Publications

Balusamy, B, Nandhini, AR, Kadry, S & Gandomi, AH 2021, Big Data Concepts, Technology, and Architecture, John Wiley & Sons.
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Executives and managers who lead teams responsible for keeping or understanding large datasets will also benefit from this book.

Gao, H, Kim, JY, Hussain, W, Iqbal, M & Duan, Y 2021, Intelligent Processing and IT Tools for E-Commerce Data, Information, and Knowledge, Springer.
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This book discusses recent research and applications about intelligent processing practices and tools for e-commerce data, information and knowledge.

Guo, YJ & Ziolkowski, RW 2021, Advanced Antenna Array Engineering for 6G and Beyond Wireless Communications, Wiley.
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Nguyen, LD, Duong, TQ & Tuan, HD 2021, Real Time Convex Optimisation for 5G Networks and Beyond, Institution of Engineering and Technology.
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Qu, Y, Nosouhi, MR, Cui, L & Yu, S 2021, Personalized Privacy Protection in Big Data, Springer Singapore.
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Rohde, PP 2021, The Quantum Internet, Cambridge University Press.
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Following the emergence of quantum computing, the subsequent quantum revolution will be that of interconnecting individual quantum computers at the global level. In the same way that classical computers only realised their full potential with the emergence of the internet, a fully-realised quantum internet is the next stage of evolution for quantum computation. This cutting-edge book examines in detail how the quantum internet would evolve in practise, focusing not only on the technology itself, but also the implications it will have economically and politically, with numerous non-technical sections throughout the text providing broader context to the discussion. The book begins with a description of classical networks before introducing the key concepts behind quantum networks, such as quantum internet protocols, quantum cryptography, and cloud quantum computing. Written in an engaging style and accessible to graduate students in physics, engineering, computer science and mathematics.

Ying, M & Feng, Y 2021, Model Checking Quantum Systems Principles and Algorithms, Cambridge University Press.
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The first book introducing computer aided verification techniques for quantum systems with quantum computing and communication hardware.

Abualigah, L, Kareem, NK, Omari, M, Elaziz, MA & Gandomi, AH 2021, 'Survey on Twitter Sentiment Analysis: Architecture, Classifications, and Challenges' in Signals and Communication Technology, Springer International Publishing, pp. 1-18.
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Adak, C, Chaudhuri, BB & Blumenstein, M 2021, 'A Deep Reinforcement Learning-based Study on Handwriting Difficulty Analysis' in Series on Language Processing, Pattern Recognition, and Intelligent Systems, WORLD SCIENTIFIC, pp. 97-117.
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Altaee, A, Swain, S, Bhol, P, Yadav, S, Saxena, M, Misra, PK & Samal, AK 2021, 'Functionalized Nanomaterials (FNMs) for Environmental Applications' in Functionalized Nanomaterials for Catalytic Application, Wiley.
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Nanomaterials empower the advances of innovative solutions to environmental problems. Among the broad range of nanomaterials, functionalized nanomate- rials emerged as promising catalyst for environmental issues. Functionalization of nanomaterials offers a great advantage on cost effectiveness and enhances the properties in many folds. Implementation of bare nanomaterials has a few chal- lenges as well as limitations such as instability, agglomeration and reusability. Functionalization of the nanomaterials facilitates to overcome these problems. Addition of functional groups helps in tailoring the nanomaterial surface to enrich the specific sites on the surface level. This chapter discusses the engineer- ing of functionalized nanomaterials and focuses on environmental applications. Different materials such as cellulose, chitosan, silica, metal oxides, and other poly- mers are added to nanomaterials and the introduction of functional groups on the surface which results composite materials of enhanced capability. Different pro- cess of functionalization namely direct functionalization, postsynthetic function- alization, grafting-to, grafting-from, and grafting-through methods are illustrated. Nanomaterial and functional group interaction such as covalent and noncovalent bonding is described. This chapter highlights the limitations of different materi- als in various aspects of environmental applications. It provides a clear overview

Arndt, F, Katic, M, Mistry, A & Nafei, S 2021, 'Dynamic capabilities of global value chains' in The Routledge Companion to Global Value Chains, Routledge, pp. 23-34.
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Bah, AO & Bird, TS 2021, 'Measurement of Mutual Coupling Effects', Wiley, pp. 389-411.
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Bhavya, MB, Swain, S, Bhol, P, Yadav, S, Altaee, A, Saxena, M, Misra, PK & Samal, AK 2021, 'Functionalized Nanomaterials (FNMs) for Environmental Applications' in Functionalized Nanomaterials for Catalytic Application, Wiley, pp. 109-134.
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Nanomaterials (NMs) are the building units for advanced science and technol- ogy. The high surface area and porosity of the NMs excel its properties in many folds compared to the bulk counterpart. NMs allow easy functionalization of surface, tailoring the electronic properties, conductivity, catalytic property, and response to physical events. The functionalization of NMs allows inclusion of functional groups and species over the surface, resulting to an improved structure with novel characteristics. As compared to homogeneous catalyst, heterogeneous catalyst has shown emerging performance in the various chemical reactions due to synergistic effect developed from intimate contact between different elements incorporated. In this chapter, various fabrication techniques such as co-precipitation, impregnation, ion-exchange, immobilization, sol-gel, chemical vapor deposition, hydrothermal, microemulsion, and thermal decomposition methods used to synthesize functionalized NMs have been briefly discussed. The characteristic features of carbon-based materials and their functionaliza- tion technique such as covalent and noncovalent functionalization based on the π-conjugation system or force of attraction are emphasized. This chapter also discusses various functionalized based materials such as Pd, Pt, Ag, graphene, carbon nanotubes, biomaterials, and metal oxides and their usefulness in influ- encing the catalytic performance with examples.

Boroomandnia, A, Bozorg-Haddad, O, Pradhan, B & Datta, A 2021, 'GIS Application in Water Resource Management' in Springer Water, Springer Singapore, pp. 125-152.
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Chamoli, U, Lam, M & Diwan, AD 2021, 'Replacing the Nucleus Pulposus for Degenerative Disc Disease and Disc Herniation: Disc Preservation Following Discectomy' in Handbook of Spine Technology, Springer International Publishing, pp. 1111-1129.
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de Groot, W, Kenning, G, van den Hoven, E & Eggen, B 2021, 'Exploring How a Multisensory Media Album Can Support Dementia Care Staff' in Dementia Lab 2021: Supporting Ability Through Design, Springer International Publishing, pp. 51-61.
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Devitt, S 2021, 'The Quantum Sneakernet' in The Quantum Internet, Cambridge University Press, pp. 177-178.
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A highly interdisciplinary overview of the emerging topic of the Quantum Internet.

Du, J, Xue, Y, Jin, E, Hu, X, Yuan, Y & Zhang, F 2021, 'Performance Evaluation on the Intelligent Operation and Maintenance Mode of Public-Private Partnership Urban Infrastructure Projects' in AI-Based Services for Smart Cities and Urban Infrastructure, IGI Global, pp. 190-214.
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With the participation of social capital, the operation and maintenance performance of Public-Private Partnership (PPP) urban infrastructure is becoming the key value-added point. This chapter summarizes and analyzes the problems of traditional urban infrastructure operation and maintenance (UIOM), and innovatively proposes the UIOM mode under the PPP background. Combined with the characteristics of the new generation of information technology, this chapter puts forward an efficient and intelligent UIOM mode with the features of intelligent decision-making, fine maintenance, technical optimization, capital saving, resource integration, and sustainability. This chapter takes the Highway Tunnel as the example, propose the Evaluation Index System for Intelligent UIOM Mode and implement the evaluation process using the AHP. The proposed intelligent UIOM Mode provides a scientific solution to the improvement of the subsequent evaluation system and the optimization of operation and maintenance decisions in PPP urban infrastructure projects.

Du, W, Di, X & Su, QP 2021, 'Studying Mitochondrial Network Formation by In Vivo and In Vitro Reconstitution Assay' in Methods in Molecular Biology, Springer US, pp. 333-341.
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Fattah, IMR, Ong, HC, Mahlia, TMI & Mofijur, M 2021, 'Diesel Emissions and Approaches to their Mitigation' in Petrodiesel Fuels, CRC Press, pp. 1079-1093.
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Ghildyal, A, Ru, D, Chang, E, Joiner, K, Saberi, M & Despande, A 2021, 'Conceptual Framework for Measuring Project Benefits Using Belief—Plausibility and Type 1 Fuzzy Inference System' in Recent Developments and the New Direction in Soft-Computing Foundations and Applications, Springer International Publishing, pp. 243-256.
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Ghosh, A, Islam, MS, Rahimi-Gorji, M, Das, R & Saha, SC 2021, 'Modeling of Pharmaceutical Aerosol Transport in the Targeted Region of Human Lung Airways Due to External Magnetic Field' in Handbook of Lung Targeted Drug Delivery Systems, CRC Press, pp. 443-457.
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Gul, M, Mujtaba, MA, Masjuki, HH, Kalam, MA & Zulkifli, NWM 2021, 'Methods for Biodiesel Production', Wiley, pp. 267-283.
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Guo, W, Ngo, HH, Deng, L, Surampalli, RY & Zhang, TC 2021, 'Resource recovery from electronic waste' in Guo, W, Ngo, HH, Surampalli, RY & Zhang, TC (eds), Sustainable Resource Management Technologies for Recovery and Reuse of Energy and Waste Materials, John Wiley & Sons, Weinheim, Germany, pp. 723-753.
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The rapid growth of electronic products manufacturing and consumption has led to global concern about the generation of electronic waste (e-waste), which poses serious threats to the environment and human health. As e-waste contains a wide range of chemicals, metals, plastics and other substances, the appropriate management of e-waste is facing big challenges due to environmental, technical, economic and health issues during the treatment and recycling processes. This chapter provides comprehensive information about the current status of e-waste generation, health impacts of e-waste and the benefits of e-waste recycling. Recent advances in recycling and recovery of valuable materials such as metals, plastics, lithium-ion batteries and waste solar PV panels have been presented. As a critical environmental problem worldwide, future perspectives and the needs of research on better e-waste management are also addressed.

Hossain, N, Chowdhury, T, Chowdhury, H, Ahmed, A, Nizamuddin, S, Griffin, G, Mahlia, TMI & Park, Y-K 2021, 'Edible bio-oil production from microalgae and application of nano-technology' in Microalgae, Elsevier, pp. 91-116.
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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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Kumar, AS, Tesfaye Jule, L, Ramaswamy, K, Sountharrajan, S, Yuuvaraj, N & Gandomi, AH 2021, 'Analysis of false data detection rate in generative adversarial networks using recurrent neural network' in Generative Adversarial Networks for Image-to-Image Translation, Elsevier, pp. 289-312.
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Larpruenrudee, P, Islam, MS, Paul, G, Paul, AR, Gu, YT & Saha, SC 2021, 'Model for Pharmaceutical aerosol transport through stenosis airway' in Handbook of Lung Targeted Drug Delivery Systems, CRC Press, USA, pp. 91-128.
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Air pollution is the leading cause of different respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD) that commonly affect respiratory health. Computational fluid dynamics (CFD) has been used to predict the airflow pattern and particle transport within human lungs under disease conditions like obstructed airways. Nevertheless, the combination of the obstructed airways and the aging impact on these diseases under the various flow rates and particle diameters, has not been considered in previous studies. This chapter provides a clear understanding of airflow characteristics and particle transport through obstructions and smaller airways due to aging based on an asymmetric lung model generating from the trachea to the fourth generation. Eight different lung models were used for the numerical simulation. The ANSYS Fluent 19.2 was employed to solve the problems under the finite volume discretization technique. Appropriate grid refinement has been performed for all cases. The results indicate that airflow pattern always changes at the stenosis area. The velocity significantly increases at stenosis area for the first two generations and the smallest diameter size. The maximum pressure drop was located at stenosis area for the first generation of right lung and the fourth generation for the smallest diameter case, whereas the highest pressure was found in the trachea for both conditions. Stenosis areas at first two generations significantly affect higher turbulence intensity while smaller diameters generate lower turbulent fluctuation. The deposition efficiency and deposition fraction were based on the airway volume, particle size, and flow rate. The results of this study enhance the knowledge of airflow characteristics and particle deposition within asymmetric human lungs with stenosis area and smaller diameters.

Liu, X, Wei, W & Ni, B 2021, 'Photocatalytic and Photoelectrochemical Reforming of Biomass', Wiley, pp. 389-417.
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López-Rubio, P, Mas-Tur, A, Merigó, JM & Roig-Tierno, N 2021, 'Leading Trends in Technology Transfer' in International Studies in Entrepreneurship, Springer International Publishing, pp. 21-41.
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Lui, HSA & Bird, TS 2021, 'Mutual Coupling in Beamforming and Interferometric Antennas' in Mutual Coupling Between Antennas, Wiley, pp. 287-323.
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This important text: Provides a theoretical background for understanding mutual coupling between various types of antennas Describes the interaction that occurs between antennas, both planned and unplanned Explores a key aspect of arrays in ...

Lui, HSA & Bird, TS 2021, 'Techniques for Minimizing Mutual Coupling Effects in Arrays' in Mutual Coupling Between Antennas, Wiley, pp. 325-356.
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This important text: Provides a theoretical background for understanding mutual coupling between various types of antennas Describes the interaction that occurs between antennas, both planned and unplanned Explores a key aspect of arrays in ...

M. B, B, Yadav, S, Saxena, M, Altaee, A, Kumari Misra, P & Kumar Samal, A 2021, 'Role of Surfactants in Facet Dependent Synthesis of Anisotropic Nanostructures' in Chemical Modification of Solid Surfaces by the Use of Additives, Bentham Books, pp. 1-20.
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Anisotropic nanostructures (ANs) are increasingly attractive materials in the ongoing decades due to their direction-dependent properties associated with them. The materialization of ANs is highly delicate, where sharp edges and vertices synthesis are very crucial. There are a few factors play an important role in the synthesis of ANs such as surfactants, pH, temperature, etchants, choice of reducing agent, metal precursor, light, the time duration of the reaction, solvent etc. In the present chapter, the most part centers on how surfactants affect the growth of ANs. Although several surfactants are used for the synthesis of ANs, however, we have focused on the surfactants particularly used for the synthesis of Au, Ag and Cu nanostructures such as Cetyltrimethylammonium bromide (CTAB), Polyvinylpyrrolidone (PVP), Cetyltrimethylammonium chloride (CTAC) and also binary surfactant mixture. An overview of various types of surfactants, the importance of the choice of surfactant, which is necessary for the synthesis of the particular nanostructure is presented. Also, change in nanostructure formation with the change in percentage assay of surfactant is discussed. The contemporary strategies and advantages of binary surfactant mixture over the mono-surfactant are comprehensively reviewed. Finally, the challenges and future outlook for the synthesis of particular nanostructure with required size and morphology are highlighted.

Meena, NK, Nimbalkar, S & Fatahi, B 2021, 'Finite Element Analysis of Soil Arching in Piled Embankment' in Challenges and Innovations in Geomechanics, Springer International Publishing, 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.

Moore, S & Dickson-Deane, C 2021, 'Behind the Curtain: Understanding the Review and Publishing Process for a Peer-Reviewed Research Journal in Higher Education' in Hartshorne, R, Ferdig, R & Bull, G (eds), What Journal Editors Wish Authors Knew About Academic Publishing, Association for the Advancement of Computing in Education (AACE), pp. 61-61.
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Getting published in academic journals is not an easy task, even for experienced researchers. Authors may spend months or years working on a submission; when the work fails to reach its audience, the result is lost time and potentially millions of dollars of lost effort for the field. This edited collection is written by journal editors who share the same goal of supporting authors. These editors represent more than a century of experience and practice in publishing. Each chapter is penned by editors of a specific journal, providing that journal’s mission and purpose, an explanation of the submission and review process, and advice for authors. Chapters include a best practices section where editors describe what they wish authors knew and considered prior to submission of a manuscript to the journal. As you read each of the chapters, we hope that you will be able to see both the overlap as well as specific nuanced differences between individual journals.

Ngo, HH, Cheng, D & Guo, W 2021, 'Approaches towards Resource Recovery from Breeding Wastewater' in Guo, W, Ngo, H, Surampalli, RY & Zhang, TC (eds), Sustainable Resource Management: Technologies for Recovery and Reuse of Energy and Waste Materials, John Wiley & Sons, Weinheim, Germany, pp. 559-599.
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To make a sustainable society, it is necessary to reduce the use of fossil fuel and the generation of pollutants. The increasing production of breeding wastewater brings environmental challenges. Besides, breeding wastewater can be used as a valuable resource instead of a waste considering the large amounts of organic matters and nutrients in it. Therefore, with the increasing production of breeding wastewater, the global concern about its disposal recently has shift to its resource recovery to achieve the above two goals simultaneously. The primary objective of this chapter is to present the main characteristic of breeding wastewater, the potentially valuable resources in breeding wastewater and main approaches for resources recovery. Through the applications of biological, physiochemical, plant-based and thermochemical technologies, multiple value-added products, nutrients, and renewable bioenergy can be recovered from breeding wastewater. However, the application of these technologies still has scientific and technical barriers requiring to be studied further.

Ngo, QT, Phan, KT, Xiang, W, Mahmood, A & Slay, J 2021, 'Two-Tier Cache-Aided Full-Duplex Content Delivery in Satellite–Terrestrial Networks' in Computational Data and Social Networks, Springer International Publishing, pp. 291-302.
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Nguyen, LN & Nghiem, LD 2021, 'Nanofiltration Bioreactors', Wiley, pp. 691-705.
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Nguyen, TT & Indraratna, B 2021, 'Fluidization of Subgrade Soil Under Rail Tracks Through CFD-DEM Coupling' in Challenges and Innovations in Geomechanics, Springer International Publishing, Switzerland, 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.

Ou, Y, Mihăiţă, AS & Chen, F 2021, 'Big data processing and analysis on the impact of COVID-19 on public transport delay' in Data Science for COVID-19: Volume 2: Societal and Medical Perspectives, Academic Press, The Netherlands, pp. 257-278.
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The coronavirus disease 2019 (COVID-19) pandemic that started at the beginning of the year 2020 has significantly disrupted people’s daily life around the world. Understanding and quantifying the impact of such a large-scale disruption will help people mitigate the pandemic and enhance the resilience for future preparation of similar events. In this chapter, we present a research work studying the impact of COVID-19 on public transport in terms of bus delay, which involves big data processing and analysis on multisource datasets containing COVID-19 case data, bus GTFS (General Transit Feed Specification) data, and LGA (Local Government Area) boundary data. The datasets in use are heterogeneous, arrive in large volumes and in real time, and have a spatiotemporal distribution, which brings true challenges to this research. To quantify the bus delay changes, we propose a methodology consisting of real-time data crawling, map-matching, arrival time estimation, and bus delay calculation and aggregation. The methodology is applied to a case study focusing on the Sydney metropolitan region across different stages of the COVID-19 pandemic from February to March 2020. The case study shows that during March 2020, the COVID-19 pandemic has significantly impacted people’s travel behaviors in Sydney, but the influence varies in different areas. The most affected areas are the central and eastern suburbs, which recorded a drop of 9.5 min of bus delay during afternoon peak hours. The findings are helpful to understand and mitigate the restriction impact in different city areas with different conditions. The quantified delay reduction also reveals the potential of better transport performance, which could be used as a benchmark of transport performance improvement after the pandemic. The main contributions of this work include the methodology to quantify travel behavior changes under large disruptions such as COVID-19 pandemic and the case study on large-scale and long-period tr...

Pathak, N, Shon, H & Vigneswaran, S 2021, 'Advanced Membrane Bioreactor Hybrid Systems' in Sustainable Technologies for Water and Wastewater Treatment, CRC Press, pp. 317-342.
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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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Rostamian, M, Kashani, AR, Camp, CV & Gandomi, AH 2021, 'Experimental Comparison of Constraint Handling Schemes in Particle Swarm Optimization' in Constraint Handling in Metaheuristics and Applications, Springer Singapore, pp. 81-99.
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Sahni, M, Jha, BK, Merigó, JM & Verma, R 2021, 'Preface', pp. ix-xi.

Salgotra, R & Gandomi, AH 2021, 'Time series analysis of the COVID-19 pandemic in Australia using genetic programming' in Data Science for COVID-19, Elsevier, pp. 399-411.
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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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Samadi, A, Zhao, S, Fu, Q & Yi, G 2021, 'Supramolecular Membranes for Liquid Separation' in Li, X, Lin, J & Zhao, S (eds), Advances in Functional Separation Membranes, The Royal Society of Chemistry, pp. 232-255.
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To minimize the cost and energy consumption of liquid separations, there is a widespread research interest in developing newly emerging materials, such as supramolecular materials. Supramolecular materials with non-covalent interactions and dynamic bonds show robust and reversible physiochemical properties and have advantages for liquid separations due to their easy synthesis by simply mixing appropriate counterparts, fascinating reversibility of the ensembles, and diverse functionalities. Molecular recognition-driven self-assembly plays a key role in the fabrication of supramolecular aggregates and networks through cross-linking and block copolymerization. Block copolymers prohibit the degradation or depolymerization of supramolecular structures under various physicochemical and biological conditions. Water-based supramolecular membranes that are sustainable in harsh conditions and effective in contaminants’ removal have attained much interest. Polyelectrolytes are the other suitable precursors for the preparation of supramolecular materials. Self-healing and stimuli-responsiveness are also discussed as the novel features of these materials with diverse applications as membranes and adsorbents. Finally, the main challenges and perspectives in the fabrication of supramolecular membranes are represented.

Shafaghat, A & Khabbaz, H 2021, 'Numerical Evaluation of Bearing Capacity of Step-Tapered Piles Using P-Y Curves Analysis' in Advancements in Geotechnical Engineering, Springer International Publishing, pp. 200-212.
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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 International Publishing, 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, Kozanoglu, DC & 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, Springer International Publishing, 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.

Singh, AK & Lin, C-T 2021, 'Content Augmentation in Virtual Reality with Cognitive-Conflict-Based Brain-Computer Interface' in Handbook of Neuroengineering, Springer Singapore, pp. 1-22.
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Sohaib, O, Kang, K & Miliszewska, I 2021, 'Uncertainty Avoidance and Consumer Cognitive Innovativeness in E-Commerce' in Research Anthology on E-Commerce Adoption, Models, and Applications for Modern Business, IGI Global, pp. 1797-1818.
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This article describes how despite the extensive academic interest in e-commerce, an investigation of consumer cognitive innovativeness towards new product purchase intention has been neglected. Based on the stimulus–organism–response (S–O–R) model, this study investigates the consumer cognitive innovativeness and the moderating role of the individual consumer-level uncertainty avoidance cultural value towards new product purchase intention in business-to-consumer (B2C) e-commerce. Structural equation modelling, such as partial least squares (PLS) path modelling was used to test the model, using a sample of 255 participants in Australia who have had prior online shopping experience. The findings show that the online store web atmosphere influences consumers' cognitive innovativeness to purchase new products in countries with diverse degrees of uncertainty avoidance such as Australia. The results provide some guidance for a B2C website design based on how individual's uncertainty avoidance and cognitive innovativeness can aid the online consumer purchasing decision-making process.

Sohaib, O, Kang, K & Miliszewska, I 2021, 'Uncertainty Avoidance and Consumer Cognitive Innovativeness in E-Commerce' in Research Anthology on Strategies for Using Social Media as a Service and Tool in Business, IGI Global, pp. 1664-1685.
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This article describes how despite the extensive academic interest in e-commerce, an investigation of consumer cognitive innovativeness towards new product purchase intention has been neglected. Based on the stimulus–organism–response (S–O–R) model, this study investigates the consumer cognitive innovativeness and the moderating role of the individual consumer-level uncertainty avoidance cultural value towards new product purchase intention in business-to-consumer (B2C) e-commerce. Structural equation modelling, such as partial least squares (PLS) path modelling was used to test the model, using a sample of 255 participants in Australia who have had prior online shopping experience. The findings show that the online store web atmosphere influences consumers' cognitive innovativeness to purchase new products in countries with diverse degrees of uncertainty avoidance such as Australia. The results provide some guidance for a B2C website design based on how individual's uncertainty avoidance and cognitive innovativeness can aid the online consumer purchasing decision-making process.

Sojoudi, A, Sefidan, AM, Alam, KCA & Saha, SC 2021, 'Hydrogen production via electrolysis: Mathematical modeling approach' in Bioenergy Resources and Technologies, Elsevier, pp. 199-235.
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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), IFIP Advances in Information and Communication Technology, Springer International Publishing, Switzerland, pp. 285-296.
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AbstractFor 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.

Tiwari, A, Paul, AR, Jain, A & Saha, SC 2021, 'Design of Efficient Dry Powder Inhalers' in Handbook of Lung Targeted Drug Delivery Systems, CRC Press, pp. 129-153.
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Voinov, A 2021, 'Participatory Modeling for Group Decision Support' in Handbook of Group Decision and Negotiation, Springer International Publishing, pp. 395-411.
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Vu, HP, Nguyen, LN, Zdarta, J, Jesionowski, T & Nghiem, LD 2021, 'Valorizing agricultural residues as biorefinery feedstocks: current advancements and challenges' in Clean Energy and Resources Recovery, Elsevier, pp. 25-48.
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Abbasi, M, Abbasi, E & Li, L 2021, 'New transformer‐less DC–DC converter topologies with reduced voltage stress on capacitors and increased voltage conversion ratio', IET Power Electronics, vol. 14, no. 6, pp. 1173-1192.
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Abbasi, M, Nazari, Y, Abbasi, E & Li, L 2021, 'A new transformer‐less step‐up DC–DC converter with high voltage gain and reduced voltage stress on switched‐capacitors and power switches for renewable energy source applications', IET Power Electronics, vol. 14, no. 7, pp. 1347-1359.
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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, pp. 102244-102244.
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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 2021, 'Integrating semantic edges and segmentation information for building extraction from aerial images using UNet', Machine Learning with Applications, vol. 6, pp. 100194-100194.
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Abdollahi, A, Pradhan, B & Alamri, A 2021, 'RoadVecNet: a new approach for simultaneous road network segmentation and vectorization from aerial and google earth imagery in a complex urban set-up', GIScience & Remote Sensing, vol. 58, no. 7, pp. 1151-1174.
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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, vol. 49, no. 3, pp. 569-583.
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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.]

Abdollahi, A, Pradhan, B, Sharma, G, Maulud, KNA & Alamri, A 2021, 'Improving Road Semantic Segmentation Using Generative Adversarial Network', IEEE Access, vol. 9, pp. 64381-64392.
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Abdollahi, M, Gao, X, Mei, Y, Ghosh, S, Li, J & Narag, M 2021, 'Substituting clinical features using synthetic medical phrases: Medical text data augmentation techniques', Artificial Intelligence in Medicine, vol. 120, pp. 102167-102167.
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Abdollahi, M, Ni, W, Abolhasan, M & Li, S 2021, 'Software-Defined Networking-Based Adaptive Routing for Multi-Hop Multi-Frequency Wireless Mesh', IEEE Transactions on Vehicular Technology, vol. 70, no. 12, pp. 13073-13086.
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While multi-hop multi-frequency mesh has been extensively studied in the past decades, only several deployable and relatively bulky systems have been developed to support small numbers of hops under stationary settings. This paper presents a new software-defined networking (SDN)-based design of multi-hop multi-frequency mesh. A new lightweight hardware platform is developed to support adaptive routing and frequency selection, by modifying and integrating commercial-off-the-shelf WiFi modules. We also extend the celebrated Dijkstra's algorithm in support of the new multi-hop multi-frequency platform, where non-overlapping frequency bands are selected together with the routing paths by maintaining N^2 Dijkstra processes for N frequency bands. These processes interact to recursively select the optimal upstream node and frequency for each downstream frequency of a node. Mininet-WiFi is used to evaluate the routing of the new system under dense network settings. The results indicate that our system improves the end-to-end throughput by taking background WiFi traffic into account and adaptively selecting the routes and frequencies, as compared to the shortest-path-based routing strategy.

Abdulganiy, RI, Wen, S, Feng, Y, Zhang, W & Tang, N 2021, 'Adapted block hybrid method for the numerical solution of Duffing equations and related problems', AIMS Mathematics, vol. 6, no. 12, pp. 14013-14034.
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<abstract><p>Problems of non-linear equations to model real-life phenomena have a long history in science and engineering. One of the popular of such non-linear equations is the Duffing equation. An adapted block hybrid numerical integrator that is dependent on a fixed frequency and fixed step length is proposed for the integration of Duffing equations. The stability and convergence of the method are demonstrated; its accuracy and efficiency are also established.</p></abstract>

Abedin, B, Jafarzadeh, H & Olszak, CM 2021, 'Thirty Six Years of Information Systems Management: A Bibliometric and Thematic Analysis', Information Systems Management, vol. 38, no. 2, pp. 151-164.
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Abedini, A, Abedin, B & Zowghi, D 2021, 'Adult learning in online communities of practice: A systematic review', British Journal of Educational Technology, vol. 52, no. 4, pp. 1663-1694.
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Abeywickrama, A, Indraratna, B, Nguyen, TT & Rujikiatkamjorn, C 2021, 'LABORATORY INVESTIGATION ON THE USE OF VERTICAL DRAINS TO MITIGATE MUD PUMPING UNDER RAIL TRACKS', Australian Geomechanics Journal, vol. 56, no. 3, pp. 117-126.
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The build-up of excess pore water pressure (EPWP) in undrained soft subgrade under repeated rail loads is the key mechanism causing soil to fluidise, consequently yielding slurry tracks (i.e., mud pumping). This issue has substantially reduced transport efficiency associated with immense cost for track maintenance though considerable effort has been made over the past years. Therefore, this study is carried out to investigate how prefabricated vertical drains (PVDs) can be used to mitigate the accumulated EPWP and associated mud pumping. A series of cyclic triaxial tests including undrained (i.e., without PVDs) and PVD-assisted drained soils are conducted, and their results are compared to evaluate the effect of PVDs on cyclic soil behaviour. In this investigation, subgrade soil collected from a mud pumping site is used while loading parameters including the frequency, confining pressure and cyclic stress ratio (CSR) are considered with respect to heavy rail load condition in the field. The results show that PVDs can help dissipate effectively the accumulated EPWP, thus mitigating soil fluidisation. The current study shows that for undrained condition, lower frequency loading (i.e., slower trains) takes a smaller number of cycles to cause soil failure, whereas for drained cases (i.e., PVDs-assisted specimens), an opposite trend is observed. The study proves that installing PVDs into shallow layer (i.e., 3-5 m depth) is an effective approach to stabilise soft subgrade soil under rail tracks.

Aboulkheyr Es, H, Bigdeli, B, Zhand, S, Aref, AR, Thiery, JP & Warkiani, ME 2021, 'Mesenchymal stem cells induce PD‐L1 expression through the secretion of CCL5 in breast cancer cells', Journal of Cellular Physiology, vol. 236, no. 5, pp. 3918-3928.
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Various factors in the tumor microenvironment (TME) regulate the expression of PD-L1 in cancer cells. In TME, mesenchymal stem cells (MSCs) play a crucial role in tumor progression, metastasis, and drug resistance. Emerging evidence suggests that MSCs can modulate the immune-suppression capacity of TME through the stimulation of PD-L1 expression in various cancers; nonetheless, their role in the induction of PD-L1 in breast cancer remained elusive. Here, we assessed the potential of MSCs in the stimulation of PD-L1 expression in a low PD-L1 breast cancer cell line and explored its associated cytokine. We assessed the expression of MSCs-related genes and their correlation with PD-L1 across 1826 breast cancer patients from the METABRIC cohort. After culturing an ER+/differentiated/low PD-L1 breast cancer cells with MSCs conditioned-medium (MSC-CM) in a microfluidic device, a variety of in-vitro assays was carried out to determine the role of MSC-CM in breast cancer cells' phenotype plasticity, invasion, and its effects on induction of PD-L1 expression. In-silico analysis showed a positive association between MSCs-related genes and PD-L1 expression in various types of breast cancer. Through functional assays, we revealed that MSC-CM not only prompts a phenotype switch but also stimulates PD-L1 expression at the protein level through secretion of various cytokines, especially CCL5. Treatment of MSCs with cytokine inhibitor pirfenidone showed a significant reduction in the secretion of CCL5 and consequently, expression of PD-L1 in breast cancer cells. We concluded that MSCs-derived CCL5 may act as a PD-L1 stimulator in breast cancer.

Aboutorab, H, Hussain, OK, Saberi, M, Hussain, FK & Chang, E 2021, 'A survey on the suitability of risk identification techniques in the current networked environment', Journal of Network and Computer Applications, vol. 178, pp. 102984-102984.
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Abraham, MT, Satyam, N & Pradhan, B 2021, 'Forecasting Landslides Using Mobility Functions: A Case Study from Idukki District, India', Indian Geotechnical Journal, vol. 51, no. 4, pp. 684-693.
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Abraham, MT, Satyam, N, Jain, P, Pradhan, B & Alamri, A 2021, 'Effect of spatial resolution and data splitting on landslide susceptibility mapping using different machine learning algorithms', Geomatics, Natural Hazards and Risk, vol. 12, no. 1, pp. 3381-3408.
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Abraham, MT, Satyam, N, Reddy, SKP & Pradhan, B 2021, 'Runout modeling and calibration of friction parameters of Kurichermala debris flow, India', Landslides, vol. 18, no. 2, pp. 737-754.
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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, pp. 105147-105147.
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Abraham, MT, Satyam, N, Shreyas, N, Pradhan, B, Segoni, S, Abdul Maulud, KN & Alamri, AM 2021, 'Forecasting landslides using SIGMA model: a case study from Idukki, India', Geomatics, Natural Hazards and Risk, vol. 12, no. 1, pp. 540-559.
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Abualigah, L, Diabat, A, Mirjalili, S, Abd Elaziz, M & Gandomi, AH 2021, 'The Arithmetic Optimization Algorithm', Computer Methods in Applied Mechanics and Engineering, vol. 376, pp. 113609-113609.
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Abualigah, L, Diabat, A, Sumari, P & Gandomi, AH 2021, 'Applications, Deployments, and Integration of Internet of Drones (IoD): A Review', IEEE Sensors Journal, vol. 21, no. 22, pp. 25532-25546.
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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, vol. 51, no. 4, pp. 2353-2376.
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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.

Abualigah, L, Yousri, D, Abd Elaziz, M, Ewees, AA, Al-qaness, MAA & Gandomi, AH 2021, 'Aquila Optimizer: A novel meta-heuristic optimization algorithm', Computers & Industrial Engineering, vol. 157, pp. 107250-107250.
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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, vol. 15, no. 3, pp. 221-246.
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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.

Adegbosin, AE, Warnken, J & Sun, J 2021, 'Mapping the quality of basic and comprehensive emergency obstetric care services in Haiti', International Journal for Quality in Health Care, vol. 33, no. 4.
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Abstract Objective To investigate geographical inequalities and changes in the quality of emergency obstetric care services available in Haiti over time. Methods We utilized data from the Service Provision Assessment survey of all health facilities in Haiti in 2013 and 2017.We developed a quality index for basic emergency obstetric care (BEmOC) and comprehensive emergency obstetric care (CEmOC) based on the items in the signal functions of an emergency obstetric care framework, using a structure, process and outcome framework. We measured the quality index of all facilities in 2013 and 2017. We also assessed geographical trends and changes in quality between 2013 and 2017 using geospatial analysis. Result Our analysis showed that basic structure items such as connection to electricity grid, manual vacuum extractors, vacuum aspirators and dilation and curettage kits were widely unavailable at healthcare facilities. There was a significant improvement in indicators of structure (P &lt; 0.001) and BEmOC (P = 0.03) in primary facilities; however, there was no significant change in the quality of CEmOC in primary facilities (P = 0.18). Similarly, there was no significant change in any of the structure or process indicators at secondary care facilities. Conclusion The availability of BEmOC at several Haitian facilities remains poor; however, there was significant improvement at primary care facilities, with little to no change in overall quality at secondary hea...

Afsari, M, Shon, HK & Tijing, LD 2021, 'Janus membranes for membrane distillation: Recent advances and challenges', Advances in Colloid and Interface Science, vol. 289, pp. 102362-102362.
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Aftab, MU, Oluwasanmi, A, Alharbi, A, Sohaib, O, Nie, X, Qin, Z & Son, NT 2021, 'Secure and dynamic access control for the Internet of Things (IoT) based traffic system.', PeerJ Comput. Sci., vol. 7, pp. e471-e471.
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Today, the trend of the Internet of Things (IoT) is increasing through the use of smart devices, vehicular networks, and household devices with internet-based networks. Specifically, the IoT smart devices and gadgets used in government and military are crucial to operational success. Communication and data sharing between these devices have increased in several ways. Similarly, the threats of information breaches between communication channels have also surged significantly, making data security a challenging task. In this context, access control is an approach that can secure data by restricting unauthorized users. Various access control models exist that can effectively implement access control yet, and there is no single state-of-the-art model that can provide dynamicity, security, ease of administration, and rapid execution all at once. In combating this loophole, we propose a novel secure and dynamic access control (SDAC) model for the IoT networks (smart traffic control and roadside parking management). Our proposed model allows IoT devices to communicate and share information through a secure means by using wired and wireless networks (Cellular Networks or Wi-Fi). The effectiveness and efficiency of the proposed model are demonstrated using mathematical models and discussed with many example implementations.

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, vol. 69, no. 8, pp. 4447-4456.
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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.

Aghlmandi, A, Nikshad, A, Safaralizadeh, R, Warkiani, ME, Aghebati-Maleki, L & Yousefi, M 2021, 'Microfluidics as efficient technology for the isolation and characterization of stem cells.', EXCLI J, vol. 20, pp. 426-443.
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The recent years have been passed with significant progressions in the utilization of microfluidic technologies for cellular investigations. The aim of microfluidics is to mimic small-scale body environment with features like optical transparency. Microfluidics can screen and monitor different cell types during culture and study cell function in response to stimuli in a fully controlled environment. No matter how the microfluidic environment is similar to in vivo environment, it is not possible to fully investigate stem cells behavior in response to stimuli during cell proliferation and differentiation. Researchers have used stem cells in different fields from fundamental researches to clinical applications. Many cells in the body possess particular functions, but stem cells do not have a specific task and can turn into almost any type of cells. Stem cells are undifferentiated cells with the ability of changing into specific cells that can be essential for the body. Researchers and physicians are interested in stem cells to use them in testing the function of the body's systems and solving their complications. This review discusses the recent advances in utilizing microfluidic techniques for the analysis of stem cells, and mentions the advantages and disadvantages of using microfluidic technology for stem cell research.

Ahmad, S, Alnowibet, K, Alqasem, L, Merigo, JM & Zaindin, M 2021, 'Generalized OWA operators for uncertain queuing modeling with application in healthcare', Soft Computing, vol. 25, no. 6, pp. 4951-4962.
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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.

Ahmadi Choukolaei, H, Jahangoshai Rezaee, M, Ghasemi, P & Saberi, M 2021, 'Efficient Crisis Management by Selection and Analysis of Relief Centers in Disaster Integrating GIS and Multicriteria Decision Methods: A Case Study of Tehran', Mathematical Problems in Engineering, vol. 2021, pp. 1-22.
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In Iran, location is usually done by temporary relief organizations without considering the necessary standards or conditions. The inappropriate and unscientific location may have led to another catastrophe, even far greater than the initial tragedy. In this study, the proposed locations of crisis management in the region and the optimal points proposed by the Geographic Information System (GIS), taking into account the opinions of experts and without the opinion of experts, were evaluated according to 18 criteria. First, the optimal areas have been evaluated according to standard criteria extracted by GIS and the intended locations of the region for accommodation in times of crisis. Then, the position of each place is calculated concerning each criterion. The resulting matrix of optimal options was qualitatively entered into the Preference Ranking Organization Method for Evaluation (PROMETHEE) for analysis. The triangular fuzzy aggregation method for weighting and standard classification of criteria for extracting optimal areas using GIS and integrating entropy and the Multiobjective Optimization Based on Ratio Analysis (MOORA) method for prioritizing places in the region are considered in this research. Finally, by applying constraints and using net input and output flows, optimal and efficient options are identified by PROMETHEE V. Among the research options, only four options were optimal and efficient. A case study of the Tehran metropolis is provided to show the ability of the proposed approach for selecting the points in three modes, with/without applying weights and applying crisis management.

