang, J, Yang, M & Zhang, J 2021, 'Constructing multilayer locality-constrained matrix regression framework for noise robust face super-resolution', PATTERN RECOGNITION, vol. 110.
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Gao, H, Hussain, W, Yin, Y, Zhao, W & Iqbal, M 2021, 'Editorial: AI-based mobile multimedia computing for data-smart processing', COMPUTER NETWORKS, vol. 195.
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Gao, J, Wang, L, Luo, Z & Gao, L 2021, 'IgaTop: an implementation of topology optimization for structures using IGA in MATLAB', Structural and Multidisciplinary Optimization.
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Gao, Y, Sun, S, Zhang, X, Liu, Y, Hu, J, Huang, Z, Gao, M & Pan, H 2021, 'Amorphous Dual-Layer Coating: Enabling High Li-Ion Conductivity of Non-Sintered Garnet-Type Solid Electrolyte', ADVANCED FUNCTIONAL MATERIALS, vol. 31, no. 15.
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Gao, Y, Zhu, S, Yang, C & Wen, S 2021, 'State bounding for fuzzy memristive neural networks with bounded input disturbances', Neural Networks, vol. 134, pp. 163-172.
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© 2020 Elsevier Ltd This paper investigates the state bounding problem of fuzzy memristive neural networks (FMNNs) with bounded input disturbances. By using the characters of Metzler, Hurwitz and nonnegative matrices, this paper obtains the exact delay-independent and delay-dependent boundary ranges of the solution, which have less conservatism than the results in existing literatures. The validity of the results is verified by two numerical examples.

Gavhane, RS, Kate, AM, Soudagar, MEM, Wakchaure, VD, Balgude, S, Fattah, IMR, Nik-Ghazali, N-N, Fayaz, H, Khan, TMY, Mujtaba, MA, Kumar, R & Shahabuddin, M 2021, 'Influence of Silica Nano-Additives on Performance and Emission Characteristics of Soybean Biodiesel Fuelled Diesel Engine', Energies.
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Ge, Z, Chen, L, Yang, L, Gomez-Garcia, R & Zhu, X 2021, 'On-Chip Millimeter-Wave Integrated Absorptive Bandstop Filter in (Bi)-CMOS Technology', IEEE ELECTRON DEVICE LETTERS, vol. 42, no. 1, pp. 114-117.
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Ghanbari, F, Wang, Q, Hassani, A, Waclawek, S, Rodriguez-Chueca, J & Lin, K-YA 2021, 'Electrochemical activation of peroxides for treatment of contaminated water with landfill leachate: Efficacy, toxicity and biodegradability evaluation', CHEMOSPHERE, vol. 279.
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Ghani Varzaneh, M, Rajaei, A, Forouzesh, M, Siwakoti, Y & Blaabjerg, F 2021, 'A Single-Stage Multi-Port Buck-Boost Inverter', IEEE Transactions on Power Electronics.
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IEEE This paper presents a novel inverter topology with a multi-port structure, which aims to connect two independent DC sources to a three-phase load by using single-stage power conversion. The proposed inverter has been developed to be used in hybrid renewable energy applications such as photovoltaic (PV), fuel cell (FC), and battery energy storage systems. Compared to the conventional hybrid dual-source inverters that use a multi-input DC-DC converter to provide a DC-link voltage at the input of the inverter stage, the proposed dual-source inverter uses an integrated DC-AC power conversion stage. The conventional topologies use bulky electrolytic capacitors at the input of the inverter stage, which leads to lower voltage gain and reliability due to high parasitic ESR/ESL and short lifetime of these capacitors. Moreover, compared to existing multi-port voltage source inverters, the proposed topology uses lower semiconductors, cost, and weight and has higher voltage gain. Besides, the proposed topology draws continuous current from both input ports and there is magnetic isolation between the input sources. The analysis and performance of the proposed inverter are verified through both computer simulations and experimental results of a 600 W - 50 Hz laboratory prototype using the Simple Boost-SPWM modulation method.

Gharleghi, R, Wright, H, Luvio, V, Jepson, N, Luo, Z, Senthurnathan, A, Babaei, B, Prusty, BG, Ray, T & Beier, S 2021, 'A multi-objective optimization of stent geometries', JOURNAL OF BIOMECHANICS, vol. 125.
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Ghasemian, R, Shamshirian, A, Heydari, K, Malekan, M, Alizadeh-Navaei, R, Ebrahimzadeh, MA, Ebrahimi Warkiani, M, Jafarpour, H, Razavi Bazaz, S, Rezaei Shahmirzadi, A, Khodabandeh, M, Seyfari, B, Motamedzadeh, A, Dadgostar, E, Aalinezhad, M, Sedaghat, M, Razzaghi, N, Zarandi, B, Asadi, A, Yaghoubi Naei, V, Beheshti, R, Hessami, A, Azizi, S, Mohseni, AR & Shamshirian, D 2021, 'The role of vitamin D in the age of COVID-19: A systematic review and meta-analysis.', Int J Clin Pract, p. e14675.
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BACKGROUND: Evidence recommends that vitamin D might be a crucial supportive agent for the immune system, mainly in cytokine response regulation against COVID-19. Hence, we carried out a systematic review and meta-analysis in order to maximise the use of everything that exists about the role of vitamin D in the COVID-19. METHODS: A systematic search was performed in PubMed, Scopus, Embase and Web of Science up to December 18, 2020. Studies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review. RESULTS: Twenty-three studies containing 11 901 participants entered into the meta-analysis. The meta-analysis indicated that 41% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 29%-55%), and in 42% of patients, levels of vitamin D were insufficient (95% CI, 24%-63%). The serum 25-hydroxyvitamin D concentration was 20.3 ng/mL among all COVID-19 patients (95% CI, 12.1-19.8). The odds of getting infected with SARS-CoV-2 are 3.3 times higher among individuals with vitamin D deficiency (95% CI, 2.5-4.3). The chance of developing severe COVID-19 is about five times higher in patients with vitamin D deficiency (OR: 5.1, 95% CI, 2.6-10.3). There is no significant association between vitamin D status and higher mortality rates (OR: 1.6, 95% CI, 0.5-4.4). CONCLUSION: This study found that most of the COVID-19 patients were suffering from vitamin D deficiency/insufficiency. Also, there is about three times higher chance of getting infected with SARS-CoV-2 among vitamin-D-deficient individuals and about five times higher probability of developing the severe disease in vitamin-D-deficient patients. Vitamin D deficiency showed no significant association with mortality rates in this population.

Gheisari, S, Shariflou, S, Phu, J, Kennedy, PJ, Agar, A, Kalloniatis, M & Golzan, SM 2021, 'A combined convolutional and recurrent neural network for enhanced glaucoma detection', Scientific Reports.
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Ghobadi, R, Altaee, A, Zhou, JL, Karbassiyazdi, E & Ganbat, N 2021, 'Effective remediation of heavy metals in contaminated soil by electrokinetic technology incorporating reactive filter media', Science of the Total Environment, vol. 794.
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Soil contamination is increasingly a global problem with serious implications for human health. Among different soil decontamination approaches, electrokinetic (EK) remediation is a relatively new technology for treating organic and inorganic contaminants in soil. This research aims to develop an enhanced EK treatment method incorporating a compost-based reactive filter media (RFM) with the advantages of low-cost and strong affinity for heavy metals and test and improve the treatment efficiency for multiple heavy metals in natural soil. A series of EK operations were performed to investigate the performance of EK-RFM under different operating conditions such as the electric current and voltage, processing time, and the amount of RFM. The electric current and treatment time demonstrated a significant positive impact on removing Zn, Cd and Mn ions while changing the amount of RFM had an insignificant impact on the efficiency of heavy metals removal. Overall, 51.6%–72.1% removal of Zn, Cd, and Mn was achieved at 30.00 mA of electric current and 14 days of treatment duration. The energy consumption of the EK process was 0.17 kWh kg−1. The soil organic matter adversely affected the mobilization and migration of heavy metals such as Cu and Pb during EK treatment. The results are valuable in optimizing the design of the EK-RFM system, which will extend its application to field-scale soil decontamination practices.

Ghobadi, R, Altaee, A, Zhou, JL, McLean, P, Ganbat, N & Li, D 2021, 'Enhanced copper removal from contaminated kaolinite soil by electrokinetic process using compost reactive filter media', JOURNAL OF HAZARDOUS MATERIALS, vol. 402.

Ghosh, B, Fatahi, B, Khabbaz, H, Nguyen, HH & Kelly, R 2021, 'Field study and numerical modelling for a road embankment built on soft soil improved with concrete injected columns and geosynthetics reinforced platform', GEOTEXTILES AND GEOMEMBRANES, vol. 49, no. 3, pp. 804-824.
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Gibbons, J, Jones, CMS, Bennett, NS & Marques-Hueso, J 2021, 'Determination of the refractive index of BaY2F8:Er3+ (0.5 mol% to 30 mol%) in the 300 nm to 1800 nm range by ellipsometry; a record-breaking upconversion material', Journal of Luminescence.
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Gong, S, Guo, Z, Wen, S & Huang, T 2021, 'Finite-Time and Fixed-Time Synchronization of Coupled Memristive Neural Networks With Time Delay', IEEE TRANSACTIONS ON CYBERNETICS, vol. 51, no. 6, pp. 2944-2955.
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Gonzales, RR, Abdel-Wahab, A, Adham, S, Han, DS, Phuntsho, S, Suwaileh, W, Hilal, N & Shon, HK 2021, 'Salinity gradient energy generation by pressure retarded osmosis: A review', DESALINATION, vol. 500.
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Goodswen, SJ, Kennedy, PJ & Ellis, JT 2021, 'Applying Machine Learning to Predict the Exportome of Bovine and Canine Babesia Species That Cause Babesiosis', Pathogens.
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Goodswen, SJ, Kennedy, PJ & Ellis, JT 2021, 'Computational antigen discovery for eukaryotic pathogens using vacceed', vol. 2183, pp. 29-42.
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© Springer Science+Business Media, LLC, part of Springer Nature 2021. Bioinformatics programs have been developed that exploit informative signals encoded within protein sequences to predict protein characteristics. Unfortunately, there is no program as yet that can predict whether a protein will induce a protective immune response to a pathogen. Nonetheless, predicting those pathogen proteins most likely from those least likely to induce an immune response is feasible when collectively using predicted protein characteristics. Vacceed is a computational pipeline that manages different standalone bioinformatics programs to predict various protein characteristics, which offer supporting evidence on whether a protein is secreted or membrane -associated. A set of machine learning algorithms predicts the most likely pathogen proteins to induce an immune response given the supporting evidence. This chapter provides step by step descriptions of how to configure and operate Vacceed for a eukaryotic pathogen of the user’s choice.

Gowripalan, N, Shakor, P & Rocker, P 2021, 'Pressure exerted on formwork by self-compacting concrete at early ages: A review', Case Studies in Construction Materials.
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Gu, X, Cao, Z, Jolfaei, A, Xu, P, Wu, D, Jung, TP & Lin, CT 2021, 'EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications', IEEE/ACM Transactions on Computational Biology and Bioinformatics.
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Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.

Guan, J, Wang, Q & Ying, M 2021, 'AN HHL-BASED ALGORITHM FOR COMPUTING HITTING PROBABILITIES OF QUANTUM WALKS', QUANTUM INFORMATION & COMPUTATION, vol. 21, no. 5-6, pp. 395-408.
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Guan, Q, Huang, Y, Luo, Y, Liu, P, Xu, M & Yang, Y 2021, 'Discriminative Feature Learning for Thorax Disease Classification in Chest X-ray Images', IEEE Transactions on Image Processing.
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This paper focuses on the thorax disease classification problem in chest X-ray (CXR) images. Different from the generic image classification task, a robust and stable CXR image analysis system should consider the unique characteristics of CXR images. Particularly, it should be able to: 1) automatically focus on the disease-critical regions, which usually are of small sizes; 2) adaptively capture the intrinsic relationships among different disease features and utilize them to boost the multi-label disease recognition rates jointly. In this paper, we propose to learn discriminative features with a two-branch architecture, named ConsultNet, to achieve those two purposes simultaneously. ConsultNet consists of two components. First, an information bottleneck constrained feature selector extracts critical disease-specific features according to the feature importance. Second, a spatial-and-channel encoding based feature integrator enhances the latent semantic dependencies in the feature space. ConsultNet fuses these discriminative features to improve the performance of thorax disease classification in CXRs. Experiments conducted on the ChestX-ray14 and CheXpert dataset demonstrate the effectiveness of the proposed method.

Guff, T, McMahon, NA, Sanders, YR & Gilchrist, A 2021, 'A resource theory of quantum measurements', JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, vol. 54, no. 22.
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Gui, L, Xu, S, Xiao, F, Shu, F & Yu, S 2021, 'Non-Line-of-Sight Localization of Passive UHF RFID Tags in Smart Storage Systems', IEEE Transactions on Mobile Computing, pp. 1-1.
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Gunatilake, A, Piyathilaka, L, Tran, A, Viswanathan, VK, Thiyagarajan, K & Kodagoda, S 2021, 'Stereo Vision Combined with Laser Profiling for Mapping of Pipeline Internal Defects', IEEE Sensors Journal.
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IEEE Underground potable water pipes are essential infrastructure assets for any country. A significant proportion of those assets are deteriorating due to pipe corrosion which results in premature failure of pipes causing enormous disruptions to the public and loss to the economy. To address such adverse effects, the water utilities in Australia exploit advanced pipelining technologies with a motive of extending the service life of their pipe assets. However, the linings are prone to defects due to improper liner application and unfavorable environmental conditions during the liner curing phase. To monitor the imperfections of the pipe linings, in this article, we propose a mobile robotic sensing system that can scan, detect, locate and measure pipeline internal defects by generating three-dimensional RGB-Depth maps using stereo camera vision combined with infrared laser profiling unit. The system does not require complex calibration procedures and it utilizes orientation correction to provide accurate real-time RGB-D maps. The defects are identified and color mapped for easier visualization. The robotic sensing system was extensively tested in laboratory conditions followed by field deployments in buried water pipes in Sydney, Australia. The experimental results show that the RGB-D maps were generated with millimeter (mm) level accuracy with demonstrated liner defect quantification.

Guo, J & Ying, M 2021, 'Software Pipelining for Quantum Loop Programs.', CoRR, vol. abs/2012.12700.

Haering, M, Bano, M, Zowghi, D, Kearney, M & Maalej, W 2021, 'Automating the Evaluation of Education Apps with App Store Data', IEEE Transactions on Learning Technologies, pp. 1-1.
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Hamdan, M, Hassan, E, Abdelaziz, A, Elhigazi, A, Mohammed, B, Khan, S, Vasilakos, AV & Marsono, MN 2021, 'A comprehensive survey of load balancing techniques in software-defined network', JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, vol. 174.
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Han, B, Tsang, IW, Xiao, X, Chen, L, Fung, S-F & Yu, CP 2021, 'Privacy-Preserving Stochastic Gradual Learning', IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 33, no. 8, pp. 3129-3140.
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Han, R, Diao, J, Kumar, S, Lyalin, A, Taketsugu, T, Casillas, G, Richardson, C, Liu, F, Yoon, CW, Liu, H, Sun, X & Huang, Z 2021, 'Boron nitride for enhanced oxidative dehydrogenation of ethylbenzene', JOURNAL OF ENERGY CHEMISTRY, vol. 57, pp. 477-484.
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Han, R, Diao, J, Kumar, S, Lyalin, A, Taketsugu, T, Casillas, G, Richardson, C, Liu, F, Yoon, CW, Liu, H, Sun, X & Huang, Z 2021, 'Boron nitride for enhanced oxidative dehydrogenation of ethylbenzene', Journal of Energy Chemistry.
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Han, Y, Wu, A, Zhu, L & Yang, Y 2021, 'Visual commonsense reasoning with directional visual connections', Frontiers of Information Technology and Electronic Engineering, vol. 22, no. 5, pp. 625-637.
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To boost research into cognition-level visual understanding, i.e., making an accurate inference based on a thorough understanding of visual details, visual commonsense reasoning (VCR) has been proposed. Compared with traditional visual question answering which requires models to select correct answers, VCR requires models to select not only the correct answers, but also the correct rationales. Recent research into human cognition has indicated that brain function or cognition can be considered as a global and dynamic integration of local neuron connectivity, which is helpful in solving specific cognition tasks. Inspired by this idea, we propose a directional connective network to achieve VCR by dynamically reorganizing the visual neuron connectivity that is contextualized using the meaning of questions and answers and leveraging the directional information to enhance the reasoning ability. Specifically, we first develop a GraphVLAD module to capture visual neuron connectivity to fully model visual content correlations. Then, a contextualization process is proposed to fuse sentence representations with visual neuron representations. Finally, based on the output of contextualized connectivity, we propose directional connectivity to infer answers and rationales, which includes a ReasonVLAD module. Experimental results on the VCR dataset and visualization analysis demonstrate the effectiveness of our method.

Han, Z, Xu, C, Xiong, Z, Zhao, G & Yu, S 2021, 'On-Demand Dynamic Controller Placement in Software Defined Satellite-Terrestrial Networking', IEEE Transactions on Network and Service Management.
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Software defined satellite-terrestrial networking has been identified as a promising approach to support the diversity of network services. As the fundamental issue to improve the flexibility of network management, the controller placement problem has been attracted increasing attentions for the integration of satellite and terrestrial networking. However, the impact of the dynamic coverage demands on the controller placement have not been well investigated in existed works, which makes them fail to adjust the coverage dynamically according to the actual demands, and leads to an obvious increase of networking response latency to the terminals. Aiming to address this issue, we propose a novel on-demand dynamic controller placement scheme, which can optimize the placement of controllers to improve networking response latency while meeting the dynamic coverage demands. Firstly, to optimize the number of controllers and meet the dynamic coverage demands, we define the coverage redundancy and propose the redundancy-based satellite subnet division method to establish the reliable satellite subnets. Secondly, we quantify the networking response latency of the distributed satellite subnet, and build an optimization mathematical model to optimize the number and location of controllers. Then, we formulate the controller placement problem into the capacitated facility location problem and build the mathematical model for it. Moreover, the on-demand dynamic approximation algorithm is proposed to obtain the approximation solution. Finally, the simulation results demonstrate that the proposed algorithm can effectively optimize the network latency compared with related algorithms.

Hao, M, Zhang, W, Wang, Y, Lu, G, Wang, F & Vasilakos, AV 2021, 'Fine-Grained Powercap Allocation for Power-Constrained Systems Based on Multi-Objective Machine Learning', IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, vol. 32, no. 7, pp. 1789-1801.
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Harley, W, Yoshie, H & Gentile, C 2021, 'Three-Dimensional Bioprinting for Tissue Engineering and Regenerative Medicine in Down Under: 2020 Australian Workshop Summary.', ASAIO Journal.
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Hasan, M, Zhao, J, Jia, F, Wu, H, Ahmad, F, Huang, Z, Wei, D, Ma, L & Jiang, Z 2021, 'Optimisation of sintering parameters for bonding nanocrystalline cemented tungsten carbide powder and solid high strength steel', Composite Interfaces, pp. 1-16.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. In this study we examined the effects of compaction pressure for bonding nanocrystalline cemented tungsten carbide (WC-10Co) and high-strength steel (AISI4340) and successfully fabricated a bilayered composite of ceramic and steel. The obtained results were compared with our previous studies, and then the optimised sintering conditions were suggested. The compaction pressure examined varied from 120–200 MPa at 1150°C for 20 min. The study shows that the change in experimental parameters has significant effects on both the sintering properties of nanocrystalline WC-10Co powders and their bonding with AISI4340 steel. The microstructure reveals a successful metallurgical bonding between ceramic and steel. Bonding temperature determines, to a great extent, the diffusion processes across the bonding interface and has found to be the most influential variable compared to sintering time and compaction pressure. The obtained average maximum bonding strength of the bimetal composite is 226 MPa, which is higher than that of previous studies.

Hasanpour, S, Siwakoti, YP, Mostaan, A & Blaabjerg, F 2021, 'New Semiquadratic High Step-Up DC/DC Converter for Renewable Energy Applications', IEEE Transactions on Power Electronics, vol. 36, no. 1, pp. 433-446.
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© 1986-2012 IEEE. In this article, a new semiquadratic high step-up coupled-inductor dc/dc converter (SQHSUCI) with continuous input current and low voltage stress on semiconductor components is presented. The proposed structure employs a coupled-inductor (CI) and two power switches with simultaneous operation to achieve an extremely high voltage conversion ratio in a semiquadratic form. The voltage stress across the main power switch is clamped by two regenerative clamp capacitors. Here, the switching losses of both MOSFETs have been reduced by applying quasi-resonance operation of the circuit created by the leakage inductance of the CI along with the balancing and clamp capacitors. Therefore, by considering the high gain conversion ratio along with low voltage stress on components, the magnetic and semiconductors losses of the SQHSUCI are reduced significantly. Also, the energy stored in the leakage inductance of CI is recycled to the output capacitor. These features make the proposed SQHSUCI more suitable for industrial applications. The operation principle, steady state, and also comparisons with other related converters in continuous conduction mode (CCM) are discussed in detail. Finally, experimental results of a prototype with 20 V input and 200 W-200 V output at 50 kHz switching frequency, verify the theoretical advantages of the proposed strategy.

Hassan, M, Hossain, MJ & Shah, R 2021, 'Impact of Meshed HVDC Grid Operation and Control on the Dynamics of AC/DC Systems', IEEE Systems Journal.
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IEEE The efficacy of long-distance and bulk power transmission largely depends on the efficient control and reliable operation of a multiterminal high-voltage direct current (MT-HVdc) grid, more precisely, a meshed HVdc grid. The capability of enduring the dc grid fault eventually enhances the reliability and improves the dynamic performance of the grid. This article investigates the operation and control of an AC/multiterminal dc (MTDC) system with bipolar topology incorporating the dc grid protection schemes. Based on the scale of a circuit breaker's operating time, the performance of three different protection strategies is compared and analyzed using DIgSILENT PowerFactory. Simulation results explicitly reveal that the dynamic performance of the MTDC grid significantly deteriorates with the slow functioning of the protection schemes, followed by a dc grid fault. Besides, prolonged recovery time causes a substantial loss of power infeed and affects the ac/dc grid's stability. Finally, to assess the frailty of the MTDC grid, a transient energy stability index is proposed considering the voltage variation in the prestate and poststate fault clearing interval. Relevant case studies are performed on the MTDC grid using an analytical approach and nonlinear simulation studies to validate the effectiveness of the proposed index.

Hazrat, MA, Rasul, MG, Khan, MMK, Mofijur, M, Ahmed, SF, Ong, HC, Vo, D-VN & Show, PL 2021, 'Techniques to improve the stability of biodiesel: a review', ENVIRONMENTAL CHEMISTRY LETTERS, vol. 19, no. 3, pp. 2209-2236.
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He, D, Xiao, J, Wang, D, Liu, X, Fu, Q, Li, Y, Du, M, Yang, Q, Liu, Y, Wang, Q, Ni, B-J, Song, K, Cai, Z, Ye, J & Yu, H 2021, 'Digestion liquid based alkaline pretreatment of waste activated sludge promotes methane production from anaerobic digestion', WATER RESEARCH, vol. 199.
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He, X, Deng, L, Yang, Y & Feng, B 2021, 'Multifunctional ultrathin reflective metasurface via polarization-decoupled phase for arbitrary circularly or elliptically polarized waves', Optics Express, vol. 29, no. 8, pp. 12736-12749.
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Metasurface offers a promising platform in the design of multifunctional devices owing to its unique ability for EMWs manipulation. However, wave-manipulation capabilities for metasurfaces face challenges in manipulating orthogonal EMWs with arbitrary circularly or elliptically polarized EMWs in the microwave region. Herein, single-layer reflective metasurfaces are proposed for independent manipulation of an arbitrary set of orthogonal circularly or elliptically polarized EMWs via polarization-decoupled phase. Taking advantage of single-layer anisotropic meta-atoms, the proposed metasurface can act as a tandem phase modulator, which introduces polarization-decoupled phase profiles for arbitrary circularly and elliptically polarized EMWs based on the Jones matrix. In this way, the proposed metasurface can distinguish a set of orthogonal EMWs with circular or elliptical polarization states and impose arbitrary phase profiles on them independently and simultaneously. For proof-of-concept, bifunctional metasurfaces operating in the microwave region are presented for independent manipulation of three different sets of orthogonal circularly or elliptically polarized EMWs. They create dual independent channels associated with a pair of orthogonal polarization states, performing functions including polarization beam splitting and orbital angular momentum (OAM) multiplexing. Measured and simulated results show a good agreement, confirming that the proposed single-layer reflective metasurfaces are efficient devices that enable meta-devices to independently control arbitrary circular and elliptical polarized EMWs, achieving arbitrary functionalities.

He, X, Wang, F, Li, W & Sheng, D 2021, 'Efficient reliability analysis considering uncertainty in random field parameters: Trained neural networks as surrogate models', Computers and Geotechnics, vol. 136.
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This paper presents an efficient reliability analysis framework, by using trained artificial neural networks (ANNs) as surrogate models, for geotechnical problems where the random field parameters like the mean and standard deviation are themselves uncertain. Random field theory has been extensively used to model soil uncertainty and spatial variability. However, due to limited availability of data, random field parameters can rarely be estimated accurately, often estimated in confidence intervals (uncertain parameters). Monte Carlo based reliability analysis is computationally extremely demanding because the function to map outcomes of random fields to structural response can only be calculated via numerical simulations. The authors have used trained ANNs as surrogate models in reliability analysis. However, these ANNs are specific for random fields with deterministic parameters. This paper presents a new framework in which trained ANN models are for random fields with variable parameters. A key component is the design of experiments – generating representative outcomes. In the prediction of the bearing capacity for strip footings, the efficiency and accuracy of this framework are successfully demonstrated. This framework is also efficient in reliability sensitivity studies. One main finding is that ignoring random field parameter uncertainty could lead to underestimated failure probability and hence unsafe design.

He, X, Yang, Y, Deng, L, Li, S & Feng, B 2021, '3D Printed Sub-Terahertz All-Dielectric Lens for Arbitrary Manipulation of Quasi-Nondiffractive Orbital Angular Momentum Waves', ACS Applied Materials and Interfaces, vol. 13, no. 17, pp. 20770-20778.
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Terahertz (THz) vortex waves carrying orbital angular momentum (OAM) hold great potential in dealing with the capacity crunch in wireless high-speed communication systems. Nevertheless, it is quite a challenge for the widespread applications of OAM in the THz regime due to the beam divergence and stringent alignment requirement. To address this issue, an all-dielectric lens (ADL) is proposed for the arbitrary manipulation of quasi-nondiffractive THz OAM waves (QTOWs). On the basis of the concept of the optical conical lens and the multivorticity metasurface, the beam number, the topological charge (TC), and the deflection angle as well as the nondiffractive depth of the generated THz OAM waves are controllable. For proof-of-concept, two ADLs are 3D printed to create single and dual deflected QTOWs, respectively. Remarkably, measured by a THz imaging camera, the desired QTOWs with high mode purity are observed in predesigned directions with a nondiffractive depth predefined theoretically. The proposed designs and experiments, for the first time, verified that the QTOWs could be achieved with a nondiffractive range of 55.58λg (λg = wavelength at 140 GHz) and large deflection angles of 30° and 45°.

Hembram, TK, Saha, S, Pradhan, B, Abdul Maulud, KN & Alamri, AM 2021, 'Robustness analysis of machine learning classifiers in predicting spatial gully erosion susceptibility with altered training samples', Geomatics Natural Hazards and Risk.
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Hesamian, MH, Jia, W, He, X, Wang, Q & Kennedy, PJ 2021, 'Synthetic CT images for semi-sequential detection and segmentation of lung nodules', Applied Intelligence.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Accurately detecting and segmenting lung nodules from CT images play a critical role in the earlier diagnosis of lung cancer and thus have attracted much interest from the research community. However, due to the irregular shapes of nodules, and the low-intensity contrast between the nodules and other lung areas, precisely segmenting nodules from lung CT images is a very challenging task. In this paper, we propose a highly effective and robust solution to this problem by innovatively utilizing the changes of nodule shapes over continuous slices (inter-slice changes) and develop a deep learning based end-to-end system. Different from the existing 2.5D or 3D methods that attempt to explore the inter-slice features, we propose to create a novel synthetic image to depict the unique changing pattern of nodules between slices in distinctive colour patterns. Based on the new synthetic images, we then adopt the deep learning based image segmentation techniques and develop a modified U-Net architecture to learn the unique color patterns formed by nodules. With our proposed approach, the detection and segmentation of nodules can be achieved simultaneously with an accuracy significantly higher than the state of the arts by 10% without introducing high computation cost. By taking advantage of inter-slice information and form the proposed synthetic image, the task of lung nodule segmentation is done more accurately and effectively.

Hesam-Shariati, N, Newton-John, T, Singh, AK, Tirado Cortes, CA, Do, T-TN, Craig, A, Middleton, JW, Jensen, MP, Trost, Z, Lin, C-T & Gustin, SM 2021, 'Evaluation of the Effectiveness of a Novel Brain-Computer Interface Neuromodulative Intervention to Relieve Neuropathic Pain Following Spinal Cord Injury: Protocol for a Single-Case Experimental Design With Multiple Baselines (Preprint)', JMIR Research Protocols.
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Background: Neuropathic pain is a debilitating secondary condition for many individuals with spinal cord injury (SCI). SCI neuropathic pain often remains poorly responsive to existing pharmacological and non-pharmacological treatments. A growing body of evidence supports the potential for brain-computer interface (BCI) systems to reduce SCI neuropathic pain via electroencephalography (EEG) neurofeedback. However, further studies are needed to provide more definitive evidence regarding the effectiveness of this intervention. Objective: The primary objective of this study is to evaluate the effectiveness of a multi-day course of BCI-N intervention in a gaming environment to provide pain relief for individuals with neuropathic pain following SCI. Methods: We have developed a novel BCI-based neuromodulative (BCI-N) intervention for SCI neuropathic pain. Our BCI-N treatment includes an interactive gaming interface, and a neuromodulation protocol targeted to suppress theta (4-8 Hz) and high beta (20-30 Hz) frequency powers, and enhance alpha (9-12 Hz) power. A single-case experimental design (SCED) with multiple baselines will be used to examine the effectiveness of our self-developed BCI-N intervention for the treatment of SCI neuropathic pain. Three participants with SCI neuropathic pain will be recruited. Each participant will be randomly allocated to a different baseline phase (i.e., 7, 10 or 14 days), which will then be followed by 20 sessions of 30-min BCI-N intervention over a 4-week period. The visual analogue scale assessing average pain intensity will serve as the primary outcome measure. Pain interference will also be assessed as a secondary outcome domain. Generalisation measures will assess quality of life, sleep quality, anxiety and depressive symptoms as well as resting-state EEG and thalamic γ-aminobutyric acid concentration (GABA). Results: This study was approved by the Human Research Committees of the University of New South Wales (HC190411) i...

Hickey, BA, Chalmers, T, Newton, P, Lin, C-T, Sibbritt, D, McLachlan, CS, Clifton-Bligh, R, Morley, J & Lal, S 2021, 'Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review', Sensors.
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Hill, M & Nham, T 2021, 'Global miRNA to miRNA Interactions: Impacts for miR-21', TRENDS IN CELL BIOLOGY, vol. 31, no. 1, pp. 3-5.
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Ho-Le, TP, Tran, HTT, Center, JR, Eisman, JA, Nguyen, HT & Nguyen, TV 2021, 'Assessing the clinical utility of genetic profiling in fracture risk prediction: a decision curve analysis.', Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, vol. 32, no. 2, pp. 271-280.
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Using decision curve analysis on 2188 women and 1324 men, we found that an osteogenomic profile constructed from 62 genetic variants improved the clinical net benefit of fracture risk prediction over and above that of clinical risk factors and BMD.

Introduction

Genetic profiling is a promising tool for assessing fracture risk. This study sought to use the decision curve analysis (DCA), a novel approach to determine the impact of genetic profiling on fracture risk prediction.

Methods

The study involved 2188 women and 1324 men, aged 60 years and above, who were followed for up to 23 years. Bone mineral density (BMD) and clinical risk factors were obtained at baseline. The incidence of fracture and mortality were recorded. A weighted individual genetic risk score (GRS) was constructed from 62 BMD-associated genetic variants. Four models were considered: CRF (clinical risk factors); CRF + GRS; Garvan model (GFRC) including CRF and femoral neck BMD; and GFRC + GRS. The DCA was used to evaluate the clinical net benefit of predictive models at a range of clinically reasonable risk thresholds.

Results

In both women and men, the full model GFRC + GRS achieved the highest net benefits. For 10-year risk threshold > 18% for women and > 15% for men, the GRS provided net benefit above those of the CRF models. At 20% risk threshold, adding the GRS could help to avoid 1 additional treatment per 81 women or 1 per 24 men compared with the Garvan model. At lower risk thresholds, there was no significant difference between the four models.

Conclusions

The addition of genetic profiling into the clinical risk factors can improve the net clinical benefit at higher risk thresholds of fracture. Although the contribution of genetic profiling was modest in the presence of BMD + CRF, it appeared to be able to replace BMD for fracture prediction.

Hong, H, Li, X, Pan, Y & Tsang, I 2021, 'Domain-adversarial Network Alignment', IEEE Transactions on Knowledge and Data Engineering.
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Network alignment is a critical task to a wide variety of fields. Many existing works leverage on representation learning to accomplish this task without eliminating domain representation bias induced by domain-dependent features, which yield inferior alignment performance. This paper proposes a unified deep architecture (DANA) to obtain a domain-invariant representation for network alignment via an adversarial domain classifier. Specifically, we employ the graph convolutional networks to perform network embedding under the domain adversarial principle, given a small set of observed anchors. Then, the semi-supervised learning framework is optimized by maximizing a posterior probability distribution of observed anchors and the loss of a domain classifier simultaneously. We also develop a few variants of our model, such as, direction-aware network alignment, weight-sharing for directed networks and simplification of parameter space. Experiments on three real-world social network datasets demonstrate that our proposed approaches achieve state-of-the-art alignment results.

Hoque, MA-A, Pradhan, B, Ahmed, N & Sohel, MSI 2021, 'Agricultural drought risk assessment of Northern New South Wales, Australia using geospatial techniques', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 756.
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Hoque, MA-A, Pradhan, B, Ahmed, N, Ahmed, B & Alamri, AM 2021, 'Cyclone vulnerability assessment of the western coast of Bangladesh', Geomatics Natural Hazards and Risk.
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Hoseini, SA, Fallahpour, A, Wong, KY, Mahdiyar, A, Saberi, M & Durdyev, S 2021, 'Sustainable Supplier Selection in Construction Industry through Hybrid Fuzzy-Based Approaches', Sustainability.
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Hossain Lipu, MS, Hannan, MA, Karim, TF, Hussain, A, Saad, MHM, Ayob, A, Miah, MS & Indra Mahlia, TM 2021, 'Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook', Journal of Cleaner Production, vol. 292, pp. 126044-126044.
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Hossain, SM, Park, H, Kang, H-J, Mun, JS, Tijing, L, Rhee, I, Kim, J-H, Jun, Y-S & Shon, HK 2021, 'Facile synthesis and characterization of anatase TiO2/g-CN composites for enhanced photoactivity under UV-visible spectrum', CHEMOSPHERE, vol. 262.
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Hossain, SM, Tijing, L, Suzuki, N, Fujishima, A, Kim, J-H & Shon, HK 2021, 'Visible light activation of photocatalysts formed from the heterojunction of sludge-generated TiO₂ and g-CN towards NO removal.', Journal of hazardous materials, vol. 422, p. 126919.
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The feasibility of preparing TiO₂/g-CN heterojunction from Ti-incorporated dried dye wastewater sludge is explored in this study. Two reaction routes of composite formation were evaluated. In the initial approach, one-step calcination of dried sludge and melamine mixture @600 °C was carried out. Detailed morphological and chemical characterizations showed that the one-step calcination route did not create TiO₂/g-CN composites; instead, only N-doped anatase TiO₂ composites were formed. Moreover, due to the non-uniform composition of organic content in the dried sludge, it was not easy to control the N doping level by varying melamine content (0-100%) in the precursor mix. However, successful formation of anatase TiO₂ and g-CN was observed when a two-step calcination route was followed, i.e., via synthesis of anatase TiO₂ from dried sludge, and later development of heterojunction by calcining (@550 °C) the TiO₂ and melamine mixture. X-ray diffraction along with infrared and X-ray photoelectron spectroscopy verified the effective heterojunction. In addition, maximum atmospheric NO removal under UV and visible light were observed for the prepared composite when the melamine content in the precursor mixture was 70%. After 1 h of UV and visible light irradiation, the best TiO₂/g-CN composite removed 25.71% and 13.50% of NO, respectively. Optical characterization suggested that the enhanced NO oxidation under UV/visible light was due to the bandgap narrowing and diminished photogenerated electron-hole recombination.

Hu, G, Wang, J, Qiao, H, Zhao, L, Li, Z & Tang, L 2021, 'An experimental study of a two‐degree‐of‐freedom galloping energy harvester', International Journal of Energy Research, vol. 45, no. 2, pp. 3365-3374.
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Hu, J, Yan, C, Liu, X, Li, Z, Ren, C, Zhang, J, Peng, D & Yang, Y 2021, 'An integrated classification model for incremental learning.', Multimedia tools and applications, pp. 1-16.
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Incremental Learning is a particular form of machine learning that enables a model to be modified incrementally, when new data becomes available. In this way, the model can adapt to the new data without the lengthy and time-consuming process required for complete model re-training. However, existing incremental learning methods face two significant problems: 1) noise in the classification sample data, 2) poor accuracy of modern classification algorithms when applied to modern classification problems. In order to deal with these issues, this paper proposes an integrated classification model, known as a Pre-trained Truncated Gradient Confidence-weighted (Pt-TGCW) model. Since the pre-trained model can extract and transform image information into a feature vector, the integrated model also shows its advantages in the field of image classification. Experimental results on ten datasets demonstrate that the proposed method outperform the original counterparts.

Hu, S, Ni, W, Wang, X, Jamalipour, A & Ta, D 2021, 'Joint Optimization of Trajectory, Propulsion, and Thrust Powers for Covert UAV-on-UAV Video Tracking and Surveillance', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 16, pp. 1959-1972.
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Hu, X, Ye, D, Zhu, T & Huo, H 2021, 'A Differentially Private Auction Mechanism in Online Social Networks', Journal of Systems Science and Systems Engineering.
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The growing popularity of users in online social network gives a big opportunity for online auction. The famous Information Diffusion Mechanism (IDM) is an excellent method even meet the incentive compatibility and individual rationality. Although the existing auction in online social network has considered the buyers’ information which is not known by the seller, current mechanism still can not preserve the privacy information of users in online social network. In this paper, we propose a novel mechanism based on the IDM and differential privacy. Our mechanism can successfully process the auction and at the same time preserve clients’ price information from neighbours. We achieved these by adding virtual nodes to each node and Laplace noise for its price in the auction process. We also formulate this mechanism on the real network and the random network, scale-free network to show the feasibility and effectiveness of the proposed mechanism. The evaluation shows that the result of our methods only depend on the noise added to the agents. It is independent from the agents’ original price.

Hu, Y, Xiong, F, Pan, S, Xiong, X, Wang, L & Chen, H 2021, 'Bayesian personalized ranking based on multiple-layer neighborhoods', INFORMATION SCIENCES, vol. 542, pp. 156-176.
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Hu, Z, Lin, W, Chen, Z, Wang, W, Wang, S, Li, J & Al-Sheikh, A 2021, 'Omnidirectional Circularly Polarized Antenna Based on Counter-Wound Helices', IEEE Transactions on Antennas and Propagation.
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An innovative counter-wound helical antenna with omnidirectional circular polarization (OCP) is reported in this communication, with a significantly enhanced radiation resistance and matching bandwidth compared with the conventional normal-mode CP helix counterpart. The antenna is realized by combining several helical radiators with opposite winding directions. A 3-segment OCP counter-wound helix is studied, featuring one right-handed helix in the middle and two left-handed helices sandwiching it from the top and the bottom. Besides achieving a stable OCP pattern with gain variations of less than 0.2 dB, the radiation resistance at the operating frequency f0 is increased to 26.7 Ω compared with 3.3 Ω of the traditional uniform-wound helix. Theoretical analysis and simulations are presented. To further enhance the radiation resistance, a 5-segment helical antenna prototype is demonstrated and tested. The radiation resistance rises further to 80.9 Ω at f0 and the measured matching bandwidth reaches 2.8%, surpassing that of the conventional OCP helix by four times. Stable OCP radiation with a realized gain of up to 2.5 dBic is observed. Being compact and simple, the developed helical antenna could represent an ideal candidate for various wireless applications, e.g. Device-to-Device (D2D) communications that require OCP radiations.

Hua, W, Sui, Y, Wan, Y, Liu, G & Xu, G 2021, 'FCCA: Hybrid Code Representation for Functional Clone Detection Using Attention Networks', IEEE Transactions on Reliability, vol. 70, no. 1, pp. 304-318.
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Code cloning, which reuses a fragment of source code via copy-and-paste with or without modifications, is a common way for code reuse and software prototyping. However, the duplicated code fragments often affect software quality, resulting in high maintenance cost. The existing clone detectors using shallow textual or syntactical features to identify code similarity are still ineffective in accurately finding sophisticated functional code clones in real-world code bases. This article proposes functional code clone detector using attention ( FCCA ), a deep-learning-based code clone detection approach on top of a hybrid code representation by preserving multiple code features, including unstructured (code in the form of sequential tokens) and structured (code in the form of abstract syntax trees and control-flow graphs) information. Multiple code features are fused into a hybrid representation, which is equipped with an attention mechanism that pays attention to important code parts and features that contribute to the final detection accuracy. We have implemented and evaluated FCCA using 275 777 real-world code clone pairs written in Java. The experimental results show that FCCA outperforms several state-of-the-art approaches for detecting functional code clones in terms of accuracy, recall, and F1 score.

Huang, J & Ji, J 2021, 'Vibration control of coupled Duffing oscillators in flexible single-link manipulators', JVC/Journal of Vibration and Control.
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© The Author(s) 2020. Motion-induced oscillations of the flexible single link and its payload at the tip have negative impact on the anticipated performance of the flexible manipulators and thus should be suppressed to achieve tip positioning accuracy and high-speed operation. Because of the structural flexibility, the dynamics of the flexible manipulator can be described by coupled Duffing oscillators when considering the inherent structural nonlinearity of the flexible link into the dynamic modeling. However, little research has been focused on addressing the dynamic coupling issue in the nonlinear modeling of flexible-link manipulators using coupled Duffing oscillators. This article presents coupled Duffing oscillators for the nonlinear modeling of flexible single-link manipulators and then proposes a control method for suppressing the nonlinear vibrations of the coupled Duffing oscillators. Simulated and experimental results obtained from a flexible single-link manipulator test bench are in good agreement with the proposed nonlinear modeling and also demonstrate the effectiveness of the proposed control techniques for vibration suppression of the flexible manipulator.

Huang, J, Li, S, Zhou, Y, Xu, T, Li, Y, Wang, H & Wang, S 2021, 'A heavy-duty magnetorheological fluid mount with flow and squeeze model', Smart Materials and Structures.

Huang, L, Liu, Z, Wu, C & Liang, J 2021, 'Interaction between a tunnel and alluvial valley under plane SV waves of earthquakes by IBIEM', European Journal of Environmental and Civil Engineering.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This paper investigates the dynamic interaction between a lined tunnel and an alluvial valley under plane SV waves with the indirect boundary integral equation method (IBIEM). The impact of different parameters on the displacement of sedimentary valley and the dynamic stress concentration factors (DSCF) of the lining inner and outer walls are studied. The dynamic response of a tunnel embedded in an alluvial valley is considerably different from that of a tunnel not embedded in an alluvial valley. Numerical results indicate that under the same frequency, different buried depths can change the distribution of displacement in the sedimentary area. The deeper the tunnel is buried, the more resonance points the response spectrum curve has. In general, the DSCF on the inner surfaces of the tunnels embedded in the sedimentary valley is obviously greater than that of the tunnels in the half space. However, the DSCF on the outer surfaces of the former is smaller than that of the latter. The results reported here will provide a quantitative basis to the security assessment and seismic design of lined tunnels.

Huang, Q, Wang, C, Hao, D, Wei, W, Wang, L & Ni, BJ 2021, 'Ultralight biodegradable 3D-g-C3N4 aerogel for advanced oxidation water treatment driven by oxygen delivery channels and triphase interfaces', Journal of Cleaner Production, vol. 288.
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© 2020 Elsevier Ltd The development of highly efficient and separation-free, low-cost photocatalysts have crucial prospect for sustainable wastewater treatment, because it is able to eliminate the hazards of organic pollutant with facile operation. However, the relatively high cost of previous photocatalysts highly obstructs the application of these materials. Herein, we report a cost-effective and distinct konjac/graphitic carbon nitride (KCN) aerogel, which has superior performance for advanced oxidation water treatment. The abundant porous structure of the ultralight aerogel ensures the rapid adsorption of pollutants, which is much helpful for the further photodegradation process. During the working process, the aerogel is half submerged in pollutant solution and half exposed in air, forming a distinctive gas-solid-liquid triphase system, where oxygen can be rapidly delivered into the solution via the porous channels, boosting the generation of hydroxyl and superoxide radicals. Meanwhile, the aerogel structure can separate the g-C3N4, obstruct its stacking, as well as improve the light absorption rate. The synthesis, utilization and readily biodegradable treatment of the KCN aerogels are all green and eco-friendly, which is extremely constructive for strategies to develop novel highly efficient photocatalytic materials.

Huang, S, Samali, B & Li, J 2021, 'Numerical and experimental investigations of a thermal break composite façade mullion under four-point bending', Journal of Building Engineering, vol. 34.
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© 2020 Elsevier Ltd This paper presents numerical and experimental investigations on a typical thermal break composite façade profile under four-point bending. The purpose of this study is to gain the knowledge of the interfacial behaviour between aluminum extrusion and polyamide insert beyond elastic range. Understanding the behaviour of this energy efficient façade profile within plastic range is important for the design under extreme loading, such as earthquakes, strong wind conditions and even blast loads. The experimental investigation was carried out on four types of beam specimens. The specimens were grouped by their span lengths with three specimens for each span length. As the specimens’ geometry and composite action are complicated, seven strain gauges were used per specimen including small strain gauges to fit in the limited space of the thermal break section. A three stage failure process was observed during the experiments. A numerical investigation was carried out by using Finite Element modelling to simulate behaviour of the thermal break composite façade profile under similar loading condition in order to compare with the testing results as well as to capture the corresponding failure mechanisms. Numerical simulations were setup by applying a proposed partitioned multi-phase failure model to simulate three stage failure process discovered by experiments. The results from FE models were compared and discussed with experimental counterparts. In summary, FE models showed consistent results to the experimental counterparts and it also provided the insight and more details of failure mechanism and stress distribution including interfacial condition details. Behaviour of the thermal break façade profile in the plastic range displayed excellent ductility and high strength capacity of this type of thermal break section in the plastic range after slip.

Huang, X, Le, AT & Guo, J 2021, 'Transmit Beamforming for Communication and Self-Interference Cancellation in Full Duplex MIMO Systems: A Trade-Off Analysis', IEEE Transactions on Wireless Communications, pp. 1-1.
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The performance of transmit beamforming for both optimized precoding and self-interference cancellation (SIC) in full duplex multiple input multiple output (MIMO) transceivers is analysed in this paper. With sub-space dimension larger than that of the null-space of the self-interference channels, the precoding error is reduced but the interference suppression ratio (ISR) is degraded, resulting in a trade-off between multibeam communication and MIMO SIC. An analytical approach for the ISR evaluation is proposed assuming known eigenvalue distribution of the self-interference channels, and a closed-form ISR expression is derived after applying a uniform distribution approximation. The ISR and precoding error trade-off curves are also formulated. Joint SIC by transmit beamforming and beam-based analog adaptive filters over both propagation and analog domains is proposed to achieve better SIC performance and enable more flexible receive antenna selection. Simulation results verify the theoretical analyses.

Huang, X, Le, AT & Jay Guo, Y 2021, 'ALMS Loop Analyses with Higher-Order Statistics and Strategies for Joint Analog and Digital Self-Interference Cancellation', IEEE Transactions on Wireless Communications.
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Joint analog and digital self-interference cancellation (SIC) is essential for enabling in-band full duplex (IBFD) communications. Analog least mean square (ALMS) loop is a promising low-complexity high-performance analog SIC technique with multi-tap adaptive filtering capability, but its properties on the tap coefficient variation have not been fully understood. In this paper, analysis based on higher-order statistics of the transmitted signal is performed to solve the problem of evaluating the variance of the ALMS loop’s weighting coefficient error, which reveals two additional types of irreducible residual self-interference (SI) produced by an ALMS loop if it runs freely. The residual SI channel impulse response in digital baseband is also analysed and its unique properties are investigated. By introducing a simple track and hold control to the ALMS loop’s tap coefficients, a joint analog and digital SIC scheme is proposed to stop the tap coefficient variation and achieve very low residual SI close to the IBFD receiver’s noise floor. In a coordinated application scenario, the noise figure of the digital SIC algorithm is proved to be only 1.76 dB at most. Simulation results are provided to verify the theoretical analyses.

Huang, Y, Lei, C, Liu, C-H, Perez, P, Forehead, H, Kong, S & Zhou, JL 2021, 'A review of strategies for mitigating roadside air pollution in urban street canyons*', ENVIRONMENTAL POLLUTION, vol. 280.
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Huang, Y, Song, R, Argha, A, Celler, BG, Savkin, AV & Su, SW 2021, 'Human motion intent description based on bumpless switching mechanism for rehabilitation robot', IEEE Transactions on Neural Systems and Rehabilitation Engineering.
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Huang, Y, Surawski, NC, Zhuang, Y, Zhou, JL & Hong, G 2021, 'Dual injection: An effective and efficient technology to use renewable fuels in spark ignition engines', Renewable and Sustainable Energy Reviews, vol. 143.
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Modern spark ignition engines mostly use one injection system to deliver gasoline into the combustion chamber, using either direct injection or port fuel injection. Both technologies have their respective advantages. To integrate their advantages and to promote the use of renewable fuels, dual injection engines are in development in recent years. Dual injection represents an advanced combustion system and is a novel technology to address the urgent issues of sustainability and environmental protection. This study reviews the state-of-the-art research on dual injection spark ignition engines with a focus on renewable fuels, their advantages and engine performance. The main advantages of dual injection include greater control flexibility, enhanced cooling effect, knock mitigation, engine downsizing, extended lean-burn limits, higher thermal efficiency and reductions of several emission species. The most promising renewable fuels for dual injection are ethanol, methanol and hydrogen. Each renewable fuel is aimed at different advantages of dual injection. Alcohol-gasoline dual injection provides great anti-knock ability by taking advantage of alcohols' large enthalpies of vaporisation and high octane numbers, while hydrogen-gasoline dual injection provides extended lean-burn limits by taking advantage of hydrogen's low ignition energy, wide flammability limit and high flame speed. Direct injection of renewable fuels is the optimal injection strategy because it effectively utilises the strong cooling effect of alcohols or avoids the volumetric efficiency reduction and pre-ignition of hydrogen. Dual injection generally demonstrates higher thermal efficiency than single injection. In addition, dual injection effectively reduces particulate emissions while there are usually trade-offs among gaseous emissions.

Huang, Y, Xu, H, Gao, H, Ma, X & Hussain, W 2021, 'SSUR: An Approach to Optimizing Virtual Machine Allocation Strategy Based on User Requirements for Cloud Data Center', IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING, vol. 5, no. 2, pp. 670-681.
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Huang, Y, Zhu, F, Porter, AL, Zhang, Y, Zhu, D & Guo, Y 2021, 'Exploring Technology Evolution Pathways to Facilitate Technology Management: From a Technology Life Cycle Perspective', IEEE Transactions on Engineering Management.
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IEEE Technological innovation is a dynamic process that spans the life cycle of an idea, from scientific research to production. Within this process, there are often a few key innovations that significantly impact a technology's development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In this article, we present a framework for identifying the technology's main evolutionary pathway. What is unique about this framework is that we introduce new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology's development. We also show how information about a family of patents can be used to build a comprehensive patent citation network. Finally, we apply integrated approaches of main path analysis (MPA)—namely global MPA and global key-route main analysis—for extracting technological trajectories at different technological stages. We illustrate this approach with dye-sensitized solar cells (DSSCs), a low-cost solar cell belonging to the group of thin-film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision makers manage the technological stages of their innovation processes more effectively.

Ibrahim, I, Bhoopal, V, Seo, DH, Afsari, M, Shon, HK & Tijing, LD 2021, 'Biomass-based photothermal materials for interfacial solar steam generation: a review', Materials Today Energy, vol. 21, pp. 100716-100716.
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Ibrahim, I, Seo, DH, McDonagh, AM, Shon, HK & Tijing, L 2021, 'Semiconductor photothermal materials enabling efficient solar steam generation toward desalination and wastewater treatment', Desalination.
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© 2020 Elsevier B.V. Water scarcity issues around the world have renewed interest in the use of solar water evaporation as a means of providing fresh water. Advances in photothermal materials and thermal management, together with new interfacial system designs, have considerably improved the overall efficiency of solar steam generation (SSG) for desalination and wastewater treatment. Several classes of rationally-designed photothermal materials (PTMs) and nanostructures have enabled effective absorption of broad solar spectrum resulting in improved solar evaporation efficiency. Among several classes of PTMs, semiconductor-based PTMs have demonstrated great potential for SSG. In this review, we highlight the progress and prospects in SSG with emphasis on the use and evolution of advanced semiconductor materials for PTMs and their various designs and engineered architectures. Applications and future prospects for desalination and wastewater treatment are also discussed.

Ibrar, I, Yadav, S, Ganbat, N, Samal, AK, Altaee, A, Zhou, JL & Nguyen, TV 2021, 'Feasibility of H2O2 cleaning for forward osmosis membrane treating landfill leachate', JOURNAL OF ENVIRONMENTAL MANAGEMENT, vol. 294.
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Ikram, MM, Saha, G & Saha, SC 2021, 'Conjugate forced convection transient flow and heat transfer analysis in a hexagonal, partitioned, air filled cavity with dynamic modulator', INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, vol. 167.
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Inayat, A, Shahbaz, M, Khan, Z, Inayat, M, Mofijur, M, Ahmed, SF, Ghenai, C & Ahmad, AA 2021, 'Heat integration modeling of hydrogen production from date seeds via steam gasification', International Journal of Hydrogen Energy, vol. 46, no. 59, pp. 30592-30605.
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The purpose of the current study is to identify the potential of energy-efficient hydrogen (H2) production from date seeds as biomass via steam gasification process along with heat integration in Gulf countries. A reaction kinetics model has been established for steam gasification with in-situ carbon dioxide (CO2) capture of date seeds using MATLAB software. The kinetics of reactions involved in the gasification process was calculated using the optimization parameters fitting approach. The heat integration model has been developed via mixed integer nonlinear programming (MINLP) in MATLAB. In the parametric study, temperature and steam/biomass ratio considered their impact on syngas composition and energy recovery. Results showed that both variables have a strong positive effect on H2 production and depicted maximum production of 68 mol% at a temperature of 750 °C with steam/biomass ratio of 1.2. Methane (CH4) and CO2 production were low in the product gas, which showed the activity of water gas shift reaction, methanation reaction, and carbonation reaction. Utilization of waste heat via process heat integration within the system reduced system's external heat load. More than 70% of energy recovered, which could be utilized for gasification and steam production. Energy analysis and process heat integration proved a prospective approach for energy-efficient and sustainable hydrogen production from date seeds.

Indraratna, B, Nghi, MP, Thanh, TN & Huang, J 2021, 'Simulating Subgrade Soil Fluidization Using LBM-DEM Coupling', INTERNATIONAL JOURNAL OF GEOMECHANICS, vol. 21, no. 5.
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Indraratna, B, Ngo, T, Ferreira, FB, Rujikiatkamjorn, C & Tucho, A 2021, 'Large-scale testing facility for heavy haul track', TRANSPORTATION GEOTECHNICS, vol. 28.
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Indraratna, B, Soomro, MHAA & Rujikiatkamjorn, C 2021, 'Semi-empirical analytical modelling of equivalent dynamic shear strength (EDSS) of rock joint', Transportation Geotechnics, vol. 29.
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A systematic dynamic triaxial series of tests on replicated rough rock joints were carried out, and results clearly highlight the strength attenuation as a function of joint degradation with respect to the number of loading cycles. A novel semi-empirical mathematical model to evaluate the equivalent dynamic shear strength (EDSS) of rock joint is proposed and validated with experimental results based on two sets of rock joints using rough (JRC = 12.6) and relatively smoother (JRC = 7.2) joint specimens.

Ishac, K & Eager, D 2021, 'Evaluating Martial Arts Punching Kinematics Using a Vision and Inertial Sensing System', Sensors.
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Islam, MR, Lu, H, Hossain, MJ & Li, L 2021, 'Optimal Coordination of Electric Vehicles and Distributed Generators for Voltage Unbalance and Neutral Current Compensation', IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, vol. 57, no. 1, pp. 1069-1080.
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Islam, MS, Larpruenrudee, P, Hossain, SI, Rahimi-Gorji, M, Gu, Y, Saha, SC & Paul, G 2021, 'Polydisperse Aerosol Transport and Deposition in Upper Airways of Age-Specific Lung', International Journal of Environmental Research and Public Health.
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Islam, MS, Larpruenrudee, P, Paul, AR, Paul, G, Gemci, T, Gu, Y & Saha, SC 2021, 'SARS CoV-2 aerosol: How far it can travel to the lower airways?', Physics of Fluids.
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Iwanaga, T, Wang, H-H, Hamilton, SH, Grimm, V, Koralewski, TE, Salado, A, Elsawah, S, Razavi, S, Yang, J, Glynn, P, Badham, J, Voinov, A, Chen, M, Grant, WE, Peterson, TR, Frank, K, Shenk, G, Barton, CM, Jakeman, AJ & Little, JC 2021, 'Socio-technical scales in socio-environmental modeling: Managing a system-of-systems modeling approach', ENVIRONMENTAL MODELLING & SOFTWARE, vol. 135.
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Iyer, S, Velmurugan, T, Gandomi, AH, Noor Mohammed, V, Saravanan, K & Nandakumar, S 2021, 'Structural health monitoring of railway tracks using IoT-based multi-robot system', Neural Computing and Applications.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. A multi-robot-based fault detection system for railway tracks is proposed to eliminate manual human visual inspection. A hardware prototype is designed to implement a master–slave robot mechanism capable of detecting rail surface defects, which include cracks, squats, corrugations, and rust. The system incorporates ultrasonic sensor inputs coupled with image processing using OpenCV and deep learning algorithms to classify the surface faults detected. The proposed Convolutional Neural Network (CNN) model fared better compared to the Artificial Neural Network (ANN), random forest, and Support Vector Machine (SVM) algorithms based on accuracy, R-squared value, F1 score, and Mean-Squared Error (MSE). To eliminate manual inspection, the location and status of the fault can be conveyed to a central location enabling immediate attention by utilizing GSM, GPS, and cloud storage-based technologies. The system is extended to a multi-robot framework designed to optimize energy utilization, increase the lifetime of individual robots, and improve the overall network throughput. Thus, the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol is simulated using 100 robot nodes, and the corresponding performance metrics are obtained.