Ahmadi, VE, Butun, I, Altay, R, Bazaz, SR, Alijani, H, Celik, S, Warkiani, ME & Koşar, A 2021, 'The effects of baffle configuration and number on inertial mixing in a curved serpentine micromixer: Experimental and numerical study', Chemical Engineering Research and Design, vol. 168, pp. 490-498.
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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, pp. 115079-115079.
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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, AA, Pradhan, B, Chakraborty, S, Alamri, A & Lee, C-W 2021, 'An Optimized Deep Neural Network Approach for Vehicular Traffic Noise Trend Modeling', IEEE Access, vol. 9, pp. 107375-107386.
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Ahmed, F, Afzal, MU, Hayat, T, Esselle, KP & Thalakotuna, DN 2021, 'A dielectric free near field phase transforming structure for wideband gain enhancement of antennas', Scientific Reports, vol. 11, no. 1.
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AbstractThe gain of some aperture antennas can be significantly increased by making the antenna near-field phase distribution more uniform, using a phase-transformation structure. A novel dielectric-free phase transforming structure (DF-PTS) is presented in this paper for this purpose, and its ability to correct the aperture phase distribution of a resonant cavity antenna (RCA) over a much wider bandwidth is demonstrated. As opposed to printed multilayered metasurfaces, all the cells in crucial locations of the DF-PTS have a phase response that tracks the phase error of the RCA over a large bandwidth, and in addition have wideband transmission characteristics, resulting in a wideband antenna system. The new DF-PTS, made of three thin metal sheets each containing modified-eight-arm-asterisk-shaped slots, is significantly stronger than the previous DF-PTS, which requires thin and long metal interconnects between metal patches. The third advantage of the new DF-PTS is, all phase transformation cells in it are highly transparent, each with a transmission magnitude greater than − 1 dB at the design frequency, ensuring excellent phase correction with minimal effect on aperture amplitude distribution. With the DF-PTS, RCA gain increases to 20.1 dBi, which is significantly greater than its 10.7 dBi gain without the DF-PTS. The measured 10-dB return loss bandwidth and the 3-dB gain bandwidth of the RCA with DF-PTS are 46% and 12%, respectively.

Ahmed, MB, Rahman, MS, Alom, J, Hasan, MDS, Johir, MAH, Mondal, MIH, Lee, D-Y, Park, J, Zhou, JL & Yoon, M-H 2021, 'Microplastic particles in the aquatic environment: A systematic review', Science of The Total Environment, vol. 775, pp. 145793-145793.
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Microplastics (MPs) pollution has become one of the most severe environmental concerns today. MPs persist in the environment and cause adverse effects in organisms. This review aims to present a state-of-the-art overview of MPs in the aquatic environment. Personal care products, synthetic clothing, air-blasting facilities and drilling fluids from gas-oil industries, raw plastic powders from plastic manufacturing industries, waste plastic products and wastewater treatment plants act as the major sources of MPs. For MPs analysis, pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS), Py-MS methods, Raman spectroscopy, and FT-IR spectroscopy are regarded as the most promising methods for MPs identification and quantification. Due to the large surface area to volume ratio, crystallinity, hydrophobicity and functional groups, MPs can interact with various contaminants such as heavy metals, antibiotics and persistent organic contaminants. Among different physical and biological treatment technologies, the MPs removal performance decreases as membrane bioreactor (> 99%) > activated sludge process (~98%) > rapid sand filtration (~97.1%) > dissolved air floatation (~95%) > electrocoagulation (> 90%) > constructed wetlands (88%). Chemical treatment methods such as coagulation, magnetic separations, Fenton, photo-Fenton and photocatalytic degradation also show moderate to high efficiency of MP removal. Hybrid treatment technologies show the highest removal efficacies of MPs. Finally, future research directions for MPs are elaborated.

Ahmed, N, Hoque, MA-A, Pradhan, B & Arabameri, A 2021, 'Spatio-Temporal Assessment of Groundwater Potential Zone in the Drought-Prone Area of Bangladesh Using GIS-Based Bivariate Models', Natural Resources Research, vol. 30, no. 5, pp. 3315-3337.
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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, vol. 199, pp. 105408-105408.
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Ahmed, SF, Hafez, MG, Chu, Y-M & Mofijur, M 2021, 'Turbulent energy motion of fiber suspensions in a rotating frame', Alexandria Engineering Journal, vol. 60, no. 3, pp. 3345-3352.
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Ahmed, SF, Liu, G, Mofijur, M, Azad, AK, Hazrat, MA & Chu, Y-M 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.

Ahmed, SF, Mofijur, M, Nuzhat, S, Chowdhury, AT, Rafa, N, Uddin, MA, Inayat, A, Mahlia, TMI, Ong, HC, Chia, WY & Show, PL 2021, 'Recent developments in physical, biological, chemical, and hybrid treatment techniques for removing emerging contaminants from wastewater', Journal of Hazardous Materials, vol. 416, pp. 125912-125912.
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Ahmed, SF, Mofijur, M, Tarannum, K, Chowdhury, AT, Rafa, N, Nuzhat, S, Kumar, PS, Vo, D-VN, Lichtfouse, E & Mahlia, TMI 2021, 'Biogas upgrading, economy and utilization: a review', Environmental Chemistry Letters, vol. 19, no. 6, pp. 4137-4164.
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Biogas production is rising in the context of fossil fuel decline and the future circular economy, yet raw biogas requires purification steps before use. Here, we review biogas upgrading using physical, chemical and biological methods such as water scrubbing, physical absorption, pressure swing adsorption, cryogenic separation, membrane separation, chemical scrubbing, chemoautotrophic methods, photosynthetic upgrading and desorption. We also discuss their techno-economic feasibility. We found that physical and chemical upgrading technologies are near-optimal, but still require high energy and resources. Biological methods are less explored despite their promising potential. High-pressure water scrubbing is more economic for small-sized plants, whereas potassium carbonate scrubbing provides the maximum net value for large-sized plants.

Ahmed, SF, Saha, SC, Debnath, JC, Liu, G, Mofijur, M, Baniyounes, A, Chowdhury, SMEK & Vo, D-VN 2021, 'Data-driven modelling techniques for earth-air heat exchangers to reduce energy consumption in buildings: a review', Environmental Chemistry Letters, vol. 19, no. 6, pp. 4191-4210.
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Ahmed, T, Hassan, S, F. Hasan, M, M. Molla, M, A. Taher, M & C. Saha, S 2021, 'Lattice Boltzmann Simulation of Magnetic Field Effect on ElectricallyConducting Fluid at Inclined Angles in Rayleigh-B閚ard Convection', Energy Engineering, vol. 118, no. 1, pp. 15-36.
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Ahmed, T, Hossain, SM & Hossain, MA 2021, 'Reducing completion time and optimizing resource use of resource-constrained construction operation by means of simulation modeling', International Journal of Construction Management, vol. 21, no. 4, pp. 404-415.
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© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. Traditionally, critical path method (CPM) is widely used with manual resource assignment for planning and scheduling of the construction projects. This is predominant in the construction industry of Bangladesh. Therefore, the industry often faces challenges to complete construction projects in minimum possible time with optimum use of resources. Although, concurrency-based scheduling is an efficient tool to reduce construction completion time, manual formulation of plans for construction operations using the technique is cumbersome (because of complex nature of real-life construction operations, for instance a real-life construction work involves complex interaction between activities, limited number of resources and resource-sharing among activities, etc.) With this viewpoint, the present study aims to introduce computer-aided simulation modelling-based approach to reduce the completion time of a resource-constrained construction operation utilizing the features of concurrency-based scheduling. The study also proposes a method to optimize the resource use of construction operation. A real-life construction work has been considered as the case construction operation for this study. Results indicate that the simulation model developed for the case project can efficiently generate work-flow plans with reduced construction durations compared to the work-flow plan of the actual schedule. The model can also help to optimize the use of resources. Furthermore, the model developed for the case project can easily be reshaped, expanded and applied to other construction operations.

Akar, S, Taheri, A, Bazaz, R, Warkiani, E & Shaegh, M 2021, 'Twisted architecture for enhancement of passive micromixing in a wide range of Reynolds numbers', Chemical Engineering and Processing - Process Intensification, vol. 160, pp. 108251-108251.
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Akbari, M, Meshram, SG, Krishna, RS, Pradhan, B, Shadeed, S, Khedher, KM, Sepehri, M, Ildoromi, AR, Alimerzaei, F & Darabi, F 2021, 'Identification of the Groundwater Potential Recharge Zones Using MCDM Models: Full Consistency Method (FUCOM), Best Worst Method (BWM) and Analytic Hierarchy Process (AHP)', Water Resources Management, vol. 35, no. 14, pp. 4727-4745.
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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, vol. 9, pp. 1-9.
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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, pp. 119309-119309.
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Akther, N, Lin, Y, Wang, S, Phuntsho, S, Fu, Q, Ghaffour, N, Matsuyama, H & Shon, HK 2021, 'In situ ultrathin silica layer formation on polyamide thin-film composite membrane surface for enhanced forward osmosis performances', Journal of Membrane Science, vol. 620, pp. 118876-118876.
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© 2020 Elsevier B.V. Polyamide (PA) based thin-film composite (TFC) membranes experience a high degree of organic fouling due to their hydrophobic and rough membrane surfaces during forward osmosis (FO) process. In this study, an ultrathin silica layer was grown in situ on the PA surface to enhance the antifouling property of TFC membrane by silicification process. Surface characterization confirmed the development of a silica layer on the PA surface. The superhydrophilic surface of silica-deposited TFC membrane (contact angle of 20°) with 3 h silicification time (STFC-3h) displayed a 53% higher water flux than the pristine TFC membrane without significantly affecting the membrane selectivity. The silica-modified TFC FO membranes also exhibited excellent stability when subjected to long-term cross-flow shear stress rinsing using deionized (DI) water, including exposure to salty, acidic and basic solutions. Moreover, the fouling tests showed that STFC-3h membrane lost only 4.2%, 9.1% and 12.1% of its initial flux with bovine serum albumin (BSA), humic acid (HA) and sodium alginate (SA), respectively, which are considerably lower compared to the pristine TFC FO membrane where flux losses were 18.7%, 23.2% and 37.2%, respectively. The STFC-3h membrane also revealed higher flux recovery ratio (FRR) of 99.6%, 96.9% and 94.4% with BSA, HA and SA, respectively, after physical cleaning than the pristine membrane (91.4%, 88.7%, and 81.2%, respectively). Overall, the in situ formation of an ultrathin hydrophilic silica layer on the PA surface reported in this work shows that the TFC membrane's water flux and antifouling property could be improved without diminishing the membrane selectivity.

Akther, N, Sanahuja-Embuena, V, Górecki, 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, pp. 114795-114795.
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Al zahrani, S, Islam, MS & Saha, SC 2021, 'Heat transfer enhancement investigation in a novel flat plate heat exchanger', International Journal of Thermal Sciences, vol. 161, pp. 106763-106763.
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Al zahrani, S, Islam, MS & Saha, SC 2021, 'Heat transfer enhancement of modified flat plate heat exchanger', Applied Thermal Engineering, vol. 186, pp. 116533-116533.
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Al-Abadi, AM, Fryar, AE, Rasheed, AA & Pradhan, B 2021, 'Assessment of groundwater potential in terms of the availability and quality of the resource: a case study from Iraq', Environmental Earth Sciences, vol. 80, no. 12.
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Alabsi, MI & Gill, AQ 2021, 'A Review of Passenger Digital Information Privacy Concerns in Smart Airports', IEEE Access, vol. 9, pp. 33769-33781.
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Alam, MA, Muhammad, G, Khan, MN, Mofijur, M, Lv, Y, Xiong, W & Xu, J 2021, 'Choline chloride-based deep eutectic solvents as green extractants for the isolation of phenolic compounds from biomass', Journal of Cleaner Production, vol. 309, pp. 127445-127445.
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Alanazi, F, Gay, V, N., M & 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.

Al-Bawi, AJ, Al-Abadi, AM, Pradhan, B & Alamri, AM 2021, 'Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers', Geomatics, Natural Hazards and Risk, vol. 12, no. 1, pp. 3035-3062.
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Alderighi, T, Malomo, L, Bickel, B, Cignoni, P & Pietroni, N 2021, 'Volume decomposition for two-piece rigid casting', ACM Transactions on Graphics, vol. 40, no. 6, pp. 1-14.
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We introduce a novel technique to automatically decompose an input object's volume into a set of parts that can be represented by two opposite height fields. Such decomposition enables the manufacturing of individual parts using two-piece reusable rigid molds. Our decomposition strategy relies on a new energy formulation that utilizes a pre-computed signal on the mesh volume representing the accessibility for a predefined set of extraction directions. Thanks to this novel formulation, our method allows for efficient optimization of a fabrication-aware partitioning of volumes in a completely automatic way. We demonstrate the efficacy of our approach by generating valid volume partitionings for a wide range of complex objects and physically reproducing several of them.

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, pp. 108470-108470.
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Alfaro-García, VG, Merigó, JM, Gil-Lafuente, AM & Monge, RG 2021, 'Group-decision making with induced ordered weighted logarithmic aggregation operators', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1761-1772.
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This paper presents the induced generalized ordered weighted logarithmic aggregation (IGOWLA) operator, this operator is an extension of the generalized ordered weighted logarithmic aggregation (GOWLA) operator. It uses order-induced variables that modify the reordering process of the arguments included in the aggregation. The principal advantage of the introduced induced mechanism is the consideration of highly complex attitude from the decision makers. We study some families of the IGOWLA operator as measures for the characterization of the weighting vector. This paper presents the general formulation of the operator and some special cases, including the induced ordered weighted logarithmic geometric averaging (IOWLGA) operator and the induced ordered weighted logarithmic aggregation (IOWLA). Further generalizations using quasi-arithmetic mean are also proposed. Finally, an illustrative example of a group decision-making procedure using a multi-person analysis and the IGOWLA operator in the area of innovation management is analyzed.

Alfouneh, M, Ji, J & Luo, Q 2021, 'Damping design of harmonically excited flexible structures with graded materials to minimize sound pressure and radiation', Engineering Optimization, vol. 53, no. 2, pp. 348-367.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. Topology optimization is an effective method in the design of acoustic media. This article presents optimization for graded damping materials to minimize sound pressure at a reference point or sound power radiation under harmonic excitation. The Helmholtz integral equation is used to calculate an acoustic field to satisfy the Sommerfeld conditions. The equation of motion is solved using a unit dynamic load method. Formulations for the sound pressure or sound power radiation in an integral form are derived in terms of mutual kinetic and strain energy densities. These integrals lead to novel physical response functions for solving the proposed optimization problem to design graded damping materials. The response function derived for individual frequency is utilized to solve the multi-objective optimization problem of minimizing sound pressure at the reference point for excitations with a range of frequencies. Numerical examples are presented to verify the efficiency of the present formulations.

Al-Hadhrami, Y & Hussain, FK 2021, 'DDoS attacks in IoT networks: a comprehensive systematic literature review', World Wide Web, vol. 24, no. 3, pp. 971-1001.
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The Internet of Things (IoT) is a rapidly emerging technology in the consumer and industrial market. This technology has the potential to radically transform the consumer experience, as it will change our daily scenes, starting from the way we drink coffee to how smart objects interact with industrial applications. Such rapid development and deployment face multifarious challenges, including the sheer amount of data generated, network scale, network heterogeneity, as well as security and privacy concerns. In recent years, Distributed Denial-of-Service (DDoS) attacks in IoT networks are considered one of the growing challenges that need to be shed light on. DDoS attacks utilize the limited resources in IoT devices, such as storage limitation and network capacity, that cause this issue in the IoT application. This paper comprehensively reviews the attacks that can lead to DDoS, which eventually will cause serious damage to existing systems. Additionally, the paper investigates the available solutions used to counter these attacks and explore their limitations from the perspective of the constrained device. Furthermore, a detailed analysis of the existing solution placement was implemented, including heterogeneity and their performance for IoT based networks. Finally, the paper will reveal and discuss interesting research direction on the future IoT security and current trends.

Alharbi, A & Sohaib, O 2021, 'Technology Readiness and Cryptocurrency Adoption: PLS-SEM and Deep Learning Neural Network Analysis.', IEEE Access, vol. 9, pp. 21388-21394.
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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, pp. 1-7.
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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, A, Fathalla, A, Salah, A, Bekhit, M & Eldesouky, E 2021, 'Marine Data Prediction: An Evaluation of Machine Learning, Deep Learning, and Statistical Predictive Models', Computational Intelligence and Neuroscience, vol. 2021, pp. 1-13.
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Nowadays, ocean observation technology continues to progress, resulting in a huge increase in marine data volume and dimensionality. This volume of data provides a golden opportunity to train predictive models, as the more the data is, the better the predictive model is. Predicting marine data such as sea surface temperature (SST) and Significant Wave Height (SWH) is a vital task in a variety of disciplines, including marine activities, deep-sea, and marine biodiversity monitoring. The literature has efforts to forecast such marine data; these efforts can be classified into three classes: machine learning, deep learning, and statistical predictive models. To the best of the authors’ knowledge, no study compared the performance of these three approaches on a real dataset. This paper focuses on the prediction of two critical marine features: the SST and SWH. In this work, we proposed implementing statistical, deep learning, and machine learning models for predicting the SST and SWH on a real dataset obtained from the Korea Hydrographic and Oceanographic Agency. Then, we proposed comparing these three predictive approaches on four different evaluation metrics. Experimental results have revealed that the deep learning model slightly outperformed the machine learning models for overall performance, and both of these approaches greatly outperformed the statistical predictive model.

Ali, SM, Im, S-J, Jang, A, Phuntsho, S & Shon, HK 2021, 'Forward osmosis system design and optimization using a commercial cellulose triacetate hollow fibre membrane module for energy efficient desalination', Desalination, vol. 510, pp. 115075-115075.
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Ali, SM, Kim, Y, Qamar, A, Naidu, G, Phuntsho, S, Ghaffour, N, Vrouwenvelder, JS & Shon, HK 2021, 'Dynamic feed spacer for fouling minimization in forward osmosis process', Desalination, vol. 515, pp. 115198-115198.
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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, vol. 14, no. 12, pp. 3529-3529.
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Automotive applications often experience conflicting-objective optimization problems focusing on performance parameters that are catered through precisely developed cost functions. Two such conflicting objectives which substantially affect the working of traction machine drive are maximizing its speed performance and minimizing its energy consumption. In case of an electric vehicle (EV) powertrain, drive energy is bounded by battery dynamics (charging and capacity) which depend on the consumption of drive voltage and current caused by driving cycle schedules, traffic state, EV loading, and drive temperature. In other words, battery consumption of an EV depends upon its drive energy consumption. A conventional control technique improves the speed performance of EV at the cost of its drive energy consumption. However, the proposed optimized energy control (OEC) scheme optimizes this energy consumption by using robust linear parameter varying (LPV) control tuned by genetic algorithms which significantly improves the EV powertrain performance. The analysis of OEC scheme is conducted on the developed vehicle simulator through MATLAB/Simulink based simulations as well as on an induction machine drive platform. The accuracy of the proposed OEC is quantitatively assessed to be 99.3% regarding speed performance which is elaborated by the drive speed, voltage, and current results against standard driving cycles.

Alibeikloo, M, Khabbaz, H, Fatahi, B & Le, TM 2021, 'Reliability Assessment for Time-Dependent Behaviour of Soft Soils Considering Cross Correlation between Visco-Plastic Model Parameters', Reliability Engineering & System Safety, vol. 213, pp. 107680-107680.
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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, vol. 9, pp. 59184-59195.
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This paper presents the design of three-port converters (TPCs) for smooth transitions (i.e., fast settling time, and no obvious overshoot/undershoot) of 7 distinctive operating modes, depending on sources and loads scheduling. Two viable converter configurations have been identified and selected for further analysis and design of PV-battery systems. Conventionally, mode transition is achieved by assigning specific switching patterns through feedback signals and appropriate control algorithms. This incurs a delay in the response and unavoidable noise in the circuit. Additionally, in TPCs, three voltage sensors and three current sensors are generally required for decision making in mode selection, where errors in sensors may lead to an inaccurate response. This paper presents a new control strategy where the number of switching patterns is significantly reduced to 3 patterns instead of minimum 5 patterns for existing reported topologies. Therefore, decisions are simplified so that the transition occurs naturally based on the power availability and load demand but not deliberately as in the conventional method. In addition, instead of six sensors, three voltage sensors and only one current sensor are required to achieve all the necessary operations, namely, MPPT, battery protection, and output regulation. Moreover, these sensors do not participate in mode selection decision, which leads to seamless and fast mode transition. In addition, this work considers two bidirectional ports as compared with only one bidirectional port in most reported topologies. This configuration enables both standalone and DC grid-connected applications. Experimental results are reported to verify the proposed solution.

Aljarajreh, H, Lu, DD-C, Siwakoti, YP, Tse, CK & See, KW 2021, 'Synthesis and Analysis of Three-Port DC/DC Converters with Two Bidirectional Ports Based on Power Flow Graph Technique', Energies, vol. 14, no. 18, pp. 5751-5751.
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This paper presents a systematic topological study to derive all possible basic and non-isolated three-port converters (TPCs) using power flow diagrams. Unlike most reported TPCs with one bidirectional port, this paper considers up to two bidirectional ports and provides a comprehensive analytical tool. This tool acts as a framework for all power flow combinations, selection, and design. Some viable converter configurations have been identified and selected for further analysis.

Alkalbani, AM & Hussain, W 2021, 'Cloud service discovery method: A framework for automatic derivation of cloud marketplace and cloud intelligence to assist consumers in finding cloud services', International Journal of Communication Systems, vol. 34, no. 8.
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Almansor, EH, Hussain, FK & Hussain, OK 2021, 'Supervised ensemble sentiment-based framework to measure chatbot quality of services', Computing, vol. 103, no. 3, pp. 491-507.
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© 2020, Springer-Verlag GmbH Austria, part of Springer Nature. Developing an intelligent chatbot has evolved in the last few years to become a trending topic in the area of computer science. However, a chatbot often fails to understand the user’s intent, which can lead to the generation of inappropriate responses that cause dialogue breakdown and user dissatisfaction. Detecting the dialogue breakdown is essential to improve the performance of the chatbot and increase user satisfaction. Recent approaches have focused on modeling conversation breakdown using serveral approaches, including supervised and unsupervised approaches. Unsupervised approach relay heavy datasets, which make it challenging to apply it to the breakdown task. Another challenge facing predicting breakdown in conversation is the bias of human annotation for the dataset and the handling process for the breakdown. To tackle this challenge, we have developed a supervised ensemble automated approach that measures Chatbot Quality of Service (CQoS) based on dialogue breakdown. The proposed approach is able to label the datasets based on sentiment considering the context of the conversion to predict the breakdown. In this paper we aim to detect the affect of sentiment change of each speaker in a conversation. Furthermore, we use the supervised ensemble model to measure the CQoS based on breakdown. Then we handle this problem by using a hand-over mechanism that transfers the user to a live agent. Based on this idea, we perform several experiments across several datasets and state-of-the-art models, and we find that using sentiment as a trigger for breakdown outperforms human annotation. Overall, we infer that knowledge acquired from the supervised ensemble model can indeed help to measure CQoS based on detecting the breakdown in conversation.

Almuntashiri, A, Hosseinzadeh, A, Volpin, F, Ali, SM, Dorji, U, Shon, H & Phuntsho, S 2021, 'Removal of pharmaceuticals from nitrified urine', Chemosphere, vol. 280, pp. 130870-130870.
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Al-Najjar, HAH & Pradhan, B 2021, 'Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks', Geoscience Frontiers, vol. 12, no. 2, pp. 625-637.
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Aloqaily, AA, Tafavogh, S, Harvey, BL, Catchpoole, DR & Kennedy, PJ 2021, 'Feature prioritisation on big genomic data for analysing gene-gene interactions', International Journal of Bioinformatics Research and Applications, vol. 17, no. 2, pp. 158-158.
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Complex diseases are not caused by single genes but result from intricate non-linear interactions among them. There is a critical need to implement approaches that take into account non-linear gene-gene interactions in searching for markers that jointly cause diseases. Determining the interaction between more than two single nucleotide polymorphisms (SNP) within the whole genome data is computationally expensive and often infeasible. In this paper, we develop an approach to classify patients with Acute Lymphoblastic Leukaemia by analysing multiple SNP interactions. A novel feature prioritisation algorithm called interaction effect quantity (IEQ) selects SNPs with high potential of interaction by analysing their distribution throughout the genomic data and enables deeper analysis of non-linear interactions within large datasets. We show that IEQ enables analyses of interactions between up to four SNPs, with F-measure for classification greater than 89% obtained. Such an analysis is typically much more computationally challenging if IEQ is not implemented.

Alqaisi, R, Le, TM & Khabbaz, H 2021, 'Combined effects of eggshell powder and hydrated lime on the properties of expansive soils', Australian Geomechanics Journal, vol. 56, no. 1, pp. 107-118.
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This study involves the utilization of eggshell powder (ESP) as a supplementary additive to lime stabilization of expansive soil and evaluates its potential in enhancing the performance of expansive soil treated with lime. Eggshell is a waste material obtained from several sources. Some of the challenges associated with dumping eggshell are odour, insect growth, disposal costs and availability of disposal sites. In order to reduce these environmental issues, eggshells can be processed into ESP and play a role as a soil stabilizing agent. Calcium oxide is considered to be the main ingredient of the ESP. Therefore, an experimental program is carried out to test a mixture of kaolinite, bentonite and Sydney fine sand, which is simulated to be as an artificial expansive soil. The eggshell powder was used as an additive to 5% lime in four percentages of 5%, 10%, 15% and 20% by total dry weight of the soil mass. Results of linear shrinkage, proctor compaction, and unconfined compressive strength tests after various curing time are presented in detail and compared with untreated soil samples. The outcomes of these experimental investigations indicated that the combination of eggshell powder and hydrated lime led to a further decrease in linear shrinkage and the maximum dry density of expansive soil samples. It was found that the improved geotechnical characteristics were more pronounced for 5% ESP treated expansive soil. At this percentage, the compressive strength at failure and the corresponding strain increased slightly by 18% and 9%, respectively, compared to the untreated expansive soil after 28 days of curing. Moreover, in comparison with lime (5%) only stabilized expansive soil, the combined lime (5%) and ESP (5%), induced approximately 15% build-up in the compressive strength of samples. Based on the reasonable laboratory test results, this addition is recommended to improve the shrinkage properties and stabilize the expansive soils where the high performance of ...

Alsahafi, YA & Gay, V 2021, 'Erratum to ‘An overview of electronic personal health records’ [Health Policy and Technology 7 (2018) 427-432]', Health Policy and Technology, vol. 10, no. 4, pp. 100566-100566.
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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, pp. 107385-107385.
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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...

Alsmadi, L, Kong, X, Sandrasegaran, K & Fang, G 2021, 'An Improved Indoor Positioning Accuracy Using Filtered RSSI and Beacon Weight', IEEE Sensors Journal, vol. 21, no. 16, pp. 18205-18213.
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Alzahrani, AS, Gay, V, Alturki, R & AlGhamdi, MJ 2021, 'Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications', Journal of Healthcare Engineering, vol. 2021, pp. 1-8.
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Mobile application (app) use is increasingly becoming an essential part of our daily lives. Due to their significant usefulness, people rely on them to perform multiple tasks seamlessly in almost all aspects of everyday life. Similarly, there has been immense progress in artificial intelligence (AI) technology, especially deep learning, computer vision, natural language processing, and robotics. These technologies are now actively being implemented in smartphone apps and healthcare, providing multiple healthcare services. However, several factors affect the usefulness of mobile healthcare apps, and usability is an important one. There are various healthcare apps developed for each specific task, and the success of these apps depends on their performance. This study presents a systematic review of the existing apps and discusses their usability attributes. It highlights the usability models, outlines, and guidelines proposed in previous research for designing apps with improved usability characteristics. Thirty-nine research articles were reviewed and examined to identify the usability attributes, framework, and app design conducted. The results showed that satisfaction, efficiency, and learnability are the most important usability attributes to consider when designing eHealth mobile apps. Surprisingly, other significant attributes for healthcare apps, such as privacy and security, were not among the most indicated attributes in the studies.

Al-Zahrani, S, Islam, MS & Saha, SC 2021, 'Comparison of flow resistance and port maldistribution between novel and conventional plate heat exchangers', International Communications in Heat and Mass Transfer, vol. 123, pp. 105200-105200.
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AlZainati, N, Saleem, H, Altaee, A, Zaidi, SJ, Mohsen, M, Hawari, A & Millar, GJ 2021, 'Pressure retarded osmosis: Advancement, challenges and potential', Journal of Water Process Engineering, vol. 40, pp. 101950-101950.
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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.

Alzoubi, Y & Gill, A 2021, 'The Critical Communication Challenges Between Geographically Distributed Agile Development Teams: Empirical Findings', IEEE Transactions on Professional Communication, vol. 64, no. 4, pp. 322-337.
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Amin, BMR, Taghizadeh, S, Maric, S, Hossain, MJ & Abbas, R 2021, 'Smart Grid Security Enhancement by Using Belief Propagation', IEEE Systems Journal, vol. 15, no. 2, pp. 2046-2057.
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Amin, U, Hossain, MJ, Tushar, W & Mahmud, K 2021, 'Energy Trading in Local Electricity Market With Renewables—A Contract Theoretic Approach', IEEE Transactions on Industrial Informatics, vol. 17, no. 6, pp. 3717-3730.
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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, vol. 11, no. 1.
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AbstractContinuous and accurate sensing of water content in soil is an essential and useful measure in the agriculture industry. Traditional sensors developed to perform this task suffer from limited lifetime and also need to be calibrated regularly. Further, maintenance, support, and deployment of these sensors in remote environments provide additional challenges to the use of conventional soil moisture sensors. In this paper, a metamaterial perfect absorber (MPA) based soil moisture sensor is introduced. The ability of MPAs to absorb electromagnetic signals with near 100% efficiency facilitates the design of highly accurate and low-profile radio frequency passive sensors. MPA based sensor can be fabricated from highly durable materials and can therefore be made more resilient than traditional sensors. High resolution sensing is achieved through the creation of physical channels in the substrate integrated waveguide (SIW) cavity. The proposed sensor does not require connection for both electromagnetic signals or for adding a testing sample. Importantly, an external power supply is not needed, making the MPA based sensor the perfect solution for remote and passive sensing in modern agriculture. The proposed MPA based sensor has three absorption bands due to the various resonance modes of the SIW cavity. By changing the soil moisture level, the absorption peak shifts by 10 MHz, 23.3 MHz, and 60 MHz, which is correlated with the water content percentage at the first, second and third absorption bands, respectively. Finally, a $$6 \times 6$$ 6 × 6 cell array with a total s...

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, pp. 195401-195401.
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Abstract The potential of transition metal dichalcogenides such as MoS2 for energy storage has been significantly limited so far by the lack of conductivity and structural stability. Employing highly conductive, graphitic materials in combination with transition metal dichalcogenides can address this gap. Here, we explore the use of a layered electrode structure for solid-state supercapacitors, made of MoS2 and epitaxial graphene (EG) on cubic silicon carbide for on-silicon energy storage. We show that the energy storage of the solid-state supercapacitors can be significantly increased by creating layered MoS2/graphene electrodes, yielding a substantial improvement as compared to electrodes using either EG or MoS2 alone. We conclude that the conductivity of EG and the growth morphology of MoS2 on graphene play an enabling role in the successful use of transition metal dichalcogenides for on-chip energy storage.

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, pp. 2150082-2150082.
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Currently, there is no unified criterion to evaluate the failure of single-layer latticed domes, and an accurate nonlinear time-history analysis (NTHA) is generally required; however, this does not consider the uncertainties found in practice. The seismic instability of domes subjected to earthquake ground motions has not been thoroughly investigated. In this paper, a new approach is developed to automatically capture the instability points in the incremental dynamic analysis (IDA) of single-layer lattice domes by integrating different efficient and robust methods. First, a seismic fragility analysis with instability parameters is performed using the bootstrap calibration method for the perfect dome. Second, based on the Sobol sequence, the quasi-Monte Carlo (QMC) sampling method is used to efficiently calculate the failure probability of the dome with uncertain parameters, in which the truncated distributions of random parameters are considered. Third, the maximum entropy principle (MEP) method is used to improve the computational efficiency in the analyses of structures with uncertainties. Last, the uncertain interval of the domes is determined based on the IDA method. The proposed method has been used to investigate the instability of single-layer lattice domes with uncertain parameters. The results show that it can determine the probability of structural failure with high efficiency and reliability. Additionally, the limitations of the proposed method for parallel computation are discussed.

An, Y, Wang, J, Lu, H & Zhao, W 2021, 'Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm', International Transactions on Electrical Energy Systems, vol. 31, no. 12.
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Andaryani, S, Nourani, V, Ball, J, Jahanbakhsh Asl, S, Keshtkar, H & Trolle, D 2021, 'A comparison of frameworks for separating the impacts of human activities and climate change on river flow in existing records and different near‐future scenarios', Hydrological Processes, vol. 35, no. 7.
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Anil Kumar, B, Ling, SH, Chiung Ching, PH & TORII, S 2021, 'Guest Editorial: Artificial‐intelligence‐based network security and computing technologies in wireless networks', IET Networks, vol. 10, no. 3, pp. 101-102.
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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, pp. 105044-105044.
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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, vol. 69, no. 7, pp. 3758-3770.
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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.

Anshar, M & Williams, M-A 2021, 'Simplified Pain Matrix Method for Artificial Pain Activation Embedded into Robot Framework', International Journal of Social Robotics, vol. 13, no. 2, pp. 187-195.
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Anwar, MJ, Gill, AQ, Hussain, FK & Imran, M 2021, 'Secure big data ecosystem architecture: challenges and solutions.', EURASIP J. Wirel. Commun. Netw., vol. 2021, pp. 130-130.
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Ao, S, Zhou, T, Long, G, Lu, Q, Zhu, L & Jiang, J 2021, 'CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum', Advances in Neural Information Processing Systems, vol. 13, pp. 10444-10456.
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Goal-conditioned reinforcement learning (RL) usually suffers from sparse reward and inefficient exploration in long-horizon tasks. Planning can find the shortest path to a distant goal that provides dense reward/guidance but is inaccurate without a precise environment model. We show that RL and planning can collaboratively learn from each other to overcome their own drawbacks. In “CO-PILOT”, a learnable path-planner and an RL agent produce dense feedback to train each other on a curriculum of tree-structured sub-tasks. Firstly, the planner recursively decomposes a long-horizon task to a tree of sub-tasks in a top-down manner, whose layers construct coarse-to-fine sub-task sequences as plans to complete the original task. The planning policy is trained to minimize the RL agent’s cost of completing the sequence in each layer from top to bottom layers, which gradually increases the sub-tasks and thus forms an easy-to-hard curriculum for the planner. Next, a bottom-up traversal of the tree trains the RL agent from easier sub-tasks with denser rewards on bottom layers to harder ones on top layers and collects its cost on each sub-task train the planner in the next episode. CO-PILOT repeats this mutual training for multiple episodes before switching to a new task, so the RL agent and planner are fully optimized to facilitate each other’s training. We compare CO-PILOT with RL (SAC, HER, PPO), planning (RRT*, NEXT, SGT), and their combination (SoRB) on navigation and continuous control tasks. CO-PILOT significantly improves the success rate and sample efficiency. Our code is available at https://github.com/Shuang-AO/CO-PILOT.

Arabameri, A, Chandra Pal, S, Costache, R, Saha, A, Rezaie, F, Seyed Danesh, A, Pradhan, B, Lee, S & Hoang, N-D 2021, 'Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms', Geomatics, Natural Hazards and Risk, vol. 12, no. 1, pp. 469-498.
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Arandiyan, H, S. Mofarah, S, Sorrell, CC, Doustkhah, E, Sajjadi, B, Hao, D, Wang, Y, Sun, H, Ni, B-J, Rezaei, M, Shao, Z & Maschmeyer, T 2021, 'Defect engineering of oxide perovskites for catalysis and energy storage: synthesis of chemistry and materials science', Chemical Society Reviews, vol. 50, no. 18, pp. 10116-10211.
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The present work provides a critical review of the science and technological state-of-the-art of defect engineering applied to oxide perovskites in thermocatalytic, electrocatalytic, photocatalytic, and energy-storage applications.

Arivalagan, J, Rujikiatkamjorn, C, Indraratna, B & Warwick, A 2021, 'The role of geosynthetics in reducing the fluidisation potential of soft subgrade under cyclic loading', Geotextiles and Geomembranes, vol. 49, no. 5, pp. 1324-1338.
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The instability of railway tracks including mud pumping, ballast degradation, and differential settlement on weak subgrade soils occurs due to cyclic stress from heavy haul trains. Although geotextiles are currently being used as a separator in railway and highway embankments, their ability to prevent the migration of fine particles and reduce cyclic pore pressure has to be investigated under adverse hydraulic conditions to prevent substructure failures. This study primarily focuses on using geosynthetics to mitigate the migration of fine particles and the accumulation of excess pore pressure (EPP) due to mud pumping (subgrade fluidisation) using dynamic filtration apparatus. The role that geosynthetics play in controlling and preventing mud pumping is analysed by assessing the development of EPP, the change in particle size distribution and the water content of subgrade soil. Using 3 types of geotextiles, the potential for fluidisation is assessed by analysing the time-dependent excess pore pressure gradient (EPPG) inside the subgrade. The experimental results are then used to evaluate the performance of selected geotextiles under heavy haul loading.

Atiqah Rochin Demong, N, Lu, J & Khadeer Hussain, F 2021, 'An Adaptive Personalized Property Investment Risk Analysis Method Based on Data-Driven Approach', International Journal of Information Technology & Decision Making, vol. 20, no. 02, 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.

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, vol. 138, pp. 111425-111425.
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Avilés-Ochoa, E, Flores-Sosa, M & Merigó, JM 2021, 'A bibliometric overview of volatility', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1997-2009.
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Price volatility is a matter of importance for making decisions in the finance world. The growing studies regarding volatility have focused on minimizing the risks through modeling, estimating and forecasting. This paper presents a bibliometric overview of the most important authors, institutions and countries that work on the topic. Additionally, a historical analysis of how the agents have interrelated is presented. For the purposes of the analysis and the design of tables and graphics, tools from the Web of Science Core Collection and the VOSviewer software were used. The results show the importance of volatility in the study of business economics and decision making.

Awang, MSN, Mohd Zulkifli, NW, Abbas, MM, Amzar Zulkifli, S, Kalam, MA, Ahmad, MH, Mohd Yusoff, MNA, Mazlan, M & Daud, WMAW 2021, 'Effect of Addition of Palm Oil Biodiesel in Waste Plastic Oil on Diesel Engine Performance, Emission, and Lubricity', ACS Omega, vol. 6, no. 33, pp. 21655-21675.
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Azadi, S, Tafazzoli Shadpour, M & Warkiani, ME 2021, 'Characterizing the effect of substrate stiffness on the extravasation potential of breast cancer cells using a 3D microfluidic model', Biotechnology and Bioengineering, vol. 118, no. 2, pp. 823-835.
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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, vol. 36, no. 16, pp. 1785-1803.
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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, 'A Stacked LSTM-Based Approach for Reducing Semantic Pose Estimation Error', IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-14.
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Azzam, R, Kong, FH, Taha, T & Zweiri, Y 2021, 'Pose-Graph Neural Network Classifier for Global Optimality Prediction in 2D SLAM', IEEE Access, vol. 9, pp. 80466-80477.
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Ba, X, Wang, P, Zhang, C, Zhu, JG & Guo, Y 2021, 'Improved Deadbeat Predictive Current Control to Enhance the Performance of the Drive System of Permanent Magnet Synchronous Motors', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-4.
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Badeti, U, Pathak, NK, Volpin, F, Dorji, U, Freguia, S, Shon, HK & Phuntsho, S 2021, 'Impact of source-separation of urine on effluent quality, energy consumption and greenhouse gas emissions of a decentralized wastewater treatment plant', Process Safety and Environmental Protection, vol. 150, pp. 298-304.
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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, pp. 144901-144901.
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Bai, J, Liu, Y, Ren, Y, Nie, Z & Guo, YJ 2021, 'Efficient Synthesis of Linearly Polarized Shaped Patterns Using Iterative FFT via Vectorial Least-Square Active Element Pattern Expansion', IEEE Transactions on Antennas and Propagation, vol. 69, no. 9, pp. 6040-6045.
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Bai, L, Yao, L, Wang, X, Li, C & Zhang, X 2021, 'Deep spatial–temporal sequence modeling for multi-step passenger demand prediction', Future Generation Computer Systems, vol. 121, pp. 25-34.
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Bai, X, Ni, J, Beretov, J, Wasinger, VC, Wang, S, Zhu, Y, Graham, P & Li, Y 2021, 'Activation of the eIF2α/ATF4 axis drives triple-negative breast cancer radioresistance by promoting glutathione biosynthesis', Redox Biology, vol. 43, pp. 101993-101993.
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Baier-Fuentes, H, Merigó, J, Miranda, L & Martínez-López, F 2021, 'Strategic planning research through fifty years of long range planning: A bibliometric overview', Strategic Management, vol. 26, no. 1, pp. 3-25.
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Long Range Planning (LRP) is the first journal focused on strategic planning. It was created in 1968 by the Long Range Planning Society, and it celebrated its 50 th anniversary in 2018. This event led to the presentation of a complete bibliometric study aimed at identifying the most significant results that occurred in the journal during this period. For this purpose, bibliometric data were collected from the Web of Science Core Collection database, and two bibliometric approaches were used to analyze the journal's publications: a performance analysis and a graphical mapping of the literature. The first of these uses a wide range of productivity and influence indicators that include the number of publications and citations, the h-index, and citations by paper, among others. The second approach uses the VOSviewer software to deliver a graphical view of the various intellectual connections within LRP. The results of both bibliometric approaches are consistent and confirm LRP as a leading journal in strategic planning and management, with increasing participation of authors and universities from countries around the world.

Bailo, F & Goldsmith, BE 2021, 'No paradox here? Improving theory and testing of the nuclear stability–instability paradox with synthetic counterfactuals', Journal of Peace Research, vol. 58, no. 6, pp. 1178-1193.
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This article contributes to both the theoretical elaboration and empirical testing of the ‘stability–instability paradox’, the proposition that while nuclear weapons deter nuclear war, they also increase conventional conflict among nuclear-armed states. Some recent research has found support for the paradox, but quantitative studies tend to pool all international dyads while qualitative and theoretical studies focus almost exclusively on the USA–USSR and India–Pakistan dyads. This article argues that existing empirical tests lack clearly relevant counterfactual cases, and are vulnerable to a number of inferential problems, including selection on the dependent variable, unintentionally biased inference, and extrapolation from irrelevant cases. The limited evidentiary base coincides with a lack of consideration of the theoretical conditions under which the paradox might apply. To address these issues this article theorizes some scope conditions for the paradox. It then applies synthetic control, a quantitative method for valid comparison when appropriate counterfactual cases are lacking, to model international conflict between India–Pakistan, China–India, and North Korea–USA, before and after nuclearization. The article finds only limited support for the paradox when considered as a general theory, or within the theorized scope conditions based on the balance of resolve and power within each dyad.

Bailo, F, Meese, J & Hurcombe, E 2021, 'The Institutional Impacts of Algorithmic Distribution: Facebook and the Australian News Media', Social Media + Society, vol. 7, no. 2, pp. 205630512110249-205630512110249.
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Since changing its algorithm in January 2018 to boost the content of family and friends over other content (including news), Facebook has signaled that it is less interested in news. However, the field is still trying to understand the long-term impacts of this change for news publishers. This is a problem because policymakers and legislators across the world are becoming concerned about the relationship between platforms and publishers. In particular, there are worries that platforms’ ability to make unilateral decisions about how their algorithms operate may harm the economic sustainability of journalism. This article provides some clarity around the relationship between these two parties through a longitudinal study of the Australian news media sector’s relationship with Facebook from 2014 to 2020, with a particular focus on the January 2018 algorithm change. We do this by analyzing Facebook data (2,082,804 posts from CrowdTangle) and external traffic data from 32 major Australian news outlets. These data are contextualized by additional desk research. We identify a range of trends including the decline of news sharing, the collapse in the performance of “social news,” the variable position of social media as a source of referral traffic, and, most critically, the diffused nature of the 2018 algorithm change. Our approach cannot make direct causal inferences. We can only identify trends in on-platform performance and referral traffic, which we then contextualize with industry reportage. However, the data provide vital longitudinal insights into the performance and responses of individual media outlets, news categories, and the Australian media sector as a whole during a critical moment of algorithmic change.

Bakirov, R, Fay, D & Gabrys, B 2021, 'Automated adaptation strategies for stream learning', Machine Learning, vol. 110, no. 6, pp. 1429-1462.
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AbstractAutomation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism.

Balogun, A-L, Yekeen, ST, Pradhan, B & Wan Yusof, KB 2021, 'Oil spill trajectory modelling and environmental vulnerability mapping using GNOME model and GIS', Environmental Pollution, vol. 268, pp. 115812-115812.
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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 Softw., vol. 38, no. 1, pp. 7-12.
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Bao, T, Damtie, MM, Wei, W, Phong Vo, HN, 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, pp. 125068-125068.
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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, pp. 05021004-05021004.
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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.

Baral, P, Rujikiatkamjorn, C, Indraratna, B, Leroueil, S & Yin, J-H 2021, 'Closure to “Radial Consolidation Analysis Using Delayed Consolidation Approach” by Pankaj Baral, Cholachat Rujikiatkamjorn, Buddhima Indraratna, Serge Leroueil, and Jian-Hua Yin', Journal of Geotechnical and Geoenvironmental Engineering, vol. 147, no. 1, pp. 07020025-07020025.
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Barani, A, Mosaddegh, P, Haghjooy Javanmard, S, Sepehrirahnama, S & Sanati-Nezhad, A 2021, 'Numerical and experimental analysis of a hybrid material acoustophoretic device for manipulation of microparticles', Scientific Reports, vol. 11, no. 1.
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AbstractAcoustophoretic microfluidic devices have been developed for accurate, label-free, contactless, and non-invasive manipulation of bioparticles in different biofluids. However, their widespread application is limited due to the need for the use of high quality microchannels made of materials with high specific acoustic impedances relative to the fluid (e.g., silicon or glass with small damping coefficient), manufactured by complex and expensive microfabrication processes. Soft polymers with a lower fabrication cost have been introduced to address the challenges of silicon- or glass-based acoustophoretic microfluidic systems. However, due to their small acoustic impedance, their efficacy for particle manipulation is shown to be limited. Here, we developed a new acoustophoretic microfluid system fabricated by a hybrid sound-hard (aluminum) and sound-soft (polydimethylsiloxane polymer) material. The performance of this hybrid device for manipulation of bead particles and cells was compared to the acoustophoretic devices made of acoustically hard materials. The results show that particles and cells in the hybrid material microchannel travel to a nodal plane with a much smaller energy density than conventional acoustic-hard devices but greater than polymeric microfluidic chips. Against conventional acoustic-hard chips, the nodal line in the hybrid microchannel could be easily tuned to be placed in an off-center position by changing the frequency, effective for particle separation from a host fluid in parallel flow stream models. It is also shown that the hybrid acoustophoretic device deals with smaller temperature rise which is safer for the actuation of bioparticles. This new device eliminates the limitations of each sound-soft and sound-hard materials in terms of cost, adjusting the position of nodal plane, temperature rise, fragility, production cost and disposability, making it desirable for developing the next generation of ...

Barbar, M & Sui, Y 2021, 'Compacting points-to sets through object clustering', Proceedings of the ACM on Programming Languages, vol. 5, no. OOPSLA, pp. 1-27.
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Inclusion-based set constraint solving is the most popular technique for whole-program points-to analysis whereby an analysis is typically formulated as repeatedly resolving constraints between points-to sets of program variables. The set union operation is central to this process. The number of points-to sets can grow as analyses become more precise and input programs become larger, resulting in more time spent performing unions and more space used storing these points-to sets. Most existing approaches focus on improving scalability of precise points-to analyses from an algorithmic perspective and there has been less research into improving the data structures behind the analyses. Bit-vectors as one of the more popular data structures have been used in several mainstream analysis frameworks to represent points-to sets. To store memory objects in bit-vectors, objects need to mapped to integral identifiers. We observe that this object-to-identifier mapping is critical for a compact points-to set representation and the set union operation. If objects in the same points-to sets (co-pointees) are not given numerically close identifiers, points-to resolution can cost significantly more space and time. Without data on the unpredictable points-to relations which would be discovered by the analysis, an ideal mapping is extremely challenging. In this paper, we present a new approach to inclusion-based analysis by compacting points-to sets through object clustering. Inspired by recent staged analysis where an auxiliary analysis produces results approximating a more precise main analysis, we formulate points-to set compaction as an optimisation problem solved by integer programming using constraints generated from the auxiliary analysis’s results in order to produce an effective mapping. We then develop a more approximate mapping, yet much more efficiently, using hierarchical clustering to compact bit-vectors. We...

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, vol. 16, no. 2, pp. e0245886-e0245886.
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The restrictive measures implemented in response to the COVID-19 pandemic have triggered sudden massive changes to travel behaviors of people all around the world. This study examines the individual mobility patterns for all transport modes (walk, bicycle, motorcycle, car driven alone, car driven in company, bus, subway, tram, train, airplane) before and during the restrictions adopted in ten countries on six continents: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States. This cross-country study also aims at understanding the predictors of protective behaviors related to the transport sector and COVID-19. Findings hinge upon an online survey conducted in May 2020 (N = 9,394). The empirical results quantify tremendous disruptions for both commuting and non-commuting travels, highlighting substantial reductions in the frequency of all types of trips and use of all modes. In terms of potential virus spread, airplanes and buses are perceived to be the riskiest transport modes, while avoidance of public transport is consistently found across the countries. According to the Protection Motivation Theory, the study sheds new light on the fact that two indicators, namely income inequality, expressed as Gini index, and the reported number of deaths due to COVID-19 per 100,000 inhabitants, aggravate respondents’ perceptions. This research indicates that socio-economic inequality and morbidity are not only related to actual health risks, as well documented in the relevant literature, but also to the perceived risks. These findings document the global impact of the COVID-19 crisis as well as provide guidance for transportation practitioners in developing future strategies.

Bardhan, A, Samui, P, Ghosh, K, Gandomi, AH & Bhattacharyya, S 2021, 'ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions', Applied Soft Computing, vol. 110, pp. 107595-107595.
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Barolli, L, Hussain, F & Takizawa, M 2021, 'Special issue on intelligent Edge, Fog, Cloud and Internet of Things (IoT)-based services', Computing, vol. 103, no. 3, pp. 357-360.
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Barua, PD, Dogan, S, Tuncer, T, Baygin, M & Acharya, UR 2021, 'Novel automated PD detection system using aspirin pattern with EEG signals', Computers in Biology and Medicine, vol. 137, pp. 104841-104841.
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Barzegarkhoo, R, Lee, SS, Khan, SA, Siwakoti, Y & Lu, DD-C 2021, 'A Novel Generalized Common-Ground Switched-Capacitor Multilevel Inverter Suitable for Transformerless Grid-Connected Applications', IEEE Transactions on Power Electronics, vol. 36, no. 9, pp. 10293-10306.
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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, no. 9, 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, vol. 14, no. 12, pp. 2084-2098.
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Barzegarkhoo, R, Siwakoti, YP, Aguilera, RP, Khan, MNH, Lee, SS & Blaabjerg, F 2021, 'A Novel Dual-Mode Switched-Capacitor Five-Level Inverter With Common-Ground Transformerless Concept', IEEE Transactions on Power Electronics, vol. 36, no. 12, pp. 13740-13753.
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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 Trans. Ind. Electron., vol. 68, no. 2, pp. 1374-1387.
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Basack, S, Goswami, G & Nimbalkar, S 2021, 'Analytical and Numerical Solutions to Selected Research Problems in Geomechanics and Geohydraulics', WSEAS TRANSACTIONS ON APPLIED AND THEORETICAL MECHANICS, vol. 16, pp. 222-231.
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Geomechanical and geohydraulic engineering is a promising study area with several emerging research concerns. Most of such problems requires advanced level of mathematics to arrive at specific solutions. A wide range of approaches includes several analytical and numerical techniques for better understanding of such problems. In this paper, a few selected research problems are identified, and their solution techniques are demonstrated. The specific areas relevant to such problems are soil-structure interaction, ground improvement and groundwater hydraulics. This paper presents the problem identification, their mathematical solutions and results as well as pertinent analyses and useful interpretations to practice.

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, pp. 04020326-04020326.
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Basha, JS, Jafary, T, Vasudevan, R, Bahadur, JK, Ajmi, MA, Neyadi, AA, Soudagar, MEM, Mujtaba, MA, Hussain, A, Ahmed, W, Shahapurkar, K, Rahman, SMA & Fattah, IMR 2021, 'Potential of Utilization of Renewable Energy Technologies in Gulf Countries', Sustainability, vol. 13, no. 18, pp. 10261-10261.
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This critical review report highlights the enormous potentiality and availability of renewable energy sources in the Gulf region. The earth suffers from extreme air pollution, climate changes, and extreme problems due to the enormous usage of underground carbon resources applications materialized in industrial, transport, and domestic sectors. The countries under Gulf Cooperation Council, i.e., Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates, mainly explore those underground carbon resources for crude oil extraction and natural gas production. As a nonrenewable resource, these are bound to be exhausted in the near future. Hence, this review discusses the importance and feasibility of renewable sources in the Gulf region to persuade the scientific community to launch and explore renewable sources to obtain the maximum benefit in electric power generation. In most parts of the Gulf region, solar and wind energy sources are abundantly available. However, attempts to harness those resources are very limited. Furthermore, in this review report, innovative areas of advanced research (such as bioenergy, biomass) were proposed for the Gulf region to extract those resources at a higher magnitude to generate surplus power generation. Overall, this report clearly depicts the current scenario, current power demand, currently installed capacities, and the future strategies of power production from renewable power sources (viz., solar, wind, tidal, biomass, and bioenergy) in each and every part of the Gulf region.

Bayazidy-Hasanabad, M, Vayghan, SS, Ghasemkhani, N, Pradhan, B & Alamri, A 2021, 'Developing a volunteered geographic information-based system for rapidly estimating damage from natural disasters', Arabian Journal of Geosciences, vol. 14, no. 17.
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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, pp. 399-399.
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This research suggests a novel distributed cooperative control methodology for a secondary controller in islanded microgrids (MGs). The proposed control technique not only brings back the frequency/voltage to its reference values, but also maintains precise active and reactive power-sharing among distributed generation (DG) units by means of a sparse communication system. Due to the dynamic behaviour of distributed secondary control (DSC), stability issues are a great concern for a networked MG. To address this issue, the stability analysis is undertaken systematically, utilizing the small-signal state-space linearized model of considering DSC loops and parameters. As the dynamic behaviour of DSC creates new oscillatory modes, an intelligent fuzzy logic-based parameter-tuner is proposed for enhancing the system stability. Accurate tuning of the DSC parameters can develop the functioning of the control system, which increases MG stability to a greater extent. Moreover, the performance of the offered control method is proved by conducting a widespread simulation considering several case scenarios in MATLAB/Simscape platform. The proposed control method addresses the dynamic nature of the MG by supporting the plug-and-play functionality, and working even in fault conditions. Finally, the convergence and comparison study of the offered control system is shown.

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, pp. 106823-106823.
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Behera, TM, Nanda, S, Mohapatra, SK, Samal, UC, Khan, MS & Gandomi, AH 2021, 'CH Selection via Adaptive Threshold Design Aligned on Network Energy', IEEE Sensors Journal, vol. 21, no. 6, pp. 8491-8500.
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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, no. 2, pp. 673-688.
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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.

Bei, X, Chen, S, Guan, J, Qiao, Y & Sun, X 2021, 'From Independent Sets and Vertex Colorings to Isotropic Spaces and Isotropic Decompositions: Another Bridge between Graphs and Alternating Matrix Spaces', SIAM Journal on Computing, vol. 50, no. 3, pp. 924-971.
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Belotti, Y, McGloin, D & Weijer, CJ 2021, 'Effects of spatial confinement on migratory properties of Dictyostelium discoideum cells', Communicative & Integrative Biology, vol. 14, no. 1, pp. 5-14.
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Berger, PR, Hussain, MM, Iacopi, F, Schulze, J, Ye, P, Rachmady, W, Wen, H-C & Krishnan, S 2021, 'Foreword Special Issue on Low-Temperature Processing of Electronic Materials for Cutting Edge Devices', IEEE Transactions on Electron Devices, vol. 68, no. 7, pp. 3138-3141.
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Bhol, P, Yadav, S, Altaee, A, Saxena, M, Misra, PK & Samal, AK 2021, 'Graphene-Based Membranes for Water and Wastewater Treatment: A Review', ACS Applied Nano Materials, vol. 4, no. 4, pp. 3274-3293.
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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, vol. 15, no. 8, pp. 1739-1746.
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Biswas, PC, Rani, S, Hossain, MA, Islam, MR & Canning, J 2021, 'Recent Developments in Smartphone Spectrometer Sample Analysis', IEEE Journal of Selected Topics in Quantum Electronics, vol. 27, no. 6, pp. 1-12.
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Bliuc, D, Tran, T, Adachi, JD, Atkins, GJ, Berger, C, Bergh, J, Cappai, R, Eisman, JA, Geel, T, Geusens, P, Goltzman, D, Hanley, DA, Josse, R, Kaiser, S, Kovacs, CS, Langsetmo, L, Prior, JC, Nguyen, TV, Solomon, LB, Stapledon, C & Center, JR 2021, 'Cognitive decline is associated with an accelerated rate of bone loss and increased fracture risk in women: a prospective study from the Canadian Multicentre Osteoporosis Study', Journal of Bone and Mineral Research, vol. 36, no. 11, pp. 2106-2115.
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Bliuc, D, Tran, T, Alarkawi, D, Chen, W, Blank, RD, Ensrud, KE, Blith, F, March, L & Center, J 2021, 'Multimorbidity Increases Risk of Osteoporosis Under-Diagnosis and Under-Treatment in Patients at High Fracture Risk: 45 and up a Prospective Population Based-Study', Journal of the Endocrine Society, vol. 5, no. Supplement_1, pp. A248-A249.
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Abstract Background: Management of osteoporosis following fracture is suboptimal. Multimorbidity adds to clinical management complexity in the elderly but its contribution to the osteoporosis treatment gap has never been investigated. Objectives: To determine the impact of multimorbidity on fracture risk and on osteoporosis investigation and treatment in patients at high fracture risk. Design and Setting: The 45 and Up Study is a prospective population-based cohort study in NSW, Australia with questionnaire data linked to hospital records by the Centre for Health Record Linkage (CHeReL) and the Pharmaceutical Benefits Scheme (PBS) and Medicare Benefits Scheme (MBS) data provided by Department of Human Services. Fractures identified from hospital records, comorbidities from questionnaires, hospital and PBS records. Bone mineral density (BMD) investigation obtained from MBS and treatment for osteoporosis from PBS. Participants: 16191 women and 9089 men with incident low-trauma fracture (2000 - 2017) classified in a high and low-risk group based on 10-year fracture risk threshold of 20% from the Garvan Fracture Risk Calculator (age, gender, prior fracture and falls). Main Outcome Measurements: Association of Charlson comorbidity index (CCI) with fracture. Likelihood of BMD investigation and treatment initiation. Outcomes ascertained by logistic regression and re-fracture risk by Cox models. Results: Individuals at high fracture risk were significantly older [women (mean age ±SD) 77 ± 10 vs 57 ±4 for high- vs low risk and men 86±5 vs 65±8 for high vs low risk] and had a higher morbidity burden [women, CCI ≥ 2 40% vs 12% for high- vs low-risk and men 53% vs 26% for high vs low risk]. Being in the high-risk group as well as a higher CCI were independently associated with &gt; 2-fold higher risk of re-fracture. However, in the high-risk group, only 28% (48% women and 17% men) had a BMD investigation and 31% (24% women ...

Bore, JC, Li, P, Jiang, L, Ayedh, WMA, Chen, C, Harmah, DJ, Yao, D, Cao, Z & Xu, P 2021, 'A Long Short-Term Memory Network for Sparse Spatiotemporal EEG Source Imaging', IEEE Transactions on Medical Imaging, vol. 40, no. 12, pp. 3787-3800.
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EEG inverse problem is underdetermined, which poses a long standing challenge in Neuroimaging. The combination of source-imaging and analysis of cortical directional networks enables us to noninvasively explore the underlying neural processes. However, existing EEG source imaging approaches mainly focus on performing the direct inverse operation for source estimation, which will be inevitably influenced by noise and the strategy used to find the inverse solution. Here, we develop a new source imaging technique, Deep Brain Neural Network (DeepBraiNNet), for robust sparse spatiotemporal EEG source estimation. In DeepBraiNNet, considering that Recurrent Neural Network (RNN) are usually “deep” in temporal dimension and thus suitable for time sequence modelling, the RNN with Long Short-Term Memory (LSTM) is utilized to approximate the inverse operation for the lead field matrix instead of performing the direct inverse operation, which avoids the possible effect of the direct inverse operation on the underdetermined lead field matrix prone to be influenced by noise. Simulations on various source patterns and noise conditions confirmed that the proposed approach could actually recover the spatiotemporal sources well, outperforming existing state of-the-art methods. DeepBraiNNet also estimated sparse MI related activation patterns when it was applied to a real Motor Imagery dataset, consistent with other findings based on EEG and fMRI. Based on the spatiotemporal sources estimated from DeepBraiNNet, we constructed MI related cortical neural networks, which clearly exhibited strong contralateral network patterns for the two MI tasks. Consequently, DeepBraiNNet may provide an alternative way different from the conventional approaches for spatiotemporal EEG source imaging.

Boroon, L, Abedin, B & Erfani, E 2021, 'The Dark Side of Using Online Social Networks', Journal of Global Information Management, vol. 29, no. 6, pp. 1-21.
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Research on online social networks (OSNs) has focused overwhelmingly on their benefits and potential, with their negative effects overlooked. This study builds on the limited existing work on the so-called ‘dark side’ of using OSNs. The authors conducted a systematic review of selected databases and identified 46 negative effects of using OSNs from the users’ perspective, which is a rich spectrum of users’ negative experiences. This article then proposed nomenclature and taxonomy for the dark side of using OSNs by grouping these negative effects into six themes: cost of social exchange, cyberbullying, low performance, annoying content, privacy concerns and security threats. This study then conducted structured interviews with experts to confirm the sense-making and validity of the proposed taxonomy. This study discusses the confirmed taxonomy and outlines directions for future research.

Bown, O, Ferguson, S, Dias Pereira Dos Santos, A & Mikolajczyk, K 2021, 'Hacking the Medium: Shaping the creative constraints of network architectures in multiplicitous media artworks', Organised Sound, vol. 26, no. 3, pp. 305-316.
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In this article we discuss our practice-based research into effective architectures and creative workflows for creatively coding massive multidevice light and sound installation artworks. We discuss the challenges of working with networked multidevice systems and illustrate these challenges with examples of the type of content that one may wish to display on these systems. We then consider how the structuring of a creative framework can strongly influence how an artist approaches the creation of such work, eases the process of creative search and discovery and reduces the time cost and risk of solving technical problems of architecture design. We take a design perspective on how to make effective creativity support tools and also consider a holistic perspective on creative practice that attempts to satisfy creative ideals grounded in the reality of practice.

Brohl, AS, Sindiri, S, Wei, JS, Milewski, D, Chou, H-C, Song, YK, Wen, X, Kumar, J, Reardon, HV, Mudunuri, US, Collins, JR, Nagaraj, S, Gangalapudi, V, Tyagi, M, Zhu, YJ, Masih, KE, Yohe, ME, Shern, JF, Qi, Y, Guha, U, Catchpoole, D, Orentas, RJ, Kuznetsov, IB, Llosa, NJ, Ligon, JA, Turpin, BK, Leino, DG, Iwata, S, Andrulis, IL, Wunder, JS, Toledo, SRC, Meltzer, PS, Lau, C, Teicher, BA, Magnan, H, Ladanyi, M & Khan, J 2021, 'Immuno-transcriptomic profiling of extracranial pediatric solid malignancies', Cell Reports, vol. 37, no. 8, pp. 110047-110047.
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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...

Bubb, KJ, Tang, O, Gentile, C, Moosavi, SM, Hansen, T, Liu, C-C, Di Bartolo, BA & Figtree, GA 2021, 'FXYD1 Is Protective Against Vascular Dysfunction', Hypertension, vol. 77, no. 6, pp. 2104-2116.
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Nitric oxide (NO) production by eNOS (endothelial NO synthase) is critical for vascular health. Oxidative stress-induced uncoupling of eNOS leads to decreased NO bioavailability, compounded by increased superoxide generation. FXYD1 (FXYD domain containing ion transport regulator 1), a caveolar protein, protects against oxidative inhibition of the Na + -K + -ATPase. We hypothesized that FXYD1 may afford a similar inhibition of oxidative dysregulation of eNOS, providing a broader protection within caveolae. FXYD1-eNOS colocalization was demonstrated by co-immunoprecipitation in heart protein and by proximity ligation assay in human umbilical vein endothelial cells. The functional nature of this partnership was shown by silencing FXYD1 in human umbilical vein endothelial cells, where 50% decreased NO and 2-fold augmented superoxide was shown. Three-dimensional cocultured cardiac spheroids generated from FXYD1 knockout mice were incapable of acetylcholine-induced NO production. Overexpression of FXYD1 in HEK293 cells revealed a possible mechanism, where FXYD1 protected against redox modification of eNOS cysteines. In vivo, vasodilation in response to increasing doses of bradykinin was impaired in knockout mice, and this was rescued in mice by delivery of FXYD1 protein packaged in exosomes. Bloods vessels extracted from knockout mice exhibited increased oxidative and nitrosative stress with evidence of reduce eNOS phosphorylation. Impaired vascular function and augmented superoxide generation were also evident in diabetic knockout mice. Despite this, blood pressure was similar in wildtype and knockout mice, but after chronic angiotensin II infusion, knockout of FXYD1 was associated with a heightened blood pressure response. FXYD1 protects eNOS from dysregulated redox signaling and is protective against both hypertension and diabetic vascular oxidative stress....

Buchlak, QD, Esmaili, N, Leveque, J-C, Bennett, C, Farrokhi, F & Piccardi, M 2021, 'Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review', Journal of Clinical Neuroscience, vol. 89, pp. 177-198.
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Budati, AK & Ling, SSH 2021, 'Guest editorial: Machine Learning in Wireless Networks', CAAI Transactions on Intelligence Technology, vol. 6, no. 2, pp. 133-134.
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Bui, HT, Hussain, OK, Saberi, M & Hussain, F 2021, 'Assessing the authenticity of subjective information in the blockchain: a survey and open issues', World Wide Web, vol. 24, no. 2, pp. 483-509.
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Bui, X-T, Fujioka, T & Nghiem, LD 2021, 'Green Technologies for Sustainable Water (GTSW)', Environmental Technology & Innovation, vol. 21, pp. 101192-101192.
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Cai, X, Li, JJ, Liu, T, Brian, O & Li, J 2021, 'Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design', Briefings in Functional Genomics, vol. 20, no. 5, pp. 289-303.
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Abstract Messenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer. This review summarises the current progresses of mRNA vaccines designed for a range of diseases including COVID-19. As epitope study is a primary component in the in silico design of mRNA vaccines, we also survey on advanced bioinformatics and machine learning algorithms which have been used for epitope prediction, and review on user-friendly software tools available for this purpose. Finally, we discuss some of the unanswered concerns about mRNA vaccines, such as unknown long-term side effects, and present with our perspectives on future developments in this exciting area.

Canning, J 2021, 'Mortar-diatom composites for smart sensors and buildings', Optical Materials Express, vol. 11, no. 2, pp. 457-457.
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Cao, J, Gowripalan, N, Sirivivatnanon, V & South, W 2021, 'Accelerated test for assessing the potential risk of alkali-silica reaction in concrete using an autoclave', Construction and Building Materials, vol. 271, pp. 121871-121871.
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To rapidly assess the potential risk of alkali-silica reaction (ASR) in concrete, an accelerated test using an autoclave by adopting multi-cycle 80 °C steam warming at atmospheric pressure is proposed. The influence of autoclave steam warming temperature, cycles/duration, and alkali dosage on expansion of mortar bars and concrete prisms was evaluated. Mechanical properties of concrete under accelerated ASR test were investigated. Furthermore, SEM-EDS analysis confirmed ASR products and indicated that the expansion is caused by ASR. The expansion limits considered for classifying aggregates were discussed. The experimental results demonstrated that the period required for assessing the potential risk of ASR in concrete (dacite aggregate in this study) can be shortened to 37 days.

Cao, L & Zhu, C 2021, 'Table2Vec-automated universal representation learning of enterprise data DNA for benchmarkable and explainable enterprise data science', Scientific Reports, vol. 11, no. 1.
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AbstractEnterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. They involve enterprise DNA associated with domain-oriented transactions and master data, informational and operational metadata, and relevant external data. A critical challenge in enterprise data science is to enable an effective ‘whole-of-enterprise’ data understanding and data-driven discovery and decision-making on all-round enterprise DNA. Accordingly, here we introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes (similar to biological genomes and DNA in organisms) and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. Such automated universal enterprise representation and learning cannot be addressed by existing enterprise data warehouses (EDWs), business intelligence and corporate analytics systems, where ‘enterprise big tables’ are constructed with reporting and analytics conducted by specific analysts on respective domain subjects and goals. It addresses critical limitations and gaps of existing representation learning, enterprise analytics and cloud analytics, which are analytical subject, task and data-specific, creating analytical silos in an enterprise. We illustrate Ta...

Cao, X & Tsang, I 2021, 'Distribution Disagreement via Lorentzian Focal Representation', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Error disagreement-based active learning (AL) selects the data that maximally update the error of a classification hypothesis. However, poor human supervision (e.g. few labels, improper classifier parameters) may weaken or clutter this update; moreover, the computational cost of performing a greedy search to estimate the errors using a deep neural network is intolerable. In this paper, a novel disagreement coefficient based on distribution, not error, provides a tighter bound on label complexity, which further guarantees its generalization in hyperbolic space. The focal points derived from the squared Lorentzian distance, present more effective hyperbolic representations on aspherical distribution from geometry, replacing the typical Euclidean, kernelized, and Poincar centroids. Experiments on different deep AL tasks show that, the focal representation adopted in a tree-likeliness splitting, significantly perform better than typical baselines of geometric centroids and error disagreement, and state-of-the-art neural network architectures-based AL, dramatically accelerating the learning process.

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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Cao, Y, Lv, T, Ni, W & Lin, Z 2021, 'Sum-Rate Maximization for Multi-Reconfigurable Intelligent Surface-Assisted Device-to-Device Communications', IEEE Transactions on Communications, vol. 69, no. 11, pp. 7283-7296.
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Cao, Y, Sheng, L, Cheng, H, Wang, C, Sun, Y & Fu, Q 2021, 'In situ synthesis of metal‐free N‐GQD@g‐C ₃ N ₄ photocatalyst for enhancing photocatalytic activity', Micro & Nano Letters, vol. 16, no. 1, pp. 77-82.
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Cao, Y, Wang, S, Guo, Z, Huang, T & Wen, S 2021, 'Event-based passification of delayed memristive neural networks', Information Sciences, vol. 569, pp. 344-357.
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Cao, Z, John, AR, Chen, H-T, Martens, KE, Georgiades, M, Gilat, M, Nguyen, HT, Lewis, SJG & Lin, C-T 2021, 'Identification of EEG Dynamics During Freezing of Gait and Voluntary Stopping in Patients With Parkinson’s Disease', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1774-1783.
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Cao, Z, Lin, C-T, Deng, Y & Weber, G-W 2021, 'Guest Editorial: Fuzzy Systems Toward Human-Explainable Artificial Intelligence and Their Applications', IEEE Transactions on Fuzzy Systems, vol. 29, no. 12, pp. 3577-3578.
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Cao, Z, Wong, K & Lin, C-T 2021, 'Weak Human Preference Supervision for Deep Reinforcement Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 12, pp. 5369-5378.
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Carpentieri, D, Catchpoole, D & Vercauteren, S 2021, 'Special Issue on Biobanking for Pediatric Research', Biopreservation and Biobanking, vol. 19, no. 2, pp. 97-97.
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Cetindamar Kozanoglu, D & Abedin, B 2021, 'Understanding the role of employees in digital transformation: conceptualization of digital literacy of employees as a multi-dimensional organizational affordance', Journal of Enterprise Information Management, vol. 34, no. 6, pp. 1649-1672.
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PurposeMuch of recent academic and professional interest in exploring digital transformation and enterprise systems has focused on the technology or the organizations' external forces, leaving internal factors, in particular employees, overlooked. The purpose of this paper is to explore digital literacy of employees as an organizational affordance to capture contextual factors within which digital technologies are situated and are used.Design/methodology/approachWe used the evidence-based practice for information systems approach, and undertook a systematic literature review of 30 papers coupled with brainstorming with 11 professional experts on the neglected topic of digital literacy and its assessment.FindingsThis paper draws upon affordance theory, and develops a novel framework for conceptualization of digital literacy of employees as an organizational affordance. We do this by distinguishing digital literacy at the individual level and organizational level, and by assessing digital literacy through Information/Cognitive and Social Practice/Articulation affordances.Research limitations/implicationsThe current paper contributes to the notion of organizational affordances by examining the effect of interactions between employee-technology through digital literacy of employees in using digital technologies. We offer a novel conceptualization of digital literacy to improve understanding of the role of employee in digital transformation and utilization of enterprise systems. Thus, our definition of digital literacy offers an extension to the recent discussions in the IS literature regarding the actu...

Cetindamar, D, Lammers, T, Kocaoglu, DF & Zhang, Y 2021, 'The Anniversary Tribute of PICMET: 1989-2018.', IEEE Trans. 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.

Chai, M, Moradi, S, Erfani, E, Asadnia, M, Chen, V & Razmjou, A 2021, 'Application of Machine Learning Algorithms to Estimate Enzyme Loading, Immobilization Yield, Activity Retention, and Reusability of Enzyme–Metal–Organic Framework Biocatalysts', Chemistry of Materials, vol. 33, no. 22, pp. 8666-8676.
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Chai, M, Razavi Bazaz, S, Daiyan, R, Razmjou, A, Ebrahimi Warkiani, M, Amal, R & Chen, V 2021, 'Biocatalytic micromixer coated with enzyme-MOF thin film for CO2 conversion to formic acid', Chemical Engineering Journal, vol. 426, pp. 130856-130856.
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Chakraborty, S, Dann, C, Mandal, A, Dann, B, Paul, M & Hafeez-Baig, A 2021, 'Effects of rubric quality on marker variation in higher education', Studies in Educational Evaluation, vol. 70, pp. 100997-100997.
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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}.

Chalmers, T, Maharaj, S & Lal, S 2021, 'Associations Between Workplace Factors and Depression and Anxiety in Australian Heavy Vehicle Truck Drivers', Annals of Work Exposures and Health, vol. 65, no. 5, pp. 581-590.
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Abstract Introduction A number of health issues have been identified as prevalent within the Australian heavy vehicle driving population. Mental illnesses, such as depression and anxiety, are among those disorders that have been regularly reported, however, the contributing factors are yet to be elucidated. Methods This study aimed to assess the associations between workplace factors such as years of employment, social interaction and shift length, with depressive and anxious symptomology in a cohort of 60 Australian heavy vehicle drivers. Results Significant positive associations were identified between depression and alcohol use (P = 0.044), coffee consumption (P = 0.037), number of accidents during career (P = &lt; 0.004), and number of hours driving per shift (P ≤ 0.001). Anxiety was found to be positively associated with a number of hours driving per week (P ≤ 0.001), and the number of accidents or near misses during a driving career (P = 0.039). Conclusion Several workplace factors were identified as being correlated to depression or anxiety within this cohort, suggesting potential changes to rostering systems and education regarding alcohol use may benefit the mental health of this driver population.

Chan, Y, Mehta, M, Paudel, KR, Madheswaran, T, Panneerselvam, J, Gupta, G, Su, QP, Hansbro, PM, MacLoughlin, 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, D & Alammari, AM 2021, 'Influence of Culture on Knowledge Sharing Attitude among Academic Staff in eLearning Virtual Communities in Saudi Arabia', Information Systems Frontiers, vol. 23, no. 6, pp. 1563-1572.
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Knowledge sharing is a significant component of success in knowledge management. In Saudi Arabia, knowledge management is often lacking when it comes to knowledge sharing adoption, especially between academic staff. This research aims to investigate various factors of knowledge sharing adoption for eLearning communities in Saudi Arabia and to examine the effect of culture as a moderating role on the relationships between these factors and academics’ attitude. Therefore, a framework is aimed at sharing knowledge within the eLearning communities is developed. Data has been collected from public universities in Saudi Arabia. Partial Least Square approach has been applied to analyse the data. The results show individual factors (such as openness in communication, interpersonal trust) and technology acceptance factors (perceived usefulness and perceived ease of use) significantly influence knowledge sharing attitude, while the relationship between people self-motivation and knowledge sharing attitude is insignificant. Subjective norm and attitude significantly impact behavioral intention toward knowledge sharing adoption in Saudi universities’ eLearning communities.

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, vol. 143, pp. 113489-113489.
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Chang, Y-C, Shi, Y, Dostovalova, A, Cao, Z, Kim, J, Gibbons, D & Lin, C-T 2021, 'Interpretable Fuzzy Logic Control for Multirobot Coordination in a Cluttered Environment', IEEE Transactions on Fuzzy Systems, vol. 29, no. 12, pp. 3676-3685.
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Mobile robot navigation is an essential problem in robotics. We propose a method for constructing and training fuzzy logic controllers (FLCs) to coordinate a robotic team performing collision-free navigation and arriving simultaneously at a target location in an unknown environment. Our FLCs are organised in a multi-layered architecture to reduce the number of tunable parameters and improve the scalability of the solution. In addition, in contrast to simple traditional switching mechanisms between target seeking and obstacle avoidance, we develop a novel rule-embedded FLC to improve the navigation performance. Moreover, we design a grouping and merging mechanism to obtain transparent fuzzy sets and integrate this mechanism into the training process for all FLCs, thus increasing the interpretability of the fuzzy models. We train the proposed FLCs using a novel multi-objective hybrid approach combining a genetic algorithm and particle swarm optimisation. Simulation results demonstrate the effectiveness of our algorithms in reliably solving the proposed navigation problem.

Chang, Y-C, Wang, Y-K, Pal, NR & Lin, C-T 2021, 'Exploring Covert States of Brain Dynamics via Fuzzy Inference Encoding', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 2464-2473.
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Chaudhary, P, Gupta, BB, Chang, X, Nedjah, N & Chui, KT 2021, 'Enhancing big data security through integrating XSS scanner into fog nodes for SMEs gain', Technological Forecasting and Social Change, vol. 168, pp. 120754-120754.
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Chen, B, Guo, R, Yu, S & Yu, Y 2021, 'An active noise control method of non-stationary noise under time-variant secondary path', Mechanical Systems and Signal Processing, vol. 149, pp. 107193-107193.
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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, vol. 33, no. 23, pp. 2008847-2008847.
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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, C-Y, Chen, W-H, Lim, S, Ong, HC & Ubando, AT 2021, 'Synergistic interaction and biochar improvement over co-torrefaction of intermediate waste epoxy resins and fir', Environmental Technology & Innovation, vol. 21, pp. 101218-101218.
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Chen, D, Liu, Y, Chen, S-L, Qin, P-Y & Guo, YJ 2021, 'A Wideband High-Gain Multilinear Polarization Reconfigurable Antenna', IEEE Transactions on Antennas and Propagation, vol. 69, no. 7, pp. 4136-4141.
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Chen, J, Indraratna, B, Vinod, JS, Ngo, NT, Gao, R & Liu, Y 2021, 'Stress-dilatancy behaviour of fouled ballast: experiments and DEM modelling', Granular Matter, vol. 23, no. 4.
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Chen, J, Wen, S, Shi, K & Yang, Y 2021, 'Highly parallelized memristive binary neural network', Neural Networks, vol. 144, pp. 565-572.
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Chen, J, Wu, D, Zhao, Y, Sharma, N, Blumenstein, M & Yu, S 2021, 'Fooling intrusion detection systems using adversarially autoencoder', Digital Communications and Networks, vol. 7, no. 3, pp. 453-460.
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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, L, Chen, L, Ge, Z, Sun, Y, Hamilton, TJ & Zhu, X 2021, 'A 90-GHz Asymmetrical Single-Pole Double-Throw Switch With >19.5-dBm 1-dB Compression Point in Transmission Mode Using 55-nm Bulk CMOS Technology', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 68, no. 11, pp. 4616-4625.
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The millimeter-wave (mm-wave) single-pole double-throw (SPDT) switch designed in bulk CMOS technology has limited power-handling capability in terms of 1-dB compression point (P1dB) inherently. This is mainly due to the low threshold voltage of the switching transistors used for shunt-connected configuration. To solve this issue, an innovative approach is presented in this work, which utilizes a unique passive ring structure. It allows a relatively strong RF signal passing through the TX branch, while the switching transistors are turned on. Thus, the fundamental limitation for P1dB due to reduced threshold voltage is overcome. To prove the presented approach is feasible in practice, a 90-GHz asymmetrical SPDT switch is designed in a standard 55-nm bulk CMOS technology. The design has achieved an insertion loss of 3.2 dB and 3.6 dB in TX and RX mode, respectively. Moreover, more than 20 dB isolation is obtained in both modes. Because of using the proposed passive ring structure, a remarkable P1dB is achieved. No gain compression is observed at all, while a 19.5 dBm input power is injected into the TX branch of the designed SPDT switch. The die area of this design is only 0.26 mm².