Jacob, A, Ashok, B, Alagumalai, A, Chyuan, OH & Phung, TKL 2021, 'Critical review on third generation micro algae biodiesel production and its feasibility as future bioenergy for IC engine applications', ENERGY CONVERSION AND MANAGEMENT, vol. 228.
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Jahirul, M, Rasul, MG, Brown, RJ, Senadeera, W, Hosen, MA, Haque, R, Saha, SC & Mahlia, TM 2021, 'Investigation of correlation between chemical composition and properties of biodiesel using principal component analysis (PCA) and artificial neural network (ANN)', RENEWABLE ENERGY, vol. 168, pp. 632-646.
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Jamil, S, Loganathan, P, Khan, SJ, McDonald, JA, Kandasamy, J & Vigneswaran, S 2021, 'Enhanced nanofiltration rejection of inorganic and organic compounds from a wastewater-reclamation plant's micro-filtered water using adsorption pre-treatment', SEPARATION AND PURIFICATION TECHNOLOGY, vol. 260.
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Jangirala, S, Das, AK, Wazid, M & Vasilakos, AV 2021, 'Designing Secure User Authentication Protocol for Big Data Collection in IoT-Based Intelligent Transportation System', IEEE Internet of Things Journal, pp. 1-1.
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Jankowska, K, Grzywaczyk, A, Piasecki, A, Kijenska-Gawronska, E, Nguyen, LN, Zdarta, J, Nghiem, LD, Pinelo, M & Jesionowski, T 2021, 'Electrospun biosystems made of nylon 6 and laccase and its application in dyes removal', ENVIRONMENTAL TECHNOLOGY & INNOVATION, vol. 21.
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Jaradat, Y & Far, H 2021, 'Optimum Stiffness Values for Impact Element Models to Determine Pounding Forces between Adjacent Buildings', Structural Engineering and Mechanics, vol. 77, no. 2.
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Structural failure due to seismic pounding between two adjacent buildings is one of the major concerns in the context of structural damage. Pounding between adjacent structures is a commonly observed phenomenon during major earthquakes. When modelling the structural response, stiffness of impact spring elements is considered to be one of the most important parameters when the impact force during collision of adjacent buildings is calculated. Determining valid and realistic stiffness values is essential in numerical simulations of pounding forces between adjacent buildings in order to achieve reasonable results. Several impact model stiffness values have been presented by various researchers to simulate pounding forces between adjacent structures. These values were mathematically calculated or estimated. In this study, a linear spring impact element model is used to simulate the pounding forces between two adjacent structures. An experimental model reported in literature was adopted to investigate the effect of different impact element stiffness k on the force intensity and number of impacts simulated by Finite Element (FE) analysis. Several numerical analyses have been conducted using SAP2000 and the collected results were used for further mathematical evaluations. The results of this study concluded the major factors that may actualise the stiffness value for impact element models. The number of impacts and the maximum impact force were found to be the core concept for finding the optimal range of stiffness values. For the experimental model investigated, the range of optimal stiffness values has also been presented and discussed.

Jarman, LR, Elliott, JL, Lees, T, Clifton-Bligh, R, Simpson, AM, Nassif, N & Lal, S 2021, 'Heart Rate Variability as a Potential Non-invasive Marker of Blood Glucose Level', Human Physiology, vol. 47, no. 2, pp. 209-218.
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Abstract: Currently, monitoring of blood glucose level (BGL) is constrained by the invasive nature of BGL measures. We investigated heart rate variability (HRV) parameters as potential non-invasive markers of BGL. Healthy volunteers (n = 25; aged 27 ± 9 years) uninhibited by regular medications or chronic illness were recruited for this study. BGL and HRV were assessed during fasting (9:00 am), postprandial (12:00 pm), and postabsorptive (3:00 pm) periods using self-monitoring of blood glucose techniques and ten-minute electrocardiogram, respectively. Frequency-domain HRV measures, which estimate contributions of sympathetic and parasympathetic systems to autonomic modulation of the heart, were correlated against BGL data with the following significant (p < 0.05) findings. The change in BGL from fasting to postprandial levels was negatively correlated with fasting low frequency (LF) power and total power (TP). Postprandial BGL was negatively associated with fasting LF and TP, as well as with postprandial LF, high frequency (HF), and TP. The change in BGL from postprandial to postabsorptive levels was positively correlated with fasting LF power, as well as with postprandial LF, HF, and TP. Frequency-domain HRV parameters may be useful in predicting the magnitude and direction of acute fluctuations in BGL, and further research could develop them as non-invasive markers of BGL.

Jayaraman, S, Ramachandran, M, Patan, R, Daneshmand, M & Gandomi, AH 2021, 'Fuzzy Deep Neural Learning Based on Goodman and Kruskal&#x0027;s Gamma for Search Engine Optimization', IEEE Transactions on Big Data.
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IEEE Search engine optimization (SEO) is a significant problem for enhancing a website's visibility with search engine results. SEO issues, such as Site Popularity, Content Quality, Keyword Density, and Publicity, were not considered during the search engine optimization process. Therefore, the retrieval rate of the existing techniques is inadequate. In this study, Triangular Fuzzy Deep Structured Learning-Based Predictive Page Ranking (TFDSL-PPR) Technique is proposed to solve these limitations. First, the TFDSL-PPR technique takes a number of user queries as input in the input layer, and then it employs four hidden layers in order to deeply analyze the web pages based on an input query. The first hidden layer determines the keywords from the user query. The second hidden layer measures the site popularity, content quality, keyword density and publicity of all web pages in the search engine. It then accomplishes Goodman and Kruskal's Gamma Predictive Ranking process in the third hidden layer, where it ranks the web pages by considering their similarities. The proposed TFDSL-PPR technique is applied to the ClueWeb09 Dataset with respect to a variety of user queries. The results are benchmarked by existing methods based on several metrics such as retrieval rate, time, and false-positive rate.

Jelich, C, Karimi, M, Kessissoglou, N & Marburg, S 2021, 'Efficient solution of block Toeplitz systems with multiple right-hand sides arising from a periodic boundary element formulation', Engineering Analysis with Boundary Elements.
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Jena, R, Pradhan, B, Naik, SP & Alamri, AM 2021, 'Earthquake risk assessment in NE India using deep learning and geospatial analysis', Geoscience Frontiers.
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Jia, M, Gabrys, B & Musial, K 2021, 'Directed closure coefficient and its patterns.', PLoS One, vol. 16, no. 6, p. e0253822.
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The triangle structure, being a fundamental and significant element, underlies many theories and techniques in studying complex networks. The formation of triangles is typically measured by the clustering coefficient, in which the focal node is the centre-node in an open triad. In contrast, the recently proposed closure coefficient measures triangle formation from an end-node perspective and has been proven to be a useful feature in network analysis. Here, we extend it by proposing the directed closure coefficient that measures the formation of directed triangles. By distinguishing the direction of the closing edge in building triangles, we further introduce the source closure coefficient and the target closure coefficient. Then, by categorising particular types of directed triangles (e.g., head-of-path), we propose four closure patterns. Through multiple experiments on 24 directed networks from six domains, we demonstrate that at network-level, the four closure patterns are distinctive features in classifying network types, while at node-level, adding the source and target closure coefficients leads to significant improvement in link prediction task in most types of directed networks.

Jia, M, Srinivasan, R, Ries, RJ, Bharathy, G & Weyer, N 2021, 'Investigating the Impact of Actual and Modeled Occupant Behavior Information Input to Building Performance Simulation', Buildings.
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Jiang, H, Xu, K, Zhang, Q, Yang, Y, Karmokar, DK, Chen, S, Zhao, P, Wang, G & Peng, L 2021, 'Backward-to-Forward Wide-Angle Fast Beam-Scanning Leaky-Wave Antenna With Consistent Gain', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 69, no. 5, pp. 2987-2992.
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Jiang, J, Phuntsho, S, Pathak, N, Wang, Q, Cho, J & Shon, HK 2021, 'Critical flux on a submerged membrane bioreactor for nitrification of source separated urine', Process Safety and Environmental Protection, vol. 153, pp. 518-526.
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Membrane fouling is the biggest challenge in membrane based technology operation. Studies on critical flux mainly focused on membrane bioreactor for municipal wastewater and/or greywater treatment, which can significantly differ from the ultrafiltration membrane bioreactor (UF-MBRs) to treat source separated urine. In this work, the inhibitory factors on nitrifying bacteria activity were investigated for fast acclimation of nitrifying bacteria with high ammonium concentration and optimization of a high-rate partial nitrification MBR. The maximum nitrification rate of 447 ± 50 mgN L–1 d–1 was achieved when concentration of ammonia in feed urine is approximately 4006.3 ± 225.8 mg N L–1 by maintaining desired pH around 6.2 and FA concentrations below 0.5 mgL−1. Furthermore, for the first time, the impact of different operational and filtration conditions (i.e. aeration intensity, filtration method, imposed flux, intermittent relaxation, biomass concentration) on the reversibility of membrane fouling was carried out for enhancement of membrane flux and fouling mitigation. Fouling mechanisms for minor irreversible fouling observed under sub-critical condition were pore blocking and polarization. To mitigate membrane fouling, the UF module with effective membrane surface area of 0.02 m2 is recommended to be operated at the aeration intensity of 0.4 m3 h−1, intermittent relaxation of 15 min, biomass concentration of 3.5 g L−1.

Jiang, Y, Zhang, Y, Lin, C, Wu, D & Lin, C-T 2021, 'EEG-Based Driver Drowsiness Estimation Using an Online Multi-View and Transfer TSK Fuzzy System', IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol. 22, no. 3, pp. 1752-1764.
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Jin, D, Wen, S, Liu, Y, Wang, F, Lin, G, zhou, J, Shi, B & Suh, YD 2021, 'Nanobarcodes with multidimensional optical information beyond diffraction limit'.
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Abstract Precise design and fabrication of heterogeneous nanostructures will enable nanoscale devices to integrate multiple desirable functionalities. But due to the diffraction limit (~200 nm), the optical uniformity and diversity within the heterogeneous functional nanostructures are hardly controlled and characterized. Here we report a set of nanobarcodes, each optically active section has its unique nonlinear responses to donut illumination patterns, so that one can discern each unit with super resolution. To achieve this, we first realized an approach of highly controlled epitaxial growth and produced a range of one-dimensional heterogeneous structures. Each section along the nanorod structure display tunable upconversion emissions, in four optically orthogonal dimensions, including colour, lifetime, excitation wavelength, and power dependency. Moreover, we demonstrated a 210 nm single nanorod as the smallest polychromatic light source for the on-demand generation of RGB photonic emissions. Remarkably, within a space of 50 nm, only 1/20th of the excitation wavelength, multiple codes can be successfully coded and decoded in 4 optical dimensions. This precision control enables the fabrication of super capacity geometrical barcodes with theoretical coding capacity up to (24-1)4. This work benchmarks our new ability towards the full control of sub-diffraction-limit optical diversities of single heterogeneous nanoparticles.

Jin, Y, Jiang, W, Yang, Y & Mu, Y 2021, 'Zero-Shot Video Event Detection with High-Order Semantic Concept Discovery and Matching', IEEE Transactions on Multimedia, pp. 1-1.
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Jin, Z, Sun, X, Cai, Y, Zhu, J, Lei, G & Guo, Y 2021, 'Comprehensive Sensitivity and Cross-Factor Variance Analysis-Based Multi-Objective Design Optimization of a 3-DOF Hybrid Magnetic Bearing', IEEE TRANSACTIONS ON MAGNETICS, vol. 57, no. 2.
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John, CB, Raja, SA, Deepanraj, B & Ong, HC 2021, 'Palm stearin biodiesel: preparation, characterization using spectrometric techniques and the assessment of fuel properties', BIOMASS CONVERSION AND BIOREFINERY.
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Johnston, E, Szabo, PSB & Bennett, NS 2021, 'Cooling silicon photovoltaic cells using finned heat sinks and the effect of inclination angle', Thermal Science and Engineering Progress, vol. 23.
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This study investigates the reduction in temperature within a silicon solar module by increasing the heat transfer coefficient of its rear surface. Specifically, a combined simulation and experimental study is carried out to determine how the performance of a heat sink mounted on the rear of a module is dependent on orientation, and how fin height influences the heat sink's ability to reduce temperature. With a heat sink fin height of 20 mm, the temperatures of the front and rear surfaces of the module reached 62 °C and 51 °C, respectively. When the fins are extended to 300 mm, these temperatures reduced to 45 °C and 30 °C, respectively. It is shown that the heat sink's performance in removing heat decreases when the orientation is rotated through 180° (from facing upwards to facing downwards). The accompanying effect on the power output is also investigated. When the 20 mm heat sink is applied to a solar module, the power output increased by 11.3% (relative), compared to the power generated from a bare solar module. Similarly, the application of a 100 mm heat sink resulted in an increase in power output of 15.3% (relative).

Jones, GT, Siwakoti, YP & Rogers, DJ 2021, 'Active Gate Drive to Increase the Power Capacity of Hard-Switched IGBTs', IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, vol. 9, no. 2, pp. 2247-2257.
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Kamali, S & Far, H 2021, 'Numerical Investigation on Shear Deflection of Steel Welded I Sections with Varying Span to Depth Ratios International Journal of Steel Structures', International Journal of Steel Structures, vol. 21, no. 1.
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Deflection of the steel I-sections is an important phenomenon that needs to be taken into account to ensure that the serviceability limit state criteria of the Australian Standards are met. The method that is widely used to calculate the deflection of steel I-sections is by the use of existing formulae that only accommodate the bending stiffness of the beams. A numerical investigation is performed in this study to find the contribution of shear effects in the final deflection of the Welded-Beams (WB) and Welded-Columns (WC). The numerical analyses were carried out in SAP2000 and numerical model was first validated using the experimental results of welded plate girders. The model was then used to analyse simply supported WB and WC sections under uniformly distributed load (UDL) with varying span lengths. The results of the numerical analyses are reported in this study which compare the mid-span deflection values from the simply supported deflection formula with the numerical model deflection values. The data acquired from the numerical analyses were used to establish a span to depth ratio for WB and WC sections below which the shear deflection becomes significant. The analysis of the results obtained from the numerical investigation suggests that a predication error begins to emerge in the result that is acquired from flexure deflection formulae at a certain span-depth ratio.

Kan, ME, Indraratna, B & Rujikiatkamjorn, C 2021, 'On numerical simulation of vertical drains using linear 1-dimensional drain elements', Computers and Geotechnics, vol. 132, pp. 103960-103960.
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Karimi, F, Green, D, Matous, P, Varvarigos, M & Khalilpour, KR 2021, 'Network of networks: A bibliometric analysis', Physica D: Nonlinear Phenomena, vol. 421.
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This study explores the evolving structure of the rising field of “network of networks” (NoN). Reviewing publications dating back to 1931, we describe the evolution of major NoN research themes in different scientific disciplines and the gradual emergence of an integrated field. We analyse the co-occurrence networks of keywords used in all 7818 scientific publications in Scopus database that mention NoN and other related terms (i.e., “interconnected networks”, “multilayer networks”, “multiplex networks”, “interdependent networks”, “multinetworks”, “multilevel networks”, and “multidimensional networks”). The results show that the NoN began to form as a field mainly in the 1990s around research on neural networks. Diverse aspects of NoN research, indicated by dominant keywords such as “interconnection”, “multilayer”, and “interdependence”, gradually spread to computer and physical sciences. As of 2018, network interdependence – with its application in network resilience and prevention of cascading failure – seems to be one of the key topics attracting broad academic attention. Another noteworthy observation is the emergence of a distinct cluster of terms relevant to nanoscience and nanotechnology. It is envisaged from the analysis that NoN concepts will develop stronger ties with nanoscience with increasing understanding and data acquisition from the molecular, atomic, and subatomic levels.

Karimi, M, Croaker, P, Skvortsov, A, Maxit, L & Kirby, R 2021, 'Simulation of airfoil surface pressure due to incident turbulence using realizations of uncorrelated wall plane waves', JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, vol. 149, no. 2, pp. 1085-1096.
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Karki, D & Far, H 2021, 'State-of-the-Art on Composite Cold-formed Steel Flooring Systems', Steel Construction: Design and Research, vol. 14, no. 1, pp. 1-11.
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The current study presents a comprehensive review of the state-of-the-art on composite cold-formed steel flooring research over the past couple of years. The most relevant and significant literature references were reviewed to provide some profundity in the trends and development of the composite cold-formed steel floors. Advantages of this type of composite flooring system are also highlighted. A broad description of mainly two types of the composite floors mainly consisting cold-formed steel and concrete, and cold-formed steel and timber-based floorboards have been outlined in this study. The experimental and numerical investigations that have been carried out worldwide is likewise discussed in the paper. The most important aspects covered are shear connection behaviour, flexural and dynamic behaviour of the floors, and a brief description of fire testing.

Kashani, AR, Chiong, R, Mirjalili, S & Gandomi, AH 2021, 'Particle Swarm Optimization Variants for Solving Geotechnical Problems: Review and Comparative Analysis', Archives of Computational Methods in Engineering.
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© 2020, CIMNE, Barcelona, Spain. Optimization techniques have drawn much attention for solving geotechnical engineering problems in recent years. Particle swarm optimization (PSO) is one of the most widely used population-based optimizers with a wide range of applications. In this paper, we first provide a detailed review of applications of PSO on different geotechnical problems. Then, we present a comprehensive computational study using several variants of PSO to solve three specific geotechnical engineering benchmark problems: the retaining wall, shallow footing, and slope stability. Through the computational study, we aim to better understand the algorithm behavior, in particular on how to balance exploratory and exploitative mechanisms in these PSO variants. Experimental results show that, although there is no universal strategy to enhance the performance of PSO for all the problems tackled, accuracies for most of the PSO variants are significantly higher compared to the original PSO in a majority of cases.

Katic, M, Cetindamar Kozanoglu, D & Agarwal, R 2021, 'Deploying ambidexterity through better management practice: The case of High-Variety, Low-Volume Manufacturing', Journal of Manufacturing Technology Management, pp. 1-21.
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have proven essential in today’s organisations, an understanding of how these so-called ambidextrous capabilities are deployed remains elusive. Thus, the authors aim to investigate the role of better management practices (BMP), as organisational routines, in deploying ambidextrous capabilities in practice. Design/methodology/approach – High-variety, low-volume (HVLV) manufacturers are adopted as exemplar ambidextrous organisations. A conceptual model was developed where BMP, including human resource management (HRM) and production planning and control (PPC), are considered as mediators in the relationship between ambidextrous capabilities and organisational performance outcomes. Partial least squares structural equation modelling was adopted to analyse the results of a survey undertaken by Australian HVLV manufacturers. Findings – The results suggest that merely holding ambidextrous capabilities is not enough – demonstrating a fully mediating role of BMP between ambidextrous capabilities and HVLV manufacturer performance outcomes. However, the individual effects of PPC and HRM prove varied in their unique impact on HVLV manufacturer performance. Practical implications – This study also provides a rare account of how HVLV manufacturers can leverage their inherently ambidextrous design towards greater organisational performance and highlights critical considerations in the selection of organisational capabilities. Originality/value – By exemplifying the explanatory power of BMP in ambidextrous capability deployment, this study moves beyond the more prevalent stance on the links between BMP and ambidextrous capabilities as that of capability building through management practices, to one concerning the deployment of the capability itself.

Kaw, HY, Jin, X, Liu, Y, Cai, L, Zhao, X, Wang, J, Zhou, JL, He, M & Li, D 2021, 'Gas-liquid microextraction coupled with magnetic-assisted dispersive solid-phase extraction clean-up for multi-residue pesticide analysis in fatty foods of animal origin', LWT-FOOD SCIENCE AND TECHNOLOGY, vol. 137.
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Khan, HU, Alomari, MK, Khan, S, Nazir, S, Gill, AQ, Al-Maadid, AA, Abu-Shawish, ZK & Hassan, MK 2021, 'Systematic Analysis of Safety and Security Risks in Smart Homes', CMC-COMPUTERS MATERIALS & CONTINUA, vol. 68, no. 1, pp. 1409-1428.
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Khan, TT, Bari, GAKMR, Kang, H-J, Lee, T-G, Park, J-W, Hwang, HJ, Hossain, SM, Mun, JS, Suzuki, N, Fujishima, A, Kim, J-H, Shon, HK & Jun, Y-S 2021, 'Synthesis of N-Doped TiO2 for Efficient Photocatalytic Degradation of Atmospheric NOx', Catalysts.
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Khawaldeh, HA, Al-Soeidat, M, Farhangi, M, Lu, DD-C & Li, L 2021, 'Efficiency Improvement Scheme for PV Emulator Based on a Physical Equivalent PV-Cell Model', IEEE Access.
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Khawaldeh, HA, Al‐soeidat, M, Lu, DD & Li, L 2021, 'Simple and Fast Dynamic Photovoltaic Emulator based on a Physical Equivalent PV‐cell Model', The Journal of Engineering.
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Khuat, TT & Gabrys, B 2021, 'Accelerated learning algorithms of general fuzzy min-max neural network using a novel hyperbox selection rule', Information Sciences.
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Khuat, TT, Chen, F & Gabrys, B 2021, 'An Effective Multiresolution Hierarchical Granular Representation Based Classifier Using General Fuzzy Min-Max Neural Network', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 29, no. 2, pp. 427-441.
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Khuat, TT, Ruta, D & Gabrys, B 2021, 'Hyperbox-based machine learning algorithms: a comprehensive survey', Soft Computing, vol. 25, pp. 1325-1363.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially. Along with this trend, machine learning algorithms have been formed and evolved continuously to discover new information and knowledge from different data sources. Learning algorithms using hyperboxes as fundamental representational and building blocks are a branch of machine learning methods. These algorithms have enormous potential for high scalability and online adaptation of predictors built using hyperbox data representations to the dynamically changing environments and streaming data. This paper aims to give a comprehensive survey of the literature on hyperbox-based machine learning models. In general, according to the architecture and characteristic features of the resulting models, the existing hyperbox-based learning algorithms may be grouped into three major categories: fuzzy min–max neural networks, hyperbox-based hybrid models and other algorithms based on hyperbox representations. Within each of these groups, this paper shows a brief description of the structure of models, associated learning algorithms and an analysis of their advantages and drawbacks. Main applications of these hyperbox-based models to the real-world problems are also described in this paper. Finally, we discuss some open problems and identify potential future research directions in this field.

Kiani, M, Andreu-Perez, J, Hagras, H, Papageorgiou, EI, Prasad, M & Lin, C-T 2021, 'Effective Brain Connectivity for fNIRS with Fuzzy Cognitive Maps in Neuroergonomics', IEEE Transactions on Cognitive and Developmental Systems, pp. 1-1.
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Kim, J, Guivant, J, Sollie, ML, Bryne, TH & Johansen, TA 2021, 'Compressed pseudo-SLAM: Pseudorange-integrated compressed simultaneous localisation and mapping for unmanned aerial vehicle navigation', Journal of Navigation, vol. 74, no. 5, pp. 1091-1103.
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This paper addresses the fusion of the pseudorange/pseudorange rate observations from the global navigation satellite system and the inertial-visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles. This work extends the previous work on a simulation-based study [Kim et al. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localisation and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22-36] to a real-flight dataset collected from a fixed-wing unmanned aerial vehicle platform. The dataset consists of measurements from visual landmarks, an inertial measurement unit, and pseudorange and pseudorange rates. We propose a novel all-source navigation filter, termed a compressed pseudo-SLAM, which can seamlessly integrate all available information in a computationally efficient way. In this framework, a local map is dynamically defined around the vehicle, updating the vehicle and local landmark states within the region. A global map includes the rest of the landmarks and is updated at a much lower rate by accumulating (or compressing) the local-to-global correlation information within the filter. It will show that the horizontal navigation error is effectively constrained with one satellite vehicle and one landmark observation. The computational cost will be analysed, demonstrating the efficiency of the method.

Kirby, R 2021, 'Atmospheric sound propagation in a moving fluid above an impedance plane: Application of the semi-analytic finite element method', JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, vol. 149, no. 2, pp. 1285-1295.
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Koli, MNY, Afzal, MU, Esselle, KP & Mehta, A 2021, 'Use of Narrower Reflection Cancelling Slots to Design Linearly Polarized Radial Line Slot Arrays with Improved Radiation Performance', IEEE Antennas and Wireless Propagation Letters, pp. 1-1.
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Kołodziejczak-Radzimska, A, Nghiem, LD & Jesionowski, T 2021, 'Functionalized Materials as a Versatile Platform for Enzyme Immobilization in Wastewater Treatment', Current Pollution Reports.
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Kousik, N, Natarajan, Y, Raja, RA, Kallam, S, Patan, R & Gandomi, AH 2021, 'Improved salient object detection using hybrid Convolution Recurrent Neural Network', EXPERT SYSTEMS WITH APPLICATIONS, vol. 166.
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Krishankumar, R, Nimmagadda, SS, Rani, P, Mishra, AR, Ravichandran, KS & Gandomi, AH 2021, 'Solving renewable energy source selection problems using a q-rung orthopair fuzzy-based integrated decision-making approach', JOURNAL OF CLEANER PRODUCTION, vol. 279.
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Kumar, A, Esmaili, N & Piccardi, M 2021, 'Topic-Document Inference with the Gumbel-Softmax Distribution', IEEE Access, vol. 9, pp. 1313-1320.
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© 2013 IEEE. Topic modeling is an important application of natural language processing (NLP) that can automatically identify the set of main topics of a given, typically large, collection of documents. In addition to identifying the main topics in the given collection, topic modeling infers which combination of topics is addressed by each individual document (the so-called topic-document inference), which can be useful for their classification and organization. However, the distributional assumptions for this inference are typically restricted to the Dirichlet family which can limit the performance of the model. For this reason, in this paper we propose modeling the topic-document inference with the Gumbel-Softmax distribution, a distribution recently introduced to expand differentiability in deep networks. To set up a performing system, the proposed approach integrates Gumbel-Softmax topic-document inference in a state-of-the-art topic model based on a deep variational autoencoder. Experimental results over two probing datasets show that the proposed approach has been able to outperform the original deep variational autoencoder and other popular topic models in terms of test-set perplexity and two topic coherence measures.

Kwon, S, Tomonaga, A, Lakshmi Bhai, G, Devitt, SJ & Tsai, J-S 2021, 'Gate-based superconducting quantum computing', Journal of Applied Physics.
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Labeeuw, L, Commault, AS, Kuzhiumparambil, U, Emmerton, B, Nguyen, LN, Nghiem, LD & Ralph, PJ 2021, 'A comprehensive analysis of an effective flocculation method for high quality microalgal biomass harvesting', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 752.
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Lazaar, A, Pradhan, B, Naiji, Z, Gourfi, A, El Hammouti, K, Andich, K & Monir, A 2021, 'The manifestation of VIS-NIRS spectroscopy data to predict and map soil texture in the Triffa plain (Morocco)', Kuwait Journal of Science, vol. 48, no. 1, pp. 111-121.
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The use of standard laboratory methods to estimate the soil texture is complicated, expensive, and time-consuming and needs considerable effort. The reflectance spectroscopy represents an alternative method for predicting a large range of soil physical properties and provides an inexpensive, rapid, and reproducible analytical method. This study aimed to assess the feasibility of Visible (VIS: 350-700 nm) and Near-Infrared and Short-Wave-Infrared (NIRS: 701-2500 nm) spectroscopy for predicting and mapping the clay, silt, and sand fractions of the soils of Triffa plain (north-east of Morocco). A total of 100 soil samples were collected from the non-root zone of soil (0-20 cm) and then analyzed for texture using the VIS-NIRS spectroscopy and the traditional laboratory method. The partial least squares regression (PLSR) technique was used to assess the ability of spectral data to predict soil texture. The results of prediction models showed excellent performance for the VIS-NIRS spectroscopy to predict the sand fraction with a coefficient of determination R = 0.93 and Root Mean Squares Error (RMSE) =3.72, good prediction for the silt fraction (R =0.87; RMSE = 4.55), and acceptable prediction for the clay fraction (R = 0.53; RMSE = 3.72). Moreover, the range situated between 2150 and 2450 nm is the most significant for predicting the sand and silt fractions, while the spectral range between 2200 and 2440 nm is the optimal to predict the clay fraction. However, the maps of predicted and measured soil texture showed an excellent spatial similarity for the sand fraction, a certain difference in the variability of clay fraction, while the maps of silt fraction show a lower difference. 2 2 2

Le Gentil, C, Vidal-Calleja, T & Huang, S 2021, 'IN2LAAMA: Inertial Lidar Localization Autocalibration and Mapping', IEEE Transactions on Robotics, vol. 37, no. 1, pp. 275-290.
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IEEE In this article, we present inertial lidar localization autocalibration and mapping: an offline probabilistic framework for localization, mapping, and extrinsic calibration based on a 3-D lidar and a six-degree-of-freedom inertial measurement unit. Most of today’s lidars collect geometric information about the surrounding environment by sweeping lasers across their field of view. Consequently, 3-D points in one lidar scan are acquired at different timestamps. If the sensor trajectory is not accurately known, the scans are affected by the phenomenon known as motion distortion. The proposed method leverages preintegration with a continuous representation of the inertial measurements to characterize the system’s motion at any point in time. It enables precise correction of the motion distortion without relying on any explicit motion model. The system’s pose, velocity, biases, and time shift are estimated via a full batch optimization that includes automatically generated loop closure constraints. The autocalibration and the registration of lidar data rely on planar and edge features matched across pairs of scans. The performance of the framework is validated through simulated and real-data experiments.

Le, AT, Huang, X & Jay Guo, Y 2021, 'Analog Self-interference Cancellation in Dual-polarization Full-duplex MIMO Systems', IEEE Communications Letters.
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Full-duplex (FD) technology combined with dual-polarization (DP) multiple-input multiple-output (MIMO) systems is attractive to improve spectral efficiency and to enhance link capacity. Cancelling self-interference (SI) in such DPFD MIMO systems using beamforming techniques is very challenging due to a significant difference of the co-polarization and cross-polarization SI channels. In this letter, an analog adaptive filter structure is proposed to mitigate both co-polarization and cross-polarization SIs in DPFD MIMO systems. Stationary analysis is applied to evaluate the performance of the proposed structure. Simulation results show that about 45 dB to 55 dB of SI cancellation can be achieved regardless of the isolation differences between cross-polarization and co-polarization channels.

Le, AT, Le Chung Tran, Huang, X, Guo, YJ & Hanzo, L 2021, 'Analog Least Mean Square Adaptive Filtering for Self-Interference Cancellation in Full Duplex Radios', IEEE wireless communications, vol. 28, no. 1, pp. 12-18.
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Le, NP, Tran, LC, Huang, X, Dutkiewicz, E, Ritz, C, Phung, SL, Bouzerdoum, A, Franklin, D & Hanzo, L 2021, 'Energy-Harvesting Aided Unmanned Aerial Vehicles for Reliable Ground User Localization Under Lognormal-Nakagami-m Fading Channels', IEEE Transactions on Vehicular Technology, pp. 1-1.
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Lee, S, Ryu, I, Ngoduy, D, Hoang, NH & Choi, K 2021, 'A stochastic behaviour model of a personal mobility under heterogeneous low-carbon traffic flow', Transportation Research Part C: Emerging Technologies, vol. 128.
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The study proposes a mathematical framework to explain the stochastic behavioural patterns of personal mobility (PM) devices under low-carbon heterogeneous traffic conditions in shared lanes. We create a set of anticipation factors in a stochastic PM behaviour model to tackle sensitivities to both space headway and relative speed against intra- and inter-modes. The proposed behaviour model involves a deterministic and a stochastic force. In the deterministic force, the anticipation factors are used in an optimal velocity model and a full velocity difference model. In the stochastic force, the Langevin equation is used to capture PMs’ stochastic characteristics against movements of other PMs, pedestrians, and bicycles, and the effect of lateral interactions. We carried out real-world circular experiments of mixed sustainable modes to verify the performance of the proposed models. Five models’ performances are compared under four different traffic conditions, including bike-mixed, pedestrian-mixed, low-speed, and high-speed conditions. We confirmed that newly created anticipation factors play a significant role in all models under all conditions to partially influence the following PM devices’ behaviour from the leading two different sustainable modes. The validation results illustrate the excellence of the proposed method. Consequently, behavioural uncertainty is well captured by the stochastic PM devices following models under all traffic conditions, although it requires more parameters than the deterministic PM behavioural models. The proposed method paves the way for the stochastic CF model's applicability to describe PM devices’ behavioural dynamics under mixed traffic conditions using anticipation factors. Besides, it lays the foundation stone of PM devices’ dynamics in a shared lane to construct effective regulations and safety standards.

Lee, SS, Yang, Y & Siwakoti, YP 2021, 'A Novel Single-Stage Five-Level Common-Ground-Boost-Type Active Neutral-Point-Clamped (5L-CGBT-ANPC) Inverter', IEEE TRANSACTIONS ON POWER ELECTRONICS, vol. 36, no. 6, pp. 6192-6196.
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Lee, SS, Yang, Y, Siwakoti, YP & Lee, K-B 2021, 'A Novel Boost Cascaded Multilevel Inverter.', IEEE Trans. Ind. Electron., vol. 68, pp. 8072-8080.
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Lee, XJ, Ong, HC, Gao, W, Ok, YS, Chen, W-H, Goh, BHH & Chong, CT 2021, 'Solid biofuel production from spent coffee ground wastes: Process optimisation, characterisation and kinetic studies', FUEL, vol. 292.
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Lei, F, Lv, X, Fang, J, Li, Q & Sun, G 2021, 'Nondeterministic multi-objective and multi-case discrete optimization of functionally-graded front-bumper structures for pedestrian protection', Thin-Walled Structures, vol. 167.
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Pedestrian lower-leg protection and lower-speed crashworthiness often present two important yet competing criteria on the design of front-bumper structures. Conventional design optimization is largely focused on a single loading condition without considering multiple impact cases. Furthermore, design of front-bumper structures is usually discrete in engineering practice and impacting conditions are commonly random. To cope with such a sophisticated nondeterministic design problem, this study aimed to develop a successive multiple attribute decision making (MADM) algorithm for optimizing a functionally graded thickness (FGT) front-bumper structure subject to multiple impact loading cases. The finite element (FE) model of front-end vehicle was constructed and validated with the in-house experimental tests under the loads of both Flexible Pedestrian Legform Impactor (Flex-PLI) impact and lower-speed impact. In the proposed successive MADM algorithm, the order preference by similarity to ideal solution (TOPSIS) based upon relative entropy was coupled with the analytic hierarchy process (AHP) to develop a MADM model for converting multiple conflicting objectives into a unified single cost function. The presented optimization procedure is algorithmically iterated using the successive Taguchi method to deal with a large number of design variables and design levels. The results showed that not only the algorithm enabled to generate an optimal design efficiently, but also the robustness of Flex-PLI impact is significantly enhanced. The proposed algorithm can be potentially used for other engineering design problems with similar complexity.

Lei, F, Lv, X, Fang, J, Pang, T, Li, Q & Sun, G 2021, 'Injury biomechanics-based nondeterministic optimization of front-end structures for safety in pedestrian–vehicle impact', Thin-Walled Structures, vol. 167.
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Lower extremity is the most frequently injured body region in a pedestrian–vehicle impact. To evaluate lower extremity injuries, both the Flexible Pedestrian Legform Impactor (FlexPLI) and the Flexible Pedestrian Legform Impactor with Upper Body Mass (FlexPLI-UBM) have been used in practice. In general, UBM would have considerable influence on the design of front-end structures. In this study, a sedan was used to perform the experimental tests first for evaluating the effects of different lower extremities. The experimental results indicated that placement of UBM can lead to a higher risk evaluation of knee ligament damage and a more significant increase in femur bending moment than that in tibia bending moment. Second, a new multiobjective discrete robust optimization (MODRO) algorithm was developed to optimize front-end structures subject to FlexPLI-UBM impact involving uncertainties. In the proposed MODRO algorithm, the order preference by similarity to ideal solution (TOPSIS) was coupled with the fuzzy approach for developing a fuzzy multiple attribute decision making (MADM) model for converting multiple conflicting objectives into a single unified cost function. The presented optimization procedure is iterated using the successive orthogonal experiment to deal with a large number of design variables and design levels. The optimal results showed that in contrast to the structures subject to the FlexPLI impact, the front-end structures under FlexPLI-UBM impact require a higher stiffness of tibia contact area but a lower stiffness of knee and femur contact area. This study provides automotive engineers with new insights into the injury biomechanics-based design of frontal structure from a road safety perspective.

Leung, D, Winter, A & Yu, N 2021, 'LOCC protocols with bounded width per round optimize convex functions', Reviews in Mathematical Physics.
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Lezana, P, Norambuena, M & Aguilera, R 2021, 'Dual-Stage Control Strategy for a Flying Capacitor Converter Based on Model Predictive and Linear Controllers', IEEE Transactions on Industrial Informatics.
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Li, B, Wen, G, Peng, Z, Wen, S & Huang, T 2021, 'Time-varying formation control of general linear multi-agent systems under markovian switching topologies and communication noises', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 4, pp. 1303-1307.
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This brief studies the time-varying formation (TVF) control of linear multi-agent systems (MASs), where the communication topology switches from several different topologies and the switching signal is depicted by a right-continuous Markov process. The communication noises are taken into account simultaneously, which are described as independent white noises with noisy intensities. A class of stochastic-approximation type control protocols is given and certain sufficient conditions are derived for realizing the TVF stabilization in mean square sense. Moreover, the indicative function and infinitesimal generator are imported in Lyapunov functions to help proving that the MASs can be formed into stable TVF shapes with the proposed control protocols. In the end, an simulation example is performed to state the availability of the approach.

Li, F, Cao, Z, Xu, P, Yi, C, Liao, Y, Jiang, Y, Si, Y, Song, L, Zhang, T, Yao, D & Zhang, Y 2021, 'Reconfiguration of Brain Network between Rest-ing-state and P300 task', IEEE Transactions on Cognitive and Developmental Systems.
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IEEE Previous studies explore the power spectra from the resting-state condition to the oddball task, but whether brain network existing significant difference is still unclear. Our study aims to address how the brain reconfigures its architecture from a resting-state condition (i.e., baseline) to the P300 task in the visual oddball task. In this study, electroencephalograms (EEGs) were collected from 24 subjects, who were required to only mentally count the number of target stimulus; afterwards, EEG networks constructed in different bands were compared between baseline and task to evaluate the reconfiguration of functional connectivity. Compared to the baseline, our results showed the significantly enhanced delta/theta functional connectivity and decreased alpha default mode network in the progress of brain reconfiguration to the task. Furthermore, the reconfigured coupling strengths were found to relate to P300 amplitudes, which were then regarded as features to train a classifier to differentiate the brain states and the high and low P300 groups with an accuracy of 100% and 77.78%, respectively. The findings of our study help us to under-stand the updates in functional connectivity from resting-state to the oddball task, and the reconfigured network structure has the potential for the selection of good subjects for P300-based brain-computer interface.

Li, F, Jiang, L, Liao, Y, Si, Y, Yi, C, Zhang, Y, Zhu, X, Yang, Z, Yao, D, Cao, Z & Xu, P 2021, 'Brain variability in dynamic resting-state networks identified by fuzzy entropy: a scalp EEG study.', Journal of neural engineering.
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Objective

Exploring the temporal variability in spatial topology during the resting state attracts growing interest and becomes increasingly useful to tackle the cognitive process of brain networks. In particular, the temporal brain dynamics during the resting state may be delineated and quantified aligning with cognitive performance, but few studies investigated the temporal variability in the electroencephalogram (EEG) network as well as its relationship with cognitive performance.

Approach

In this study, we proposed an EEG-based protocol to measure the nonlinear complexity of the dynamic resting-state network by applying the fuzzy entropy. To further validate its applicability, the fuzzy entropy was applied into simulated and two independent datasets (i.e., decision-making and P300).

Main results

The simulation study first proved that compared to the existing methods, this approach could not only exactly capture the pattern dynamics in time series but also overcame the magnitude effect of time series. Concerning the two EEG datasets, the flexible and robust network architectures of the brain cortex at rest were identified and distributed at the bilateral temporal lobe and frontal/occipital lobe, respectively, whose variability metrics were found to accurately classify different groups. Moreover, the temporal variability of resting-state network property was also either positively or negatively related to individual cognitive performance.

Significance

This outcome suggested the potential of fuzzy entropy for evaluating the temporal variability of the dynamic resting-state brain networks, and the fuzzy entropy is also helpful for uncovering the fluctuating network variability that accounts for the individual decision differences.

Li, G, Guo, G, Peng, S, Wang, C, Yu, S, Niu, J & Mo, J 2021, 'Matrix Completion via Schatten Capped p Norm', IEEE Transactions on Knowledge and Data Engineering.
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The low-rank matrix completion problem is fundamental in both machine learning and computer vision fields with many important applications, such as recommendation system, motion capture, face recognition, and image inpainting. In order to avoid solving the rank minimization problem which is NP-hard, several surrogate functions of the rank have been proposed in the literature. However, the matrix restored from the optimization problem based on the existing surrogate functions seriously deviates from the original one. In this paper, we first design a new non-convex Schatten capped p norm which generalizes several existing non-convex matrix norms and balances between the rank and the nuclear norm of the matrix. Then, a matrix completion method based on the Schatten capped p norm is proposed by exploiting the framework of the alternating direction method of multipliers. Meanwhile, the Schatten capped p norm regularized least squares subproblem is analyzed in detail and is solved explicitly. Finally, we evaluate the performance of the proposed matrix completion method based on extensive experiments in the field of image inpainting. All the experimental results demonstrate that the proposed method can indeed improve the accuracy of matrix completion compared with the existing methods.

Li, G, Zhou, H, Feng, B, Zhang, Y & Yu, S 2021, 'Efficient Provision of Service Function Chains in Overlay Networks using Reinforcement Learning', IEEE Transactions on Cloud Computing.
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IEEE Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies facilitate deploying Service Function Chains (SFCs) at clouds in efficiency and flexibility. However, it is still challenging to efficiently chain Virtualized Network Functions (VNFs) in overlay networks without knowledge of underlying network configurations. Although there are many deterministic approaches for VNF placement and chaining, they have high complexity and depend on state information of substrate networks. Fortunately, Reinforcement Learning (RL) brings opportunities to alleviate this challenge as it can learn to make suitable decisions without prior knowledge. Therefore, in this paper, we propose an RL approach for efficient SFC provision in overlay networks, where the same VNFs provided by multiple vendors are with different performance. Specifically, we first formulate the problem into an Integer Linear Programming (ILP) model for benchmarking. Then, we present the online SFC path selection into a Markov Decision Process (MDP) and propose a corresponding policy-gradient-based solution. Finally, we evaluate our proposed approach with extensive simulations with randomly generated SFC requests and a real-world video streaming dataset, and implement an emulation system for feasibility verification. Related results demonstrate that performance of our approach is close to the ILP-based method and better than deep Q-learning, random, and load-least-greedy methods.

Li, H, Askari, M, Li, J, Li, Y & Yu, Y 2021, 'A novel structural seismic protection system with negative stiffness and controllable damping', Structural Control and Health Monitoring.
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Li, H, Li, J, Yu, Y & Li, Y 2021, 'Modified adaptive negative stifiness device with variable negative stifiness and geometrically nonlinear damping for seismic protection of structures', International Journal of Structural Stability and Dynamics.
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Adaptive negative sti®ness device is one of the promising seismic protection devices since it can generate seismic isolation e®ect through negative sti®ness when it is mostly needed and achieve similar vibration mitigation as a semi-active control device. However, the adaptive negative sti®ness device generally combined with linear viscous damping underpins the drawback of degrading the vibration isolation e®ect during the high-frequency region. In this paper, a modi¯ed adaptive negative sti®ness device (MANSD) with the ability to provide both lateral negative sti®ness and nonlinear damping by con¯guring linear springs and linear viscous dampers is proposed to address the above issue. The negative sti®ness and nonlinear damping are realised through a linkage mechanism. The fundamentals and dynamic characteristics of a SDOF system with such a device are analyzed and formulated using the Harmonic Balance Method, with a special focus on the amplitude–frequency response and transmissibility of the system. The system with damping nonlinearity as a function of displacement and velocity has been proven to have attractive advantages over linear damping in reducing the transmissibility in the resonance region without increasing that in the high-frequency region. The e®ect of nonlinear damping on suppressing displacement and acceleration responses is numerically veri¯ed under di®erent sinusoidal excitations and earthquakes with di®erent intensities. Compared with linear damping, the MANSD with nonlinear damping could achieve additional reductions on displacement and acceleration under scaled earthquakes, especially intensive earthquakes.

Li, H, Yu, Y, Li, J & Li, Y 2021, 'Analysis and optimization of a typical quasi-zero stiffness vibration isolator', Smart Structures and Systems, vol. 27, no. 3, pp. 525-536.
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Li, H, Yu, Y, Li, J, Li, Y & Askari, M 2021, 'Multi-objective optimisation for improving the seismic protection performance of a multi-storey adaptive negative stiffness system based on modified NSGA-II with DCD', Journal of Building Engineering.

Li, J, Liang, W, Xu, W, Xu, Z, Jia, X, Zhou, W & Zhao, J 2021, 'Maximizing User Service Satisfaction for Delay-Sensitive IoT Applications in Edge Computing', IEEE Transactions on Parallel and Distributed Systems, pp. 1-1.
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Li, K, Lu, J, Zuo, H & Zhang, G 2021, 'Multi-Source Contribution Learning for Domain Adaptation', IEEE Transactions on Neural Networks and Learning Systems.
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Transfer learning becomes an attractive technology to tackle a task from a target domain by leveraging previously acquired knowledge from a similar domain (source domain). Many existing transfer learning methods focus on learning one discriminator with single-source domain. Sometimes, knowledge from single-source domain might not be enough for predicting the target task. Thus, multiple source domains carrying richer transferable information are considered to complete the target task. Although there are some previous studies dealing with multi-source domain adaptation, these methods commonly combine source predictions by averaging source performances. Different source domains contain different transferable information; they may contribute differently to a target domain compared with each other. Hence, the source contribution should be taken into account when predicting a target task. In this article, we propose a novel multi-source contribution learning method for domain adaptation (MSCLDA). As proposed, the similarities and diversities of domains are learned simultaneously by extracting multi-view features. One view represents common features (similarities) among all domains. Other views represent different characteristics (diversities) in a target domain; each characteristic is expressed by features extracted in a source domain. Then multi-level distribution matching is employed to improve the transferability of latent features, aiming to reduce misclassification of boundary samples by maximizing discrepancy between different classes and minimizing discrepancy between the same classes. Concurrently, when completing a target task by combining source predictions, instead of averaging source predictions or weighting sources using normalized similarities, the original weights learned by normalizing similarities between source and target domains are adjusted using pseudo target labels to increase the disparities of weight values, which is desired to improve the perfo...

Li, M, Yang, Y, Iacopi, F, Yamada, M & Nulman, J 2021, 'Compact Multilayer Bandpass Filter Using Low-Temperature Additively Manufacturing Solution', IEEE Transactions on Electron Devices, pp. 1-7.
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Li, P, Li, W, Sun, Z, Shen, L & Sheng, D 2021, 'Development of sustainable concrete incorporating seawater: A critical review on cement hydration, microstructure and mechanical strength', CEMENT & CONCRETE COMPOSITES, vol. 121.
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Li, P, Xu, Y, Wei, Y & Yang, Y 2021, 'Self-Correction for Human Parsing', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Li, R, Dai, Q, Qin, L, Wang, G, Xiao, X, Yu, JX & Qiao, S 2021, 'Signed Clique Search in Signed Networks: Concepts and Algorithms', IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 33, no. 2, pp. 710-727.
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Li, S, Li, W, Wen, S, Shi, K, Yang, Y, Zhou, P & Huang, T 2021, 'Auto-FERNet: A Facial Expression Recognition Network with Architecture Search', IEEE Transactions on Network Science and Engineering.
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Deep convolutional neural networks have achieved great success in facial expression datasets both under laboratory conditions and in the wild. However, most of these related researches use general image classification networks (e.g., VGG, GoogLeNet) as backbones, which leads to inadaptability while applying to Facial Expression Recognition (FER) task, especially those in the wild. In the meantime, these manually designed networks usually have large parameter size. To tackle with these problems, we propose an appropriative and lightweight Facial Expression Recognition Network Auto-FERNet, which is automatically searched by a differentiable Neural Architecture Search (NAS) model directly on FER dataset. Furthermore, for FER datasets in the wild, we design a simple yet effective relabeling method based on Facial Expression Similarity (FES) to alleviate the uncertainty problem caused by natural factors and the subjectivity of annotators. Experiments have shown the effectiveness of the searched Auto-FERNet on FER task. Concretely, our architecture achieves a test accuracy of 73.78\% on FER2013 without ensemble or extra training data. And noteworthily, experimental results on CK+ and JAFFE outperform the state-of-the-art with an accuracy of 98.89\% (10 folds) and 97.14\%, respectively, which also validate the robustness of our system.

Li, S, Zhou, X & Feng, Y 2021, 'Qubit Mapping Based on Subgraph Isomorphism and Filtered Depth-Limited Search', IEEE Transactions on Computers, pp. 1-1.
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Li, W, Yang, M, Long, R, Mamaril, K & Chi, Y 2021, 'Treatment of electric vehicle battery waste in china: A review of existing policies', Journal of Environmental Engineering and Landscape Management, vol. 29, no. 2, pp. 111-122.
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This paper reviews existing policies for supporting the treatment of electric vehicle (EV) battery waste in China, and identifies some of their major shortcomings that policy makers may like to consider while making policy decisions. The shortcomings of existing policies identified in this paper include: 1) no clear provisions for historical and orphan batteries; 2) no target for battery collection; 3) unclear definition of the scope of authority among various central and local agencies involved in the regulation of waste battery treatment; 4) unclear requirements for data auditing and verification for tracking the entire life cycle of EV batteries; 5) limited consideration of the challenges to ensure stakeholder cooperation; and 6) no explicit specification of the mechanisms for financing waste battery treatment. This paper also makes some practical policy suggestions for overcoming these shortcomings.

Li, X, Xiang, J, Wang, J, Li, J, Wu, F-X & Li, M 2021, 'FUNMarker: Fusion network-based method to identify prognostic and heterogeneous breast cancer biomarkers.', IEEE/ACM transactions on computational biology and bioinformatics, vol. PP.
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Breast cancer is a heterogeneous disease with many clinically distinguishable molecular subtypes each corresponding to a cluster of patients. Identification of prognostic and heterogeneous biomarkers for breast cancer is to detect cluster-specific gene biomarkers which can be used for accurate survival prediction of breast cancer outcomes. In this study, we proposed a FUsion Network-based method (FUNMarker) to identify prognostic and heterogeneous breast cancer biomarkers by considering the heterogeneity of patient samples and biological information from multiple sources. To reduce the affect of heterogeneity of patients, samples were first clustered using the K-means algorithm based on the principal components of gene expression. For each cluster, to comprehensively evaluate the influence of genes on breast cancer, genes were weighted from three aspects: biological function, prognostic ability and correlation with known disease genes. Then they were ranked via a label propagation model on a fusion network that combined physical protein interactions from seven types of networks and thus could reduce the impact of incompleteness of interactome. We compared FUNMarker with three state-of-the-art methods and the results showed that biomarkers identified by FUNMarker were biological interpretable and had stronger discriminative power than the existing methods in differentiating patients with different prognostic outcomes.

Li, X, Zhang, J, Shen, L, Qin, L, Fu, Q, Sun, Y & Liu, Y 2021, 'Magnetoresistive micro-displacement sensor based on magnetorheological fluid', SMART MATERIALS AND STRUCTURES, vol. 30, no. 4.
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Li, Y, Lei, G, Bramerdorfer, G, Peng, S, Sun, X & Zhu, J 2021, 'Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions', Applied Sciences.
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Li, Y, Shi, W, Liu, Z, Li, J, Wang, Q, Yan, X, Cao, Z & Wang, G 2021, 'Effective Brain State Estimation During Propofol-Induced Sedation Using Advanced EEG Microstate Spectral Analysis.', IEEE journal of biomedical and health informatics, vol. 25, no. 4, pp. 978-987.
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Brain states are patterns of neuronal synchrony, and the electroencephalogram (EEG) microstate provides a promising tool to characterize and analyze the synchronous neural firing. However, the topographical spectral information for each predominate microstate is still unclear during the switch of consciousness, such as sedation, and the practical usage of the EEG microstate is worth probing. Also, the mechanism behind the anesthetic-induced alternations of brain states remains poorly understood. In this study, an advanced EEG microstate spectral analysis was utilized using multivariate empirical mode decomposition in Hilbert-Huang transform. The practicability was further investigated in scalp EEG recordings during the propofol-induced transition of consciousness. The process of transition from the awake baseline to moderate sedation was accompanied by apparent increases in microstate (A, B, and F) energy, especially in the whole-brain delta band, frontal alpha band and beta band. In comparison to other effective EEG-based parameters that commonly used to measure anesthetic depth, using the selected spectral features reached better performance (80% sensitivity, 90% accuracy) to estimate the brain states during sedation. The changes in microstate energy also exhibited high correlations with individual behavioral data during sedation. In a nutshell, the EEG microstate spectral analysis is an effective method to estimate brain states during propofol-induced sedation, giving great insights into the underlying mechanism. The generated spectral features can be promising markers to dynamically assess the consciousness level.

Li, Y, Wang, D, Yang, G, Yuan, X, Li, H, Wang, Q, Ni, B, He, D, Fu, Q, Jiang, L, Tang, W, Yang, F & Chen, H 2021, 'Comprehensive investigation into in-situ chemical oxidation of ferrous iron/sodium percarbonate (Fe(II)/SPC) processing dredged sediments for positive feedback of solid–liquid separation', Chemical Engineering Journal, vol. 425.
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Before disposal of dredged sediments (DS), filtrating DS is commonly used for their volume reduction. The work, for the first time, investigated Fe(II)/SPC processing DS to advance their solid–liquid separation from filtering feasibility, operational mechanism, technic reinforcement to potential implication. 16 mg Fe(II)/TSS & 60 mg SPC/TSS treatment elevated solid content of DS from 25.7% to 55.7% (vacuum filtration for 10 min), along with filtrate volume increased from 45.0 mL to 77.5 mL. •OH and Fe(III) with their hydrolyzed polymers, from Fe(II)/SPC system, are mainly lying behind the improved solid–liquid separation. Detailedly, the dilapidation of extracellular polymeric substances (EPS) with the destruction of biomolecules in EPS was completed by •OH invasion, which might rearrange the extracellular/intracellular protein configuration, with the increments of β-sheet & random coil but the decrement of α-helices. Simultaneously, Fe(III) and their hydrolyzed polymers promoted the relief of electrostatic repulsive-forces and the squeezing of double-electric layers, and the gathered DS could be held by integration of Fe(III) with –COOH and –OH. Additionally, CaO strengthened the filtering velocity/extent of Fe(II)/SPC-treated DS. After 70 mg/g CaO treatment, its solid content further elevated to 61.7% after vacuum filtration for 5.5 min, mainly resulting from skeleton construction by CaO, charge neutrality by released Ca2+, bridging cell debris and biopolymers by released Ca2+, compression of colloids double layers by released Ca2+, and binding PO43- in outer centrate liquid by released Ca2+.

Li, Y, Wang, D, Yang, G, Yuan, X, Yuan, L, Li, Z, Xu, Q, Liu, X, Yang, Q, Tang, W, Jiang, L, Li, H, Wang, Q & Ni, B 2021, 'In-depth research on percarbonate expediting zero-valent iron corrosion for conditioning anaerobically digested sludge', Journal of Hazardous Materials.
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Li, Y, Zeng, X, Zhou, J, Shi, Y, Umar, HA, Long, G & Xie, Y 2021, 'Development of an eco-friendly ultra-high performance concrete based on waste basalt powder for Sichuan-Tibet Railway', Journal of Cleaner Production, vol. 312.
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Generally, tunnel waste is stacked in the slag field nearby for landfilling, which is harmful to sustainable development. The broken rocks and rock powder among the tunnel waste can be recycled to produce machine-made sand, producing many by-products calling rock powder. Based on the practical project, three types of waste basalt powder (BP), from tunnel excavation waste and by-products (rock powder) of machine-made sand producing from tunnel excavation waste in Sichuan-Tibet railway construction sites, was used to prepare an eco-friendly UHPC. The BP is used to replace the cement and is included in the design UHPC based on Modified Andreasen &Andersen particle packing model (MAA). Moreover, the chemical and physical behaviors and ecological evaluation of the designed UHPC and UHPC pasted were discussed. The results showed that when BP (Specific surface area 4.6582 m2/g) replaces up to 15%, the highest compressive strength of designed UHPC (220 MPa) was obtained. Compared with quartz powder, the pozzolanic activity of BP was generally low and increased with the increase of reaction temperature. However, the presence of BP and its fineness in UHPC pastes increased the values of the total autogenous shrinkage and decreased the total heat release at an early age of designed UHPC pastes, this effect is more pronounced with temperature increasing. Based on a quartering method with embodied carbon dioxide emissions and the compressive strength, UHPC with waste BP reduced embodied carbon dioxide and possessed higher compressive strength and lower environmental impact than the control samples of UHPC.

Li, Z, Tang, Y, Huang, T & Wen, S 2021, 'Formation Control of Multiagent Networks: Cooperative and Antagonistic Interactions', IEEE Transactions on Systems, Man, and Cybernetics: Systems.
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This article studies the formation control problem of second-order multiagent networks, in which cooperative and antagonistic interactions of the agents spontaneously coexist in the communication process. Based on the convex analysis theory, several convex polytopes that do not require some kinds of system constraints are constructed in the presence of these interactions. Then, the matrix perturbation theory and some mathematical techniques are utilized to analyze these convex polytopes. The obtained results show that the agents with cooperative interactions monotonously converge to their own specified formation shape while maintaining the desired relative position of the other agents with antagonistic interactions. Subsequently, two numerical examples are presented to illustrate the obtained results.

Li, ZX, Zhang, X, Shi, Y, Wu, C & Li, J 2021, 'Finite element modeling of FRP retrofitted RC column against blast loading', Composite Structures, vol. 263.
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Fiber-reinforced polymer (FRP) wrap could considerably improve the shear capacity and ductility of RC columns. FRP is therefore considered a potential material to strengthen the RC column against blast loading. Due to the high expense and safety concern of field blast tests, a very limited number of explosion tests on FRP retrofitted RC columns have been conducted, which hinders the understanding of the response of FRP retrofitted RC columns against blast loading. With advanced computational technology, it is convenient to develop a Finite Element (FE) model that can accurately capture the structural response of FRP retrofitted columns under blast loading. In this paper, a refined FE model was established to simulate the FRP retrofitted RC columns under blast loading. Strain rate effects on the concrete and steel reinforcing bar as well as the FRP composite of which the strain rate effect was commonly ignored, were all considered in the model. Comprehensive modifications were made to the Karagozian and Case concrete (KCC) model to accurately capture the mechanical properties of FRP-confined concrete. Finally, the FE model was validated with several available experimental tests. The developed FE model could capture the blast response of FRP retrofitted columns with good accuracy.