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, vol. 28, pp. 384-388.
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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, Q, Peng, W, Yu, R, Tao, G & Nimbalkar, S 2021, 'Laboratory Investigation on Particle Breakage Characteristics of Calcareous Sands', Advances in Civil Engineering, vol. 2021, pp. 1-8.
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Many studies have demonstrated the fragility of calcareous sands even under small stresses. This bears an adverse influence on their engineering properties. A series of laboratory tests were carried out on poor-graded calcareous sands to investigate the crushability mechanism. Einav’s relative breakage and fractal dimension were used as the particle breakage indices. The results show that the particles broke into smaller fragments at the low-stress level by attrition which was caused by friction and slip between particles. In contrast, particles broke in the form of crushing at the relatively higher stresses. The evolution of the particle size was reflected by the variation in Einav’s relative breakage and fractal dimension. As testing commenced, the breakage index rapidly increased. When the stress was increased to 400 kPa, the rate of increase in the breakage index was retarded. As the stress was further increased beyond 800 kPa, the rate of increase in the fractal index became much smaller. This elucidated that the well-graded calcareous sands could resist crushing depending on the range of applied stresses. Based on the test findings, a new breakage law is proposed.

Chen, Q, Yu, R, Li, Y, Tao, G & Nimbalkar, S 2021, 'Cyclic stress-strain characteristics of calcareous sand improved by polyurethane foam adhesive', Transportation Geotechnics, vol. 31, pp. 100640-100640.
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Chen, Q, Yu, R, Tao, G, Zhang, J & Nimbalkar, S 2021, 'Shear behavior of polyurethane foam adhesive improved calcareous sand under large-scale triaxial test', Marine Georesources & Geotechnology, vol. 39, no. 12, pp. 1449-1458.
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Chen, Q-S, Peng, W, Tao, G-L & 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, vol. 6, no. 2, pp. 2296-2303.
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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, vol. 15, no. 6, pp. 606-619.
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Chen, R-S, Wong, S-W, Lin, J-Y, Yang, Y, Li, Y, Zhang, L, He, Y & Zhu, L 2021, 'Reconfigurable Cavity Bandpass Filters Using Fluid Dielectric', IEEE Transactions on Industrial Electronics, vol. 68, no. 9, pp. 8603-8614.
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Chen, R-S, Zhu, L, Lin, J-Y, Wong, S-W, Yang, Y, Li, Y, Zhang, L & He, Y 2021, 'High-Isolation In-Band Full-Duplex Cavity-Backed Slot Antennas in a Single Resonant Cavity', IEEE Transactions on Antennas and Propagation, vol. 69, no. 11, pp. 7092-7102.
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Chen, S, Fu, A, Yu, S, Ke, H & Su, M 2021, 'DP-QIC: A differential privacy scheme based on quasi-identifier classification for big data publication', Soft Computing, vol. 25, no. 11, pp. 7325-7339.
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Chen, S, Wang, W, Xia, B, You, X, Peng, Q, Cao, Z & Ding, W 2021, 'CDE-GAN: Cooperative Dual Evolution-Based Generative Adversarial Network', IEEE Transactions on Evolutionary Computation, vol. 25, no. 5, pp. 986-1000.
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Chen, W-H, Cheng, C-L, Lee, K-T, Lam, SS, Ong, HC, Ok, YS, Saeidi, S, Sharma, AK & Hsieh, T-H 2021, 'Catalytic level identification of ZSM-5 on biomass pyrolysis and aromatic hydrocarbon formation', Chemosphere, vol. 271, pp. 129510-129510.
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Chen, W-H, Chiu, G-L, Chyuan Ong, H, Shiung Lam, S, Lim, S, Sik Ok, Y & E.Kwon, E 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, pp. 120642-120642.
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Chen, W-H, Du, J-T, Lee, K-T, Ong, HC, Park, Y-K & Huang, C-C 2021, 'Pore volume upgrade of biochar from spent coffee grounds by sodium bicarbonate during torrefaction', Chemosphere, vol. 275, pp. 129999-129999.
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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, W-H, Lin, B-J, Lin, Y-Y, Chu, Y-S, Ubando, AT, Show, PL, Ong, HC, Chang, J-S, Ho, S-H, Culaba, AB, Pétrissans, A & Pétrissans, M 2021, 'Progress in biomass torrefaction: Principles, applications and challenges', Progress in Energy and Combustion Science, vol. 82, pp. 100887-100887.
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Chen, W-H, Lo, H-J, Yu, K-L, Ong, H-C & Sheen, H-K 2021, 'Valorization of sorghum distillery residue to produce bioethanol for pollution mitigation and circular economy', Environmental Pollution, vol. 285, pp. 117196-117196.
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Chen, X, Hu, Z, Xie, H, Ngo, HH, Guo, W & Zhang, J 2021, 'Enhanced biocatalysis of phenanthrene in aqueous phase by novel CA-Ca-SBE-laccase biocatalyst: Performance and mechanism', Colloids and Surfaces A: Physicochemical and Engineering Aspects, vol. 611, pp. 125884-125884.
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Chen, X, Huo, P, Liu, J, Li, F, Yang, L, Li, X, Wei, W, Liu, Y & Ni, B-J 2021, 'Model predicted N2O production from membrane-aerated biofilm reactor is greatly affected by biofilm property settings', Chemosphere, vol. 281, pp. 130861-130861.
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Chen, X, Lu, Z, Ni, W, Wang, X, Wang, F, Zhang, S & Xu, S 2021, 'Cooling-Aware Optimization of Edge Server Configuration and Edge Computation Offloading for Wirelessly Powered Devices', IEEE Transactions on Vehicular Technology, vol. 70, no. 5, pp. 5043-5056.
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Chen, X, Tang, M-C, Yi, D & Ziolkowski, RW 2021, 'Wideband, Compact Antennas With Interdigitated Magnetic-Based Near-Field Resonant Parasitic Elements', IEEE Transactions on Antennas and Propagation, vol. 69, no. 8, pp. 5036-5041.
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Chen, X, Wang, K, Lin, X, Zhang, W, Qin, L & Zhang, Y 2021, 'Efficiently Answering Reachability and Path Queries on Temporal Bipartite Graphs.', Proc. VLDB Endow., vol. 14, no. 10, pp. 1845-1858.
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Bipartite graphs are naturally used to model relationships between two different types of entities, such as people-location, authorpaper, and customer-product. When modeling real-world applications like disease outbreaks, edges are often enriched with temporal information, leading to temporal bipartite graphs. While reachability has been extensively studied on (temporal) unipartite graphs, it remains largely unexplored on temporal bipartite graphs. To fill this research gap, in this paper, we study the reachability problem on temporal bipartite graphs. Specifically, a vertex u reaches a vertex w in a temporal bipartite graph G if u and w are connected through a series of consecutive wedges with time constraints. Towards efficiently answering if a vertex can reach the other vertex, we propose an index-based method by adapting the idea of 2-hop labeling. Effective optimization strategies and parallelization techniques are devised to accelerate the index construction process. To better support real-life scenarios, we further show how the index is leveraged to efficiently answer other types of queries, e.g., singlesource reachability query and earliest-arrival path query. Extensive experiments on 16 real-world graphs demonstrate the effectiveness and efficiency of our proposed techniques.

Chen, X, Zhang, T, Shen, S, Zhu, T & Xiong, P 2021, 'An optimized differential privacy scheme with reinforcement learning in VANET', Computers & Security, vol. 110, pp. 102446-102446.
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Chen, Y, Huang, S, Zhao, L & Dissanayake, G 2021, 'Cramér–Rao Bounds and Optimal Design Metrics for Pose-Graph SLAM', IEEE Transactions on Robotics, vol. 37, no. 2, pp. 627-641.
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Chen, Y, Li, C, White, S, Nonahal, M, Xu, Z-Q, Watanabe, K, Taniguchi, T, Toth, M, Tran, TT & Aharonovich, I 2021, 'Generation of High-Density Quantum Emitters in High-Quality, Exfoliated Hexagonal Boron Nitride', ACS Applied Materials & Interfaces, vol. 13, no. 39, pp. 47283-47292.
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Chen, Y, Liu, J, Zhang, Z, Wen, S & Xiong, W 2021, 'MöbiusE: Knowledge Graph Embedding on Möbius Ring', Knowledge-Based Systems, vol. 227, pp. 107181-107181.
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Chen, Y, Westerhausen, MT, Li, C, White, S, Bradac, C, Bendavid, A, Toth, M, Aharonovich, I & Tran, TT 2021, 'Solvent-Exfoliated Hexagonal Boron Nitride Nanoflakes for Quantum Emitters', ACS Applied Nano Materials, vol. 4, no. 10, pp. 10449-10457.
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Chen, Y, Wu, D, Yu, Y & Gao, W 2021, 'An improved theory in the determination of aerodynamic damping for a horizontal axis wind turbine (HAWT)', Journal of Wind Engineering and Industrial Aerodynamics, vol. 213, pp. 104619-104619.
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Chen, Y, Wu, D, Yu, Y & Gao, W 2021, 'Do cyclone impacts really matter for the long-term performance of an offshore wind turbine?', Renewable Energy, vol. 178, pp. 184-201.
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With the transition on planning and construction of offshore wind turbine (OWT) from North Europe to other regions like America and East Asia, challenges are proposed for the direct application of international OWT experience to these territories due to disparate natural condition like cyclones. This article is intended to evaluate the impact of cyclone on the long-term performance of an OWT to be installed in cyclone-prone regions. To have a comprehensive consideration on the aero-hydro-structural-soil interaction, an improved decoupled method is proposed and validated for an onshore wind turbine before its application to an OWT. Two cyclone models combined with two wave theories are considered in the fatigue evaluation of an OWT under different working status, and their implications on the final estimation of fatigue damage are compared and discussed. The results obtained from this study indicate that the fatigue life reduction which is caused by cyclone, for an OWT can be conspicuous for a reasonable cyclone strength and average recurrence interval. This implies that potential premature failure of an OWT tower and relevant economic losses can be encountered during its service life if the cyclone contribution to fatigue damage is ignored in the initial conceptual design.

Chen, Y, Xu, X, Li, C, Bendavid, A, Westerhausen, MT, Bradac, C, Toth, M, Aharonovich, I & Tran, TT 2021, 'Bottom‐Up Synthesis of Hexagonal Boron Nitride Nanoparticles with Intensity‐Stabilized Quantum Emitters', Small, vol. 17, no. 17, pp. 2008062-2008062.
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Chen, Z, Wei, W & Ni, B-J 2021, 'Cost-effective catalysts for renewable hydrogen production via electrochemical water splitting: Recent advances', Current Opinion in Green and Sustainable Chemistry, vol. 27, pp. 100398-100398.
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Chen, Z, Zheng, R, Graś, M, Wei, W, Lota, G, Chen, H & Ni, B-J 2021, 'Tuning electronic property and surface reconstruction of amorphous iron borides via W-P co-doping for highly efficient oxygen evolution', Applied Catalysis B: Environmental, vol. 288, pp. 120037-120037.
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Chen, Z, Zheng, R, Zou, W, Wei, W, Li, J, Wei, W, Ni, B-J & Chen, H 2021, 'Integrating high-efficiency oxygen evolution catalysts featuring accelerated surface reconstruction from waste printed circuit boards via a boriding recycling strategy', Applied Catalysis B: Environmental, vol. 298, pp. 120583-120583.
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Chen, Z, Zou, W, Zheng, R, Wei, W, Wei, W, Ni, B-J & Chen, H 2021, 'Synergistic recycling and conversion of spent Li-ion battery leachate into highly efficient oxygen evolution catalysts', Green Chemistry, vol. 23, no. 17, pp. 6538-6547.
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A one-pot synergetic recycling and regeneration strategy to develop highly efficient tri-metal OER electrocatalysts from spent LIB leachates is demonstrated.

Cheng, D, Liu, Y, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, S, Luo, G & Bui, XT 2021, 'Sustainable enzymatic technologies in waste animal fat and protein management', Journal of Environmental Management, vol. 284, pp. 112040-112040.
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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, EJ, Prasad, M, Yang, J, Zheng, DR, Tao, X, Mery, D, Young, KY & Lin, CT 2021, 'A novel online self-learning system with automatic object detection model for multimedia applications', Multimedia Tools and Applications, vol. 80, no. 11, pp. 16659-16681.
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Cheng, H, Liu, Y, Huang, D, Cai, B & Wang, Q 2021, 'Rebooting kernel CCA method for nonlinear quality-relevant fault detection in process industries', Process Safety and Environmental Protection, vol. 149, pp. 619-630.
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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, vol. 117, pp. 210-220.
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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, H, Yang, G, Li, D, Li, M, Cao, Y, Fu, Q & Sun, Y 2021, 'Ultralow Icing Adhesion of a Superhydrophobic Coating Based on the Synergistic Effect of Soft and Stiff Particles', Langmuir, vol. 37, no. 41, pp. 12016-12026.
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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, Liu, Z, Du, X, Yu, S & Mostarda, L 2021, 'IEEE Access Special Section Editorial: Security and Privacy in Emerging Decentralized Communication Environments', IEEE Access, vol. 9, pp. 68880-68887.
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Cheng, X, Wang, H, Hua, J, Xu, G & Sui, Y 2021, 'DeepWukong', ACM Transactions on Software Engineering and Methodology, vol. 30, no. 3, pp. 1-33.
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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 D eep W ukong , 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.

Chetty, K, Xie, S, Song, Y, McCarthy, T, Garbe, U, Li, X & Jiang, G 2021, 'Self-healing bioconcrete based on non-axenic granules: A potential solution for concrete wastewater infrastructure', Journal of Water Process Engineering, vol. 42, pp. 102139-102139.
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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, vol. 40, no. 13, pp. 2367-2381.
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AbstractHistone deacetylase (HDAC) inhibitors are effective in MYCN-driven cancers, because of a unique need for HDAC recruitment by the MYCN oncogenic signal. However, HDAC inhibitors are much more effective in combination with other anti-cancer agents. To identify novel compounds which act synergistically with HDAC inhibitor, such as suberanoyl hydroxamic acid (SAHA), we performed a cell-based, high-throughput drug screen of 10,560 small molecule compounds from a drug-like diversity library and identified a small molecule compound (SE486-11) which synergistically enhanced the cytotoxic effects of SAHA. Effects of drug combinations on cell viability, proliferation, apoptosis and colony forming were assessed in a panel of neuroblastoma cell lines. Treatment with SAHA and SE486-11 increased MYCN ubiquitination and degradation, and markedly inhibited tumorigenesis in neuroblastoma xenografts, and, MYCN transgenic zebrafish and mice. The combination reduced ubiquitin-specific protease 5 (USP5) levels and increased unanchored polyubiquitin chains. Overexpression of USP5 rescued neuroblastoma cells from the cytopathic effects of the combination and reduced unanchored polyubiquitin, suggesting USP5 is a therapeutic target of the combination. SAHA and SE486-11 directly bound to USP5 and the drug combination exhibited a 100-fold higher binding to USP5 than individual drugs alone in microscale thermophoresis assays. MYCN bound to the USP5 promoter and induced USP5 gene expression suggesting that USP5 and MYCN expression created a forward positive feedback loop in neuroblastoma cells. Thus, USP5 acts as an oncogenic cofactor with MYCN in neuroblastoma and the novel combination of HDAC inhibitor with SE486-11 represents a novel therapeutic approach for the treatment of MYCN-driven neuroblastoma.

Chi, C, Li, C, Buys, N, Wang, W, Yin, C & Sun, J 2021, 'Effects of Probiotics in Preterm Infants: A Network Meta-analysis', Pediatrics, vol. 147, no. 1.
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CONTEXT: Probiotics have proven to be effective in promoting premature infants’ health, but the optimal usage is unknown. OBJECTIVE: To compare probiotic supplements for premature infants. DATA SOURCES: We searched PubMed, Embase, Cochrane, and ProQuest from inception of these databases to June 1, 2020. STUDY SELECTION: Randomized trials of probiotic supplement intervention for preterm infants were screened by 2 reviewers independently. The primary outcomes were mortality and the morbidity of necrotizing enterocolitis (NEC). Secondary outcomes were morbidity of sepsis, time to achieve full enteral feeding, and length of hospital stay. DATA EXTRACTION: The data of primary and secondary outcomes were extracted by 2 reviewers and pooled with a random-effects model. RESULTS: The meta-analysis included 45 trials with 12 320 participants. Bifidobacterium plus Lactobacillus was associated with lower rates of mortality (risk ratio 0.56; 95% credible interval 0.34–0.84) and NEC morbidity (0.47; 0.27–0.79) in comparison to the placebo; Lactobacillus plus prebiotic was associated with lower rates of NEC morbidity (0.06; 0.01–0.41) in comparison to the placebo; Bifidobacterium plus prebiotic had the highest probability of having the lowest rate of mortal...

Chi, H, Liu, F, Yang, W, Lan, L, Liu, T, Han, B, Cheung, WK & Kwok, JT 2021, 'TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation', Advances in Neural Information Processing Systems, vol. 25, pp. 20970-20982.
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In few-shot domain adaptation (FDA), classifiers for the target domain are trained with accessible labeled data in the source domain (SD) and few labeled data in the target domain (TD). However, data usually contain private information in the current era, e.g., data distributed on personal phones. Thus, the private data will be leaked if we directly access data in SD to train a target-domain classifier (required by FDA methods). In this paper, to prevent privacy leakage in SD, we consider a very challenging problem setting, where the classifier for the TD has to be trained using few labeled target data and a well-trained SD classifier, named few-shot hypothesis adaptation (FHA). In FHA, we cannot access data in SD, as a result, the private information in SD will be protected well. To this end, we propose a targetoriented hypothesis adaptation network (TOHAN) to solve the FHA problem, where we generate highly-compatible unlabeled data (i.e., an intermediate domain) to help train a target-domain classifier. TOHAN maintains two deep networks simultaneously, in which one focuses on learning an intermediate domain and the other takes care of the intermediate-to-target distributional adaptation and the target-risk minimization. Experimental results show that TOHAN outperforms competitive baselines significantly.

Chiniforush, AA, Gharehchaei, M, Akbar Nezhad, A, Castel, A, Moghaddam, F, Keyte, L, Hocking, D & Foster, S 2021, 'Minimising risk of early-age thermal cracking and delayed ettringite formation in concrete – A hybrid numerical simulation and genetic algorithm mix optimisation approach', Construction and Building Materials, vol. 299, pp. 124280-124280.
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Chinnaraj, S, Palani, V, Yadav, S, Arumugam, M, Sivakumar, M, Maluventhen, V & Singh, M 2021, 'Green synthesis of silver nanoparticle using goniothalamus wightii on graphene oxide nanocomposite for effective voltammetric determination of metronidazole', Sensing and Bio-Sensing Research, vol. 32, pp. 100425-100425.
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Chivukula, AS, Yang, X, Liu, W, Zhu, T & Zhou, W 2021, 'Game Theoretical Adversarial Deep Learning With Variational Adversaries', IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 11, pp. 3568-3581.
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Chou, KP, Prasad, M, Yang, J, Su, S-Y, Tao, X, Saxena, A, Lin, W-C & Lin, C-T 2021, 'A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications', Multimedia Tools and Applications, vol. 80, no. 11, pp. 16635-16657.
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Chowdhury, H, Chowdhury, T, Hossain, N, Chowdhury, P, Salam, B, Sait, SM & Mahlia, TMI 2021, 'Exergetic sustainability analysis of industrial furnace: a case study', Environmental Science and Pollution Research, vol. 28, no. 10, pp. 12881-12888.
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Chowdhury, L, Kamal, MS, Ripon, SH, Parvin, S, Hussain, OK, Ashour, A & Chowdhury, BR 2021, 'A Biological Data-Driven Mining Technique by Using Hybrid Classifiers With Rough Set', International Journal of Ambient Computing and Intelligence, vol. 12, no. 3, pp. 123-139.
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Biological data classification and analysis are significant for living organs. A biological data classification is an approach that classifies the organs into a particular group based on their features and characteristics. The objective of this paper is to establish a hybrid approach with naive Bayes, apriori algorithm, and KNN classifier that generates optimal classification rules for finding biological pattern matching. The authors create combined association rules by using naïve Bayes and apriori approach with a rough set for next sequence prediction. First, the large DNA sequence is reduced by using k-nearest approach. They apply association rules by using naïve Bayes and apriori approach for the next sequence pattern. The hybrid approach provides more accuracy than single classifier for biological sequence prediction. The optimized hybrid process needs less execution time for rule generation for massive biological data analysis. The results established that the hybrid approach generally outperforms the other association rule generation approach.

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, vol. 9, pp. 9584-9594.
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Chu, L, Shi, J & Braun, R 2021, 'The Impacts of Material Uncertainty in Electro-Migration of SAC Solder Electronic Packaging by Monte Carlo-Based Stochastic Finite-Element Model', IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 11, no. 11, pp. 1864-1876.
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Chu, L, Zhou, P, Shi, J & Braun, R 2021, 'Sensitivity Analysis for Geometrical Parameters of BGA in Flip-Chip Packaging Under Random Shear Stress and Thermal Temperature', IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 11, no. 5, pp. 765-777.
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Clarke, C, Singh, M, Tawfik, SA, Xu, X, Spencer, MJS, Ramanathan, R, Reineck, P, Bansal, V & Ton-That, C 2021, 'Mono- to few-layer non-van der Waals 2D lanthanide-doped NaYF₄nanosheets with upconversion luminescence', 2D Materials, vol. 8, no. 1, pp. 015005-015005.
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AbstractNaYF4is an efficient host material for lanthanide-based upconversion luminescence and has attracted immense interest for potential applications in photovoltaics, lasers and bioimaging. However, being a non-van der Waals (non-vdW) material, there have been thus far no reports on exfoliation of bulk NaYF4to nanosheets and their upconversion luminescence properties. Here, we demonstrate for the first time the fabrication of lanthanide-containing NaYF42D nanosheets using a soft liquid-phase exfoliation method and report on their optical, electronic and chemical characteristics. The nanosheets exfoliated from NaYF4:Yb,Er microcrystals consisting mainly ofβ-NaYF4become enriched inα-NaYF4post exfoliation and have a large micron-sized planar area with a preferential (100) surface orientation. X-ray absorption spectroscopy confirms that both Yb and Er doping ions are retained in the exfoliated nanosheets. Through centrifugation, NaYF42D nanosheets are successfully obtained with thicknesses ranging from a monolayer to tens of layers. Optical analysis of individual nanosheets shows that they exhibit both optical down-conversion and upconversion properties, albeit with reduced emission intensities compared with the parent microparticles. Further exploration of their electronic structure by density functional theory (DFT) calculations and photoelectron spectroscopy reveals the formation of surface F atom defects and a shrinkage of the electronic bandgap in ultrathin nanosheets. Our findings will trigger further interest in non-vdW 2D upconversion nanomaterials.

Clegg, SR & Burdon, S 2021, 'Exploring creativity and innovation in broadcasting', Human Relations, vol. 74, no. 6, pp. 791-813.
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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.

Cong Nguyen, N, Thi Nguyen, H, Cong Duong, H, Chen, S-S, Quang Le, H, Cong Duong, C, Thuy Trang, L, Chen, C-K, Dan Nguyen, P, Thanh Bui, X, Guo, W & Hao Ngo, H 2021, 'A breakthrough dynamic-osmotic membrane bioreactor/nanofiltration hybrid system for real municipal wastewater treatment and reuse', Bioresource Technology, vol. 342, pp. 125930-125930.
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Cong, HP, Perry, S, Cheng, E, Trinh, VA & Hoang, XV 2021, 'ỔN ĐỊNH CHẤT LƯỢNG ẢNH LIGHT FIELD DỰA TRÊN BỘ MÃ HÓA VIDEO PHÂN TÁN THẾ HỆ MỚI (Consistent Quality Control for Light Field image based on distributed video coding)', Tạp chí Khoa học Công nghệ thông tin và Truyền thông (Journal of Science and Technology on Information and Communications), vol. 1, no. 3.
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Distributed video coding (DVC) is a promising encoding solution for low complexity video applications such as wireless sensor networks or video surveillance systems. The requirement for image quality stability is one of the important issues in advanced display systems. However, most recent distributed video encoding solutions, which are developed based on H.264/AVC or H.265/HEVC standards, cannot provide video with stable quality. In this paper, we propose a distributed video encoding solution with quantization matrices (QMs) applied to Light Field data, ensuring stability in display quality and improving compression performance. In the proposed DVC solution, the latest video coding standard, Video Versatile Coding - VVC is selected appropriately to compress Key frames. Meanwhile, to achieve stability in video quality, quantization parameters (QPs) choose to compress key frames and quantization matrices (QMs) choose to compress Wyner-Ziv frames (WZ) will be explained in detail in the article. The test results show that the proposed distributed video encoding solution with integrated VVC codec (D-VVC) is significantly superior to other related DVC encoding solutions, especially distributed encoders. DISCOVER and recent DVC-HEVC solution, in terms of compression performance while providing stability for better picture quality for decoded video.

Consoli, NC, Tonini de Araújo, M, Tonatto Ferrazzo, S, de Lima Rodrigues, V & Gravina da Rocha, C 2021, 'Increasing density and cement content in stabilization of expansive soils: Conflicting or complementary procedures for reducing swelling?', Canadian Geotechnical Journal, vol. 58, no. 6, pp. 866-878.
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The present study makes three contributions to the literature of expansive soils: (i) it proposes equations to predict soil swelling based on dry density and cement content, (ii) it checks the developed general equation by predicting the swelling of different expansive soils from the literature, and (iii) it designs experiments that investigate factors that have a significant influence on swelling. An experimental programme was carried out to analyse the expansion of bentonite–kaolin–cement blends. Different proportions of bentonite–kaolin, cement content, dry density, and moisture content were evaluated. A unique relation of the cement/porosity index was obtained for cement-stabilized expansive soils’ swelling; this index has been used before to portray strength, stiffness, and loss of mass of stabilized soils and is now shown to be applicable to describe swelling of expansive soils treated with Portland cement. In the present research, cement content and dry density are seen as conflicting parameters regarding the swelling of expansive soils, because increasing the amount of Portland cement reduces swelling and increasing the density (through compaction) causes higher expansion. A general swelling model is proposed and successfully checked with data from the literature; it is able to predict the swelling of expansive soils with different densities, expansive mineral, moisture content, and cement content.

Coronado, FC, Merigó, JM & Cancino, CA 2021, 'Business and management research in Latin America: A country-level bibliometric analysis', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1865-1878.
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Bibliometrics is a scientific discipline that studies quantitatively the bibliographic material of a particular topic. This study analyzes management research published by Latin American countries between 1990 and 2019. The work uses the Web of Science database and provides several country-level bibliometric indicators including the total number of publications and citations, and the h-index. The results indicate that Brazil, Chile and Mexico have constantly led the region’s scientific publications. The temporal evolution shows a significant increase on the number of publications during the last years that seems to continue in the future. The results also show that operations research and finance are the most significant topics in the region.

Cortes, CAT, Chen, H-T, Sturnieks, DL, Garcia, J, Lord, SR & Lin, C-T 2021, 'Evaluating Balance Recovery Techniques for Users Wearing Head-Mounted Display in VR', IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 1, pp. 204-215.
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Cowled, CJL, Crews, K & Gover, D 2021, 'Influence of loading protocol on the structural performance of timber-framed shear walls', Construction and Building Materials, vol. 288, pp. 123103-123103.
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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, vol. 115, no. 9-10, pp. 3089-3104.
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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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D’Ambrosio, U & Shannon, AG 2021, 'The IJMEST Editorial Board over the decades: a personal retrospective perspective', International Journal of Mathematical Education in Science and Technology, vol. 52, no. 2, pp. 324-329.
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Dabbaghi, F, Dehestani, M, Yousefpour, H, Rasekh, H & Navaratnam, S 2021, 'Residual compressive stress–strain relationship of lightweight aggregate concrete after exposure to elevated temperatures', Construction and Building Materials, vol. 298, pp. 123890-123890.
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Dai, M, Su, Z, Li, R & Yu, S 2021, 'A Software-Defined-Networking-Enabled Approach for Edge-Cloud Computing in the Internet of Things', IEEE Network, vol. 35, no. 5, pp. 66-73.
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Daly, L, Lee, MR, Darling, JR, McCarrol, I, Yang, L, Cairney, J, Forman, LV, Bland, PA, Benedix, GK, Fougerouse, D, Rickard, WDA, Saxey, DW, Reddy, SM, Smith, W & Bagot, PAJ 2021, 'Developing Atom Probe Tomography of Phyllosilicates in Preparation for Extra‐Terrestrial Sample Return', Geostandards and Geoanalytical Research, vol. 45, no. 3, pp. 427-441.
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Dang, B-T, Bui, X-T, Itayama, T, Ngo, HH, Jahng, D, Lin, C, Chen, S-S, Lin, K-YA, Nguyen, T-T, Nguyen, DD & Saunders, T 2021, 'Microbial community response to ciprofloxacin toxicity in sponge membrane bioreactor', Science of The Total Environment, vol. 773, pp. 145041-145041.
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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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In this investigation, a ground-modification technique utilising a fibre-reinforced load-transfer platform (FRLTP) and columns-supported (CS) embankment constructed on multi-layered soft soils is proposed and investigated. After validating the proposed model with published data in the literature, numerical analysis was firstly conducted on the two-dimensional finite element model of a CS embankment without or with FRLTP to examine the influence of the FRLTP inclusion into the CS embankment system. Secondly, an extensive parametric study was performed to further investigate the effects of the FRLTP essential parameters — including platform thickness, shear strength, and tensile strength properties — and deformation modulus on the embankment performance during the construction and post-construction stages. Additionally, the influence of the embankment design parameters, such as column spacing, column length, and diameter, was examined. The numerical results reveal that the FRLTP inclusion can be effective in enhancing the CS embankment behaviour. It is also found that when increasing the platform thickness, the shear strength properties of FRLTP play a significant role in improving the overall performance of a column embankment with FRLTP.

Dang, KB, Nguyen, TT, Ngo, HH, Burkhard, B, Müller, F, Dang, VB, Nguyen, H, Ngo, VL & Pham, TPN 2021, 'Integrated methods and scenarios for assessment of sand dunes ecosystem services', Journal of Environmental Management, vol. 289, pp. 112485-112485.
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Dang, LC, Khabbaz, H & Ni, B-J 2021, 'Improving engineering characteristics of expansive soils using industry waste as a sustainable application for reuse of bagasse ash', Transportation Geotechnics, vol. 31, pp. 100637-100637.
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Dang, Z, Li, L, Ni, W, Liu, R, Peng, H & Yang, Y 2021, 'How does rumor spreading affect people inside and outside an institution', Information Sciences, vol. 574, pp. 377-393.
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Daniel, J & Merigó, JM 2021, 'Developing a new multidimensional model for selecting strategic plans in balanced scorecard', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1817-1826.
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The main motivation of this research is to develop an innovative multidimensional model through multi attribute decision making (MADM) methods for strategic plans selection process in the Balanced Scorecard (BSC). The current study adopted MADM analytical methods including AHP, ELECTRE, BORDA, TOPSIS and SAW to rank the initiatives / strategic plans in BSC. Then the results of those methods were compared against each other in order to find a robust model for selecting strategic plans. The correlation coefficient between methods indicated that multidimensional and ELECTRE methods with 0.944 are the best performing and AHP with negative correlation (–0.455) is the worst performing method for selecting strategic plans in BSC. The high correlation demonstrates that the model can be a useful and effective tool to finding the critical aspects of evaluation criteria as well as the gaps to improve company performance for achieving desired level. Developing multidimensional model is the core model for the selection of strategic plans. This study addresses the problem and issues of group decision making process for selecting strategic plans in BSC. It has numerous contributions that particularly includes; 1) Determination of the explicit criteria sub-criteria and criteria to improve ranking strategic plans in BSC, 2) Adopting MADM analytical methods including AHP, ELECTRE, BORDA, TOPSIS and SAW for the selection of strategic plans decision problem in BSC, 3) Developing multidimensional model to address the selection of strategic plans problems in BSC. The proposed model will provide an approach to facilitate strategic plans decision problem in BSC.

Dansana, D, Kumar, R, Parida, A, Sharma, R, Das Adhikari, J, Van Le, H, Thai Pham, B, Kant Singh, K & Pradhan, B 2021, 'Using Susceptible-Exposed-Infectious-Recovered Model to Forecast Coronavirus Outbreak', 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, vol. 15, no. 1, pp. 1077-1085.
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Darabi, H, Torabi Haghighi, A, Rahmati, O, Jalali Shahrood, A, Rouzbeh, S, Pradhan, B & Tien Bui, D 2021, 'A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation', Journal of Hydrology, vol. 603, pp. 126854-126854.
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In regions with lack of hydrological and hydraulic data, a spatial flood modeling and mapping is an opportunity for the urban authorities to predict the spatial distribution and the intensity of the flooding. It helps decision-makers to develop effective flood prevention and management plans. In this study, flood inventory data were prepared based on the historical and field surveys data by Sari municipality and regional water company of Mazandaran, Iran. The collected flood data accompanied with different variables (digital elevation model and slope have been considered as topographic variables, land use/land cover, precipitation, curve number, distance to river, distance to channel and depth to groundwater as environmental variables) were applied to novel hybridized model based on neural network and swarm intelligence-grey wolf algorithm (NN-SGW) to map flood-inundation. Several confusion matrix criteria were used for accuracy evaluation by cutoff-dependent and independent metrics (e.g., efficiency (E), positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC)). The accuracy of the flood inundation map produced by the NN-SGW model was compared with that of maps produced by four state-of-the-art benchmark models: random forest (RF), logistic model tree (LMT), classification and regression trees (CART), and J48 decision tree (J48DT). The NN-SGW model outperformed all benchmark models in both training (E = 90.5%, PPV = 93.7%, NPV = 87.3%, AUC = 96.3%) and validation (E = 79.4%, PPV = 85.3%, NPV = 73.5%, AUC = 88.2%). As the NN-SGW model produced the most accurate flood-inundation map, it can be employed for robust flood contingency planning. Based on the obtained results from NN-SGW model, distance from channel, distance from river, and depth to groundwater were identified as the most important variables for spatial prediction of urban flood inundation. This work can serve as a basis for future s...

Dardor, D, Al Maas, M, Minier-Matar, J, Janson, A, Abdel-Wahab, A, Shon, HK & Adham, S 2021, 'Evaluation of pretreatment and membrane configuration for pressure-retarded osmosis application to produced water from the petroleum industry', Desalination, vol. 516, pp. 115219-115219.
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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, pp. 104688-104688.
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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.

Deepanraj, B, Senthilkumar, N, Ranjitha, J, Jayaraj, S & Ong, HC 2021, 'Biogas from food waste through anaerobic digestion: optimization with response surface methodology', Biomass Conversion and Biorefinery, vol. 11, no. 2, pp. 227-239.
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Deliri, S, Varesi, K, Siwakoti, YP & Blaabjerg, F 2021, 'A boost type switched‐capacitor multi‐level inverter for renewable energy sources with Self‐Voltage balancing of capacitors', International Journal of Energy Research, vol. 45, no. 10, pp. 15217-15230.
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Deliri, S, Varesi, K, Siwakoti, YP & Blaabjerg, F 2021, 'Generalized diamond‐type single DC‐source switched‐capacitor based multilevel inverter with step‐up and natural voltage balancing capabilities', IET Power Electronics, vol. 14, no. 6, pp. 1208-1218.
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Deng, J, Chen, X, Fan, Z, Jiang, R, Song, X & Tsang, IW 2021, 'The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting', ACM Transactions on Knowledge Discovery from Data, vol. 15, no. 6, pp. 1-25.
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Transportation demand forecasting is a topic of large practical value. However, the model that fits the demand of one transportation by only considering the historical data of its own could be vulnerable since random fluctuations could easily impact the modeling. On the other hand, common factors like time and region attribute, drive the evolution demand of different transportation, leading to a co-evolving intrinsic property between different kinds of transportation. In this work, we focus on exploring the co-evolution between different modes of transport, e.g., taxi demand and shared-bike demand. Two significant challenges impede the discovery of the co-evolving pattern: (1) diversity of the co-evolving correlation, which varies from region to region and time to time. (2) Multi-modal data fusion. Taxi demand and shared-bike demand are time-series data, which have different representations with the external factors. Moreover, the distribution of taxi demand and bike demand are not identical. To overcome these challenges, we propose a novel method, known as co-evolving spatial temporal neural network (CEST). CEST learns a multi-view demand representation for each mode of transport, extracts the co-evolving pattern, then predicts the demand for the target transportation based on multi-scale representation, which includes fine-scale demand information and coarse-scale pattern information. We conduct extensive experiments to validate the superiority of our model over the state-of-art models.

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, pp. 126016-126016.
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Deng, ZX, Tao, JW, Zhang, W, Mu, HJ, Wu, HJ, Wang, YB, Xu, XX & Zheng, W 2021, 'Effect of protein adsorption on electrospun hemoglobin/gelatin-MWCNTs microbelts modified electrode: Toward electrochemical measurement of hydrogen peroxide', Materials Chemistry and Physics, vol. 257, pp. 123827-123827.
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Deshpande, NM, Gite, S, Pradhan, B, Kotecha, K & Alamri, A 2021, 'Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia', Mathematical Biosciences and Engineering, vol. 19, no. 2, pp. 1970-2001.
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<abstract> <p>The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique.</p> </abstract>

Devda, V, Chaudhary, K, Varjani, S, Pathak, B, Patel, AK, Singhania, RR, Taherzadeh, MJ, Ngo, HH, Wong, JWC, Guo, W & Chaturvedi, P 2021, 'Recovery of resources from industrial wastewater employing electrochemical technologies: status, advancements and perspectives', Bioengineered, vol. 12, no. 1, pp. 4697-4718.
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Deveci, Ö & Shannon, AG 2021, 'A note on balanced incomplete block designs and projective geometry', International Journal of Mathematical Education in Science and Technology, vol. 52, no. 5, pp. 807-813.
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An advanced topology of single-phase five-level transformerless grid-connected inverter is presented in this paper, which offers a common ground between the input source and the null of the grid. This alleviated the concern of variable common-mode voltage and the leakage current problem, which makes the inverter suitable for grid-tied photovoltaic (PV)-based applications. The proposed topology is operated by a series-parallel switching conversion of a switched-capacitor (SC) cell. It consists of a single dc source, two power diodes/capacitors alongside six power switches. Using the SC technique, a single-stage two times voltage boosting inverter with a self-voltage balancing of the capacitors is achieved. By employing the SC cell, five distinct voltage levels are also made at the inverter's output, so a small L-Type filter can be utilized. The control/modulation of the proposed inverter is also on the basis of a new peak current controller (PCC) technique. The circuit description along with the proposed PCC operation is discussed and a brief comparative study besides the experimental results are given at the end to demonstrate the feasibility of the proposed topology.

Deveci, Ö & Shannon, AG 2021, 'Matrix manipulations for properties of Fibonacci p-numbers and their generalizations', Annals of the Alexandru Ioan Cuza University - Mathematics, 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.

Deveci, Ö & Shannon, AG 2021, 'The complex-type k-Fibonacci sequences and their applications', Communications in Algebra, vol. 49, no. 3, pp. 1352-1367.
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© 2020 Taylor & Francis Group, LLC. In this article, we define the complex-type k-Fibonacci numbers and then give the relationships between the k-step Fibonacci numbers and the complex-type k-Fibonacci numbers. Also, we obtain miscellaneous properties of the complex-type k-Fibonacci numbers such as the Binet formulas, the combinatorial, permanental, determinantal representations and the sums. In addition, we study the complex-type k-Fibonacci sequence modulo m and then we give some results concerning the periods and the ranks of the complex-type k-Fibonacci sequences for any k and m which are related the periods of the k-step Fibonacci sequences modulo m. Furthermore, we extend the complex-type k-Fibonacci sequences to groups. Finally, we obtain the periods of the complex-type 2-Fibonacci sequences in the dihedral group (Formula presented.) with respect to the generating pairs (x, y) and (y, x).

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, vol. 21, no. 4, pp. 1651-1658.
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Diao, K, Sun, X, Lei, G, Bramerdorfer, G, Guo, Y & Zhu, J 2021, 'Robust Design Optimization of Switched Reluctance Motor Drive Systems Based on System-Level Sequential Taguchi Method', IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 3199-3207.
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Diao, K, Sun, X, Lei, G, Bramerdorfer, G, Guo, Y & Zhu, J 2021, 'System-Level Robust Design Optimization of a Switched Reluctance Motor Drive System Considering Multiple Driving Cycles', IEEE Transactions on Energy Conversion, vol. 36, no. 1, pp. 348-357.
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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, vol. 36, no. 3, pp. 2217-2226.
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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.