Li, ZX, Zhang, X, Shi, Y, Wu, C & Li, J 2021, 'Predication of the residual axial load capacity of CFRP-strengthened RC column subjected to blast loading using artificial neural network', Engineering Structures, vol. 242.
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In this study, two genetic algorithm optimized backpropagation neural networks (GA-BPNN) were established to predict the ratio of residual axial load capacity to the maximum axial load capacity (referred to as RCI hereafter) of the non- and CFRP-strengthened RC columns based on a huge amount of simulation data. The first one can be used to predict the residual axial load capacity of the damaged non- and CFRP-strengthened RC columns induced by blast load with the input of several parameters including column dimensions, concrete strength, transverse reinforcement ratio, longitudinal reinforcement ratio, axial load ratio, CFRP stiffness, carbon fiber strength, peak pressure and impulse of the blast load. Therefore it can be used for the blast-resistant design of non- and CFRP-strengthened RC columns. The input variables of the second GA-BPNN were changed to be the ratio of residual mid-height deflection to the column height after the explosion, column dimensions, concrete strength, transverse reinforcement ratio, longitudinal reinforcement ratio, CFRP stiffness and carbon fiber strength. Since the input variables of the second GA-BPNN could be easily derived after the explosion, thus it could be used for the rapid damage assessment of RC columns. Damage assessments for three non- and CFRP-strengthened columns were also conducted using the first GA-BPNN.

Liang, W, Ma, Y, Xu, W, Xu, Z, Jia, X & Zhou, W 2021, 'Request Reliability Augmentation with Service Function Chain Requirements in Mobile Edge Computing', IEEE Transactions on Mobile Computing.
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In this paper, we study a novel service reliability augmentation problem in an Edge Computing network, where mobile users request network services with a service function chain (SFC) and reliability expectation requirements. To enhance the service reliability of user requests, it is a common practice to make use of redundant network function (VNF) instance placement in case their primary VNF instances fail. To this end, we first formulate a novel service reliability augmentation problem for each admitted request and show the NP-hardness of the problem. We then provide an admission framework of user requests by placing primary VNF instances of network functions in the SFC to different cloudlets, and then develop a randomized algorithm with a good approximation ratio and high probability for the service reliability augmentation problem, at the expense of moderate resource constraint violations. We also devise a deterministic heuristic for the problem without any resource violation. We thirdly extend the proposed algorithm for a single request to the service reliability augmentation problem for a set of requests. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising and superior to their analytical counterparts.

Lidfors Lindqvist, A, Zhou, S & Walker, PD 2021, 'Direct yaw moment control of an ultra-lightweight solar-electric passenger vehicle with variation in loading conditions', Vehicle System Dynamics: international journal of vehicle mechanics and mobility.
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Large variations load-to-curb weight ratios are linked to significant changes in parameters critical to control design for vehicle stability control system. Unique and highly customised vehicles, such as the lightweight solar car in this paper, are more susceptible to the impact of such variations when developing control methods. The purpose of this study is to study the influence of variation in loading conditions, the effect of ignoring changes in inertial parameters, and develop and compare a number of alternative vehicle stability control methods that can be applied to rear-wheel driven vehicles via in-wheel motors. In this paper a Sliding Mode Control (SMC) both nominal and when including uncertainty, Dynamic Curvature Control (DCC) and a Proportional–Integral Control (PI) strategies are compared to the baseline open-loop control case. Each controller is implemented through co-simulation via MATLAB® Simulink® and Siemens Amesim™ using a 15-DOF non-linear vehicle model. The results show that SMC achieves the best performance, whilst DCC tends to overshoot target conditions prior to settling, indicating that SMC is the preferred control strategy. It is also demonstrated that by ignoring the change in the inertial parameters in simulation environments can produce an incorrect translation of the control performance.

Lin, C-T & Tien-Thong, ND 2021, 'Direct-Sense Brain-Computer Interfaces and Wearable Computers', IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, vol. 51, no. 1, pp. 298-312.
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Lin, C-T, Chuang, C-H, Hung, Y-C, Fang, C-N, Wu, D & Wang, Y-K 2021, 'A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks.', IEEE Transactions on Cybernetics.
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Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level. Electroencephalography (EEG) has been demonstrated to be effective for monitoring changes in the human brain state and behavior. Thirty-seven subjects participated in this driving experiment and performed a perform lane-keeping task in a visual-reality environment. Three domains, namely, frequency, temporal, and 2-D spatial information, of the EEG channel location were comprehensively considered. A 4-D convolutional neural-network (4-D CNN) algorithm was then proposed to associate all information from the EEG signals and the changes in the human state and behavioral performance. A 4-D CNN achieves superior forecasting performance over 2-D CNN, 3-D CNN, and shallow networks. The results showed a 3.82% improvement in the root mean-square error, a 3.45% improvement in the error rate, and a 11.98% improvement in the correlation coefficient with 4-D CNN compared with 3-D CNN. The 4-D CNN algorithm extracts the significant θ and alpha activations in the frontal and posterior cingulate cortices under distinct fatigue levels. This work contributes to enhancing our understanding of deep learning methods in the analysis of EEG signals. We even envision that deep learning might serve as a bridge between translation neuroscience and further real-world applications.

Lin, C-T, King, J-T, John, AR, Huang, K-C, Cao, Z & Wang, Y-K 2021, 'The Impact of Vigorous Cycling Exercise on Visual Attention: A Study With the BR8 Wireless Dry EEG System', Frontiers in Neuroscience.
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Lin, C-T, Wang, C-Y, Huang, K-C, Horng, S-J & Liao, L-D 2021, 'Wearable, Multimodal, Biosignal Acquisition System for Potential Critical and Emergency Applications', Emergency Medicine International.
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Lin, JY, Yang, Y, Wong, SW & Li, Y 2021, 'High-Order Modes Analysis and Its Applications to Dual-Band Dual-Polarized Filtering Cavity Slot Arrays', IEEE Transactions on Microwave Theory and Techniques.
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In this article, a series of filtering cavity slot arrays using high-order modes are investigated. It is found that each unit of the cavity slot arrays in the proposed high-order mode resonator is in phase with the same amplitude, which helps enhance the antenna gain and reduce the sidelobe level. Meanwhile, the filtering function is integrated into the design for frequency selectivity and harmonic mode suppression. The higher order response can be achieved by cascading more high-order mode resonators with required external quality factor (Qₑ) and coupling coefficient (K). The fractional bandwidth (FBW) and out-of-band suppression of proposed designs are also discussed. For proof-of-concept, a single-band third-order 4 x 5 filtering cavity slot array using a TM₄₅₀ mode resonator and a dual-band dual-polarized third-order 4 x 3 filtering cavity slot array, using TM₄₃₀ and TM₃₄₀ mode resonators, are fabricated and tested. The good agreement between the simulated and measured results verifies that the proposed design methodology is feasible for designing high-order mode filtering cavity slot array antennas.

Lin, S, Liao, S, Yang, Y, Che, W & Xue, Q 2021, 'Gain Enhancement of Low-Profile Omnidirectional Antenna Using Annular Magnetic Dipole Directors', IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 20, no. 1, pp. 8-12.
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Lin, Z, Lv, T, Ni, W, Zhang, JA & Liu, RP 2021, 'Nested Hybrid Cylindrical Array Design and DoA Estimation for Massive IoT Networks', IEEE Journal on Selected Areas in Communications.
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IEEE Reducing cost and power consumption while maintaining high network access capability is a key physical-layer requirement of massive Internet of Things (mIoT) networks. Deploying a hybrid array is a cost-and energy-efficient way to meet the requirement, but would penalize system degree of freedom (DoF) and channel estimation accuracy. This is because signals from multiple antennas are combined by a radio frequency (RF) network of the hybrid array. This paper presents a novel hybrid uniform circular cylindrical array (UCyA) for mIoT networks. We design a nested hybrid beamforming structure based on sparse array techniques and propose the corresponding channel estimation method based on the second-order channel statistics. As a result, only a small number of RF chains are required to preserve the DoF of the UCyA. We also propose a new tensor-based two-dimensional (2-D) direction-of-arrival (DoA) estimation algorithm tailored for the proposed hybrid array. The algorithm suppresses the noise components in all tensor modes and operates on the signal data model directly, hence improving estimation accuracy with an affordable computational complexity. Corroborated by a Cramér-Rao lower bound (CRLB) analysis, simulation results show that the proposed hybrid UCyA array and the DoA estimation algorithm can accurately estimate the 2-D DoAs of a large number of IoT devices.

Lin, Z, Lv, T, Ni, W, Zhang, JA, Zeng, J & Liu, RP 2021, 'Joint Estimation of Multipath Angles and Delays for Millimeter-Wave Cylindrical Arrays with Hybrid Front-ends', IEEE Transactions on Wireless Communications.
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Linares-Mustarós, S, Ferrer-Comalat, JC, Corominas-Coll, D & Merigó, JM 2021, 'The weighted average multiexperton', Information Sciences.
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© 2020 Experton theory, a generalization of probabilistic set theory, that is of great usefulness to group decision analysis, was first proposed as a means of improving the processing and analysis of opinions issued by experts. This theory produces an information-fusion mathematical object, the experton, which can be used in predictive problems to justify decisions based on well-constructed reasoning. The aim of this paper is to present an aggregative method of several expertons, with the idea that some of the groups of experts involved in producing these expertons may have more influence than others in the decision-making process. In this article, we carry out an aggregation analysis of expertons, not experts, which culminates in the creation of a new mathematical object. This object, which is called the weighted average multiexperton, is coherent with an experton-type object created from a weighting of the initial data provided by all experts. Since the aggregation method presented has been devised to represent the decision-maker's attitude regarding the importance of different groups of experts, this approach represents a new tool for dealing with group decision-making problems. Additionally, the study presents some properties of the new object. Finally, the paper ends with an application for business decision-making.

Ling, Y, Wang, K, Wang, X & Li, W 2021, 'Prediction of engineering properties of fly ash-based geopolymer using artificial neural networks', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Fly ash-based geopolymer has been studied extensively in recent years due to its comparable properties to Portland cement and its environmental benefits. However, the uncertainty and complexity of design parameters, such as the SiO2/Na2O mole ratio in alkaline solution, the alkaline solution concentration in liquid phase, and the liquid-to-fly ash mass ratio (L/F), have made it very difficult to create a systematic approach for geopolymer mix design. These mix design parameters, along with fly ash properties and curing conditions (temperature and time), significantly influence key properties of the material, such as setting time and compressive strength. In this study, an artificial neural network (ANN) was used to develop models for predicting the key properties of high-calcium fly ash-based geopolymer according to its mix design parameters. The correlations between experimental measurements and ANN model predictions of setting time, compressive strength, and heat of geopolymerization were established based on the results of tests on 36, 273, and 72 geopolymer mixes, respectively. The results show that the correlations between the experimental measurements and ANN model predictions of the properties studied are all strong. ANN modeling was found to be a suitable computing method to analyze the effects of design parameters on geopolymer properties and showed that L/F exhibited the greatest effect on setting time, alkaline solution concentration had the greatest influence on compressive strength, and a mole ratio larger than 1.5 significantly impacted heat at the geopolymerization peak. The developed ANN models can be used as guidance for mix design of high-calcium fly ash geopolymer in engineering applications.

Liu, A, Lu, J & Zhang, G 2021, 'Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 293-307.
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Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved to be an efficient method of handling concept drift. However, the best way to create and maintain ensemble diversity with evolving streams is still a challenging problem. In contrast to estimating diversity via inputs, outputs, or classifier parameters, we propose a diversity measurement based on whether the ensemble members agree on the probability of a regional distribution change. In our method, estimations over regional distribution changes are used as instance weights. Constructing different region sets through different schemes will lead to different drift estimation results, thereby creating diversity. The classifiers that disagree the most are selected to maximize diversity. Accordingly, an instance-based ensemble learning algorithm, called the diverse instance-weighting ensemble (DiwE), is developed to address concept drift for data stream classification problems. Evaluations of various synthetic and real-world data stream benchmarks show the effectiveness and advantages of the proposed algorithm.

Liu, B, Ding, M, Shaham, S, Rahayu, W, Farokhi, F & Lin, Z 2021, 'When Machine Learning Meets Privacy', ACM Computing Surveys.
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Liu, B, Ni, W, Liu, RP, Zhu, Q, Guo, YJ & Zhu, H 2021, 'Novel Integrated Framework of Unmanned Aerial Vehicle and Road Traffic for Energy-Efficient Delay-Sensitive Delivery', IEEE Transactions on Intelligent Transportation Systems.
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Unmanned aerial vehicle (UAV) has demonstrated its usefulness in goods delivery. However, the delivery distances are often restrained by the battery capacity of UAVs. This paper integrates UAVs into intelligent transportation systems for energy-efficient, delay-sensitive goods delivery. Dynamic programming (DP) is first applied to minimize the energy consumption of a UAV and ensure its timely arrival at its destination, by optimizing the control policy of the UAV. The control policy involves decisions including flight speed, hitchhiking (on collaborative ground vehicles), or recharging at roadside charging stations. Another key aspect is that we reveal the conditions of the remaining flight distance or the elapsed time, only under which the optimal action of the UAV changes. Accordingly, thresholds are derived, and the optimal control policy can be instantly made by comparing the remaining flight distance and the elapsed time with the thresholds. Simulations show that the proposed algorithms can improve the flight distance by 48%, as compared with existing alternatives. The proposed threshold-based technique can achieve the same performance as the DP-based solution, while significantly reducing the computational complexity.

Liu, C, Indraratna, B & Rujikiatkamjorn, C 2021, 'An analytical model for particle-geogrid aperture interaction', Geotextiles and Geomembranes, vol. 49, no. 1, pp. 41-44.
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The shear strength of geogrid-reinforced ballast is often dependent on the aperture size of geogrids and the nominal size of ballast. This paper presents a theoretical analysis based on probabilistic mechanics of how aperture size affects the interaction between particles and geogrid. Unlike past literature, in this study, the properties of the particle size distribution is analysed using a Weibull distribution. The probability of grain interlock is proposed to describe the interactive mechanisms between particles and geogrids based on the relative particle size, which is defined as the ratio of particle size to aperture size. The mathematical model is calibrated by a set of large-scale direct shear tests with almost single-size (highly uniform) ballast aggregates, and then validated by independent set of data taken from both literature and current study. The study concludes that more uniform particle size distribution increases the probability of grain interlock at the optimum aperture size but decreases it at non-optimum aperture sizes.

Liu, C, Wang, D, Wang, S, Niu, F, Wang, Y, Lei, G & Zhu, J 2021, 'Design and Analysis of a New Permanent Magnet Claw Pole Machine With S-Shape Winding', IEEE TRANSACTIONS ON MAGNETICS, vol. 57, no. 2.
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Liu, F, Han, R, Naficy, S, Casillas, G, Sun, X & Huang, Z 2021, 'Few-Layered Boron Nitride Nanosheets for Strengthening Polyurethane Hydrogels', ACS Applied Nano Materials, vol. 4, no. 8, pp. 7988-7994.
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Liu, F, Han, R, Nattestad, A, Sun, X & Huang, Z 2021, 'Carbon- and oxygen-doped hexagonal boron nitride for degradation of organic', SURFACE INNOVATIONS, vol. 9, no. 4, pp. 222-230.
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Liu, H, Zhu, X, Wang, Y, Men, K & Yeo, KS 2021, 'A 60 GHz 8-Way Combined Power Amplifier in 0.18 &#x03BC;m SiGe BiCMOS', IEEE Transactions on Circuits and Systems II: Express Briefs.
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IEEE A 60 GHz fully-integrated 8-way combined power amplifier (PA) is developed in a standard 0.18 μm SiGe BiCMOS technology. The 8-way power splitter and combiner are co-optimized with transformer based baluns inside the eight differential PA cells, and hence resulting in minimum loss and high gain, linearity and efficiency. The measurement shows that the PA can achieve a gain of 22.2 dB around 60 GHz and 3-dB bandwidth from 53.5 GHz to 66.5 GHz, which covers all the channels specified in IEEE 802.11ad standard. It also attains a 1-dB power compression point (P1dB) of 21.8 dBm and saturated output power (PSAT) of 22.6 dBm, with power-added-efficiency of 10.7% and 12%, respectively.

Liu, J, Li, H, Ji, J & Luo, J 2021, 'Bipartite Consensus Control for a Swarm of Robots', JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME, vol. 143, no. 1.
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Liu, J, Wang, X, Shen, S, Yue, G, Yu, S & Li, M 2021, 'A Bayesian Q-Learning Game for Dependable Task Offloading Against DDoS Attacks in Sensor Edge Cloud', IEEE Internet of Things Journal.
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To enhance dependable resource allocation against increasing DDoS attacks, in this paper, we investigate interactions between a sensor device-edgeVM pair and a DDoS attacker using a game-theoretic framework, under the constraints of the task time, resource budget, and incomplete knowledge of the processing time of machine learning tasks. In this game, the sensor device expects an edgeVM to cooperate and choose its resource allocation strategy with the objective of satisfying the minimum resource required of machine learning tasks at the corresponding sensor device. Similarly, the attacker’s objective is to strategically allocate resources so that the resource constraint of the machine learning tasks is not satisfied. Owing to a lack of complete information of the processing time of the machine learning tasks, this strategic resource allocation problem between the two players is modeled as a Bayesian Q-learning game, in which the optimal strategies of the sensor device-edgeVM pair and the attacker are analyzed. Furthermore, probability distributions are employed by the corresponding players to model the incomplete nature of the game and a greedy Q-learning algorithm is proposed to dependable resource allocation against DDoS attacks. Numerical simulation results demonstrate that the proposed mechanism is superior to other dependable resource allocation mechanisms under incomplete information for DDoS attacks in the sensor edge cloud.

Liu, J, Wu, C, Liu, Z, Li, J, Xu, S, Liu, K, Su, Y & Chen, G 2021, 'Investigations on the response of ceramic ball aggregated and steel fibre reinforced geopolymer-based ultra-high performance concrete (G-UHPC) to projectile penetration', Composite Structures, vol. 255.
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© 2020 Elsevier Ltd This paper presents experimental and numerical studies on projectile impact resistance of ceramic ball aggregated and steel fibre reinforced geopolymer-based ultra-high performance concrete (G-UHPC) targets. Compared with plain G-UHPC, ceramic ball aggregated G-UHPC enhanced projectile impact resistance regarding crack propagation, crater damage and depth of penetration (DOP). A further improvement of projectile impact resistance was observed if a combined addition of steel fibres and ceramic balls was used. Numerical simulations were then performed to further comprehend the projectile impact on G-UHPC targets using the HJC constitutive model in the finite element software LS-DYNA. Numerically simulated DOP, projectile velocity and displacement histories were obtained and then validated through comparing with the existing models. The numerical perforation limits for 20 vol-% ceramic ball aggregated and 1.5 vol-% steel fibre reinforced G-UHPC were 240 mm at 568 m/s and 380 mm at 798 m/s, respectively.

Liu, L, Fryc, S, Wu, L, Vu, TL, Paul, G & Vidal-Calleja, T 2021, 'Active and Interactive Mapping With Dynamic Gaussian Process Implicit Surfaces for Mobile Manipulators', IEEE Robotics and Automation Letters.
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Liu, L, Jiang, J, Jia, W, Amirgholipour, S, Wang, Y, Zeibots, M & He, X 2021, 'DENet: A Universal Network for Counting Crowd With Varying Densities and Scales', IEEE Transactions on Multimedia, vol. 23, pp. 1060-1068.
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Liu, L, Zhou, T, LONG, G, Jiang, J & Zhang, C 2021, 'Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Liu, P, Li, Y, Cheng, W, Gao, X & Huang, X 2021, 'Intelligent Reflecting Surface Aided NOMA for Millimeter-Wave Massive MIMO with Lens Antenna Array', IEEE Transactions on Vehicular Technology.
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Liu, Q, Huang, H, Xuan, J, Zhang, G, Gao, Y & Lu, J 2021, 'A Fuzzy Word Similarity Measure for Selecting Top-k Similar Words in Query Expansion', IEEE Transactions on Fuzzy Systems, pp. 1-1.
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Liu, Q, Lu, J, Zhang, G, Shen, T, Zhang, Z & Huang, H 2021, 'Domain-specific meta-embedding with latent semantic structures', Information Sciences.
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Liu, S, Wang, S, Liu, X, Lin, C-T & Lv, Z 2021, 'Fuzzy Detection Aided Real-Time and Robust Visual Tracking Under Complex Environments', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 29, no. 1, pp. 90-102.
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Liu, W, Wang, H, Zhang, Y, Wang, W, Qin, L & Lin, X 2021, 'EI-LSH: An early-termination driven I/O efficient incremental c-approximate nearest neighbor search', The VLDB Journal.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Nearest neighbor in high-dimensional space has been widely used in various fields such as databases, data mining and machine learning. The problem has been well solved in low-dimensional space. However, when it comes to high-dimensional space, due to the curse of dimensionality, the problem is challenging. As a trade-off between accuracy and efficiency, c-approximate nearest neighbor (c-ANN) is considered instead of an exact NN search in high-dimensional space. A variety of c-ANN algorithms have been proposed, one of the important schemes for the c-ANN problem is called Locality-sensitive hashing (LSH), which projects a high-dimensional dataset into a low-dimensional dataset and can return a c-ANN with a constant probability. In this paper, we propose a new aggressive early-termination (ET) condition which stops the algorithm with LSH scheme earlier under the same theoretical guarantee, leading to a smaller I/O cost and less running time. Unlike the “conservative” early termination conditions used in previous studies, we propose an “aggressive” early termination condition which can stop much earlier. Though it is not absolutely safe and may result in the probability of failure, we can still devise more efficient algorithms under the same theoretical guarantee by carefully considering the failure probabilities brought by LSH scheme and early termination. Furthermore, we also introduce an incremental searching strategy. Unlike the previous LSH methods, which expand the bucket width in an exponential way, we employ a more natural search strategy to incrementally access the hash values of the objects. We also provide a rigorous theoretical analysis to underpin our incremental search strategy and the new early termination technique. Our comprehensive experiment results show that, compared with the state-of-the-art I/O efficient c-ANN techniques, our proposed algorithm, namely EI-LSH, can achieve much bette...

Liu, X, Wu, Y, Xu, Q, Du, M, Wang, D, Yang, Q, Yang, G, Chen, H, Zeng, T, Liu, Y, Wang, Q & Ni, B-J 2021, 'Mechanistic insights into the effect of poly ferric sulfate on anaerobic digestion of waste activated sludge', WATER RESEARCH, vol. 189.
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Liu, X, Xie, L, Wang, Y, Zou, J, Xiong, J, Ying, Z & Vasilakos, AV 2021, 'Privacy and Security Issues in Deep Learning: A Survey', IEEE Access.
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Deep Learning (DL) algorithms based on artificial neural networks have achieved remarkable success and are being extensively applied in a variety of application domains, ranging from image classification, automatic driving, natural language processing to medical diagnosis, credit risk assessment, intrusion detection. However, the privacy and security issues of DL have been revealed that the DL model can be stolen or reverse engineered, sensitive training data can be inferred, even a recognizable face image of the victim can be recovered. Besides, the recent works have found that the DL model is vulnerable to adversarial examples perturbed by imperceptible noised, which can lead the DL model to predict wrongly with high confidence. In this paper, we first briefly introduces the four types of attacks and privacy-preserving techniques in DL. We then review and summarize the attack and defense methods associated with DL privacy and security in recent years. To demonstrate that security threats really exist in the real world, we also reviewed the adversarial attacks under the physical condition. Finally, we discuss current challenges and open problems regarding privacy and security issues in DL.

Liu, Y, Zhou, Z, Wang, F, Kewes, G, Wen, S, Burger, S, Ebrahimi Wakiani, M, Xi, P, Yang, J, Yang, X, Benson, O & Jin, D 2021, 'Axial localization and tracking of self-interference nanoparticles by lateral point spread functions', Nature Communications.
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Liu, Z, Li, Y, Yao, L, Wang, X & Nie, F 2021, 'Agglomerative Neural Networks for Multiview Clustering', IEEE Transactions on Neural Networks and Learning Systems.
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Conventional multiview clustering methods seek a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, pairwise comparison cannot portray the interview relationship precisely if some of the subviews can be further agglomerated. To address the above challenge, we propose the agglomerative analysis to approximate the optimal consensus view, thereby describing the subview relationship within a view structure. We present an agglomerative neural network (ANN) based on constrained Laplacian rank to cluster multiview data directly without a dedicated postprocessing step (e.g., using K-means). We further extend ANN with a learnable data space to handle data of complex scenarios. Our evaluations against several state-of-the-art multiview clustering approaches on four popular data sets show the promising view-consensus analysis ability of ANN. We further demonstrate ANN's capability in analyzing complex view structures, extensibility through our case study and robustness and effectiveness of data-driven modifications.

Liu, Z, Xiao, F, Lin, C-T, Kang, BH & Cao, Z 2021, 'A Generalized Golden Rule Representative Value for Multiple-Criteria Decision Analysis', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-12.
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Lou, B, Barbieri, DM, Passavanti, M, Hui, C, Gupta, A, Hoff, I, Lessa, DA, Sikka, G, Chang, K, Fang, K, Lam, L, Maharaj, B, Ghasemi, N, Qiao, Y, Adomako, S, Foroutan Mirhosseini, A, Naik, B, Banerjee, A, Wang, F, Tucker, A, Liu, Z, Wijayaratna, K, Naseri, S, Yu, L, Chen, H, Shu, B, Goswami, S, Peprah, P, Hessami, A, Abbas, M & Agarwal, N 2021, 'Air pollution perception in ten countries during the COVID-19 pandemic', Ambio.
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Lu, J, Zheng, X, Tang, L, Zhang, T, Sheng, QZ, Wang, C, Jin, J, Yu, S & Zhou, W 2021, 'Can Steering Wheel Detect Your Driving Fatigue', IEEE Transactions on Vehicular Technology.
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Automated Driving System (ADS) has attracted increasing attention but the state-of-the-art ADS largely depend on vehicle driving parameters and facial features, which lacks reliability. Approaches using physiological based sensors (e.g., electroencephalogram or electrocardiogram) are either too clumsy to wear or impractical to install. In this paper, we propose a novel driver fatigue detection method by embedding surface electromyography (sEMG) sensors on a steering wheel. Compared with the existing methods, our approach is able to collect bio-signals in a non-intrusive way and detect driver fatigue at an earlier stage. The experimental results show that our approach outperforms existing methods with the weighted average F1 scores of about 90%. We also propose promising future directions to deploy this approach in real-life settings, such as applying multimodal learning using several supplementary sensors.

Lu, L, Ren, X, Cui, C, Luo, Y & Huang, M 2021, 'Tensor mutual information and its applications', Concurrency Computation.
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© 2020 John Wiley & Sons, Ltd. Correlation analysis has long been a question of great interest in measuring the relationship among different variables and has been applied in many fields, such as dimension reduction, classification, and so on. However, current methods of correlation analysis take into account the linear relationship between multiple variables and only few works on nonlinear interaction of two variables have been considered. In this article, we first present a nonlinear analysis method of multiple (two or more) variables based on mutual information for tensor analysis (MITA). In addition, we extend the mutual-information matrix analysis directly to MITA and show the multivariable mutual information formula based on Venn diagram. Experiments on multiview dimension reduction, including attacking internet traffic prediction, advertisement classification, and biometric structure prediction illustrate the effectiveness of the proposed method, especially in the case of low-dimensional subspace.

Lu, Z, Shi, L, Geng, L, Zhang, J, Li, X & Guo, X 2021, 'Non-cooperative game pricing strategy for maximizing social welfare in electrified transportation networks', International Journal of Electrical Power and Energy Systems, vol. 130.
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This paper proposes a non-cooperative game pricing strategy framework by the approach of profit-sharing and user equilibrium principles, - to maximize the social welfare of the electrified transportation system stakeholders consisting of electricity wholesalers, fast charging stations, and electric vehicle users. Electricity wholesalers propose profit-sharing contracts to sell electricity to each fast charging station. Fast charging stations compete with each other to develop the optimal retail price while considering their electricity selling revenue and the traveling cost of electric vehicle users for the purpose of maximal social welfare. Non-cooperative game competition between fast charging stations is formulated as a generalized Nash game. Wardrop user equilibrium principle is applied for path selection for electric vehicle users. A Newton-type fixed-point algorithm is developed to solve the generalized Nash equilibrium point. Meanwhile, the nonlinear program is solved by the commercial solver KNITRO. A case study demonstrates the effectiveness of the proposed pricing strategy in maximizing the total profits of the fast charging station retailers, wholesalers, and electric vehicle users.

Lu, Z-H, Wu, S-Y, Tang, Z, Zhao, Y-G & Li, W 2021, 'Effect of chloride-induced corrosion on the bond behaviors between steel strands and concrete', MATERIALS AND STRUCTURES, vol. 54, no. 3.
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Luo, S, Chu, VW, Li, Z, Wang, Y, Zhou, J, Chen, F & Wong, RK 2021, 'Multi-task learning by hierarchical Dirichlet mixture model for sparse failure prediction', International Journal of Data Science and Analytics.
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Luo, Z, Li, W, Gan, Y, He, X, Castel, A & Sheng, D 2021, 'Nanoindentation on micromechanical properties and microstructure of geopolymer with nano-SiO2 and nano-TiO2', CEMENT & CONCRETE COMPOSITES, vol. 117.
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Luo, Z, Li, W, Li, P, Wang, K & Shah, SP 2021, 'Investigation on effect of nanosilica dispersion on the properties and microstructures of fly ash-based geopolymer composite', CONSTRUCTION AND BUILDING MATERIALS, vol. 282.
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Luo, Z, Li, W, Wang, K, Castel, A & Shah, SP 2021, 'Comparison on the properties of ITZs in fly ash-based geopolymer and Portland cement concretes with equivalent flowability', CEMENT AND CONCRETE RESEARCH, vol. 143.
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Lyu, J, Ling, SH, Banerjee, S, Zheng, JY, Lai, KL, Yang, D, Zheng, YP, Bi, X, Su, S & Chamoli, U 2021, 'Ultrasound volume projection image quality selection by ranking from convolutional RankNet', COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, vol. 89.
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Ma, C, Zhang, J, Wang, J, Yang, N, Liu, Q, Zuo, S, Wu, X, Wang, P, Li, J & Fang, J 2021, 'Analytical Model of Open-Circuit Air-Gap Field Distribution in Interior Permanent Magnet Machines Based on Magnetic Equivalent Circuit Method and Boundary Conditions of Macroscopic Equations', IEEE TRANSACTIONS ON MAGNETICS, vol. 57, no. 3.
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Ma, F, Wu, Y, Yu, X & Yang, Y 2021, 'Learning With Noisy Labels via Self-Reweighting From Class Centroids', IEEE Transactions on Neural Networks and Learning Systems.
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Although deep neural networks have been proved effective in many applications, they are data hungry, and training deep models often requires laboriously labeled data. However, when labeled data contain erroneous labels, they often lead to model performance degradation. A common solution is to assign each sample with a dynamic weight during optimization, and the weight is adjusted in accordance with the loss. However, those weights are usually unreliable since they are measured by the losses of corrupted labels. Thus, this scheme might impede the discriminative ability of neural networks trained on noisy data. To address this issue, we propose a novel reweighting method, dubbed self-reweighting from class centroids (SRCC), by assigning sample weights based on the similarities between the samples and our online learned class centroids. Since we exploit statistical class centers in the image feature space to reweight data samples in learning, our method is robust to noise caused by corrupted labels. In addition, even after reweighting the noisy data, the decision boundaries might still suffer distortions. Thus, we leverage mixed inputs that are generated by linearly interpolating two random images and their labels to further regularize the boundaries. We employ the learned class centroids to evaluate the confidence of our generated mixed data via measuring feature similarities. During the network optimization, the class centroids are updated as more discriminative feature representations of original images are learned. In doing so, SRCC will generate more robust weighting coefficients for noisy and mixed data and facilitates our feature representation learning in return. Extensive experiments on both the synthetic and real image recognition tasks demonstrate that our method SRCC outperforms the state of the art on learning with noisy data.

Maeda, EE, Haapasaari, P, Helle, I, Lehikoinen, A, Voinov, A & Kuikka, S 2021, 'Black Boxes and the Role of Modeling in Environmental Policy Making', Frontiers in Environmental Science.
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Mahmudul, HM, Rasul, MG, Akbar, D, Narayanan, R & Mofijur, M 2021, 'A comprehensive review of the recent development and challenges of a solar-assisted biodigester system', Science of the Total Environment, vol. 753.
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© 2020 Elsevier B.V. The extensive use of fossil fuels and the environmental effect of their combustion products have attracted researchers to look into renewable energy sources. In addition, global mass production of waste has motivated communities to recycle and reuse the waste in a sustainable way to lower landfill waste and associated problems. The development of waste to energy (WtE) technology including the production of bioenergy, e.g. biogas produced from various waste through Anaerobic Digestion (AD), is considered one of the potential measures to achieve the sustainable development goals of the United Nations (UN). Therefore, this study reviews the most recent studies from relevant academic literature on WtE technology (particularly AD technology) for biogas production and the application of a solar-assisted biodigester (SAB) system aimed at improving performance. In addition, socio-economic factors, challenges, and perspectives have been reported. From the analysis of different technologies, further work on effective low-cost technologies is recommended, especially using SAB system upgrading and leveraging the opportunities of this system. The study found that the performance of the AD system is affected by a variety of factors and that different approaches can be applied to improve performance. It has also been found that solar energy systems efficiently raise the biogas digester temperature and through this, they maximize the biogas yield under optimum conditions. The study revealed that the solar-assisted AD system produces less pollution and improves performance compared to the conventional AD system.

Mai, L, Ding, Y, Zhang, X, Fan, L, Yu, S & Xu, Z 2021, 'Energy efficiency with service availability guarantee for Network Function Virtualization', Future Generation Computer Systems, vol. 119, pp. 140-153.
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Following the trend of Network Function Virtualization (NFV), dedicated hardware middleboxes are replaced with innovative and flexible software middleboxes also known as Virtual Network Functions (VNFs). An ordered sequence of VNFs composing a logical service is called Service Function Chain (SFC). VNFs are generally run on commodity servers. In this way, the capital and operational expenditures of buying and maintaining dedicated hardware for telecom operators can be greatly reduced. One of the key issues in NFV is the optimal VNF placement and service chaining to achieve energy efficiency. However, the current NFV energy saving approaches seem to consider energy minimization as the only objective to be optimized. Little or no attention is given to other important aspects, e.g., service availability, which is paramountly important to fulfill Service Level Agreement (SLA) for telecom operators. This paper investigates the energy efficiency optimization with service availability guarantee in NFV-enabled networks. We firstly propose a novel green orchestration NFV architecture. Then, an energy-efficient VNF placement framework guaranteeing service availability is presented under the proposed architecture, and evaluated by extensive simulations. Open research issues and technical challenges in this emerging area are also presented.

Malik, K, Kumar, D, Perissin, D & Pradhan, B 2021, 'Estimation of ground subsidence of New Delhi, India using and Multi-sensor Radar data', Advances in Space Research.
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Man, X, Xia, B, Luo, Z, Liu, J, Li, K & Nie, Y 2021, 'Engineering three-dimensional labyrinthine fractal acoustic metamaterials with low-frequency multi-band sound suppression', JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, vol. 149, no. 1, pp. 308-319.
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Mann, L, Chang, R, Chandrasekaran, S, Coddington, A, Daniel, S, Cook, E, Crossin, E, Cosson, B, Turner, J, Mazzurco, A & others 2021, 'From problem-based learning to practice-based education: A framework for shaping future engineers', European Journal of Engineering Education, pp. 1-21.

Mansoor, A & Bano, M 2021, 'Decolonial pedagogy and English literary studies: problematics in a Pakistani context', Compare: a journal of comparative and international education.
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© 2019, © 2019 British Association for International and Comparative Education. We propose a decolonial pedagogy in the teaching of English Literature at the undergraduate level in postcolonial Pakistan. We argue that the English literary texts that are taught in conjunction with different supplementary materials retain a West-centric tilt that requires disbanding. Therefore, we administered carefully designed worksheets. These encouraged marginal thinking across the differential socio-historical canvases that outline the prescribed texts, online lectures, supplementary reading materials and the students’ situatedness. The students used these resources as tools for practising a decolonial hermeneutics. By directing these tools towards their own lived situation, they intervened within the Western means of knowledge production and undid their alterity and historical silencing. By analysing the students’ assignments as sites of decolonial articulation, we propose that decolonial pedagogy can be used to generate an intercultural intellectual solidarity that allows the formerly colonised to rethink themselves in a manner that no longer talks back to the Empire.

Mao, T, Mihaita, A-S, Chen, F & Vu, HL 2021, 'Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization', IEEE Transactions on Intelligent Transportation Systems.
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Marjanovic, O 2021, 'A Novel Mechanism for Business Analytics Value Creation: Improvement of Knowledge-Intensive Business Processes', Journal of Knowledge Management.
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This research focuses on the ‘how’ of Business Analytics (BA) value creation, which remains an open research and practical challenge. The main purpose of this paper is to propose a novel BA value creation mechanism that is BA-enabled improvement of Knowledge Intensive Business Processes (KIBPs), with experiential knowledge of decision makers as the key to a more sustainable BA-enabled competitive differentiation. This research uses a qualitative research case study, conducted in a large retail distribution company. The research insights were observed through a combined lens of Work Systems Theory (WST) and the Knowledge-based View (KBV) of the firm, using an interpretivist approach. The proposed theoretical model identifies three stages of KIBP improvement through BA, and explains how they lead to a sustainable BA-enabled competitive differentiation. Stage 1 focuses on BA support for individual knowledge-intensive tasks, Stage 2 focuses on individual decision makers and their ability to gain KIBP-related analytical insights and turn them into action; and Stage 3 on sharing of the acquired experiential knowledge among decision-makers using BA. In addition to proposing a novel mechanism for BA value creation, this research demonstrates the importance of leveraging experiential knowledge of decision makers as a pathway to a more sustainable competitive differentiation through BA. This in turn creates new opportunities for KM researchers to engage in BA-related research. It also opens a new approach for BA researchers to investigate BA value creation mechanisms through the lens of KBV, rather than more common Resource-based (RB) or Capability-based (CB) views.

Mateos, MK, Marshall, GM, Barbaro, PM, Quinn, MC, George, C, Mayoh, C, Sutton, R, Revesz, T, Giles, JE, Barbaric, D, Alvaro, F, Mechinaud, F, Catchpoole, D, Lawson, JA, Chenevix-Trench, G, MacGregor, S, Kotecha, RS, Dalla-Pozza, L & Trahair, TN 2021, 'Methotrexate-related central neurotoxicity: clinical characteristics, risk factors and genome-wide association study in children treated for acute lymphoblastic leukemia.', Haematologica.
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Symptomatic methotrexate-related central neurotoxicity, 'MTX neurotoxicity', is a severe toxicity experienced during acute lymphoblastic leukemia (ALL) therapy with potential long-term neurologic complications. Risk factors and long-term outcomes require further study. We conducted a systematic, retrospective review of 1251 consecutive Australian children enrolled on BFM or COG-based protocols between 1998-2013. Clinical risk predictors for MTX neurotoxicity were analyzed using regression. A genome-wide association study (GWAS) was performed on 48 cases and 537 controls. The incidence of MTX neurotoxicity was 7.6% (n=95/1251), at a median of 4 months from ALL diagnosis and 8 days after intravenous or intrathecal MTX. Grade 3 elevation of serum aspartate aminotransferase (P=0.005, OR 2.31 (1.28-4.16)) in induction/consolidation was associated with MTX neurotoxicity, after accounting for the only established risk factor, age a10 years. Cumulative incidence of CNS relapse was increased in children where intrathecal MTX was omitted following symptomatic MTX neurotoxicity (n=48) compared to where intrathecal MTX was continued throughout therapy (n=1174) (P=0.047). Five-year CNS relapsefree survival was 89.2%±4.6% when intrathecal MTX was ceased compared to 95.4%±0.6% when intrathecal MTX was continued. Recurrence of MTX neurotoxicity was low (12.9%) for patients whose intrathecal MTX was continued after their first episode. The GWAS identified SNPs associated with MTX neurotoxicity near genes regulating neuronal growth, neuronal differentiation and cytoskeletal organization (P>1E-06). In conclusion, increased serum aspartate aminotransferase and age a10 years at diagnosis were independent risk factors for MTX neurotoxicity. Our data do not support cessation of intrathecal MTX after a first MTX neurotoxicity event.

McCarthy, PX, Gong, X, Eghbal, S, Falster, DS & Rizoiu, M-A 2021, 'Evolution of diversity and dominance of companies in online activity', PLOS ONE.
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Mehrabi, N & Khabbaz, H 2021, 'A trustful transition zone for high-speed rail using stone columns', Australian Journal of Civil Engineering.
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The high-speed railway projects have encountered several geotechnical challenges. One of the most important challenges is the differential settlement control in transition zones. Cement-treated soil is a common method to prevent the differential settlement at transition zones. An alternative method uses stone columns for controlling the differential settlement in approaching embankment of bridges. In this study, numerical modelling using PLAXIS 2D is selected for the assessment of stone columns in the reduction of total and differential settlements. One of the overpass bridges of the track constructed for the Tehran–Isfahan railway, the first high-speed railway in the country, is chosen as the case study. Three models are created based on the properties of the selected case study. The first one is a typical approaching embankment. The second one is the bridge abutment section, and the last one is a typical reinforced approaching embankment with stone columns.

Mehta, M, Paudel, KR, Shukla, SD, Allam, VSRR, Kannaujiya, VK, Panth, N, Das, A, Parihar, VK, Chakraborty, A, Ali, K, Jha, NK, Xenaki, D, Su, QP, Wich, PR, Adams, J, Hansbro, PM, Chellappan, DK, Oliver, BGG & Dua, K 2021, 'Recent trends of NFκB decoy oligodeoxynucleotide-based nanotherapeutics in lung diseases', Journal of Controlled Release.
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Mei, C, Zhang, Y, Wang, D, Wu, C & Xu, Y 2021, 'Parameter optimal investigation of modular prefabricated two-side connected buckling-restrained steel plate shear wall', Structures, vol. 29, pp. 2028-2043.
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This paper research the optimal scope of width-to-thickness ratio α1 of connection steel plate (CSP), thickness ratio α2 between CSP and inner steel plates (ISP), and width-to-thickness ratio α3 between the width of CSP and the thickness of the ISP of modular fabricated two-side connected buckling-restrained steel plate (MTBSP) shear wall. The finite element model (FEM) of the MTBSP shear wall is established based on the ABAQUS platform, which, considering the plastic-damage constitutive model of both concrete and steel materials. Through the result of a comparison between the test and FEM, it can be concluded that the FEM can effectively reflect the mechanical behavior of the MTBSP shear wall. Based on the verified FEM, the mechanical behavior of 93 computational cases with different ratios of α1, α2, and α3 of MTBSP shear wall is investigated and the corresponding optimal scope of α1, α2, and α3 are explored in detail. The results show that the mechanical performance of the MTBSP shear wall can be ensured effectively when the ratio α1, α2, and α3 is in the scope from 10 to 20, from 3 to 5, and from 60 to 80, respectively. Research of this study can be provided supporting the reliable design of the MTBSP shear wall.

Mei, C, Zhao, Z, Zhang, Y, Wang, D & Wu, C 2021, 'Performance Evaluation and Shear Resistance of Modular Prefabricated Two-Side Connected Composite Shear Walls', KSCE JOURNAL OF CIVIL ENGINEERING, vol. 25, no. 8, pp. 2936-2950.
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Meilianda, E, Lavigne, F, Pradhan, B, Wassmer, P, Darusman, D & Dohmen-Janssen, M 2021, 'Barrier Islands Resilience to Extreme Events: Do Earthquake and Tsunami Play a Role?'.
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Meng, Q, Wu, C, Li, J, Wu, P, Xu, S & Wang, Z 2021, 'A study of pressure characteristics of methane explosion in a 20 m buried tunnel and influence on structural behaviour of concrete elements', Engineering Failure Analysis, vol. 122.
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With increasing use of natural gas in urban metropolitan areas, utility tunnels that contain utility and gas lines have become critical infrastructure. A leak within a gas pipe may cause methane-air explosions in the tunnel, leading to structural damage and casualties. Thus, it is necessary to investigate the response of structural members against explosion loads in the tunnel. Few studies in the open literature have studied the effects of a methane-air explosion in a full-scale concrete tunnel. This study presents two 9.5% methane-air explosions in a tunnel with a dimension of 20000 mm × 1800 mm × 600 mm. The pressure characteristics are summarized and compared with existing pressure–time curve of the methane-air explosion in typical vented containers. Similarities of pressure characteristics between the current study and previous studies avaliable from the literature are identified. Apart from explosion pressure characteristics, concrete structural specimens with a dimension of 1800 mm × 400 mm × 90 mm are also investigated. Geopolymer concrete, ultra high performance concrete (UHPC) and conventional concrete with compressive strength of approximately 70 MPa, 150 MPa and 30 MPa, respectively, were used to manufacture the testing specimens subjected to the methane-air explosions in the tunnel. In this study, the cracks were observed on all specimens. Due to large size of the tunnel and gas leakage during blast, the pressure distribution in the tunnel is not as uniform as observed in methane-air explosion chamber from the previous literatures. The conventional concrete specimen positioned between two pressure sensors is selected to validate the numerical model in the present study. The calibrated numerical model is then used to study the structural responses of concrete specimen subjected to the captured pressure.

Merenda, A, Bortolassi, ACC, Rodriguez-Andres, J, Al-Attabi, R, Schütz, JA, Kujawski, W, Shon, HK & Dumée, LF 2021, 'Hybrid polymer/ionic liquid electrospun membranes with tunable surface charge for virus capture in aqueous environments', Journal of Water Process Engineering, vol. 43, pp. 102278-102278.
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Metia, S, Nguyen, HAD & Ha, QP 2021, 'IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with Extended Fractional-Order Kalman Filtering', Sensors.
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Miao, J, Wu, Y & Yang, Y 2021, 'Identifying Visible Parts via Pose Estimation for Occluded Person Re-Identification', IEEE Transactions on Neural Networks and Learning Systems.
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We focus on the occlusion problem in person re-identification (re-id), which is one of the main challenges in real-world person retrieval scenarios. Previous methods on the occluded re-id problem usually assume that only the probes are occluded, thereby removing occlusions by manually cropping. However, this may not always hold in practice. This article relaxes this assumption and investigates a more general occlusion problem, where both the probe and gallery images could be occluded. The key to this challenging problem is depressing the noise information by identifying bodies and occlusions. We propose to incorporate the pose information into the re-id framework, which benefits the model in three aspects. First, it provides the location of the body. We then design a Pose-Masked Feature Branch to make our model focus on the body region only and filter those noise features brought by occlusions. Second, the estimated pose reveals which body parts are visible, giving us a hint to construct more informative person features. We propose a Pose-Embedded Feature Branch to adaptively re-calibrate channel-wise feature responses based on the visible body parts. Third, in testing, the estimated pose indicates which regions are informative and reliable for both probe and gallery images. Then we explicitly split the extracted spatial feature into parts. Only part features from those commonly visible parts are utilized in the retrieval. To better evaluate the performances of the occluded re-id, we also propose a large-scale data set for the occluded re-id with more than 35 000 images, namely Occluded-DukeMTMC. Extensive experiments show our approach surpasses previous methods on the occluded, partial, and non-occluded re-id data sets.

Min, C, Bu, Y, Wu, D, Ding, Y & Zhang, Y 2021, 'Identifying citation patterns of scientific breakthroughs: A perspective of dynamic citation process', INFORMATION PROCESSING & MANAGEMENT, vol. 58, no. 1.
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Ming, Y, Wu, D, Wang, YK, Shi, Y & Lin, CT 2021, 'EEG-Based Drowsiness Estimation for Driving Safety Using Deep Q-Learning', IEEE Transactions on Emerging Topics in Computational Intelligence.
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IEEE Fatigue is the most vital factor of road fatalities, and one manifestation of fatigue during driving is drowsiness. In this paper, we propose using deep Q-learning to study the correlation between drowsiness and driving performance. This study is carried out by analyzing an electroencephalogram (EEG) dataset captured during a simulated endurance driving test. Driving safety research using EEG data represents an important brain-computer interface (BCI) paradigm from an application perspective. To formulate the drowsiness estimation problem as an optimization of a Q-learning task, we adapt the terminologies in the driving test to fit the reinforcement learning framework. Based on that, a deep Q-network (DQN) is tailored by referring to the latest DQN technologies. The designed network merits the characteristics of the EEG data and can generate actions to indirectly estimate drowsiness. The results show that the trained model can trace the variations of mind state in a satisfactory way against the testing EEG data, which confirms the feasibility and practicability of this new computation paradigm. By comparison, it also reveals that our method outperforms the supervised learning counterpart and is superior for real applications. To the best of our knowledge, we are the first to introduce the deep reinforcement learning method to this BCI scenario, and our method can potentially be generalized to other BCI cases.

Minh, HT, Indraratna, B, Thanh, TN, Carter, J & Rujikiatkamjorn, C 2021, 'Analysis of undrained cyclic response of saturated soils', COMPUTERS AND GEOTECHNICS, vol. 134.
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Mirzaei, S, Vafakhah, M, Pradhan, B & Alavi, SJ 2021, 'Flood susceptibility assessment using extreme gradient boosting (EGB), Iran', Earth Science Informatics, vol. 14, pp. 51-67.
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Flood occurs as a result of high intensity and long-term rainfalls accompanied by snowmelt which flow out of the main river channel onto the flood prone areas and damage the buildings, roads, and facilities and cause life losses. This study aims to implement extreme gradient boosting (EGB) method for the first time in flood susceptibility modelling and compare its performance with three advanced benchmark models including Frequency Ratio (FR), Random Forest (RF), and Generalized Additive Model (GAM). Flood susceptibility map is an efficient tool to make decision for flood control. To do this, the altitude, slope degree, profile curvature, topographic wetness index (TWI), distance from rivers, normalized difference vegetation index, plan curvature, rainfall, land use, stream power index, and lithology were fed to the models. To run the models, 243 flood locations were detected by field surveys and national reports. The same number of locations were randomly created in the study regions and considered as non-flood locations. The flood and non-flood locations were split in 70% ratio for the training dataset and 30% ratio for the testing dataset. Both flood and non-flood locations were fed into the models and output flood susceptibility maps were produced. In order to evaluate the performance of the algorithms, receiver operating characteristics (ROC) curve was implemented. The results of the current research show that the RF model and EGB have the best performances with the area under ROC curve (AUC) of 0.985, and 0.980, followed by the GAM and FR algorithms with AUC values of 0.97, and 0.953, respectively. The results of variable importance by the RF model show that distance from rivers has an important influence on flood susceptibility mapping (FSM), followed by profile curvature, slope, TWI, and altitude. Considering the high performances of the RF and EGB models in flood susceptibility modelling, application of these models is recommended for such studies.

Mishra, DK, Ghadi, MJ, Azizivahed, A, Li, L & Zhang, J 2021, 'A review on resilience studies in active distribution systems', RENEWABLE & SUSTAINABLE ENERGY REVIEWS, vol. 135.
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Mo, W, Wu, J, Walker, PD & Zhang, N 2021, 'Shift characteristics of a bilateral Harpoon-shift synchronizer for electric vehicles equipped with clutchless AMTs', MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 148.
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Moallemi, EA, de Haan, FJ, Hadjikakou, M, Khatami, S, Malekpour, S, Smajgl, A, Smith, MS, Voinov, A, Bandari, R, Lamichhane, P, Miller, KK, Nicholson, E, Novalia, W, Ritchie, EG, Rojas, AM, Shaikh, MA, Szetey, K & Bryan, BA 2021, 'Evaluating Participatory Modeling Methods for Co‐creating Pathways to Sustainability', Earth's Future.
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Mofijur, M, Ahmed, SF, Rahman, SMA, Arafat Siddiki, SY, Islam, ABMS, Shahabuddin, M, Ong, HC, Mahlia, TMI, Djavanroodi, F & Show, PL 2021, 'Source, distribution and emerging threat of micro- and nanoplastics to marine organism and human health: Socio-economic impact and management strategies', Environmental Research, vol. 195.
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The nature of micro- and nanoplastics and their harmful consequences has drawn significant attention in recent years in the context of environmental protection. Therefore, this paper aims to provide an overview of the existing literature related to this evolving subject, focusing on the documented human health and marine environment impacts of micro- and nanoplastics and including a discussion of the economic challenges and strategies to mitigate this waste problem. The study highlights the micro- and nanoplastics distribution across various trophic levels of the food web, and in different organs in infected animals which is possible due to their reduced size and their lightweight, multi-coloured and abundant features. Consequently, micro- and nanoplastics pose significant risks to marine organisms and human health in the form of cytotoxicity, acute reactions, and undesirable immune responses. They affect several sectors including aquaculture, agriculture, fisheries, transportation, industrial sectors, power generation, tourism, and local authorities causing considerable economic losses. This can be minimised by identifying key sources of environmental plastic contamination and educating the public, thus reducing the transfer of micro- and nanoplastics into the environment. Furthermore, the exploitation of the potential of microorganisms, particularly those from marine origins that can degrade plastics, could offer an enhanced and environmentally sound approach to mitigate micro- and nanoplastics pollution.

Mofijur, M, Fattah, IMR, Alam, MA, Islam, ABMS, Ong, HC, Rahman, SMA, Najafi, G, Ahmed, SF, Uddin, MA & Mahlia, TMI 2021, 'Impact of COVID-19 on the social, economic, environmental and energy domains: Lessons learnt from a global pandemic', SUSTAINABLE PRODUCTION AND CONSUMPTION, vol. 26, pp. 343-359.
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Mojiri, A, Zhou, JL, Ratnaweera, H, Ohashi, A, Ozaki, N, Aoi, Y, Vakili, M & Kindaichi, T 2021, 'Performance optimization of a chitosan/anammox reactor in nitrogen removal from synthetic wastewater', JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING, vol. 9, no. 3.
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Mong, GR, Chong, CT, Ng, J-H, Chong, WWF, Ong, HC & Tran, M-V 2021, 'Multivariate optimisation study and life cycle assessment of microwave-induced pyrolysis of horse manure for waste valorisation and management', ENERGY, vol. 216.
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Mora, A, Cardenas, R, Aguilera, RP, Angulo, A, Lezana, P & Lu, DD-C 2021, 'Predictive Optimal Switching Sequence Direct Power Control for Grid-Tied 3L-NPC Converters', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 68, no. 9, pp. 8561-8571.
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Mousavi, M & Gandomi, AH 2021, 'Structural health monitoring under environmental and operational variations using MCD prediction error', Journal of Sound and Vibration, pp. 116370-116370.
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Mousavi, M, Holloway, D, Olivier, JC & Gandomi, AH 2021, 'Beam damage detection using synchronisation of peaks in instantaneous frequency and amplitude of vibration data', MEASUREMENT, vol. 168.
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Muhammad, G, Alam, A, Mofijur, M, Jahirul, MI, Lv, Y, Xiong, W, Ong, HC & Xu, J 2021, 'Modern developmental aspects in the field of economical harvesting and biodiesel production from microalgae biomass', Renewable and Sustainable Energy Reviews.
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Munasinghe, N & Paul, G 2021, 'Radial Slicing for Helical-Shaped Advanced Manufacturing Applications', The International Journal of Advanced Manufacturing Technology.
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The fourth industrial revolution (Industry 4.0) is transforming industries all around the world focusing on areas including advanced robotics and automation, sensor technology and data analytics. The authors are involved in a project developing a multi-robot material extrusion 3D printer to print a Gravity Separation Spiral (GSS), an instrument used in the mining industry to separate mineral slurry into different density components. Compared to traditional mould-based manufacturing, this new additive manufacturing method will significantly reduce manufacturing tooling costs, improve the customisation to enable the production of bespoke GSS that each process different minerals, and reduce worker exposure to hazardous materials. Slicing and printing large scale helical objects in conventional horizontal layer addition would result in an unreasonable amount of waste material from support structures, and poor surface quality due to step-wise bumps. This paper presents a novel slicing algorithm using concentric vertical ray lines to slice objects radially, enabling layers to be deposited progressively in the same fashion. This method can be applied in large scale additive manufacturing where objects are printed by a robot in a radial direction, which is different from layered vertical printing in conventional additive systems. An example GSS is sliced to generate motion plans for a print head affixed to the end effector of a robot arm. Then through simulations, it is shown how a robot's expected manipulability measure can be used to predict and ensure the successful completion of the print.

N.Usefi, Sharafi, P, Mortazavi, M, Ronagh, H & Samali, B 2021, 'Structural performance and sustainability assessment of hybrid-cold formed modular steel frame', Journal of Building Engineering, vol. 34.
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© 2020 Elsevier Ltd Hybrid cold-formed steel (HCFS) structures are new structural systems in the light steel construction industry offering new possibilities, in particular with regard to the applications in mid-rise construction. The structural performance, sustainability as well as the economic and social costs of these structures are of great importance for decision-makers when it comes to deciding on employing these systems and comparing them with their conventional counterparts. In this study, the HCFS systems are evaluated with respect to sustainability, structural performance, economic cost, and social impacts. The results then are compared with those of Ordinary Moment Resisting Frames (OMRF), as the most popular conventional HRS framed system. The methodology consists of both qualitative and quantitative analyses that include the overview of the positive and negative points of each construction method in the form of a comparative study. The results of the structural analysis of the two construction systems show that the hybrid system exhibits better structural performance with regard to the storey shear and drift. It is also shown that in terms of most environmental performance indicators, HCFS framed structures can lead to less environmental impact than OMRF systems. Moreover, the economic assessment demonstrates that HCFS framed structures can save up to the 23% in framing costs, compared to OMRF systems, primarily owing to the fact that lightweight flooring system can be easily incorporated to the design of HCFS structure. Their great potential for prefabrication, on the other hand, makes HCFS a better option with respect to many social compact indicators such as noise, air, vibration and dust pollution and traffic.

Nagy, Z, Seneviratne, JA, Kanikevich, M, Chang, W, Mayoh, C, Venkat, P, Du, Y, Jiang, C, Salib, A, Koach, J, Carter, DR, Mittra, R, Liu, T, Parker, MW, Cheung, BB & Marshall, GM 2021, 'An ALYREF-MYCN coactivator complex drives neuroblastoma tumorigenesis through effects on USP3 and MYCN stability', Nature Communications.
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Namasudra, S, Deka, GC, Johri, P, Hosseinpour, M & Gandomi, AH 2021, 'The Revolution of Blockchain: State-of-the-Art and Research Challenges', Archives of Computational Methods in Engineering.
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© 2020, CIMNE, Barcelona, Spain. With the rapid development of Information Technology (IT) industries, data or information security has become one of the critical issues. Nowadays, Blockchain technology is widely using for improving data security. It is a tool for the individual and organization to interchange the digital asset without the intervention of a trusted third party i.e. a central administrator. This technology has given the ability to create digital tokens for representing assets, innovation and likely reshaping the scenery of entrepreneurship. Blockchain has several key properties, such as decentralization, immutability and transparency without using a trusted third party. It can be used in several fields, such as healthcare, digital voting, Internet of Things (IoT) and many more. This study aims to discuss the fundamentals of Blockchain. In this paper, the technology or working procedure of Blockchain including many applications in several fields are discussed. Finally, future work directions and open research challenges in the domain of Blockchain have been also discussed in detail.