Dickson-Deane, C 2021, 'Influencing the design outcome', Educational Technology Research and Development, vol. 69, no. 1, pp. 269-271.
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Dickson-Deane, C 2021, 'Moving practical learning online', Educational Technology Research and Development, vol. 69, no. 1, pp. 235-237.
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Dickson-Deane, C & Edwards, M 2021, 'Transcribing accounting lectures: Enhancing the pedagogical practice by acknowledging student behaviour', Journal of Accounting Education, vol. 54, pp. 100709-100709.
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Dikshit, A & Pradhan, B 2021, 'Explainable AI in drought forecasting', Machine Learning with Applications, vol. 6, pp. 100192-100192.
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Dikshit, A & Pradhan, B 2021, 'Interpretable and explainable AI (XAI) model for spatial drought prediction', Science of The Total Environment, vol. 801, pp. 149797-149797.
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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, pp. 142638-142638.
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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, vol. 100, pp. 290-301.
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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, pp. 111979-111979.
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Ding, A, Song, R, Cui, H, Cao, H, Ngo, HH, Chang, H, Nan, J, Li, G & Ma, J 2021, 'Presence of powdered activated carbon/zeolite layer on the performances of gravity-driven membrane (GDM) system for drinking water treatment: Ammonia removal and flux stabilization', Science of The Total Environment, vol. 799, pp. 149415-149415.
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Ding, A, Zhang, R, Ngo, HH, He, X, Ma, J, Nan, J & Li, G 2021, 'Life cycle assessment of sewage sludge treatment and disposal based on nutrient and energy recovery: A review', Science of The Total Environment, vol. 769, pp. 144451-144451.
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With the acceleration of urbanization, the production of urban sludge is increasing rapidly. To minimize resource input and waste output, it is crucial to execute analyses of environmental impact and assessments of sustainability on different technical strategies involving sludge disposal based on Life Cycle Assessment (LCA), which is a great potential mean of environmental management adopted internationally in the 21st century. This review aims to compare the environmental sustainability of existing sludge management schemes with a purpose of nutrient recovery and energy saving, respectively, and also to include the substitution benefits of alternative sludge products. Simultaneously, LCA research regarding the emerging sludge management technologies and sludge recycling (cement, adsorbent, bricks) is analyzed. Additionally, the key aspects of the LCA process are worth noting in the context of the current limitations reviewed here. It is worth emphasizing that no technical remediation method can reduce all environmental damage simultaneously, and these schemes are typically more applicable to the assumed local conditions. Future LCA research should pay more attention to the toxic effects of different sludge treatment methods, evaluate the technical ways of adding pretreatment technology to the ‘front end’ of the sludge treatment process, and further explore how to markedly reduce environmental damage in order to maximize energy and nutrient recovery from the LCA perspective.

Ding, L, Radfar, P, Rezaei, M & Warkiani, ME 2021, 'An easy-to-operate method for single-cell isolation and retrieval using a microfluidic static droplet array', Microchimica Acta, vol. 188, no. 8.
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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, pp. 124968-124968.
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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, Wang, Y-K & Lin, C-T 2021, 'Memory augmented convolutional neural network and its application in bioimages', Neurocomputing, vol. 466, pp. 128-138.
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Ding, 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, vol. 5, no. 1, pp. 2-5.
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Ding, W, Pedrycz, W, Triguero, I, Cao, Z & Lin, C-T 2021, 'Multigranulation Supertrust Model for Attribute Reduction', IEEE Transactions on Fuzzy Systems, vol. 29, no. 6, pp. 1395-1408.
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IEEE As big data often contains a significant amount of uncertain, unstructured and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this paper, we present a novel multigranulation super-trust fuzzy-rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation super-trust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme is adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular-coevolution is employed to ensure a wide range of balancing of exploration and exploitation and can classify super elitists’ preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables.

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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Ding, X, Chen, L, Zhou, P, Xu, Z, Wen, S, Lui, JCS & Jin, H 2021, 'Dynamic online convex optimization with long-term constraints via virtual queue', Information Sciences, vol. 577, pp. 140-161.
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Do, T-TN, Jung, T-P & Lin, C-T 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, T-TN, Lin, C-T & Gramann, K 2021, 'Human brain dynamics in active spatial navigation', Scientific Reports, vol. 11, no. 1.
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AbstractSpatial navigation is a complex cognitive process based on multiple senses that are integrated and processed by a wide network of brain areas. Previous studies have revealed the retrosplenial complex (RSC) to be modulated in a task-related manner during navigation. However, these studies restricted participants’ movement to stationary setups, which might have impacted heading computations due to the absence of vestibular and proprioceptive inputs. Here, we present evidence of human RSC theta oscillation (4–8 Hz) in an active spatial navigation task where participants actively ambulated from one location to several other points while the position of a landmark and the starting location were updated. The results revealed theta power in the RSC to be pronounced during heading changes but not during translational movements, indicating that physical rotations induce human RSC theta activity. This finding provides a potential evidence of head-direction computation in RSC in healthy humans during active spatial navigation.

Do, T-TN, Wang, Y-K & Lin, C-T 2021, 'Increase in Brain Effective Connectivity in Multitasking but not in a High-Fatigue State', IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 3, pp. 566-574.
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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, pp. 103941-103941.
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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.

Dogan, A, Akay, M, Barua, PD, Baygin, M, Dogan, S, Tuncer, T, Dogru, AH & Acharya, UR 2021, 'PrimePatNet87: Prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition', Computers in Biology and Medicine, vol. 138, pp. 104867-104867.
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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. 718-718.
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Absorptive capacity is a common barrier to knowledge transfer at the individual level. However, technology absorptive capacity can enhance an individual’s learning behaviour. This study investigates that technology readiness, the tools for knowledge sources, social influences, and social networks influence an individual’s absorptive capacity on an adaptation of the individual learning behaviour. A quantitative approach is used to assess the presence of a causal relationship from the constructs mentioned above. Data were collected from university students in Australia to examine the hypotheses. With 199 responses, a partial least squares structural equation modelling (PLS-SEM) approach was used for the analysis. The results generated mixed findings. Individual’s technological belief in optimism and innovation and social influences had a significantly weaker effect on individual absorptive capacity, which in turn had a significantly weaker impact on their learning behaviour.

Dombrowski, U, Deuse, J, Ortmeier, C, Henningsen, N, Wullbrandt, J & Nöhring, F 2021, 'Auswahlsystematik für Methoden und Werkzeuge Ganzheitlicher Produktionssysteme 4.0', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 116, no. 6, pp. 398-402.
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Abstract Das produzierende Gewerbe steht aktuell vor der Herausforderung, bestehende Produktionssysteme mit Industrie-4.0-Lösungen zu erweitern, um deren Potenziale zu nutzen und somit die Wettbewerbsfähigkeit zu sichern. Es fehlen insbesondere kleinen und mittleren Unternehmen (KMU) jedoch die Transparenz über die Wirkung und die Auswahl von Industrie-4.0-Lösungen. Im Rahmen des Forschungsprojekts „Ganzheitliche Produktionssysteme 4.0“ (GaProSys 4.0) wurde diese Problematik aufgegriffen. In diesem Beitrag werden die Vorgehensweise zur Entwicklung von GaProSys-4.0-Methoden anhand des elektronischen Shopfloor Managements aufgezeigt und die im Rahmen des Forschungsprojektes erarbeitete Auswahlsystematik präsentiert.

Dominici, L, Fleck, R, Gill, RL, Pettit, TJ, Irga, PJ, Comino, E & Torpy, FR 2021, 'Analysis of lighting conditions of indoor living walls: Effects on CO2 removal', Journal of Building Engineering, vol. 44, pp. 102961-102961.
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Vertical greening systems, or living walls, are becoming increasingly used indoors for improving the sustainability of buildings, including for the mitigation of excess CO2 levels, derived from human respiration. However, light provision within indoor environments is often insufficient for the efficient functioning of many plant species, leading to low photosynthetic CO2 removal rates, and the need for supplementary light sources. In this study, we investigated the performance of supplementary lighting employed for indoor living wall systems, and whether optimised lighting conditions could lead to improved CO2 removal. In situ trials with several medium-large indoor living walls were performed to sample the lighting scenarios currently employed. We concluded that the majority of plants in existing systems were exposed to suboptimal lighting and will have a net-zero CO2 removal efficiency. Sealed chamber experiments using two common living wall plant species were conducted to explore the effect of varying lighting conditions on CO2 removal efficiency. Comparisons on optimal and “best case” in situ conditions were carried out, showing that CO2 removal efficiency was significantly correlated with both leaf and stem angles, which suggest phototropism may influence in situ CO2 removal. After a ten-day experimental period, the highest CO2 removal efficiency for both test plant species was observed at 200 μmol m−2 s−1 light flux density (~10500 lux) at 15° from the vertical growing surface. Our results indicate that most current lighting systems are inadequate for healthy plant photosynthesis and CO2 removal, and that modified lighting systems could improve this performance. The estimation of the CO2 removal ability of a 5 m2 passive living wall decreases from an ACH of 0.21 h−1, achieved in an optimal light exposure condition, to only 0.03 h−1 when plants are exposed to sub-optimal conditions. To reduce maintenance costs, technical guidelines for indoor living wall lig...

Dong, J, Cong, Y, Sun, G, Fang, Z & Ding, Z 2021, 'Where and How to Transfer: Knowledge Aggregation-Induced Transferability Perception for Unsupervised Domain Adaptation', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Dong, M, Yao, L, Wang, X, Benatallah, B, Zhang, S & Sheng, QZ 2021, 'Gradient Boosted Neural Decision Forest', IEEE Transactions on Services Computing, pp. 1-1.
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Dong, S, Zhao, Y, Li, JJ & Xing, D 2021, 'Global Research Trends in Revision Total Knee Arthroplasty: A Bibliometric and Visualized Study', Indian Journal of Orthopaedics, vol. 55, no. 5, pp. 1335-1347.
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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, pp. 128834-128834.
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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, pp. 100563-100563.
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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 and Concrete Composites, vol. 118, pp. 103962-103962.
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Dong, X, Yang, Y, Wei, S-E, Weng, X, Sheikh, Y & Yu, S-I 2021, 'Supervision by Registration and Triangulation for Landmark Detection'.
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We present Supervision by Registration and Triangulation (SRT), an unsupervised approach that utilizes unlabeled multi-view video to improve the accuracy and precision of landmark detectors. Being able to utilize unlabeled data enables our detectors to learn from massive amounts of unlabeled data freely available and not be limited by the quality and quantity of manual human annotations. To utilize unlabeled data, there are two key observations: (1) the detections of the same landmark in adjacent frames should be coherent with registration, i.e., optical flow. (2) the detections of the same landmark in multiple synchronized and geometrically calibrated views should correspond to a single 3D point, i.e., multi-view consistency. Registration and multi-view consistency are sources of supervision that do not require manual labeling, thus it can be leveraged to augment existing training data during detector training. End-to-end training is made possible by differentiable registration and 3D triangulation modules. Experiments with 11 datasets and a newly proposed metric to measure precision demonstrate accuracy and precision improvements in landmark detection on both images and video. Code is available at https://github.com/D-X-Y/landmark-detection.

Donnelly, S & Tran, N 2021, 'Commandeering the mammalian Ago2 miRNA network: a newly discovered mechanism of helminth immunomodulation', Trends in Parasitology, vol. 37, no. 12, pp. 1031-1033.
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MicroRNAs (miRNAs) are a class of noncoding RNAs that contribute to a broad range of biological processes through post-transcriptional regulation of gene expression. Helminths exploit this system to target mammalian gene expression, to modulate the host immune response. Recent discoveries have shed new light on the mechanisms involved.

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, G, Huang, N, Zhao, Y, Lei, G & Zhu, J 2021, 'Comprehensive Sensitivity Analysis and Multiphysics Optimization of the Rotor for a High Speed Permanent Magnet Machine', IEEE Transactions on Energy Conversion, vol. 36, no. 1, pp. 358-367.
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Du, H, Gao, H & Jia, W 2021, 'Joint Frequency and DOA Estimation with Automatic Pairing Using the Rayleigh–Ritz Theorem', Computers, Materials & Continua, vol. 67, no. 3, pp. 3907-3919.
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Du, J, Dong, P, Sugumaran, V & Castro‐Lacouture, D 2021, 'Dynamic decision support framework for production scheduling using a combined genetic algorithm and multiagent model', Expert Systems, vol. 38, no. 1.
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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, L, Gao, T, Ni, W & Wang, W 2021, 'A hybrid intelligent service recommendation by latent semantics and explicit ratings', International Journal of Intelligent Systems, vol. 36, no. 12, pp. 7867-7894.
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Duong, HC, Cao, HT, Hoang, NB & Nghiem, LD 2021, 'Reverse osmosis treatment of condensate from ammonium nitrate production: Insights into membrane performance', Journal of Environmental Chemical Engineering, vol. 9, no. 6, pp. 106457-106457.
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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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Dutta, S & Gandomi, AH 2021, 'Erratum for “Bilevel Data-Driven Modeling Framework for High-Dimensional Structural Optimization under Uncertainty Problems” by Subhrajit Dutta and Amir H. Gandomi', Journal of Structural Engineering, vol. 147, no. 7.
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Dzaklo, CK, Rujikiatkamjorn, C, Indraratna, B & Kelly, R 2021, 'Cyclic behaviour of compacted black soil-coal wash matrix', Engineering Geology, vol. 294, pp. 106385-106385.
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Eager, D, Hossain, I, Ishac, K & Robins, S 2021, 'Analysis of Racing Greyhound Path Following Dynamics Using a Tracking System', Animals, vol. 11, no. 9, pp. 2687-2687.
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The University of Technology Sydney (UTS) has been working closely with the Australasian greyhound industry for more than 5 years to reduce greyhound race-related injuries. During this period, UTS has developed and deployed several different techniques including inertial measurement units, drones, high-frame-rate cameras, track geometric surveys, paw print analysis, track soil spring-force analysis, track maintenance data, race injury data, race computer simulation and modelling to assist in this task. During the period where the UTS recommendations have been adopted, the injury rate has dropped significantly. This has been achieved by animal welfare interventions that lower racing congestion, and lower transient forces and jerk rates the greyhounds experience during a race. This study investigated the use of a greyhound location tracing system where small and lightweight signal emitting devices were placed inside a pocket in the jackets of racing greyhounds. The system deployed an enhanced version of a player tracking system currently used to track the motion of human athletes. Greyhounds gallop at speeds of almost 20 m/s and are known to change their heading direction to exceed a yaw rate of 0.4 rad/s. The high magnitudes of velocity, acceleration and jerk posed significant technical challenges, as the greyhounds pushed the human tracking system beyond its original design limits. Clean race data gathered over a six-month period were analysed and presented for a typical 2-turn greyhound racing track. The data confirmed that on average, greyhounds ran along a path that resulted in the least energy wastage, which includes smooth non-linear paths that resemble easement curves at the transition between the straights to the semi-circular bends. This study also verified that the maximum jerk levels greyhounds experienced while racing were lower than the jerk levels that had been predicted with simulations and modelling for the track path. Furthermore, the resu...

Easttom, C, Bianchi, L, Valeriani, D, Nam, CS, Hossaini, A, Zapala, D, Roman-Gonzalez, A, Singh, AK, Antonietti, A, Sahonero-Alvarez, G & Balachandran, P 2021, 'A Functional Model for Unifying Brain Computer Interface Terminology', IEEE Open Journal of Engineering in Medicine and Biology, vol. 2, pp. 91-96.
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Easttom, C, Bianchi, L, Valeriani, D, Nam, CS, Hossaini, A, Zapała, D, Roman-Gonzalez, A, Singh, AK, Antonietti, A, Sahonero-Alvarez, G & Balachandran, P 2021, 'A functional BCI model by the P2731 working group: control interface', Brain-Computer Interfaces, vol. 8, no. 4, pp. 154-160.
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Echaroj, S, Pannucharoenwong, N, Ong, HC & Rattanadecho, P 2021, 'Production of bio-fuel from alcohothermal liquefaction of rice straw over sulfated-graphene oxide', Energy Reports, vol. 7, pp. 744-752.
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Ejaz, A, Babar, H, Ali, HM, Jamil, F, Janjua, MM, Fattah, IMR, Said, Z & Li, C 2021, 'Concentrated photovoltaics as light harvesters: Outlook, recent progress, and challenges', Sustainable Energy Technologies and Assessments, vol. 46, pp. 101199-101199.
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Ejegwa, PA, Wen, S, Feng, Y, Zhang, W & Chen, J 2021, 'Some new Pythagorean fuzzy correlation techniques via statistical viewpoint with applications to decision-making problems', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 9873-9886.
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Pythagorean fuzzy set is a reliable technique for soft computing because of its ability to curb indeterminate data when compare to intuitionistic fuzzy set. Among the several measuring tools in Pythagorean fuzzy environment, correlation coefficient is very vital since it has the capacity to measure interdependency and interrelationship between any two arbitrary Pythagorean fuzzy sets (PFSs). In Pythagorean fuzzy correlation coefficient, some techniques of calculating correlation coefficient of PFSs (CCPFSs) via statistical perspective have been proposed, however, with some limitations namely; (i) failure to incorporate all parameters of PFSs which lead to information loss, (ii) imprecise results, and (iii) less performance indexes. Sequel, this paper introduces some new statistical techniques of computing CCPFSs by using Pythagorean fuzzy variance and covariance which resolve the limitations with better performance indexes. The new techniques incorporate the three parameters of PFSs and defined within the range [-1, 1] to show the power of correlation between the PFSs and to indicate whether the PFSs under consideration are negatively or positively related. The validity of the new statistical techniques of computing CCPFSs is tested by considering some numerical examples, wherein the new techniques show superior performance indexes in contrast to the similar existing ones. To demonstrate the applicability of the new statistical techniques of computing CCPFSs, some multi-criteria decision-making problems (MCDM) involving medical diagnosis and pattern recognition problems are determined via the new techniques.

Ejeian, M, Grant, A, Shon, HK & Razmjou, A 2021, 'Is lithium brine water?', Desalination, vol. 518, pp. 115169-115169.
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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, vol. 232, no. 3.
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Ekanayake, UGM, Barclay, M, Seo, DH, Park, MJ, MacLeod, J, O'Mullane, AP, Motta, N, Shon, HK & Ostrikov, KK 2021, 'Utilization of plasma in water desalination and purification', Desalination, vol. 500, pp. 114903-114903.
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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.

Elgharabawy, A, Prasad, M & Lin, C-T 2021, 'Subgroup Preference Neural Network', Sensors, vol. 21, no. 18, pp. 6104-6104.
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Subgroup label ranking aims to rank groups of labels using a single ranking model, is a new problem faced in preference learning. This paper introduces the Subgroup Preference Neural Network (SGPNN) that combines multiple networks have different activation function, learning rate, and output layer into one artificial neural network (ANN) to discover the hidden relation between the subgroups’ multi-labels. The SGPNN is a feedforward (FF), partially connected network that has a single middle layer and uses stairstep (SS) multi-valued activation function to enhance the prediction’s probability and accelerate the ranking convergence. The novel structure of the proposed SGPNN consists of a multi-activation function neuron (MAFN) in the middle layer to rank each subgroup independently. The SGPNN uses gradient ascent to maximize the Spearman ranking correlation between the groups of labels. Each label is represented by an output neuron that has a single SS function. The proposed SGPNN using conjoint dataset outperforms the other label ranking methods which uses each dataset individually. The proposed SGPNN achieves an average accuracy of 91.4% using the conjoint dataset compared to supervised clustering, decision tree, multilayer perceptron label ranking and label ranking forests that achieve an average accuracy of 60%, 84.8%, 69.2% and 73%, respectively, using the individual dataset.

El-Haddad, BA, Youssef, AM, Pourghasemi, HR, Pradhan, B, El-Shater, A-H & El-Khashab, MH 2021, 'Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt', Natural Hazards, vol. 105, no. 1, pp. 83-114.
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El-Magd, SAA, 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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Emami, Y, Wei, B, Li, K, Ni, W & Tovar, E 2021, 'Joint Communication Scheduling and Velocity Control in Multi-UAV-Assisted Sensor Networks: A Deep Reinforcement Learning Approach', IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 10986-10998.
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Eslahi, H, Hamilton, TJ & Khandelwal, S 2021, 'Small signal model and analog performance analysis of negative capacitance FETs', Solid-State Electronics, vol. 186, pp. 108161-108161.
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Esmaili, N, Buchlak, QD, Piccardi, M, Kruger, B & Girosi, F 2021, 'Multichannel mixture models for time-series analysis and classification of engagement with multiple health services: An application to psychology and physiotherapy utilization patterns after traffic accidents', Artificial Intelligence in Medicine, vol. 111, pp. 101997-101997.
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Espinoza-Audelo, LF, León-Castro, E, Mellado-Cid, C, Merigó, JM & Blanco-Mesa, F 2021, 'Uncertain induced prioritized aggregation operators in the analysis of the imports and exports', Journal of Multiple-Valued Logic and Soft Computing, vol. 36, no. 6, pp. 543-568.
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Interval numbers are widely used in many fields to provide information about different scenarios. This paper presents several new uncertain average formulations using the ordered weighted average, prioritized, probabilistic and induced operators. First, the work introduces the uncertain prioritized induced probabilistic ordered weighted average (UPIPOWA) operator that its main applicability is in complex group decision making problems. Also, a wide range of special cases and extensions using quasi-arithmetic means are presented, such is the case of the quasi-arithmetic UPIPOWA (QUPIPOWA) operator. The study analyzes the applicability of this new approach in economic variables, specifically are imports and exports. Particularly, the paper focuses on measuring the imports and exports for Latin America for 2017.

Esselle, KP 2021, 'Distinguished Lecturer Program News And New Appointments [Distinguished Lecturers]', IEEE Antennas and Propagation Magazine, vol. 63, no. 3, pp. 138-141.
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Eyni, H, Ghorbani, S, Nazari, H, Hajialyani, M, Razavi Bazaz, S, Mohaqiq, M, Ebrahimi Warkiani, M & Sutherland, DS 2021, 'Advanced bioengineering of male germ stem cells to preserve fertility', Journal of Tissue Engineering, vol. 12, pp. 204173142110605-204173142110605.
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In modern life, several factors such as genetics, exposure to toxins, and aging have resulted in significant levels of male infertility, estimated to be approximately 18% worldwide. In response, substantial progress has been made to improve in vitro fertilization treatments (e.g. microsurgical testicular sperm extraction (m-TESE), intra-cytoplasmic sperm injection (ICSI), and round spermatid injection (ROSI)). Mimicking the structure of testicular natural extracellular matrices (ECM) outside of the body is one clear route toward complete in vitro spermatogenesis and male fertility preservation. Here, a new wave of technological innovations is underway applying regenerative medicine strategies to cell-tissue culture on natural or synthetic scaffolds supplemented with bioactive factors. The emergence of advanced bioengineered systems suggests new hope for male fertility preservation through development of functional male germ cells. To date, few studies aimed at in vitro spermatogenesis have resulted in relevant numbers of mature gametes. However, a substantial body of knowledge on conditions that are required to maintain and mature male germ cells in vitro is now in place. This review focuses on advanced bioengineering methods such as microfluidic systems, bio-fabricated scaffolds, and 3D organ culture applied to the germline for fertility preservation through in vitro spermatogenesis.

Faisal, SN, Amjadipour, M, Izzo, K, Singer, JA, Bendavid, A, Lin, C-T & Iacopi, F 2021, 'Non-invasive on-skin sensors for brain machine interfaces with epitaxial graphene', Journal of Neural Engineering, vol. 18, no. 6, pp. 066035-066035.
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Abstract Objective. Brain–machine interfaces are key components for the development of hands-free, brain-controlled devices. Electroencephalogram (EEG) electrodes are particularly attractive for harvesting the neural signals in a non-invasive fashion. Approach. Here, we explore the use of epitaxial graphene (EG) grown on silicon carbide on silicon for detecting the EEG signals with high sensitivity. Main results and significance. This dry and non-invasive approach exhibits a markedly improved skin contact impedance when benchmarked to commercial dry electrodes, as well as superior robustness, allowing prolonged and repeated use also in a highly saline environment. In addition, we report the newly observed phenomenon of surface conditioning of the EG electrodes. The prolonged contact of the EG with the skin electrolytes functionalize the grain boundaries of the graphene, leading to the formation of a thin surface film of water through physisorption and consequently reducing its contact impedance more than three-fold. This effect is primed in highly saline environments, and could be also further tailored as pre-conditioning to enhance the performance and reliability of the EG sensors.

Fan, J, Li, J, Wang, J, Wei, Z & Hsieh, M-H 2021, 'Asymmetric Quantum Concatenated and Tensor Product Codes With Large Z-Distances', IEEE Transactions on Communications, vol. 69, no. 6, pp. 3971-3983.
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Fan, Q, Bao, G, Ge, D, Wang, K, Sun, M, Liu, T, Liu, J, Zhang, Z, Xu, X, Xu, X, He, B, Rao, J & Zheng, Y 2021, 'Effective easing of the side effects of copper intrauterine devices using ultra-fine-grained Cu-0.4Mg alloy', Acta Biomaterialia, vol. 128, pp. 523-539.
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Fan, W, Xiao, F, Chen, X, Cui, L & Yu, S 2021, 'Efficient Virtual Network Embedding of Cloud-Based Data Center Networks into Optical Networks', IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 11, pp. 2793-2808.
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Fan, X, Xiang, C, Chen, C, Yang, P, Gong, L, Song, X, Nanda, P & He, X 2021, 'BuildSenSys: Reusing Building Sensing Data for Traffic Prediction With Cross-Domain Learning', IEEE Transactions on Mobile Computing, vol. 20, no. 6, pp. 2154-2171.
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With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outp...

Fan, Z, Holmes, DW, Sauret, E, Islam, MS, Saha, SC, Ristovski, Z & Gu, Y 2021, 'A multiscale modeling method incorporating spatial coupling and temporal coupling into transient simulations of the human airways', International Journal for Numerical Methods in Fluids, vol. 93, no. 9, pp. 2905-2920.
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In this article, a novel multiscale modeling method is proposed for transient computational fluid dynamics (CFD) simulations of the human airways. The developed method is the first attempt to incorporate spatial coupling and temporal coupling into transient human airway simulations, aiming to improve the flexibility and the efficiency of these simulations. In this method, domain decomposition was used to separate the complex airway model into different scaled domains. Each scaled domain could adopt a suitable mesh and timestep, as necessary: the coarse mesh and large timestep were employed in the macro regions to reduce the computational cost, while the fine mesh and small timestep were used in micro regions to maintain the simulation accuracy. The radial point interpolation method was used to couple data between the coarse mesh and the fine mesh. The continuous micro solution–intermittent temporal coupling method was applied to bridge different timesteps. The developed method was benchmarked using a well-studied four-generation symmetric airway model under realistic normal breath conditions. The accuracy and efficiency of the method were verified separately in the inhalation phase and the exhalation phase. Similar airflow behavior to previous studies was observed from the multiscale airway model. The developed multiscale method has the potential to improve the flexibility and efficiency of transient human airway simulations without sacrificing accuracy.

Fang, C, Liu, C, Wang, Z, Sun, Y, Ni, W, Li, P & Guo, S 2021, 'Cache-Assisted Content Delivery in Wireless Networks: A New Game Theoretic Model', IEEE Systems Journal, vol. 15, no. 2, pp. 2653-2664.
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With the rapid increase of mobile data traffic, efficient content delivery in wireless networks is increasingly important to provide broadcast and multicast services. To reduce network cost and enrich end-user quality of experience, Internet service providers (ISPs) and content providers (CPs) are expected to collaborate to improve content delivery services. However, the influence of content popularity has been generally overlooked, and profit split problem between ISPs and CPs has not been studied. We investigate the novel economic behaviors between ISPs and CPs in the presence of edge caches, and formulate the profit split problem as a centralized model, which can achieve optimal content caching and maximal network benefits. A Stackelberg game is designed to obtain its distributed win-win solution, where a feasible backward induction is proposed and the influence of edge caches and content popularity is analyzed. Simulation results show that the performance of our proposed Stackelberg game model is comparable to the centralized alternative and much better than existing ISP-CP cooperative schemes not considering cache deployment in the network.

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, pp. 118586-118586.
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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, pp. 117988-117988.
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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, F, Xu, R-Z, Huang, Y-Q, Luo, J-Y, Xie, W-M, Ni, B-J & Cao, J-S 2021, 'Exploring the feasibility of nitrous oxide reduction and polyhydroxyalkanoates production simultaneously by mixed microbial cultures', Bioresource Technology, vol. 342, pp. 126012-126012.
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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, pp. 112833-112833.
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Fang, G, Lu, H, Rodriguez de la Fuente, L, Law, AMK, Lin, G, Jin, D & Gallego‐Ortega, D 2021, 'Mammary Tumor Organoid Culture in Non‐Adhesive Alginate for Luminal Mechanics and High‐Throughput Drug Screening', Advanced Science, vol. 8, no. 21, pp. 2102418-2102418.
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Mammary tumor organoids have become a promising in vitro model for drug screening and personalized medicine. However, the dependency on the basement membrane extract (BME) as the growth matrices limits their comprehensive application. In this work, mouse mammary tumor organoids are established by encapsulating tumor pieces in non-adhesive alginate. High-throughput generation of organoids in alginate microbeads is achieved utilizing microfluidic droplet technology. Tumor pieces within the alginate microbeads developed both luminal- and solid-like structures and displayed a high similarity to the original fresh tumor in cellular phenotypes and lineages. The mechanical forces of the luminal organoids in the alginate capsules are analyzed with the theory of the thick-wall pressure vessel (TWPV) model. The luminal pressure of the organoids increase with the lumen growth and can reach 2 kPa after two weeks’ culture. Finally, the mammary tumor organoids are treated with doxorubicin and latrunculin A to evaluate their application as a drug screening platform. It is found that the drug response is related to the luminal size and pressures of organoids. This high-throughput culture for mammary tumor organoids may present a promising tool for preclinical drug target validation and personalized medicine.

Fang, J, Wu, C, Rabczuk, T, Wu, C, Sun, G & Li, Q 2021, 'Correction to: Phase field fracture in elasto-plastic solids: a length-scale insensitive model for quasi-brittle materials', Computational Mechanics.
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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, 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, vol. 32, no. 10, pp. 4309-4322.
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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.

Far, H & Nejadi, S 2021, 'Experimental investigation on flexural behaviour of composite PVC encased macro-synthetic fibre reinforced concrete walls', Construction and Building Materials, vol. 273, pp. 121756-121756.
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Composite PVC encased concrete walls provide substantial advantages in terms of structural strength and durability enhancement, ultraviolet radiation and pest infestation resistance, design flexibility, ease of construction and excellent resistance to impact. In this study, the effects of using macro-synthetic fibre reinforced concrete on flexural behaviour of composite PVC encased walls in comparison with composite PVC encased walls filled with conventional plain concrete and reinforced concrete have been experimentally investigated. Fifteen composite PVC encased concrete wall specimens were cast and tested using three-point bending test. Based on the load-deflection curves resulting from the three-point bending tests, flexural parameters including ultimate loads, ultimate flexural strengths, stiffness and flexural rigidity values for cracked and uncracked conditions were determined for three different cases including i) test specimens filled with plain concrete, ii) test specimens filled with macro-synthetic fibre reinforced concrete, and iii) test specimens filled with reinforced concrete. The determined parameters as well as the measured load-deflection curves for the three cases were compared and the final findings have been discussed. Based on this study, it has become apparent that using BarChip 48 macro-synthetic fibre reinforced concrete in composite PVC encased walls instead of plain concrete can lead to 43.5% flexural strength improvement and 25% stiffness enhancement at the age of 28 days. Based on the experimental measurements and theoretical comparison in this study, it has been concluded that composite PVC encased walls filled with BarChip 48 macro-synthetic fibre reinforced concrete, without steel reinforcement, are deemed suitable for sway-prevented structures such as retaining walls. If using steel reinforcement in composite PVC encased retaining walls is not an option due to high-risk of steel corrosion in harsh environment; it is highly recomm...

Far, H & Nejadi, S 2021, 'Experimental investigation on interface shear strength of composite PVC encased macro-synthetic fibre reinforced concrete walls', Structures, vol. 34, pp. 729-737.
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Far, H, Nejadi, S & Aghayarzadeh, M 2021, 'Experimental investigation on in‐plane lateral stiffness and degree of ductility of composite PVC reinforced concrete walls', Structural Concrete, vol. 22, no. 4, pp. 2126-2137.
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Farahmandian, S & Hoang, DB 2021, 'Policy-based Interaction Model for Detection and Prediction of Cloud Security Breaches', Journal of Telecommunications and the Digital Economy, vol. 9, no. 2, pp. 92-116.
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The ever-increasing number and gravity of cyberattacks against the cloud's assets, together with the introduction of new technologies, have brought about many severe cloud security issues. The main challenge is finding effective mechanisms for constructing dynamic isolation boundaries for securing cloud assets at different cloud infrastructure levels. Our security architecture tackles these issues by introducing a policy-driven interaction model. The model is governed by cloud system security policies and constrained by cloud interacting entities' locations and levels. Security policies are used to construct security boundaries between cloud objects at their interaction level. The novel interaction model relies on its unique parameters to develop an agile detection and prediction mechanism of security threats against cloud resources. The proposed policy-based interaction model and its interaction security algorithms are developed to protect cloud resources. The model deals with external and internal interactions among entities representing diverse participating elements of different complexity levels in a cloud environment. We build a security controller and simulate various scenarios for testing the proposed interaction model and security algorithms.

Fardjahromi, MA, Ejeian, F, Razmjou, A, Vesey, G, Mukhopadhyay, SC, Derakhshan, A & Warkiani, ME 2021, 'Enhancing osteoregenerative potential of biphasic calcium phosphates by using bioinspired ZIF8 coating', Materials Science and Engineering: C, vol. 123, pp. 111972-111972.
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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, pp. 104220-104220.
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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.

Fatema, I, Kong, X & Fang, G 2021, 'Electricity demand and price forecasting model for sustainable smart grid using comprehensive long short term memory', International Journal of Sustainable Engineering, vol. 14, no. 6, pp. 1714-1732.
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Feng, B, Zhou, H, Li, G, Zhang, Y, Sood, K & Yu, S 2021, 'Enabling Machine Learning with Service Function Chaining for Security Enhancement at 5G Edges', IEEE Network, vol. 35, no. 5, pp. 196-201.
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Feng, L, Hu, C, Yu, J, Jiang, H & Wen, S 2021, 'Fixed-time Synchronization of Coupled Memristive Complex-valued Neural Networks', Chaos, Solitons & Fractals, vol. 148, pp. 110993-110993.
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Feng, M, Krunz, M & Zhang, W 2021, 'Joint Task Partitioning and User Association for Latency Minimization in Mobile Edge Computing Networks', IEEE Transactions on Vehicular Technology, vol. 70, no. 8, pp. 8108-8121.
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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, pp. 125278-125278.
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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, pp. 103053-103053.
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Feng, Y, Li, S & Ying, M 2021, 'Verification of Distributed Quantum Programs.', CoRR, vol. abs/2104.14796.

Feng, Y, Wang, Q, Wu, D, Luo, Z, Chen, X, Zhang, T & Gao, W 2021, 'Machine learning aided phase field method for fracture mechanics', International Journal of Engineering Science, vol. 169, pp. 103587-103587.
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Feng, Y, Zhang, JA, Cheng, B, He, X & Chen, J 2021, 'Magnetic Sensor-Based Multi-Vehicle Data Association', IEEE Sensors Journal, vol. 21, no. 21, pp. 24709-24719.
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Sensors have been playing an increasingly important role in smart cities. Using small roadside magnetic sensors provides a cost-efficient method for monitoring vehicle traffic. However, there are significant challenges associated with vehicle data misalignment due to the timing-offsets between sensors and missed or increased data because of vehicle lane-changing. In this paper, we propose a novel traffic information acquisition and vehicle state estimation scheme using multiple road magnetic sensors. To efficiently solve the multi-sensor registration problem in the presence of timing-offset, we develop a linear discrimination analysis method to achieve vehicle separation and classification. To handle the situation of lane-changing, we propose a data smoothing technique based on a multi-hypotheses tracker that exploits vehicle correlation. The road density effect on the probability of correct data association is investigated, with numerical and experimental results provided. The results show that our proposed scheme can effectively detect vehicles with a 95.5% accuracy rate. It also outperforms some other speed sensing methods in terms of the vehicle speed estimation accuracy.

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, pp. 123254-123254.
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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.

Ferreira, FGDC, Gandomi, AH & Cardoso, RTN 2021, 'Artificial Intelligence Applied to Stock Market Trading: A Review', IEEE Access, vol. 9, pp. 30898-30917.
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Fisher, BM, Tang, KD, Warkiani, ME, Punyadeera, C & Batstone, MD 2021, 'A pilot study for presence of circulating tumour cells in adenoid cystic carcinoma', International Journal of Oral and Maxillofacial Surgery, vol. 50, no. 8, pp. 994-998.
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Flores-Sosa, M, Avilés-Ochoa, E, Merigó, JM & Yager, RR 2021, 'Volatility GARCH models with the ordered weighted average (OWA) operators', Information Sciences, vol. 565, pp. 46-61.
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Fonseka, C, Ryu, S, Choo, Y, Mullett, M, Thiruvenkatachari, R, Naidu, G & Vigneswaran, S 2021, 'Selective Recovery of Rare Earth Elements from Mine Ore by Cr-MIL Metal–Organic Frameworks', ACS Sustainable Chemistry & Engineering, vol. 9, no. 50, pp. 16896-16904.
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Freguia, S, Sharma, K, Benichou, O, Mulliss, M & Shon, HK 2021, 'Sustainable engineering of sewers and sewage treatment plants for scenarios with urine diversion', Journal of Hazardous Materials, vol. 415, pp. 125609-125609.
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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, vol. 135, pp. 110283-110283.
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Fu, X, Yu, J, Su, X, Jiang, H, Wu, H, Cheng, F, Deng, X, Zhang, J, Jin, L, Yang, Y, Xu, L, Hu, C, Huang, A, Huang, G, Qiang, X, Deng, M, Xu, P, Xu, W, Liu, W, Zhang, Y, Deng, Y, Wu, J & Feng, Y 2021, 'Quingo: A Programming Framework for Heterogeneous Quantum-Classical Computing with NISQ Features', ACM Transactions on Quantum Computing, vol. 2, no. 4, pp. 1-37.
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The increasing control complexity of Noisy Intermediate-Scale Quantum (NISQ) systems underlines the necessity of integrating quantum hardware with quantum software. While mapping heterogeneous quantum-classical computing (HQCC) algorithms to NISQ hardware for execution, we observed a few dissatisfactions in quantum programming languages (QPLs), including difficult mapping to hardware, limited expressiveness, and counter-intuitive code. In addition, noisy qubits require repeatedly performed quantum experiments, which explicitly operate low-level configurations, such as pulses and timing of operations. This requirement is beyond the scope or capability of most existing QPLs. We summarize three execution models to depict the quantum-classical interaction of existing QPLs. Based on the refined HQCC model, we propose the Quingo framework to integrate and manage quantum-classical software and hardware to provide the programmability over HQCC applications and map them to NISQ hardware. We propose a six-phase quantum program life-cycle model matching the refined HQCC model, which is implemented by a runtime system. We also propose the Quingo programming language, an external domain-specific language highlighting timer-based timing control and opaque operation definition, which can be used to describe quantum experiments. We believe the Quingo framework could contribute to the clarification of key techniques in the design of future HQCC systems.

Fu, Z, Xu, W, Hu, R, Long, G & Jiang, J 2021, 'MHieR-encoder: Modelling the high-frequency changes across stocks', Knowledge-Based Systems, vol. 224, pp. 107092-107092.
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Gan, YY, Chen, W-H, Ong, HC, Lin, Y-Y, Sheen, H-K, Chang, J-S & Ling, TC 2021, 'Effect of wet torrefaction on pyrolysis kinetics and conversion of microalgae carbohydrates, proteins, and lipids', Energy Conversion and Management, vol. 227, pp. 113609-113609.
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Gandomi, M, Kashani, AR, Farhadi, A, Akhani, M & Gandomi, AH 2021, 'Spectral acceleration prediction using genetic programming based approaches', Applied Soft Computing, vol. 106, pp. 107326-107326.
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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, vol. 9.
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Background: Conservative treatment, Ponseti method, has been considered as a standard method to correct the clubfoot deformity among Orthopedic society. Although the result of conservative methods have been reported with higher success rates than surgical methods, many more problems have been reported due to improper casting, casting pressure or bracing discomfort. Nowadays, infrared thermography (IRT) is widely used as a diagnostic tool to assess musculoskeletal disorders or injuries by detecting temperature abnormalities. Similarly, the foot skin temperature evaluation can be added along with the current subjective evaluation to predict if there is any casting pressure, excessive manipulation, or overcorrections of the foot, and other bracing pressure-related complications.Purpose: The main purpose of this study was to explore the foot skin temperature changes before and after using of manipulation and weekly castings.Methods: This is an explorative study design. Infrared Thermography (IRT), E33 FLIR thermal imaging camera model, was used to collect the thermal images of the clubfoot before and after casting intervention. A total of 120 thermal images (Medial region of the foot–24, Lateral side of the foot–24, Dorsal side of the foot−24, Plantar side of the foot−24, and Heel area of the foot–24) were collected from the selected regions of the clubfoot.Results: The results of univariate statistical analysis showed that significant temperature changes in some regions of the foot after casting, especially, at the 2nd (M = 32.05°C, SD = 0.77, p = 0.05), 3rd (M = 31.61, SD = 1.11; 95% CI: 31.27–31.96; p = 0.00), and 6th week of evaluation on the lateral side of the foot (M = 31.15°C, SD = 1.59; 95% CI: 30.75–31.54, p = 0.000). There was n...

Gao, F, He, X & Zhang, S 2021, 'Pumping effect of rainfall-induced excess pore pressure on particle migration', Transportation Geotechnics, vol. 31, pp. 100669-100669.
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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, pp. 107539-107539.
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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, pp. 108197-108197.
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Gao, J, Dong, S, Li, JJ, Ge, L, Xing, D & Lin, J 2021, 'New technology‐based assistive techniques in total knee arthroplasty: A Bayesian network meta‐analysis and systematic review', The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 17, no. 2.
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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, vol. 64, no. 3, pp. 1669-1700.
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Gao, K, Liu, Z, Wu, C, Li, J, Liu, K, Liu, Y & Li, S 2021, 'Effect of low gas concentration in underground return tunnels on characteristics of gas explosions', Process Safety and Environmental Protection, vol. 152, pp. 679-691.
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Gao, L, Liu, G, Zamyadi, A, Wang, Q & Li, M 2021, 'Life-cycle cost analysis of a hybrid algae-based biological desalination – low pressure reverse osmosis system', Water Research, vol. 195, pp. 116957-116957.
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Gao, S, Yu, S & Yao, S 2021, 'An efficient protein homology detection approach based on seq2seq model and ranking', Biotechnology & Biotechnological Equipment, vol. 35, no. 1, pp. 633-640.
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Gao, X, Xu, Z, Li, Y, Zhang, L, Li, G, Nghiem, LD & Luo, W 2021, 'Bacterial dynamics for gaseous emission and humification in bio-augmented composting of kitchen waste', Science of The Total Environment, vol. 801, pp. 149640-149640.
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Gao, X, Zhang, T, Du, J, An, J, Bu, X & Guo, J 2021, 'A dual-beam lens-free slot-array antenna coupled high-T _c superconducting fundamental mixer at the W-band', Superconductor Science and Technology, vol. 34, no. 12, pp. 125006-125006.
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Abstract This paper presents a W-band high-T c superconducting (HTS) Josephson-junction fundamental mixer which is coupled using a dual-beam lens-free slot-array antenna. The antenna features a uniplanar six-element slot array fed by an ungrounded coplanar waveguide line, of which each element is a long slot loaded by four rectangular loops. Highly directional radiation is therefore realized by utilizing the long slots and array synthesis to form a relatively large antenna aperture. The antenna also enables asymmetric dual-beam radiation in opposite directions, which not only reduces the RF coupling losses but greatly facilitates the quasi-optics design for the integration of the HTS mixer into a cryocooler. The electromagnetic simulations show that a coupling efficiency as high as −2.2 dB, a realized gain of 13 dB and a front-to-back ratio of 10 dB are achieved at the frequency of 84 GHz. Using this on-chip antenna, a W-band HTS fundamental mixer module is experimentally developed and characterized for different operating temperatures. The measured conversion gain is −10 dB at 20 K and −14.6 dB at 40 K, respectively. The mixer noise temperature is predicted to be around 780 K at 20 K and 1600 K at 40 K, respectively. It is also analyzed that the mixer performance can be further improved if the Josephson junction parameters were optimized.

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, pp. 2009692-2009692.
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Gao, Y, Sun, S, Zhang, X, Liu, Y, Hu, J, Huang, Z, Gao, M & Pan, H 2021, 'Solid State Electrolytes: Amorphous Dual‐Layer Coating: Enabling High Li‐Ion Conductivity of Non‐Sintered Garnet‐Type Solid Electrolyte (Adv. Funct. Mater. 15/2021)', Advanced Functional Materials, vol. 31, no. 15, pp. 2170100-2170100.
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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.

Gao, Z-K, Liu, A-A, Wang, Y, Small, M, Chang, X & Kurths, J 2021, 'IEEE Access Special Section Editorial: Big Data Learning and Discovery', IEEE Access, vol. 9, pp. 158064-158073.
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Garcia Marin, J, Biloria, N, Robertson, H & Fornes, M 2021, 'Urban Health and Wellbeing in the Contemporary City', HealthManagement, vol. 21, no. 6.
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This paper explores and debates the intricate connection between our built environment and an increasingly technocentric approach to distinguish health and wellbeing from a multidisciplinary perspective. The authors profess the dire need for rethinking the ‘smart’ within the city by reconsidering models of urban development and focusing on the democratisation of technology for the purpose of enhancing our lived urban experience and psychophysiological wellbeing.

Gaur, VK, Sharma, P, Gaur, P, Varjani, S, Ngo, HH, Guo, W, Chaturvedi, P & Singhania, RR 2021, 'Sustainable mitigation of heavy metals from effluents: Toxicity and fate with recent technological advancements', Bioengineered, vol. 12, no. 1, pp. 7297-7313.
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Gavhane, RS, Kate, AM, Soudagar, MEM, Wakchaure, VD, Balgude, S, Rizwanul Fattah, IM, 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, vol. 14, no. 5, pp. 1489-1489.
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The present study examines the effect of silicon dioxide (SiO2) nano-additives on the performance and emission characteristics of a diesel engine fuelled with soybean biodiesel. Soybean biofuel was prepared using the transesterification process. The morphology of nano-additives was studied using scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy-dispersive X-ray spectroscopy (EDS). The Ultrasonication process was used for the homogeneous blending of nano-additives with biodiesel, while surfactant was used for the stabilisation of nano-additives. The physicochemical properties of pure and blended fuel samples were measured as per ASTM standards. The performance and emissions characteristics of different fuel samples were measured at different loading conditions. It was found that the brake thermal efficiency (BTE) and brake specific fuel consumption (BSFC) increased by 3.48–6.39% and 5.81–9.88%, respectively, with the addition of SiO2 nano-additives. The carbon monoxide (CO), hydrocarbon (HC) and smoke emissions for nano-additive added blends were decreased by 1.9–17.5%, 20.56–27.5% and 10.16–23.54% compared to SBME25 fuel blends.

Ge, M, Pineda, JA, Sheng, D, Burton, GJ & Li, N 2021, 'Microstructural effects on the wetting-induced collapse in compacted loess', Computers and Geotechnics, vol. 138, pp. 104359-104359.
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Ge, Z, Chen, L, Gomez-Garcia, R & Zhu, X 2021, 'Millimeter-Wave Wide-Band Bandpass Filter in CMOS Technology Using a Two-Layered Highpass-Type Approach With Embedded Upper Stopband', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 5, pp. 1586-1590.
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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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Geissinger, A, Laurell, C, Öberg, C, Sandström, C, Sick, N & Suseno, Y 2021, 'Social media analytics for knowledge acquisition of market and non-market perceptions in the sharing economy', Journal of Knowledge Management, vol. 25, no. 2, pp. 500-512.
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Purpose Using the case of Foodora, this paper aims to assess the impact of technological innovation of an emerging actor in the sharing economy through stakeholders’ perceptions in the market and non-market domains. Design/methodology/approach Using a methodological approach called social media analytics (SMA) to explore the case of Foodora, 3,250 user-generated contents in social media are systematically gathered, coded and analysed. Findings The findings indicate that, while Foodora appears to be a viable provider in the marketplace, there is mounting public concern about the working conditions of its employees. In the market domain, Foodora manages its status as an online delivery platform and provider well, but at the same time, it struggles with its position in the non-market sphere, suggesting that the firm is vulnerable to regulatory change. These insights highlight the importance of simultaneously exploring and balancing market and non-market perceptions when assessing the impact of disruptive innovation. Originality/value This study offers originality by providing an integrative approach to consider both the market and non-market domains. It is also novel in its use of SMA as a tool for knowledge acquisition and management to evaluate the impact of emerging technologies in the sharing economy.

Geng, L, Lu, Z, Guo, X, Zhang, J, Li, X & He, L 2021, 'Coordinated operation of coupled transportation and power distribution systems considering stochastic routing behaviour of electric vehicles and prediction error of travel demand', IET Generation, Transmission & Distribution, vol. 15, no. 14, pp. 2112-2126.
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Gentile, CG 2021, 'Printability, Durability, Contractility and Vascular Network Formation in 3D Bioprinted Cardiac Endothelial Cells Using Alginate–Gelatin Hydrogels', Frontiers in Bioengineering and Biotechnology, vol. 9.
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Ghabrial, A, Franklin, DR & Zaidi, H 2021, 'A Monte Carlo simulation study of scatter fraction and the impact of patient BMI on scatter in long axial field-of-view PET scanners', Zeitschrift für Medizinische Physik, vol. 31, no. 3, pp. 305-315.
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Ghadi, MJ, Azizivahed, A, Mishra, DK, Li, L, Zhang, J, Shafie-khah, M & Catalão, JPS 2021, 'Application of small-scale compressed air energy storage in the daily operation of an active distribution system', Energy, vol. 231, pp. 120961-120961.
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Ghanbari, F, Wang, Q, Hassani, A, Wacławek, S, Rodríguez-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, pp. 130610-130610.
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Ghane, S, Jolfaei, A, Kulik, L, Ramamohanarao, K & Puthal, D 2021, 'Preserving Privacy in the Internet of Connected Vehicles', IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 8, pp. 5018-5027.
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Ghantous, GB & Gill, AQ 2021, 'Evaluating the DevOps Reference Architecture for Multi-cloud IoT-Applications.', SN Comput. Sci., vol. 2, pp. 123-123.
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Gharehbaghi, S, Gandomi, M, Plevris, V & Gandomi, AH 2021, 'Prediction of seismic damage spectra using computational intelligence methods', Computers & Structures, vol. 253, pp. 106584-106584.
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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, pp. 110575-110575.
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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', International Journal of Clinical Practice, vol. 75, no. 11, 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.

Ghavidel, S, Rajabi, A, Jabbari Ghadi, M, Azizivahed, A, Li, L & Zhang, J 2021, 'Hybrid power plant bidding strategy for voltage stability improvement, electricity market profit maximization, and congestion management', IET Energy Systems Integration, vol. 3, no. 2, pp. 130-141.
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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, vol. 11, no. 1.
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AbstractGlaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g., optic nerve cupping) as well as fundus videos (e.g., vascular pulsatility index). Current convolutional neural networks (CNNs) developed to detect glaucoma are all based on spatial features embedded in an image. We developed a combined CNN and recurrent neural network (RNN) that not only extracts the spatial features in a fundus image but also the temporal features embedded in a fundus video (i.e., sequential images). A total of 1810 fundus images and 295 fundus videos were used to train a CNN and a combined CNN and Long Short-Term Memory RNN. The combined CNN/RNN model reached an average F-measure of 96.2% in separating glaucoma from healthy eyes. In contrast, the base CNN model reached an average F-measure of only 79.2%. This proof-of-concept study demonstrates that extracting spatial and temporal features from fundus videos using a combined CNN and RNN, can markedly enhance the accuracy of glaucoma detection.

Ghezelbash, F, Eskandari, AH, Shirazi-Adl, A, Kazempour, M, Tavakoli, J, Baghani, M & Costi, JJ 2021, 'Modeling of human intervertebral disc annulus fibrosus with complex multi-fiber networks', Acta Biomaterialia, vol. 123, pp. 208-221.
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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, pp. 148668-148668.
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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, pp. 123891-123891.
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Gholampour, A, Gandomi, AH, Ozbakkaloglu, T & Xie, T 2021, 'Corrigendum to “New formulations for mechanical properties of recycled aggregate concrete using gene expression programming” [Constr. Build. Mater. 130 (2017) 122–145]', Construction and Building Materials, vol. 278, pp. 122930-122930.
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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, vol. 230, pp. 117639-117639.
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Gite, S, Pradhan, B, Alamri, A & Kotecha, K 2021, 'ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks', IEEE Access, vol. 9, pp. 99312-99326.
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Goldman, S, Bramante, J, Vrdoljak, G, Guo, W, Wang, Y, Marjanovic, O, Orlowicz, S, Di Lorenzo, R & Noestheden, M 2021, 'The analytical landscape of cannabis compliance testing', Journal of Liquid Chromatography & Related Technologies, vol. 44, no. 9-10, pp. 403-420.
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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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Gong, Y, Li, Z, Zhang, J, Liu, W, Yin, Y & Zheng, Y 2021, 'Missing Value Imputation for Multi-view Urban Statistical Data via Spatial Correlation Learning', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Gong, Y, Zhang, L, Liu, R, Yu, K & Srivastava, G 2021, 'Nonlinear MIMO for Industrial Internet of Things in Cyber–Physical Systems', IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5533-5541.
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Gong, Z, Zhang, C, Ba, X & Guo, Y 2021, 'Improved Deadbeat Predictive Current Control of Permanent Magnet Synchronous Motor Using a Novel Stator Current and Disturbance Observer', IEEE Access, vol. 9, pp. 142815-142826.
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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, pp. 114841-114841.
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Gonzales, RR, Abdel-Wahab, A, Han, DS, Matsuyama, H, Phuntsho, S & Shon, HK 2021, 'Control of the antagonistic effects of heat-assisted chlorine oxidative degradation on pressure retarded osmosis thin film composite membrane surface', Journal of Membrane Science, vol. 636, pp. 119567-119567.
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Gonzales, RR, Zhang, L, Guan, K, Park, MJ, Phuntsho, S, Abdel-Wahab, A, Matsuyama, H & Shon, HK 2021, 'Aliphatic polyketone-based thin film composite membrane with mussel-inspired polydopamine intermediate layer for high performance osmotic power generation', Desalination, vol. 516, pp. 115222-115222.
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Gonzales, RR, Zhang, L, Sasaki, Y, Kushida, W, Matsuyama, H & Shon, HK 2021, 'Facile development of comprehensively fouling-resistant reduced polyketone-based thin film composite forward osmosis membrane for treatment of oily wastewater', Journal of Membrane Science, vol. 626, pp. 119185-119185.
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Goodswen, SJ, Barratt, JLN, Kennedy, PJ, Kaufer, A, Calarco, L & Ellis, JT 2021, 'Machine learning and applications in microbiology', FEMS Microbiology Reviews, vol. 45, no. 5.
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ABSTRACT To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.

Goodswen, SJ, Kennedy, PJ & Ellis, JT 2021, 'Applying Machine Learning to Predict the Exportome of Bovine and Canine Babesia Species That Cause Babesiosis', Pathogens, vol. 10, no. 6, pp. 660-660.
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Babesia infection of red blood cells can cause a severe disease called babesiosis in susceptible hosts. Bovine babesiosis causes global economic loss to the beef and dairy cattle industries, and canine babesiosis is considered a clinically significant disease. Potential therapeutic targets against bovine and canine babesiosis include members of the exportome, i.e., those proteins exported from the parasite into the host red blood cell. We developed three machine learning-derived methods (two novel and one adapted) to predict for every known Babesia bovis, Babesia bigemina, and Babesia canis protein the probability of being an exportome member. Two well-studied apicomplexan-related species, Plasmodium falciparum and Toxoplasma gondii, with extensive experimental evidence on their exportome or excreted/secreted proteins were used as important benchmarks for the three methods. Based on 10-fold cross validation and multiple train–validation–test splits of training data, we expect that over 90% of the predicted probabilities accurately provide a secretory or non-secretory indicator. Only laboratory testing can verify that predicted high exportome membership probabilities are creditable exportome indicators. However, the presented methods at least provide those proteins most worthy of laboratory validation and will ultimately save time and money.

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.

Goswami, K, Giarmatzi, C, Monterola, C, Shrapnel, S, Romero, J & Costa, F 2021, 'Experimental characterisation of a non-Markovian quantum process', Phys. Rev. A, vol. 104, p. 022432.
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Every quantum system is coupled to an environment. Such system-environment interaction leads to temporal correlation between quantum operations at different times, resulting in non-Markovian noise. In principle, a full characterisation of non-Markovian noise requires tomography of a multi-time processes matrix, which is both computationally and experimentally demanding. In this paper, we propose a more efficient solution. We employ machine learning models to estimate the amount of non-Markovianity, as quantified by an information-theoretic measure, with tomographically incomplete measurement. We test our model on a quantum optical experiment, and we are able to predict the non-Markovianity measure with $90\%$ accuracy. Our experiment paves the way for efficient detection of non-Markovian noise appearing in large scale quantum computers.

Gour, G & Tomamichel, M 2021, 'Entropy and Relative Entropy From Information-Theoretic Principles', IEEE Transactions on Information Theory, vol. 67, no. 10, pp. 6313-6327.
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We introduce an axiomatic approach to entropies and relative entropies that relies only on minimal information-theoretic axioms, namely monotonicity under mixing and data-processing as well as additivity for product distributions. We find that these axioms induce sufficient structure to establish continuity in the interior of the probability simplex and meaningful upper and lower bounds, e.g., we find that every relative entropy satisfying these axioms must lie between the Rényi divergences of order 0 and infty . We further show simple conditions for positive definiteness of such relative entropies and a characterisation in terms of a variant of relative trumping. Our main result is a one-to-one correspondence between entropies and relative entropies.

Gravina da Rocha, C, Marin, EJB, Quiñónez Samaniego, RA & Consoli, NC 2021, 'Decision-Making Model for Soil Stabilization: Minimizing Cost and Environmental Impacts', Journal of Materials in Civil Engineering, vol. 33, no. 2, pp. 06020024-06020024.
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Gu, M, Gu, Y, Luo, W, Xu, G, Yang, Z, Zhou, J & Qu, W 2021, 'From text to graph: a general transition-based AMR parsing using neural network', Neural Computing and Applications, vol. 33, no. 11, pp. 6009-6025.
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Gu, P, Han, Y, Gao, W, Xu, G & Wu, J 2021, 'Enhancing session-based social recommendation through item graph embedding and contextual friendship modeling', Neurocomputing, vol. 419, pp. 190-202.
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Gu, X, Cao, Z, Jolfaei, A, Xu, P, Wu, D, Jung, T-P & Lin, C-T 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, vol. 18, no. 5, pp. 1645-1666.
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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, 'quantum walks.', Quantum Inf. Comput., vol. 21, 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, vol. 30, pp. 2476-2487.
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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.

Guan, W, Song, X, Gan, T, Lin, J, Chang, X & Nie, L 2021, 'Cooperation Learning From Multiple Social Networks: Consistent and Complementary Perspectives', IEEE Transactions on Cybernetics, vol. 51, no. 9, pp. 4501-4514.
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Gudigar, A, Raghavendra, U, Nayak, S, Ooi, CP, Chan, WY, Gangavarapu, MR, Dharmik, C, Samanth, J, Kadri, NA, Hasikin, K, Barua, PD, Chakraborty, S, Ciaccio, EJ & Acharya, UR 2021, 'Role of Artificial Intelligence in COVID-19 Detection', Sensors, vol. 21, no. 23, pp. 8045-8045.
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The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.

Guertler, MR & Sick, N 2021, 'Exploring the enabling effects of project management for SMEs in adopting open innovation – A framework for partner search and selection in open innovation projects', International Journal of Project Management, vol. 39, no. 2, pp. 102-114.
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© 2020 Open Innovation (OI) facilitates a multitude of innovation opportunities through allowing access to a broad variety of external partners, expertise and knowledge. Although OI has been established in academia and the corporate world, implementation by SMEs remains a formidable challenge, especially concerning the identification and selection of suitable OI partners. Given methodical support for such an endeavour is currently lacking, this article investigates how project management can support OI projects. Based on evidence from an exploratory multi-case study with four SMEs, this article develops a Situational Open Innovation framework that provides methodical support for SMEs in leveraging the complementarities between OI and project management towards effective partner search and selection. The findings illustrate how sensing capabilities for OI opportunities can benefit from systematic problem and stakeholder analyses as they allow for identifying and focussing on the most relevant innovation tasks and partners.

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, pp. 225301-225301.
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Abstract Resource theories are broad frameworks that capture how useful objects are in performing specific tasks. In this paper we devise a formal resource theory quantum measurements, focusing on the ability of a measurement to acquire information. The objects of the theory are equivalence classes of positive operator-valued measures, and the free transformations are changes to a measurement device that can only deteriorate its ability to report information about a physical system. We show that catalysis and purification, protocols that are possible in other resource theories, are impossible in our resource theory for quantum measurements. Standard measures of information gain are shown to be resource monotones, and the resource theory is applied to the task of quantum state discrimination.

Gul, M, Zulkifli, NWM, Kalam, MA, Masjuki, HH, Mujtaba, MA, Yousuf, S, Bashir, MN, Ahmed, W, Yusoff, MNAM, Noor, S, Ahmad, R & Hassan, MT 2021, 'RSM and Artificial Neural Networking based production optimization of sustainable Cotton bio-lubricant and evaluation of its lubricity & tribological properties', Energy Reports, vol. 7, pp. 830-839.
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Gunatilake, A, Piyathilaka, L, Tran, A, Vishwanathan, VK, Thiyagarajan, K & Kodagoda, S 2021, 'Stereo Vision Combined With Laser Profiling for Mapping of Pipeline Internal Defects', IEEE Sensors Journal, vol. 21, no. 10, pp. 11926-11934.
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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.

Gunawan, Y, Putra, N, Hakim, II, Agustina, D & Mahlia, TMI 2021, 'Withering of tea leaves using heat pipe heat exchanger by utilizing low-temperature geothermal energy', International Journal of Low-Carbon Technologies, vol. 16, no. 1, pp. 146-155.
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Abstract The volume of Indonesian tea exports to the European Union (EU) decreased by 43% in 2014 because of the EU setting a maximum residue limit of anthraquinone (AQ) for tea as 0.02 mg/kg. The content of AQ in tea leaves increases when there is incomplete combustion in the combustion of firewood for the energy source of withering and drying of tea leaves. This study aims to develop and test a new concept for the direct use of low-temperature geothermal energy with a heat pipe heat exchanger (HPHE) for the withering of tea leaves as a solution for energy sources free from AQ. The geothermal fluid simulators use water, which is heated by heater and flowed by a pump. The HPHE used consists of 42 heat pipes and 181 fins. The heat pipe used has a length of 700 mm with an outer diameter of 10 mm. Each fin is made of aluminum with a thickness of 0.105 mm and a size of 76 × 345 mm2. The results show that the effectiveness of the HPHE varies from 66% to 79.59%. For 100 g of fresh tea leaves, the heating energy produced ranges from 15.21 W to 45.07 W, meaning it can wither tea leaves from 80% (w.b.) to 54% (w.b.) in a variety of 11 h 56 min to only 49.6 min. The Page mathematical model is the best model to represent the behavior of the tea leaves with this HPHE system.

Gunness, R, Wee, H, Lee, R, Nguyen, LN & Nghiem, LD 2021, 'Solar driven produced water treatment for beneficial uses', The APPEA Journal, vol. 61, no. 1, pp. 25-25.
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This study evaluates the feasibility of an emerging technology – a concentrated solar multi-effect distiller (CSMED) – to supply high quality water for beneficial use at Eromanga, which is located in a remote and dry region of Australia. Produced water from the Kenmore oil field is the only reliable water source at Eromanga and has been approved for livestock watering. The process utilises concentrated solar technology to drive a multi-effect distiller, making the process ideal for the Australian outback. Historical water parameters of the produced water were assessed against the water guidelines for irrigation, livestock watering, municipal and potable use. The proposed treatment will further improve key water quality parameters to exceed guideline requirements for unrestricted water uses mentioned above. The CSMED produces a high-quality distillate (i.e. treated water) free of all mineral salts that can be mixed with the produced water to increase the final product water volume. An Excel based model was developed to determine suitable blending ratios while maintaining the water quality for each beneficial use. For production of livestock watering and potable use, a blending ratio (of at least 19% v/v) between the CSMED distillate and produced water can be applied, significantly increasing the final water volume. The Excel based model could also indicate chemical addition for adjustment of sodium absorption ratio in the case of irrigation application. The brine from the CSMED can potentially be used to prepare drilling mud for oil field operation. An on-site performance study of the CSMED system has been planned to validate these results.

Guo, H, Wang, Y, Tian, L, Wei, W, Zhu, T & Liu, Y 2021, 'Insight into the enhancing short-chain fatty acids (SCFAs) production from waste activated sludge via polyoxometalates pretreatment: Mechanisms and implications', Science of The Total Environment, vol. 800, pp. 149392-149392.
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Guo, H, Wang, Y, Tian, L, Wei, W, Zhu, T & Liu, Y 2021, 'Unveiling the mechanisms of a novel polyoxometalates (POMs)-based pretreatment technology for enhancing methane production from waste activated sludge', Bioresource Technology, vol. 342, pp. 125934-125934.
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Guo, J, Gong, Z & Cao, L 2021, 'dhCM: Dynamic and Hierarchical Event Categorization and Discovery for Social Media Stream', ACM Transactions on Intelligent Systems and Technology, vol. 12, no. 5, pp. 1-25.
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The online event discovery in social media based documents is useful, such as for disaster recognition and intervention. However, the diverse events incrementally identified from social media streams remain accumulated, ad hoc, and unstructured. They cannot assist users in digesting the tremendous amount of information and finding their interested events. Further, most of the existing work is challenged by jointly identifying incremental events and dynamically organizing them in an adaptive hierarchy. To address these problems, this article proposes d ynamic and h ierarchical C ategorization M odeling (dhCM) for social media stream. Instead of manually dividing the timeframe, a multimodal event miner exploits a density estimation technique to continuously capture the temporal influence between documents and incrementally identify online events in textual, temporal, and spatial spaces. At the same time, an adaptive categorization hierarchy is formed to automatically organize the documents into proper categories at multiple levels of granularities. In a nonparametric manner, dhCM accommodates the increasing complexity of data streams with automatically growing the categorization hierarchy over adaptive growth. A sequential Monte Carlo algorithm is used for the online inference of the dhCM parameters. Extensive experiments show that dhCM outperforms the state-of-the-art models in terms of term coherence, category abstraction and specialization, hierarchical affinity, and event categorization and discovery accuracy.

Guo, K & Guo, Y 2021, 'Corrections to “Design Optimization of Linear-Rotary Motion Permanent Magnet Generator With E-Shaped Stator” [Nov 21 Art. no. 0600705]', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-1.
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Guo, K & Guo, Y 2021, 'Design Optimization of Linear-Rotary Motion Permanent Magnet Generator With E-Shaped Stator', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-5.
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Guo, K & Guo, Y 2021, 'Electromagnetic Characteristic Analysis of BFSLRM', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-6.
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Guo, YJ, Ansari, M & Fonseca, NJG 2021, 'Circuit Type Multiple Beamforming Networks for Antenna Arrays in 5G and 6G Terrestrial and Non-Terrestrial Networks', IEEE Journal of Microwaves, vol. 1, no. 3, pp. 704-722.
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Guo, YJ, Ansari, M, Ziolkowski, RW & Fonseca, NJG 2021, 'Quasi-Optical Multi-Beam Antenna Technologies for B5G and 6G mmWave and THz Networks: A Review', IEEE Open Journal of Antennas and Propagation, vol. 2, pp. 807-830.
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Gupta, A, Agrawal, RK, Kirar, JS, Andreu-Perez, J, Ding, W-P, Lin, C-T & Prasad, M 2021, 'On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 5, pp. 3080-3092.
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Gupta, BB, Yadav, K, Razzak, I, Psannis, K, Castiglione, A & Chang, X 2021, 'A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment', Computer Communications, vol. 175, pp. 47-57.
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Gupta, PK, Kumar, A & Joshi, S 2021, 'A review of knowledge, attitude, and practice towards COVID ‐19 with future directions and open challenges', Journal of Public Affairs, vol. 21, no. 4.
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Haering, M, Bano, M, Zowghi, D, Kearney, M & Maalej, W 2021, 'Automating the Evaluation of Education Apps With App Store Data.', IEEE Trans. Learn. Technol., vol. 14, no. 1, pp. 16-27.
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Hafiz, M, Alfahel, R, Hawari, AH, Hassan, MK & Altaee, A 2021, 'A Hybrid NF-FO-RO Process for the Supply of Irrigation Water from Treated Wastewater: Simulation Study', Membranes, vol. 11, no. 3, pp. 191-191.
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Municipal treated wastewater could be considered as a water source for food crop irrigation purposes. Enhancing the quality of treated wastewater to meet irrigation standards has become a necessary practice. Nanofiltration (NF) was used in the first stage to produce permeate at relatively low energy consumption. In the second stage, two membrane combinations were tested for additional water extraction from the brine generated by the NF process. The simulation results showed that using a hybrid forward osmosis (FO)–reverse osmosis (RO) system is more efficient than using the RO process alone for the further extraction of water from the brine generated by the NF process. The total specific energy consumption can be reduced by 27% after using FO as an intermediate process between NF and RO. In addition, the final permeate water quality produced using the hybrid FO-RO system was within the allowable standards for food crops irrigation.

Hafiz, M, Hawari, AH, Alfahel, R, Hassan, MK & Altaee, A 2021, 'Comparison of Nanofiltration with Reverse Osmosis in Reclaiming Tertiary Treated Municipal Wastewater for Irrigation Purposes', Membranes, vol. 11, no. 1, pp. 32-32.
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This study compares the performance of nanofiltration (NF) and reverse osmosis (RO) for the reclamation of ultrafiltered municipal wastewater 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 Food and Agriculture Organization (FAO) standards at pressures applied between 10 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 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%, which was achieved at a 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%.

Halat, DM, Snyder, RL, Sundararaman, S, Choo, Y, Gao, KW, Hoffman, ZJ, Abel, BA, Grundy, LS, Galluzzo, MD, Gordon, MP, Celik, H, Urban, JJ, Prendergast, D, Coates, GW, Balsara, NP & Reimer, JA 2021, 'Modifying Li⁺ and Anion Diffusivities in Polyacetal Electrolytes: A Pulsed-Field-Gradient NMR Study of Ion Self-Diffusion', Chemistry of Materials, vol. 33, no. 13, pp. 4915-4926.
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Hamilton, T 2021, 'The best of both worlds', Nature Machine Intelligence, vol. 3, no. 3, pp. 194-195.
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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, C, Wang, X, Peng, J, Xia, Q, Chou, S, Cheng, G, Huang, Z & Li, W 2021, 'Recent Progress on Two-Dimensional Carbon Materials for Emerging Post-Lithium (Na+, K+, Zn2+) Hybrid Supercapacitors', Polymers, vol. 13, no. 13, pp. 2137-2137.
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The hybrid ion capacitor (HIC) is a hybrid electrochemical energy storage device that combines the intercalation mechanism of a lithium-ion battery anode with the double-layer mechanism of the cathode. Thus, an HIC combines the high energy density of batteries and the high power density of supercapacitors, thus bridging the gap between batteries and supercapacitors. Two-dimensional (2D) carbon materials (graphite, graphene, carbon nanosheets) are promising candidates for hybrid capacitors owing to their unique physical and chemical properties, including their enormous specific surface areas, abundance of active sites (surface and functional groups), and large interlayer spacing. So far, there has been no review focusing on the 2D carbon-based materials for the emerging post-lithium hybrid capacitors. This concept review considers the role of 2D carbon in hybrid capacitors and the recent progress in the application of 2D carbon materials for post-Li (Na+, K+, Zn2+) hybrid capacitors. Moreover, their challenges and trends in their future development are discussed.

Han, DS, Solayman, KMD, Shon, HK & Abdel-Wahab, A 2021, 'Pyrite (FeS2)-supported ultrafiltration system for removal of mercury (II) from water', Emergent Materials, vol. 4, no. 5, pp. 1441-1453.
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AbstractThis study investigated the Hg(II) removal efficiencies of the reactive adsorbent membrane (RAM) hybrid filtration process, a removal process that produces stable final residuals. The reaction mechanism between Hg(II) and pyrite and the rejection of the solids over time were characterized with respect to flux decline, pH change, and Hg and Fe concentration in permeate water. Effects of the presence of anions (Cl−, SO42−, NO3−) or humic acid (HA) on the rejection of the Hg(II)-contacted pyrite were studied. The presence of both HA and Hg(II) increased the rate of flux decline due to the formation of irreversible gel-like compact cake layers as shown in the experimental data and modeling related to the flux decline and the SEM images. Stability experiments of the final residuals retained on the membrane using a thiosulfate solution (Na2S2O3) show that the Hg(II)-laden solids were very stable due to little or no detection of Hg(II) in the permeate water. Experiment on the possibility of continuously removing Hg(II) by reusing the Hg/pyrite-laden membrane shows that almost all Hg(II) was adsorbed onto the pyrite surface regardless of the presence of salts or HA, and the Hg(II)-contacted pyrite residuals were completely rejected by the DE/UF system. Therefore, a membrane filter containing pyrite-Hg(II) could provide another reactive cake layer capable of further removal of Hg(II) without post-chemical treatment for reuse.

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, S, Hu, C, Yu, J, Jiang, H & Wen, S 2021, 'Stabilization of inertial Cohen-Grossberg neural networks with generalized delays: A direct analysis approach', Chaos, Solitons & Fractals, vol. 142, pp. 110432-110432.
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Han, Y, Gu, P, Gao, W, Xu, G & Wu, J 2021, 'Aspect-level sentiment capsule network for micro-video click-through rate prediction', World Wide Web, vol. 24, no. 4, pp. 1045-1064.
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Han, Y, Wu, A, Zhu, L & Yang, Y 2021, 'Visual commonsense reasoning with directional visual connections', Frontiers of Information Technology & 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, vol. 18, no. 3, pp. 2915-2928.
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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.

Hannan, MA, Al-Shetwi, AQ, Ker, PJ, Begum, RA, Mansor, M, Rahman, SA, Dong, ZY, Tiong, SK, Mahlia, TMI & Muttaqi, KM 2021, 'Impact of renewable energy utilization and artificial intelligence in achieving sustainable development goals', Energy Reports, vol. 7, pp. 5359-5373.
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Hannan, MA, How, DNT, Lipu, MSH, Mansor, M, Ker, PJ, Dong, ZY, Sahari, KSM, Tiong, SK, Muttaqi, KM, Mahlia, TMI & Blaabjerg, F 2021, 'Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model', Scientific Reports, vol. 11, no. 1.
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AbstractAccurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

Hannan, MA, Wali, SB, Ker, PJ, Rahman, MSA, Mansor, M, Ramachandaramurthy, VK, Muttaqi, KM, Mahlia, TMI & Dong, ZY 2021, 'Battery energy-storage system: A review of technologies, optimization objectives, constraints, approaches, and outstanding issues', Journal of Energy Storage, vol. 42, pp. 103023-103023.
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Hao, D, Chen, Z-G, Figiela, M, Stepniak, I, Wei, W & Ni, B-J 2021, 'Emerging alternative for artificial ammonia synthesis through catalytic nitrate reduction', Journal of Materials Science & Technology, vol. 77, pp. 163-168.
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Hao, D, Huang, Q, Wei, W, Bai, X & Ni, B-J 2021, 'A reusable, separation-free and biodegradable calcium alginate/g-C3N4 microsphere for sustainable photocatalytic wastewater treatment', Journal of Cleaner Production, vol. 314, pp. 128033-128033.
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Hao, D, Liu, Y, Gao, S, Arandiyan, H, Bai, X, Kong, Q, Wei, W, Shen, PK & Ni, B-J 2021, 'Emerging artificial nitrogen cycle processes through novel electrochemical and photochemical synthesis', Materials Today, vol. 46, pp. 212-233.
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Hao, D, Ren, J, Wang, Y, Arandiyan, H, Garbrecht, M, Bai, X, Shon, HK, Wei, W & Ni, B-J 2021, 'A Green Synthesis of Ru Modified g-C ₃ N ₄ Nanosheets for Enhanced Photocatalytic Ammonia Synthesis', Energy Material Advances, vol. 2021, pp. 1-12.
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Nitrate is a crucial environmental pollutant, and its risk on ecosystem keeps increasing. Photocatalytic conversion of nitrate to ammonia can simultaneously achieve the commercialization of environmental hazards and recovery of valuable ammonia, which is green and sustainable for the planet. However, due to the thermodynamic and kinetic energy barriers, photocatalytic nitrate reduction usually involves a higher selectivity of the formation of nitrogen that largely limits the ammonia synthesis activity. In this work, we reported a green and facile synthesis of novel metallic ruthenium particle modified graphitic carbon nitride photocatalysts. Compare with bulk graphitic carbon nitride, the optimal sample had 2.93-fold photocatalytic nitrate reduction to ammonia activity (2.627 mg/h/g cat ), and the NH 3 selectivity increased from 50.77% to 77.9%. According to the experimental and calculated results, the enhanced photocatalytic performance is attributed to the stronger light absorption, nitrate adsorption, and lower energy barrier for the generation of ammonia. This work may provide a facile way to prepare metal modified photocatalysts to achieve highly efficient nitrate reduction to ammonia.

Hao, S, Shi, C, Cao, L, Niu, Z & Guo, P 2021, 'Learning deep relevance couplings for ad-hoc document retrieval', Expert Systems with Applications, vol. 183, pp. 115335-115335.
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Haq, S, Seah, TH, Chao, KC & Rujikiatkamjorn, C 2021, 'A numerical approach to cyclic consolidation of saturated clays', Geotechnical Engineering, vol. 51, no. 4, pp. 52-60.
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A finite-difference numerical code is written in MATLAB to predict excess pore pressures and settlements under stepped/square wave cyclic loads. The numerical code is developed by approximating the Terzaghi's 1D consolidation equation under time-dependent loading using the Crank Nicolson scheme. A method of applying the stepped/square wave cyclic loads is proposed. The code considers the nonlinear inelastic stress ~ strain relationship and can be used for both homogeneous and heterogeneous layers. The code is validated by comparing the results with analytical, experimental, and field monitoring data in the literature. A good agreement of the results shows that the code is well developed and can be used in predicting the settlements in practice. The analyses show that the maximum steady-state degree of consolidation calculated based on settlement and the maximum steady-state average degree of consolidation calculated based on dissipation of excess pore pressures decrease as the time period decreases. Below a specific time period, both remain unchanged. For a specific time period, both increase as the percentage of loaded portion in a cycle increases. Besides, the maximum steady-state degree of consolidation based on settlement, for a specific time period, increases with an increase in stress levels, which is due to the nonlinear stress ~ strain behavior.

Harandizadeh, H, Armaghani, DJ, Asteris, PG & Gandomi, AH 2021, 'TBM performance prediction developing a hybrid ANFIS-PNN predictive model optimized by imperialism competitive algorithm', Neural Computing and Applications, vol. 33, no. 23, pp. 16149-16179.
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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, vol. 67, no. 4, pp. 363-369.
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Hasan, ASMM, Tuhin, RA, Ullah, M, Sakib, TH, Thollander, P & Trianni, A 2021, 'A comprehensive investigation of energy management practices within energy intensive industries in Bangladesh', Energy, vol. 232, pp. 120932-120932.
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Industrial energy efficiency is acknowledged as a cost-effective mean contributing to sustainable development and industrial competitiveness. Implementing energy management practices becomes even more imperative for developing countries, considering their energy usage trends and economic development forecasts. Based on the circumstances, an empirical investigation is conducted on energy efficiency and management practices, as well as barriers and drivers to energy efficiency in the energy-intensive industries of Bangladesh. The study finds that majority of the companies barely implement the energy management practices. Energy audits represent the mostly implemented energy management practice at the industries, though a comprehensive approach on a detailed level is still lacking. In addition, this study finds that the number of dedicated and specialised energy professionals employed in the industries is yet negligible. The cumulated results show that energy efficiency is mostly disrupted due to inadequate support from preeminent administration and bureaucratic intricacy. Energy blueprint cost-saving due to less use of energy and rules and regulations were distinctively signified as most imperative drivers for energy efficiency. On the other hand, lack of information is found to be the most significant barrier to consult energy service companies. Analysis of the country's energy usage and supply-demand relationship points towards insufficient energy efficiency measures and energy management practices in the country. The study also finds that energy efficiency could be improved by 8%–10% through the practice of energy management. Our findings, besides pointing out specific issues to be tackled in the specific context of investigation, pave the way for further research over industrial energy efficiency in developing countries.