Nascimben, M, Wang, YK, King, JT, Jung, TP, Touryan, J, Lance, BJ & Lin, CT 2021, 'Alpha correlates of practice during mental preparation for motor imagery', IEEE Transactions on Cognitive and Developmental Systems.
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IEEE In this study we quantified performance variations of motor imagery (MI)-based brain-computer interface (BCI) systems induced by practice. Two experimental sessions were recorded from ten healthy subjects while playing a BCI-oriented videogame for two weeks. The analysis focused on the exploration of electroencephalographic changes during mental preparation between novice and practiced subjects. EEG changes were quantified using global field power (GFP), dynamic time warping (TW) and mutual information (MutInf): GFP represents the strength of the electric field, TW measures signal similarities and MutInf signals inter-dependency. Each metric was selected to relate insights extracted from mental preparation to the three experimental hypotheses associating practice with BCI performance. Significant results were identified in lower alpha for GFP and upper alpha for TW and MutInf. GFP in lower alpha during mental preparation assessed not only novice vs practiced variations but also “intra-session” differences. Findings suggest that EEG changes during mental preparation provide a quantitative measure of practice level. These metrics extracted before motor intention could be applied to BCI models targeting MI to monitor a user’s degree of training.

Nekoei, M, Moghaddas, SA, Golafshani, EM & Gandomi, AH 2021, 'Introduction of ABCEP as an automatic programming method', INFORMATION SCIENCES, vol. 545, pp. 575-594.
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Neri, A, Cagno, E, Lepri, M & Trianni, A 2021, 'A triple bottom line balanced set of key performance indicators to measure the sustainability performance of industrial supply chains', SUSTAINABLE PRODUCTION AND CONSUMPTION, vol. 26, pp. 648-691.
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Ng, ECY, Huang, Y, Hong, G, Zhou, JL & Surawski, NC 2021, 'Reducing vehicle fuel consumption and exhaust emissions from the application of a green-safety device under real driving', Science of the Total Environment, vol. 793.
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Vehicle emissions have a significantly negative impact on climate change, air quality and human health. Drivers of vehicles are the last major and often overlooked factor that determines vehicle performance. Eco-driving is a relatively low-cost and immediate measure to reduce fuel consumption and emissions significantly. This paper reports investigation of the effects of an on-board green-safety device on fuel consumption and emissions for both experienced and inexperienced drivers. A portable emissions measurement system (PEMS) was installed on a diesel light goods vehicle (LGV) to measure real-driving emissions (RDE), including total hydrocarbons (THC), CO CO2, NO, NO2 and particulate matter (PM). In addition, driving parameters (e.g. vehicle speed and acceleration) and environmental parameters (e.g. ambient temperature, humidity and pressure) were recorded in the experiments. The experimental results were evaluated using the Vehicle Specific Power (VSP) methodology to understand the effects of driving behavior on fuel consumption and emissions. The results indicated that driving behavior was improved for both experienced and inexperienced drivers after activation of the on-board green-safety device. In addition, the average time spent was shifted from higher to lower VSP modes by avoiding excessive speed, and aggressive accelerations and decelerations. For experienced drivers, the average fuel consumption and NO, NO2 and soot emissions were reduced by 5%, 56%, 39% and 35%, respectively, with the on-board green-safety device. For inexperienced drivers, the average reductions were 6%, 65%, 50% and 19%, respectively. Moreover, the long-term formed habits of experienced drivers are harder to be changed to accept the assistance of the green-safety device, whereas inexperienced drivers are likely to be more receptive to change and improve their driving behaviors.

Nguyen, AQ, Vu, HP, Nguyen, LN, Wang, Q, Djordjevic, SP, Donner, E, Yin, H & Nghiem, LD 2021, 'Monitoring antibiotic resistance genes in wastewater treatment: Current strategies and future challenges', Science of the Total Environment, vol. 783.
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Antimicrobial resistance (AMR) is a growing threat to human and animal health. Progress in molecular biology has revealed new and significant challenges for AMR mitigation given the immense diversity of antibiotic resistance genes (ARGs), the complexity of ARG transfer, and the broad range of omnipresent factors contributing to AMR. Municipal, hospital and abattoir wastewater are collected and treated in wastewater treatment plants (WWTPs), where the presence of diverse selection pressures together with a highly concentrated consortium of pathogenic/commensal microbes create favourable conditions for the transfer of ARGs and proliferation of antibiotic resistant bacteria (ARB). The rapid emergence of antibiotic resistant pathogens of clinical and veterinary significance over the past 80 years has re-defined the role of WWTPs as a focal point in the fight against AMR. By reviewing the occurrence of ARGs in wastewater and sludge and the current technologies used to quantify ARGs and identify ARB, this paper provides a research roadmap to address existing challenges in AMR control via wastewater treatment. Wastewater treatment is a double-edged sword that can act as either a pathway for AMR spread or as a barrier to reduce the environmental release of anthropogenic AMR. State of the art ARB identification technologies, such as metagenomic sequencing and fluorescence-activated cell sorting, have enriched ARG/ARB databases, unveiled keystone species in AMR networks, and improved the resolution of AMR dissemination models. Data and information provided in this review highlight significant knowledge gaps. These include inconsistencies in ARG reporting units, lack of ARG/ARB monitoring surrogates, lack of a standardised protocol for determining ARG removal via wastewater treatments, and the inability to support appropriate risk assessment. This is due to a lack of standard monitoring targets and agreed threshold values, and paucity of information on the ARG-pathogen host...

Nguyen, HG, Le, N, Nguyen-Duong, KH, Ho-Pham, LT & Nguyen, T 2021, 'Reference values of body composition parameters for Vietnamese men and women', EUROPEAN JOURNAL OF CLINICAL NUTRITION.
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Nguyen, HG, Lieu, KB, Ho-Le, TP, Ho-Pham, LT & Nguyen, TV 2021, 'Discordance between quantitative ultrasound and dual-energy X-ray absorptiometry in bone mineral density: The Vietnam Osteoporosis Study', Osteoporosis and Sarcopenia.
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Nguyen, L, Kodagoda, S, Ranasinghe, R & Dissanayake, G 2021, 'Mobile robotic sensors for environmental monitoring using gaussian markov random field', Robotica.
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© 2020 Georg Thieme Verlag. All rights reserved. This paper addresses the issue of monitoring spatial environmental phenomena of interest utilizing information collected by a network of mobile, wireless, and noisy sensors that can take discrete measurements as they navigate through the environment. It is proposed to employ Gaussian Markov random field (GMRF) represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field. It then derives a GMRF-based approach to effectively predict the field at unmeasured locations, given available observations, in both centralized and distributed manners. Furthermore, a novel but efficient optimality criterion is then proposed to design centralized and distributed adaptive sampling strategies for the mobile robotic sensors to find the most informative sampling paths in taking future measurements. By taking advantage of conditional independence property in the GMRF, the adaptive sampling optimization problem is proven to be resolved in a deterministic time. The effectiveness of the proposed approach is compared and demonstrated using pre-published data sets with appealing results.

Nguyen, N-T, Nguyena, DN, Hoang, DT, Van Huynh, N, Dutkiewicz, E, Nguyenb, N-H & Nguyenc, Q-T 2021, 'Time Scheduling and Energy Trading for Heterogeneous Wireless-Powered and Backscattering-based IoT Networks', IEEE Transactions on Wireless Communications.
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Nguyen, P-D, Tran, N-ST, Nguyen, T-T, Dang, B-T, Le, M-TT, Bui, X-T, Mukai, F, Kobayashi, H & Ngo, HH 2021, 'Long-term operation of the pilot scale two-stage anaerobic digestion of municipal biowaste in Ho Chi Minh City.', Science of the Total Environment, vol. 766, pp. 142562-142562.
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A pilot-scale two-stage anaerobic digestion system, which includes a feed tank (0.4 m³), a hydrolysis reactor (1.2 m³) followed by a methane fermenter (4.0 m³) was set up and run at the municipal solid waste landfill located in Ho Chi Minh City (HCMC), Vietnam. The feed that was separated from urban organic solid waste was collected at households and restaurants in District 1, HCMC. This study aimed to investigate the resource recovery performance of the pilot two-stage anaerobic digestion system, in terms of carbon recovery via biogas production and nutrient recovery from digestate. The average organic loading rate (OLR) of the system was step increased from 1.6 kg volatile solids (VS)·m^-3·d^-1, 2.5 kg VS·m^-3·d^-1 and 3.8 kg VS·m^-3·d^-1 during 400 days of operation. During the long-term operation at three OLRs, pH values and alkalinity were stable at both hydrolysis and methanogenesis stages without any addition of alkalinity for the methanogenesis phase. High buildup of propanoic acid and total volatile fatty acid concentrations in the fermenter did not drop pH values and inhibit the methanogenic process at high OLRs (2.5-3.8 kg VS m^-3·d^-1). The obtained total chemical oxygen demand (tCOD) removal performance was 83-87% at the OLRs ranging from 2.5 kg VS·m^-3·d^-1 and 3.8 kg VS·m^-3·d^-1, respectively. The highest biogas yield of 263 ± 64 L·kg^-1 tCOD removed obtained at OLR of 2.5 kg VS·m^-3·d^-1. It is expected that a full scale 2S-AD plant with capacity of 5200 tons day^-1 of biowaste collected currently from municipal solid waste in HCMC may create daily electricity of 552 MWh, thermal energy of 630 MWh, and recovery of 16.1 tons of NH₄⁺-N, 11.4 tons of organic-N, and 2.1 tons of TP as both organic liquid and solid fertilizers.

Nguyen, QD, Castel, A, Kim, T & Khan, MSH 2021, 'Performance of fly ash concrete with ferronickel slag fine aggregate against alkali-silica reaction and chloride diffusion', CEMENT AND CONCRETE RESEARCH, vol. 139.
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Nguyen, TH, Nguyen, AT, Loganathan, P, Nguyen, TV, Vigneswaran, S, Nguyen, THH & Tran, HN 2021, 'Low-cost laterite-laden household filters for removing arsenic from groundwater in Vietnam and waste management', PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, vol. 152, pp. 154-163.
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Nguyen, TKL, Ngo, HH, Guo, W, Nghiem, LD, Qian, G, Liu, Q, Liu, J, Chen, Z, Bui, XT & Mainali, B 2021, 'Assessing the environmental impacts and greenhouse gas emissions from the common municipal wastewater treatment systems.', The Science of the total environment, vol. 801, p. 149676.
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This study measured the environmental impacts from three same-size wastewater treatment systems, specifically activated sludge, a constructed wetland, and a high rate algal pond. Detailed data inventories were employed using SimaPro 9 software to calculate the entire consequences by ReCiPe 2016 and Greenhouse Gas Protocol method. The environmental outcomes caused by substance emissions and resource extraction are presented in several impact categories at the endpoint level. For a better comparison, the single score tool was applied to aggregate all factors into three areas of protection: human health, ecosystem, and resource shortage. Results showed that concrete and steel are the main contributors to the construction phase, while electricity is responsible for the operation stage. The single score calculation indicates that the proportion of construction activities could be equal to or even higher than the operation stage for a small capacity plant. The total environmental impact of the conventional system was 2.3-fold and 3-fold higher than that of constructed wetland and high rate algal pond, respectively. High rate algal pond has the best environmental performance when generating the least burdens and greenhouse gas emissions of 0.72 kg CO₂ equivalent per m³. Constructed wetland produces 5.69 kg CO_2, higher than an algal pond but much lower than activated sludge plant, emitting 11.42 kg CO₂ per m³.

Nguyen, TT & Indraratna, B 2021, 'Rail track degradation under mud pumping evaluated through site and laboratory investigations', INTERNATIONAL JOURNAL OF RAIL TRANSPORTATION.
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Nguyen, TT, Indraratna, B & Singh, M 2021, 'DYNAMIC PARAMETERS OF SUBGRADE SOILS PRONE TO MUD PUMPING CONSIDERING THE INFLUENCE OF KAOLIN CONTENT AND THE CYCLIC STRESS RATIO', Transportation Geotechnics.
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Nguyen, TV 2021, 'Personalized fracture risk assessment: where are we at?', Expert Review of Endocrinology and Metabolism, vol. 16, no. 4, pp. 191-200.
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Introduction: Osteoporotic fracture imposes a significant health care burden globally. Personalized assessment of fracture risk can potentially guide treatment decisions. Over the past decade, a number of risk prediction models, including the Garvan Fracture Risk Calculator (Garvan) and FRAX®, have been developed and implemented in clinical practice. Areas covered: This article reviews recent development and validation results concerning the prognostic performance of the two tools. The main areas of review are the need for personalized fracture risk prediction, purposes of risk prediction, predictive performance in terms of discrimination and calibration, concordance between the Garvan and FRAX tools, genetic profiling for improving predictive performance, and treatment thresholds. In some validation studies, FRAX tended to underestimate fracture by as high as 50%. Studies have shown that the predicted risk from the Garvan tool is highly concordant with clinical decision. Expert opinion: Although there are some discrepancy in fracture risk prediction between Garvan and FRAX, both tools are valid and can aid patients and doctors communicate about risk and make informed decision. The ideal of personalized risk assessment for osteoporosis patients will be realized through the incorporation of genetic profiling into existing fracture risk assessment tools.

Nguyen, XC, Nguyen, TTH, Bui, X-T, Tran, XV, Tran, TCP, Hoang, NTT, La, DD, Chang, SW, Ngo, HH & Nguyen, DD 2021, 'Status of water use and potential of rainwater harvesting for replacing centralized supply system in remote mountainous areas: a case study.', Environmental science and pollution research international.
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The failure of the centralized water supply system forced XY community to become more dependent on uncertain and unstable water sources. The results of surveying 50 households showed that 89.18% of total households depended on water collected from rivers, which contributed 58.3% of the total water volume used for the domestic demands. The average water volume consumed was 19.5 liters/person/day (l/p/d), and 86.5% of households used more than one source; 13.5% of households collected water only from rivers, and 45.94% of families had rainwater harvesting (RWH) for their activities (domestic water demand); however, RWH only provided 9.9% of total water consumption. In this study, basic methods were applied to calculate the storage tanks necessary to balance the water deficit created by drought months. Three levels of water demand (14, 20, and 30 l/p/d) can be the best choices for RWH; for a higher demand (40 and 60 l/p/d), small roof area (30-40 m²), and many people (six to seven) per family, RWH might be impractical because of unsuitable rainfall or excessively large storage tanks.

Ni, Z, Zhang, JA, Huang, X, Yang, K & Yuan, J 2021, 'Uplink Sensing in Perceptive Mobile Networks With Asynchronous Transceivers', IEEE Transactions on Signal Processing.
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Nie, X, Takalkar, MA, Duan, M, Zhang, H & Xu, M 2021, 'GEME: Dual-stream multi-task GEnder-based micro-expression recognition', Neurocomputing, vol. 427, pp. 13-28.
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© 2020 Elsevier B.V. Recognition of micro-expressions remains a topic of concern considering its brief span and low intensity. This issue is addressed through convolutional neural networks (CNNs) by developing multi-task learning (MTL) method to effectively leverage a side task: gender detection. A dual-stream multi-task framework called GEME is introduced that recognises micro-expressions by incorporating unique gender characteristics and subsequently improves the micro-expression recognition accuracy. This research aims to examine how gender differences influence the way micro-expressions are displayed. The current study proves that selecting relevant features of micro-expressions distinctive to the gender and added to the micro-expression features improves the micro-expression recognition accuracy. This network learns gender-specific features and micro-expression features and adds them together to learn the combination of shared and task-specific representations. A multi-class focal loss is used to mitigate the class imbalance issue by down-weighing the easy samples and concentrate more on misclassified samples. The Class-Balanced (CB) focal loss is also implemented for a better class balancing during Leave-One-Subject-Out (LOSO) validations where CB loss re-balances and re-weights the loss. The experimental results on three widely used databases demonstrate the improved performance of the proposed network and achieve comparable results with the state-of-the-art methods.

Nikshad, A, Aghlmandi, A, Safaralizadeh, R, Aghebati-Maleki, L, Warkiani, ME, Khiavi, FM & Yousefi, M 2021, 'Advances of microfluidic technology in reproductive biology', LIFE SCIENCES, vol. 265.
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Niu, T, Wang, J, Lu, H, Yang, W & Du, P 2021, 'A Learning System Integrating Temporal Convolution and Deep Learning for Predictive Modeling of Crude Oil Price', IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 17, no. 7, pp. 4602-4612.
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Nizami, MSH, Hossain, MJ & Mahmud, K 2021, 'A Nested Transactive Energy Market Model to Trade Demand-Side Flexibility of Residential Consumers', IEEE TRANSACTIONS ON SMART GRID, vol. 12, no. 1, pp. 479-490.
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Noushini, A, Nguyen, QD & Castel, A 2021, 'Assessing alkali-activated concrete performance in chloride environments using NT Build 492', MATERIALS AND STRUCTURES, vol. 54, no. 2.
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Oberst, S, Martin, R, Halkon, B, Lai, J, Evans, T & Saadatfar, M 2021, '"Submillimetre mechanistic designs of termite-built structures"', Journal of the Royal Society Interface, vol. 18, no. 178, pp. 33947222-33947222.

Ollerton, RL & Shannon, AG 2021, 'A note on brousseau’s summation problem', Fibonacci Quarterly, vol. 58, no. 5, pp. 190-199.
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This paper takes a historical view of some long-standing problems associated with the development of sums of Fibonacci numbers in which the latter have powers of integers as coefficients. The sequences of coefficients of these polynomials are arrayed in matrices with links to The On-Line Encyclopedia of Integer Sequences. This is an extension of previous work on the summation problem of Ledin because Brousseau introduced some elegant techniques for contracting the summations and the papers of both authors link with some interesting matrices.

Onasanya, BO, Wen, S, Feng, Y, Zhang, W & Xiong, J 2021, 'Fuzzy Coefficient of Impulsive Intensity in a Nonlinear Impulsive Control System', Neural Processing Letters.
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Many works on impulsive control theory have considered some constant coefficient of impulsive intensity which, in reality, is not the case. Then, in the recent time, a model in which the coefficient of the impulsive intensity was assumed not to be certain but some J≤ βI was proposed. Unfortunately, this was also for some fixed β. Hence, the result of this model can only give information about that particular β. In actual sense, errors are not fixed as suggested by this recent model but vary under different circumstances. Hence, in this work, a model for infinitely many such (fuzzy) coefficients of impulsive intensity was presented and its effectiveness was demonstrated with some numerical illustrations. This idea tends to give a generalisation of the existing models. The model achieves control in less time than the aforementioned recent model.

Ong, HC, Tiong, YW, Goh, BHH, Gan, YY, Mofijur, M, Fattah, IMR, Chong, CT, Alam, MA, Lee, HV, Silitonga, AS & Mahlia, TMI 2021, 'Recent advances in biodiesel production from agricultural products and microalgae using ionic liquids: Opportunities and challenges', ENERGY CONVERSION AND MANAGEMENT, vol. 228.
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Osman, I, Pileggi, SF, Ben Yahia, S & Diallo, G 2021, 'An Alignment-Based Implementation of a Holistic Ontology Integration Method', MethodsX, vol. 8, pp. 101460-101460.
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Ouyang, D, Shao, J, Jiang, H, Wen, S & Nguang, SK 2021, 'Finite-time stability of coupled impulsive neural networks with time-varying delays and saturating actuators', Neurocomputing.
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The paper considers the stability of coupled impulsive neural networks with time-varying delays and saturating actuators in finite time. Based on a delayed state feedback controller, the stability of coupled impulsive neural networks with time-varying delays and saturating actuators can be achieved in finite time. Combined with Lyapunov-based finite-time stability theory, some sufficient conditions are obtained to ensure the stability of coupled impulsive neural networks with time-varying delays and saturating actuators in finite time by using polytopic representation approach and sector nonlinearity model approach, respectively. Moreover, the setting time of coupled impulsive neural networks with saturating actuators is given, and it is found to be related to both the initial state and impulse effect. Furthermore, as special cases, some finite-time stability results of coupled impulsive neural networks with saturating actuators are given under a memoryless controller. Finally, two simulation examples are used to test the effectiveness of the obtained results.

Pan, Y, Xu, X, Ding, X, Huang, S, Wang, Y & Xiong, R 2021, 'GEM: online globally consistent dense elevation mapping for unstructured terrain', IEEE Transactions on Instrumentation and Measurement, vol. 70.
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IEEE Online dense mapping gives a representation of the unstructured terrain, which is indispensable for safe robotic motion planning. In this paper, we propose such an elevation mapping system, namely GEM, to generate a dense local elevation map in constant real-time for fast responsive local planning, and maintain a globally consistent dense map for path routing at the same time. We model the global elevation map as a collection of submaps. When the trajectory estimation of the robot is corrected by SLAM, only relative poses between submaps are updated without re-building the submap. As a result, this deformable global dense map representation is able to keep the global consistency online. Besides, we accelerate the local mapping by integrating traversability analysis into the mapping system to save the computation cost by obstacle awareness. The system is implemented by CPU-GPU coordinated processing to guarantee constant real-time performance for in-time handling of dynamic obstacles. Substantial experimental results on both simulated and real-world dataset validate the efficiency and effectiveness of GEM.

Pang, G, Cao, L & Chen, L 2021, 'Homophily outlier detection in non-IID categorical data', Data Mining and Knowledge Discovery.
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Park, MJ, Wang, C, Seo, DH, Gonzales, RR, Matsuyama, H & Shon, HK 2021, 'Inkjet printed single walled carbon nanotube as an interlayer for high performance thin film composite nanofiltration membrane', Journal of Membrane Science, vol. 620.
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© 2020 Elsevier B.V. Inkjet printing process enables rapid deposition of inks with precise amount and location. Moreover, the process can be automated and provide control such as repetitive printing of the inks. Utilizing the advantageous features of the inkjet printing process, we demonstrate the synthesis of thin film composite (TFC) flat-sheet membrane for NF application where single walled carbon nanotube (SWCNT) was deposited via an inkjet printing process, acting as an interlayer between the polyamide (PA) selective layer and polyethersulfone (PES) MF membrane support. By controlling the number of SWCNT printings on the PES membrane, we investigated how the SWCNT interlayer thickness influences the formation of PA selective layer. The best membrane performance was achieved from the TFC membrane synthesized using 15 cycles of SWCNT printing, where both high water flux (18.24 ± 0.43 L m−2 h−1 bar−1) and the high Na2SO4 salt rejection (97.88 ± 0.33%) rates were demonstrated. SWCNT interlayer provided highly porous, interconnected structure with uniform pore size distribution which led to the formation of a defect-free ultrathin PA selective layer. Designing of TFC membrane using the SWCNT deposition via inkjet printing is the new approach and successfully demonstrated the significant improvement in the NF membrane performances.

Paryani, S, Neshat, A & Pradhan, B 2021, 'Improvement of landslide spatial modeling using machine learning methods and two Harris hawks and bat algorithms', The Egyptian Journal of Remote Sensing and Space Science.
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Passavanti, M, Argentieri, A, Barbieri, DM, Lou, B, Wijayaratna, K, Foroutan Mirhosseini, AS, Wang, F, Naseri, S, Qamhia, I, Tangerås, M, Pelliciari, M & Ho, C-H 2021, 'The psychological impact of COVID-19 and restrictive measures in the world', Journal of Affective Disorders.
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Pateetin, P, Hutvagner, G, Bajan, S, Padula, MP, McGowan, EM & Boonyaratanakornkit, V 2021, 'Author Correction: Triple SILAC identified progestin-independent and dependent PRA and PRB interacting partners in breast cancer', Scientific Data.
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Paul, A, Shukla, N, Paul, SK & Trianni, A 2021, 'Sustainable Supply Chain Management and Multi-Criteria Decision-Making Methods: A Systematic Review', Sustainability, vol. 13, no. 13, pp. 7104-7104.
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Multi-criteria decision-making (MCDM) methods are smart tools to deal with numerous criteria in decision-making. These methods have been widely applied in the area of sustainable supply chain management (SSCM) because of their computational capabilities. This paper conducts a systematic literature review on MCDM methods applied in different areas of SSCM. From the literature search, a total of 106 published journal articles have been selected and analyzed. Both individual and integrated MCDM methods applied in SSCM are reviewed and summarized. In addition, contributions, methodological focuses, and findings of the reviewed articles are discussed. It is observed that MCDM methods are widely used for analyzing barriers, challenges, drivers, enablers, criteria, performances, and practices of SSCM. In recent years, studies have focused on integrating more than one MCDM method to highlight methodological contributions in SSCM; however, in the literature, limited research papers integrate multiple MCDM methods in the area of SSCM. Most of the published articles integrate only two MCDM methods, and integration with other methods, such as optimization and simulation techniques, is missing in the literature. This review paper contributes to the literature by analyzing existing research, identifying research gaps, and proposing new future research opportunities in the area of sustainable supply chain management applying MCDM methods.

Peng, L, Fan, SQ, Xie, GJ, Ni, BJ, Liu, Y, Xu, Y, Liu, BF, Xing, DF, Han, HJ, Song, S & Ren, NQ 2021, 'Modeling nitrate/nitrite dependent anaerobic methane oxidation and Anammox process in a membrane granular sludge reactor', Chemical Engineering Journal, vol. 403.
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© 2020 Elsevier B.V. The granular bioreactor, characterized by excellent settling velocity, high rate and low cost is an ideal choice for achieving coupled nitrate/nitrite dependent denitrifying anaerobic methane oxidation (n-DAMO) and anaerobic ammonium oxidation (Anammox) process. To fundamentally understand its underlining mechanisms and provide suggestions for process optimization, a granule-based model framework was developed to describe simultaneous anaerobic methane and ammonium oxidation by functional microbes. The proposed model was evaluated based on long-term experimental data from two membrane granular sludge reactors (MGSRs) with different operational conditions. The model possessed of good predictive ability to reproduce removal rates and effluent concentrations of nitrogen species. The predicted biomass abundance in two MGSRs and stratified microbial distribution along granule depth were consistent with experimental observations. The estimated parameter values, with good identifiability and reliability indicated a stimulated growth of DAMO archaea in MGSRs. Both hydraulic retention times (HRTs) and granule sizes have influences on microbial abundance in the MGSR and community distribution inside granules. Within the investigated HRTs from 1.4 h to 20 h and granule sizes from 500 μm to 2900 μm, it was revealed that a proper control of relatively short HRTs and small granule sizes resulted in an increased fraction of DAMO archaea and a reduced DAMO bacteria abundance with AnAOB less impacted, which would lower the required nitrite nitrogen to ammonium nitrogen ratio in the nitritation reactor (prior unit) and thus minimize operational cost in sidestream treatment lines.

Perez, MA, Ceballos, S, Konstantinou, G, Pou, J & Aguilera, RP 2021, 'Modular Multilevel Converters: Recent Achievements and Challenges', IEEE Open Journal of the Industrial Electronics Society.
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Pérez-Escamillla, B, Benrimoj, SI, Martínez-Martínez, F, Gastelurrutia, MÁ, Varas-Doval, R, Musial-Gabrys, K & Garcia-Cardenas, V 2021, 'Using network analysis to explore factors moderating the implementation of a medication review service in community pharmacy.', Research in social & administrative pharmacy : RSAP.
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Background

Implementation factors are hypothesised to moderate the implementation of innovations. Although individual barriers and facilitators have been identified for the implementation of different evidence-based services in pharmacy, relationships between implementation factors are usually not considered.

Objectives

To examine how a network of implementation factors and the position of each factor within this network structure influences the implementation of a medication review service in community pharmacy.

Methods

A mixed methods approach was used. Medication review with follow-up service was the innovation to be implemented over 12 months in community pharmacies. A network analysis to model relationships between implementation factors was undertaken. Two networks were created.

Results

Implementation factors hindering the service implementation with the highest centrality measures were time, motivation, recruitment, individual identification with the organization and personal characteristics of the pharmacists. Three hundred and sixty-nine different interrelationships between implementation factors were identified. Important causal relationships between implementation factors included: workflow-time; characteristics of the pharmacy-time; personal characteristics of the pharmacists-motivation. Implementation factors facilitating the implementation of the service with highest centrality scores were motivation, individual identification with the organization, beliefs, adaptability, recruitment, external support and leadership. Four hundred and fifty-six different interrelationships were identified. The important causal relationships included: motivation-external support; structure-characteristics of the pharmacy; demographics-location of the pharmacy.

Conclusion

Network analysis has proven to be a useful technique to explore networks of factors moderating the implementation of a pharmacy service. Relationships were complex ...

Pettit, T, Torpy, FR, Surawski, NC, Fleck, R & Irga, PJ 2021, 'Effective reduction of roadside air pollution with botanical biofiltration', Journal of Hazardous Materials.
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Pham, HM, Tuan, HD, Tran, ST & Poor, HV 2021, 'Qualitative HD Image and Video Recovery via High-Order Tensor Augmentation and Completion', IEEE Journal on Selected Topics in Signal Processing.
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IEEE This paper presents a new framework for severely distorted image and video recovery via tensor augmentation and completion. By considering a task of representing a matrix by a high-order-n tensor as that of encoding the matrix two-dimension (2D) indices (i, j) by n-digit words i1i2… in, we then develop a new high order tensor augmentation to cast a third order tensor of color images or video sequences containing missing pixels into a higher order tensor, which likes the ket augmentation of quantum physics, is capable of capturing all correlations and entanglements between entries of the original third order tensor. Accordingly, the resultant high-order tensor is completed by our previously developed parallel matrix factorization via tensor train. Simulations are provided to show the clear advantages of our approach to enhance important metrics of the visual quality such as relative square error and structural similarity index in image and video processing that help to achieve high recovery rates even for high-definition images and videos with 95% missing pixels.

Phung, MD & Ha, QP 2021, 'Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization', Applied Soft Computing.
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Pileggi, SF 2021, 'Knowledge interoperability and re-use in Empathy Mapping: an ontological approach', EXPERT SYSTEMS WITH APPLICATIONS, vol. 180.
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Pileggi, SF 2021, 'Life before COVID-19: how was the World actually performing?', Quality & Quantity.
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Poostchi, H & Piccardi, M 2021, 'BiLSTM-SSVM: Training the BiLSTM with a Structured Hinge Loss for Named-Entity Recognition', IEEE Transactions on Big Data, pp. 1-1.
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Building on the achievements of the BiLSTM-CRF in named-entity recognition (NER), this paper introduces the BiLSTM-SSVM, an equivalent neural model where training is performed using a structured hinge loss. The typical loss functions used for evaluating NER are entity-level variants of the F1 score such as the CoNLL and MUC losses. Unfortunately, the common loss function used for training NER - the cross entropy - is only loosely related to the evaluation losses. For this reason, in this paper we propose a training approach for the BiLSTM-CRF that leverages a hinge loss bounding the CoNLL loss from above. In addition, we present a mixed hinge loss that bounds either the CoNLL loss or the Hamming loss based on the density of entity tokens in each sentence. The experimental results over four benchmark languages (English, German, Spanish and Dutch) show that training with the mixed hinge loss has led to small but consistent improvements over the cross entropy across all languages and four different evaluation measures

Popovi, M, Thomas, F, Papatheodorou, S, Funk, N, Vidal-Calleja, T & Leutenegger, S 2021, 'Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation', IEEE Robotics and Automation Letters.
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Pour, AB, Sekandari, M, Rahmani, O, Crispini, L, Laeufer, A, Park, Y, Hong, JK, Pradhan, B, Hashim, M, Hossain, MS, Muslim, AM & Mehranzamir, K 2021, 'Identification of Phyllosilicates in the Antarctic Environment Using ASTER Satellite Data: Case Study from the Mesa Range, Campbell and Priestley Glaciers, Northern Victoria Land', REMOTE SENSING, vol. 13, no. 1.
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Pour, AB, Zoheir, B, Pradhan, B & Hashim, M 2021, 'Editorial for the Special Issue: Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas', Remote Sensing.
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Pradeep, P, Krishnamoorthy, S & Vasilakos, AV 2021, 'A holistic approach to a context-aware IoT ecosystem with Adaptive Ubiquitous Middleware', PERVASIVE AND MOBILE COMPUTING, vol. 72.
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Pradhan, B 2021, 'Energy: Preface', Geoscience Frontiers.
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Prior, DD, Saberi, M, Janjua, NK & Jie, F 2021, 'Can i trust you? incorporating supplier trustworthiness into supplier selection criteria', ENTERPRISE INFORMATION SYSTEMS.
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Punetha, P, Nimbalkar, S & Khabbaz, H 2021, 'Simplified geotechnical rheological model for simulating viscoelasto-plastic response of ballasted railway substructure', INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS.
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Qamar, A, Kerdi, S, Ali, SM, Shon, HK, Vrouwenvelder, JS & Ghaffour, N 2021, 'Novel hole-pillar spacer design for improved hydrodynamics and biofouling mitigation in membrane filtration', Scientific Reports.
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Qi, L, Shi, K, Yang, C & Wen, S 2021, 'Mean Square Stabilization of Neural Networks with Weighted Try once Discard Protocol and State Observer', Neural Processing Letters, vol. 53, no. 1, pp. 829-842.
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This paper investigates mean square stabilization problem of neural networks with weighted try once discard protocol and state observer. In general, sensor nodes access conflict happens frequently in the communication transmission networks. Aiming at this problem, we have adopted weighted try once discard protocol (WTOD) to reduce the occurrence of the conflict, The strategy is applied to discontinuous time systems, and only one node can obtain the access token to update signal transmission for the network. In addition, data transmission in the channel is large -scale and packet loss often occurs. Therefore, we adopt the method of quantized signal to reduce the pressure of channel transmission and ensure the stability of network transmission. As the system state is difficult to obtain, this paper design an observer to estimate the system state, the purpose of this paper is to guarantee the system to achieve the mean square stabilization with bounded output via the action of observer of signal output and controller, then solve the optimization problem of inequality through group of reasonable parameters of the controller and the observer. Finally, an example is given to prove the effectiveness of the proposed controller and observer.

Qi, L, Song, H, Zhang, X, Srivastava, G, Xu, X & Yu, S 2021, 'Compatibility-Aware Web API Recommendation for Mashup Creation via Textual Description Mining', ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, vol. 17, no. 1.
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Qi, M, Qin, J, Yang, Y, Wang, Y & Luo, J 2021, 'Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval', IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 30, pp. 2989-3004.
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Qi, Y, Indraratna, B, Ngo, T & Ferreira, FB 2021, 'Advancements in Geo-Inclusions for Ballasted Track: Constitutive Modelling and Numerical Analysis', SUSTAINABILITY, vol. 13, no. 16.
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Qian, H, Li, J, Zong, Z, Wu, C & Pan, Y 2021, 'Behavior of precast segmental utility tunnel under ground surface Explosion: A numerical study', Tunnelling and Underground Space Technology, vol. 115.
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Over the past few decades, a large number of precast segmental utility tunnels have been constructed. Intensive research efforts have been devoted to understanding their performance under service and seismic loads. However, the vulnerability of precast segmental utility tunnels against accidental blast loads is seldom discussed. The present study performs a comprehensive numerical investigation on precast segmental utility tunnels against ground surface explosion. The numerical model is verified against testing data based on buried RC structures and semi-analytical equations in TM 5–855-1. Three types of precast tunnel connections are investigated, i.e., connection without shear keys, connection with trapezoid-shaped shear keys, and connection with wedge-shaped shear keys. The effects of detonation location and prestressing conditions are evaluated. The performances of precast segmental utility tunnels under low-level, medium-level and high-level threat explosions are obtained and compared with those of the conventional monolithic utility tunnel. Discussions on the dynamic responses and energy dissipation (ED) of different precast segmental utility tunnels are made. The influences of energy dissipating (ED) bar, shear link and burial depth on the dynamic responses of the precast segmental utility tunnel are also investigated numerically.

Qian, H, Zong, Z, Wu, C, Li, J & Gan, L 2021, 'Numerical study on the behavior of utility tunnel subjected to ground surface explosion', Thin-Walled Structures, vol. 161.
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Over the past few decades, underground utility tunnels have become increasingly popular in many countries, especially in North America and China. Utility tunnels host multiple services’ infrastructures inside an accessible underground space that allows regular inspection, maintenance, and easy replacement. Previous studies on the safety of underground utility tunnels were mainly focused on the seismic performances, their dynamic behaviors under accidental/hostile blast loads were seldomly investigated. TM 5-855-1 is frequently employed to calculate the ground shock load and responses of buried structures. However, it is only applicable for the cases with a scaled distance larger than 0.4 m/kg . The present study performs a comprehensive numerical investigation on the blast performance of utility tunnel subjected to ground surface explosion with small scaled distance. With validated material and structural models, a FEM model based on a utility tunnel project is established. The influence of charge weight on blast resistance of the utility tunnel is studied numerically. The effect of the reinforcement ratio, shear reinforcement arrangement, buried depth and wall thickness are investigated under the scenario of a sedan car bomb overhead explosion. It is found that over 200 kg TNT ground surface explosion will cause a great threat to the utility tunnel with 2 m burial depth (scaled distance smaller than 0.4 m/kg ). The shear reinforcement arrangement is of great importance to the blast resistance of the utility tunnel. The failure mode of the roof can be changed from combined shear and flexural failure to flexural failure, with an increased reinforcement ratio. Increased wall thickness and buried depth can improve the blast resistance of the utility tunnel as expected. Safety buried depth, and wall thickness for a sedan car bomb overhead explosion are discussed in the present study. 1/3 1/3

Qiu, S, Ji, J, Sun, W, Pei, J, He, J, Li, Y, Li, JJ & Wang, G 2021, 'Recent advances in surface manipulation using micro-contact printing for biomedical applications', Smart Materials in Medicine, vol. 2, pp. 65-73.
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Qu, F, Li, W, Dong, W, Tam, VWY & Yu, T 2021, 'Durability deterioration of concrete under marine environment from material to structure: A critical review', JOURNAL OF BUILDING ENGINEERING, vol. 35.
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Qu, F, Li, W, Tang, Z & Wang, K 2021, 'Property degradation of seawater sea-sand cementitious mortars with GGBFS and glass fibers subjected to elevated temperature', Journal of Materials Research and Technology.
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Qu, Y, Yu, S, Zhou, W, Chen, S & Wu, J 2021, 'Customizable Reliable Privacy-Preserving Data Sharing in Cyber-Physical Social Network', IEEE Transactions on Network Science and Engineering.
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IEEE Privacy leakage becomes increasingly serious because massive volumes of data are constantly shared in diverse booming cyber-physical social networks (CPSN). Differential privacy is one of the dominating privacy-preserving methods, but most of its extensions assume all data users share the same privacy requirement, which fails to satisfy various privacy expectations in practice. To address this issue, customizable privacy preservation based on differential privacy is a potentially promising countermeasure. However, we found that customizable protection will trigger the composition mechanism of differential privacy and leads to unexpected correlations among injected noises that weakens privacy protection and reveal more sensitive inforamtion. As a result, customizable privacy protection is vulnerable to two primary attacks: background knowledge attack and collusion attack. To optimize the tradeoff between customizable privacy preservation and data utility, we propose a customizable reliable differential privacy model (CRDP), which provides customizable protection on each individual while being attack-proof. We define social distance as the shortest path between two nodes, which is used as an index to customize the privacy protection levels, followed by quantitatively modeling the attacks under the framework of differential privacy. We develop a modified Laplacian mechanism in which the noise generation complies with a Markov stochastic process. Consequently, the correlations of noises are properly de-coupled so that CRDP can simultaneously minimize background knowledge attacks and eliminate collusion attacks in this particular scenario. The evaluation results from real-world datasets show the feasibility and superiority of CRDP in terms of customizable privacy preservation and reliable attack resistance.

Quan, R, Zhu, L, Wu, Y & Yang, Y 2021, 'Holistic LSTM for Pedestrian Trajectory Prediction', IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 30, pp. 3229-3239.
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Rad, DM, Rad, MA, Bazaz, SR, Kashaninejad, N, Jin, D & Warkiani, ME 2021, 'A Comprehensive Review on Intracellular Delivery', ADVANCED MATERIALS, vol. 33, no. 13.
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Radfar, P, Bazaz, SR, Mirakhorli, F & Warkiani, ME 2021, 'The role of 3D printing in the fight against COVID-19 outbreak', Journal of 3D Printing in Medicine.
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Rafiee, A, Khalilpour, KR, Prest, J & Skryabin, I 2021, 'Biogas as an energy vector', BIOMASS & BIOENERGY, vol. 144.
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Raghunathan, K, Soundarapandian, RK, Gandomi, AH, Ramachandran, M, Patan, R & Madda, RB 2021, 'Duo-Stage Decision: A Framework for Filling Missing Values, Consistency Check, and Repair of Decision Matrices in Multicriteria Group Decision Making', IEEE Transactions on Engineering Management.
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IEEE With high uncertainty and vagueness in the decision-making process, maintaining consistency in the decision matrix is an open challenge. Previous studies on the intuitionistic fuzzy (IF) theory focused on the consistency of preference relation but ignored consistency of the decision matrix. In this article, efforts are made to propose a new duo-stage decision framework in the context of IF set to better circumvent the challenge. Often, decision makers (DMs) hesitate to provide certain values in the decision matrix that are filled randomly, resulting in inaccuracies in the decision-making process. To alleviate this issue, a new systematic procedure is developed that sensibly fills the missing data in the first stage. Following the first stage, consistency of the decision matrix is determined by extending Cronbach's alpha coefficient to IF context. Furthermore, efforts are made to repair inconsistent decision matrix iteratively. In the second stage, a new aggregation operator is presented for aggregation of DMs’ preferences. Also, a new mathematical model is proposed for criteria weight estimation, and a procedure is developed for ranking objects. The practical use of the proposed framework is demonstrated using a numerical example, and the strengths and weaknesses of the framework are investigated.

Rahim, MS, Nguyen, KA, Stewart, RA, Giurco, D & Blumenstein, M 2021, 'Advanced household profiling using digital water meters.', J Environ Manage, vol. 288, p. 112377.
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Advanced householder profiling using digital water metering data analytics has been acknowledged as a core strategy for promoting water conservation because of its ability to provide near real-time feedback to customers and instil long-term conservation behaviours. Customer profiling based on household water consumption data collected through digital water meters helps to identify the water consumption patterns and habits of customers. This study employed advanced customer profiling techniques adapted from the machine learning research domain to analyse high-resolution data collected from residential digital water meters. Data analytics techniques were applied on already disaggregated end-use water consumption data (e.g., shower and taps) for creating in-depth customer profiling at various intervals (e.g., 15, 30, and 60 min). The developed user profiling approach has some learning functionality as it can ascertain and accommodate changing behaviours of residential customers. The developed advanced user profiling technique was shown to be beneficial since it identified residential customer behaviours that were previously unseen. Furthermore, the technique can identify and address novel changes in behaviours, which is an important feature for promoting and sustaining long-term water conservation behaviours. The research has implications for researchers in data analytics and water demand management, and also for practitioners and government policy advisors seeking to conserve valuable potable-water resources.

Rahimi, I, Chen, F & Gandomi, AH 2021, 'A review on COVID-19 forecasting models', Neural Computing and Applications.
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Rahimi, I, Gandomi, AH, Asteris, PG & Chen, F 2021, 'Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases', Information.
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Rahman, ML, Zhang, JA, Huang, X, Guo, YJ & Lu, Z 2021, 'Gaussian-Mixture-Model Based Clutter Suppression in Perceptive Mobile Networks', IEEE COMMUNICATIONS LETTERS, vol. 25, no. 1, pp. 152-156.
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Rahman, SMA, Fattah, IMR, Maitra, S & Mahlia, TMI 2021, 'A ranking scheme for biodiesel underpinned by critical physicochemical properties', Energy Conversion and Management, vol. 229.
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© 2020 Elsevier Ltd Diminishing oil reserve, escalating energy dependence, and the environmental impact of fossil fuel utilization has led to research on renewable energy resources with a cleaner carbon footprint. Biofuel, especially biodiesel, has become a feasible substitute for petroleum diesel as it can be directly used in existing transport infrastructure without significant alteration. This paper starts by discussing some critical physicochemical properties and their effect on engine performance and emission. The research then proposes a ranking scheme to select the most suitable biodiesel based on six vital physicochemical properties: density, viscosity, heating value, flash point, cetane number and oxidation stability. The solution developed is independent of supervision, contrary to popular learning algorithms and can operate on the only intelligence whether an attribute is favourable by its higher/lower values. The novelty of the work consists in ensuring that the rarer properties pick up the greater weights and in establishing a simple ranker based on descriptive statistics. This scheme first generates transactions against each biodiesel which helps in association rule mining, which is later used to score/rank the biodiesels. The three phases and their subordinate sub-steps have been carried out using the platforms: Python, R and Tableau, respectively. The study endorses Brassica juncea, Cardoon (Cynara cardunculu), and poppyseed oil as the most desirable biodiesel feedstocks. On the other hand, cedar, castor and hiptage were ranked as least desirable in the list of 71 feedstocks based on the proposed ranking scheme. The proposed ranking scheme will help decision-makers such to analyze and obtain tailored biodiesel feedstock for their purposes.

Ran, H, Wen, S, Li, Q, Yang, Y, Shi, K, Feng, Y, Zhou, P & Huang, T 2021, 'Memristor-Based Edge Computing of Blaze Block for Image Recognition.', IEEE Transactions on Neural Networks and Learning Systems, vol. PP.
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In this article, a novel edge computing system is proposed for image recognition via memristor-based blaze block circuit, which includes a memristive convolutional neural network (MCNN) layer, two single-memristive blaze blocks (SMBBs), four double-memristive blaze blocks (DMBBs), a global Avg-pooling (GAP) layer, and a memristive full connected (MFC) layer. SMBBs and DMBBs mainly utilize the depthwise separable convolution neural network (DwCNN) that can be implemented with a much smaller memristor crossbar (MC). In the backward propagation, we use batch normalization (BN) layers to accelerate the convergence. In the forward propagation, this circuit combines DwCNN layers/CNN layers with nonseparate BN layers, which means that the required number of operational amplifiers is cut by half as long as the greatly reduced power consumption. A diode is added after the rectified linear unit (ReLU) layer to limit the output of the circuit below the threshold voltage Vt of the memristor; thus, the circuit is more stable. Experiments show that the proposed memristor-based circuit achieves an accuracy of 84.38% on the CIFAR-10 data set with advantages in computing resources, calculation time, and power consumption. Experiments also show that, when the number of multistate conductance is 2⁸ and the quantization bit of the data is 8, the circuit can achieve its best balance between power consumption and production cost.

Ran, H, Wen, S, Shi, K & Huang, T 2021, 'Stable and compact design of Memristive GoogLeNet Neural Network', Neurocomputing, vol. 441, pp. 52-63.
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According to the requirements of edge intelligence for circuit volume, power consumption and computing performance, a Memristive GoogLeNet Neural Network (MGNN) circuit is designed using memristor which is a new device integrating storage and computing as the basic circuit element. This circuit adopts 1×1 convolution and multi-scale convolution feature fusion to reduce the number of layers required by the network while ensuring the recognition accuracy of circuit. In order to reduce the size of the memristor crossbars in the circuit, we design word-line pruning and bit-line pruning methods of Memristive Convolution (MC) layers. We also use the parameter distribution of the memristive neural network to further reduce the size of memristor crossbars. The Memristive Batch Normalization (MBN) layer and Memristive Dropout (MD) are merged into front MC layers according mathematical analysis for cutting the number of network layers and decreasing the power consumption of the circuit. We also design the channel optimization and layer optimization methods of MC layers which greatly reduce the negative effect of multi-state conductance of memristors on the accuracy, improve the stability of the circuit, and reduce the circuit volume and power consumption. Experiments show that this circuit can get 89.83% accuracy on the CIFAR-10 data set, and the power consumption of a single neuron is only 1.3μW. When the number of memristor multi-state conductance is 2 =16, the accuracy of the MGNN circuit close to float MGNN can still be obtained. 4

Ran, H, Wen, S, Wang, S, Cao, Y, Zhou, P & Huang, T 2021, 'Memristor-based Edge Computing of ShuffleNetV2 for Image Classification', IEEE Transactions on Computer - Aided Design of Integrated Circuits and Systems.
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IEEE In this paper, we propose a memristor-based ShuffleNetV2 for image classification. Because of the low power consumption and high integration, this circuit is suitable for edge computing. The memristor-based ShuffleNetV2 is divided into four kinds of units, and each unit is composed by a series of basic memristive neural circuits, such as memristive Convolutional Neural Networks (MCNNs), Memristive Batch Normalization (MBN) layers, Memristive Fully Connection (MFC) layers, Rectified Linear Unit (ReLU) layers and max-pooling layers etc.. To solve the imbalance of required memristor crossbars in MCNN and the huge power consumption of the input voltage inverting, the inverters are shifted behind the memristor crossbar.This circuit uses (M+1)×2 memristor crossbars instead of the original (2M+1)×1 memristor crossbars. The world lines of memristor crossbars required can be reduced from (2M+1) to (M+1). The number of inverters in MCNN for a kernel can be decreased to one, and the power consumption in MCNN is greatly decreased. The MBN is designed by combining operational amplifiers with memristors. An output limiter circuit is added behind the ReLU layer implemented by diode. The output voltage of ReLU layer is limited to the threshold voltage Vp of memristor. The memristive ShuffleNetV2 is established for image classification in experiments. The effectiveness of the circuit is verified on the FER2013 dataset along with the analysis of the memristor resources, calculation periods and circuit power consumption of the network. The experimental results show that this that the inference time of this memristive ShuffleNetV2 could be as less as 137ns with a max power consumption 2.8μW of a memristive neurons.

Ranjbar-Zahedani, M, Keshavarzi, A, Khabbaz, H & Ball, JE 2021, 'Optimizing flow diversion structure as an effective pier-scour countermeasure', JOURNAL OF HYDRAULIC RESEARCH.
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Rasal, AS, Yadav, S, Yadav, A, Kashale, AA, Manjunatha, ST, Altaee, A & Chang, J-Y 2021, 'Carbon Quantum Dots for Energy Applications: A Review', ACS Applied Nano Materials.
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Rasouli, H & Fatahi, B 2021, 'Geosynthetics reinforced interposed layer to protect structures on deep foundations against strike-slip fault rupture', GEOTEXTILES AND GEOMEMBRANES, vol. 49, no. 3, pp. 722-736.
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Rathee, M, Kumar, S, Gandomi, AH, Dilip, K, Balusamy, B & Patan, R 2021, 'Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks', IEEE Transactions on Engineering Management.
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IEEE Existing routing protocols for wireless sensor networks (WSNs) focus primarily either on energy efficiency, quality of service (QoS), or security issues. However, a more holistic view of WSNs is needed, as many applications require both QoS and security guarantees along with the requirement of prolonging the lifetime of the network. The limited energy capacity of sensor nodes forces a tradeoff to be made between network lifetime, QoS, and security. To address these issues, an ant colony optimization based QoS aware energy balancing secure routing (QEBSR) algorithm for WSNs is proposed in this article. Improved heuristics for calculating the end-to-end delay of transmission and the trust factor of the nodes on the routing path are proposed. The proposed algorithm is compared with two existing algorithms: distributed energy balanced routing and energy efficient routing with node compromised resistance. Simulation results show that the proposed QEBSR algorithm performed comparatively better than the other two algorithms.

Razzaq, L, Mujtaba, MA, Soudagar, MEM, Ahmed, W, Fayaz, H, Bashir, S, Fattah, IMR, Ong, HC, Shahapurkar, K, Afzal, A, Wageh, S, Al-Ghamdi, A, Ali, MS & EL-Seesy, A 2021, 'Engine performance and emission characteristics of palm biodiesel blends with graphene oxide nanoplatelets and dimethyl carbonate additives', JOURNAL OF ENVIRONMENTAL MANAGEMENT, vol. 282.
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Rezaei, K, Pradhan, B, Vadiati, M & Nadiri, AA 2021, 'Suspended sediment load prediction using artificial intelligence techniques: comparison between four state-of-the-art artificial neural network techniques', ARABIAN JOURNAL OF GEOSCIENCES, vol. 14, no. 3.
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Rezaei, M, Khalilpour, KR & Mohamed, MA 2021, 'Co-production of electricity and hydrogen from wind: A comprehensive scenario-based techno-economic analysis', INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, vol. 46, no. 35, pp. 18242-18256.
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Rezaei, M, Radfar, P, Winter, M, McClements, L, Thierry, B & Warkiani, ME 2021, 'Simple-to-Operate Approach for Single Cell Analysis Using a Hydrophobic Surface and Nanosized Droplets', ANALYTICAL CHEMISTRY, vol. 93, no. 10, pp. 4584-4592.
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Rezaei, M, Razavi Bazaz, S, Zhand, S, Sayyadi, N, Jin, D, Stewart, MP & Ebrahimi Warkiani, M 2021, 'Point of Care Diagnostics in the Age of COVID-19.', Diagnostics (Basel, Switzerland), vol. 11, no. 1.
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The recent outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated serious respiratory disease, coronavirus disease 2019 (COVID-19), poses a major threat to global public health. Owing to the lack of vaccine and effective treatments, many countries have been overwhelmed with an exponential spread of the virus and surge in the number of confirmed COVID-19 cases. Current standard diagnostic methods are inadequate for widespread testing as they suffer from prolonged turn-around times (>12 h) and mostly rely on high-biosafety-level laboratories and well-trained technicians. Point-of-care (POC) tests have the potential to vastly improve healthcare in several ways, ranging from enabling earlier detection and easier monitoring of disease to reaching remote populations. In recent years, the field of POC diagnostics has improved markedly with the advent of micro- and nanotechnologies. Due to the COVID-19 pandemic, POC technologies have been rapidly innovated to address key limitations faced in existing standard diagnostic methods. This review summarizes and compares the latest available POC immunoassay, nucleic acid-based and clustered regularly interspaced short palindromic repeats- (CRISPR)-mediated tests for SARS-CoV-2 detection that we anticipate aiding healthcare facilities to control virus infection and prevent subsequent spread.

Ricafrente, A, Nguyen, H, Tran, N & Donnelly, S 2021, 'An Evaluation of the Fasciola hepatica miRnome Predicts a Targeted Regulation of Mammalian Innate Immune Responses', Frontiers in Immunology.
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Richards, C, Sesperez, K, Chhor, M, Ghorbanpour, S, Rennie, C, Ming, CLC, Evenhuis, C, Nikolic, V, Orlic, NK, Mikovic, Z, Stefanovic, M, Cakic, Z, McGrath, K, Gentile, C, Bubb, K & McClements, L 2021, 'Characterisation of cardiac health in the reduced uterine perfusion pressure model and a 3D cardiac spheroid model, of preeclampsia', Biology of Sex Differences.
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Roche, CD, Sharma, P, Ashton, AW, Jackson, C, Xue, M & Gentile, C 2021, 'Printability, Durability, Contractility and Vascular Network Formation in 3D Bioprinted Cardiac Endothelial Cells Using Alginate–Gelatin Hydrogels', Frontiers in Bioengineering and Biotechnology.
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Roy Chowdhury, R, Chattopadhyay, S & Adak, C 2021, 'CAHPHF: Context-Aware Hierarchical QoS Prediction with Hybrid Filtering', IEEE Transactions on Services Computing.
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IEEE With the proliferation of Internet-of-Things and continuous growth in the number of web-services at the Internet-scale, service-recommendation is becoming a challenge nowadays. One of the prime aspects influencing the service-recommendation is the Quality-of-Service(QoS) parameter, which depicts the performance of a web-service. In general, the service provider furnishes the QoS values before service deployment. In reality, the QoS values of service vary across different users, time, locations, etc. Therefore, estimating the QoS value of service before its execution is an important task. Thus, QoS-prediction has gained significant attention. Multiple approaches are available in the literature for predicting QoS. However, these approaches are yet to reach the desired accuracy level. Here, we study the QoS-prediction problem across different users and propose a novel solution by considering the contextual information of both services and users. Our proposal includes two key-steps: (a)hybrid-filtering, (b)hierarchical-prediction-mechanism. On one hand, the hybrid-filtering aims to obtain a set of similar users and services, given a target user and a service. On the other hand, the goal of the hierarchical-prediction-mechanism is to estimate the QoS value accurately by leveraging hierarchical-neural-regression. We evaluated our framework on WS-DREAM datasets. The experimental results show our framework outperformed the major state-of-the-art approaches.

Rozali, S, Abd Latif, Z, Adnan, NA, Hussin, Y, Blackburn, A & Pradhan, B 2021, 'Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique', Geocarto International.
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Ryan, ST, Hosseini-Beheshti, E, Afrose, D, Ding, X, Xia, B, Grau, GE, Little, CB, McClements, L & Li, JJ 2021, 'Extracellular Vesicles from Mesenchymal Stromal Cells for the Treatment of Inflammation-Related Conditions', International Journal of Molecular Sciences.
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Ryu, SC, Naidu, G, Moon, H & Vigneswaran, S 2021, 'Continuous and selective copper recovery by multi-modified and granulated SBA-15', Chemosphere, vol. 271.
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Continuous and selective recovery of copper (Cu) from heavy metal wastewater not only mitigates the pollution of environment but also can be applied for industrial field. Due to several advantages such as large pore size, easy modification, physical and chemical stabilities, mesoporous silica material, SBA-15, has been synthesized via hydrothermal reaction in this study. For enhancing the adsorption capacity and selectivity for Cu ions, prepared SBA-15 was modified with manganese loading and amine-grafting (MN-SBA) then granulated by alginic-acid (GMN-SBA), successfully. Adsorption capacities for heavy metals such as Cu, Zn, Ni and Mn were 2.11, 1.24, 1.74 and 1.25 mmol/g on MN-SBA and decreased to 1.23, 0.68, 0.86 and 0.65 when it was granulated. Even though the adsorption capacities of GMN-SBA for heavy metals decreased by 40–50%, it enabled easy regeneration and separation process when applied for continuous fixed-bed column adsorption mode. Specifically, the results demonstrated that GMN-SBA was able to be reused for 5 times while maintaining over 80% adsorption capacities. Fixed-bed adsorption results were well explained by dynamic adsorption model incorporated with linear driving force approximation (LDFA) model. The simulation of fixed-bed adsorption tests was proceeded in terms of bed length, feeding concentration and flow rate, and it showed the breakthrough times were shifted in the axis of time. In multi-component adsorption, LDFA model showed a high overshoot phenomenon of the breakthrough curves for Zn, Ni and Mn compared to Cu. This reflected the high affinity of Cu towards GMN-SBA compared to other heavy metals.

Sabetamal, H, Carter, JP, Zhang, X & Sheng, D 2021, 'Coupled analysis of full flow penetration problems in soft sensitive clays', COMPUTERS AND GEOTECHNICS, vol. 133.
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Sadeghi Rad, H, Monkman, J, Warkiani, ME, Ladwa, R, O'Byrne, K, Rezaei, N & Kulasinghe, A 2021, 'Understanding the tumor microenvironment for effective immunotherapy.', Medicinal research reviews.
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Advances in immunotherapy have led to durable and long-term benefits in a subset of patients across a number of solid tumor types. Understanding of the subsets of patients that respond to immune checkpoint inhibitors at the cellular level, and in the context of their tumor microenvironment (TME) is becoming increasingly important. The TME is composed of a heterogeneous milieu of tumor and immune cells. The immune landscape of the TME can inhibit or promote tumor initiation and progression; thus, a deeper understanding of tumor immunity is necessary to develop immunotherapeutic strategies. Recent developments have focused on characterizing the TME immune contexture (type, density, and function) to discover mechanisms and biomarkers that may predict treatment outcomes. This has, in part, been powered by advancements in spatial characterization technologies. In this review article, we address the role of specific immune cells within the TME at various stages of tumor progression and how the immune contexture determinants affecting tumor growth are used therapeutically.