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, vol. 28, no. 5, pp. 477-492.
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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, Forouzesh, M, Siwakoti, Y & Blaabjerg, F 2021, 'A New High-Gain, High-Efficiency SEPIC-Based DC–DC Converter for Renewable Energy Applications', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 2, no. 4, pp. 567-578.
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Hasanpour, S, Forouzesh, M, Siwakoti, Y & Blaabjerg, F 2021, 'A Novel Full Soft-Switching High-Gain DC/DC Converter Based on Three-Winding Coupled-Inductor', IEEE Transactions on Power Electronics, vol. 36, no. 11, pp. 12656-12669.
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Hasanpour, S, Siwakoti, Y & Blaabjerg, F 2021, 'Analysis of a New Soft-Switched Step-Up Trans-Inverse DC/DC Converter Based on Three-Winding Coupled-Inductor', IEEE Transactions on Power Electronics, pp. 1-1.
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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, 'DC Fault Identification in Multiterminal HVDC Systems Based on Reactor Voltage Gradient', IEEE Access, vol. 9, pp. 115855-115867.
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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, vol. 15, no. 4, pp. 5209-5220.
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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.

Hayat, T, Afzal, MU, Ahmed, F, Zhang, S, Esselle, KP & Vardaxoglou, J 2021, 'The Use of a Pair of 3D-Printed Near Field Superstructures to Steer an Antenna Beam in Elevation and Azimuth', IEEE Access, vol. 9, pp. 153995-154010.
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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, pp. 117198-117198.
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He, D, Xiao, J, Wang, D, Liu, X, Li, Y, Fu, Q, Li, C, Yang, Q, Liu, Y & Ni, B-J 2021, 'Understanding and regulating the impact of tetracycline to the anaerobic fermentation of waste activated sludge', Journal of Cleaner Production, vol. 313, pp. 127929-127929.
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He, F, Huang, X, Wang, X, Qiu, S, Jiang, F & Ling, SH 2021, 'A neuron image segmentation method based Deep Boltzmann Machine and CV model', Computerized Medical Imaging and Graphics, vol. 89, pp. 101871-101871.
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He, L, Lu, Z, Zhang, J, Geng, L, Cai, Y & Li, X 2021, 'Economic dispatch of multi-area integrated electricity and natural gas systems considering emission and hourly spinning reserve constraints', International Journal of Electrical Power & Energy Systems, vol. 132, pp. 107177-107177.
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He, M, Zhang, X, Huang, J, Li, J, Yan, C, Kim, J, Chen, Y, Yang, L, Cairney, JM, Zhang, Y, Chen, S, Kim, J, Green, MA & Hao, X 2021, 'High Efficiency Cu ₂ ZnSn(S,Se) ₄ Solar Cells with Shallow Li _Zn Acceptor Defects Enabled by Solution‐Based Li Post‐Deposition Treatment', Advanced Energy Materials, vol. 11, no. 13, pp. 2003783-2003783.
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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. 12736-12736.
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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, pp. 104212-104212.
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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 & 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°.

He, Y, Wang, K, Zhang, W, Lin, X & Zhang, Y 2021, 'Exploring cohesive subgraphs with vertex engagement and tie strength in bipartite graphs', Information Sciences, vol. 572, pp. 277-296.
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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, vol. 12, no. 1, pp. 794-828.
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Hernandez, O, Buldain, I, Ederra, I, Ziolkowski, RW & Liberal, I 2021, 'Highly-Directive Cross-Polarized Backscatterers Integrated With a Ground Plane', IEEE Transactions on Antennas and Propagation, vol. 69, no. 10, pp. 6739-6751.
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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, vol. 51, no. 3, pp. 1616-1628.
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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.

Hewa, TM, Hu, Y, Liyanage, M, Kanhare, SS & Ylianttila, M 2021, 'Survey on Blockchain-Based Smart Contracts: Technical Aspects and Future Research', IEEE Access, vol. 9, pp. 87643-87662.
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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, vol. 21, no. 10, pp. 3461-3461.
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Recently, there has been an increase in the production of devices to monitor mental health and stress as means for expediting detection, and subsequent management of these conditions. The objective of this review is to identify and critically appraise the most recent smart devices and wearable technologies used to identify depression, anxiety, and stress, and the physiological process(es) linked to their detection. The MEDLINE, CINAHL, Cochrane Central, and PsycINFO databases were used to identify studies which utilised smart devices and wearable technologies to detect or monitor anxiety, depression, or stress. The included articles that assessed stress and anxiety unanimously used heart rate variability (HRV) parameters for detection of anxiety and stress, with the latter better detected by HRV and electroencephalogram (EGG) together. Electrodermal activity was used in recent studies, with high accuracy for stress detection; however, with questionable reliability. Depression was found to be largely detected using specific EEG signatures; however, devices detecting depression using EEG are not currently available on the market. This systematic review highlights that average heart rate used by many commercially available smart devices is not as accurate in the detection of stress and anxiety compared with heart rate variability, electrodermal activity, and possibly respiratory rate.

Hieu, NQ, Hoang, DT, Niyato, D & Kim, DI 2021, 'Optimal Power Allocation for Rate Splitting Communications With Deep Reinforcement Learning', IEEE Wireless Communications Letters, vol. 10, no. 12, pp. 2820-2823.
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Hill, M & Tran, N 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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Hill, M & Tran, N 2021, 'miRNA interplay: mechanisms and consequences in cancer', Disease Models & Mechanisms, vol. 14, no. 4.
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ABSTRACT Canonically, microRNAs (miRNAs) control mRNA expression. However, studies have shown that miRNAs are also capable of targeting non-coding RNAs, including long non-coding RNAs and miRNAs. The latter, termed a miRNA:miRNA interaction, is a form of self-regulation. In this Review, we discuss the three main modes of miRNA:miRNA regulation: direct, indirect and global interactions, and their implications in cancer biology. We also discuss the cell-type-specific nature of miRNA:miRNA interactions, current experimental approaches and bioinformatic techniques, and how these strategies are not sufficient for the identification of novel miRNA:miRNA interactions. The self-regulation of miRNAs and their impact on gene regulation has yet to be fully understood. Investigating this hidden world of miRNA self-regulation will assist in discovering novel regulatory mechanisms associated with disease pathways.

Hinge, G, Surampalli, RY, Goyal, MK, Gupta, BB & Chang, X 2021, 'Soil carbon and its associate resilience using big data analytics: For food Security and environmental management', Technological Forecasting and Social Change, vol. 169, pp. 120823-120823.
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Hirzallah, M, Krunz, M, Kecicioglu, B & Hamzeh, B 2021, '5G New Radio Unlicensed: Challenges and Evaluation', IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 3, pp. 689-701.
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Hlalele, TG, Zhang, J, Naidoo, RM & Bansal, RC 2021, 'Multi-objective economic dispatch with residential demand response programme under renewable obligation', Energy, vol. 218, pp. 119473-119473.
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Ho, LV, Nguyen, DH, Mousavi, M, De Roeck, G, Bui-Tien, T, Gandomi, AH & Wahab, MA 2021, 'A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks', Computers & Structures, vol. 252, pp. 106568-106568.
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Hoang, AT, Nizetic, S, Ong, HC, Chong, CT, Atabani, AE & Pham, VV 2021, 'Acid-based lignocellulosic biomass biorefinery for bioenergy production: Advantages, application constraints, and perspectives', Journal of Environmental Management, vol. 296, pp. 113194-113194.
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Hoang, AT, Nižetić, S, Ong, HC, Mofijur, M, Ahmed, SF, Ashok, B, Bui, VTV & Chau, MQ 2021, 'Insight into the recent advances of microwave pretreatment technologies for the conversion of lignocellulosic biomass into sustainable biofuel', Chemosphere, vol. 281, pp. 130878-130878.
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Hoang, AT, Ong, HC, Fattah, IMR, Chong, CT, Cheng, CK, Sakthivel, R & Ok, YS 2021, 'Progress on the lignocellulosic biomass pyrolysis for biofuel production toward environmental sustainability', Fuel Processing Technology, vol. 223, pp. 106997-106997.
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Hoang, AT, Sandro Nižetić, Olcer, AI, Ong, HC, Chen, W-H, Chong, CT, Thomas, S, Bandh, SA & Nguyen, XP 2021, 'Impacts of COVID-19 pandemic on the global energy system and the shift progress to renewable energy: Opportunities, challenges, and policy implications', Energy Policy, vol. 154, pp. 112322-112322.
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Hoang, DK, Le, NM, Vo‐Thi, UP, Nguyen, HG, Ho‐Pham, LT & Nguyen, TV 2021, 'Mechanography assessment of fall risk in older adults: the Vietnam Osteoporosis Study', Journal of Cachexia, Sarcopenia and Muscle, vol. 12, no. 5, pp. 1161-1167.
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Hoang, H-G, Lin, C, Chiang, C-F, Bui, X-T, Lukkhasorn, W, Bui, T-P-T, Tran, H-T, Vo, T-D-H, Le, V-G & Nghiem, LD 2021, 'The Individual and Synergistic Indexes for Assessments of Heavy Metal Contamination in Global Rivers and Risk: a Review', Current Pollution Reports, vol. 7, no. 3, pp. 247-262.
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Hoang, LM, Zhang, JA, Nguyen, DN, Huang, X, Kekirigoda, A & Hui, K-P 2021, 'Suppression of Multiple Spatially Correlated Jammers', IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 10489-10500.
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Hoang, PM, Tuan, HD, Son, TT & Poor, HV 2021, 'Qualitative HD Image and Video Recovery via High-Order Tensor Augmentation and Completion', IEEE Journal of Selected Topics in Signal Processing, vol. 15, no. 3, pp. 688-701.
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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.

Hoang, TM, Duong, TQ, Tuan, HD, Lambotharan, S & Hanzo, L 2021, 'Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines', IEEE Access, vol. 9, pp. 31595-31607.
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Hobbie, JG, Gandomi, AH & Rahimi, I 2021, 'A Comparison of Constraint Handling Techniques on NSGA-II', Archives of Computational Methods in Engineering, vol. 28, no. 5, pp. 3475-3490.
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Hofer, OJ, McKinlay, CJD, Tran, T & Crowther, CA 2021, 'Antenatal corticosteroids, maternal body mass index and infant morbidity within the ASTEROID trial', Australian and New Zealand Journal of Obstetrics and Gynaecology, vol. 61, no. 3, pp. 380-385.
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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, 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.

Ho-Le, TP, Tran, TS, Bliuc, D, Pham, HM, Frost, SA, Center, JR, Eisman, JA & Nguyen, TV 2021, 'Epidemiological transition to mortality and refracture following an initial fracture', eLife, vol. 10.
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This study sought to redefine the concept of fracture risk that includes refracture and mortality, and to transform the risk into 'skeletal age'. We analysed data obtained from 3521 women and men aged 60 years and older, whose fracture incidence, mortality, and bone mineral density (BMD) have been monitored since 1989. During the 20-year follow-up period, among 632 women and 184 men with a first incident fracture, the risk of sustaining a second fracture was higher in women (36%) than in men (22%), but mortality risk was higher in men (41%) than in women (25%). The increased risk of mortality was not only present with an initial fracture, but was accelerated with refractures. Key predictors of post-fracture mortality were male gender (hazard ratio [HR] 2.4; 95% CI, 1.79–3.21), advancing age (HR 1.67; 1.53–1.83), and lower femoral neck BMD (HR 1.16; 1.01–1.33). A 70-year-old man with a fracture is predicted to have a skeletal age of 75. These results were incorporated into a prediction model to aid patient-doctor discussion about fracture vulnerability and treatment decisions.

Holmes, NP, Chambon, S, Holmes, A, Xu, X, Hirakawa, K, Deniau, E, Lartigau-Dagron, C & Bousquet, A 2021, 'Organic semiconductor colloids: From the knowledge acquired in photovoltaics to the generation of solar hydrogen fuel', Current Opinion in Colloid & Interface Science, vol. 56, pp. 101511-101511.
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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, pp. 143600-143600.
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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, vol. 12, no. 1, pp. 198-221.
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Horry, MJ, Chakraborty, S, Pradhan, B, Fallahpoor, M, Chegeni, H & Paul, M 2021, 'Factors determining generalization in deep learning models for scoring COVID-CT images', Mathematical Biosciences and Engineering, vol. 18, no. 6, pp. 9264-9293.
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<abstract> <p>The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.</p> </abstract>

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, vol. 13, no. 3, pp. 1413-1413.
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Due to increase in the public and stakeholders’ awareness regarding economic, environmental, and social issues, the construction industry tends to follow the sustainability policies and practices in supply chain management. Hence, one of the most crucial aspects for a construction company in this regard is sustainable supplier selection, and, to this end, an accurate and reliable model is required. In this paper a hybrid fuzzy best-worst method and fuzzy inference system model is developed for sustainable supplier selection. In the first phase of this study, after determining 19 criteria in three main aspects, the final weight of each aspect and criterion is obtained using fuzzy best-worst method approach. In the second phase, the most sustainable supplier is selected by running the weighted fuzzy inference system both in aspect and criterion level, providing more accurate results compared to the use of other available models. Finally, two different tests are employed to validate the results and evaluate the robustness of the proposed model. The novel developed model enables the decision-maker to simulate the decision-making process, reduce the calculations loads, consider a large number of criteria in decision making, and resolve the inherited uncertainties in experts’ responses.

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, MA, Brito-Rodriguez, B, Sedger, LM & Canning, J 2021, 'A Cross-Disciplinary View of Testing and Bioinformatic Analysis of SARS-CoV-2 and Other Human Respiratory Viruses in Pandemic Settings', IEEE Access, vol. 9, pp. 163716-163734.
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Hossain, N, Hoong, LL, Barua, P, Soudagar, MEM & Mahlia, TMI 2021, 'The effect of enzymatic hydrolysis of pretreated wastepaper for bioethanol production', Korean Journal of Chemical Engineering, vol. 38, no. 12, pp. 2493-2499.
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Hossain, N, Mahlia, TMI, Miskat, MI, Chowdhury, T, Barua, P, Chowdhury, H, Nizamuddin, S, Ahmad, NB, Zaharin, NAB, Mazari, SA & Soudagar, MEM 2021, 'Bioethanol production from forest residues and life cycle cost analysis of bioethanol-gasoline blend on transportation sector', Journal of Environmental Chemical Engineering, vol. 9, no. 4, pp. 105542-105542.
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Hossain, SI, Luo, Z, Deplazes, E & Saha, SC 2021, 'Shape matters—the interaction of gold nanoparticles with model lung surfactant monolayers', Journal of The Royal Society Interface, vol. 18, no. 183.
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The lung surfactant monolayer (LSM) forms the main biological barrier for any inhaled particles to enter our bloodstream, including gold nanoparticles (AuNPs) present as air pollutants and under investigation for use in biomedical applications. Understanding the interaction of AuNPs with lung surfactant can assist in understanding how AuNPs enter our lungs. In this study, we use coarse-grained molecular dynamics simulations to investigate the effect of four different shape D AuNPs (spherical, box, icosahedron and rod) on the structure and dynamics of a model LSM, with a particular focus on differences resulting from the shape of the AuNP. Monolayer-AuNP systems were simulated in two different states: the compressed state and the expanded state, representing inhalation and exhalation conditions, respectively. Our results indicate that the compressed state is more affected by the presence of the AuNPs than the expanded state. Our results show that in the compressed state, the AuNPs prevent the monolayer from reaching the close to zero surface tension required for normal exhalation. In the compressed state, all four nanoparticles (NPs) reduce the lipid order parameters and cause a thinning of the monolayer where the particles drag surfactant molecules into the water phase. Comparing the different properties shows no trend concerning which shape has the biggest effect on the monolayer, as shape-dependent effects vary among the different properties. Insights from this study might assist future work of how AuNP shapes affect the LSM during inhalation or exhalation conditions.

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, pp. 128004-128004.
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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, 'Synthesis and NOx removal performance of anatase S–TiO2/g-CN heterojunction formed from dye wastewater sludge', Chemosphere, vol. 275, pp. 130020-130020.
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Hosseini, MR, Jupp, J, Papadonikolaki, E, Mumford, T, Joske, W & Nikmehr, B 2021, 'Position paper: digital engineering and building information modelling in Australia', Smart and Sustainable Built Environment, vol. 10, no. 3, pp. 331-344.
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PurposeThis position paper urges a drive towards clarity in the key definitions, terminologies and habits of speech associated with digital engineering and building information modelling (BIM). The ultimate goal of the paper is to facilitate the move towards arriving at an ideal definition for both concepts.Design/methodology/approachThis paper takes the “explanation building” review approach in providing prescriptive guidelines to researchers and industry practitioners. The aim of the review is to draw upon existing studies to identify, describe and find application of principles in a real-world context.FindingsThe paper highlights the definitional challenges surrounding digital engineering and BIM in Australia, to evoke a debate on BIM and digital engineering boundaries, how and why these two concepts may be linked, and how they relate to emerging concepts.Originality/valueThis is the first scholarly attempt to clarify the definition of digital engineering and address the confusion between the concepts of BIM and digital engineering.

Hosseini, SM, Kalhori, H & Al-Jumaily, A 2021, 'Active vibration control in human forearm model using paired piezoelectric sensor and actuator', Journal of Vibration and Control, vol. 27, no. 19-20, pp. 2231-2242.
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An active vibration control system to monitor and suppress the human forearm tremor is proposed in this article. The forearm is modelled as a uniform flexible continuous beam supported by a pin joint and a rotational spring at one end, whereas the other end is free. The beam is covered with a layer of piezoelectric sensor on its top surface and a layer of piezoelectric actuator on its bottom surface to form a control system, through which a closed-loop active control paradigm is implemented for tremor suppression. The governing equation of motion is derived using the Hamilton principle as well as the Galerkin procedure, leading to a second-order ordinary differential equation in time. The vibration response of the structure to an external harmonic excitation, analogous to tremor, is obtained analytically, enabling parametric study of the control system for tremor reduction. Using the obtained analytical expression, the effects of various parameters such as the control gain, the piezoelectric coefficient and the dielectric constant on the vibration response are studied. The results indicated that the proposed active vibration control system is an effective tool for active vibration control. Increasing the control gain of the control system as well as the magnitude of the piezoelectric constant decreased the amplitude of vibration, whereas the dielectric constant of the piezoelectric material did not show to have a significant effect on the beam vibration. The obtained results will pave the way for further experimental exploration to take and fabricate the most appropriate piezoelectric material and to design an effective active vibration control system for tremor suppression in people with Parkinson’s disease.

Hosseinzadeh, A, Najafpoor, AA, Navaei, AA, Zhou, JL, Altaee, A, Ramezanian, N, Dehghan, A, Bao, T & Yazdani, M 2021, 'Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling', Water, vol. 13, no. 19, pp. 2754-2754.
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This study aimed to assess, optimize and model the efficiencies of Fenton, photo-Fenton and ozonation/Fenton processes in formaldehyde elimination from water and wastewater using the response surface methodology (RSM) and artificial neural network (ANN). A sensitivity analysis was used to determine the importance of the independent variables. The influences of different variables, including H2O2 concentration, initial formaldehyde concentration, Fe dosage, pH, contact time, UV and ozonation, on formaldehyde removal efficiency were studied. The optimized Fenton process demonstrated 75% formaldehyde removal from water. The best performance with 80% formaldehyde removal from wastewater was achieved using the combined ozonation/Fenton process. The developed ANN model demonstrated better adequacy and goodness of fit with a R2 of 0.9454 than the RSM model with a R2 of 0. 9186. The sensitivity analysis showed pH as the most important factor (31%) affecting the Fenton process, followed by the H2O2 concentration (23%), Fe dosage (21%), contact time (14%) and formaldehyde concentration (12%). The findings demonstrated that these treatment processes and models are important tools for formaldehyde elimination from wastewater.

Hosseinzadeh, A, Zhou, JL, Navidpour, AH & Altaee, A 2021, 'Progress in osmotic membrane bioreactors research: Contaminant removal, microbial community and bioenergy production in wastewater', Bioresource Technology, vol. 330, pp. 124998-124998.
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Hou, J, Sun, L, Shu, T, Xiao, Y & Krunz, M 2021, 'Economics of Strategic Network Infrastructure Sharing: A Backup Reservation Approach', IEEE/ACM Transactions on Networking, vol. 29, no. 2, pp. 665-680.
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Hou, S, Ni, W, Wang, M, Liu, X, Tong, Q & Chen, S 2021, 'Bottleneck-Aware Resource Allocation for Service Processes', International Journal of Web Services Research, vol. 18, no. 3, pp. 1-21.
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In 5G systems and beyond, traditional generic service models are no longer appropriate for highly customized and intelligent services. The process of reinventing service models involves allocating available resources, where the performance of service processes is determined by the activity node with the lowest service rate. This paper proposes a new bottleneck-aware resource allocation approach by formulating the resource allocation as a max-min problem. The approach can allocate resources proportional to the workload of each activity, which can guarantee that the service rates of activities within a process are equal or close-to-equal. Based on the business process simulator (i.e., BIMP) simulation results show that the approach is able to reduce the average cycle time and improve resource utilization, as compared to existing alternatives. The results also show that the approach can effectively mitigate the impact of bottleneck activity on the performance of service processes.

Hsieh, MH 2021, 'Preface', Leibniz International Proceedings in Informatics, LIPIcs, vol. 197.
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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, vol. 80, no. 11, pp. 17275-17290.
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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, Chen, X, Ni, W, Hossain, E & Wang, X 2021, 'Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications', IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1458-1493.
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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, Wong, S, Li, Y, Lin, J, Yang, Y, Sun, G & Zhang, L 2021, 'Broadband high‐gain slot grid array antenna for millimeter wave applications', International Journal of RF and Microwave Computer-Aided Engineering, vol. 31, no. 1.
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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.

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, H, Savkin, AV & Ni, W 2021, 'Navigation of a UAV Team for Collaborative Eavesdropping on Multiple Ground Transmitters', IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 10450-10460.
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Huang, H, Zhang, J, Zhang, J, Xu, J & Wu, Q 2021, 'Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification', IEEE Transactions on Multimedia, vol. 23, pp. 1666-1680.
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Huang, J & Ji, J 2021, 'Vibration control of coupled Duffing oscillators in flexible single-link manipulators', Journal of Vibration and Control, vol. 27, no. 17-18, pp. 2058-2068.
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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, vol. 30, no. 8, pp. 085012-085012.
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Huang, K-C, John, AR, Jung, T-P, Tsai, W-F, Yu, Y-H & Lin, C-T 2021, 'Comparing the Differences in Brain Activities and Neural Comodulations Associated With Motion Sickness Between Drivers and Passengers', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1259-1267.
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Huang, L, Chen, X, Zhang, Y, Zhu, Y, Li, S & Ni, X 2021, 'Dynamic network analytics for recommending scientific collaborators', Scientometrics, vol. 126, no. 11, pp. 8789-8814.
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Collaboration is one of the most important contributors to scientific advancement and a crucial aspect of an academic’s career. However, the explosion in academic publications has, for some time, been making it more challenging to find suitable research partners. Recommendation approaches to help academics find potential collaborators are not new. However, the existing methods operate on static data, which can render many suggestions less useful or out of date. The approach presented in this paper simulates a dynamic network from static data to gain further insights into the changing research interests, activities and co-authorships of scholars in a field–all insights that can improve the quality of the recommendations produced. Following a detailed explanation of the entire framework, from data collection through to recommendation modelling, we provide a case study on the field of information science to demonstrate the reliability of the proposed method, and the results provide empirical insights to support decision-making in related stakeholders—e.g., scientific funding agencies, research institutions and individual researchers in the field.

Huang, L, Liu, F & Zhang, Y 2021, 'Overlapping Community Discovery for Identifying Key Research Themes', IEEE Transactions on Engineering Management, vol. 68, no. 5, pp. 1321-1333.
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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, vol. 25, no. 12, pp. 2217-2235.
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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, B-J 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, pp. 125091-125091.
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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, pp. 101590-101590.
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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, W & Xu, RYD 2021, 'Gaussian process latent variable model factorization for context-aware recommender systems', Pattern Recognition Letters, vol. 151, pp. 281-287.
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Huang, X, Tuyen Le, A & Guo, YJ 2021, 'ALMS Loop Analyses With Higher-Order Statistics and Strategies for Joint Analog and Digital Self-Interference Cancellation', IEEE Transactions on Wireless Communications, vol. 20, no. 10, pp. 6467-6480.
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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, X, Tuyen Le, A & Guo, YJ 2021, 'Transmit Beamforming for Communication and Self-Interference Cancellation in Full Duplex MIMO Systems: A Trade-Off Analysis', IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 3760-3769.
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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, 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, pp. 116971-116971.
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Huang, Y, Ng, ECY, Zhou, JL, Surawski, NC, Lu, X, Du, B, Forehead, H, Perez, P & Chan, EFC 2021, 'Impact of drivers on real-driving fuel consumption and emissions performance', Science of The Total Environment, vol. 798, pp. 149297-149297.
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Eco-driving has attracted great attention as a cost-effective and immediate measure to reduce fuel consumption significantly. Understanding the impact of driver behaviour on real driving emissions (RDE) is of great importance for developing effective eco-driving devices and training programs. Therefore, this study was conducted to investigate the performance of different drivers using a portable emission measurement system. In total, 30 drivers, including 15 novice and 15 experienced drivers, were recruited to drive the same diesel vehicle on the same route, to minimise the effect of uncontrollable real-world factors on the performance evaluation. The results show that novice drivers are less skilled or more aggressive than experienced drivers in using the accelerator pedal, leading to higher vehicle and engine speeds. As a result, fuel consumption rates of novice drivers vary in a slightly greater range than those of experienced drivers, with a marginally higher (2%) mean fuel consumption. Regarding pollutant emissions, CO and THC emissions of all drivers are well below the standard limits, while NOx and PM emissions of some drivers significantly exceed the limits. Compared with experienced drivers, novice drivers produce 17% and 29% higher mean NOx and PM emissions, respectively. Overall, the experimental results reject the hypothesis that driver experience has significant impacts on fuel consumption performance. The real differences lie in the individual drivers, as the worst performing drivers have significantly higher fuel consumption rates than other drivers, for both novice and experienced drivers. The findings suggest that adopting eco-driving skills could deliver significant reductions in fuel consumption and emissions simultaneously for the worst performing drivers, regardless of driving experience.

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, vol. 29, pp. 673-682.
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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, pp. 110921-110921.
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Huang, Y, Wang, Q, Jia, W, Lu, Y, Li, Y & He, X 2021, 'See more than once: Kernel-sharing atrous convolution for semantic segmentation', Neurocomputing, vol. 443, pp. 26-34.
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Huang, Y, Wu, Q, Xu, J, Zhong, Y & Zhang, Z 2021, 'Unsupervised Domain Adaptation with Background Shift Mitigating for Person Re-Identification', International Journal of Computer Vision, vol. 129, no. 7, pp. 2244-2263.
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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, Zhang, Y, Wu, M, Porter, A & Barrangou, R 2021, 'Determination of Factors Driving the Genome Editing Field in the CRISPR Era Using Bibliometrics', The CRISPR Journal, vol. 4, no. 5, pp. 728-738.
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Over the past two decades, the discovery of CRISPR-Cas immune systems and the repurposing of their effector nucleases as biotechnological tools have revolutionized genome editing. The corresponding work has been captured by 90,000 authors representing 7,600 affiliations in 126 countries, who have published more than 19,000 papers spanning medicine, agriculture, and biotechnology. Here, we use tech mining and an integrated bibliometric and networks framework to investigate the CRISPR literature over three time periods. The analysis identified seminal papers, leading authors, influential journals, and rising applications and topics interconnected through collaborative networks. A core set of foundational topics gave rise to diverging avenues of research and applications, reflecting a bona fide disruptive emerging technology. This analysis illustrates how bibliometrics can identify key factors, decipher rising trends, and untangle emerging applications and technologies that dynamically shape a morphing field, and provides insights into the trajectory of genome editing.

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, vol. 68, no. 5, pp. 1347-1359.
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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.

Huang, Z, Shivakumara, P, Lu, T, Pal, U, Blumenstein, M, Chetty, B & Kumar, GH 2021, 'Improved Ring Radius Transform-Based Reconstruction for Video Character Recognition', International Journal of Pattern Recognition and Artificial Intelligence, vol. 35, no. 07, pp. 2150023-2150023.
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Character shape reconstruction in video is challenging due to low contrast, complex backgrounds and arbitrary orientation of characters. This work proposes an Improved Ring Radius Transform (IRRT) for reconstructing impaired characters through medial axis prediction. At first, the technique proposes a novel idea based on the Tangent Vector (TV) concept that identifies each actual pair of end pixels caused by gaps in impaired character components. Next, the actual direction to predict medial axis pixels using IRRT for each pair of end pixels is proposed with a new normal vector concept. The process of prediction repeats iteratively to find all the medial axis pixels for every gap in question. Further, medial axis pixels with their radii are used to reconstruct the shapes of impaired characters. The proposed technique is tested on benchmark datasets consisting of video, natural scenes, objects and multi-lingual data to demonstrate that it reconstructs shapes well, even for heterogeneous data. Comparative studies with different binarization and character recognition methods show that the proposed technique is effective, useful and outperforms existing methods.

Huo, L, Jiao Li, J, Chen, L, Yu, Z, Hutvagner, G & Li, J 2021, 'Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects', Briefings in Bioinformatics, vol. 22, no. 6.
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AbstractSingle-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. In practice, single-cell multi-omics sequencing actually detects multiple traits such as DNA, RNA, methylation information and/or protein profiles from the same cell for many individuals in a tissue sample. Multi-omics sequencing has been widely applied to systematically unravel interplay mechanisms of key components and pathways in cell. This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic. We also summarize machine learning and bioinformatics techniques used in the analysis of the intercorrelated multilayer heterogeneous data. We observed that variational inference and graph-based learning are popular approaches, and Seurat V3 is a commonly used tool to transfer the missing variables and labels. We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods. The survey is concluded with some open questions and opportunities for this extraordinary field.

Huo, X, Luo, Q, Li, Q & Sun, G 2021, 'Measurement of fracture parameters based upon digital image correlation and virtual crack closure techniques', Composites Part B: Engineering, vol. 224, pp. 109157-109157.
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Hussain, FK, Rahayu, W & Takizawa, M 2021, 'Special issue on Intelligent Fog and Internet of Things (IoT)-Based Services', World Wide Web, vol. 24, no. 3, pp. 925-927.
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Hussain, T, Muhammad, K, Ullah, A, Ser, JD, Gandomi, AH, Sajjad, M, Baik, SW & de Albuquerque, VHC 2021, 'Multiview Summarization and Activity Recognition Meet Edge Computing in IoT Environments', IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9634-9644.
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Hussain, W, Merigo, JM, Gao, H, Alkalbani, AM & Rabhi, FA 2021, 'Integrated AHP-IOWA, POWA Framework for Ideal Cloud Provider Selection and Optimum Resource Management', IEEE Transactions on Services Computing, pp. 1-1.
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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, Angeloski, A, McDonagh, A, Shon, HK & Tijing, LD 2021, '3D microflowers CuS/Sn2S3 heterostructure for highly efficient solar steam generation and water purification', Solar Energy Materials and Solar Cells, vol. 232, pp. 111377-111377.
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Solar-driven interfacial steam generation is a promising method to produce potable water using renewable energy and help solve global clean water scarcity problems. However, the design of photothermal materials (PTMs) with excellent light absorption that can localize heat at the air/water interface, and facilitate water vapor generation remains a key challenge for its practical implementation. In this work, we demonstrate the synthesis of heterostructure microflowers composed of vertically aligned CuS/Sn2S3 nanosheets (3D CSS-NS MF) using a single-step solvothermal method for solar steam generation application. The microflower structures and the abundant nanocavities between the vertically aligned nanosheets resulted in significant sunlight harvesting over the solar spectrum, excellent heat localization through trapping and re-absorbing the heat, and fast escape of water vapor. Under 1 sun (1 kW m-2) illumination, a high water evaporation rate of 1.42 kg m-2 h-1, corresponding to an efficiency of 82.93% was obtained. The 3D CSS-NS MF based solar evaporator exhibited remarkable salt ions rejection efficiency and good reusability over 10 cycles. Furthermore, efficient removal of organic dyes was observed in application geared towards wastewater treatment with a rejection ∼99.9%. Our work demonstrates the potential of using novel semiconductor-based nanocomposites as effective photothermal materials for high-performance solar steam generation in water desalination and wastewater treatment applications.

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, vol. 500, pp. 114853-114853.
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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.

Ibrahim, IA & Hossain, MJ 2021, 'Low Voltage Distribution Networks Modeling and Unbalanced (Optimal) Power Flow: A Comprehensive Review', IEEE Access, vol. 9, pp. 143026-143084.
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Ibrahim, IA, Sabah, S, Abbas, R, Hossain, MJ & Fahed, H 2021, 'A novel sizing method of a standalone photovoltaic system for powering a mobile network base station using a multi-objective wind driven optimization algorithm', Energy Conversion and Management, vol. 238, pp. 114179-114179.
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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, pp. 113024-113024.
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Ideris, F, Shamsuddin, AH, Nomanbhay, S, Kusumo, F, Silitonga, AS, Ong, MY, Ong, HC & Mahlia, TMI 2021, 'Optimization of ultrasound-assisted oil extraction from Canarium odontophyllum kernel as a novel biodiesel feedstock', Journal of Cleaner Production, vol. 288, pp. 125563-125563.
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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, pp. 120786-120786.
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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, Ngo, T, Ferreira, FB, Rujikiatkamjorn, C & Tucho, A 2021, 'Large-scale testing facility for heavy haul track', Transportation Geotechnics, vol. 28, pp. 100517-100517.
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Indraratna, B, Nguyen, TT, Singh, M, Rujikiatkamjorn, C, Carter, JP, Ni, J & Truong, MH 2021, 'Cyclic loading response and associated yield criteria for soft railway subgrade – Theoretical and experimental perspectives', Computers and Geotechnics, vol. 138, pp. 104366-104366.
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Indraratna, B, Phan, NM, Nguyen, TT & Huang, J 2021, 'Simulating Subgrade Soil Fluidization Using LBM-DEM Coupling', International Journal of Geomechanics, vol. 21, no. 5, pp. 04021039-04021039.
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Indraratna, B, Qi, Y, Jayasuriya, C, Rujikiatkamjorn, C & Arachchige, CMK 2021, 'Use of recycled rubber inclusions with granular waste for enhanced track performance', Transportation Engineering, vol. 6, pp. 100093-100093.
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Indraratna, B, Singh, M, Nguyen, TT, Leroueil, S, Abeywickrama, A, Kelly, R & Neville, T 2021, 'Correction: Laboratory study on subgrade fluidization under undrained cyclic triaxial loading', Canadian Geotechnical Journal, vol. 58, no. 11, pp. 1790-1790.
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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, pp. 100569-100569.
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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.

Iqbal, J 2021, 'Landslide susceptibility assessment along the Dubair-Dudishal section of the Karakoram Highway, Northwestern Himalayas, Pakistan', Acta Geodynamica et Geomaterialia, pp. 137-155.
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The primary objective of this study is to analyze and characterize landslides in North Pakistan along Karakoram Highway (KKH) to produce a landslide susceptibility map using GIS and remote sensing technology. Using satellite images followed by field investigations, spatial distribution of landslide database was generated. Next, an integrated study was undertaken in the study area to perform the landslide susceptibility mapping. Dubaur-Dudishal section of KKH (about 150 km) which is a part of Kohistan Island Arc, is investigated in this study with a buffer zone of about 8 km along both sides of the KKH. Several thematic maps, e.g., lithology, distance to faults, distance to streams, distance to roads, normalized difference vegetation index (NDVI), slope, aspect, elevation, relative relief, plan-curvature and profile-curvature were prepared. Subsequently, these thematic data layers were analyzed by frequency ratio (FR) model and weights-of-evidence (WoE) model to generate the landslide susceptibility maps. In order to check the accuracy of the models, the area under the curve (AUC) was to quantitatively compare the two models used in this study. The predictive ability of AUC values indicate that the success rates of FR model and WoE model are 0.807 and 0.866, whereas the prediction rates are 0.785 and 0.846, respectively. Both methods show that nearly 50 % landslides in the study area fall in either high or very high susceptibility zones. The landslide susceptibility maps presented in this study are of great importance to the policy makers and the engineers for highway construction as well as the mega dams construction projects (Dasu dam and Bhasha dam which lie within the vicinity of the study area); so that proper prevention as well as mitigation could be done in advance to avoid the possible economic as well as the human loss in future.

Iranpour, H, Hosseini, SN, Hosseini Far, H, Zhand, S, Mohammadi Ghanbarlu, M, Shahsavarani, H, Bouzari, S & Shokrgozar, MA 2021, 'Self-assembling of chimeric mussel-inspired bio-adhesives originated from Mytilus californianus and Anabaena flos-aquae: A new approach to develop underwater adhesion', International Journal of Adhesion and Adhesives, vol. 110, pp. 102938-102938.
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Irshad, UB, Nizami, MSH, Rafique, S, Hossain, MJ & Mukhopadhyay, SC 2021, 'A Battery Energy Storage Sizing Method for Parking Lot Equipped With EV Chargers', IEEE Systems Journal, vol. 15, no. 3, pp. 4459-4469.
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Ishac, K & Eager, D 2021, 'Evaluating Martial Arts Punching Kinematics Using a Vision and Inertial Sensing System', Sensors, vol. 21, no. 6, pp. 1948-1948.
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Martial arts has many benefits not only in self-defence, but also in improving physical fitness and mental well-being. In our research we focused on analyzing the velocity, impulse, momentum and impact force of the Taekwondo sine-wave punch and reverse-step punch. We evaluated these techniques in comparison with the martial arts styles of Hapkido and Shaolin Wushu and investigated the kinematic properties. We developed a sensing system which is composed of an ICSensor Model 3140 accelerometer attached to a punching bag for measuring dynamic acceleration, Kinovea motion analysis software and 2 GoPro Hero 3 cameras, one focused on the practitioner’s motion and the other focused on the punching bag’s motion. Our results verified that the motion vectors associated with a Taekwondo practitioner performing a sine-wave punch, uses a unique gravitational potential energy to optimise the impact force of the punch. We demonstrated that the sine-wave punch on average produced an impact force of 6884 N which was higher than the reverse-step punch that produced an average impact force of 5055 N. Our comparison experiment showed that the Taekwondo sine-wave punch produced the highest impact force compared to a Hapkido right cross punch and a Shaolin Wushu right cross, however the Wushu right cross had the highest force to weight ratio at 82:1. The experiments were conducted with high ranking black belt practitioners in Taekwondo, Hapkido and Shaolin Wushu.

Isherwood, ZJ, Huynh-Thu, Q, Arnison, M, Monaghan, D, Toscani, M, Perry, S, Honson, V & Kim, J 2021, 'Surface properties and the perception of color', Journal of Vision, vol. 21, no. 2, pp. 7-7.
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Islam, MR, Liu, S, Biddle, R, Razzak, I, Wang, X, Tilocca, P & Xu, G 2021, 'Discovering dynamic adverse behavior of policyholders in the life insurance industry', Technological Forecasting and Social Change, vol. 163, pp. 120486-120486.
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© 2020 Elsevier Inc. Adverse selection (AS) is one of the significant causes of market failure worldwide. Analysis and deep insights into the Australian life insurance market show the existence of adverse activities to gain financial benefits, resulting in loss to insurance companies. Understanding the behavior of policyholders is essential to improve business strategies and overcome fraudulent claims. However, policyholders’ behavior analysis is a complex process, usually involving several factors depending on their preferences and the nature of data such as data which is missing useful private information, the presence of asymmetric information of policyholders, the existence of anomalous information at the cell level rather than the data instance level and a lack of quantitative research. This study aims to analyze the life insurance policyholder's behavior to identify adverse behavior (AB). In this study, we present a novel association rule learning-based approach ‘ARLAS’ to detect the AS behavior of policyholders. In addition to the original data, we further created a synthetic AS dataset by randomly flipping the attribute values of 10% of the records in the test set. The experiment results on 31,800 Australian life insurance users show that the proposed approach achieves significant gains in performance comparatively.