Sadeghi, F, Yu, Y, Zhu, X & Li, J 2021, 'Damage identification of steel-concrete composite beams based on modal strain energy changes through general regression neural network', Engineering Structures, vol. 244, pp. 112824-112824.
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Saha, S, Kundu, B, Paul, GC, Mukherjee, K, Pradhan, B, Dikshit, A, Abdul Maulud, KN & Alamri, AM 2021, 'Spatial assessment of drought vulnerability using fuzzy-analytical hierarchical process: a case study at the Indian state of Odisha', Geomatics Natural Hazards and Risk.
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Saha, S, Paul, GC, Pradhan, B, Abdul Maulud, KN & Alamri, AM 2021, 'Integrating multilayer perceptron neural nets with hybrid ensemble classifiers for deforestation probability assessment in Eastern India', GEOMATICS NATURAL HAZARDS & RISK, vol. 12, no. 1, pp. 29-62.
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Salehpour, MJ, Alishavandi, AM, Hossain, MJ, Rostami, SMH, Wang, J & Yu, X 2021, 'A stochastic decentralized model for the privately interactive operation of a multi-carrier energy system', SUSTAINABLE CITIES AND SOCIETY, vol. 64.
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Salgotra, R, Singh, U, Saha, S & Gandomi, AH 2021, 'Self adaptive cuckoo search: Analysis and experimentation', Swarm and Evolutionary Computation, vol. 60.
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Cuckoo search (CS) algorithm, based on the brood parasitic behaviour of cuckoo species, since its inception, has proved its worth in various fields of research and can be considered as an efficient problem solver. Though CS is a very good algorithm, its performance degrades as the problem complexity increases. So a new version namely self-adaptive CS (SACS) is proposed to improve its performance. The algorithm employs adaptive parameters and hence no parameter tuning is required to be done. Here proportional population reduction based on the fitness of current best and previous best solution is followed. Secondly, in order to improve the exploration and exploitation tendencies, the Gaussian sampling mechanism known as bare-bones variant along with division of population and generations is added. The concept of Weibull distributed probability switching is also added to increase the balance between the exploration and exploitation processes. Further, CS as an extension of differential evolution (DE), genetic algorithm (GA), stability analysis with respect to Von Neumann's stability criteria are also presented. For performance evaluation, the SACS algorithm is tested on CEC 2017 benchmark problems and compared with respect to self-adaptive DE (SaDE), JADE, success-history based adaptive DE (SHADE), SHADE with linear population size reduction (LSHADE), mean-variance mapping optimization (MVMO), cuckoo version 1.0 (CV1.0), cuckoo version new (CV ) and others. Experimental results and statistical analysis show that the proposed SACS algorithm performs better than SaDE, JADE, CV1.0, CV and CS whereas comparable with respect to LSHADE, SHADE, and MVMO. Convergence profiles further validate the results. new new

Samadi, A, Xie, M, Li, J, Shon, H, Zheng, C & Zhao, S 2021, 'Polyaniline-based adsorbents for aqueous pollutants removal: A review', CHEMICAL ENGINEERING JOURNAL, vol. 418.
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Samani, BH, Behruzian, M, Najafi, G, Fayyazi, E, Ghobadian, B, Behruzian, A, Mofijur, M, Mazlan, M & Yue, J 2021, 'The rotor-stator type hydrodynamic cavitation reactor approach for enhanced biodiesel fuel production', Fuel.
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Sang, L, Xu, M, Qian, S & Wu, X 2021, 'Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network', Neurocomputing, vol. 454, pp. 417-429.
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Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem in recommender system. However, existing KG based recommendation methods mainly rely on handcrafted meta-path features or simple triple-level entity embedding, which cannot automatically capture entities’ long-term relational dependencies for the recommendation. Specially, entity embedding learning is not properly designed to combine user-item interaction information with KG context information. In this paper, a two-channel neural interaction method named Knowledge Graph enhanced Neural Collaborative Filtering with Residual Recurrent Network (KGNCF-RRN) is proposed, which leverages both long-term relational dependencies KG context and user-item interaction for recommendation. (1) For the KG context interaction channel, we propose Residual Recurrent Network (RRN) to construct context-based path embedding, which incorporates residual learning into traditional recurrent neural networks (RNNs) to efficiently encode the long-term relational dependencies of KG. The self-attention network is then applied to the path embedding to capture the polysemy of various user interaction behaviours. (2) For the user-item interaction channel, the user and item embeddings are fed into a newly designed two-dimensional interaction map. (3) Finally, above the two-channel neural interaction matrix, we employ a convolutional neural network to learn complex correlations between user and item. Extensive experimental results on three benchmark datasets show that our proposed approach outperforms existing state-of-the-art approaches for knowledge graph based recommendation.

Sang, L, Xu, M, Qian, S & Wu, X 2021, 'Knowledge graph enhanced neural collaborative recommendation', Expert Systems with Applications, vol. 164.
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Existing neural collaborative filtering (NCF) recommendation methods suffer from severe sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem. However, pure NCF models can hardly model the high-order connectivity in KG, and ignores complex pairwise correlations between user/item embedding dimensions. To address these problems, we propose a novel Knowledge graph enhanced Neural Collaborative Recommendation (K-NCR) framework, which effectively combines user–item interaction information and auxiliary knowledge information for recommendation task into three parts: (1) For items, the proposed propagating model learns the representation of item entity. It recursively aggregates information from its multi-hop neighbours in KG, and employs an attention mechanism to discriminate the importance of the relation type to mine users’ potential preferences. (2) For users, another heterogeneous attention weights are leveraged to strengthen the embedding learning of users. (3) The user and item embeddings are then fed into a newly designed two-dimensional interaction map with convolutional hidden layers to model the complex pairwise correlations between their embedding dimensions explicitly. Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our K-NCR framework.

Sang, R, Stratton, B, Engel, A & Deng, W 2021, 'Liposome technologies towards colorectal cancer therapeutics', Acta Biomaterialia, vol. 127, pp. 24-40.
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Colorectal cancer (CRC) is the third most common cancer and the fourth most common deadly cancer worldwide. After treatment with curative intent recurrence rates vary with staging 0-13% in Stage 1, 11-61% in S2 and 28-73% in Stage 3. The toxicity to healthy tissues from chemotherapy and radiotherapy and drug resistance severely affect the quality of life and cancer specific outcomes of CRC patients. To overcome some of these limitations, many efforts have been made to develop nanomaterial-based drug delivery systems. Among these nanocarriers, liposomes represented one of the most successful candidates in delivering targeted oncological treatment, improving safety profile and therapeutic efficacy of encapsulated drugs. In this review we will discuss liposome design with a particular focus on the targeting feature and triggering functions. We will also summarise the recent advances in liposomal delivery system for CRC treatment in both the preclinical and clinical studies. We will finally provide our perspectives on the liposome technology development for the future clinical translation. Statement of significance: Conventional treatments for colorectal cancer (CRC) severely affect the therapeutic effects for advanced patients. With the development of nanomedicines, liposomal delivery system appears to be one of the most promising nanocarriers for CRC treatment. In last three years several reviews in this area have been published focusing on the preclinical research and drug delivery function, which is a fairly narrow focus in the field of liposome technology for CRC therapy. Our review presented the most recent advances of the liposome technology (both clinical and preclinical applications) for CRC with strong potential for further clinical translation. We believe it will attract lots of attention from various audiences, including researchers, clinicians and the industry.

Saputra, YM, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2021, 'A Novel Mobile Edge Network Architecture with Joint Caching-Delivering and Horizontal Cooperation', IEEE Transactions on Mobile Computing, vol. 20, no. 1, pp. 19-31.
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Mobile edge caching/computing (MEC) has been emerging as a promising paradigm to provide ultra-high rate, ultra-reliable, and/or low-latency communications in future wireless networks. In this paper, we introduce a novel MEC network architecture that leverages the optimal joint caching-delivering with horizontal cooperation among mobile edge nodes (MENs). To that end, we first formulate the content-access delay minimization problem by jointly optimizing the content caching and delivering decisions under various network constraints (e.g., network topology, storage capacity and users’ demands at each MEN). However, the strongly mutual dependency between the decisions makes the problem a nested dual optimization that is proved to be NP-hard. To deal with it, we propose a novel transformation method to transform the nested dual problem to an equivalent mixed-integer nonlinear programming (MINLP) optimization problem. Then, we design a centralized solution using an improved branch-and-bound algorithm with the interior-point method to find the joint caching and delivering policy which is within 1 percent of the optimal solution. Since the centralized solution requires the full network topology and information from all MENs, to make our solution scalable, we develop a distributed algorithm which allows each MEN to make its own decisions based on its local observations. Extensive simulations demonstrate that the proposed solutions can reduce the total average delay for the whole network up to 40 percent compared with other current caching policies. Furthermore, the proposed solutions also increase the cache hit ratio for the network up to 4 times, thereby dramatically reducing the traffic load on the backhaul network

Satya, A, Harimawan, A, Sri Haryani, G, Johir, MAH, Nguyen, LN, Nghiem, LD, Vigneswaran, S, Ngo, HH & Setiadi, T 2021, 'Fixed-bed adsorption performance and empirical modelingof cadmium removal using adsorbent prepared from the cyanobacterium Aphanothece sp cultivar', Environmental Technology and Innovation.
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© 2020 Elsevier B.V. Water contamination by cadmium (Cd), which is a toxic heavy metal widely used in many industrial processes, is a pervasive environmental problem. This study investigated the removal of Cd by dry cyanobacterium Aphanothece sp cultivar in adsorption columns The effect of inlet Cd concentrations (1.00 – 4.85mg/L), flow rates (0.30-0.60 L/h) and bed height (4.6 -7.2 cm) on the breakthrough characteristics of the adsorption column was investigated. The maximum adsorption capacity and efficiency were found to be 8.20 mg/g and 89.07 %, respectively with a flow rate of 0.60 L/h and 4.85 mg/L inlet concentration of Cd. The fixed bed adsorption data were fitted to three well known fixed bed empirical models namely Thomas, Adam–Bohart and Yoon–Nelson. The experimental results well fitted with the models mentioned above with R2 of greater than 0.98 at different conditions. The regeneration efficiency of benthic cyanobacterium Aphanothece sp cultivar based adsorbent was studied using 0.1 M HCl. Repetitive adsorption–regeneration experiment show that, at the end of the fifth cycle, the desorption efficiency decreased by 21%. Thus, further research is necessary to improve the reusability of dried biomass of cyanobacterium Aphanothece sp adsorbent.

Scheuermann Filho, HC, Beck Saldanha, R, Gravina da Rocha, C & Cesar Consoli, N 2021, 'Sustainable Binders Stabilizing Dispersive Clay', Journal of Materials in Civil Engineering, vol. 33, no. 3, pp. 06020026-06020026.
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Schleppi, J, Bromiley, G, Odling, N & Bennett, NS 2021, 'In-situ resource utilisation manufacturing of optically transparent glass from lunar regolith simulant', JOURNAL OF MATERIALS SCIENCE, vol. 56, no. 21, pp. 12132-12153.
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Seo, DH, Xie, M, Murdock, AT, van der Laan, T, Lawn, M, Park, MJ, Woo, YC, Pineda, S, Hong, JM, Grigore, M, Yick, S, Han, Z, Millar, G, Gray, S, Ostrikov, K, Shon, HK & Bendavid, A 2021, 'Rejection of harsh pH saline solutions using graphene membranes', Carbon, vol. 171, pp. 240-247.
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© 2020 Elsevier Ltd Water security is a pressing issue for global citizens in the 21st century. Concerns over clean water supply, and the environmental impact of industrial waste water, make water treatment a world-wide problem requiring a simple and effective solution. Membrane distillation is an emerging water purification technique that complements state-of-the-art reverse osmosis processes. Membrane distillation achieves high rejection over a range of salt concentrations while maintaining flux, using a temperature differential as opposed to pressure across the membrane. Importantly the process can be driven using low grade waste heat energy. Current membranes used in membrane distillation do not guarantee stable membrane performance under harsh (high salt and acid or base concentrations) conditions. Here we report, a permeable graphene membrane operating in harsh conditions with no observable degradation. A permeate of pH neutral water with a flux of 25 ± 1 L m−2 h−1 is produced by this membrane through near (99.9 ± 0.1%) ion rejection from 0.6 M sodium chloride at pH 1 and pH 13, over 144 h. More complex ion solutions, including real acid mine drainage waste-water, were also successfully purified. These findings may present a membrane that is suitable to improve water supply and reduce the environmental impact of industrial waste-water.

Shafaghat, A & Khabbaz, H 2021, 'Recent advances and past discoveries on tapered pile foundations: a review', Geomechanics and Geoengineering, pp. 1-30.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. The growing tendency to study the behaviour of tapered piles in the last two decades has made it necessary to gain a deeper insight into this specific kind of deep foundation. Tapered piles have been investigated through analytical, experimental, and numerical studies. These piles have revealed different behaviour under various loading conditions. Hence, reviewing and assessing these efforts to comprehend their response can be of great significance. In this paper firstly, it is attempted to go over experimental studies, conducted on tapered piles. Then, the proposed mathematical and numerical solutions, employed to calculate the bearing capacity of single tapered piles, are compared to have a better vision of how these piles behave. In the third section, the optimum tapering angles of tapered piles in loose, medium, and dense sand are discussed. All the efforts are investigated technically to find the advantages, disadvantages, and the research gaps for this specific kind of piles. In addition, another section entitled the directions and ideas for future research on tapered piles is provided comprising the most recent achievements in this area. Moreover, the implementation of tapered piles in a significant project as a case study is discussed.

Shafaghat, A, Khabbaz, H & Fatahi, B 2021, 'Analytical and Numerical Approaches to Attain the Optimum Tapering Angle for Axially-Loaded Bored Piles in Sandy Soils', INTERNATIONAL JOURNAL OF GEOMECHANICS, vol. 21, no. 7.
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Shakor, P, Nejadi, S, Paul, G & Gowripalan, N 2021, 'Effects of Different Orientation Angle, Size, Surface Roughness, Heat Curing on Mechanical Behaviour of 3D Printed Cement Mortar with/without Glass Fibre in Powder-based 3DP', 3D Printing and Additive Manufacturing.
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Shan, X, Wang, F, Wang, D, Wen, S, Chen, C, Di, X, Nie, P, Liao, J, Liu, Y, Ding, L, Reece, PJ & Jin, D 2021, 'Optical tweezers beyond refractive index mismatch using highly doped upconversion nanoparticles', NATURE NANOTECHNOLOGY, vol. 16, no. 5.
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Sharma, P, Wang, X, Ming, CLC, Vettori, L, Figtree, G, Boyle, A & Gentile, C 2021, 'Considerations for the Bioengineering of Advanced Cardiac In Vitro Models of Myocardial Infarction', SMALL, vol. 17, no. 15.
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Shen, J, Wang, Y & Zhang, J 2021, 'ASDN: A Deep Convolutional Network for Arbitrary Scale Image Super-Resolution', Mobile Networks and Applications.
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Shen, S, Zhu, T, Wu, D, Wang, W & Zhou, W 2021, 'From distributed machine learning to federated learning: In the view of data privacy and security', Concurrency and Computation: Practice and Experience.
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Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micromanaging the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a submodel locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multiparty computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm.

Shen, S, Zhu, T, Ye, D, Wang, M, Zuo, X & Zhou, A 2021, 'A novel differentially private advising framework in cloud server environment', Concurrency and Computation: Practice and Experience.
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Due to the rapid development of the cloud computing environment, it is widely accepted that cloud servers are important for users to improve work efficiency. Users need to know servers' capabilities and make optimal decisions on selecting the best available servers for users' tasks. We consider the process of learning servers' capabilities by users as a multiagent reinforcement learning process. The learning speed and efficiency in reinforcement learning can be improved by sharing the learning experience among learning agents which is defined as advising. However, existing advising frameworks are limited by the requirement that during advising all learning agents in a reinforcement learning environment must have exactly the same actions. To address the above limitation, this article proposes a novel differentially private advising framework for multiagent reinforcement learning. Our proposed approach can significantly improve the application of conventional advising frameworks when agents have one different action. The approach can also widen the applicable field of advising and speed up reinforcement learning by triggering more potential advising processes among agents with different actions.

Shen, Z-H, Ni, H, Ding, C, Sui, G-R, Jia, H-Z, Gao, X-M & Wang, N 2021, 'Improving the Energy-Conversion Efficiency of a PV-TE System With an Intelligent Power-Track Switching Technique and Efficient Thermal-Management Scheme', IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY, vol. 11, no. 6, pp. 963-973.
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Sheng, Z, Tuan, HD, Duong, TQ & Hanzo, L 2021, 'UAV-Aided Two-Way Multi-User Relaying', IEEE TRANSACTIONS ON COMMUNICATIONS, vol. 69, no. 1, pp. 246-260.
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Shi, Y, Tuan, HD, Savkin, A, Lin, C-T, Zhu, JG & Poor, HV 2021, 'Distributed model predictive control for joint coordination of demand response and optimal power flow with renewables in smart grid', APPLIED ENERGY, vol. 290.
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Shi, Z, Zhang, JA, Xu, YDR & Cheng, Q 2021, 'Environment-Robust Device-free Human Activity Recognition with Channel-State-Information Enhancement and One-Shot Learning', IEEE Transactions on Mobile Computing, pp. 1-1.
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Shrestha, J, Ryan, ST, Mills, O, Zhand, S, Razavi Bazaz, S, Hansbro, PM, Ghadiri, M & Ebrahimi Warkiani, M 2021, 'A 3D-printed microfluidic platform for simulating the effects of CPAP on the nasal epithelium', BIOFABRICATION, vol. 13, no. 3.
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Siami, M, Naderpour, M & Lu, J 2021, 'A Mobile Telematics Pattern Recognition Framework for Driving Behavior Extraction', IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1459-1472.
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Mobile telematics is a relatively new innovation that involves collecting data on driving behavior using the internal sensors in a smartphone rather than from an in-vehicle data recorder. However, telematics data are usually not labeled, which makes extracting driving patterns from them very difficult. Therefore, unsupervised learning algorithms play an important role in this field. In addition, most current research is based on datasets developed in a laboratory or from site investigations and questionnaires, which are very different from real-world driving behaviors. To advance unsupervised learning techniques in this field, and to fill the gap in findings based on real-world data, we have developed an unsupervised pattern recognition framework for mobile telematics data. The framework comprises three main components: a self-organizing map, a nine-layers deep auto-encoder, and partitive clustering algorithms. The SOM algorithm reduces the complexity of the data, the deep auto-encoder extracts the features, and the clustering algorithm groups driving events with similar patterns into behaviors. Further, given clustering with mobile telematics data is an under-researched area, we undertook an empirical comparison of five well-known clustering algorithms to determine the strengths and weaknesses of each method and which is best suited to categorizing driving styles. The study was conducted with a real-world insurance dataset containing 500,000 journeys by 2500 drivers, and the results were evaluated against three measures - Davis Boulding, Calinski Harabasz, and execution time. Overall, we find that k-means clustering and a self-organizing map were able to extract more accurate patterns than others. A statistical analysis of the 29 clusters produced by SOM and k-means, revealed 29 unique driving styles, all of which can be found in the transportation literature.

Singh, AK, Gramann, K, Chen, HT & Lin, CT 2021, 'The impact of hand movement velocity on cognitive conflict processing in a 3D object selection task in virtual reality', NeuroImage, vol. 226.
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© 2020 Detecting and correcting incorrect body movements is an essential part of everyday interaction with one's environment. The human brain provides a monitoring system that constantly controls and adjusts our actions according to our surroundings. However, when our brain's predictions about a planned action do not match the sensory inputs resulting from that action, cognitive conflict occurs. Much is known about cognitive conflict in 1D/2D environments; however, less is known about the role of movement characteristics associated with cognitive conflict in 3D environment. Hence, we devised an object selection task in a virtual reality (VR) environment to test how the velocity of hand movements impacts human brain responses. From a series of analyses of EEG recordings synchronized with motion capture, we found that the velocity of the participants’ hand movements modulated the brain's response to proprioceptive feedback during the task and induced a prediction error negativity (PEN). Additionally, the PEN originates in the anterior cingulate cortex and is itself modulated by the ballistic phase of the hand's movement. These findings suggest that velocity is an essential component of integrating hand movements with visual and proprioceptive information during interactions with real and virtual objects.

Singh, M, Indraratna, B & Nguyen, TT 2021, 'Experimental insights into the stiffness degradation of subgrade soils prone to mud pumping', Transportation Geotechnics, vol. 27.
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Recent laboratory investigations conducted on soils prone to mud pumping have shown that the subgrade soil experiences softening associated with an internal redistribution of moisture when the cyclic stress ratio (CSR) exceeds a certain critical level. This article aims to evaluate the stiffness degradation of these problematic subgrade soils subjected to a wide range of loading conditions. The test results revealed that the instability of the specimens is caused by the early softening behaviour, and this is accompanied by a sharp reduction in the specimen stiffness. As high as 70–80% reduction in the initial small-strain stiffness was observed even for densely compacted specimens (ρd = 1790 kg/m3). In this regard, a new method for predicting the threshold number of cycles, Nthr and residual axial strains required for the onset of subgrade instability is proposed. As a useful practical guide, a quasi-linear relationship between the threshold strain and Nthr is also determined.

Siwakoti, Y, Shaw, P, Hasan, S & Khan, MNH 2021, 'A New Single-Stage Continuous Input Current Based High Gain Boost Inverter: Analysis and Implementation', International Journal of Circuit Theory and Applications.
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Sohn, W, Guo, W, Ngo, HH, Deng, L & Cheng, D 2021, 'Powdered activated carbon addition for fouling control in anaerobic membrane bioreactor', Bioresource Technology Reports, vol. 15.
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The effect of powdered activated carbon (PAC) addition to a conventional anaerobic membrane bioreactor (AnMBR) on removal performance and membrane fouling was explored in this study. The optimal one-off PAC dose could increase average chemical oxygen demand (COD) and total organic carbon (TOC) removal rates up to 15.7% and 15.6%, respectively. The PAC addition exhibited not only lower TMP increase rate, but also reduced soluble microbial product (SMP) and extracellular polymeric substances (EPS). Higher protein to polysaccharide ratio of SMP and higher zeta potential in AnMBR with PAC could enhance hydrophobicity and flocculation ability, thereby effectively alleviating membrane fouling. Lower total membrane resistance and pore blocking resistance indicated that PAC addition could prevent both severe pore blocking and irreversible fouling, due to the lower polysaccharide level in SMP of the cake layer. Moreover, PAC addition facilitated the decrease in fouling-related bacteria such as Cloacibacterium and Paludibacter.

Sohn, W, Guo, W, Ngo, HH, Deng, L, Cheng, D & Zhang, X 2021, 'A review on membrane fouling control in anaerobic membrane bioreactors by adding performance enhancers', JOURNAL OF WATER PROCESS ENGINEERING, vol. 40.
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Song, Y, Lu, J, Liu, A, Lu, H & Zhang, G 2021, 'A Segment-Based Drift Adaptation Method for Data Streams', IEEE Transactions on Neural Networks and Learning Systems.
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In concept drift adaptation, we aim to design a blind or an informed strategy to update our best predictor for future data at each time point. However, existing informed drift adaptation methods need to wait for an entire batch of data to detect drift and then update the predictor (if drift is detected), which causes adaptation delay. To overcome the adaptation delay, we propose a sequentially updated statistic, called drift-gradient to quantify the increase of distributional discrepancy when every new instance arrives. Based on drift-gradient, a segment-based drift adaptation (SEGA) method is developed to online update our best predictor. Drift-gradient is defined on a segment in the training set. It can precisely quantify the increase of distributional discrepancy between the old segment and the newest segment when only one new instance is available at each time point. A lower value of drift-gradient on the old segment represents that the distribution of the new instance is closer to the distribution of the old segment. Based on the drift-gradient, SEGA retrains our best predictors with the segments that have the minimum drift-gradient when every new instance arrives. SEGA has been validated by extensive experiments on both synthetic and real-world, classification and regression data streams. The experimental results show that SEGA outperforms competitive blind and informed drift adaptation methods.

Sood, K, Karmakar, KK, Varadharajen, V, Kumar, N, Xiang, Y & Yu, S 2021, 'Plug-in over Plug-in Evaluation in Heterogeneous 5G Enabled Networks and beyond', IEEE Network, vol. 35, no. 2, pp. 34-39.
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With the cool upcoming wave of 5G, currently, the networking and telecommunication industries are facing various digital transformations, which are changing the very fundamental nature of the existing network management infrastructure. Besides the Internet of Things (IoT) domain, we also notice that the 5G network in itself is composed of millions of heterogeneous physical entities and nodes, multiple domains, complex protocols and technologies, different gateways, and so on. This heterogeneity imposes critical impacts on the application specific quality of service (QoS) requirements, performance and utilization of network resources, and data and user security. In order to alleviate the above impacts, researchers propose to use different technologies such as software-defined networking, network function virtualization, blockchain, and artificial intelligence in 5G-enabled IoT networking. We notice that the layers over layers (of protocols and technologies) act like a plug-in over plug-in (PoP) in the network in order to accomplish various aims, including meeting QoS demands, enhancing security, load balancing, and so on. On one hand, we agree that this integration of different technologies in 5G networks bring numerous advantages, but on the other hand, we realize that this has posed a lot of unique critical issues in modern 5G network management. In this article, we point out that this straightforward approach of PoP is eventually not a healthy approach for network transformation. In this regard, using open source MANO (OSM), we provide a proof of concept (PoC) to show that at varying degrees of heterogeneity, PoP adds the delay in the VNF deployment process and further impacts the VIM CPU performance. This eventually affects the QoS requirements of IoT nodes or applications. Following this, we propose a high-level holistic approach that helps to alleviate the PoP issue. Finally, in this context, we also discuss the associated challenges and research opportuniti...

Soon, JL, Raman, GP, Peng, JC-H & Lu, DD-C 2021, 'Current Ripple Reduction Using AC Core Biasing in DC-DC Converters', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 68, no. 10, pp. 10058-10067.
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Stender, M, Tiedemann, M, Spieler, D, Schöpflin, D, Hoffmann, N & Oberst, S 2021, 'Deep learning for brake squeal: Brake noise detection, characterization and prediction', Mechanical Systems and Signal Processing, vol. 149, pp. 107181-107181.
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Despite significant advances in modeling of friction-induced vibrations and brake squeal, the majority of industrial research and design is still conducted experimentally, since many aspects of squeal and its mechanisms involved remain unknown. In practice, measurement data is available in large amounts. We report here for the first time on novel strategies for handling data-intensive vibration testings to gain better insights into friction brake system vibrations and noise generation mechanisms. Machine learning-based methods to detect and characterize vibrations, to understand sensitivities and to predict brake squeal are applied with the aim to illustrate how interdisciplinary approaches can leverage the potential of data science techniques for classical mechanical engineering challenges. In the first part, a deep learning brake squeal detector is developed to identify several classes of typical friction noise recordings. The detection method is rooted in recent computer vision techniques for object detection based on convolutional neural networks (CNN). It allows to overcome limitations of classical approaches that solely rely on instantaneous spectral properties of the recorded noise. Results indicate superior detection and characterization quality when compared to a state-of-the-art brake squeal detector. In the second part, a recurrent neural network (RNN) is employed to learn the parametric patterns that determine the dynamic stability of an operating brake system. Given a set of multivariate loading conditions, the RNN learns to predict the noise generation of the structure. The validated RNN represents a virtual twin model for the squeal behavior of a specific brake system. It is found that this model can predict the occurrence and the onset of brake squeal with high accuracy and that it can identify the complicated patterns and temporal dependencies in the loading conditions that drive the dynamical structure into regimes of instability. Large data se...

Stewart, MG & Li, J 2021, 'Risk-based assessment of blast-resistant design of ultra-high performance concrete columns', Structural Safety, vol. 88.
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© 2020 Elsevier Ltd In conventional structural protective design against blast loads conservative structural designs are anticipated. However, unknown factors that include threat uncertainty, blast load variation, construction methods, material quality, etc., could impact the accuracy of assessment and design, sometimes even leading to an overestimation of structural capacity to explosive blast effects or an underestimation of actual blast pressures. In the present study, structural safety and reliability analyses of Ultra-high Performance Concrete (UHPC) columns under varying blast scenarios are performed. The variation in column dimensions, steel reinforcement, UHPC material strength, explosive range and mass, and numerical and blast load model errors are considered. The peak reflected pressure and impulse from the selected blast scenarios are derived based on variation in the explosive mass and standoff distance. Failure probabilities of columns made of this emerging high performance concrete material are then estimated. It was found that for a UHPC column designed for blast the probability of major damage given an explosive blast load varies from 1 × 10−2 to 1 × 10−5 for explosive ordnance and terrorism blast scenarios. This provides a reasonable margin of safety against major structural damage. It was also found that the risk reducing benefit of blast-resistant UHPC columns can be considerable.

Su, G, Ong, HC, Ibrahim, S, Fattah, IMR, Mofijur, M & Chong, CT 2021, 'Valorisation of medical waste through pyrolysis for a cleaner environment: Progress and challenges*', ENVIRONMENTAL POLLUTION, vol. 279.
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Su, Q, Wei, H-L, Liu, Y, Chen, C, Guan, M, Wang, S, Su, Y, Wang, H, Chen, Z & Jin, D 2021, 'Six-photon upconverted excitation energy lock-in for ultraviolet-C enhancement', Nature Communications.
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Su, Q, Wei, H-L, Wang, S, Chen, C, Ming, G, Su, Y, Wang, H, Chen, Z & Jin, D 2021, 'Upconverted excitation energy lock-in for deep-ultraviolet enhancement'.
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Abstract Photon upconversion of near-infrared (NIR) irradiation into deep-ultraviolet (UV) emission offers many exciting opportunities for drug release in deep tissues, photodynamic therapy, solid-state lasing, energy storage, and photocatalysis. However, NIR-to-deep-UV upconversion remains a daunting challenge due to low quantum efficiency. Here, we report an unusual six-photon upconversion process in Gd3+/Tm3+-codoped nanoparticles comprising a heterogeneous, core-multishell nanostructure. This multishell design efficiently suppresses energy consumption induced by interior energy traps, maximizes cascade sensitizations of the NIR excitation, and promotes upconverted deep-UV emission from high-lying excited states. We released the intense six-photon-upconverted UV emissions at 253 nm under 808-nm excitation. This work provides new insight into mechanistic understanding of the upconversion process within the heterogeneous architecture, while offering exciting opportunities for developing nanoscale deep-UV emitters that can be remotely controlled in deep tissues upon NIR illumination.

Sukarno, R, Putra, N, Hakim, II, Rachman, FF & Mahlia, TMI 2021, 'Utilizing heat pipe heat exchanger to reduce the energy consumption of airborne infection isolation hospital room HVAC system', JOURNAL OF BUILDING ENGINEERING, vol. 35.
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Sun, B, Cao, Y, Guo, Z, Yan, Z, Wen, S, Huang, T & Chen, Y 2021, 'Sliding Mode Stabilization of Memristive Neural Networks With Leakage Delays and Control Disturbance.', IEEE transactions on neural networks and learning systems.
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In this article, we investigate a class of memristive neural networks (MNNs) with time-varying delays and leakage delays via sliding mode control (SMC) with and without control disturbance. SMC is used to ensure MNNs' stability. According to the characteristics of the MNNs, we consider the following three models: the first is the MNNs with time-varying delays, the second is the MNNs with time-varying delays and the control disturbance, and the third is the MNNs with time-varying delays, leakage delays, and the control disturbance. We quote some assumptions and lemmas to ensure that our main results are true. The sliding surface, the corresponding sliding mode controller, and the Lyapunov functions are constructed in different models to ensure MNNs' stability. Finally, some examples and simulations verify the validity of our main results by solving linear matrix inequality (LMI), and the conclusions and analysis of the results are given.

Sun, G, Li, L, Fang, J & Li, Q 2021, 'On lower confidence bound improvement matrix-based approaches for multiobjective Bayesian optimization and its applications to thin-walled structures', THIN-WALLED STRUCTURES, vol. 161.
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Sun, G, Li, W, Luo, Q & Li, Q 2021, 'Modal identification of vibrating structures using singular value decomposition and nonlinear iteration based on high-speed digital image correlation', THIN-WALLED STRUCTURES, vol. 163.
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Sun, L, Ma, B, Pei, L, Zhang, X & Zhou, JL 2021, 'The relationship of human activities and rainfall-induced landslide and debris flow hazards in Central China', NATURAL HAZARDS, vol. 107, no. 1, pp. 147-169.
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Sun, R, Chen, C, Wang, X, Zhang, Y & Wang, X 2021, 'Stable Community Detection in Signed Social Networks', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Community detection is one of the most fundamental problems in social network analysis, while most existing research focuses on unsigned graphs. In real applications, social networks involve not only positive relationships but also negative ones. It is important to exploit the signed information to identify more stable communities. In this paper, we propose a novel model, named stable k-core, to measure the stability of a community in signed graphs. The stable k-core model not only emphasizes user engagement, but also eliminates unstable structures. We show that the problem of finding the maximum stable k-core is NP-hard. To scale for large graphs, novel pruning strategies and searching methods are proposed. We conduct extensive experiments on 6 real-world signed networks to verify the efficiency and effectiveness of proposed model and techniques.

Sun, X, Cao, J, Lei, G, Guo, Y & Zhu, J 2021, 'A Composite Sliding Mode Control for SPMSM Drives Based on a New Hybrid Reaching Law With Disturbance Compensation', IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, vol. 7, no. 3, pp. 1427-1436.
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Sun, X, Diao, K, Yang, Z, Lei, G, Guo, Y & Zhu, J 2021, 'Direct Torque Control Based on a Fast Modeling Method for a Segmented-Rotor Switched Reluctance Motor in HEV Application', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 1, pp. 232-241.
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Sun, X, Shi, Z, Lei, G, Guo, Y & Zhu, J 2021, 'Multi-Objective Design Optimization of an IPMSM Based on Multilevel Strategy', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 68, no. 1, pp. 139-148.
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Sun, X, Wu, M, Lei, G, Guo, Y & Zhu, J 2021, 'An Improved Model Predictive Current Control for PMSM Drives Based on Current Track Circle', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 68, no. 5, pp. 3782-3793.
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Sun, X, Zhang, Y, Lei, G, Guo, Y & Zhu, J 2021, 'An Improved Deadbeat Predictive Stator Flux Control with Reduced-Order Disturbance Observer for In-Wheel PMSMs', IEEE/ASME Transactions on Mechatronics.
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In this paper, an improved deadbeat predictive stator flux control (DPSFC) based on disturbance observer is proposed to improve the control performance of in-wheel permanent magnet synchronous motors (PMSMs) with parameter mismatch and disturbance. First, the sensitivity of conventional deadbeat predictive current control to the parameter variation, including flux linkage, stator resistance and stator inductance, is analyzed. Then, a reduced-order observer based on additional disturbance state variables is designed to predict the future stator flux and observe the system disturbance caused by parameter mismatch. The proposed DPSFC method is able to enhance the robustness of the drive performance effectively via the compensations of one-step delay and stator voltage. Finally, the performance of the proposed control method is validated by simulations and experiments on a prototype of an in-wheel PMSM drive.

Sun, Z, Yao, Y, Xiao, J, Zhang, L, Zhang, J & Tang, Z 2021, 'Exploiting textual queries for dynamically visual disambiguation', PATTERN RECOGNITION, vol. 110.
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Swain, S, Bhol, P, Bhavya, MB, Yadav, S, Altaee, A, Saxena, M, Misra, PK & Samal, AK 2021, 'Synthesis of Functionalized Nanomaterial (FNM)–Based Catalytic Materials'.
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Syed Abdul Rahman, SAF, Abdul Maulud, KN, Pradhan, B, Syed Mustorpha, SNA & Che Ani, AI 2021, 'Impact of evacuation design parameter on users' evacuation time using a multi-agent simulation', Ain Shams Engineering Journal.
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© 2020 THE AUTHORS Emergency management is a fundamental issue in building management. Therefore, this study was established to simulate the required times and movement conditions for user evacuation from Campus Infrastructure Building in a 3D environment. Eight simulations are carried out by a multi-agent evacuation simulator, Pathfinder, considering 100% and 70% users capacity with a combination of random, divide, zone 1 and zone 2 access selection. The users evacuated were handled by the A* algorithm in the steering mode of 3D evacuation simulation. The evacuation flow was investigated with the user density heat map and Fruin's Level of Service (LOS). This study found the best path planning obtained from the divide access factor and the impact of the user's capacity is relying on total evacuation time. The critical space is caused by the access width fewer than 1.5 m for every floor, and by widening some spaces helped to improve the evacuation flow.

Syed, MS, Marquis, C, Taylor, R & Warkiani, ME 2021, 'A two-step microengineered system for high-density cell retention from bioreactors', SEPARATION AND PURIFICATION TECHNOLOGY, vol. 254.
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Szemes, M, Melegh, Z, Bellamy, J, Park, JH, Chen, B, Greenhough, A, Catchpoole, D & Malik, K 2021, 'Transcriptomic analyses of mycn-regulated genes in anaplastic wilms’ tumour cell lines reveals oncogenic pathways and potential therapeutic vulnerabilities', Cancers, vol. 13, no. 4, pp. 1-21.
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The MYCN proto-oncogene is deregulated in many cancers, most notably in neuroblastoma, where MYCN gene amplification identifies a clinical subset with very poor prognosis. Gene expression and DNA analyses have also demonstrated overexpression of MYCN mRNA, as well as focal amplifications, copy number gains and presumptive change of function mutations of MYCN in Wilms’ tumours with poorer outcomes, including tumours with diffuse anaplasia. Surprisingly, however, the expression and functions of the MYCN protein in Wilms’ tumours still remain obscure. In this study, we assessed MYCN protein expression in primary Wilms’ tumours using immunohistochemistry of tissue microarrays. We found MYCN protein to be expressed in tumour blastemal cells, and absent in stromal and epithelial components. For functional studies, we used two anaplastic Wilms’ tumour cell-lines, WiT49 and 17.94, to study the biological and transcriptomic effects of MYCN depletion. We found that MYCN knockdown consistently led to growth suppression but not cell death. RNA sequencing identified 561 MYCN-regulated genes shared by WiT49 and 17.94 cell-lines. As expected, numerous cellular processes were downstream of MYCN. MYCN positively regulated the miRNA regulator and known Wilms’ tumour oncogene LIN28B, the genes encoding methylosome proteins PRMT1, PRMT5 and WDR77, and the mitochondrial translocase genes TOMM20 and TIMM50. MYCN repressed genes including the developmental signalling receptor ROBO1 and the stromal marker COL1A1. Importantly, we found that MYCN also repressed the presumptive Wilms’ tumour suppressor gene REST, with MYCN knockdown resulting in increased REST protein and concomitant repression of RE1-Silencing Transcription factor (REST) target genes. Together, our study identifies regulatory axes that interact with MYCN, providing novel pathways for potential targeted therapeutics for poor-prognosis Wilms’ tumour.

Tafti, G, Canning, J, Wang, S, Luo, Y, Cook, K & Peng, GD 2021, 'Pressure Effects on Structured Optical Fibre Drawing by Modified Single-Capillary Modelling', Optical Fiber Technology, vol. 63.
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The impact of drawing pressure on the structure of solid-core structured optical fibres (SOFs) is investigated. A modified single-capillary model to cater for multi-capillary structural constraint in the radial axis within a larger single capillary draw, whilst retaining analytical simplicity, is proposed and shown to give improved fits with experimental data. For the cases we have experimented, an effective pressure is found approximately one-fifth less than that of the applied air pressure, based on the model and contributed from the interplay of applied pressure and the surface tension associated with the structure. In addition, the proposed model indicates that when birefringent SOFs with asymmetric holes having thinner walls were examined, the reduction in effective pressure is found to be about one-quarter of the pressure applied – it is larger than the uniform case and supports the explanation. Thus the model offers a simple and useful relation between the structure and draw pressure in fabricating structured optical fibre.

Taghikhah, F, Voinov, A, Shukla, N & Filatova, T 2021, 'Shifts in consumer behavior towards organic products: Theory-driven data analytics', Journal of Retailing and Consumer Services.
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Taghikhah, F, Voinov, A, Shukla, N, Filatova, T & Anufriev, M 2021, 'Integrated modeling of extended agro-food supply chains: A systems approach', EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, vol. 288, no. 3, pp. 852-868.
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Takalkar, MA, Thuseethan, S, Rajasegarar, S, Chaczko, Z, Xu, M & Yearwood, J 2021, 'LGAttNet: Automatic micro-expression detection using dual-stream local and global attentions', KNOWLEDGE-BASED SYSTEMS, vol. 212.
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Tam, VW, Butera, A, Le, KN & Li, W 2021, 'CO2 concrete and its practical value utilising living lab methodologies', Cleaner Engineering and Technology.
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Tan, S, Wang, Y & Vasilakos, AV 2021, 'Distributed Population Dynamics for Searching Generalized Nash Equilibria of Population Games With Graphical Strategy Interactions', IEEE Transactions on Systems, Man, and Cybernetics: Systems.
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Evolutionary games and population dynamics are finding increasing applications in design learning and control protocols for a variety of resource allocation problems. The implicit requirement for full communication has been the main limitation of the evolutionary game dynamic approach in engineering tasks with various information constraints. This article intends to build population games and dynamics with both static and dynamical graphical communication structures. To this end, we formulate a population game model with graphical strategy interactions and derive its corresponding population dynamics. In particular, we first introduce the concept of generalized Nash equilibria for population games with graphical strategy interactions, and establish the equivalence between the set of generalized Nash equilibria and the set of rest points of its distributed population dynamics. Furthermore, the conditions for convergence to generalized Nash equilibrium and particularly to Nash equilibrium are obtained for the distributed population dynamics with both static and dynamical graphical structures. These results provide a new approach to design distributed Nash equilibrium seeking algorithms for population games with both static and dynamical communication networks, and hence, expand the applicability of the population game dynamics in the design of learning and control protocols under distributed circumstances.

Tang, J, Liu, H & Yang, Y 2021, 'Balanced Dual-Band Superconducting Filter Using Stepped-Impedance Resonators With High Band-to-Band Isolation and Wide Stopband', IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, vol. 68, no. 1, pp. 131-135.
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Tang, Q, Yang, J, Liu, H, Guo, Z & Jia, W 2021, 'Single image deraining using Context Aggregation Recurrent Network', Journal of Visual Communication and Image Representation, vol. 75, pp. 103039-103039.
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Tang, Y, Wang, H, Zhan, X, Luo, X, Zhou, Y, Zhou, H, Yan, Q, Sui, Y & Keung, JW 2021, 'A Systematical Study on Application Performance Management Libraries for Apps', IEEE Transactions on Software Engineering.
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Tang, Z, Li, W, Peng, Q, Tam, VWY & Wang, K 2021, 'Study on the failure mechanism of geopolymeric recycled concrete using digital image correlation method', JOURNAL OF SUSTAINABLE CEMENT-BASED MATERIALS.
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Tapas, MJ, Vessalas, K, Thomas, P & Sirivivatnanon, V 2021, 'Influence of Limestone Mineral Addition in Cements on the Efficacy of SCMs in Mitigating Alkali-Silica Reaction Assessed by Accelerated Mortar Bar Test', Journal of Materials in Civil Engineering, vol. 33, no. 6, pp. 04021106-04021106.
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Tariq, MU, Babar, M, Jan, MA, Khattak, AS, Alshehri, MD & Yahya, A 2021, 'Security Requirement Management for Cloud-Assisted and Internet of Things-Enabled Smart City', CMC-COMPUTERS MATERIALS & CONTINUA, vol. 67, no. 1, pp. 625-639.
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Tawk, M & Indraratna, B 2021, 'Role of Rubber Crumbs on the Stress-Strain Response of a Coal Wash Matrix', Journal of Materials in Civil Engineering, vol. 33, no. 3.
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The recycling of waste materials (e.g., coal wash, steel slag, recycled rubber, and fly ash) is becoming increasingly popular in ground engineering projects. While the physical properties of these waste materials can be different from those of traditional aggregates, their strength properties are often comparable to traditional quarried materials. This paper examines the stress-strain properties of four mixtures of coal wash and rubber crumbs (CWRC) under monotonic, consolidated drained conditions considering four confining pressures (i.e., 10, 25, 50, and 75 kPa). A three-dimensional (3D) function is proposed to describe the effect of rubber inclusion on the peak friction angle and the shear strength of the mixture. Also, the role of rubber inclusion on the characteristic state, peak state, and critical state is addressed and quasi-critical state parameters are defined to incorporate the ongoing rubber deformation even after large axial strains (i.e., > 20%). Given that one component of the mixture is highly compressible (i.e., rubber), a new equation that accounts for the compression of rubber particles and the associated change in the volume of the solid phase is proposed to estimate the void ratio. Alternatively, a modified void ratio that is independent of the volume of rubber within the mixture is defined and a dilatancy model is proposed accordingly.

Tawk, M, Qi, Y, Indraratna, B, Rujikiatkamjorn, C & Heitor, A 2021, 'Behavior of a Mixture of Coal Wash and Rubber Crumbs under Cyclic Loading', Journal of Materials in Civil Engineering.
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Teng, J, Yan, H, Liang, S, Zhang, S & Sheng, D 2021, 'Generalising the Kozeny-Carman equation to frozen soils', JOURNAL OF HYDROLOGY, vol. 594.
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Thao, PH-L, Tran, TS, Bliuc, D, Pham, HM, Frost, SA, Center, JR, Eisman, JA & Nguyen, T 2021, 'Epidemiological transition to mortality and refracture following an initial fracture', ELIFE, vol. 10.
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Tran, H-T, Lin, C, Xuan-Thanh, B, Ngo, H-H, Cheruiyot, NK, Hoang, H-G & Vu, C-T 2021, 'Aerobic composting remediation of petroleum hydrocarbon-contaminated soil. Current and future perspectives', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 753.
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Tran, N, Ricafrente, A, To, J, Lund, M, Marques, TM, Gama-Carvalho, M, Cwiklinski, K, Dalton, JP & Donnelly, S 2021, 'Fasciola hepatica hijacks host macrophage miRNA machinery to modulate early innate immune responses', Scientific Reports.
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Tran, TTV, Vo, D-VN, Nguyen, ST, Luu, SDN, Mofijur, M & Vu, CM 2021, 'In situ sintered silver decorated 3D structure of cellulose scaffold for highly thermoconductive electromagnetic interference shielding epoxy nanocomposites', JOURNAL OF APPLIED POLYMER SCIENCE, vol. 138, no. 40.
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Trianni, A, Cagno, E, Dolšak, J & Hrovatin, N 2021, 'Implementing energy efficiency measures: do other production resources matter? A broad study in Slovenian manufacturing small and medium-sized enterprises', Journal of Cleaner Production, vol. 287.
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© 2020 Elsevier Ltd Literature has largely investigated barriers to energy efficiency in industrial firms. Recently, research is looking at the non-energy benefits accompanying the adoption of energy efficiency measures that may contribute to overcoming these barriers. In our study we take an innovative perspective by specifically exploring the relationships between energy efficiency measures and other production resources, being assessed by their importance and capability of firms to manage them efficiently. By analysing 10% of Slovenian small and medium-sized manufacturing firms, our exploratory findings show that decision-makers carefully look at the multiple effects (either positive or negative) energy efficiency measures may have on a number of other production resources, particularly on those closer to the production (shop floor). Additionally, companies seem to struggle in efficiently managing the most important production resources, thus suggesting that energy efficiency measures should be looked in close consideration to other resources, which represents a new barrier to energy efficiency not accounted by previous research. Further, we could not detect significant differences between clusters of small versus medium-sized firms and energy intensive versus non-energy intensive ones, differently from previous research that was emphasizing the larger perception of barriers in smaller and less energy intensive firms. Our findings may challenge the discussion over incentive schemes for energy efficiency measures by promoting those with the largest (positive) implications for other production resources.

Trung, N, Indraratna, B & Ferreira, F 2021, 'Influence of synthetic inclusions on the degradation and deformation of ballast under heavy-haul cyclic loading', INTERNATIONAL JOURNAL OF RAIL TRANSPORTATION.
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Tu, J, Chen, H, Wang, M & Gandomi, AH 2021, 'The Colony Predation Algorithm', Journal of Bionic Engineering.
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Tuan, HD, Nasir, AA, Savkin, AV, Poor, HV & Dutkiewicz, E 2021, 'MPC-based UAV navigation for simultaneous solar-energy harvesting and two-way communications', IEEE Journal on Selected Areas in Communications, pp. 1-1.
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Uddin, MB, Chow, CM, Ling, SH & Su, SW 2021, 'A novel algorithm for automatic diagnosis of sleep apnea from airflow and oximetry signals', PHYSIOLOGICAL MEASUREMENT, vol. 42, no. 1.
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Ulapane, N, Thiyagarajan, K, Miro, JV & Kodagoda, S 2021, 'Surface Representation of Pulsed Eddy Current Sensor Signals for Improved Ferromagnetic Material Thickness Quantification', IEEE Sensors Journal, vol. 21, no. 4, pp. 5413-5422.
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Valdés-Mora, F, Salomon, R, Gloss, BS, Law, AMK, Venhuizen, J, Castillo, L, Murphy, KJ, Magenau, A, Papanicolaou, M, Rodriguez de la Fuente, L, Roden, DL, Colino-Sanguino, Y, Kikhtyak, Z, Farbehi, N, Conway, JRW, Sikta, N, Oakes, SR, Cox, TR, O'Donoghue, SI, Timpson, P, Ormandy, CJ & Gallego-Ortega, D 2021, 'Single-cell transcriptomics reveals involution mimicry during the specification of the basal breast cancer subtype', Cell Reports.
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van den Hoven, E, Orth, D & Zijlema, A 2021, 'Possessions and memories.', Current opinion in psychology, vol. 39, pp. 94-99.
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People often acquire souvenirs and photographs to facilitate remembering, but possessions and memories can relate to each other in a variety of ways. This review paper presents four different connection types found between meaningful things in our everyday lives and our personal memories. Each connection type either focuses on possessions or memories and the connection between the two is either active or lost. These perspectives will be detailed through examples of studies and design cases from different fields and research areas. More studies have been found focusing on existing connections between possessions and memories, such as in human-computer interaction, design, material culture, psychology and marketing, than those lost, which were specifically focused around ageing, forgetting, heirlooms, identity and hoarding behaviour. Our review of connections between possessions and memories accumulate to suggest the attachment people ascribe to certain possessions is mirrored by the ability of objects to fulfil people's desire to preserve, embody, showcase and recollect certain memories.

Van Huynh, N, Nguyen, DN, Hoang, DT & Dutkiewicz, E 2021, 'Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach', IEEE Transactions on Communications.
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Varjani, S, Rakholiya, P, Shindhal, T, Shah, AV & Ngo, HH 2021, 'Trends in dye industry effluent treatment and recovery of value added products', Journal of Water Process Engineering, vol. 39.
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© 2020 Elsevier Ltd Increased population and industrialization generate a large number of organic pollutants that create problems on the planet earth. The level of freshwater is reducing which has pushed the society to reuse/recycle wastewater. Eco-friendly and economically sound treatment of industrial wastewater has attracted global attention and hence is a thrust area of research. Organic compounds rich wastewater can be used to generate bioenergy and value-added products from the resource recovery point of view. Wastewater treatment(s) can be used to trap energy from industrial effluents in form of biofuel, bioenergy and biogas. Recovered products can be used in various ways such as recovered nutrients for (bio)fertilizer production and algal biomass for bioplastic production. Microbial electrochemical technology is a promising approach for resource recovery. This review article aims to present and discuss trends and scientific developments about recovery of value-added products from dye industry effluent. It also provides state-of-art technical information about technologies for remediation of pollutants from dye industry effluent with emphasis on nanotechnological approaches and microbial electrochemical technologies (METs). It narrates literature on classification and properties of dyes, effects of dye pollutants on environment and human health and factors affecting degradation of dyes. Generation of bioenergy and recovery of valuables from dye industrial wastewater along with challenges and perspectives of this research area have been covered.

Vazquez, S, Zafra, E, Aguilera, RP, Geyer, T, Leon, JI & Garcia Franquelo, L 2021, 'Prediction Model with Harmonic Load Current Components for FCS-MPC of an Uninterruptible Power Supply', IEEE Transactions on Power Electronics, pp. 1-1.
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Vu, HP, Nguyen, LN, Vu, MT, Labeeuw, L, Emmerton, B, Commault, AS, Ralph, PJ, Mahlia, TM & Nghiem, LD 2021, 'Harvesting Porphyridium purpureum using polyacrylamide polymers and alkaline bases and their impact on biomass quality', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 755.
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Vu, MT, Nguyen, LN, Hasan Johir, MA, Zhang, X, Nghiem, LD & Elimelech, M 2021, 'Biogas sparging to control fouling and enhance resource recovery from anaerobically digested sludge centrate by forward osmosis', Journal of Membrane Science, vol. 625.
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© 2021 This study demonstrates the proof-of-concept of biogas sparging to control membrane fouling during sludge centrate pre-concentration by forward osmosis (FO). Sludge centrate sparging by biogas reduced membrane fouling (measured by water flux decline) and filtration time by two and eight times, respectively, compared to FO operation without biogas sparging at the same water recovery of 60%. In addition, the water flux was almost fully recovered by physical flushing when biogas sparging was applied. Biogas sparging also resulted in a significant improvement in the enrichment of organic, ammonia, and phosphate to close to the theoretical value based on mass balance calculation. In other words, organic matter and nutrients were retained in the bulk solution for subsequent recovery. Fouling mitigation and nutrient enrichment improvement by biogas sparging could be attributed to carbonate buffering to maintain a near neutral pH for preventing calcium phosphate precipitation on the membrane surface and ammonia volatilisation.

Vu, MT, Nguyen, LN, Johir, MAH, Ngo, HH, Skidmore, C, Fontana, A, Galway, B, Bustamante, H & Nghiem, LD 2021, 'Phosphorus removal from aqueous solution by steel making slag - Mechanisms and performance optimisation', JOURNAL OF CLEANER PRODUCTION, vol. 284.
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Waheed, N, He, X, Ikram, M, Usman, M & Hashmi, SS 2021, 'Security and Privacy in IoT Using Machine Learning and Blockchain: Threats and Countermeasures', ACM COMPUTING SURVEYS, vol. 53, no. 6.
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Wang, B, Lau, Y-S, Huang, Y, Organ, B, Chuang, H-C, Ho, SSH, Qu, L, Lee, S-C & Ho, K-F 2021, 'Chemical and toxicological characterization of particulate emissions from diesel vehicles', JOURNAL OF HAZARDOUS MATERIALS, vol. 405.
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Wang, B, Liu, W, Li, JJ, Chai, S, Xing, D, Yu, H, Zhang, Y, Yan, W, Xu, Z, Zhao, B, Du, Y & Jiang, Q 2021, 'A low dose cell therapy system for treating osteoarthritis: In vivo study and in vitro mechanistic investigations', Bioactive Materials.
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Wang, C, Huang, Y, Ding, W & Cao, Z 2021, 'Attribute reduction with fuzzy rough self-information measures', Information Sciences, vol. 549, pp. 68-86.
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The fuzzy rough set is one of the most effective methods for dealing with the fuzziness and uncertainty of data. However, in most cases this model only considers the information provided by the lower approximation of a decision when it is used to attribute reduction. In a realistic environment, the uncertainty of information is related to lower approximation as well as upper approximation. In this study, we construct four kinds of uncertainty measures by combining fuzzy rough approximations with the concept of self-information. These uncertainty measures can be employed to evaluate the classification ability of attribute subsets. The relationships between these measures are discussed in detail. It is proven that the fourth measure, called relative decision self-information, is better for attribute reduction than the other measures because it considers both the lower and upper approximations of a fuzzy decision. The proposed measures are generalizations of classical measures based on fuzzy rough sets. Finally, we have designed a greedy algorithm for attribute reduction. We validate the effectiveness of the proposed method by comparing the experimental results for efficiency and accuracy with those of three other algorithms using fundamental data.

Wang, C, Ji, J, Miao, Z & Zhou, J 2021, 'Synchronization control for networked mobile robot systems based on Udwadia-Kalaba approach', NONLINEAR DYNAMICS.
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Wang, C, Park, MJ, Seo, DH & Shon, HK 2021, 'Inkjet printing of graphene oxide and dopamine on nanofiltration membranes for improved anti-fouling properties and chlorine resistance', Separation and Purification Technology, vol. 254.
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© 2020 Elsevier B.V. Anti-fouling properties and chlorine resistance nature are highly desirable features for membranes used in nanofiltration (NF). Conventional polymeric NF membranes often suffer from fouling issues and poor stability under chlorine based chemicals. Therefore, in this work, a thin film composite (TFC) NF membrane was modified by coating a binding agent polydopamine (PDA) and graphene oxide (GO) using a simple and scalable inkjet printing process where the GO deposition was controlled by the number of printing cycles. The NF test results revealed the PDA-GO printed NF membranes exhibited higher salt rejection while achieving slightly lower permeate flux compared to control membrane. Moreover, the PDA-GO printed membrane exhibited enhanced anti-fouling properties where only 20% of permeate flux reduction was observed while the control membrane displayed significant reduction in flux up to 48%. Furthermore, chlorine resistance of the PDA-GO printed membrane showed reduction in salt rejection was effectively suppressed compared to the control membrane for the chlorination time of 1 and 3 h. Our work demonstrates an effective strategy to mitigate fouling and chlorine stability issues in NF membranes as well as validate inkjet printing as a versatile technique for the formation of advanced nanomaterial based membranes with high controllability of membrane properties.

Wang, D, Yi, N, Wang, Y, Yang, J, Fu, Q, Liu, X, Yang, Q, Cai, Z, Ye, J, Liu, Y, Wang, Q & Ni, BJ 2021, 'Triclosan degradation in sludge anaerobic fermentation and its impact on hydrogen production', Chemical Engineering Journal, vol. 421.
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Triclosan (TCS), a widely used antibacterial agent, was accumulated at significant levels in waste activated sludge (WAS). To date, however, the interaction between TCS and sludge anaerobic fermentation was rarely reported. Hence, this work aimed to deeply understanding the degradation of TCS in sludge anaerobic fermentation and its impact on hydrogen production. Experimental results showed that ~ 45% of TCS was degraded in long-term anaerobic fermentation, with 2,4-dichlorophenol as its main intermediate. Based on the information from high performance liquid chromatography–mass spectrometry analysis, three pathways i.e., dechlorination, hydroxylation, and cleavage of ether bonds, were proposed for TCS degradation. It was found that the maximum hydrogen yield decreased from 18.6 to 12.8 mL/g VSS with the increase of TCS from 12 to 487 mg/kg TSS. One possible reason for the decreased hydrogen yield was that a part of hydrogen generated might serve as electron donors for TCS dechlorination. Besides, the presence of TCS significantly suppressed acidogenesis (an important step responsible for hydrogen generation). This inhibition to acidogenesis is likely due to that the high-affinity functional groups of TCS such as hydroxyl groups could bind to the active sites of acetate kinase (AK, a key enzyme in acidogenesis), which reduced the active sites available for original fermentation substrates. Microbial analysis revealed that TCS increased the relative abundances of potential contaminant decomposers such as Guggeheimella but inhibited the populations of hydrogen producers such as Proteiniborus, which was consistent with the results obtained by chemical analyses.

Wang, D, Zhang, Y, Zhu, Y, Wu, C, Zhou, Y & Han, Q 2021, 'Mechanical performance of sustainable modular prefabricated composite shear panels under cyclic loading', Journal of Constructional Steel Research, vol. 179.
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This study focuses on the seismic pxerformance of a new sustainable modular prefabrication composite shear panel (MPCSP) system. 1:3 scaled MPCSP specimens with vertial, horizontal and cross assembly ways of the modulars are investigated experimentally under quasi-static loading. Finite element(FE) model is validated with the experimental data and then numerical analyses of 44 computional cases with different parameters of the vertical assembled MPCSP system are conducted for systematic discussion on mechanical characteristics of the investigated models. It shows that the new sustainable prefabrication system is of excellent seismic performance, especially for the vertical assembled MPCSP system(MPCSP-V). Numerical analysis and tests coincide reasonably well with each other. The best seismic performance is achieved at an aspect ratio of 1 for the MPCSP-V system. Design parameters, such as height-thickness ratio of steel plate and bolt distance, have significant influence on the mechanical performance of the system. The effect of the distance of bolts, the spacing between concrete cover plates (RCCPs) and connection steel plates (CSPs), and the thickness of CSP are more significant on the mechanical properties of MPCSP system, while the effect of the bolt layout and the thickness of RCCP are shown to be less significant. The proposed formulas of three-line constitutive law and shearing capacity can well capture key performance index of shear resistance and stiffness at yield, peak and ultimate points.

Wang, F, Zhu, M, Wang, M, Khosravi, MR, Ni, Q, Yu, S & Qi, L 2021, '6g-enabled short-term forecasting for large-scale traffic flow in massive iot based on time-aware locality-sensitive hashing', IEEE Internet of Things Journal, vol. 8, no. 7, pp. 5321-5331.
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With the advent of the Internet of Things (IoT) and the increasing popularity of the intelligent transportation system, a large number of sensing devices are installed on the road for monitoring traffic dynamics in real time. These sensors can collect streaming traffic data distributed across different traffic sites, which constitute the main source of big traffic data. Analyzing and mining such big traffic data in massive IoT can help traffic administrations to make scientific and reasonable traffic scheduling decisions, so as to avoid prospective traffic congestions in the future. However, the above traffic decision making often requires frequent and massive data transmissions between distributed sensors and centralized cloud computing centers, which calls for lightweight data integrations and accurate data analyses based on large-scale traffic data. In view of this challenge, a big data-driven and nonparametric model aided by 6G is proposed in this article to extract similar traffic patterns over time for accurate and efficient short-term traffic flow prediction in massive IoT, which is mainly based on time-aware locality-sensitive hashing (LSH). We design a wide range of experiments based on a real-world big traffic data set to validate the feasibility of our proposal. Experimental reports demonstrate that the prediction accuracy and efficiency of our proposal are increased by 32.6% and 97.3%, respectively, compared with the other two competitive approaches.

Wang, G, Zhou, T, Choi, K-S & Lu, J 2021, 'A Deep-Ensemble-Level-Based Interpretable Takagi-Sugeno-Kang Fuzzy Classifier for Imbalanced Data.', IEEE Transactions on Cybernetics.
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Existing research reveals that the misclassification rate for imbalanced data depends heavily on the problematic areas due to the existence of small disjoints, class overlap, borderline, and rare data samples. In this study, by stacking zero-order Takagi-Sugeno-Kang (TSK) fuzzy subclassifiers on the minority class and its problematic areas in the deep ensemble, a novel deep-ensemble-level-based TSK fuzzy classifier (IDE-TSK-FC) for imbalanced data classification tasks is presented to achieve both promising classification performance and high interpretability of zero-order TSK fuzzy classifiers. Simultaneously, according to the stacked generalization principle, the proposed classifier lifts up oversampling from the data level to the deep ensemble level with a guarantee of enhanced generalization capability for class imbalance learning. In the structure of IDE-TSK-FC, the first interpretable zero-order TSK fuzzy subclassifier is built on the original training dataset. After that, several successive zero-order TSK fuzzy subclassifiers are stacked layer by layer on the newly identified problematic areas from the original training dataset plus the corresponding interpretable predictions obtained by the averaging strategy on all previous layers. IDE-TSK-FC simply takes the classical K-nearest neighboring algorithm at each layer to identify its problematic area that consists of the minority samples and its surrounding K majority neighbors. After randomly neglecting certain input features and randomly selecting the five Gaussian membership functions for all the chosen input features and the augmented feature in the premise of each fuzzy rule, each subclassifier can be quickly obtained by using the least learning machine to determine the consequent part of each fuzzy rule. The experimental results on both the public datasets and a real-world healthcare dataset demonstrate IDE-TSK-FC's superiority in class imbalanced learning.

Wang, H, Chang, T, Li, Y, Li, S, Zhang, G & Wang, J 2021, 'Field-frequency-dependent nonlinear rheology behavior of magnetorheological grease under large amplitude oscillatory shear', Frontiers in Materials.

Wang, H, Chang, T, Li, Y, Li, S, Zhang, G, Wang, J & Li, J 2021, 'Characterization of nonlinear viscoelasticity of magnetorheological grease under large oscillatory shear by using Fourier transform-Chebyshev analysis', Journal of Intelligent Material Systems and Structures, vol. 32, no. 6, pp. 614-631.
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Magnetorheological (MR) grease, which is a type of magneto-sensitive smart material, exhibits complex viscoelastic behavior under different magnetic fields. Storage and loss moduli from oscillatory test are commonly used to characterize such field-dependent viscoelastic behavior. However, they are not able to represent the higher harmonic nonlinearity appeared in oscillatory responses under large amplitude oscillatory (LAOS) shear. In this paper, an investigation is conducted on the characterization of the filed-dependent nonlinearity of MR grease under LAOS by utilizing of Fourier transform (FT)-Chebyshev analysis. To capture the higher harmonic nonlinearity for the modeling and analysis, a comprehensive test program has been conducted. Firstly, MR grease with 70% weight percentage of carbonyl iron particles (MRG-70) is prepared and the stress response of MRG-70 under the oscillatory shear from small to large strain amplitudes with different magnetic field are measured. Then the higher harmonics in stress response signal are used to detect the intercycle nonlinearity of MRG-70 and compare with the traditional analysis method of dynamic modulus. Further, the elastic and viscous measures from FT-Chebyshev analysis are obtained to capture the inter-/intracycle nonlinear behaviors, that is, strain stiffening/softening, shear thickening/thinning, of MRG-70 under LAOS. It is found that, in the presence of magnetic fields, the onset of the intercycle nonlinearities of MRG-70 is originated from shear thickening of MR grease. With the further increase of the shear strain, MRG-70 exhibits the nonlinearities with different combinations of strain stiffening/softening and shear thickening/thinning.

Wang, H, Lian, D, Zhang, Y, Qin, L & Lin, X 2021, 'GoGNN: Graph of graphs neural network for predicting structured entity interactions', IJCAI : proceedings of the conference / sponsored by the International Joint Conferences on Artificial Intelligence, vol. 2021-January, pp. 1317-1323.
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© 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved. Entity interaction prediction is essential in many important applications such as chemistry, biology, material science, and medical science. The problem becomes quite challenging when each entity is represented by a complex structure, namely structured entity, because two types of graphs are involved: local graphs for structured entities and a global graph to capture the interactions between structured entities. We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model. In this paper, we propose a Graph of Graphs Neural Network, namely GoGNN, which extracts the features in both structured entity graphs and the entity interaction graph in a hierarchical way. We also propose the dual-attention mechanism that enables the model to preserve the neighbor importance in both levels of graphs. Extensive experiments on real-world datasets show that GoGNN outperforms the state-of-the-art methods on two representative structured entity interaction prediction tasks: chemical-chemical interaction prediction and drug-drug interaction prediction. Our code is available at Github.

Wang, H, Roche, CD & Gentile, C 2021, 'Reply to Yurekli et al.', European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.
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Wang, H, Wei, G, Wen, S & Huang, T 2021, 'Impulsive disturbance on stability analysis of delayed quaternion-valued neural networks', Applied Mathematics and Computation, vol. 390.
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In reality, abrupt uncertainty phenomenon may disturb or even break the system stability. Under these circumstances, it is worthy of studying the stability criteria of impulsive disturbed systems. In this article, an impulsive disturbed neural network model with delays is constructed in quaternion space, and the exponential stability conditions of the delayed system are derived by utilizing generalized norms. Firstly, a general impulsive disturbed quaternion-valued delayed neural network (IQVDNN) is given by combining impulsive differential system with quaternion-valued neural networks. Since quaternion multiplication is noncommutative, the IQVDNN system is decomposed into real-valued impulsive delayed neural networks. Then, the generalized ∞-norm and 1-norm are used to research its stability, respectively. By constructing special Lyapunov-type functional, several exponential stability sufficient criteria are obtained, which can guarantee that the stability can't be destroyed by abrupt impulsive perturbations. Finally, two numerical examples and their simulation figures are given to show the effectiveness of the obtained conclusions.

Wang, J, Li, H, Wang, Y & Lu, H 2021, 'A hesitant fuzzy wind speed forecasting system with novel defuzzification method and multi-objective optimization algorithm', EXPERT SYSTEMS WITH APPLICATIONS, vol. 168.
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Wang, L, He, R, Wang, H, Xia, P, Li, Y, Wu, L, Zhou, Y, Luo, X, Sui, Y, Guo, Y & Xu, G 2021, 'Beyond the virus: a first look at coronavirus-themed Android malware', Empirical Software Engineering.
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Wang, L, Yang, Y, Deng, L, Hong, W, Zhang, C & Li, S 2021, 'Vanadium dioxide embedded frequency reconfigurable metasurface for multi-dimensional multiplexing of terahertz communication', JOURNAL OF PHYSICS D-APPLIED PHYSICS, vol. 54, no. 25.
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Wang, L, Yu, Z, Xiong, F, Yang, D, Pan, S & Yan, Z 2021, 'Influence Spread in Geo-Social Networks: A Multiobjective Optimization Perspective.', IEEE Transactions on Cybernetics.
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As an emerging social dynamic system, geo-social network can be used to facilitate viral marketing through the wide spread of targeted advertising. However, unlike traditional influence spread problem, the heterogeneous spatial distribution has to incorporated into geo-social network environment. Moreover, from the perspective of business managers, it is indispensable to balance the tradeoff between the objective of influence spread maximization and objective of promotion cost minimization. Therefore, these two goals need to be seamlessly combined and optimized jointly. In this paper, considering the requirements of real-world applications, we develop a multiobjective optimization-based influence spread framework for geo-social networks, revealing the full view of Pareto-optimal solutions for decision makers. Based on the reverse influence sampling (RIS) model, we propose a similarity matching-based RIS sampling method to accommodate diverse users, and then transform our original problem into a weighted coverage problem. Subsequently, to solve this problem, we propose a greedy-based incrementally approximation approach and heuristic-based particle swarm optimization approach. Extensive experiments on two real-world geo-social networks clearly validate the effectiveness and efficiency of our proposed approaches.

Wang, L, Zhang, J, Sun, Y, Zhang, T, Wang, L, Wang, J, Liang, Y, Hao, M & Fu, Q 2021, 'Green preparation and enhanced gas barrier property of rubber nanocomposite film based on graphene oxide-induced chemical crosslinking', POLYMER, vol. 225.
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Wang, L, Zhang, T, Ye, L, Li, JJ & Su, S 2021, 'An Efficient Calibration Method for Triaxial Gyroscope', IEEE Sensors Journal, pp. 1-1.
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Wang, LJ, Wu, CQ, Fan, LF & Wang, M 2021, 'Investigation of wave reflection at the joint with different wave impedances on two sides', Waves in Random and Complex Media.
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When the stress wave propagates through rock mass with different wave impedances on two sides. It shows different reflection tendencies compared with the traditional study of wave propagation. A characteristic line method combined with the displacement discontinuity model was proposed to study the wave reflection through such rock mass. The effects of incident frequency, joint stiffness and wave impedance ratio on the reflection coefficient were discussed. The results show that the reflection coefficient increases as the wave impedance ratio increases. The traditional study of wave reflection can be regarded as a special case of the present study when the wave impedance ratio equals 1.0. Moreover, the difference of reflection coefficients decreases as the incident frequency increases, while increases as the joint stiffness and wave impedance ratio increase when the wave impedance ratio is larger than 1.0. When the wave impedance ratio is smaller than 1.0, the difference of reflection coefficients increases firstly and then decreases with the increase of incident frequency and joint stiffness, while decreases as the wave impedance ratio increases. For the cases of large incident frequency or small joint stiffness, the effects of wave impedance ratio can be neglected for the approximated prediction of wave reflection.

Wang, L-K, Chen, X, Wei, W, Xu, Q, Sun, J, Mannina, G, Song, L & Ni, B-J 2021, 'Biological Reduction of Nitric Oxide for Efficient Recovery of Nitrous Oxide as an Energy Source.', Environmental science & technology.
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Chemical absorption-biological reduction based on Fe(II)EDTA is a promising technology to remove nitric oxide (NO) from flue gases. However, limited effort has been made to enable direct energy recovery from NO through production of nitrous oxide (N₂O) as a potential renewable energy rather than greenhouse gas. In this work, the enhanced energy recovery in the form of N₂O via biological NO reduction was investigated by conducting short-term and long-term experiments at different Fe(II)EDTA-NO and organic carbon levels. The results showed both NO reductase and N₂O reductase were inhibited at Fe(II)EDTA-NO concentration up to 20 mM, with the latter being inhibited more significantly, thus facilitating N₂O accumulation. Furthermore, N₂O accumulation was enhanced under carbon-limiting conditions because of electron competition during short-term experiments. Up to 47.5% of NO-N could be converted to gaseous N₂O-N, representing efficient N₂O recovery. Fe(II)EDTA-NO reduced microbial diversity and altered the community structure toward Fe(II)EDTA-NO-reducing bacteria-dominated culture during long-term experiments. The most abundant bacterial genus Pseudomonas, which was able to resist the toxicity of Fe(II)EDTA-NO, was significantly enriched, with its relative abundance increased from 1.0 to 70.3%, suggesting Pseudomonas could be the typical microbe for the energy recovery technology in NO-based denitrification.

Wang, N, Zhang, J-N, Liu, Z-Y, Ding, C, Sui, G-R, Jia, H-Z & Gao, X-M 2021, 'An Enhanced Thermoelectric Collaborative Cooling System With Thermoelectric Generator Serving as a Supplementary Power Source', IEEE Transactions on Electron Devices, pp. 1-8.
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Wang, P, Li, L, Wang, R, Xu, G & Zhang, J 2021, 'Socially-driven multi-interaction attentive group representation learning for group recommendation', Pattern Recognition Letters, vol. 145, pp. 74-80.
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Wang, P, Yang, M, Mamaril, K, Shi, X, Cheng, B & Zhao, D 2021, 'Explaining the slow progress of coal phase-out: The case of Guangdong-Hong Kong-Macao Greater Bay Region', Energy Policy, vol. 155.
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Prompted by the urgency of climate change, this paper analyses the impediments for coal phase-out, by using the Greater Bay Region in China as a case study. Rather than factors specific to coal production, transport and consumption (e.g., subsidies, and vested interests), as suggested by existing literature, the analysis of this paper demonstrates that coal phase-out in the region has encountered a range of market (e.g., high gas price), infrastructure (such as, inadequate network), and regulatory (e.g., prolonged project approval) impediments; these impediments have hindered the effective deployment of alternative energy sources, raising concern about coal phase-out and its crippling impacts on the security of energy supply. Redressing these impediments is therefore a key priority for promoting a smooth coal phase-out in the region. This requires a mix of policies addressing two dimensions: 1) those aimed at squeezing out coal from the energy-mix to create room for alternative low-carbon energy sources; and 2) those aimed at supporting the uptake of these sources. Implementing these policies is however a challenging task as it relies on close-centre-local, and inter- and intra-regional cooperation, especially in a resource-poor region with a disperse energy endowment. Achieving this is difficult because the energy policy process of the country has long been characterised by fragmented authority and territorial administrative divisions with a proclivity for local governments to work in isolation from each other.

Wang, Q, Liu, J & Ying, M 2021, 'Equivalence checking of quantum finite-state machines', Journal of Computer and System Sciences, vol. 116, pp. 1-21.
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© 2020 Elsevier Inc. In this paper, we introduce the model of quantum Mealy machines and study the equivalence checking and minimisation problems of them. Two efficient algorithms are developed for checking equivalence of two states in the same machine and for checking equivalence of two machines. As an application, they are used in equivalence checking of quantum circuits. Moreover, the minimisation problem is proved to be in PSPACE.

Wang, Q, Liu, J & Ying, M 2021, 'Equivalence checking of quantum finite-state machines.', J. Comput. Syst. Sci., vol. 116, pp. 1-21.
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Wang, S, Cao, Y, Guo, Z, Yan, Z, Wen, S & Huang, T 2021, 'Periodic Event-Triggered Synchronization of Multiple Memristive Neural Networks With Switching Topologies and Parameter Mismatch', IEEE TRANSACTIONS ON CYBERNETICS, vol. 51, no. 1, pp. 427-437.
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Wang, S, Jin, X, Mao, S, Vasilakos, AV & Tang, Y 2021, 'Model-Free Event-Triggered Optimal Consensus Control of Multiple Euler-Lagrange Systems via Reinforcement Learning', IEEE Transactions on Network Science and Engineering.
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IEEE This paper develops a model-free approach to solve the event-triggered optimal consensus of multiple Euler-Lagrange systems (MELSs) via reinforcement learning (RL). Firstly, an augmented system is constructed by defining a pre-compensator to circumvent the dependence on system dynamics. Secondly, the Hamilton-Jacobi-Bellman (HJB) equations are applied to the deduction of the model-free event-triggered optimal controller. Thirdly, we present a policy iteration (PI) algorithm derived from reinforcement learning (RL), which converges to the optimal policy. Then, the value function of each agent is represented through a neural network to realize the PI algorithm. Moreover, the gradient descent method is used to update the neural network only at a series of discrete event-triggered instants. The specific form of the event-triggered condition is then proposed, and it is guaranteed that the closed-loop augmented system under the event-triggered mechanism is uniformly ultimately bounded (UUB). Meanwhile, the Zeno behavior is also eliminated. Finally, the validity of this approach is verified by a simulation example.

Wang, S, Tao, J, Qiu, X & Burnett, IS 2021, 'Broadband noise insulation of windows using coiled-up silencers consisting of coupled tubes', Scientific Reports.
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Wang, S, Wang, J, Lu, H & Zhao, W 2021, 'A novel combined model for wind speed prediction – Combination of linear model, shallow neural networks, and deep learning approaches', Energy, vol. 234.
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Accurate wind speed forecasting is increasingly essential for improving the operating efficiency of electric power systems. Numerous models have been proposed to obtain the accurate and stable wind speed forecasting results. However, previous proposed models are limited by single predictive model or cannot deal with complex nonlinear data characteristic, which resulted in poor and unstable prediction results. In this paper, a novel forecasting model that combines noise processing, statistical approaches, deep learning frameworks and multi-objective optimization algorithm is proposed. Multi-objective optimization algorithms can take advantage of the merits of benchmark prediction models to address nonlinear characteristics of wind speed series. The 10-min real wind speed data from three Sites in China are adopted for verifying the effectiveness of this proposed model. The experimental results of multi-step prediction show that the model achieves MAPE1-step = 2.2109%, MAPE2-step = 3.0309%, and MAPE3-step = 4.2536% at Site 1; MAPE1-step = 2.4586%, MAPE2-step = 3.2034%, and MAPE3-step = 4.6843% at Site 2; MAPE1-step = 2.3180%, MAPE2-step = 3.0846%, and MAPE3-step = 4.4193% at Site 3. Therefore, the forecasting performance of this model is excellent, and it is beneficial to the dispatching and planning of power grid.

Wang, W, Zhang, Y, Sui, Y, Wan, Y, Zhao, Z, Wu, J, Yu, P & Xu, G 2021, 'Reinforcement-Learning-Guided Source Code Summarization via Hierarchical Attention', IEEE Transactions on Software Engineering, pp. 1-1.
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Wang, X, Cheng, E & Burnett, IS 2021, 'MSBOTS: a multiple small biological organism tracking system robust against non-ideal detection and segmentation conditions', PeerJ.
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Wang, X, Fei, Z, Andrew Zhang, J, Huang, J & Yuan, J 2021, 'Constrained Utility Maximization in Dual-Functional Radar-Communication Multi-UAV Networks', IEEE Transactions on Communications.
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IEEE In this paper, we investigate the network utility maximization problem in a dual-functional radar-communication multi-unmanned aerial vehicle (multi-UAV) network where multiple UAVs serve a group of communication users and cooperatively sense the target simultaneously. To balance the communication and sensing performance, we formulate a joint UAV location, user association, and UAV transmission power control problem to maximize the total network utility under the constraint of localization accuracy. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into three sub-problems, i.e., UAV location optimization, user association and transmission power control. Three mechanisms are then introduced to solve the three sub-problems based on spectral clustering, coalition game, and successive convex approximation, respectively. The spectral clustering result provides an initial solution for user association. Based on the three mechanisms, an overall algorithm is proposed to iteratively solve the whole problem. We demonstrate that the proposed algorithm improves the minimum user data rate significantly, as well as the fairness of the network. Moreover, the proposed algorithm increases the network utility with a lower power consumption and similar localization accuracy, compared to conventional techniques.

Wang, X, Qin, P-Y & Jin, R 2021, 'Low RCS Transmitarray Employing Phase Controllable Absorptive Frequency-Selective Transmission Elements', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 69, no. 4, pp. 2398-2403.
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Wang, X, Wang, D, Zhang, Y & Wu, C 2021, 'Research on the impact effect of AP1000 shield building subjected to large commercial aircraft', Nuclear Engineering and Technology.
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This study addresses the numerical simulation of the shield building of an AP1000 nuclear power plant (NPP) subjected to a large commercial aircraft impact. First, a simplified finite element model (F.E. model) of the large commercial Boeing 737 MAX 8 aircraft is established. The F.E. model of the AP1000 shield building is constructed, which is a reasonably simplified reinforced concrete structure. The effectiveness of both F.E. models is verified by the classical Riera method and the impact test of a 1/7.5 scaled GE-J79 engine model. Then, based on the verified F.E. models, the entire impact process of the aircraft on the shield building is simulated by the missile-target interaction method (coupled method) and by the ANSYS/LS-DYNA software, which is at different initial impact velocities and impact heights. Finally, the laws and characteristics of the aircraft impact force, residual velocity, kinetic energy, concrete damage, axial reinforcement stress, and perforated size are analyzed in detail. The results show that all of them increase with the addition to the initial impact velocity. The first four are not very sensitive to the impact height. The engine impact mainly contributes to the peak impact force, and the peak impact force is six times higher than that in the first stage. With increasing initial impact velocity, the maximum aircraft impact force rises linearly. The range of the tension and pressure of the reinforcement axial stress changes with the impact height. The perforated size increases with increasing impact height. The radial perforation area is almost insensitive to the initial impact velocity and impact height. The research of this study can provide help for engineers in designing AP1000 shield buildings.

Wang, X, Zha, X, Ni, W, Liu, RP, Guo, YJ, Niu, X & Zheng, K 2021, 'Game Theoretic Suppression of Forged Messages in Online Social Networks', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-11.
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Wang, X, Zhu, L, Wu, Y & Yang, Y 2021, 'Symbiotic Attention for Egocentric Action Recognition with Object-centric Alignment.', IEEE transactions on pattern analysis and machine intelligence, vol. PP.
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In this paper, we propose to tackle egocentric action recognition by suppressing background distractors and enhancing action-relevant interactions. The existing approaches usually utilize two independent branches to recognize egocentric actions, i.e., a verb branch and a noun branch. However, the mechanism to suppress distracting objects and exploit local human-object correlations is missing. To this end, we introduce two extra sources of information, i.e., the candidate objects' spatial location and their discriminative features, to enable concentration on the occurring interactions. We design a Symbiotic Attention withObject-centric featureAlignmentframework (SAOA) to provide meticulous reasoning between the actor and the environment. First, we introduce an object-centric feature alignment method to inject the local object features to the verb branch and noun branch. Second, we propose a symbiotic attention mechanism to encourage the mutual interaction between the two branches and select the most action-relevant candidates for classification. The framework benefits from the communication among the verb branch, the noun branch, and the local object information. Experiments based on different backbones and modalities demonstrate the effectiveness of our method. Notably, our framework achieves the state-of-the-art on the largest egocentric video dataset.

Wang, Y, Wei, S, Cavillon, M, Sapaly, B, Poumellec, B, Peng, G-D, Canning, J & Lancry, M 2021, 'Thermal Stability of Type II Modifications Inscribed by Femtosecond Laser in a Fiber Drawn from a 3D Printed Preform', Applied Sciences.
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Wang, Y, Wei, W, Dai, X & Ni, B-J 2021, 'Coconut shell ash enhances short-chain fatty acids production from anaerobic algae fermentation', BIORESOURCE TECHNOLOGY, vol. 338.
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Wang, Y, Wei, Y, Qian, X, Zhu, L & Yang, Y 2021, 'Sketch-Guided Scenery Image Outpainting', IEEE Transactions on Image Processing.
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Wang, YK, Ma, XY, Tang, L, Wang, XC, Zhang, S, Ngo, HH & Yu, M 2021, 'Capability of shallow open-water unit for emerging contaminants attenuation and ecological safety improvement in a treated effluent polishing process', Journal of Water Process Engineering, vol. 40.
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Nature-based solutions of polishing treated effluent are drawing increased attention because of their potential to mitigate ecological risks, and the shallow open water unit (SOWU) process is one of the most promising options. In this study, a pilot experiment was conducted to assess the efficiency of SOWU for effluent quality polishing in a municipal wastewater treatment plant targeting 33 residual emerging contaminants (ECs) that were categorized into six groups, namely pharmaceuticals, sunscreens, bactericides, pesticides, flame retardants and phenols. In the 14-day monitoring period, each of the ECs decayed following a first order reaction, and about 61 % total ECs removal was achieved, indicating the remarkable photolysis effect of the SOWU under natural conditions. The significant removal of ECs, especially the very high removals of pharmaceuticals (76 %) and sunscreens (77 %), was associated with the attenuation of both the acute luminescent bacterial toxicity and genotoxicity. From the linear relationship between the acute toxicity and chromophoric dissolved organic matter and that between genotoxicity and UV absorbance at 254 nm, it was likely that the attenuation of biotoxicities resulted from the decay of chromophoric and aromatic compounds. By analyzing several reactive species, the excellent photoreactivity of the SOWU was further identified.

Wang, Z, Ma, K, Zhang, Y, Zhang, X, Ngo, HH, Meng, J & Du, L 2021, 'High internal phase emulsion hierarchical porous polymer grafting polyol compounds for boron removal', Journal of Water Process Engineering, vol. 41.
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Removing micro-pollutants is an important global challenge for the development of good adsorbents and environmental engineering issues. In this work, the structure of hierarchically and interconnected porous polymer has been fabricated by a high internal phase emulsion (HIPE) with water-in-oil and used for removing boron from water. This porous polymer was synthesized via monomer radical polymerization and utilized the active sites of monomer –Cl. In this way the vicinal hydroxyl group of N-Methyl-D-glucamine (NMG) was successfully introduced and grafted into HIPE by nucleophilic substitution reaction under the catalysis of triethylamine. The maximum boron uptake capacity was 2.54 mmol/g at a boron concentration of 100 mg/L, and reached adsorption equilibrium after about 2 h. Compared to traditional adsorption membrane materials, the HIPE porous polymer had better mechanical strength and able to resist acid and alkaline in the long-term. Meanwhile the regeneration efficiency of the HIPE30 %-g-PNMG porous polymer remained at 100 % after being used for 10 cycles.

Wei, J, Li, J, Wu, C, Liu, ZX & Li, J 2021, 'Hybrid fibre reinforced ultra-high performance concrete beams under static and impact loads', Engineering Structures, vol. 245.
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High mechanical strength and ductility of fibre reinforced ultra-high performance concrete (UHPC) are beneficial to its loading capacity under static and dynamic loads. To further improve the material performance, especially the crack restraint capability and flexural capacity, hybrid fibre reinforcement in UHPC is investigated in the present study. This study first investigated the influence of hybrid straight steel fibres on the static mechanical performance of UHPC. Four mixtures with single and hybrid fibre reinforcement were developed. Fibres with three different lengths (6, 10, 15 mm) but identical diameter (0.2 mm) and volume dosage (2.5%) were incorporated. Quasi-static compression and flexural tests were conducted on these mixtures. The findings in this study indicated that while longer fibre provided an improvement of both compressive strength and elastic modulus, UHPC with hybrid fibres demonstrated better flexural performance. With notched three-point bending test results, tensile softening curves and fracture energy of UHPC were obtained and discussed. The flexural strength and fracture energy of mixture with hybrid long and short fibre was 21.84% and 20.13% greater than those with single long fibre reinforcement. Subsequently, the dynamic behaviour of UHPC beams (2000 × 168 × 168 mm) with a single and hybrid fibre reinforcement was investigated against low-velocity impact. The impact scenario was modelled with 641 kg drop hammer falling freely to the mid-span of the UHPC beams. The test results showed all specimens exhibited flexural response with minimal damage. The impact resistance of UHPC beams reinforced with hybrid fibres was found better than that with single long fibre reinforcement. The maximum and residual displacement of the mixture with hybrid long and medium fibre was 16.08% and 23.95% lower than that with long fibre reinforcement.

Wei, Q, Feng, D & Jia, W 2021, 'UDR: An Approximate Unbiased Difference-Ratio Edge Detector for SAR Images', IEEE Transactions on Geoscience and Remote Sensing, pp. 1-18.
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Wei, W, Chen, X & Ni, B-J 2021, 'Different Pathways of Microplastics Entering the Sludge Treatment System Distinctively Affect Anaerobic Sludge Fermentation Processes', ENVIRONMENTAL SCIENCE & TECHNOLOGY, vol. 55, no. 16, pp. 11274-11283.
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Wei, W, Chen, X, Liu, Y & Ni, BJ 2021, 'Aerobic sludge digestion is distinguishingly affected by the different entering pathways of zinc oxide nanoparticles', Journal of Hazardous Materials, vol. 416.
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Zinc oxide nanoparticles (ZnO NPs) are widespread emerging pollutants raising global concerns about their influences on biological wastewater treatment processes. However, the impacts of ZnO NPs on aerobic sludge digestion that is a major sludge treatment process remain unknown. Herein, this study comprehensively investigated the key influences of ZnO NPs on aerobic digestion of waste activated sludge (WAS) and the potential mechanisms involved. Two different entering pathways, i.e., ZnO NPs directly entered into aerobic sludge digester and ZnO NPs initially entered into wastewater bio-treatment reactor, were tested to evaluate the different impacts. Compared to the control, ZnO NPs initially entering into wastewater bioreactor inhibited WAS degradation by 18.2 ± 0.1%, whereas ZnO NPs immediately entered into digester inhibited it by 29.7 ± 0.1%. This was accompanied by a similar decrease in inorganic nitrogen production and oxygen consumption. ZnO NPs exposure in wastewater bioreactor changed WAS characteristics in favor of solubilization in aerobic digestion. Modelling analysis indicated that ZnO NPs inhibited WAS hydrolysis, especially for their direct entering into aerobic digester. Correspondingly, microbial community was shifted in the direction against aerobic digestion by the ZnO NPs. Excessive oxidative stress and Zn2+ release represented the primary toxicity factors for the inhibition.

Wei, W, Chen, X, Peng, L, Liu, Y, Bao, T & Ni, B-J 2021, 'The entering of polyethylene terephthalate microplastics into biological wastewater treatment system affects aerobic sludge digestion differently from their direct entering into sludge treatment system.', Water research, vol. 190, pp. 116731-116731.
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The entering of the widespread polyethylene terephthalate (PET) microplastics into biological wastewater treatment system results in their retention in sewage sludge, which inevitably enters the sludge treatment system. However, all previous studies regarding the impact of microplastics on sludge treatment system were conducted by directly adding microplastics to system and focusing on anaerobic sludge digestion, although PET microplastics commonly enter into the biological wastewater treatment system first before sludge being subsequently treated. The potential impact of the microplastics on waste activated sludge (WAS) aerobic digestion is also completely missing. Therefore, herein the influences of PET microplastics with different entry paths on WAS aerobic digestion as well as the key mechanisms involved was firstly explored. Experimental results demonstrated that compared to the control test, the entering of PET microplastics to biological wastewater treatment system inhibited WAS aerobic digestion by 10.9 ± 0.1% through the decreased hydrolysis, although WAS solubilization during aerobic digestion was improved due to the change of generated WAS characteristics. In contrast, when PET microplastics was directly added to the sludge aerobic digester, there was little impact on solubilization, while the hydrolysis were inhibited seriously, thereby suppressing WAS aerobic digestion more severely by 28.9 ± 0.1%. Further investigation revealed that PET microplastics reduced the populations of key bacteria (e.g., Saprospiraceae, Chitinophagaceae and Xanthomonadaceae) involved in aerobic digestion via induced oxidative stress or/and releasing toxic chemical. This study provided a more accurate approach to assessing the real situation regarding the influences of PET microplastics on aerobic sludge digestion.

Wen, D, Yang, B, Qin, L, Zhang, Y, Chang, L & Li, R 2021, 'Computing K-Cores in Large Uncertain Graphs: An Index-based Optimal Approach', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Wen, H, Wang, J, Ngo, HH, Zhang, H, Bai, R, Jia, H & Zhang, X 2021, 'Numerical and experimental investigation on the forward osmosis (FO) process for the operational conditions and spacer configuration optimization in microalgae dewatering', Journal of Water Process Engineering, vol. 40.
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Microalgae (Scenedesmus obliquus) dewatering using forward osmosis membrane has received considerable attention for its possible application in biofuel generation. To investigate the filtration performance by analyzing permeate water flux, five different velocities (0.23 m/s, 0.31 m/s, 0.40 m/s, 0.55 m/s, 0.66 m/s) were selected in a bench-scale experiments. The results showed that the optimal flux was with 0.55 m/s velocity. Moreover, the same velocities (0.23 m/s, 0.31 m/s, 0.40 m/s, 0.55 m/s, 0.66 m/s) and three various spacer positions (0.3 mm, 1 mm and 2 mm away from the membrane) were simulated employing the computational fluid dynamics (CFD) approach. The results showed that the pressure and velocity distribution were affected by the velocities at the module inlet and the spacer configuration. And the 0.55 m/s velocity was confirmed, while the CFD revealed that the velocity distribution was relatively uniform and exerted a higher pressure on the membrane, and 0.55 m/s velocity agreed with the experiment in optimal operation. As for the spacer configurations, they were evenly distributed in the channel and the optimum structures occurred when the spacers were 1 mm away from the membrane. The spacer is beneficial for alleviating external concentration polarization (ECP) during osmosis process.

Wen, S, Chen, J, Wu, Y, Yan, Z, Cao, Y, Yang, Y & Huang, T 2021, 'CKFO: Convolution Kernel First Operated Algorithm with Applications in Memristor-based Convolutional Neural Network', IEEE Transactions on Computer - Aided Design of Integrated Circuits and Systems.
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IEEE This paper presents a new convolution algorithm: Convolution Kernel First Operated (CKFO), which can solve the problem that the actual calculation is not reduced after pruning the weight of the convolution neural network. According to the convolution algorithm, this paper proposes a simulated memristor implementation of a Convolutional Neural Network (CNN). After that, we use the method of ex-situ training to train CNN in Tensorflow and then download the trained parameters to the Simulink system by compiling the conductance value of memristor to test the proposed simulation model. Finally, the effectiveness of the proposed model is verified. In addition, we prune the weights of CNN and retrain it, then adjust the simulation model according to the parameters after being pruned. We are surprised to find that the convolution layer designed according to the new convolution algorithm can apply the results of the pruned weight without any modification to the circuit, which is very cumbersome in other memristor-based CNN because the distribution of the pruned weight is irregular. The parameters are reduced by 75.24% and the number of multiplication operations in the convolution layer was reduced by 30.1%, while the accuracy is just reduced by 0.06%.

Wen, S, Liu, W, Yang, Y, Zhou, P, Guo, Z, Yan, Z, Chen, Y & Huang, T 2021, 'Multilabel Image Classification via Feature/Label Co-Projection', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-10.
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Wen, S, Ni, X, Wang, H, Zhu, S, Shi, K & Huang, T 2021, 'Observer-Based Adaptive Synchronization of Multiagent Systems With Unknown Parameters Under Attacks', IEEE Transactions on Neural Networks and Learning Systems.
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IEEE This article studies the observer-based adaptive synchronization of multiagent systems (MASs) with unknown parameters under attacks. First, to estimate the state of agents, the observer for MAS is introduced. When disturbance, nonlinear function, and system model uncertainty are not considered, the nominal controller is proposed to achieve synchronization and state estimation. Then, in order to eliminate the effect of unknown parameters in the disturbance, nonlinear function, and system model uncertainty, the adaptive controller with switching term is introduced. However, the attack will lead to the destruction of the network topology so as the destruction of the nominal controller. By constructing an appropriate Lyapunov function, we analyze the effect caused by attacks, and the security control law is given to make sure the synchronization of the MASs under attacks. Finally, a numerical simulation is given to verify the validness of the obtained theorem.

Wen, S, Wei, H, Yang, Y, Guo, Z, Zeng, Z, Huang, T & Chen, Y 2021, 'Memristive LSTM Network for Sentiment Analysis', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 3, pp. 1794-1804.
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This paper presents a complete solution for the hardware design of a memristor-based long short-term memory (MLSTM) network. Throughout the design process, we fully consider the external and internal structures of the long short-term memory (LSTM), both of which are efficiently implemented by memristor crossbars. In the specific design of the internal structure, the parameter sharing mechanism is used between the LSTM cells to minimize the hardware design scale. In particular, we designed a circuit that requires only one memristor crossbar for each unit in the LSTM cell. The activation function, including sigmoid and tanh (hyperbolic tangent function), involved in each unit is approximated by a piecewise function, which is designed with the corresponding hardware. To verify the effectiveness of the system we designed, we test it on IMDB and SemEval datasets. Considering the huge impact of the dimensions of the input data on the scale of the hardware design, we use word2vector instead of one-hot encoding for the input data encoding. With the parameter sharing mechanism, the transformed vectors are input in different periods, so only 65 memristive crossbars are needed in the entire system to complete the sentiment analysis of the input text. The experimental results verify the effectiveness of our proposed MLSTM system.

Wen, Z, Garg, S, Aujla, GSS, Alwasel, K, Puthal, D, Dustdar, S, Zomaya, AY & Rajan, R 2021, 'Running Industrial Workflow Applications in a Software-defined Multi-Cloud Environment using Green Energy Aware Scheduling Algorithm', IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5645-5656.
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Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and Cloud computing. To handle the work-flows from Industrial Cyber-Physical systems, more and more data centers have been built across the globe to serve the growing needs of computing and storage. This has led to an enormous increase in energy usage by cloud data centers which is not only a financial burden but also increases their carbon footprint. The private Software Defined Wide Area network (SDWAN) connects a cloud provider's data centers across the planet. This gives the opportunity to develop new scheduling strategies to manage cloud providers workload in a more energy-efficient manner. In this context, this paper addresses the problem of scheduling data-driven industrial workflow applications over a set of private SDWAN connected data centers in an energy-efficient manner while managing trade-off of a cloud provider' revenue. Our proposed algorithm aims to minimize the cloud provider's revenue and the usage of non-renewable energy by utilizing the real-world electricity prices with the availability of green energy on different cloud data centers, where the energy consumption consists of the usage of running application over multiple data centers and transferring the data among them through SDWAN. The evaluation shows that our proposed method can increase usage of green energy for the execution of industrial workflow up to 3× times with a slight increase in the cost when compared to cost-based workflow scheduling methods.

Wilkinson, S, Carmichael, M & Khonasty, R 2021, 'Towards Smart Green Wall Maintenance and Wallbot Technology', Property Management.
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The UN forecast of a 3 degree Celsius global temperature increase by 2100 will exacerbate excessive heat. Population growth, urban densification, climate change and global warming contribute to heat waves, which are more intense in high-density environments. With urbanisation, vegetation is replaced by impervious materials which contribute to the Urban Heat Island effect. Concurrently, adverse health outcomes and heat related deaths are increasing and heat stress affects labour productivity. More green infrastructure, such as green walls, is needed to mitigate these effects, however maintenance costs, OH&S issues and perceptions of fire risk inhibit take up. What if these barriers could be overcome by a green wallbot? This research examines the feasibility of integrating smart technology in the form of a Wallbot. The research design comprised two workshops with key stakeholders; comprising green wall designers and installers, green wall maintenance teams, project managers and building owners with green wall installations, horticulture scientists, designers and mechatronics engineers. The aim was to gain a deeper understanding of the issues affecting maintenance of green walls on different building types in New South Wales Australia to inform the design of a prototype robot to maintain green walls. The wallbot has great potential to overcome the perceived barriers associated with maintaining green walls and also fire risk and detection. If these barriers are addressed, other locations, such as the sides of motorways or rail corridors, could be used for more green wall installations thereby increasing mitigation of UHI. This innovation would be a welcome addition to smart building technology and property maintenance This is a pilot study and the sample of stakeholders attending the workshops was small, though experienced. The range of green walls is varied and it was decided to focus initially on a specific type of green wall design for the prototype wallbot. There...

Wong, S-W, Lin, J-Y, Yang, Y, Guo, Z-C, Zhu, L & Chu, Q-X 2021, 'Waveguide Components Based on Multiple-Mode Resonators', IEEE MICROWAVE MAGAZINE, vol. 22, no. 2, pp. 33-45.
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Woo, YC, Yao, M, Shim, WG, Kim, Y, Tijing, LD, Jung, B, Kim, SH & Shon, HK 2021, 'Co-axially electrospun superhydrophobic nanofiber membranes with 3D-hierarchically structured surface for desalination by long-term membrane distillation', Journal of Membrane Science, vol. 623.
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© 2021 Elsevier B.V. Electrospun nanofiber membranes (ENMs) have gained increasing interest in membrane distillation (MD) applications due to their high surface area, high hydrophobicity and porosity, and controllable pore size and membrane thickness. However, despite these advantages, ENMs still suffer from wetting issues in MD. Co-axial electrospinning is an attractive strategy for the one-step fabrication of non-woven membranes with core-sheath structures and improved wetting resistance for MD application. In the present study, we investigated poly (vinylidene fluoride-co-hexafluoropropylene) (PH) as the core and PH/silica aerogel (SiA) as the sheath to obtain superhydrophobic co-axial composite ENMs. The surface characterization results indicated that the active layer (i.e., PH) of the co-axial ENMs was rough, highly porous (>80%), and superhydrophobic (contact angle >160°). Further, the co-axial ENMs possessed small pore sizes (<0.39 μm) and a suitable liquid entry pressure (>1.72 bar). Upon the application in long-term (one month) direct contact MD testing using a 3.5 wt% NaCl solution as the feed, high water vapor flux and salt rejection of 14.5 L/m2h and 99.99% were achieved, respectively, when applying the optimal 4 wt% SiA solution loading at the sheath. The ENMs fabricated using the versatile co-axial electrospinning showed great potential for long-term applications in direct contact MD desalination.

Wu, A, Han, Y, Zhu, L & Yang, Y 2021, 'Instance-Invariant Domain Adaptive Object Detection via Progressive Disentanglement', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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Wu, J, Tian, Y, Walker, P & Li, Y 2021, 'Attenuation reference model based adaptive speed control tactic for automatic steering system', Mechanical Systems and Signal Processing, vol. 156.
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Ensuring passenger comfort in steering process of autonomous vehicles is a key issue in corresponding product development. However, the existing steering control system has an inability to plan steering speed, which will increase the discomfort of passengers. In this paper, a novel reference model based adaptive control tactic is proposed to regulate steering speed of automatic steering system (ASS). In order to improve the response speed and control accuracy of ASS, firstly, exponential decay function is creatively proposed as a reference model to control the decay rate of the speed error of steering motor. Subsequently, to reduce the system uncertainty range, the steering load disturbance is treated as a known time-varying term and a bounded unknown time-varying disturbance. In order to compensate the uncertainties of nonlinear system, the additional estimation of disturbance boundary and the robust term in the parameters updating law are introduced. Besides, the stability and robustness of the whole system are verified by Lyapunov theory. Finally, simulation and HIL test results demonstrate the proposed control tactic ensures the tracking performance of steering speed control and the robust stability in the bounded unknown time-varying disturbance, which provides benefit reference for ASS development in practice.

Wu, K, Zhang, A, Huang, X & Guo, YJ 2021, 'Accurate Frequency Estimation with Fewer DFT Interpolations based on Pad Approximation', IEEE Transactions on Vehicular Technology.
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Frequency estimation is a fundamental problem in many areas. The well-known A&M and its variant estimators have established an estimation framework by iteratively interpolating the discrete Fourier transform (DFT) coefficients. In general, previous estimators require two DFT interpolations per iteration, have uneven initial estimation performance against frequencies, and are incompetent for small sample numbers due to low-order approximations involved. Exploiting the iterative estimation framework of A&M, we unprecedentedly introduce the Pad Approximation to frequency estimation, unveil some features about the updating function used for refining the estimation in each iteration, and develop a simple closed-form solution to solving the residual estimation error. Extensive simulation results are provided, validating the superiority of the new estimator over the state-the-art estimators in wide ranges of key parameters.

Wu, K, Zhang, JA, Huang, X, Guo, YJ & Heath, RW 2021, 'Waveform Design and Accurate Channel Estimation for Frequency-Hopping MIMO Radar-Based Communications', IEEE TRANSACTIONS ON COMMUNICATIONS, vol. 69, no. 2, pp. 1244-1258.
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Wu, L, Lee, KMB, Liu, L & Vidal-Calleja, TA 2021, 'Faithful Euclidean Distance Field from Log-Gaussian Process Implicit Surfaces', IEEE Robotics and Automation Letters.
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Wu, L, Wei, W & Ni, BJ 2021, 'Response to Comment on "a Critical Review on Nitrous Oxide Production by Ammonia-Oxidizing Archaea"', Environmental Science and Technology.
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Wu, L, Wei, W, Xu, J, Chen, X, Liu, Y, Peng, L, Wang, D & Ni, B-J 2021, 'Denitrifying biofilm processes for wastewater treatment: developments and perspectives', ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY, vol. 7, no. 1, pp. 40-67.
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Wu, M, Sun, X, Zhu, J, Lei, G & Guo, Y 2021, 'Improved Model Predictive Torque Control for PMSM Drives Based on Duty Cycle Optimization', IEEE TRANSACTIONS ON MAGNETICS, vol. 57, no. 2.
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Wu, M, Xiong, N, Vasilakos, AV, Leung, VCM & Chen, CLP 2021, 'RNN-K: A Reinforced Newton Method for Consensus-Based Distributed Optimization and Control Over Multiagent Systems.', IEEE transactions on cybernetics, vol. PP.
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With the rise of the processing power of networked agents in the last decade, second-order methods for machine learning have received increasing attention. To solve the distributed optimization problems over multiagent systems, Newton's method has the benefits of fast convergence and high estimation accuracy. In this article, we propose a reinforced network Newton method with K-order control flexibility (RNN-K) in a distributed manner by integrating the consensus strategy and the latest knowledge across the network into local descent direction. The key component of our method is to make the best of intermediate results from the local neighborhood to learn global knowledge, not just for the consensus effect like most existing works, including the gradient descent and Newton methods as well as their refinements. Such a reinforcement enables revitalizing the traditional iterative consensus strategy to accelerate the descent of the Newton direction. The biggest difficulty to design the approximated Newton descent in distributed settings is addressed by using a special Taylor expansion that follows the matrix splitting technique. Based on the truncation on the Taylor series, our method also presents a tradeoff effect between estimation accuracy and computation/communication cost, which provides the control flexibility as a practical consideration. We derive theoretically the sufficient conditions for the convergence of the proposed RNN-K method of at least a linear rate. The simulation results illustrate the performance effectiveness by being applied to three types of distributed optimization problems that arise frequently in machine-learning scenarios.

Wu, M, Zhang, Y, Zhang, G & Lu, J 2021, 'Exploring the genetic basis of diseases through a heterogeneous bibliometric network: A methodology and case study', TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, vol. 164.
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Wu, S, Luo, Z, Li, Z, Liu, S & Zhang, L-C 2021, 'Topological design of pentamode metamaterials with additive manufacturing', COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, vol. 377.
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Wu, S-L, Wei, W, Xu, Q, Huang, X, Sun, J, Dai, X & Ni, B-J 2021, 'Revealing the Mechanism of Biochar Enhancing the Production of Medium Chain Fatty Acids from Waste Activated Sludge Alkaline Fermentation Liquor', ACS ES&T Water, vol. 1, no. 4, pp. 1014-1024.
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Wu, W, Li, B, Chen, L, Gao, J & Zhang, C 2021, 'A Review for Weighted MinHash Algorithms', IEEE Transactions on Knowledge and Data Engineering.
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Data similarity (or distance) computation is a fundamental research topic which underpins many high-level applications based on similarity measures in machine learning and data mining. However, in large-scale real-world scenarios, the exact similarity computation has become daunting due to "3V" nature (volume, velocity and variety) of big data. In this case, the hashing techniques have been verified to efficiently conduct similarity estimation in terms of both theory and practice. Currently, MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore, weighted MinHash is generalized to estimate the generalized Jaccard similarity of weighted sets. This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms. In this review, we mainly categorize the weighted MinHash algorithms into quantization-based approaches, "active index"-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash ones to the real-valued weighted MinHash ones. Also, we have developed a Python toolbox for the algorithms, and released it in our github. We experimentally conduct a comprehensive study of the standard MinHash algorithm and the weighted MinHash ones in the similarity estimation error and the information retrieval task.

Wu, W, Zhu, S, Huang, X, Wei, W & Ni, BJ 2021, 'Mechanisms of persulfate activation on biochar derived from two different sludges: Dominance of their intrinsic compositions', Journal of Hazardous Materials, vol. 408.
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Sludge-derived biochar (SDBC) has been regarded as persulfate (PS) activator during the remediation of organic contamination. However, the complexity of sludge composition makes it difficult to predict the activity of SDBC and the efficacy of PS. To improve the understanding of how the composition of sludge regulated activity of its parent SDBC towards PS activation, we used two SDBCs derived from different sludges with significantly different organic compositions and metals. Results indicated the higher content of organic and nitrogen content in sludge led to higher polymerization and condensation of carbon layer and more moieties in SDBC1, whereas more Fe species (e.g. Fe-O, Fe2+ and Fe3+) formed in SDBC2. According to the results of phenol (PN) degradation in SDBC/PS, the apparent rate constants (kobs) of SDBC2-700 (0.0037 min−1) was 2 folds higher than that of SDBC1-700 (0.0016 min−1), whereas the SDBC1-500 (6.0 × 10−4 min−1) exhibited higher kobs than that of SDBC2-500 (4.9 × 10−4 min−1). The difference of PS activation by different SDBCs mainly relied on generated reactive oxygen species (ROS). The persistent free radicals (PFRs) and Fe species acted as redox sites for generated ROS, which were depended on the organic compositions and involved metals in used sludges.

Wu, Y, Zhu, L, Wang, X, Yang, Y & Wu, F 2021, 'Learning to Anticipate Egocentric Actions by Imagination', IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 30, pp. 1143-1152.
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Wu, Z, Li, G, Shen, S, Lian, X, Chen, E & Xu, G 2021, 'Constructing dummy query sequences to protect location privacy and query privacy in location-based services', World Wide Web, vol. 24, no. 1, pp. 25-49.
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Location-based services (LBS) have become an important part of people’s daily life. However, while providing great convenience for mobile users, LBS result in a serious problem on personal privacy, i.e., location privacy and query privacy. However, existing privacy methods for LBS generally take into consideration only location privacy or query privacy, without considering the problem of protecting both of them simultaneously. In this paper, we propose to construct a group of dummy query sequences, to cover up the query locations and query attributes of mobile users and thus protect users’ privacy in LBS. First, we present a client-based framework for user privacy protection in LBS, which requires not only no change to the existing LBS algorithm on the server-side, but also no compromise to the accuracy of a LBS query. Second, based on the framework, we introduce a privacy model to formulate the constraints that ideal dummy query sequences should satisfy: (1) the similarity of feature distribution, which measures the effectiveness of the dummy query sequences to hide a true user query sequence; and (2) the exposure degree of user privacy, which measures the effectiveness of the dummy query sequences to cover up the location privacy and query privacy of a mobile user. Finally, we present an implementation algorithm to well meet the privacy model. Besides, both theoretical analysis and experimental evaluation demonstrate the effectiveness of our proposed approach, which show that the location privacy and attribute privacy behind LBS queries can be effectively protected by the dummy queries generated by our approach.

Xi, Y, Jia, W, Zheng, J, Fan, X, Xie, Y, Ren, J & He, X 2021, 'DRL-GAN: Dual-Stream Representation Learning GAN for Low-Resolution Image Classification in UAV Applications', IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
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CCBY Identifying tiny objects from extremely low resolution (LR) UAV-based remote sensing images is generally considered as a very challenging task, because of very limited information in the object areas. In recent years, there have been very limited attempts to approach this problem. These attempts intend to deal with LR image classification by enhancing either the poor image quality or image representations.In this paper, we argue that the performance improvement in LR image classification is affected by the inconsistency of the information loss and learning priority on Low-Frequency (LF) components and High-Frequency (HF) components.To address this LF-HF inconsistency problem, we propose a Dual-Stream Representation Learning Generative Adversarial Network (DRL-GAN).The core idea is to produce super image representations optimal for LR recognition by simultaneously recovering the missing information in LF and HF components, respectively, under the guidance of high-resolution (HR) images.We evaluate the performance of DRL-GAN on the challenging task of LR image classification.A comparison of the experimental results on the LR benchmark, namely HRSC and CIFAR-10, and our newly collected “WIDER-SHIP” dataset demonstrates the effectiveness of our DRL-GAN, which significantly improves the classification performance, with up to 10% gain on average.

Xia, F, Sun, K, Yu, S, Aziz, A, Wan, L, Pan, S & Liu, H 2021, 'Graph Learning: A Survey', IEEE Transactions on Artificial Intelligence, pp. 1-1.
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Xiao, F, Cao, Z & Jolfaei, A 2021, 'A Novel Conflict Measurement in Decision-Making and Its Application in Fault Diagnosis', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 29, no. 1, pp. 186-197.
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Xiao, H, He, L, Li, X, Zhang, Q & Li, W 2021, 'Texture synthesis: A novel method for generating digital models with heterogeneous diversity of rock materials and its CGM verification', COMPUTERS AND GEOTECHNICS, vol. 130.
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Xiao, J, Cao, J, Cheng, J, Wen, S, Zhang, R & Zhong, S 2021, 'Novel Inequalities to Global Mittag-Leffler Synchronization and Stability Analysis of Fractional-Order Quaternion-Valued Neural Networks[-.3pc].', IEEE transactions on neural networks and learning systems, vol. PP.
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This article is concerned with the problem of the global Mittag-Leffler synchronization and stability for fractional-order quaternion-valued neural networks (FOQVNNs). The systems of FOQVNNs, which contain either general activation functions or linear threshold ones, are successfully established. Meanwhile, two distinct methods, such as separation and nonseparation, have been employed to solve the transformation of the studied systems of FOQVNNs, which dissatisfy the commutativity of quaternion multiplication. Moreover, two novel inequalities are deduced based on the general parameters. Compared with the existing inequalities, the new inequalities have their unique superiorities because they can make full use of the additional parameters. Due to the Lyapunov theory, two novel Lyapunov-Krasovskii functionals (LKFs) can be easily constructed. The novelty of LKFs comes from a wider range of parameters, which can be involved in the construction of LKFs. Furthermore, mainly based on the new inequalities and LKFs, more multiple and more flexible criteria are efficiently obtained for the discussed problem. Finally, four numerical examples are given to demonstrate the related effectiveness and availability of the derived criteria.

Xiao, J, Zeng, Z, Wen, S, Wu, A & Wang, L 2021, 'A Unified Framework Design for Finite-Time and Fixed-Time Synchronization of Discontinuous Neural Networks.', IEEE Transactions on Cybernetics.
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In this article, the problems of finite-time/fixed-time synchronization have been investigated for discontinuous neural networks in the unified framework. To achieve the finite-time/fixed-time synchronization, a novel unified integral sliding-mode manifold is introduced, and corresponding unified control strategies are provided; some criteria are established for selecting suitable parameters for solving the related issue, namely, the dynamics of neural network can reach the designed sliding-mode manifold in finite/fixed time, and stay on it thereafter. Moreover, the estimations of setting time are given out. The established unified framework can bring in various protocols by choosing the different parameters of controllers and sliding-mode manifold, which extend previous related results. Finally, some numerical examples are introduced to show the effectiveness and superiority of resulting conclusions.

Xiao, J, Zeng, Z, Wen, S, Wu, A & Wang, L 2021, 'Finite-/Fixed-Time Synchronization of Delayed Coupled Discontinuous Neural Networks With Unified Control Schemes.', IEEE transactions on neural networks and learning systems, vol. PP.
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In this article, it addresses the problem of finite-/fixed-time synchronization of delayed coupled discontinuous neural networks in the unified framework. To achieve the finite-/fixed-time synchronization and precise estimations of setting time, two novel different kinds of controllers are established, in which one is switching. Then, based on the finite-/fixed-time theorem and Lyapunov function theory, some useful criteria are obtained to select suitable controllers' parameters, which can guarantee error systems converge in the finite time/fixed time with respect to coupled neural networks. Moreover, corresponding estimations of the setting time are also provided. Finally, two numerical examples are introduced to show the effectiveness of the proposed control protocols.

Xiao, J, Zhong, S & Wen, S 2021, 'Improved approach to the problem of the global Mittag-Leffler synchronization for fractional-order multidimension-valued BAM neural networks based on new inequalities', Neural Networks, vol. 133, pp. 87-100.
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© 2020 Elsevier Ltd This paper studies the problem of the global Mittag-Leffler synchronization for fractional-order multidimension-valued BAM neural networks (FOMVBAMNNs) with general activation functions (AFs). First, the unified model is established for the researched systems of FOMVBAMNNs which can be turned into the corresponding multidimension-valued systems as long as the state variables, the connection weights and the AFs of the neural networks are valued to be real, complex, or quaternion. Then, without any decomposition, the criteria in unified form are derived by constructing the new Lyapunov–Krasovskii functionals (LKFs) in vector form, combining two new inequalities and considering the easy controllers. It is worth mentioning that the obtained criteria have many advantages in higher flexibility, more diversity, smaller computation, and lower conservatism. Finally, a simulation example is provided to illustrate the availability and improvements of the acquired results.

Xiao, J, Zhong, S & Wen, S 2021, 'Unified Analysis on the Global Dissipativity and Stability of Fractional-Order Multidimension-Valued Memristive Neural Networks With Time Delay', IEEE Transactions on Neural Networks and Learning Systems.
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The unified criteria are analyzed on the global dissipativity and stability for the delayed fractional-order systems of multidimension-valued memristive neural networks (FSMVMNNs) in this article. First, based on the comprehensive knowledge about multidimensional algebra, fractional derivatives, and nonsmooth analysis, we establish the unified model for the studied FSMVMNNs in order to propose a more uniform method to analyze the dynamic behaviors of multidimensional neural networks. Then, by mainly applying the Lyapunov method, employing several new lemmas, and solving some mathematical difficulties, without any separation, we acquire the unified and concise criteria. The derived criteria have many advantages in a smaller calculation, lower conservatism, more diversity, and higher flexibility. Finally, we provide two numerical examples to express the availability and improvements of the theoretical results.

Xiao, R, Ni, B-J, Liu, S & Lu, H 2021, 'Impacts of organics on the microbial ecology of wastewater anammox processes: Recent advances and meta-analysis', WATER RESEARCH, vol. 191.
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Xiao, T, Zhao, S, Qiu, X & Halkon, B 2021, 'Using a Retro-Reflective Membrane and Laser Doppler Vibrometer for Real-Time Remote Acoustic Sensing and Control', SENSORS, vol. 21, no. 11.
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Xiao, Y, Xiao, L, Lu, X, Zhang, H, Yu, S & Poor, HV 2021, 'Deep-Reinforcement-Learning-Based User Profile Perturbation for Privacy-Aware Recommendation', IEEE Internet of Things Journal, vol. 8, no. 6, pp. 4560-4568.
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User profile perturbation protects privacy in the release of user profiles to receive recommendation services, in which the privacy budget as a privacy parameter can be controlled to effect a tradeoff between the recommendation quality and privacy protection against inference attacks. In this article, we propose a deep reinforcement learning (RL)-based user profile perturbation scheme for recommendation systems. This scheme applies differential privacy to protect user privacy and uses deep RL to choose the privacy budget against inference attackers. Based on an evaluated neural network (NN) and a target NN, this scheme enables a user device to optimize the privacy budget over time based on the sensitivity level of the clicked item, the similarities among the recommended items, and the estimated privacy loss. We provide an upper bound on the privacy protection performance of this scheme in the recommendation game and evaluate its computational complexity. Simulation results for a movie recommendation system show that this scheme increases the user privacy protection level for a given recommendation quality compared with benchmark schemes.

Xie, X, Niu, J, Liu, X, Chen, Z, Tang, S & Yu, S 2021, 'A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis', Medical Image Analysis.
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Xing, D, Wang, K, Wu, J, Zhao, Y, Liu, W, Li, JJ, Gao, T, Yan, D, Wang, L, Hao, J & Lin, J 2021, 'Clinical-Grade Human Embryonic Stem Cell-Derived Mesenchymal Stromal Cells Ameliorate the Progression of Osteoarthritis in a Rat Model', Molecules.
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Xu, B, Liu, S, Zhou, JL, Zheng, C, Weifeng, J, Chen, B, Zhang, T & Qiu, W 2021, 'PFAS and their substitutes in groundwater: Occurrence, transformation and remediation', Journal of Hazardous Materials.
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Xu, B-H, Indraratna, B, Nguyen, TT & Walker, R 2021, 'A vertical and radial consolidation analysis incorporating drain degradation based on the spectral method', Computers and Geotechnics.
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Xu, C, Zhang, T, Kuang, X, Zhou, Z & Yu, S 2021, 'Context-aware Adaptive Route Mutation Scheme: A Reinforcement Learning Approach', IEEE Internet of Things Journal.
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Moving Target Defense (MTD) is an emerging proactive defense technology, which can reduce the risk of vulnerabilities exploited by attacker. As a crucial component of MTD, route mutation (RM) faces a few fundamental problems defending against sophisticated Distributed Denial of Service (DDoS) attacks: 1) It’s unable to make optimal mutation selection due to insufficient learning in attack behaviors. 2) Because network situation is time-varying, RM also lacks self-adaptation in mutation parameters. In this paper, we propose a context-aware Q-learning algorithm for RM (CQ-RM) that can learn attack strategies to optimize the selection of mutated routes. We firstly integrate four representative attack strategies into a unified mathematical model and formalize multiple network constraints. Then, taking above network constraints into considerations, we model RM process as a Markov decision process (MDP). To look for the optimal policy of MDP, we develop a context estimation mechanism and further propose the CQ-RM scheme, which can adjust learning rate and mutation period adaptively. Correspondingly, the optimal convergence of CQ-RM is proved theoretically. Finally, extensive experimental results highlight the effectiveness of our method compared to representative solutions.

Xu, C, Zhu, L, Liu, Y, Guan, J & Yu, S 2021, 'DP-LTOD: Differential Privacy Latent Trajectory Community Discovering Services over Location-Based Social Networks', IEEE Transactions on Services Computing.
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IEEE Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis,we prove that DP-LTOD scheme can guarantee \epsilon-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking.

Xu, J, Tian, H, Wang, Z, Wang, Y, Chen, F & Kang, W 2021, 'Joint Input and Output Space Learning for Multi-Label Image Classification', IEEE Transactions on Multimedia, pp. 1-1.
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Xu, JX, Huang, M, Li, HY, Yang, Y & Zhang, XY 2021, 'Design of Balanced Filtering Rat-Race Coupler Based on Quad-Mode Dielectric Resonator', IEEE Transactions on Circuits and Systems II: Express Briefs.
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A balanced filtering rat-race coupler is proposed based on a quad-mode dielectric resonator (DR) in this paper. Due to the electromagnetic-field distributions of the DR, the desired balanced performance with high common-mode suppression is realized without additional circuits. Moreover, by setting the feeding probes at the proper locations, the quad-mode DR is also used to obtain the desired coupler topology with filtering responses. Accordingly, a balanced filtering rat-race coupler, integrating three circuit functions of the balanced circuit, rat-race coupler, and bandpass filter, can be designed into a single-cavity configuration, resulting in a very compact size. For verification, the proposed circuit is implemented, which shows good filtering responses, low amplitude imbalance, high common-mode suppression, and excellent phase characteristics.

Xu, M, Huang, CC & Feng, Y 2021, 'Measuring the constrained reachability in quantum Markov chains', Acta Informatica.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Constrained reachability is a kind of quantitative path property, which is generally specified by multiphase until formulas originated in continuous stochastic logic. In this paper, through proposing a positive operator valued measure on the set of infinite paths, we develop an exact method to solve the constrained reachability problem for quantum Markov chains. The convergence rate of the reachability is also obtained. We then analyse the complexity of the proposed method, which turns out to be in polynomial-time w.r.t. the size of the classical state space and the dimension of the accompanied Hilbert space. Finally, our method is implemented and applied to a simple quantum protocol.

Xu, Q, Fu, Q, Liu, X, Wang, D, Wu, Y, Li, Y, Yang, J, Yang, Q, Wang, Y, Li, H & Ni, BJ 2021, 'Mechanisms of potassium permanganate pretreatment improving anaerobic fermentation performance of waste activated sludge', Chemical Engineering Journal, vol. 406.
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© 2020 Elsevier B.V. Potassium permanganate (KMnO4), one typical strong oxidant, is used extensively in various applications. However, its impact on anaerobic fermentation of waste activated sludge is unknown. This paper therefore aims to investigate its effect on generation of short-chain fatty acids (SCFAs) and qualities of fermentation liquor and fermented solid sludge. Experimental results showed that when KMnO4 increased from 0 to 0.1 g/TSS, the highest SCFAs production enhanced from 33.9 to 251.8 mg COD/g VSS. The mechanism analysis revealed that KMnO4 addition increased the disintegration of sludge cells and the degradation of substantial recalcitrant organics in sludge such as humus and lignocellulose, thereby provided more substances for SCFAs generation. Ultraviolet absorption spectroscopy analysis showed that KMnO4 addition destroyed unsaturated conjugated bonds and decreased organics aromaticity, thereby promoting humus and lignocellulose degradation. GC/MS analyses showed that several micro-molecular substances (e.g., acid-like, alkane-like, and alcohol-like organics) were generated from KMnO4 pretreated humus and lignocellulose, and some of them were demonstrated to serve as substrates to produce SCFAs. The enzyme activity and model-based analyses showed that although KMnO4 restrained all the microorganisms to some extends, the activities of SCFAs generators were much higher than those of SCFAs consumers. In addition, the sludge mixture was separated into liquid and solid fraction after anaerobic fermentation, and the components in fermentation liquid and solid sludge were tested. It was found that the addition of KMnO4 effectively reduced the categories and total detected frequency of refractory organic pollutants in fermentation liquid, which improved the quality of the produced SCFAs. KMnO4 addition also increased the inactivation of fecal coliforms and degradation of emerging contaminants in fermented sludge, which were helpful to its final disp...

Xu, Q, Huang, Q-S, Luo, T-Y, Wu, R-L, Wei, W & Ni, B-J 2021, 'Coagulation removal and photocatalytic degradation of microplastics in urban waters', CHEMICAL ENGINEERING JOURNAL, vol. 416.
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Xu, Q, Luo, T-Y, Wu, R-L, Wei, W, Sun, J, Dai, X & Ni, B-J 2021, 'Rhamnolipid pretreatment enhances methane production from two-phase anaerobic digestion of waste activated sludge', WATER RESEARCH, vol. 194.
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Xu, Q, Su, Z, Zhang, K & Yu, S 2021, 'Fast Containment of Infectious Diseases with E-healthcare Mobile Social Internet of Things', IEEE Internet of Things Journal.
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Infectious disease presents great hazards to public health, due to their high infectivities and potential lethalities. One of the effective methods to hinder the spread of infectious disease is vaccination. However, due to the limitation of resource and the medical budget, vaccinating all people is not feasible in practice. Besides, the vaccinating effects are difficult to be timely observed through traditional ways, such as outpatient services. To tackle above problem, we propose an e-healthcare mobile social internet of things (MSIoTs) based targeted vaccination scheme to fast contain the spread of the infectious disease. Specifically, we first develop an e-healthcare MSIoT architecture by integrating the e-healthcare system and MSIoTs, whereby the spread status of the infectious disease is timely collected. Furthermore, a graph coloring and spreading centrality-based optional candidate searching algorithm is devised to hunt for the candidates that are powerfully capable of preventing infectious disease. Especially, in order to reduce the vaccination cost, we design an optimal vaccinated target selection algorithm to choose a minimum number of targets whose locations are differentially distributed. Extensive simulations demonstrate that the proposed scheme can effectively prevent infectious disease as compared to conventional schemes.

Xu, S, Liu, Z, Li, J, Yang, Y & Wu, C 2021, 'Dynamic behaviors of reinforced NSC and UHPC columns protected by aluminum foam layer against low-velocity impact', Journal of Building Engineering, vol. 34.
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© 2020 Elsevier Ltd Two strategies were proposed in this study to improve the safety of RC columns under low-velocity impact loading. One is setting up the protective closed-cell aluminum foam (CCAF) layer on the surfaces of RC columns for general structures, and the other one is utilizing the combination of the CCAF layer and UHPC for important structures. For verifying the effectiveness of these two strategies, both experimental and numerical investigations on the dynamic behaviors of reinforced normal strength concrete (R–NSC) and ultra-high-performance concrete (R–UHPC) columns protected by the CCAF layer against low-velocity impact were presented in this study. Two R–NSC columns and two R–UHPC columns were tested by the free-falling drop-weight system. The failure modes, failure process, time history of impact force and deflection were discussed in detail. Moreover, a 3D finite element model was developed to further investigate the impact dynamic behaviors of R–NSC columns and R–UHPC columns protected by the CCAF layer. The test results demonstrated that the CCAF layer can protect both R–NSC columns and R–UHPC columns effectively by reducing the impact force and absorbing a large amount of impact energy. Correspondingly, the safety of RC columns was also effectively improved since the impact force between the target and the impactor was reduced significantly by the adoption of the aluminum foam layer, and it verified strategy one. Furthermore, R–UHPC columns showed a better impact-resistant performance than R–NSC columns, especially, R–UHPC columns with the CCAF layer showed superior impact-resistant performance, and it verified strategy two. The finite element model can predict the dynamic behaviors of aluminum foam protecting R–NSC and R–UHPC columns with reasonable accuracy. Eventually, the energy absorption of the specimens was investigated by the numerical model.

Xu, T, Li, Y, Lai, T & Zheng, J 2021, 'A simplified design method of tuned inerter damper for damped civil structures: Theory, validation, and application', Structural Control and Health Monitoring.
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Xu, W, Li, X, Zhu, J & Wang, Q 2021, '3D Modelling and Testing of a Stator-Magnet Transverse-Flux Linear Oscillatory Machine for Direct Compressor Drive', IEEE Transactions on Industrial Electronics.
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A stator-magnet transverse-flux linear oscillatory machine is proposed for direct compressor drive. The robust transverse-flux structure with permanent magnets embedded in the stator yoke and a moving-iron translator can yield high reliability and is relatively simple to fabricate. For electromagnetic performance analysis, a linear model under the no-load condition and a nonlinear model under the loaded condition are developed by taking into account the axial leakage flux and saturation effects of iron core, respectively. The effectiveness and accuracy of the proposed analytical models are verified by comparing the results with those of the finite element analysis and the static experimental tests. Based on the measured static characteristics and damping coefficient, a system kinetic model is developed in the form of coupled equivalent electromechanical circuit, and validated by the results of dynamic test on a prototype. The key indices of the new machine are compared with those of an existing moving-magnet linear oscillatory machine, including the amount of permanent magnet usage, efficiency, and thrust density, etc. The case study results show that the proposed linear oscillatory machine is suitable for linear compressor drives.

Xu, W, Zhang, Y, Du, G, He, M & Zhu, J 2021, 'No-Load Performance Analysis of an Asymmetric-Pole Single-Phase Doubly Salient Permanent Magnet Machine', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 68, no. 4, pp. 2907-2918.
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Xu, X, Sun, G, Luo, L, Cao, H, Yu, H & Vasilakos, AV 2021, 'Latency performance modeling and analysis for hyperledger fabric blockchain network', Information Processing & Management.
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Xu, Y, Afshar, S, Wang, R, Cohen, G, Singh Thakur, C, Hamilton, TJ & van Schaik, A 2021, 'A Biologically Inspired Sound Localisation System Using a Silicon Cochlea Pair', APPLIED SCIENCES-BASEL, vol. 11, no. 4.
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Xu, Y, Gao, Y, Wu, C, Fang, J, Sun, G, Steven, GP & Li, Q 2021, 'On design of carbon fiber reinforced plastic (CFRP) laminated structure with different failure criteria', INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES, vol. 196.
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Xu, Y, Zhu, L, Yang, Y & Wu, F 2021, 'Training Robust Object Detectors From Noisy Category Labels and Imprecise Bounding Boxes', IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 30, pp. 5782-5792.
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Xu, Z, Li, J, Wu, P & Wu, C 2021, 'Experimental investigation of triaxial strength of ultra-high performance concrete after exposure to elevated temperature', Construction and Building Materials, vol. 295.
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Equipped with excellent strength and energy absorption capacity, ultra-high performance concrete (UHPC) is a promising material to improve structural resistance against extreme loads. Material and structural tests at ambient temperature have been conducted extensively on UHPC specimens in recent years, and its mechanical properties have been well-documented. In this study, a hybrid steel and polypropylene (PP) fibre reinforced UHPC is investigated under uniaxial and triaxial compression states after exposure to elevated temperatures. Cubic (50 mm) and cylindrical specimens (50 mm diameter × 100 mm height) were first heated in electric furnace to target temperatures, i.e. 200 °C, 400 °C, 600 °C, 800 °C and 1000 °C. After naturally cooled down to ambient temperature, the specimens were tested under uniaxial compression and triaxial compression with confining pressure ranging from 5 to 40 MPa. The triaxial stress–strain relationships and failure modes after exposure to elevated temperatures were then compared and discussed. Several common failure criteria for concrete material were adopted to describe the high temperature effect on UHPC's strength. An empirical model for reproducing the triaxial compression stress–strain curves of UHPC after elevated temperature was proposed.

Xu, Z, Xia, Y, Zhou, P, Li, JJ, Yang, M, Zhang, Y, Zhang, Y, Xie, Y, Li, L, Pan, H, Xu, S & Wang, G 2021, 'Silicon incorporation into hydroxyapatite nanocarrier counteracts the side effects of vancomycin for efficient chronic osteomyelitis treatment', CHEMICAL ENGINEERING JOURNAL, vol. 406.
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Xue, C, Li, W, Luo, Z, Wang, K & Castel, A 2021, 'Effect of chloride ingress on self-healing recovery of smart cementitious composite incorporating crystalline admixture and MgO expansive agent', CEMENT AND CONCRETE RESEARCH, vol. 139.
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Yadav, S, Ibrar, I, Samal, AK, Altaee, A, Déon, S, Zhou, J & Ghaffour, N 2021, 'Preparation of fouling resistant and highly perm-selective novel PSf/GO-vanillin nanofiltration membrane for efficient water purification.', Journal of hazardous materials, vol. 421, p. 126744.
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To meet the rising global demand for water, it is necessary to develop membranes capable of efficiently purifying contaminated water sources. Herein, we report a series of novel polysulfone (PSf)/GO-vanillin nanofiltration membranes highly permeable, selective, and fouling resistant. The membranes are composed of two-dimensional (2D) graphite oxide (GO) layers embedded with vanillin as porogen and PSf as the base polymer. There is a growing interest in addressing the synergistic effect of GO and vanillin on improving the permeability and antifouling characteristics of membranes. Various spectroscopic and microscopic techniques were used to perform detailed physicochemical and morphological analyses. The optimized PSf₁₆/GO_0.15-vanillin_0.8 membrane demonstrated 92.5% and 25.4% rejection rate for 2000 ppm magnesium sulphate (MgSO₄) and sodium chloride (NaCl) solutions respectively. Antifouling results showed over 99% rejection for BSA and 93.57% flux recovery ratio (FRR). Experimental work evaluated the antifouling characteristics of prepared membranes to treat landfill leachate wastewater. The results showed 84-90% rejection for magnesium (Mg⁺²) and calcium (Ca⁺²) with 90.32 FRR. The study experimentally demonstrated that adding GO and vanillin to the polymeric matrix significantly improves fouling resistance and membrane performance. Future research will focus on molecular sieving for industrial separations and other niche applications using mixed matrix membranes.

Yan, L, Li, JJ, Zhu, Y, Liu, H, Liu, R, Zhao, B & Wang, B 2021, 'Interference screws are more likely to perform better than cortical button and cross-pin fixation for hamstring autograft in ACL reconstruction: a Bayesian network meta-analysis.', Knee Surgery Sports Traumatology Arthroscopy.
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PURPOSE:Anterior cruciate ligament (ACL) reconstruction is widely accepted as the first choice of treatment for ACL injury, but there is disagreement in the literature regarding the optimal femoral fixation method. This meta-analysis assesses the evidence surrounding three common femoral fixation methods: cortical button (CB), cross-pin (CP) and interference screws (IS). METHODS:A systematic search was conducted in Medline, EMBASE and the Cochrane Library to identify studies with evidence level I or II that compared at least two femoral fixation methods with hamstring autograft for ACL reconstruction. Ten primary outcomes were collected. Risk of bias was assessed following the Cochrane Handbook for Systematic Reviews of Interventions. Standardized mean differences (SMD) were estimated using random-effects network meta-analysis in a Bayesian framework. Probability of ranking best (ProBest) and surface under the cumulative ranking curve (SUCRA) were used to rank all treatments. Funnel plots were used to identify publication bias and small-study effects. RESULTS:Sixteen clinical trials were included for analysis out of 2536 retrieved studies. Bayesian network meta-analysis showed no significant differences among the three fixation methods for the ten primary outcome measures. Based on the 10 outcome measures, the IS, CB and CP had the highest ProBest in 5, 5 and 0 outcomes, and the highest SUCRA values in 5, 4 and 1 outcomes, respectively. No substantial inconsistency between direct and indirect evidence, or publication bias was detected in the outcomes. CONCLUSION:There were no statistical differences in performance among the CP, CB and IS femoral fixation methods with hamstring autograft in ACL reconstruction, although the IS was more likely to perform better than CB and CP based on the analysis of outcome measures from the included studies. LEVEL OF EVIDENCE:1.

Yang, B, Song, B-P, Shankar, S, Guller, A & Deng, W 2021, 'Recent advances in liposome formulations for breast cancer therapeutics', CELLULAR AND MOLECULAR LIFE SCIENCES.
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Yang, T, Ding, C, Ziolkowski, RW & Guo, YJ 2021, 'An Epsilon-Near-Zero (ENZ) Based, Ultra-Wide Bandwidth Terahertz Single-Polarization Single-Mode Photonic Crystal Fiber', JOURNAL OF LIGHTWAVE TECHNOLOGY, vol. 39, no. 1, pp. 223-232.
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Yang, T, Ding, C, Ziolkowski, RW & Guo, YJ 2021, 'High Sensitivity Core-Shell Structure (CSS)-Based Fiber Sensor for Monitoring Analytes in Liquids and Gases', JOURNAL OF LIGHTWAVE TECHNOLOGY, vol. 39, no. 10, pp. 3319-3329.
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Yang, T, Wang, W, Liu, Z, Wu, C, Xu, S & Yang, Y 2021, 'Behavior of CFRP-UHPFRC-steel double skin tubular columns against low-velocity impact', Composite Structures.
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© 2020 Elsevier Ltd In this study, a total of six specimens were prepared and tested, and the dynamic response and damage mechanism of carbon fiber reinforced polymer (CFRP)-ultra-high performance fiber-reinforced concrete (UHPFRC)-steel double skin tubular (DST) columns against low-velocity impact were investigated. The effects of CFRP, impact energy, hollowness ratio and infilled concrete on the behavior of CFRP-UHPFRC-steel double skin tubular columns against low-velocity impact were studied. Moreover, the software LS-DYNA was used to simulate the CFRP-UHPFRC-steel DST columns against low-velocity impact, and the feasibility of the model was verified by comparing the simulation and experimental results. Based on the verified model, the damage evolution of the infilled concrete was investigated. Afterwards, parametric analyses were carried out using the validated finite element model. The results show that increasing the steel ratio and decreasing the slenderness ratio can improve the lateral stiffness and the lateral impact resistance of specimens. When the impact position is closer to the fixed end of the specimen, the global deflection decreases. Only binding CFRP within a certain range at the impact region can also have a good restraint effect on the specimen and can enhance the lateral impact resistance of the specimen. Furthermore, indenters with different shapes have different effects on the behavior of specimens against low-velocity impact.

Yang, W, Liang, J, Yang, J, Walker, PD & Zhang, N 2021, 'Corresponding drivability control and energy control strategy in uninterrupted multi-speed mining trucks', JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, vol. 358, no. 2, pp. 1214-1239.
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Yang, X, Liu, W, Liu, W & Tao, D 2021, 'A Survey on Canonical Correlation Analysis', IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 33, no. 6, pp. 2349-2368.
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Yang, Y, Chen, H, Heidari, AA & Gandomi, AH 2021, 'Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts', Expert Systems with Applications.
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Yang, Y, Wang, Y, Jia, H, Li, B, Xing, D & Li, JJ 2021, 'MicroRNA-1 Modulates Chondrocyte Phenotype by Regulating FZD7 of Wnt/β-Catenin Signaling Pathway.', Cartilage.
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Objective

Osteoarthritis (OA) is an incurable joint disease characterized by pronounced pain. MicroRNAs constitute epigenetic mechanisms that may affect OA progression by contributing to changes in chondrocyte phenotype. This study investigates for the first time whether there is a link between miRNA-1 (miR-1) and OA pathogenesis, and the molecular mechanisms involved.

Design

OA-associated gene expression, including MMP-13, ADAMTS5, and COL2A1 was compared in chondrocytes from non-OA and OA cartilage, and in SW1353 cells over- and underexpressing miR-1. Bioinformatics and luciferase reporter assay were conducted to confirm whether FZD7 was a target of miR-1. The effects of miR-1 on FZD7 expression and downstream Wnt/β-catenin signalling were investigated.

Results

Non-OA and OA chondrocytes differed significantly in the expression of miR-1 and OA-associated genes. MiR-1 over- and underexpression in SW1353 cells, respectively, reduced and enhanced gene expression associated with cartilage catabolism. FZD7, which has an important role in the Wnt/β-catenin signaling pathway, was shown to be a potential target of miR-1. MiR-1 binding to FZD7 increased the levels of phosphorylated (inactivated) β-catenin, thereby preventing downstream β-catenin signaling.

Conclusions

Inhibition of Wnt/β-catenin signaling by miR-1 in chondrocytes may attenuate the expression of genes that regulate the activity of catabolic enzymes. This finding may be useful for future investigations of molecular targets for OA treatment.

Yang, Y, Wu, C, Liu, Z, Du, J, Zhang, H, Xu, S & Zhou, S 2021, 'Protective effect of unbonded prestressed ultra-high performance reinforced concrete slab against gas explosion in buried utility tunnel', Process Safety and Environmental Protection, vol. 149, pp. 370-384.
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With the wide application of natural gas, in order to increase the sustainability of buried utility tunnel and prevent the explosion of the gas pipeline cabin from threatening process safety, the performance of the partition wall (slab) of the cabin against gas explosion should be investigated. This paper presents the protective effect of unbonded prestressed ultra-high performance reinforced concrete slab (UPUHPRCS) against gas explosion in buried utility tunnel. There were four scenarios in the experiment, among which the variables were the length of tunnel (12,000 mm and 20,000 mm) and gas concentration (7.5 % and 9.5 %). The normal strength reinforced concrete slab (NSRCS) and ultra-high performance reinforced concrete slab (UHPRCS) were tested as reference specimens, and the simple supported steel slab fixed by stacking sandbags was compared with the concrete slab fully constrained on the tunnel. The overpressure curves caused by gas explosions in the tunnel, the damage of each concrete slab and the change of deflections were obtained through the tests. The results indicated that with the increase of the length of the buried utility tunnel, the power of gas explosion will further increase, and the UPUHPRCS, as the partition wall of the gas pipeline independent cabin in the buried utility tunnel, will play an excellent role in against gas explosion. Moreover, the gas explosion may have little effect on the circular section pipeline in the independent cabin.

Yang, Z, Garg, H, Li, J, Srivastava, G & Cao, Z 2021, 'Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm', Neural Computing and Applications.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. Q-rung orthopair fuzzy (q-ROF) set is one of the powerful tools for handling the uncertain multi-criteria decision-making (MCDM) problems, various MCDM methods under q-ROF environment have been developed in recent years. However, most existing studies merely concerned about the relationship between the criteria but they have not investigated the interactions between membership function and non-membership function. To explore the multiple heterogeneous relationships among membership functions and criteria, we propose a novel decision algorithm based on q-ROF set to deal with these using interactive operators and Maclaurin symmetric mean (MSM) operators. Specifically, the new interaction laws in the membership pairs of q-ROF sets are explained, and their properties are analyzed as the initial stage. Then, taking into account the influence of two or more factors on decision analysis, a q-ROF interaction Maclaurin symmetry mean (q-ROFIMSM) operator is formed based on the proposed interaction law to identify these factors’ interrelationship. Thirdly, based on the proposed operator with q-ROF information, a MCDM algorithm is developed and illustrated by numerical examples. An analysis of the feasibility, sensitivity, and superiority of the proposed framework is provided to validate our proposed method.

Yao, L, Kusakunniran, W, Wu, Q, Zhang, J, Tang, Z & Yang, W 2021, 'Robust gait recognition using hybrid descriptors based on Skeleton Gait Energy Image', Pattern Recognition Letters.
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© 2019 Gait features have been widely applied in human identification. The commonly-used representations for gait recognition can be roughly classified into two categories: model-free features and model-based features. However, due to the view variances and clothes changes, model-free features are sensitive to the appearance changes. For model-based features, there is great difficulty in extracting the underlying models from gait sequences. Based on the confidence maps and the part affinity fields produced by a two-branch multi-stage CNN network, a new model-based representation, Skeleton Gait Energy Image (SGEI), has been proposed in this paper. Another contribution is that a hybrid representation has been produced, which uses SGEI to remedy the deficiency of model-free features, Gait Energy Image (GEI) for instance. The experimental performances indicate that our proposed methods are more robust to the cloth changes, and contribute to increasing the robustness of gait recognition in the unconstrained environments with view variances and clothes changes.

Yao, Q, Lu, D-D-C & Lei, G 2021, 'Accurate Online Battery Impedance Measurement Method with Low Output Voltage Ripples on Power Converters', ENERGIES, vol. 14, no. 4.
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Yao, Q, Lu, DD-C & Lei, G 2021, 'Rapid Open-Circuit Voltage Measurement Method for Lithium-Ion Batteries Using One-Cycle Bipolar-Current Pulse', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 2, no. 2, pp. 132-141.
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Ye, D, Zhu, T, Shen, S & Zhou, W 2021, 'A Differentially Private Game Theoretic Approach for Deceiving Cyber Adversaries', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 16, pp. 569-584.
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Ye, D, Zhu, T, Shen, S, Zhou, W & Yu, P 2021, 'Differentially Private Multi-Agent Planning for Logistic-like Problems', IEEE Transactions on Dependable and Secure Computing, pp. 1-1.
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Ye, F, He, X, Zheng, J, Li, Y, Li, M, Hu, Z, Wang, S, Tong, G & Li, X 2021, 'Highly stretchable and self-foaming polyurethane composite skeleton with thermally tunable microwave absorption properties.', Nanotechnology.
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Stretchable and lightweight polymer composite material possessing tunable microwave absorption properties under thermal radiations remains a significant challenge. Here, we proposed a facile strategy to fabricate stretchable, magnetic composite skeletons by incorporating the tadpole-like CNTs@Fe3O4 nanoparticles into self-foaming polyurethane (PU) matrix and the electromagnetic responsive of CNTs@Fe3O4/PU composite foams with different CNTs contents under heating-cooling cycle in a temperature range of 253~333K were carefully investigated. Enhanced complex permittivity and shifting peak frequency were observed at elevated temperatures. For instance, the 70-CNTs@Fe3O4/PU sample with 15wt% loading content at 333K exhibits excellent microwave absorption (MA) properties including a minimum reflection loss (RLm) of 66.9 dB and ultrabroad effective frequency bandwidth (RL  20 dB) of 9.98 GHz at the thickness of 1.58~3.37 mm. Meanwhile, great recoverability in terms of RL-f profile was achieved in the process of thermal cooling back to 253 K. Such adjustable MA property was attributed to the well-matched impedance and dramatic attenuation ability, benefiting from the temperature-dependant electrical conductivity, abundant interfacial polarization and interior microcellular structures. Besides, the rising temperature increased the sample elongation and electrical conductivity with a slight sacrifice of maximum tensile strength. This stretchable PU skeleton with a unique assembly of CNTs and Fe3O4 nanoparticles are expected to be promising candidates as smart absorbers for application in the harsh environments.

Ye, G, Yin, H, Chen, T, Chen, H, Cui, L & Zhang, X 2021, 'FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection.', IEEE journal of biomedical and health informatics, vol. PP.
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Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit hospitals and bear the costly treatment for a single detection. Recently, many single-sensor alternatives have been proposed to improve the cost efficiency and convenience. Among these methods, solutions based on RR-interval (i.e., the interval between two consecutive pulses) signals reach a satisfactory balance among comfort, portability and detection accuracy.

Ye, K, Ji, JC & Brown, T 2021, 'A novel integrated quasi-zero stiffness vibration isolator for coupled translational and rotational vibrations', MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 149.
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Ye, Q, Bie, H, Li, KC, Gong, XFL, He, X & Fang, G 2021, 'EdgeLoc: A Robust and Real-time Localization System Towards Heterogeneous IoT Devices', IEEE Internet of Things Journal.
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Indoor localization has become an essential demand driven by indoor location-based services (ILBSs) for mobile users. With the rising of Internet of Things (IoT), heterogeneous smartphones and wearables have become ubiquitous. However, the ILBSs for heterogeneous IoT devices confront significant challenges, such as received signal strength (RSS) variances caused by hardware heterogeneity, multipath reflections from complex environments and localization time restricted by computation resources. This paper proposes EdgeLoc, a robust and real-time indoor localization system towards heterogeneous IoT devices to solve the above challenges. In particular, the RSS fingerprinting data of Wi-Fi is employed for localization and tackling the heterogeneity of IoT devices in two folds. First, feature-level and signal-level solutions are presented to address the random RSS variances. At the feature level, this works proposes a novel capsule neural network model to efficiently extract incremental features from RSS fingerprinting data. At the signal level, a multi-step dataflow is further devised to process RSS fingerprints into image-like data, which utilizes the feature matrix to reduce absolute sensing errors introduced by hardware heterogeneity. Second, an edge-IoT framework is designed to utilize the edge server to train the deep learning model and further supports real-time localization for heterogeneous IoT devices. Extensive field experiments with over 33,600 data points are conducted to validate the effectiveness of EdgeLoc with a large-scale Wi-Fi fingerprint dataset. The results show that EdgeLoc outperforms the state-of-the-art SAE-CNN method in localization accuracy by up to 14.4%, with an average error of 0.68 m and an average positioning time of 2.05 ms.

Yeola, A, Subramanian, S, Oliver, RA, Lucas, CA, Thoms, JAI, Yan, F, Olivier, J, Chacon, D, Tursky, ML, Srivastava, P, Potas, JR, Hung, T, Power, C, Hardy, P, D., D, Kilian, KA, McCarroll, J, Kavallaris, M, Hesson, LB, Beck, D, Curtis, DJ, Wong, JWH, Hardeman, EC, Walsh, WR, Mobbs, R, Chandrakanthan, V & Pimanda, JE 2021, 'Induction of muscle-regenerative multipotent stem cells from human adipocytes by PDGF-AB and 5-azacytidine', Science Advances.
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Yin, H, Wang, Y, Tang, L, Ding, X, Huang, S & Xiong, R 2021, '3D LiDAR Map Compression for Efficient Localization on Resource Constrained Vehicles', IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, vol. 22, no. 2, pp. 837-852.
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Yin, J, Li, Q, Liu, S, Wu, Z & Xu, G 2021, 'Leveraging multi-level dependency of relational sequences for social spammer detection', Neurocomputing, vol. 428, pp. 130-141.
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Much recent research has shed light on developing the relation-dependent but the content-independent framework for social spammer detection. This is mainly because the relation among users is difficult to be altered when spammers attempt to conceal their malicious intentions. Our study investigates the spammer detection problem in the context of multi-relation social networks and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy. Specifically, we present the Multi-level Dependency Model (MDM). The MDM is able to exploit the user's long-term dependency hidden in their relational sequences along with short-term dependency. Moreover, MDM fully considers short-term relational sequences from the perspectives of individual-level and union-level, due to the fact that the type of short-term sequences is multi-folds. Experimental results on a real-world multi-relational social network demonstrate the effectiveness of our proposed MDM on multi-relational social spammer detection.

Ying, M-S, Feng, Y & Ying, S-G 2021, 'Optimal Policies for Quantum Markov Decision Processes', International Journal of Automation and Computing.
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You, X, Wang, CX, Huang, J, Gao, X, Zhang, Z, Wang, M, Huang, Y, Zhang, C, Jiang, Y, Wang, J, Zhu, M, Sheng, B, Wang, D, Pan, Z, Zhu, P, Yang, Y, Liu, Z, Zhang, P, Tao, X, Li, S, Chen, Z, Ma, X, Chih-Lin, I, Han, S, Li, K, Pan, C, Zheng, Z, Hanzo, L, Shen, XS, Guo, YJ, Ding, Z, Haas, H, Tong, W, Zhu, P, Yang, G, Wang, J, Larsson, EG, Ngo, HQ, Hong, W, Wang, H, Hou, D, Chen, J, Chen, Z, Hao, Z, Li, GY, Tafazolli, R, Gao, Y, Poor, HV, Fettweis, GP & Liang, YC 2021, 'Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts', Science China Information Sciences, vol. 64, no. 1.
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The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G ...

Yu, G, Zhang, L, Wang, X, Yu, K, Ni, W, Zhang, JA & Liu, RP 2021, 'A novel Dual-Blockchained structure for contract-theoretic LoRa-based information systems', Information Processing and Management, vol. 58, no. 3.
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LoRa serves as one of the most deployed technologies in Internet-of-Things-based information systems (IoT-IS), and self-motivated deployment is the key to the rollout of LoRa. Proper incentive can play an important role in encouraging the private deployment of LoRa, increasing coverage and promoting effective management of IoT-IS. However, existing incentive mechanisms have the vulnerabilities of insecure centralized architecture and excessive utility loss of LoRa Controllers and Gateways, due to asymmetric information between private owners of gateways and centralized controller (or service providers). Blockchain-based LoRa networks, as a promising solution, have not been comprehensively studied to address the vulnerabilities, let alone the other issues of security, scalability, and flexibility. In this paper, we propose a novel Dual-Chained LoRa-based information system (LoRa-IS) to provide globally cross-validated security. Behaviors, including state-of-the-art contract-theoretic incentive mechanism and new flow control protocol, can be secured with the tamper-resistance of Blockchains. Being part of the proposed incentive mechanism, the new self-driven flow control allows both the Dual-Chain system and the LoRa network to scale. To the best of our knowledge, the proposed system is the first comprehensive Blockchain-based LoRa-IS combined with contract theory. We also provide analysis and simulations, showing that our system can pay fair incentives under information asymmetry. With the new flow control, the system can optimize network coverage while improving the Blockchain scalability and flexibility.

Yu, H, Li, W, Chen, C, Liang, J, Gui, W, Wang, M & Chen, H 2021, 'Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis', Engineering with Computers.
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The Fruit Fly Optimization Algorithm (FOA) is a recent algorithm inspired by the foraging behavior of fruit fly populations. However, the original FOA easily falls into the local optimum in the process of solving practical problems, and has a high probability of escaping from the optimal solution. In order to improve the global search capability and the quality of solutions, a dynamic step length mechanism, abandonment mechanism and Gaussian bare-bones mechanism are introduced into FOA, termed as BareFOA. Firstly, the random and ambiguous behavior of fruit flies during the olfactory phase is described using the abandonment mechanism. The search range of fruit fly populations is automatically adjusted using an update strategy with dynamic step length. As a result, the convergence speed and convergence accuracy of FOA have been greatly improved. Secondly, the Gaussian bare-bones mechanism that overcomes local optimal constraints is introduced, which greatly improves the global search capability of the FOA. Finally, 30 benchmark functions for CEC2017 and seven engineering optimization problems are experimented with and compared to the best-known solutions reported in the literature. The computational results show that the BareFOA not only significantly achieved the superior results on the benchmark problems than other competitive counterparts, but also can offer better results on the engineering optimization design problems.

Yu, H, Lu, J & Zhang, G 2021, 'An Online Robust Support Vector Regression for Data Streams', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Yu, H, Lu, J & Zhang, G 2021, 'Topology Learning-based Fuzzy Random Neural Network for Streaming Data Regression', IEEE Transactions on Fuzzy Systems.
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IEEE As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN system can inherit the advantages of neural networks. However, for streaming data regression, EFN systems still have several drawbacks: 1) determining fuzzy sets is not robust to data sequence; 2) determining fuzzy rules is complex due to subspaces that can approximate to a Takagi-Sugeno-Kang (TSK) rule need to be obtained, and many parameters need to be optimized; 3) it is difficult to detect and adapt to changes in the data distribution, i.e., concept drift, if the output is a continuous variable. Hence, in this paper, a novel evolving-fuzzy-neuro system, called the topology learning-based fuzzy random neural network (TLFRNN), is proposed. In TLFRNN, an online topology learning algorithm is designed to self-organize each layer of TLFRNN. Different from current EFN systems, TLFRNN learns multiple fuzzy sets to reduce the impact of noises on each fuzzy set, and a randomness layer is designed, which assigning the probability of each fuzzy set. Also, TLFRNN does not utilize TSK rules instead uses a simple inference which considering fuzzy and random information of data simultaneously. More importantly, in TLFRNN, concept drift can be detected and adapted easily and rapidly. The experiments demonstrate that TLFRNN achieves superior performance compared to other EFSs.

Yu, H, Ye, L, Guo, Y & Su, S 2021, 'An Effective In-Field Calibration Method for Triaxial Magnetometers Based on Local Magnetic Inclination', IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, vol. 70.
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Yu, J, Xue, H, Liu, B, Wang, Y, Zhu, S & Ding, M 2021, 'GAN-Based Differential Private Image Privacy Protection Framework for the Internet of Multimedia Things', SENSORS, vol. 21, no. 1.
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Yu, KL, Lee, XJ, Ong, HC, Chen, W-H, Chang, J-S, Lin, C-S, Show, PL & Ling, TC 2021, 'Adsorptive removal of cationic methylene blue and anionic Congo red dyes using wet-torrefied microalgal biochar: Equilibrium, kinetic and mechanism modeling', ENVIRONMENTAL POLLUTION, vol. 272.
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Yu, S, Peng, L, Xu, Y, Song, S, Xie, G-J, Liu, Y & Ni, B-J 2021, 'Optimizing light sources for selective growth of purple bacteria and efficient formation of value-added products', JOURNAL OF CLEANER PRODUCTION, vol. 280.
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Yu, Y, Nguyen, TN, Li, J, Sanchez, LFM & Nguyen, A 2021, 'Predicting elastic modulus degradation of alkali silica reaction affected concrete using soft computing techniques: A comparative study', CONSTRUCTION AND BUILDING MATERIALS, vol. 274.
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Yuan, D, Zhou, X, Jin, W, Han, W, Chi, H, Ding, W, Huang, Y, He, Z, Gao, S & Wang, Q 2021, 'Effects of the Combined Utilization of Ultrasonic/Hydrogen Peroxide on Excess Sludge Destruction', Water.
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Yuan, S, Hao, H, Zong, Z & Li, J 2021, 'Numerical analysis of axial load effects on RC bridge columns under blast loading', Advances in Structural Engineering: an international journal.
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© The Author(s) 2020. Blast load and its effects on transportation infrastructure especially bridge structures have received considerable attention in recent years. The RC bridge columns are considered as the most critical structural members because their failure leads to collapse of the bridge. Although RC bridge columns are typical axial load-carrying components, the studies on blast-resistant capacity of RC bridge columns usually neglect the axial load effect since it is commonly assumed that neglecting the axial load leads to conservative predictions of column responses. This assumption is true when column failure is governed by flexural response since axial compressive load generates a prestress in column which compensates concrete tensile stress induced by bending response. When subjected to blast loads, column response however could be governed by shear response. In this case neglecting axial loading effect does not necessarily lead to conservative predictions of column responses. In this study, high-fidelity finite element (FE) models for both non-contact explosion and contact explosion were developed in LS-DYNA. The FE models were validated with field blast test data. Subsequently, intensive simulations of the RC bridge columns with and without axial load subjected to a wider range of blast loading scenarios, including far-field, near-field and contact explosion were conducted. The influence of axial load on the dynamic performance of RC bridge columns corresponding to different blast loading scenarios was discussed.

Yuan, X, Feng, Z, Zhang, JA, Ni, W, Liu, RP, Wei, Z & Xu, C 2021, 'Spatio-Temporal Power Optimization for MIMO Joint Communication and Radio Sensing Systems With Training Overhead', IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, vol. 70, no. 1, pp. 514-528.
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Yuan, Z, Yu, Y, Wei, L, Wang, C, Zhong, X, Sui, X, Yu, Z, Han, DS, Shon, H & Chen, Y 2021, 'Thermo-osmosis-Coupled Thermally Regenerative Electrochemical Cycle for Efficient Lithium Extraction.', ACS applied materials & interfaces.
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Lithium (Li) production based on the soda evaporation process is time-consuming and unsustainable. The emerging electrochemical Li extraction is time-efficient but requires high-concentration Li sources and significant electrical energy input. Here, we demonstrate a fast, energy-saving, and environment-friendly Li production process by coupling a thermally regenerative electrochemical cycle (TREC) using lithium manganese oxide (LMO) and nickel hexacyanoferrate (NiHCF) electrodes with poly(vinylidene fluoride) membrane-based thermo-osmosis (denoted as TO-TREC). The characterization of LMO and NiHCF electrodes confirmed that the relatively high temperature of TO-TREC has negligible adverse effects on the ion intercalation in LMO and NiHCF electrodes. The LMO/NiHCF pair has a positive temperature coefficient of 0.843 mV K-1. In the TO-TREC process, Li ions are selectively extracted from a Li-containing brine warmed by low-grade heat and then released into a room-temperature recovery solution such as LiCl with a production rate of 50-60 mmol Li+ m-2 h-1. Li source solutions are concentrated by thermo-osmosis simultaneously, making it possible to utilize previously unusable Li-containing sources, such as concentrated brines from desalination plants and industrial effluents. Besides, the TREC harvests thermal energy from the heated brine, saving >20% of electrical energy compared to conventional electrochemical methods. The new process shows the potential to meet the growing global Li demands for many applications.

Zamri, MFMA, Hasmady, S, Akhiar, A, Ideris, F, Shamsuddin, AH, Mofijur, M, Fattah, IMR & Mahlia, TM 2021, 'A comprehensive review on anaerobic digestion of organic fraction of municipal solid waste', RENEWABLE & SUSTAINABLE ENERGY REVIEWS, vol. 137.
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Zarandi, SM, Shahsavani, A, Nasiri, R & Pradhan, B 2021, 'A hybrid model of environmental impact assessment of PM2.5 concentration using multi-criteria decision-making (MCDM) and geographical information system (GIS)-a case study', ARABIAN JOURNAL OF GEOSCIENCES, vol. 14, no. 3.
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Zavahir, S, Elmakki, T, Gulied, M, Ahmad, Z, Al-Sulaiti, L, Shon, HK, Chen, Y, Park, H, Batchelor, B & Han, DS 2021, 'A review on lithium recovery using electrochemical capturing systems', Desalination.
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Zdarta, J, Nguyen, LN, Jankowska, K, Jesionowski, T & Nghiem, LD 2021, 'A contemporary review of enzymatic applications in the remediation of emerging estrogenic compounds', CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY.
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Zeweldi, HG, Bendoy, AP, Park, MJ, Shon, HK, Johnson, EM, Kim, H-S, Kim, H, Chung, W-J & Nisola, GM 2021, 'Forward osmosis with direct contact membrane distillation using tetrabutylphosphonium p-toluenesulfonate as an effective and safe thermo-recyclable osmotic agent for seawater desalination', CHEMOSPHERE, vol. 263.
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Zhand, S, Xiao, K, Bazaz, SR, Zhu, Y, Bordhan, P, Jin, D & Warkiani, ME 2021, 'Improving capture efficiency of human cancer cell derived exosomes with nanostructured metal organic framework functionalized beads', APPLIED MATERIALS TODAY, vol. 23.
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Zhang, A, Rahman, ML, Huang, X, Guo, YJ, Chen, S & Heath, RW 2021, 'Perceptive Mobile Network: Cellular Networks With Radio Vision via Joint Communication and Radar Sensing', IEEE Vehicular Technology Magazine.
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Zhang, C, Zhang, Q, Yu, S, Yu, JJQ & Song, X 2021, 'Complicating the Social Networks for Better Storytelling: An Empirical Study of Chinese Historical Text and Novel', IEEE Transactions on Computational Social Systems.
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Zhang, C, Zhang, S, Yu, JJQ & Yu, S 2021, 'FASTGNN: A Topological Information Protected Federated Learning Approach For Traffic Speed Forecasting', IEEE Transactions on Industrial Informatics.
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Federated learning has been applied to various tasks in intelligent transportation systems to protect data privacy through decentralized training schemes. The majority of the state-of-the-art models in ITS are graph neural networks (GNN)-based for spatial information learning. When applying federated learning to the ITS tasks with GNN-based models, the existing architectures can only protect the data privacy; however, ignore the one of topological information of transportation networks. In this work, we propose a novel federated learning architecture to tackle this problem. Specifically, we introduce a differential privacy-based adjacency matrix preserving approach for protecting the topological information. We also propose an adjacency matrix aggregation approach to allow local GNN-based models to access the global network for a better training effect. Furthermore, we propose a GNN-based model named Attention-based Spatial-Temporal Graph Neural Networks (ASTGNN) for traffic speed forecasting. We integrate the proposed federated learning architecture and ASTGNN as FASTGNN for traffic speed forecasting. Extensive case studies on a real-world dataset demonstrate that FASTGNN can develop accurate forecasting under the privacy preservation constraint.

Zhang, F, Du, X-S, Zhang, D-W, Wang, Y-F, Lu, H-Y & Chen, C-F 2021, 'A Green Fluorescent Nitrogen-Doped Aromatic Belt Containing a [6]Cycloparaphenylene Skeleton', ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, vol. 60, no. 28, pp. 15291-15295.
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Zhang, G, Morrison, D, Bao, G, Yu, H, Yoon, CW, Song, T, Lee, J, Ung, AT & Huang, Z 2021, 'An Amine-Borane System Featuring Room-Temperature Dehydrogenation and Regeneration', ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, vol. 60, no. 21, pp. 11725-11729.
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Zhang, G, Morrison, D, Bao, G, Yu, H, Yoon, CW, Song, T, Lee, J, Ung, AT & Huang, Z 2021, 'An Amine–Borane System Featuring Room‐Temperature Dehydrogenation and Regeneration', Angewandte Chemie, vol. 133, no. 21, pp. 11831-11835.
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Zhang, G, Ngo, HH, Peng, Y, Bux, F & Mannina, G 2021, 'Biological nutrients removal and recovery.', Bioresource technology, vol. 320, no. Pt B, p. 124377.
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Zhang, H, Huang, X & Zhanga, JA 2021, 'Adaptive Transmission with Frequency-Domain Precoding and Linear Equalization over Fast Fading Channels', IEEE Transactions on Wireless Communications.
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In this paper, the emerging orthogonal time frequency space (OTFS) modulation is firstly restructured as a precoded orthogonal frequency division multiplexing (OFDM) system, so that the well established frequency-domain approach can be applied to perform signal in fast fading channels. Then a frequency-domain minimum mean squared error (MMSE) equalizer for OTFS is introduced and its performance is analyzed based on the eigenvalue decomposition of the channel matrix. Inspired by the frequency-domain precoding structure, an adaptive transmission scheme with frequency-domain precoding matrix composed of the eigenvectors of the channel matrix is proposed to improve the system performance under MMSE equalization, and its optimized performance is derived with simple expression. Finally, considering two extreme channel conditions, the lower and upper bounds for the diversity performance of the adaptive transmission scheme are derived. Simulation results show that the proposed adaptive transmission achieves significantly better performance for short signal frames and can work well with imperfect channel state information (CSI). The derived performance bounds can serve as benchmarks for OTFS and other precoded OFDM systems.

Zhang, H, Nguyen, H, Bui, X-N, Pradhan, B, Asteris, PG, Costache, R & Aryal, J 2021, 'A generalized artificial intelligence model for estimating the friction angle of clays in evaluating slope stability using a deep neural network and Harris Hawks optimization algorithm', ENGINEERING WITH COMPUTERS.
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Zhang, J, Chen, B, Cheng, X, Huynh, TTB & Yu, S 2021, 'PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems', IEEE INTERNET OF THINGS JOURNAL, vol. 8, no. 5, pp. 3310-3322.
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Zhang, J, Liu, L, Wang, P & Zhang, J 2021, 'Exploring the auxiliary learning for long-tailed visual recognition', Neurocomputing, vol. 449, pp. 303-314.
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Real-world visual data often exhibits a long-tailed distribution, where some “head” classes have a large number of samples, yet only a few samples are available for “tail” classes. The fundamental problem of learning with the imbalanced data is that insufficient training samples easily lead to the over-fitting of feature extractor and classifier for tail classes, which can be boiled down into a dilemma: on the one hand, we prefer to increase the exposure of tail class samples to avoid the excessive dominance of head classes in the classifier training. On the other hand, oversampling tail classes makes the network prone to over-fitting, since head class samples are often consequently under-represented. To resolve this dilemma, in this paper, we propose an effective auxiliary learning approach. The key idea is to split a network into a classifier part and a feature extractor part, and then employ different training strategies for each part in an auxiliary learning manner. Specifically, to promote the awareness of tail-classes, a class-balanced sampling scheme is utilised for training both the classifier and the feature extractor as the primary task. For the feature extractor, we also introduce an auxiliary training task, which is to train a classifier under the regular random sampling scheme. In this way, the feature extractor is jointly trained from both sampling strategies and thus can take advantage of all training data and avoid the over-fitting issue. Apart from this basic auxiliary task, we further explore the benefits of different types of auxiliary tasks for improving the generality of learned features, including self-supervised learning and class-wise re-weighting. Without using any bells and whistles, our model compares favourably over state-of-the-art solutions.

Zhang, L, Gonzales, RR, Istirokhatun, T, Lin, Y, Segawa, J, Shon, HK & Matsuyama, H 2021, 'In situ engineering of an ultrathin polyamphoteric layer on polyketone-based thin film composite forward osmosis membrane for comprehensive anti-fouling performance', Separation and Purification Technology, vol. 272.
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Thin film composite (TFC) membranes easily suffer from fouling induced by oil and other pollutants during forward osmosis (FO) due to the relatively hydrophobic chemistry and rough structure of polyamide (PA). To achieve comprehensive anti-fouling properties, poly(2-methacryloyloxyethyl phosphorylcholine-co-2-aminoethyl methacrylate hydrochloride) (MPC-co-AEMA) was immobilized on top of a polyketone (PK)-based TFC membrane following a single-step simultaneous deposition with dopamine. The adhesive properties of polydopamine (PDA), as well as the covalent interactions between PDA and MPC-co-AEMA, ensured the firm immobilization of the MPC-co-AEMA on PA layer. As a result of the simultaneous deposition of PDA and MPC-co-AEMA, a high-performance and superhydrophilic and underwater superoleophobic TFC membrane was engineered. In addition, the outstanding water adsorption capacity of the polyamphoteric layer resulted in better protein adhesion mitigation. FO operation using various foulants also demonstrated a high fouling resistance of the PK-TFC-PDA/MPC membrane, especially during the treatment of wastewater emulsion containing high concentration of oil and bovine serum albumin (BSA). In summary, the findings in this study could provide insights into the preparation of anti-fouling membranes for wastewater purification using FO.

Zhang, L, He, Z, Yang, Y, Wang, L & Gao, X-B 2021, 'Tasks Integrated Networks: Joint Detection and Retrieval for Image Search.', IEEE transactions on pattern analysis and machine intelligence, vol. PP.
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The traditional object (person) retrieval (re-identification) task aims to learn a discriminative feature representation or metric on the cropped objects. However, in many real-world scenarios, the objects are seldom accurately annotated. Therefore, object-level retrieval becomes intractable without annotation, which leads to a new but challenging topic, i.e. image search with joint detection and retrieval. To address the image search issue, we introduce an end-to-end Integrated Net, which has four merits: 1) A Siamese architecture and an on-line pairing strategy for similar and dissimilar objects in the given images are designed. 2) A novel on-line pairing (OLP) loss is introduced with a dynamic feature dictionary, which alleviates the multi-task training stagnation problem, by automatically generating a number of negative pairs to restrict the positives. 3) Two modules are tailored to handle different tasks separately in the integrated framework, such that the task specification is guaranteed. 4) A class-center guided HEP loss (C2HEP) by exploiting the stored class centers is proposed, such that the intra-similarity and inter-dissimilarity can be captured. Extensive experiments on the CUHK-SYSU and PRW datasets for person search and the large-scale WebTattoo dataset for tattoo search, demonstrate that the proposed model outperforms the state-of-the-art image search models.

Zhang, M, Gao, Y, Sun, C & Blumenstein, M 2021, 'Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy', IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 30, pp. 150-162.
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Zhang, M, Li, H, Pan, S, Lyu, J, Ling, S & Su, S 2021, 'Convolutional Neural Networks based Lung Nodule Classification: A Surrogate-Assisted Evolutionary Algorithm for Hyperparameter Optimization', IEEE Transactions on Evolutionary Computation.
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This paper investigates deep neural networks (DNNs) based lung nodule classification with hyperparameter optimization. Hyperparameter optimization in DNNs is a computationally expensive problem, and a surrogate-assisted evolutionary algorithm has been recently introduced to automatically search for optimal hyperparameter configurations of DNNs, by applying computationally efficient surrogate models to approximate the validation error function of hyperparameter configurations. Different from existing surrogate models adopting stationary covariance functions (kernels) to measure the difference between hyperparameter points, this paper proposes a non-stationary kernel that allows the surrogate model to adapt to functions whose smoothness varies with the spatial location of inputs. A multi-level convolutional neural network (ML-CNN) is built for lung nodule classification, and the hyperparameter configuration is optimized by the proposed non-stationary kernel-based Gaussian surrogate model. Our algorithm searches with a surrogate for optimal setting via a hyperparameter importance based evolutionary strategy, and the experiments demonstrate our algorithm outperforms manual tuning and several well-established hyperparameter optimization methods, including random search, grid Search, the Tree-structured Parzen Estimator Approach (TPE), Gaussian processes (GP) with stationary kernels, and the recently proposed Hyperparameter Optimization via RBF and Dynamic coordinate search (HORD).

Zhang, R, Xu, L, Yu, Z, Shi, Y, Mu, C & Xu, M 2021, 'Deep-IRTarget: An Automatic Target Detector in Infrared Imagery using Dual-domain Feature Extraction and Allocation', IEEE Transactions on Multimedia.
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Recently, convolutional neural networks (CNNs) have brought impressive improvements for object detection. However, detecting targets in infrared images still remains challenging, because the poor texture information, low resolution and high noise levels of the thermal imagery restrict the feature extraction ability of CNNs. In order to deal with these difficulties in the feature extraction, we propose a novel backbone network named Deep-IRTarget, composing of a frequency feature extractor, a spatial feature extractor and a dual-domain feature resource allocation model. Hypercomplex Infrared Fourier Transform is developed to calculate the infrared intensity saliency by designing hypercomplex representations in the frequency domain, while a convolutional neural network is invoked to extract feature maps in the spatial domain. Features from the frequency domain and spatial domain are stacked to construct Dual-domain features. To efficiently integrate and recalibrate them, we propose a Resource Allocation model for Features (RAF). The well-designed channel attention block and position attention block are used in RAF to respectively extract interdependent relationships among channel and position dimensions, and capture channel-wise and position-wise contextual information. Extensive experiments are conducted on three challenging infrared imagery databases. We achieve 10.14%, 9.1% and 8.05% improvement on mAP scores, compared to the current state of the art method on MWIR, BITIR and WCIR respectively.

Zhang, T, Du, J & Guo, YJ 2021, 'An 8 to 10 GHz Low-loss Image-reject HTS Mixer Based on Cascaded Josephson Junctions', IEEE Microwave and Wireless Components Letters.
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A 7.8 to 9.8 GHz low-loss high-Tc superconducting (HTS) image-reject Josephson junction mixer is presented in this paper. Image rejection is achieved using low-loss image rejection filters and a pair of parallel dual-mode resonators. Three cascaded Josephson junctions are monolithically integrated with impedance matching networks and bandpass/lowpass filters on a single chip for low loss and compactness. The implementation of series junction array increased the overall junction equivalent impedance, so that a better impedance matching is achieved. Measurement results of the novel HTS mixer showed a low conversion loss at 5.5 dB, and an image-reject ratio over 30 dB within the desired frequency. The proposed cascaded junction configuration provides a new approach for impedance matching in HTS mixer designs, and the demonstrated mixer performance makes it an ideal candidate for X-band radar systems.

Zhang, W-W, Sanders, YR & Sanders, BC 2021, 'Channel discord and distortion', NEW JOURNAL OF PHYSICS, vol. 23, no. 8.
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Zhang, X & Fatahi, B 2021, 'Assessing axial load transfer mechanism of open-ended tubular piles penetrating in weak rocks using three-dimensional discrete element method', COMPUTERS AND GEOTECHNICS, vol. 137.
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Zhang, X, Liu, L, Long, G, Jiang, J & Liu, S 2021, 'Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task', Neural Networks, vol. 134, pp. 1-10.
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© 2020 Elsevier Ltd Typical methods to study cognitive function are to record the electrical activities of animal neurons during the training of animals performing behavioral tasks. A key problem is that they fail to record all the relevant neurons in the animal brain. To alleviate this problem, we develop an RNN-based Actor–Critic framework, which is trained through reinforcement learning (RL) to solve two tasks analogous to the monkeys’ decision-making tasks. The trained model is capable of reproducing some features of neural activities recorded from animal brain, or some behavior properties exhibited in animal experiments, suggesting that it can serve as a computational platform to explore other cognitive functions. Furthermore, we conduct behavioral experiments on our framework, trying to explore an open question in neuroscience: which episodic memory in the hippocampus should be selected to ultimately govern future decisions. We find that the retrieval of salient events sampled from episodic memories can effectively shorten deliberation time than common events in the decision-making process. The results indicate that salient events stored in the hippocampus could be prioritized to propagate reward information, and thus allow decision-makers to learn a strategy faster.

Zhang, X, Liu, Y, Ren, Z, Zhang, X, Hu, J, Huang, Z, Lu, Y, Gao, M & Pan, H 2021, 'Realizing 6.7 wt% reversible storage of hydrogen at ambient temperature with non-confined ultrafine magnesium hydrides', ENERGY & ENVIRONMENTAL SCIENCE, vol. 14, no. 4, pp. 2302-2313.
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Zhang, X, Wu, D, Ding, L, Luo, H, Lin, C-T, Jung, T-P & Chavarriaga, R 2021, 'Tiny noise, big mistakes: adversarial perturbations induce errors in brain-computer interface spellers', NATIONAL SCIENCE REVIEW, vol. 8, no. 4.
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Zhang, X, Yao, L, Wang, X, Monaghan, J, McAlpine, D & Zhang, Y 2021, 'A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers', Journal of Neural Engineering.
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Zhang, X, Ye, H, Wei, N, Tao, R & Luo, Z 2021, 'Design optimization of multifunctional metamaterials with tunable thermal expansion and phononic bandgap', Materials & Design.
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Zhang, X, Zhang, L, Zhang, W, Ren, Z, Huang, Z, Hu, J, Gao, M, Pan, H & Liu, Y 2021, 'Nano-synergy enables highly reversible storage of 9.2 wt% hydrogen at mild conditions with lithium borohydride', NANO ENERGY, vol. 83.
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Zhang, X, Zheng, Y, Zhao, Z, Liu, Y, Blumenstein, M & Li, J 2021, 'Deep learning detection of anomalous patterns from bus trajectories for traffic insight analysis', Knowledge-Based Systems, vol. 217.
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Existing data-driven methods for traffic anomaly detection are modeled on taxi trajectory datasets. The concern is that the data may contain much inaccuracy about the actual traffic situations, because taxi drivers often choose optimal routes to evade from the congestions caused by traffic anomalies. We use bus trajectory data in this work. Bus trajectories can capture real traffic conditions in the road networks without drivers’ preference, which are more objective and appropriate for accurately detecting anomalous patterns for a broad range of insight analyses on traffics. We proposed a deep learning-based feature visualization method to map 3-dimensional features into a red–green–blue (RGB) color space. A color trajectory (CT) is then derived by encoding a trajectory with the RGB colors. With the spatial and temporal properties extracted from the CT, spatio-temporal outliers are detected by a novel offline detection method. We then conduct GIS map fusion to obtain insights for better understanding the traffic anomaly locations, and more importantly the influences on the road affected by the corresponding anomalies. Extended from the offline detection, an online detection method is developed for real-time detection of anomalous patterns. Our proposed methods were tested on 3 real-world bus trajectory datasets to demonstrate the performance of high accuracies, high detection rates and relatively low false alarm rates.

Zhang, Y, Cai, X, Fry, CV, Wu, M & Wagner, C 2021, 'Topic Evolution, Disruption and Resilience in Early COVID-19 Research', vol. 126, no. 5, pp. 1-29.

Zhang, Y, Huang, Y, Chiavetta, D & Porter, AL 2021, 'Guest editorial: Tech mining for engineering management: An introduction', IEEE Transactions on Engineering Management, vol. 68, no. 5, pp. 1211-1213.
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Zhang, Y, Li, B, Liu, B, Hu, Y & Zheng, H 2021, 'A Privacy-Aware PUFs-Based Multi-Server Authentication Protocol in Cloud-Edge IoT Systems Using Blockchain', IEEE Internet of Things Journal.
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The combination of the Internet of Things (IoT) and Cloud-Edge (CE) paradigm promises to be an efficient system to aggregate and further process huge volumes of data from IoT nodes. Physical Unclonable Functions (PUFs) emerge as a prospective primitive to provide IoT nodes with lightweight physical identities for authentication. However, when integrating PUFs into multi-server authentication protocols to improve security, the following problems occur: 1) the challenge-response pairs (CRPs) of PUFs generated by devices need to be explicitly stored by each edge-server. This will cause the privacy leakage of CRPs; 2) the reliability is reduced resulting from the single point failure; 3) existing PUFs-based authentication protocols would need to put great efforts into synchronizing CRPs, to ensure consistency in multi-server systems. To overcome these problems, in this paper, we propose a privacy-aware authentication protocol for the multi-server CE-IoT systems by combining PUFs and the blockchain technique. The real correlations of CRPs are double-encoded into mapping correlations (MCs) by a one-time physical identity and the keyed-hash function. The blockchain is leveraged to store MCs, synchronize them efficiently, and incorporate the multi-receiver encryption to share the physical identity securely. The security of our protocol is formally proved by a random oracle model, and security features are discussed to show that our protocol resists various attacks. Moreover, a prototype was implemented to prove the efficiency of the protocol, and the comparison results present that our protocol accommodates CE-IoT systems. Finally, the simulation of the smart contract evaluates the scalability of our protocol.

Zhang, Y, Liu, F, Fang, Z, Yuan, B, Zhang, G & Lu, J 2021, 'Learning From a Complementary-Label Source Domain: Theory and Algorithms', IEEE Transactions on Neural Networks and Learning Systems.
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Zhang, Y, Tsang, I, Luo, Y, Hu, C, Lu, X & Yu, X 2021, 'Recursive Copy and Paste GAN: Face Hallucination from Shaded Thumbnails', IEEE Transactions on Pattern Analysis and Machine Intelligence.
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Existing face hallucination methods based on convolutional neural networks (CNNs) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in non-uniform illumination conditions. This paper proposes a Recursive Copy and Paste Generative Adversarial Network (Re-CPGAN) to recover authentic high-resolution (HR) face images while compensating for non-uniform illumination. To this end, we develop two key components in our Re-CPGAN: internal and recursive external Copy and Paste networks (CPnets). Our internal CPnet exploits facial self-similarity information residing in the input image to enhance facial details; while our recursive external CPnet leverages an external guided face for illumination compensation. Specifically, our recursive external CPnet stacks multiple external Copy and Paste (EX-CP) units in a compact model to learn normal illumination and enhance facial details recursively. By doing so, our method offsets illumination and upsamples facial details progressively in a coarse-to-fine fashion, thus alleviating the ambiguity of correspondences between LR inputs and external guided inputs. Furthermore, a new illumination compensation loss is developed to capture illumination from the external guided face image effectively. Extensive experiments demonstrate that our method achieves authentic HR images in a uniform illumination condition with a 16x magnification factor and outperforms state-of-the-art methods qualitatively and quantitatively.

Zhang, Y, Tsang, IW, Li, J, Liu, P, Lu, X & Yu, X 2021, 'Face Hallucination With Finishing Touches', IEEE Transactions on Image Processing.
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Zhang, Y, Wang, Q, Li, L, Le, Y, Liu, L, Yang, J, Li, Y, Bao, G & Yan, L 2021, 'Synthesis and preliminary structure-activity relationship study of 3-methylquinazolinone derivatives as EGFR inhibitors with enhanced antiproliferative activities against tumour cells.', J Enzyme Inhib Med Chem, vol. 36, no. 1, pp. 1205-1216.
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In this paper, a set of 3-methylquniazolinone derivatives were designed, synthesised, and studied the preliminary structure-activity relationship for antiproliferative activities. All target compounds performed significantly inhibitory effects against wild type epidermal growth factor receptor tyrosine kinase (EGFRwt-TK) and tumour cells (A431, A549, MCF-7, and NCI-H1975). In particular, compound 4d 3-fluoro-N-(4-((3-methyl-4-oxo-3,4-dihydroquinazolin-2-yl)methoxy)phenyl)benzamide showed higher antiproliferative activities against all tumour cells than Gefitinib (IC50 of 3.48, 2.55, 0.87 and 6.42 μM, respectively). Furthermore, compound 4d could induce apoptosis of MCF-7 cells and arrest in G2/M phase at the tested concentration. Molecular docking and ADMET studies showed that compound 4d could closely form many hydrogen bonds with EGFRwt-TK. Therefore, compound 4d is potential to develop as novel anti-cancer drug.

Zhang, Y, Wang, Z, Cao, Y, Zhang, L, Wang, G, Dong, F, Deng, R, Guo, B, Zeng, L, Wang, P, Dai, R, Ran, Y, Lyu, W, Miao, P & Su, S 2021, 'The effect of consecutive ambient air pollution on the hospital admission from chronic obstructive pulmonary disease in the Chengdu region, China', Air Quality, Atmosphere & Health.
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Zhang, Y, Yang, Q, Zhang, L, Ran, Y, Wang, G, Celler, B, Su, S, Xu, P & Yao, D 2021, 'Noise-assisted multivariate empirical mode decomposition based causal decomposition for brain-physiological network in bivariate and multiscale time series', JOURNAL OF NEURAL ENGINEERING, vol. 18, no. 4.
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Zhang, Y, Zhang, X & Alshehri, MD 2021, 'A machine learning-enabled intelligent application for public health and safety', NEURAL COMPUTING & APPLICATIONS.
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Zhang, YF, Zheng, J, Jia, W, Huang, W, Li, L, Liu, N, Li, F & He, X 2021, 'Deep RGB-D Saliency Detection without Depth', IEEE Transactions on Multimedia.
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IEEE The existing saliency detection models based on RGB colors only leverage appearance cues to detect salient objects. Depth information also plays a very important role in visual saliency detection and can supply complementary cues for saliency detection. Although many RGB-D saliency models have been proposed, they require to acquire depth data, which is expensive and not easy to get. In this paper, we propose to estimate depth information from monocular RGB images and leverage the intermediate depth features to enhance the saliency detection performance in a deep neural network framework. Specically, we rst use an encoder network to extract common features from each RGB image and then build two decoder networks for depth estimation and saliency detection, respectively. The depth decoder features can be fused with the RGB saliency features to enhance their capability. Furthermore, we also propose a novel dense multiscale fusion model to densely fuse multiscale depth and RGB features based on the dense ASPP model. A new global context branch is also added to boost the multiscale features. Experimental results demonstrate that the added depth cues and the proposed fusion model can both improve the saliency detection performance. Finally, our model not only outperforms state-of-the-art RGB saliency models, but also achieves comparable results compared with state-of-the-art RGB-D saliency models.

Zhang, YF, Zheng, J, Li, L, Liu, N, Jia, W, Fan, X, Xu, C & He, X 2021, 'Rethinking feature aggregation for deep RGB-D salient object detection', Neurocomputing, vol. 423, pp. 463-473.
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© 2020 Two-stream UNet based architectures are widely used in deep RGB-D salient object detection (SOD) models. However, UNet only adopts a top-down decoder network to progressively aggregate high-level features with low-level ones. In this paper, we propose to enrich feature aggregation via holistic aggregation paths and an extra bottom-up decoder network. The former aggregates multi-level features holistically to learn abundant feature interactions while the latter aggregates improved low-level features with high-level features, thus promoting their representation ability. Aiming at the two-stream architecture, we propose another early aggregation scheme to aggregate and propagate multi-modal encoder features at each level, thereby improving the encoder capability. We also propose a factorized attention module to efficiently modulate the feature aggregation action for each feature node with multiple learned attention factors. Experimental results demonstrate that all of the proposed components can gradually improve RGB-D SOD results. Consequently, our final SOD model performs favorably against other state-of-the-art methods.

Zhang, Z, Bu, J, Li, Z, Yao, C, Wang, C & Wu, J 2021, 'TigeCMN: On exploration of temporal interaction graph embedding via Coupled Memory Neural Networks', Neural Networks, vol. 140, pp. 13-26.
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With the increasing demand of mining rich knowledge in graph structured data, graph embedding has become one of the most popular research topics in both academic and industrial communities due to its powerful capability in learning effective representations. The majority of existing work overwhelmingly learn node embeddings in the context of static, plain or attributed, homogeneous graphs. However, many real-world applications frequently involve bipartite graphs with temporal and attributed interaction edges, named temporal interaction graphs. The temporal interactions usually imply different facets of interest and might even evolve over the time, thus putting forward huge challenges in learning effective node representations. Furthermore, most existing graph embedding models try to embed all the information of each node into a single vector representation, which is insufficient to characterize the node's multifaceted properties. In this paper, we propose a novel framework named TigeCMN to learn node representations from a sequence of temporal interactions. Specifically, we devise two coupled memory networks to store and update node embeddings in the external matrices explicitly and dynamically, which forms deep matrix representations and thus could enhance the expressiveness of the node embeddings. Then, we generate node embedding from two parts: a static embedding that encodes its stationary properties and a dynamic embedding induced from memory matrix that models its temporal interaction patterns. We conduct extensive experiments on various real-world datasets covering the tasks of node classification, recommendation and visualization. The experimental results empirically demonstrate that TigeCMN can achieve significant gains compared with recent state-of-the-art baselines.

Zhang, Z, Li, X, Liu, H, Gao, L & Wang, Q 2021, 'Free ammonia pretreatment enhances the removal of antibiotic resistance genes in anaerobic sludge digestion', CHEMOSPHERE, vol. 279.
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Zhang, Z, Wu, Q, Wang, Y & Chen, F 2021, 'Exploring region relationships implicitly: Image captioning with visual relationship attention', IMAGE AND VISION COMPUTING, vol. 109.
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Zhang, Z, Yang, Y, Liao, S & Xue, Q 2021, 'Omnidirectional oversized annular lens antenna with high gain for 5G millimeter-wave channel measurement', Microwave and Optical Technology Letters.
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A high gain oversized annular lens antenna for 28-GHz band channel measurement system is presented. The proposed antenna is composed of an omnidirectional feeding source and an oversized annular lens. The feeding source is a compact multi-stepped biconical antenna with a wide impedance bandwidth. By rotating a horizontally placed two-dimensional extended hemispherical lens around the vertical axis, the oversized annular lens is realized, which effectively increases the vertical radiation aperture and maintains high aperture efficiency to enhance the omnidirectional gain. Under the same envelope, the proposed antenna features a higher gain than the biconical antenna and the ordinary annular lens antenna. A prototype is fabricated and measured. The measured results demonstrate that the -10-dB impedance bandwidth is 20.7% (24-29.6 GHz), with stable vertically polarized (VP) omnidirectional radiation patterns and gain over 10.3 dBi within the entire band of interest.

Zhao, C, Yang, Y, Upadrashta, D & Zhao, L 2021, 'Design, modeling and experimental validation of a low-frequency cantilever triboelectric energy harvester', Energy, vol. 214, pp. 118885-118885.
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In this paper, a novel cantilever triboelectric energy harvester (TEH) working on the contact-separation mode is proposed for low-frequency vibration energy harvesting. An electro-mechanical model of TEH with non-parallel contact surfaces is derived by evaluating the total electrical energy between two surfaces. One merit of the proposed harvester is its simple design for easy implementation. The performance of TEH is investigated theoretically and experimentally, and the results show that it can harvest energy from broadband vibration sources. A peak output voltage of 25 V is achieved from the harvester under a base acceleration of 0.5 g with excitation frequency of 8 Hz. Good agreement is observed between the experimental results and analytical predictions. The performance of TEH can be improved by adjusting the gap distance between the top plate and the beam. The proposed triboelectric harvester is shown to be cost-effective to scavenge the low-frequency vibration energy from the ambient environment.

Zhao, E & Wu, C 2021, 'Risk probabilistic assessment of ultrahigh arch dams through regression panel modeling on deformation behavior', STRUCTURAL CONTROL & HEALTH MONITORING, vol. 28, no. 5.
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Zhao, F, Ji, J, Ye, K & Luo, Q 2021, 'An innovative quasi-zero stiffness isolator with three pairs of oblique springs', International Journal of Mechanical Sciences, vol. 192.
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© 2020 Elsevier Ltd This paper presents an innovative design of the quasi-zero stiffness (QZS) isolator with three pairs of oblique springs. Formulations of stiffness are firstly derived and parameter study is then conducted to obtain an optimal design of the proposed isolator. The proposed isolator initially has five independent parameters. By imposing stiffness and its second order derivative to be zero at the static equilibrium position, there exist three independent parameters that can be optimised to design this type of QZS isolator. A distinctive feature of the proposed isolator is that there exists a nearly horizontal straight line near the equilibrium position in the stiffness curve as the nonlinear coefficient of the Taylor expansion up to the third order of the elastic force or the Duffing differential equation is very small; thus a wider QZS region with lower transmissibility can be achieved in the present isolator. Static and dynamic analyses are presented with focusing on dynamic response in terms of displacement transmissibility. A prototype is fabricated and tested to evaluate the proposed design. The experimental results validate the present formulations in static and dynamic analyses and show lower transmissibility as compared to the corresponding QZS isolator with one pair of oblique springs and the linear isolator.

Zhao, H-Y, Indraratna, B & Ngo, T 2021, 'Numerical simulation of the effect of moving loads on saturated subgrade soil', COMPUTERS AND GEOTECHNICS, vol. 131.
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Zhao, J, Hou, T, Wang, Q, Zhang, Z, Lei, Z, Shimizu, K, Guo, W & Ngo, HH 2021, 'Application of biogas recirculation in anaerobic granular sludge system for multifunctional sewage sludge management with high efficacy energy recovery', Applied Energy, vol. 298.
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This study investigated the possibility of biogas recirculation-driven anaerobic granular sludge system for sewage sludge treatment, aiming to develop an energy sufficient and multifunctional anaerobic digestion (AD) system for sewage sludge with biogas upgrading, sludge stabilization and self-aggregation. Results show that biogas recirculation could enhance the CH4 production rate by 31–44% and shorten the lag-phase duration to 0.08–0.2 day with simultaneous increment of CH4 content (> 83% in this study). The reason is mainly associated with the stronger interspecies electron transfer under the biogas recirculation condition. In addition, 37–40% better dewaterability of the digested sludge was achieved, implying the occurrence of self-aggregation of microbial cells induced by biogas recirculation. Energy balance analysis reflects that this sewage sludge treatment system could enhance the net energy recovery by 78–85%. Moreover, almost no obvious influence was noticed on the seed granules’ composition and properties. These findings suggest that the biogas recirculation-driven anaerobic granular sludge system could be a promising alternative for sewage sludge treatment, which can improve biogas quality and sludge dewaterability simultaneously towards sludge self-aggregation with no addition of other chemicals.

Zhao, J, Li, T, Yang, T, Zhao, L & Huang, S 2021, '2D Laser SLAM with Closed Shape Features: Fourier Series Parameterization and Submap Joining', IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1527-1534.
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One of the valuable directions in feature based SLAM is to parameterize and estimate features accurately. In the real world, closed shape features are especially common. It is necessary to study the feature based SLAM problem on closed shape features. The main contribution of this letter is a 2D laser SLAM approach with Fourier series based feature parameterization and submap joining. In this letter, the Fourier series are introduced to parameterize irregular closed shape features and the SLAM problem with Fourier series feature parameterization is formulated. A submap joining process is also derived in order to reduce the high dependence on precise initial guess and the computing time. The proposed method has been evaluated on both synthetic and actual data and is able to obtain accurate trajectory and feature boundaries. The practical experiment also shows that our method surpasses Cartographer under certain scenarios. We also show that our method has the ability to be applied to the general environment.

Zhao, J, Yang, S, Huo, H, Sun, Q & Geng, X 2021, 'TBTF: an effective time-varying bias tensor factorization algorithm for recommender system', Applied Intelligence, vol. 51, no. 7, pp. 4933-4944.
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Context-aware processing is a research hotspot in the recommendation area, which achieves better recommendation accuracy by considering more context information such as time, location and etc. besides the information of the users, items and ratings. Tensor factorization is an effective algorithm in context-aware recommendation and current approaches show that adding bias to the tensor factorization model can improve the accuracy. However, users’ rating preferences fluctuate greatly over time, which makes bias fluctuate with time too. Current context-aware recommendation algorithms ignore this problem, and usually use the same bias for a user or an item in different time. Aiming at this problem, this paper first considers the time-varying effect on user bias and item bias in context-aware recommendation, and proposes a time-varying bias tensor factorization recommendation algorithm based on the bias tensor factorization model (BiasTF). We experiment on two real datasets, and the experimental results show that the proposed algorithms get better accuracy than other algorithms.

Zhao, J, Zhang, JA, Li, Q, Zhang, H & Wang, X 2021, 'Recursive Maximum Correntropy Algorithms for Second-Order Volterra Filtering', IEEE Transactions on Circuits and Systems II: Express Briefs.
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As a special case of the Volterra system, the second-order Volterra (SOV) filter is very efficient for nonlinear system identification. The improved correntorpy based on the generalized Gaussian density function has been proven robust against impulsive noise. In this brief, we propose several SOV filters based on a recursive maximum correntropy (RMC) algorithm for nonlinear system identification. We first introduce a basic RMC algorithm, which faces a trade-off between filtering accuracy and tracking capability due to the use of a fixed forgetting factor (FFF). Two RMCs with variable FF (VFF) are further proposed to enhance the tracking ability. Simulation results demonstrate that our proposed algorithms outperform existing ones in impulsive noise environments and/or in time-varying systems.

Zhao, J, Zhang, Q, Sun, Q, Huo, H, Xiao, Y & Gong, M 2021, 'FolkRank++: An optimization of folkrank tag recommendation algorithm integrating user and item information', KSII Transactions on Internet and Information Systems, vol. 15, no. 1, pp. 1-19.
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The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance.

Zhao, S, Qiu, X, Burnett, I, Rigby, M & Lele, A 2021, 'A lumped-parameter model for sound generation in gas metal arc welding', MECHANICAL SYSTEMS AND SIGNAL PROCESSING, vol. 147.
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Zhao, X, Lu, C, Yang, L, Chen, W, Xin, W, Kong, XY, Fu, Q, Wen, L, Qiao, G & Jiang, L 2021, 'Metal organic framework enhanced SPEEK/SPSF heterogeneous membrane for ion transport and energy conversion', Nano Energy, vol. 81.
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Bioinspired nanofluidic devices have drawn increasing global interest due to their giant applicable potential in a wide range of fields. By mimicking biological prototype, it is expected to achieve high energy conversion efficiency and tunable ion transport. However, the low osmotic conversion efficiency, weak ion transport capability and poor mechanical performance limit practical application. We designed a class of heterogeneous membrane consisting of a support layer and a thin top layer to meet fundamental requirements. To achieve higher power generation, we incorporated metal organic framework (MOF) nanosheets (dispersed phase) into polymer matrix (continuous phase) to afford a mixed matrix top layer. This unique structure addressed the geometric restriction associated with the polymeric specie due to their limited pore accessibility. As a result, the presented membranes produced high power density of ca. 7 W m and a high energy conversion efficiency of ca. 40% under a salinity gradient of 50 (0.5 M|0.01 M, NaCl). This work thus offers an insight into a new methodology in the development of a novel membrane technology for highly efficient energy conversion. −2

Zhao, Y, Lian, H, Tian, C, Li, H, Xu, W, Phuntsho, S & Shih, K 2021, 'Surface water treatment benefits from the presence of algae: Influence of algae on the coagulation behavior of polytitanium chloride', Frontiers of Environmental Science and Engineering, vol. 15, no. 4.
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© 2020, Higher Education Press. Titanium-based coagulation has proved to be effective for algae-laden micro-polluted water purification processes. However, the influence of algae inclusion in surface water treatment by titanium coagulation is barely reported. This study reports the influence of both Microcystis aeruginosa and Microcystis wesenbergii in surface water during polytitanium coagulation. Jar tests were performed to evaluate coagulation performance using both algae-free (controlled) and algae-laden water samples, and floc properties were studied using a laser diffraction particle size analyzer for online monitoring. Results show that polytitanium coagulation can be highly effective in algae separation, removing up to 98% from surface water. Additionally, the presence of algae enhanced organic matter removal by up to 30% compared to controlled water containing only organic matter. Polytitanium coagulation achieved significant removal of fluorescent organic materials and organic matter with a wide range of molecular weight distribution (693–4945 Da) even in the presence of algae species in surface water. The presence of algae cells and/or algal organic matter is likely to function as an additional coagulant or flocculation aid, assisting polytitanium coagulation through adsorption and bridging effects. Although the dominant coagulation mechanisms with polytitanium coagulant were influenced by the coagulant dosage and initial solution pH, algae species in surface water could enhance the charge neutralization capability of the polytitanium coagulant. Algae-rich flocs were also more prone to breakage with strength factors approximately 10% lower than those of algae-free flocs. Loose structure of the flocs will require careful handling of the flocs during coagulation-sedimentation-filtration processes. [Figure not available: see fulltext.].

Zhao, Z, Wang, Z, Tavakoli, J, Shan, G, Zhang, J, Peng, C, Xiong, Y, Zhang, X, Cheung, TS, Tang, Y, Huang, B, Yu, Z, Lam, JWY & Tang, BZ 2021, 'Revisiting an ancient inorganic aggregation‐induced emission system: An enlightenment to clusteroluminescence'.
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Zheng, C, Tao, D, Wang, J, Cui, L, Ruan, W & Yu, S 2021, 'Memory Augmented Hierarchical Attention Network for Next Point-of-Interest Recommendation', IEEE Transactions on Computational Social Systems.
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IEEE Next point-of-interest (POI) recommendation has been an important task for location-based intelligent services. However, the application of such promising technique is still limited due to the following three challenges: 1) the difficulty of capturing complicated spatiotemporal patterns of user movements; 2) the hardness of modeling fine-grained long-term preferences of users; and 3) the effective learning of interaction between long- and short-term preferences. Motivated by this, we propose a memory augmented hierarchical attention network (MAHAN), which considers both short-term check-in sequences and long-term memories. To capture the complicated interest tendencies of users within a short-term period, we design a spatiotemporal self-attention network (ST-SAN). For long-term preferences modeling, we employ a memory network to maintain fine-grained preferences of users and dynamically operate them based on users' constantly updated check-ins. Moreover, we first employ a coattention network/mechanism to integrate the proposed ST-SAN and memory network, which can fully learn the dynamic interaction between long- and short-term preferences. Our extensive experiments on two publicly available data sets demonstrate the effectiveness of MAHAN.

Zheng, D, Zhang, H, Zhang, JA & Su, SW 2021, 'Stability and controller design of switched systems with sequence-based average dwell time', International Journal of Control.
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© 2020 Informa UK Limited, trading as Taylor & Francis Group. This paper investigates the stability of both linear and nonlinear switched systems with sequence-based average preceding dwell time (SBAPDT) and sequence-based average subsequent dwell time (SBASDT). Different from existing work, we take the sequence of switch events into consideration. These two new analysis methods have the potential of reducing the conservativeness of the threshold time in continuous- and discrete-time systems. A comparative numerical example is also given to demonstrate the advantages of the new approaches. It shows that the designed controllers and calculated average dwell time thresholds are better than other methods.

Zheng, D, Zhang, H, Zhang, JA & Su, SW 2021, 'Stability of Switched Systems with Unstable Subsystems: A Sequence-Based Average Dwell Time Approach', Circuits, Systems, and Signal Processing.
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This paper proposes a new sequence-based approach to resolve the stability problems found in switched systems with unstable subsystems. In existing approaches, the sequence information of switching subsystems is seldom exploited. By exploiting the sequence information, threshold values can be less restrictive and more appropriate for the situation. We study two cases in this paper: (a) all subsystems are unstable, and (b) part of the subsystems are unstable. Both continuous-time and discrete-time systems are studied, and a numerical example is given to show the advantage of our approach.

Zheng, L, Li, K, Wang, Q, Naidu, G, Price, WE, Zhang, X & Nghiem, LD 2021, 'Proof of concept: Integrated membrane distillation-forward osmosis approaches water production in a low-temperature CO2 capture', Environmental Technology and Innovation, vol. 22.
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This study investigated the removal of CO2 from flue gas by an integrated membrane distillation-forward osmosis (MD–FO) system. Monoethanolamine (MEA) and sodium glycinate solutions were loaded with CO2 from a mixture of CO2 and N2 (1:9 in volume ratio) to simulate synthetic flue gas. CO2 desorption from the amine solution was evaluated using MD at 80 °C. Interaction between amines and the membrane polymeric matrix could alter the membrane surface hydrophobicity; however, under all experimental conditions it was still sufficiently hydrophobic for MD operation. Amine loss during MD operation for CO2 desorption was insignificant. FO was used to provide make-up water and cooling to the regenerated amine solution after CO2 desorption by MD. The results showed stable FO water flux when wastewater effluent was used as the source for make-up water. Repetitive CO2 loading and desorption experiments showed 87.0% and 88.1% CO2 re-absorption efficiency for MEA and sodium glycinate in the second cycle, respectively. Further investigation of this hybrid system is suggested to advance the CO2 desorption by MD process and water production by FO process.

Zheng, Y, Wang, Y, Luo, Z, Lu, X & Qu, J 2021, 'Concurrent design for structures and material microstructures under hybrid uncertainties', Materials & Design.
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Zheng, Z & Yang, Y 2021, 'Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation', INTERNATIONAL JOURNAL OF COMPUTER VISION.
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Zhong, J, Kirby, R & Qiu, X 2021, 'The near field, Westervelt far field, and inverse-law far field of the audio sound generated by parametric array loudspeakers', The Journal of the Acoustical Society of America.
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Zhou, F, Luo, S, Li, Z, Fan, X, Wang, Y, Sowmya, A & Chen, F 2021, 'Efficient EM-variational inference for nonparametric Hawkes process', Statistics and Computing, vol. 31, no. 4.
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The classic Hawkes process assumes the baseline intensity to be constant and the triggering kernel to be a parametric function. Differently, we present a generalization of the parametric Hawkes process by using a Bayesian nonparametric model called quadratic Gaussian Hawkes process. We model the baseline intensity and trigger kernel as the quadratic transformation of random trajectories drawn from a Gaussian process (GP) prior. We derive an analytical expression for the EM-variational inference algorithm by augmenting the latent branching structure of the Hawkes process to embed the variational Gaussian approximation into the EM framework naturally. We also use a series of schemes based on the sparse GP approximation to accelerate the inference algorithm. The results of synthetic and real data experiments show that the underlying baseline intensity and triggering kernel can be recovered efficiently and our model achieved superior performance in fitting capability and prediction accuracy compared to the state-of-the-art approaches.

Zhou, I, Makhdoom, I, Shariati, N, Raza, MA, Keshavarz, R, Lipman, J, Abolhasan, M & Jamalipour, A 2021, 'Internet of Things 2.0: Concepts, Applications, and Future Directions', IEEE Access.
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Zhou, J, Gandomi, AH, Chen, F & Holzinger, A 2021, 'Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics', Electronics.
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Zhou, J, Yang, S, Xiao, C & Chen, F 2021, 'Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from a State in Australia', SN Computer Science.
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Zhou, J, Zogan, H, Yang, S, Jameel, S, Xu, G & Chen, F 2021, 'Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia', IEEE Transactions on Computational Social Systems.
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Zhou, L, Fu, A, Mu, Y, Wang, H, Yu, S & Sun, Y 2021, 'Multicopy provable data possession scheme supporting data dynamics for cloud-based Electronic Medical Record system', INFORMATION SCIENCES, vol. 545, pp. 254-276.
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Zhou, S, Walker, P, Tian, Y & Zhang, N 2021, 'Mode switching analysis and control for a parallel hydraulic hybrid vehicle', Vehicle System Dynamics: international journal of vehicle mechanics and mobility.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. Parallel hydraulic hybrid vehicles (PHHVs) integrate conventional engines with a hydraulic pump/motor (HPM) to drive the vehicle independently or in combination. To improve the comfortability and drivability during mode switching from hydraulic driving mode to engine driving mode, this paper proposes a coordinated control strategy for the engine, clutch and HPM. To capture the transient behaviour of the powertrain, a medium-duty PHHV dynamic model is established. Based on a typical PHHV powertrain layout, the mode switching process is analysed and divided into five phases. Control strategies are designed for each phase, respectively.LQR-based closed-loop control strategy is adopted to analyse the effect of clutch engaging speed on vehicle jerk, clutch frictional work and energy consumption. HPM output torque is adjusted to compensate the engine clutch drag torque, with the aim maintaining good drivability. To do this, constant vehicle acceleration is selected as the indicator of stable output torque. The simulation results demonstrate that by using the HPM to compensate the engine clutch drag torque, vehicle drivability is guaranteed during mode switching. From the comparison between different LQR weighting matrices, multiple options are available to drivers on drivability and comfortability during mode switching.

Zhou, S, Walker, P, Tian, Y, Nguyen, CT & Zhang, N 2021, 'Comparison on Energy Economy and Vibration Characteristics of Electric and Hydraulic in-Wheel Drive Vehicles', Energies.
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Zhou, S, Walker, P, Wu, J & Zhang, N 2021, 'Power on gear shift control strategy design for a parallel hydraulic hybrid vehicle', Mechanical Systems and Signal Processing, vol. 159.
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The power on gear shift control of a parallel hydraulic hybrid vehicle (PHHV) is researched in this paper. A multibody dynamic model is built to capture the main vibration characteristics of PHHV powertrain, based on which the power on gear shift control strategy is designed and verified. To avoid the vehicle driving torque interruption during gear shift, power on gear shift control strategy is designed. In the control strategy, hydraulic pump/motor (HPM) compensates the engine torque when the engine clutch is disengaged for gear shift. The engine clutch engagement process is carefully controlled by LQR control strategy to mitigate the vehicle powertrain vibration and improve the vehicle driving comfort. Extended Kalman filter (EKF) is adopted to estimate the PHHV parameters required by LQR control. The available HPM torque depends on its working pressure which varies a lot with different accumulator state of charge. So the HPM torque compensation capability is investigated by analysing the traction force requirement during gear shift. To obtain the gear shift traction force demand, a gear shift schedule considering both vehicle fuel economy and dynamic performance is designed. The simulation results show that the HPM could satisfy most of the torque compensation requirements during gear shifts with the designed gear shift schedule in typical driving cycles.

Zhou, W, Sutton, GJ, Liu, RP, Zhang, JA & Pan, S 2021, 'Deterministic Channel Aggregation for LTE in Unlicensed Spectrum', IEEE COMMUNICATIONS LETTERS, vol. 25, no. 3, pp. 807-811.
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Zhu, A, Ma, M, Guo, S, Yu, S & Yi, L 2021, 'Adaptive Multi-Access Algorithm for Multi-Service Edge Users in 5G Ultra-Dense Heterogeneous Networks', IEEE Transactions on Vehicular Technology, vol. 70, no. 3, pp. 2807-2821.
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In the 5G ultra-dense wireless heterogeneous network system, it is a crucial issue to implement an effective network selection strategy to satisfy the demands of massive edge users and novel 5G services. In this paper, we model the network selection problem of edge users requesting different services as a bipartite graph, and propose a network selection algorithm based on weighted bipartite graph matching for 5G ultra-dense heterogeneous networks, named BGMNS. The proposed algorithm combines Analytic Hierarchy Process (AHP) and Grey Relation Analysis (GRA) to analyze the preferences of multiple services for different network attributes and obtain the Quality of Experience (QoE) of different edge users for each network. Moreover, in order to realize the fair allocation of network resources, we comprehensively consider the importance of the requested services and the obtained QoE by edge users to construct system fairness index. The BGMNS algorithm can optimize the overall QoE of users under the premise of ensuring the system fairness. Simulation results show that compared to the existing network selection algorithms, the proposed BGMNS algorithm can not only provide stable access to users when network status fluctuates randomly, but also effectively reduce user blocking probability as well as total packet loss rate, and significantly improve user average energy efficiency.

Zhu, J, Yang, Y, Mcgloin, D, Liao, S & Xue, Q 2021, 'Sub-Terahertz 3-D Printed All-Dielectric Low-Cost Low-Profile Lens-Integrated Polarization Beam Splitter', IEEE Transactions on Terahertz Science and Technology.
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Polarizing beam splitters (PBS) are essential components for polarization-multiplexed communication system. However, conventional PBS, such as Rochon prism and Wollaston prism, must be sufficiently thick to generate enough walk-off distance between two orthogonal polarizations due to the low birefringence of natural material. To reduce this requirement, we propose and demonstrate a 3-D printed low-cost low-profile Fresnel- Rochon prism. By adjusting the dimensions of the artificially engineered gratings that comprise the prism, the refractive index tensor along the x- and y-direction can differ, enabling the independent manipulation of the deflection angles of the orthogonal polarized components. Applying the Fresnel principle to this Rochon prism, the thickness can be greatly reduced while the polarization splitting and deflection angles remain unaltered. Meanwhile, the transmission efficiency of the prism is also improved as less energy is dissipated in the lossy printing material. In addition, in contrast to conventional Rochon prisms formed by two right triangle prisms, the proposed Rochon prism enjoys the benefit of simple fabrication without any further assembly procedure. Prototypes operating at 0.14-THz frequency were printed and measured to verify the idea.

Zhu, L & Yang, Y 2021, 'Label Independent Memory for Semi-Supervised Few-shot Video Classification.', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP.
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In this paper, we propose to leverage freely available unlabeled video data to facilitate few-shot video classification. In this semi-supervised few-shot video classification task, millions of unlabeled data are available for each episode during training. These videos can be extremely imbalanced, while they have profound visual and motion dynamics. To tackle the semi-supervised few-shot video classification problem, we make the following contributions. First, we propose a label independent memory (LIM) to cache label related features, which enables a similarity search over a large set of videos. LIM produces a class prototype for few-shot training. This prototype is an aggregated embedding for each class, which is more robust to noisy video features. Second, we integrate a multi-modality compound memory network to capture both RGB and flow information. We propose to store the RGB and flow representation in two separate memory networks, but they are jointly optimized via a unified loss. In this way, mutual communications between the two modalities are leveraged to achieve better classification performance. Third, we conduct extensive experiments on the few-shot Kinetics-100, Something-Something-100 datasets, which validates the effectiveness of leveraging the accessible unlabeled data for few-shot classification.

Zhu, L, Fan, H, Luo, Y, Xu, M & Yang, Y 2021, 'Few-Shot Common-Object Reasoning Using Common-Centric Localization Network', IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 30, pp. 4253-4262.
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Zhu, L, Fan, H, Luo, Y, Xu, M & Yang, Y 2021, 'Temporal Cross-Layer Correlation Mining for Action Recognition', IEEE Transactions on Multimedia.
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Neighboring frames are more correlated compared to frames from further temporal distances. In this paper, we aim to explore the temporal correlations among neighboring frames and exploit cross-layer multi-scale features for action recognition. We present a Temporal Cross-Layer Correlation (TCLC) framework for temporal correlation learning. First, we introduce a context-aware reconstruction block to enable the exploration of neighboring context. This prediction block aims to reconstruct the past frame and the future frame in a unified way. It learns to mine frame correlations and aggregate long sequences at the same time. We demonstrate that this neighborhood mining process enhances the discriminative ability of the network. Second, we propose a novel cross-layer attention and a center-guided attention mechanism to integrate features with contextual knowledge from multiple scales. Our method is a two-stage process for effective cross-layer feature learning. The first stage incorporates the cross-layer attention module to decide the importance weight of the convolutional layers. The second stage leverages the center-guided attention mechanism to aggregate local features from each layer for the generation of a final video representation. We leverage global centers to extract shared semantic knowledge among videos. We evaluate TCLC on three action recognition datasets, i.e., UCF-101, HMDB-51 and Kinetics. Our experimental results demonstrate the superiority of our proposed temporal correlation mining method.

Zhu, P, Shen, Y, Pan, X, Dong, B, Zhou, J, Zhang, W & Li, X 2021, 'Reducing odor emissions from feces aerobic composting: additives', RSC Advances.
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Zhu, Q, Dinh, TH, Phung, MD & Ha, QP 2021, 'Hierarchical Convolutional Neural Network with Feature Preservation and Autotuned Thresholding for Crack Detection', IEEE Access.
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Zhu, R, Li, S, Wang, P, Xu, M & Yu, S 2021, 'Energy-efficient Deep Reinforced Traffic Grooming in Elastic Optical Networks for Cloud-Fog Computing', IEEE Internet of Things Journal.
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Cloud-fog computing emerges to satisfy the low latency and high computation requirements of Internet of Things (IoTs) services. Elastic Optical Networks (EONs) are excellent substrate communication networks between fog datacenters and cloud datacenters. However, the uneven traffic of massive cloud-fog services incurs many spectrum fragments, leading to high extra energy consumption. To solve this problem, we propose an Energy-efficient Deep Reinforced Traffic Grooming (EDTG) algorithm based on deep reinforcement learning. Unlike existing manually network features extracting methods, we convert the traditional network modal and the service routing path into colored network images to represent their states, and extract the features automatically by MobilenetV3 according to these images. With the extracted features, we implement an Advantage Actor-Critic (A2C) algorithm, whose actor module and critic module share an Artificial Neural Network (ANN) to get optimal grooming actions. Additionally, after repeated attempts and experiments, we set up an objective reward and punishment mechanism to evaluate the grooming actions. We conduct extensive simulations for performance evaluation, and the results have shown that EDTG can significantly reduce energy consumption compared with two well-performed traffic grooming algorithms.

Zhu, R, Samuel, A, Wang, P, Li, S, Oun, BK, Li, L, Lv, P, Xu, M & Yu, S 2021, 'Protected Resource Allocation in Space Division Multiplexing-Elastic Optical Networks with Fluctuating Traffic', JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, vol. 174.
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Zhu, Y, Zhong, H, Wang, H, Ouyang, L, Liu, J, Huang, Z & Zhu, M 2021, 'Breaking the Passivation: Sodium Borohydride Synthesis by Reacting Hydrated Borax with Aluminum', Chemistry - A European Journal, vol. 27, no. 35, pp. 9087-9093.
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A significant obstacle in the large-scale applications of sodium borohydride (NaBH4) for hydrogen storage is its high cost. Herein, we report a new method to synthesize NaBH4 by ball milling hydrated sodium tetraborate (Na2B4O7 ⋅ 10H2O) with low-cost Al or Al88Si12, instead of Na, Mg or Ca. An effective strategy is developed to facilitate mass transfer during the reaction by introducing NaH to enable the formation of NaAlO2 instead of dense Al2O3 on Al surface, and by using Si as a milling additive to prevent agglomeration and also break up passivation layers. Another advantage of this process is that hydrogen in Na2B4O7 ⋅ 10H2O serves as a hydrogen source for NaBH4 generation. Considering the low cost of the starting materials and simplicity in operation, our studies demonstrate the potential of producing NaBH4 in a more economical way than the commercial process.

Zhuang, Y, Leng, Y, Zhou, J, Song, R, Li, L & Su, SW 2021, 'Voluntary Control of an Ankle Joint Exoskeleton by Able-Bodied Individuals and Stroke Survivors Using EMG-Based Admittance Control Scheme', IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, vol. 68, no. 2, pp. 695-705.
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Zuo, L, Yao, H, Li, H, Jia, F, Wei, W, Liu, Y, Ni, B-J & Chen, X 2021, 'Modeling of completely autotrophic nitrogen removal process with salt and glycine betaine addition.', Chemosphere, vol. 264, no. Pt 2, pp. 128474-128474.
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The susceptibility of the completely autotrophic nitrogen removal over nitrite (CANON) process to high salinity limits its widespread application. The addition of glycine betaine (GB), a type of compatible solutes that could resist osmotic stress, could be an effective strategy to enhance the salt tolerance ability of aerobic and anaerobic ammonium oxidizing bacteria (AOB and anammox bacteria) involved in the CANON process. This study aims to make use of mathematical modeling to systematically investigate the effects of salt and GB addition on the activities of AOB and anammox bacteria and the treatment performance of the CANON process. To this end, a series of dedicated batch tests and long-term experiments for the CANON process with salt and GB additions were conducted and the data was used to calibrate and validate the model established to consider the relationships between salt and GB concentrations and bacterial growth in the CANON process. The calibrated/validated CANON process model was then applied to simulate the long-term impacts of GB addition concentration and sludge retention time (SRT) on the CANON process. The results showed that 1 mM GB addition and a SRT of 50 days would be sufficient to protect AOB and anammox bacteria under the high salinity (30 g/L NaCl) conditions studied and therefore reduce the time needed to recover the treatment performance of the CANON process from exposure to salt inhibition by 35%-40%.

Afzal, MU, Koli, NY & Esselle, KP 2021, 'Low-Cost Radial Line Slot Array Antenna for Millimeter-Wave Backhaul Links', 2021 15th European Conference on Antennas and Propagation (EuCAP), 2021 15th European Conference on Antennas and Propagation, IEEE, Dusseldorf, Germany.
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The front-end antennas with a directed narrow beam are essential to establish a robust backhaul network for 5G network. The cost and profile of the antenna are imperative due to the large number of smaller cells envisaged for a millimeter-based 5G communication network. Radial-line slot array (RLSA) antennas are suitable due to their planar and thin height profile and single feed point. A low-cost RLSA is investigated for the millimeter-wave backhaul network without using dielectric materials. The lack of dielectric substantially reduces the fabrication cost of the antenna. The RLSA is made of two metal plates where radiating slots are on a thin-plate at the top. The results predicted through numerical simulations indicate antenna can create a narrow broadside beam without any excessive grating or side lobes.

Ahmed, F, Hayat, T, Afzal, MU, Lalbakhsh, A & Esselle, KP 2020, 'Dielectric-Free Cells for Low-Cost Near-Field Phase Shifting Metasurfaces', 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEECONF 2020 - Proceedings, IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, Montreal, QC, Canada, pp. 741-742.
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© 2020 IEEE. This paper presents a unique dielectric-free cell configuration that is useful as a building block to design low-cost transparent phase-gradient metasurfaces required for antenna beam steering using both far-field and near-field methods. Each cell is made of a few thin metal sheets each having an asterisk-shaped slot. A phase variation of 360 degrees is possible with four metal layers, with less than 2 dB drop in amplitude. This is much greater than what is possible with the conventional cross-slot which lacks such a high degree of freedom.

Al-Ghattas, H & Marjanovic, O 2021, 'Business Analytics Capabilities for Organisational Resilience', The 54 Hawaii International Conference on System Sciences HICSS-54, Hawaii & Online platform.
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Nowadays, organizations are facing unique challenges created by different disruptions, including natural disasters, new technologies, regulatory changes, and more recently, a global pandemic. Consequently, the need to build, sustain, and continuously enhance Organizational Resilience (OR) is greater than ever. An ongoing process of building OR requires high-quality data and business analytics (BA) capabilities. In this paper we aim to investigate the yet-to-be explored link between BA and OR. We achieve this aim by conducting a multidisciplinary literature review on OR and BA, focusing on BA capabilities for OR. Based on our findings, we then propose a conceptual framework of BA capabilities for OR. In doing so, we also bring a well-established area of OR to the attention of BA researchers, as a critically important area for further BA research and practice.

Altulayan, MS, Huang, C, Yao, L, Wang, X & Kanhere, S 2021, 'Contextual Bandit Learning for Activity-aware Things-of-Interest Recommendation in an Assisted Living Environment', 32th Australasian Database Conference, Springer, Dunedin, New Zealand, pp. 37-49.
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Recommendation systems are crucial for providing services to the elderly with Alzheimer’s disease in IoT-based smart home environments. Therefore, we present a Reminder Care System to help Alzheimer patients live safely and independently in their homes. The proposed recommendation system is formulated based on a contextual bandit approach to tackle dynamicity in human activity patterns for accurate recommendations meeting user needs without their feedback. Our experiment results demonstrate the feasibility and effectiveness of the proposed Reminder Care System in real-world IoT-based smart home applications.

Alyami, A, Pileggi, SF & Hawryszkiewycz, I 2021, 'ENHANCING AND CONSOLIDATING REQUIREMENTS SPECIFICATION IN TEACHING SYSTEM DESIGN BY ALIGNING CONCEPTS FROM HETEROGENEOUS DIAGRAMS', EDULEARN21 Proceedings, 13th International Conference on Education and New Learning Technologies, IATED.
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Au, W, Liu, D & Sakaue, T 2020, 'A Model for Optimizing the Size of Climbing Robots for Navigating Truss Structures', Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Las Vegas, NV, USA.
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Truss structures can be found in many buildings and civil infrastructure such as bridges and towers. But as these architectures age, their maintenance is required to keep them structurally sound. A legged robotic solution capable of climbing these structures for maintenance is sought, but determining the size and shape of such a robot to maximise structure coverage is a challenging task. This paper proposes a model in which the size of a multi-legged robot is optimised for coverage in a truss structure. A detailed representation of a truss structure is presented, which forms the novel framework for constraint modelling. With this framework, the overall truss structure coverage is modelled, given a robot's size and its climbing performance constraints. This is set up as an optimisation problem, such that its solution represents the optimum size of the robot that satisfies all constraints. Three case studies of practical climbing applications are conducted to verify the model. By intuitive analysis of the model's output data, the results show that the model accurately applies these constraints in a variety of truss structures.

Chen, X, Tang, MC, Yi, D & Ziolkowski, RW 2020, 'An Interdigitated Structure-Based, Electrically Small Dipole Antenna with Enhanced Bandwidth', 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEECONF 2020 - Proceedings, IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, Montreal, QC, Canada, pp. 355-356.
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A wideband, electrically small dipole antenna with stable radiation performance is presented. Inspired by recently reported metasurface antennas, a coax-fed dipole antenna with periodic patches acting as near-field resonant parasitic (NFRP) elements is first developed. It exhibits operating mechanisms identical to those of the metasurface antennas and, thus, realizes a wide bandwidth and stable radiation patterns across it. Introducing interdigitated structures into the NFRP elements, an electrically small version is realized. A 14.4%, -10-dB fractional impedance bandwidth is achieved along with a stable realized gain, ∼ 1.5 dBi, over it. The antenna has potential applications in future wideband space-limited systems.

Chen, Z, Wang, C, Yan, J, Sui, Y & Xue, J 2021, 'Runtime detection of memory errors with smart status', ISSTA 2021 - Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, pp. 296-308.
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C is a dominant language for implementing system software. Unfortunately, its support for low-level control of memory often leads to memory errors. Dynamic analysis tools, which have been widely used for detecting memory errors at runtime, are not yet satisfactory as they cannot deterministically and completely detect some types of memory errors, e.g., segment confusion errors, sub-object overflows, use-after-frees, and memory leaks. We propose Smatus, short for smart status, a new dynamic analysis approach that supports comprehensive runtime detection of memory errors. The key innovation is to create and maintain a small status node for each memory object. Our approach tracks not only the bounds of each pointer's referent but also the status and reference count of the referent in its status node, where the status represents the liveness and segment type of the referent. A status node is smart as it is automatically destroyed when it becomes useless. To the best of our knowledge, Smatus represents the most comprehensive approach of its kind. In terms of effectiveness (for detecting more kinds of errors), Smatus outperforms state-of-the-art tools, Google's AddressSanitizer, SoftBoundCETS and Valgrind. In terms of performance, Smatus outperforms SoftBoundCETS and Valgrind in terms of both time and memory overheads incurred, and is on par with AddressSanitizer in terms of the time and memory overheads tradeoff (with much lower memory overhead incurred).

Cheng, Q, Lin, Z, Zhang, JA, Nguyen, D, Huang, X, Kekirigoda, A & Hui, KP 2020, 'Multi-user MIMO with Jamming Suppression for Spectrum-Efficient Tactical Communications', 2020 14th International Conference on Signal Processing and Communication Systems, ICSPCS 2020 - Proceedings, International Conference on Signal Processing and Communication Systems, IEEE, Adelaide, Australia.
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Being spectrum-efficient and robust to adversarial interference caused by jammers are critical to tactical wireless systems. Leveraging multiple-input multiple-output (MIMO) techniques, this paper investigates the realization of spectrum-efficient multi-user MIMO communications in the presence of high-power jammers. Unlike most existing work that only exploits the MIMO degree of freedom to nullify the jamming signal, we also aim to improve the spectral efficiency of the system with the MIMO spatial multiplexing capability. To that end, we first design a combiner at the receiver spanning the null space of the jamming channels, which can completely remove the jamming signals and optimize the communication reception. We further propose two methods for the design of precoders at the transmitter to mitigate multi-user interference. Simulation results are presented to verify the effectiveness of the proposed schemes in radio-frequency contested environments.

Chu, NH, Hoang, DT, Nguyen, DN, Van Huynh, N & Dutkiewicz, E 2021, 'Fast or Slow: An Autonomous Speed Control Approach for UAV-assisted IoT Data Collection Networks'.
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Dann, C, O'Neill, S, Getenet, S, Verma, N, Chakraborty, S, Yu, K, Aboufarw, K, Edmondson, S, Quirke-Bolt, N, Levy, D, McFarlane, S, Quadrelli, C, Daly, M & Seifert, T 2021, 'Machine learning system to guide teacher reflection on behavior management skills', Innovate Learning Summit Online 2021, Association for the Advancement of Computing in Education (AACE), USA.

Demong, NAR, Lu, J & Hussain, FK 2021, 'An Adaptive Personalized Property Investment Risk Analysis Method Based on Data-Driven Approach', International Journal of Information Technology and Decision Making, pp. 671-706.
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Risk assessment analysis for investment decisions largely depends on expert judgment using traditional approaches and is lacking in considering investors' different preferences and limitations. This paper proposes an adaptive personalized property investment risk analysis (APPIRA) method to identify the property investment determinants using a data-driven and personalized approach to weight the risk factors using the multicriteria decision model for optimal solutions. Result for predictive modeling using value prediction technique that measures the median house price depicts that the best method used was nonseasonal ARIMA. Furthermore, classification technique indicates that in each of the three selected suburbs, different property characteristics determined the rental properties desirable. As shown in result, for the investors who plan to invest in property for rental purposes, they need to choose townhouse type or property to make it rentable while for Vaucluse, terrace houses. These results can be applied into practice and will benefit the property industry directly.

Derix, EC, Leong, TW & Prior, J 2021, '“It's The Same Conflict Every Time, On Repeat.”'.
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Do, T-TN, Singh, AK, Cortes, CAT & Lin, C-T 2020, 'Estimating the cognitive load in physical spatial navigation', 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, Canberra Australia.
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Erfani, E, Samy Helmy Hanna, A & Boroon, L 2021, 'Social Network Sites Use and Psychological distress: A Systematic Review', Proceedings of the 54th Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii International Conference on System Sciences, Hawaii In.
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Esselle, KP 2020, 'A Brief Overview of Antenna Technologies for Communications-On- The-Move Satellite Communication Mobile Terminals', 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, 2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, IEEE, Montreal, QC, Canada.
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This paper serves as the introductory first presentation to the 2020 IEEE AP-S Industry-Focused Special Session on “Beam-Steerable Antenna Systems with High Gains for Mobile Satellite Terminals”. It presents a brief overview of established and emerging antenna technologies for Communication-On- The-Move (COTM) mobile satellite communication terminals and known or expected advantages and limitations of each technology.

Gowripalan, N, Cao, J, Sirivivatnanon, V & South, W 2021, 'Comparison of the effect of ASR deterioration on the load carrying capacity of concrete structural elements in accelerated laboratory tests and in the field', 16th International Conference on Alkali Aggregate Reaction 2021, Lisbon, Portugal.

Guo, YJ, Chen, S-L & Liu, Y 2021, 'Reconfigurable Antenna Arrays for Integrated Space and Terrestrial Networks', 2020 International Symposium on Antennas and Propagation (ISAP), 2020 International Symposium on Antennas and Propagation, IEEE, Osaka, Japan.
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Integrated space and terrestrial networks are expected to be a key technology for beyond 5G communications systems. One main challenge posed for the antennas is the array performance deterioration caused by orientation changes of various elements mounted to conformal platforms. To overcome this challenge, a reconfigurable antenna element that can switch among four linear polarizations is reported and applied to a conformal array. An efficient array synthesis algorithm is utilized to attain high-gain, low-sidelobes, and low-cross-polarization characteristics. A linear conformal array is presented to verify the effectiveness of the developed algorithm.

Hashim, S, Mokhtar, ES, Halim, AIA, Mohd, WMNW, Adnan, NA & Pradhan, B 2021, 'Cross Section Intervals of Flood Intervals of Flood Inundation Mapping at Ungauged Area', IOP Conference Series Earth and Environmental Science.
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He, NA & Ferguson, S 2020, 'Multi-view Neural Networks for Raw Audio-based Music Emotion Recognition', Proceedings - 2020 IEEE International Symposium on Multimedia, ISM 2020, IEEE International Symposium on Multimedia, IEEE, Naples, Italy, pp. 168-172.
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In Music Emotion Recognition (MER) research, most existing research uses human engineered audio features as learning model inputs, which require domain knowledge and much effort for feature extraction. We propose a novel end-to-end deep learning approach to address music emotion recognition as a regression problem, using the raw audio signal as input. We adopt multi-view convolutional neural networks as feature extractors to learn feature representations automatically. Then the extracted feature vectors are merged and fed into two layers of Bidirectional Long Short-Term Memory to capture temporal context sufficiently. In this way, our model is capable of recognizing dynamic music emotion without requiring too much workload on domain knowledge learning and audio feature processing. Combined with data augmentation strategies, the experimental results show that our model outperforms the state-of-the-art baseline with a significant margin in terms of R2 score (approximately 16%) on the Emotion in Music Database.

Huang, L, Ni, X, Chen, X & Zhang, Y 2021, 'Measuring interdisciplinary interactions using citation analysis and semantic analysis', CEUR Workshop Proceedings, pp. 140-152.
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Interdisciplinary interactions and integrations have become a major feature of the current development of science and technology. How to measure the strength of interdisciplinary interactions between two disciplines is a crucial issue. In ou