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, vol. 18, no. 12, pp. 6239-6239.
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A comprehensive understanding of airflow characteristics and particle transport in the human lung can be useful in modelling to inform clinical diagnosis, treatment, and management, including prescription medication and risk assessment for rehabilitation. One of the difficulties in clinical treatment of lung disorders lies in the patients’ variable physical lung characteristics caused by age, amongst other factors, such as different lung sizes. A precise understanding of the comparison between different age groups with various flow rates is missing in the literature, and this study aims to analyse the airflow and aerosol transport within the age-specific lung. ANSYS Fluent solver and the large-eddy simulation (LES) model were employed for the numerical simulation. The numerical model was validated with the available literature and the computational results showed airway size-reduction significantly affected airflow and particle transport in the upper airways. This study reports higher deposition at the mouth-throat region for larger diameter particles. The overall deposition efficiency (DE) increased with airway size reduction and flow rate. Lung aging effected the pressure distribution and a higher pressure drop was reported for the aged lung as compared to the younger lung. These findings could inform medical management through individualised simulation of drug-aerosol delivery processes for the patient-specific lung.

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, vol. 33, no. 6, pp. 061903-061903.
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Islam, MS, Larpruenrudee, P, Saha, SC, Pourmehran, O, Paul, AR, Gemci, T, Collins, R, Paul, G & Gu, Y 2021, 'How severe acute respiratory syndrome coronavirus-2 aerosol propagates through the age-specific upper airways', Physics of Fluids, vol. 33, no. 8, pp. 081911-081911.
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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, pp. 104885-104885.
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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, vol. 33, no. 11, pp. 5897-5915.
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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.

Izadikhah, M, Azadi, M, Toloo, M & Hussain, FK 2021, 'Sustainably resilient supply chains evaluation in public transport: A fuzzy chance-constrained two-stage DEA approach', Applied Soft Computing, vol. 113, pp. 107879-107879.
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Izady, A, Khorshidi, MS, Nikoo, MR, Al-Maktoumi, A, Chen, M, Al-Mamari, H & Gandomi, AH 2021, 'Optimal Water Allocation from Subsurface Dams: A Risk-Based Optimization Approach', Water Resources Management, vol. 35, no. 12, pp. 4275-4290.
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Jacob, A, Ashok, B, Alagumalai, A, Chyuan, OH & Le, PTK 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, pp. 113655-113655.
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Jafarizadeh, S, Veitch, D, Tofigh, F, Lipman, J & Abolhasan, M 2021, 'Optimal Synchronizability in Networks of Coupled Systems: Topological View', IEEE Transactions on Network Science and Engineering, vol. 8, no. 2, pp. 1517-1530.
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Jahangoshai Rezaee, M, Eshkevari, M, Saberi, M & Hussain, O 2021, 'GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game', Knowledge-Based Systems, vol. 213, pp. 106672-106672.
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Jahirul, MI, Rasul, MG, Brown, RJ, Senadeera, W, Hosen, MA, Haque, R, Saha, SC & Mahlia, TMI 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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Jain, K & Pradhan, B 2021, 'Editorial', Journal of the Indian Society of Remote Sensing, vol. 49, no. 3, pp. 461-462.
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Jamborsalamati, P, Garmabdari, R, Hossain, J, Lu, J & Dehghanian, P 2021, 'Planning for resilience in power distribution networks: A multi‐objective decision support', IET Smart Grid, vol. 4, no. 1, pp. 45-60.
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Jamil, S, Loganathan, P, Kandasamy, J, Ratnaweera, H & Vigneswaran, S 2021, 'Comparing nanofiltration membranes effectiveness for inorganic and organic compounds removal from a wastewater-reclamation plant’s micro-filtered water', Materials Today: Proceedings, vol. 47, pp. 1389-1393.
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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, pp. 118207-118207.
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Jankowska, K, Grzywaczyk, A, Piasecki, A, Kijeńska-Gawrońska, 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, pp. 101332-101332.
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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.

Jayathilaka, P, Indraratna, B & Heitor, A 2021, 'Influence of Salinity-Based Osmotic Suction on the Shear Strength of a Compacted Clay', International Journal of Geomechanics, vol. 21, no. 5, pp. 04021041-04021041.
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Jeffry, L, Ong, MY, Nomanbhay, S, Mofijur, M, Mubashir, M & Show, PL 2021, 'Greenhouse gases utilization: A review', Fuel, vol. 301, pp. 121017-121017.
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Jena, R, Ghansar, TAA, Pradhan, B & Rai, AK 2021, 'Estimation of fractal dimension and b-value of earthquakes in the Himalayan region', Arabian Journal of Geosciences, vol. 14, no. 10.
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Jena, R, Naik, SP, Pradhan, B, Beydoun, G, Park, H-J & Alamri, A 2021, 'Earthquake vulnerability assessment for the Indian subcontinent using the Long Short-Term Memory model (LSTM)', International Journal of Disaster Risk Reduction, vol. 66, pp. 102642-102642.
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Earthquakes are one of the most destructive and unpredictable natural hazards with a long-term physical, psychological, and economic impact to the society. In the past century, more than 1100 destructive earthquakes occurred, and caused around 1.5 million deaths worldwide. Some recent studies have suggested that a future earthquake in the Himalayan region of magnitude range MW 7.5–8 can cause more than 0.2 million human lives and around 150 billion dollar financial loss. Deep learning methods in recent studies proved very useful in natural hazards forecasting and prediction modelling. Long Short-Term Memory (LSTM) model has been particularly popular in several natural hazard forecasting. In this research, for the first time, LSTM model is implemented with suitable Geospatial Information Systems (GIS) techniques to assess the earthquake vulnerability for whole of India. In India, most of the seismic vulnerability assessment available are at city level or state level using traditional techniques. Several factors such as land use, geology, geomorphology, fault distribution, transportation facility, population density were all used to develop the social, structural, and geotechnical vulnerability maps. The results show that the areas around Delhi, NE region of India, major parts of Gujrat, West Bengal plain exhibit high to very-high seismic vulnerability. This model achieved an accuracy of 87.8%, sensitivity (90%) and specificity (84.9%). The present analysis can be helpful towards prioritization of regions which are in higher need of risk reduction interventions. Also, based on this vulnerability index map, the risk metrics can be attenuated.

Jena, R, Pradhan, B, Naik, SP & Alamri, AM 2021, 'Earthquake risk assessment in NE India using deep learning and geospatial analysis', Geoscience Frontiers, vol. 12, no. 3, pp. 101110-101110.
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Ji, A, Xue, X, Ha, QP, Luo, X & Zhang, M 2021, 'Game theory–based bilevel model for multiplayer pavement maintenance management', Automation in Construction, vol. 129, pp. 103763-103763.
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Ji, JC, Luo, Q & Ye, K 2021, 'Vibration control based metamaterials and origami structures: A state-of-the-art review', Mechanical Systems and Signal Processing, vol. 161, pp. 107945-107945.
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Ji, S, Pan, S, Li, X, Cambria, E, Long, G & Huang, Z 2021, 'Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications', IEEE Transactions on Computational Social Systems, vol. 8, no. 1, pp. 214-226.
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Ji, Z, Natarajan, A, Vidick, T, Wright, J & Yuen, H 2021, 'MIP* = RE', Communications of the ACM, vol. 64, no. 11, pp. 131-138.
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Note from the Research Highlights Co-Chairs: A Research Highlights paper appearing in Communications is usually peer-reviewed prior to publication. The following paper is unusual in that it is still under review. However, the result has generated enormous excitement in the research community, and came strongly nominated by SIGACT, a nomination seconded by external reviewers. The complexity class NP characterizes the collection of computational problems that have efficiently verifiable solutions. With the goal of classifying computational problems that seem to lie beyond NP, starting in the 1980s complexity theorists have considered extensions of the notion of efficient verification that allow for the use of randomness (the class MA), interaction (the class IP), and the possibility to interact with multiple proofs, or provers (the class MIP). The study of these extensions led to the celebrated PCP theorem and its applications to hardness of approximation and the design of cryptographic protocols. In this work, we study a fourth modification to the notion of efficient verification that originates in the study of quantum entanglement. We prove the surprising result that every problem that is recursively enumerable, including the Halting problem, can be efficiently verified by a classical probabilistic polynomial-time verifier interacting with two all-powerful but noncommunicating provers sharing entanglement. The result resolves long-standin...

Jia, M, Gabrys, B & Musial, K 2021, 'Directed closure coefficient and its patterns', PLOS ONE, vol. 16, no. 6, pp. e0253822-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, R, Bharathy, G & Weyer, N 2021, 'Investigating the Impact of Actual and Modeled Occupant Behavior Information Input to Building Performance Simulation', Buildings, vol. 11, no. 1, pp. 32-32.
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Occupant behaviors are one of the most dominant factors that influence building energy use. Understanding the influences from building occupants can promote the development of energy–efficient buildings. This paper quantifies the impact of different occupant behavior information on building energy model (BEM) from multiple perspectives. For this purpose, an occupant behavior model that uses agent–based modeling (ABM) approach is implemented via co-simulation with a BEM of an existing commercial building. Then, actual occupant behavior data in correspondence to ABM output, including operations on window, door, and blinds in selected thermal zones of the building are recorded using survey logs. A simulation experiment is conducted by creating three BEMs with constant, actual, and modeled occupant behavioral inputs. The analysis of the simulation results among these scenarios helps us gain an in–depth understanding of how occupant behaviors influence building performance. This study aims to facilitate robust building design and operation with human–in–the–loop system optimization.

Jiang, C, D'Arienzo, A, Li, W, Wu, S & Bai, Q 2021, 'An Operator-Based Approach for Modeling Influence Diffusion in Complex Social Networks', Journal of Social Computing, vol. 2, no. 2, pp. 166-182.
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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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Jiao, Y, Wang, Y, Ding, X, Fu, B, Huang, S & Xiong, R 2021, '2-Entity Random Sample Consensus for Robust Visual Localization: Framework, Methods, and Verifications', IEEE Transactions on Industrial Electronics, vol. 68, no. 5, pp. 4519-4528.
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Jin, B, Chen, E, Zhao, H, Huang, Z, Liu, Q, Zhu, H & Yu, S 2021, 'Promotion of Answer Value Measurement With Domain Effects in Community Question Answering Systems', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 5, pp. 3068-3079.
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Jin, J, Sheng, G, Bi, Y, Song, Y, Liu, X, Chen, X, Li, Q, Deng, Z, Zhang, W, Zheng, J, Coombs, T, Shen, B, Zhu, J, Zhao, Y, Wang, J, Xiang, B, Tang, Y, Ren, L, Xu, Y, Shi, J, Islam, MR, Guo, Y & Zhu, J 2021, 'Applied Superconductivity and Electromagnetic Devices - Principles and Current Exploration Highlights', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-29.
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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, pp. 1-4.
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Jinqiang Liu, JL, Jinqiang Liu, YL, Yining Liu, LC & Lei Cui, SY 2021, 'MSAI: Masking Sensitive Area of Image on IoT Cameras', 網際網路技術學刊, vol. 22, no. 7, pp. 1553-1561.
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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, pp. 100902-100902.
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Jones, CM, Buchlak, QD, Oakden‐Rayner, L, Milne, M, Seah, J, Esmaili, N & Hachey, B 2021, 'Chest radiographs and machine learning – Past, present and future', Journal of Medical Imaging and Radiation Oncology, vol. 65, no. 5, pp. 538-544.
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Jones, CM, Danaher, L, Milne, MR, Tang, C, Seah, J, Oakden-Rayner, L, Johnson, A, Buchlak, QD & Esmaili, N 2021, 'Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study', BMJ Open, vol. 11, no. 12, pp. e052902-e052902.
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ObjectivesArtificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists.DesignThis prospective real-world multicentre study involved a group of radiologists using the model in their daily reporting workflow to report consecutive CXRs and recording their feedback on level of agreement with the model findings and whether this significantly affected their reporting.SettingThe study took place at radiology clinics and hospitals within a large radiology network in Australia between November and December 2020.ParticipantsEleven consultant diagnostic radiologists of varying levels of experience participated in this study.Primary and secondary outcome measuresProportion of CXR cases where use of the AI model led to significant material changes to the radiologist report, to patient management, or to imaging recommendations. Additionally, level of agreement between radiologists and the model findings, and radiologist attitudes towards the model were assessed.ResultsOf 2972 cases reviewed with the model, 92 cases (3.1%) had significant report changes, 43 cases (1.4%) had changed patient management and 29 cases (1.0%) had further imaging recommendations. In terms of agreement with the model, 2569 cases showed complete agreement (86.5%). 390 (13%) cases had one or more findings rejected by the radiologist. There were 16 findings across 13 cases (0.5%) deemed to ...

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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Joshi, S, Sharma, M, Bisht, P & Singh, S 2021, 'Explaining the Factors Influencing Consumer Perception, Adoption Readiness, and Perceived Usefulness toward Digital Transactions: Online Retailing Experience of Millennials in India', Journal of Operations and Strategic Planning, vol. 4, no. 2, pp. 202-223.
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The present study intends to identify and evaluate the factors affecting the perception and readiness of young online shoppers regarding digital transactions. Thirty variables were identified through systematic literature review that can influence consumers’ perception of digital transactions. Data were collected from 525 millennials from north India. After performing factor analysis, five broad factors were identified namely (a) personal characteristics and incentives, (b) knowledge technical capability, (c) perceived usefulness, (d) sense of security, and (e) transaction risk, which affect the perception and intention as well as adaption of young consumers regarding the digital transactions. Using regression analysis, factors affecting consumer’s perception and readiness regarding digital transactions are modeled out. The study also examines the influence on intention to adopt digital transactions and the actual adaption of digital transactions. Further, the study concludes that consumer intention to make digital transactions mediates the relationship between factors of digital transactions and digital transaction adaption behavior.

Jothiramalingam, R, Jude, A, Patan, R, Ramachandran, M, Duraisamy, JH & Gandomi, AH 2021, 'Machine learning-based left ventricular hypertrophy detection using multi-lead ECG signal', Neural Computing and Applications, vol. 33, no. 9, pp. 4445-4455.
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Ju, M, Ding, C, Guo, CA, Ren, W & Tao, D 2021, 'IDRLP: Image Dehazing Using Region Line Prior', IEEE Transactions on Image Processing, vol. 30, pp. 9043-9057.
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In this work, a novel and ultra-robust single image dehazing method called IDRLP is proposed. It is observed that when an image is divided into n regions, with each region having a similar scene depth, the brightness of both the hazy image and its haze-free correspondence are positively related with the scene depth. Based on this observation, this work determines that the hazy input and its haze-free correspondence exhibit a quasi-linear relationship after performing this region segmentation, which is named as region line prior (RLP). By combining RLP and the atmospheric scattering model (ASM), a recovery formula (RF) can be easily obtained with only two unknown parameters, i.e., the slope of the linear function and the atmospheric light. A 2D joint optimization function considering two constraints is then designed to seek the solution of RF. Unlike other comparable works, this "joint optimization" strategy makes efficient use of the information across the entire image, leading to more accurate results with ultra-high robustness. Finally, a guided filter is introduced in RF to eliminate the adverse interference caused by the region segmentation. The proposed RLP and IDRLP are evaluated from various perspectives and compared with related state-of-the-art techniques. Extensive analysis verifies the superiority of IDRLP over state-of-the-art image dehazing techniques in terms of both the recovery quality and efficiency. A software release is available at https://sites.google.com/site/renwenqi888/.

Ju, M, Ding, C, Ren, W, Yang, Y, Zhang, D & Guo, YJ 2021, 'IDE: Image Dehazing and Exposure Using an Enhanced Atmospheric Scattering Model', IEEE Transactions on Image Processing, vol. 30, pp. 2180-2192.
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Ju, R, Zhou, P, Wen, S, Wei, W, Xue, Y, Huang, X & Yang, X 2021, '3D-CNN-SPP: A Patient Risk Prediction System From Electronic Health Records via 3D CNN and Spatial Pyramid Pooling', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 2, pp. 247-261.
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IEEE The problem of extracting useful clinical representations from longitudinal electronic health record (EHR) data, also known as the computational phenotyping problem, is an important yet challenging task in the health-care academia and industry. Recent progress in the design and applications of deep learning methods has shown promising results towards solving this problem. In this paper, we propose 3D-CNN-SPP (3D Convolutional Neural Networks and Spatial Pyramid Pooling), a novel patient risk prediction system, to investigate the application of deep neural networks in modeling longitudinal EHR data. Particularly, we propose a 3D CNN structure, which is featured by SPP. Compared with 2D CNN methods, our proposed method can capture the complex relationships in EHRs more effectively and efficiently. Furthermore, previous works handle the issue of variable length in patient records by padding zeros to all vectors so that they have a fixed length. In our work, the proposed spatial pyramid pooling divides the records into several length sections for respective pooling processing, hence handling the variable length problem easily and naturally. We take heart failure and diabetes as examples to test the performance of the system, and the experiment results demonstrate great effectiveness in patient risk prediction, compared with several strong baselines.

Jung, MC, Chai, R, Zheng, J & Nguyen, H 2021, 'Sparse Gaussian process regression in real-time myoelectric control', International Journal of Modelling, Identification and Control, vol. 39, no. 1, pp. 51-51.
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Kaliaraj, GS, Vishwakarma, V, Dawn, SS, Karthik, A, Vigneshwaran, S & Naidu, GD 2021, 'Reduction of sulphate reducing bacterial survival by Cu-Ni, Zn-Ni and Cu-Zn-Ni coatings using electroless plating technique for oil/diesel pipeline applications', Materials Today: Proceedings, vol. 45, pp. 6804-6806.
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Kamal, MS, Northcote, A, Chowdhury, L, Dey, N, Crespo, RG & Herrera-Viedma, E 2021, 'Alzheimer’s Patient Analysis Using Image and Gene Expression Data and Explainable-AI to Present Associated Genes', IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-7.
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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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Kan, WH, Huang, S, Man, Z, Yang, L, Huang, A, Chang, L, Nadot, Y, Cairney, JM & Proust, G 2021, 'Effect of T6 treatment on additively-manufactured AlSi10Mg sliding against ceramic and steel', Wear, vol. 482-483, pp. 203961-203961.
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Kanti Sen, M, Dutta, S, Gandomi, AH & Putcha, C 2021, 'Case Study for Quantifying Flood Resilience of Interdependent Building–Roadway Infrastructure Systems', ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, vol. 7, no. 2, pp. 04021005-04021005.
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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, pp. 132889-132889.
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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.

Karki, D, Far, H & Saleh, A 2021, 'Numerical studies into factors affecting structural behaviour of composite cold-formed steel and timber flooring systems', Journal of Building Engineering, vol. 44, pp. 102692-102692.
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Kashani, AR, Chiong, R, Dhakal, S & Gandomi, AH 2021, 'Investigating bound handling schemes and parameter settings for the interior search algorithm to solve truss problems', Engineering Reports, vol. 3, no. 10.
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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, vol. 28, no. 3, pp. 1871-1927.
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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.

Kashif, M, Hossain, MJ, Fernandez, E, Nizami, MSH, Ali, SMN & Sharma, V 2021, 'An Optimal Allocation of Reactive Power Capable End-User Devices for Grid Support', IEEE Systems Journal, vol. 15, no. 3, pp. 3249-3260.
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Kashyap, PK, Kumar, S, Jaiswal, A, Prasad, M & Gandomi, AH 2021, 'Towards Precision Agriculture: IoT-Enabled Intelligent Irrigation Systems Using Deep Learning Neural Network', IEEE Sensors Journal, vol. 21, no. 16, pp. 17479-17491.
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Katic, M, Cetindamar, D & Agarwal, R 2021, 'Deploying ambidexterity through better management practices: an investigation based on high-variety, low-volume manufacturing', Journal of Manufacturing Technology Management, vol. 32, no. 4, pp. 952-975.
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PurposeWhilst capabilities in exploiting existing assets and simultaneously exploring new opportunities 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/approachHigh-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.FindingsThe 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 implicationsThis 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

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, vol. 137, pp. 110448-110448.
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Ke, Z, Li, Z, Cao, Z & Liu, P 2021, 'Enhancing Transferability of Deep Reinforcement Learning-Based Variable Speed Limit Control Using Transfer Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4684-4695.
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The study aims to evaluate the performance of the transfer learning algorithm to enhance the transferability of a deep reinforcement learning-based variable speed limits (VSL) control. The Double Deep Q Network (DDQN)-based VSL control strategy is proposed for reducing total time spent (TTS) on freeways. A real merging bottleneck is developed in the simulation and considered for the VSL control as the source scenario. Three types of target scenarios are considered, including the overspeed scenarios, adverse weather scenarios, and diverse capacity drop scenarios. A stable testing demand and a fluctuating testing demand are adopted to evaluate the effects of VSL control. The results show that by updating the neural networks, the transfer learning in the DDQN-based VSL control agent successfully transfers knowledge learned in the source scenario to other target scenarios. With the transfer learning, the entire training process is shortened by 32.3% to 69.8%, while keeping a similar maximum reward level, as compared to the VSL control with full learning from scratch. With the transferred DDQN-based VSL strategy, the TTS is reduced by 26.02% to 67.37% with the stable testing demand and 21.31% to 69.98% with the fluctuating testing demand in various scenarios, respectively. The results also show that when the task similarity between the source scenario and target scenario is relatively low, the transfer learning could lead to local optimum and may not achieve the global optimal control effects.

Keshavarz, R, Lipman, J, Schreurs, DMM-P & Shariati, N 2021, 'Highly Sensitive Differential Microwave Sensor for Soil Moisture Measurement', IEEE Sensors Journal, vol. 21, no. 24, pp. 27458-27464.
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Keshavarz, R, Mohammadi, A & Abdipour, A 2021, 'Linearity improvement of a dual-band Doherty power amplifier using E-CRLH transmission line', AEU - International Journal of Electronics and Communications, vol. 131, pp. 153584-153584.
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Keshavarz, S, Keshavarz, R & Abdipour, A 2021, 'COMPACT ACTIVE DUPLEXER BASED ON CSRR AND INTERDIGITAL LOADED MICROSTRIP COUPLED LINES FOR LTE APPLICATION', Progress In Electromagnetics Research C, vol. 109, pp. 27-37.
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Khade, S, Gite, S, Thepade, SD, Pradhan, B & Alamri, A 2021, 'Detection of Iris Presentation Attacks Using Hybridization of Discrete Cosine Transform and Haar Transform With Machine Learning Classifiers and Ensembles', IEEE Access, vol. 9, pp. 169231-169249.
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Khan, AA, Abolhasan, M, Ni, W, Lipman, J & Jamalipour, A 2021, 'An End-to-End (E2E) Network Slicing Framework for 5G Vehicular Ad-Hoc Networks', IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp. 7103-7112.
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Khan, AUH, Liu, Y, Naidu, R, Fang, C, Dharmarajan, R & Shon, H 2021, 'Interactions between zinc oxide nanoparticles and hexabromocyclododecane in simulated waters', Environmental Technology & Innovation, vol. 24, pp. 102078-102078.
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Khan, HU, Niazi, M, El-Attar, M, Ikram, N, Khan, SU & Gill, AQ 2021, 'Empirical Investigation of Critical Requirements Engineering Practices for Global Software Development.', IEEE Access, vol. 9, pp. 93593-93613.
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Khan, JA, Vu, MT & Nghiem, LD 2021, 'A preliminary assessment of forward osmosis to extract water from rumen fluid for artificial saliva', Case Studies in Chemical and Environmental Engineering, vol. 3, pp. 100095-100095.
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Khan, MNH, Siwakoti, YP, Scott, MJ, Li, L, Khan, SA, Lu, DD-C, Barzegarkhoo, R, Sidorski, F, Blaabjerg, F & Hasan, SU 2021, 'A Common Grounded Type Dual-Mode Five-Level Transformerless Inverter for Photovoltaic Applications.', IEEE Trans. Ind. Electron., vol. 68, no. 10, pp. 9742-9754.
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This article presents a novel dual-mode five-level common grounded type (5L-DM-CGT) transformerless inverter topology for a medium-power application with a wide input voltage range (200–400 V). It consists of nine semiconductor switches, two inner flying-capacitors, and a small LC filter at the output side. Due to the direct connection of the negative terminal of the photovoltaic to the neutral point of the grid, there is no leakage current in the 5L-DM-CGT. Depending on the magnitude of the input voltage, the converter can operate in both buck and boost mode to produce the same ac output voltage. The theoretical analysis shows the advantages of the dual-mode inverter for various industrial applications. Finally, the laboratory test results are presented to verify the theoretical analysis. Measurement results show that the proposed inverter rated at 1 kW has around 97±1% efficiency over a wide range of load with a peak efficiency of 98.96% at 130 VA in buck mode and peak efficiency of 99% at 122 VA in boost mode

Khan, P, Khan, Y, Kumar, S, Khan, MS & Gandomi, AH 2021, 'HVD-LSTM based recognition of epileptic seizures and normal human activity', Computers in Biology and Medicine, vol. 136, pp. 104684-104684.
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Khan, S, Hussain, FK & Hussain, OK 2021, 'Guaranteeing end-to-end QoS provisioning in SOA based SDN architecture: A survey and Open Issues', Future Generation Computer Systems, vol. 119, pp. 176-187.
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Khan, S, Solano-Paez, P, Suwal, T, Lu, M, Al-Karmi, S, Ho, B, Mumal, I, Shago, M, Hoffman, LM, Dodgshun, A, Nobusawa, S, Tabori, U, Bartels, U, Ziegler, DS, Hansford, JR, Ramaswamy, V, Hawkins, C, Dufour, C, André, N, Bouffet, E, Huang, A, Gonzalez CV, A, Stephens, D, Leary, S, Marrano, P, Fonseca, A, Thacker, N, Li, BK, Lindsay, HB, Lassaletta, A, Bendel, AE, Moertel, C, Morales La Madrid, A, Santa-Maria, V, Lavarino, C, Rivas, E, Perreault, S, Ellezam, B, Weil, AG, Jabado, N, Oviedo, A, Yalon-Oren, M, Amariglio, L, Toledano, H, Dvir, R, Loukides, J, Van Meter, TE, Nakamura, H, Wong, T-T, Wu, K-S, Cheng, C-J, Ra, Y-S, La Spina, M, Massimi, L, Buccoliero, AM, Reddy, A, Li, R, Gillespie, GY, Adamek, D, Fangusaro, J, Scharnhorst, D, Torkildson, J, Johnston, D, Michaud, J, LafayCousin, L, Chan, J, Van Landeghem, F, Wilson, B, Camelo-Piragua, S, Kabbara, N, Boutarbouch, M, Hanson, D, Jacobsen, C, Wright, K, Vibhakar, R, Levy, JM, Wang, Y, Catchpoole, D, Gerber, N, Grotzer, MA, Shen, V, Plant, A, Dunham, C, Joao Gil da Costa, M, Ramanujachar, R, Raabe, E, Rubens, J, Phillips, J, Gupta, N, Demir, HA, Dahl, C, Jorgensen, M, Hwang, EI, Packer, RJ, Smith, A, Tan, E, Low, S, Lu, J-Q, Ng, H-K, Kresak, JL, Gururangan, S, Pomeroy, SL, Sirachainan, N, Hongeng, S, Magimairajan, V, Sinha, R, Mushtaq, N, Antony, R, Sato, M, Samuel, D, Zapotocky, M, Afzal, S, Walter, A, Tihan, T, Tsang, DS, Gajjar, A, Wood, P, Cain, JE, Downie, PA, Gottardo, N, Branson, H, Laughlin, S, Ertl-Wagner, B, Kulkarni, AV, Taylor, MD, Drake, J, Ibrahim, GM, Dirks, PB, Rutka, JT, Somers, GR, Hazrati, L-N, Bourdeaut, F, Padovani, L, Grundy, RG, Mazewski, CM & Fouladi, M 2021, 'Clinical phenotypes and prognostic features of embryonal tumours with multi-layered rosettes: a Rare Brain Tumor Registry study', The Lancet Child & Adolescent Health, vol. 5, no. 11, pp. 800-813.
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Khan, SA, Barzegarkhoo, R, Guo, Y, Siwakoti, Y, Khan, MNH, Lu, DD-C & Zhu, J 2021, 'Topology, Modeling and Control Scheme for a new Seven-Level Inverter With Reduced DC-Link Voltage', IEEE Transactions on Energy Conversion, vol. 36, no. 4, pp. 2734-2746.
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Khan, T, Bari, G, Kang, H-J, Lee, T-G, Park, J-W, Hwang, H, Hossain, S, Mun, J, Suzuki, N, Fujishima, A, Kim, J-H, Shon, H & Jun, Y-S 2021, 'Synthesis of N-Doped TiO2 for Efficient Photocatalytic Degradation of Atmospheric NOx', Catalysts, vol. 11, no. 1, pp. 109-109.
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Titanium oxide (TiO2) is a potential photocatalyst for removing toxic NOx from the atmosphere. Its practical application is, however, significantly limited by its low absorption into visible light and a high degree of charge recombination. The overall photocatalytic activity of TiO2 remains too low since it can utilize only about 4–5% of solar energy. Nitrogen doping into the TiO2 lattice takes advantage of utilizing a wide range of solar radiation by increasing the absorption capability towards the visible light region. In this work, N-doped TiO2, referred to as TC, was synthesized by a simple co-precipitation of tri-thiocyanuric acid (TCA) with P25 followed by heat treatment at 550 degrees C. The resulting nitrogen doping increased the visible-light absorption and enhanced the separation/transfer of photo-excited charge carriers by capturing holes by reduced titanium ions. As a result, TC samples exhibited excellent photocatalytic activities of 59% and 51% in NO oxidation under UV and visible light irradiation, in which the optimum mass ratio of TCA to P25 was found to be 10.

Khan, TA & Ling, SH 2021, 'A novel hybrid gravitational search particle swarm optimization algorithm', Engineering Applications of Artificial Intelligence, vol. 102, pp. 104263-104263.
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Khanafer, D, Ibrahim, I, Yadav, S, Altaee, A, Hawari, A & Zhou, J 2021, 'Brine reject dilution with treated wastewater for indirect desalination', Journal of Cleaner Production, vol. 322, pp. 129129-129129.
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Khanh Nguyen, V, Kumar Chaudhary, D, Hari Dahal, R, Hoang Trinh, N, Kim, J, Chang, SW, Hong, Y, Duc La, D, Nguyen, XC, Hao Ngo, H, Chung, WJ & Nguyen, DD 2021, 'Review on pretreatment techniques to improve anaerobic digestion of sewage sludge', Fuel, vol. 285, pp. 119105-119105.
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Anaerobic digestion (AD) of sewage sludge is one of the most efficient, effective, and environmentally sustainable remediation techniques; however, the presence of complex floc structures, hard cell walls, and large amounts of molecular organic matter in the sludge hinder AD hydrolysis. Consequently, sewage sludge pretreatment is a prerequisite to accelerate hydrolysis and improve AD efficiency. This review focuses on pretreatment techniques for improving sewage sludge AD, which include mechanical, chemical, thermal, and biological processes. The various pretreatment process effects are discussed in terms of advantages and disadvantages, including their effectiveness, and recent achievements are reviewed for improved biogas production.

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, vol. 9, pp. 83929-83939.
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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, vol. 2021, no. 5, pp. 276-285.
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Khuat, TT & Gabrys, B 2021, 'An in-depth comparison of methods handling mixed-attribute data for general fuzzy min–max neural network', Neurocomputing, vol. 464, pp. 175-202.
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Khuat, TT & Le, MH 2021, 'Balanced Random Hyperboxes for Class Imbalanced Problems', IAENG International Journal of Computer Science, vol. 48, no. 2, pp. 1-11.
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A Random Hyperboxes (RH) classifier is a simple but powerful randomization-based ensemble model, including hyperbox-based classifiers used as base learners. Individual learners in this ensemble model are trained on random subspaces of both instance and feature spaces. This facet results in a flexible mechanism to form a high-performing classifier competitive with other ensemble models in the literature. Like other machine learning models, however, the RH classifier also faces inefficiency when dealing with class-imbalanced datasets. Meanwhile, data containing highly imbalanced class distributions are prevalent in practical applications. Hence, this paper proposes a new variant of the original RH model, namely Balance Random Hyperboxes (BRH), to bypass this drawback effectively. The proposed method uses an undersampling strategy to build individual learners instead of the random sampling method employed in the original RH model. The experiment conducted on software fault datasets, which show a highly class-imbalanced property, indicated the proposed method's efficiency compared to the original RH model and other ensemble models.

Kieu, BT, Unanue, IJ, Pham, SB, Phan, HX & Piccardi, M 2021, 'NeuSub: A Neural Submodular Approach for Citation Recommendation', IEEE Access, vol. 9, pp. 148459-148468.
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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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AbstractThis 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.

Kim, J, Light, N, Subasri, V, Young, EL, Wegman-Ostrosky, T, Barkauskas, DA, Hall, D, Lupo, PJ, Patidar, R, Maese, LD, Jones, K, Wang, M, Genome Research Laboratory, C, Tavtigian, SV, Wu, D, Shlien, A, Telfer, F, Goldenberg, A, Skapek, SX, Wei, JS, Wen, X, Catchpoole, D, Hawkins, DS, Schiffman, JD, Khan, J, Malkin, D & Stewart, DR 2021, 'Pathogenic Germline Variants in Cancer Susceptibility Genes in Children and Young Adults With Rhabdomyosarcoma', JCO Precision Oncology, no. 5, pp. 75-87.
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PURPOSE Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue sarcoma and accounts for 3% of all pediatric cancer. In this study, we investigated germline sequence and structural variation in a broad set of genes in two large, independent RMS cohorts. MATERIALS AND METHODS Genome sequencing of the discovery cohort (n = 273) and exome sequencing of the secondary cohort (n = 121) were conducted on germline DNA. Analyses were performed on 130 cancer susceptibility genes (CSG). Pathogenic or likely pathogenic (P/LP) variants were predicted using the American College of Medical Genetics and Genomics (ACMG) criteria. Structural variation and survival analyses were performed on the discovery cohort. RESULTS We found that 6.6%-7.7% of patients with RMS harbored P/LP variants in dominant-acting CSG. An additional approximately 1% have structural variants ( ATM, CDKN1C) in CSGs. CSG variants did not influence survival, although there was a significant correlation with an earlier age of tumor onset. There was a nonsignificant excess of P/LP variants in dominant inheritance genes in the patients with FOXO1 fusion–negative RMS patients versus the patients with FOXO1 fusion–positive RMS. We identified pathogenic germline variants in CSGs previously ( TP53, NF1, DICER1, mismatch repair genes), rarely ( BRCA2, CBL, CHEK2, SMARCA4), or never ( FGFR4) reported in RMS. Numerous genes ( TP53, BRCA2, mismatch repair) were on the ACMG Secondary Findings 2.0 list. CONCLUSION In two cohorts of patients with RMS, we identified pathogenic germline variants for which gene-specific therapies and surveillance guidelines may be beneficial. In families with a proband with an RMS-risk P/LP variant, genetic counseling and cascade testing should be considered...

Kiran, MR, Farrok, O, Islam, MR & Zhu, J 2021, 'Increase in the Power Transfer Capability of Advanced Magnetic Material Based High Frequency Transformer by Using a Novel Distributed Winding Topology', IEEE Transactions on Industry Applications, vol. 57, no. 6, pp. 6306-6317.
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Kiran, MR, Farrok, O, Islam, MR, Zhu, J, Kouzani, AZ & Mahmud, MAP 2021, 'The High Frequency Magnetic-Link With Distributed HTS YBCO Windings for Power Converter Applications', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-5.
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Kolekar, S, Gite, S, Pradhan, B & Kotecha, K 2021, 'Behavior Prediction of Traffic Actors for Intelligent Vehicle Using Artificial Intelligence Techniques: A Review', IEEE Access, vol. 9, pp. 135034-135058.
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Kolekar, SS, Gite, SS & Pradhan, B 2021, 'Demystifying Artificial Intelligence based Behavior Prediction of Traffic Actors for Autonomous Vehicle- A Bibliometric Analysis of Trends and Techniques', Library Philosophy and Practice, vol. 2021, pp. 1-25.
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Background: The purpose of this study is to examine, using bibliometric methods, the work done on behavior prediction of traffic actors for autonomous vehicles using various artificial intelligence algorithms from 2011 to 2020. Methods: Using one of the most common databases, Scopus, numerous papers on behavior prediction of traffic actors for autonomous vehicles were retrieved. The research papers are being considered for the period from 2011 to 2020. The Scopus analyzer is used to obtain some results of the study, such as documents by year, source, and country and so on. VOSviewer Version 1.6.16 is used for the analysis of different units such as co-authorship, co-occurrences, citation analysis etc. Results: In our study, a database search outputs a total of 275 articles on behavior prediction for autonomous vehicle from 2011 to 2020. Statistical analysis and network analysis shows the maximum articles are published in the years 2019 and 2020 with United State contributed the largest number of documents. Network analysis of different parameters shows a good potential of the topic in terms of research. Conclusions: Scopus keyword search outcome has 272 articles with English language having the largest number. Authors, documents, country, affiliation etc are statically analyzed and indicates the potential of the topic. Network analysis of different parameters indicates that, there is a lot of scope to contribute in the further research in terms of advanced algorithms of computer vision, deep learning, machine learning and explainable artificial intelligence.

Koli, MNY, Afzal, MU & Esselle, KP 2021, 'Significant Bandwidth Enhancement of Radial-Line Slot Array Antennas Using a Radially Nonuniform TEM Waveguide', IEEE Transactions on Antennas and Propagation, vol. 69, no. 6, pp. 3193-3203.
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IEEE Radial line slot array (RLSA) antennas have attractive features such as high gain, high efficiency, and planar low profile, but their gain bandwidths have been limited to less than 10%. This paper presents a method to significantly increase the gain bandwidth of RLSAs to over 30%. The key to the method is the application of a non-uniform radial TEM waveguide as opposed to the radially uniform TEM waveguide used in conventional RLSAs. Hence, the condition for maximum radiation is satisfied at a wide range of frequencies by different sections of the RLSA. To demonstrate the concept, several circularly polarised RLSA designs and one prototype are presented. The measured results of the prototype demonstrate an unprecedented 3dB gain bandwidth of 27.6%, a peak gain of 27.3 dBic, 3dB axial ratio bandwidth greater than 31.1% and a 10dB return loss bandwidth greater than 34.8%. The overall measured bandwidth of the RLSA in which gain variation and axial ratio are within 3dB and return loss is greater than 10dB is from 9.7 GHz to 12.8 GHz or 27.6%. Its extremely high measured gain bandwidth product per unit area (GBP/A) of 88 indicates excellent overall performance in terms of bandwidth, gain and area.

Koli, MNY, Afzal, MU, Esselle, KP & Mehta, A 2021, 'Use of Narrower Reflection-Canceling Slots to Design Linearly Polarized Radial Line Slot Arrays With Improved Radiation Performance', IEEE Antennas and Wireless Propagation Letters, vol. 20, no. 12, pp. 2275-2279.
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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, vol. 7, no. 3, pp. 263-276.
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Abstract Purpose of Review Untreated wastewater discharge can significantly and negatively impact the state of the environment. Rapid industrialization and economic development have directly contributed to land and water pollution resulting from the application of many chemicals such as organic dyes, pharmaceuticals, and industrial reagents. The removal of these chemicals before effluent discharge is crucial for environmental protection. This review aims to explore the importance of functionalized materials in the preparation of biocatalytic systems and consider their application in eliminating water pollutants. Recent Findings Wastewater treatment methods can be classified into three groups: (i) chemical (e.g., chemical oxidation and ozonation), (ii) physical (e.g., membrane separation and ion exchange), and (iii) biological processes. Biological treatment is the most widely used method due to its cost-effectiveness and eco-friendliness. In particular, the use of immobilized enzymes has recently become more attractive as a result of scientific progress in advanced material synthesis. The selection of an appropriate support plays an important role in the preparation of such biologically active systems. Recent studies have demonstrated the use of various materials for enzyme immobilization in the purification of water. Summary This review identifies and discusses different biocatalytic systems used in the enzymatic degradation of various water pollutants. Materials functionalized by specific groups can serve as good support matrices for enzyme immobilization, providing chemical and thermal stability to support catalytic reactions. Enzymatic biocataly...

Kordi Ghasrodashti, E & Sharma, N 2021, 'Hyperspectral image classification using an extended Auto-Encoder method', Signal Processing: Image Communication, vol. 92, pp. 116111-116111.
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Kousik, N, Natarajan, Y, Arshath Raja, R, Kallam, S, Patan, R & Gandomi, AH 2021, 'Improved salient object detection using hybrid Convolution Recurrent Neural Network', Expert Systems with Applications, vol. 166, pp. 114064-114064.
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Krätzig, O & Sick, N 2021, 'Exploring the role of entrepreneurial passion for facilitating university technology commercialization: Insights from battery research as an interdisciplinary field', Journal of Engineering and Technology Management, vol. 60, pp. 101627-101627.
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Krishankumar, R, Arun, K, Kumar, A, Rani, P, Ravichandran, KS & Gandomi, AH 2021, 'Double-hierarchy hesitant fuzzy linguistic information-based framework for green supplier selection with partial weight information', Neural Computing and Applications, vol. 33, no. 21, pp. 14837-14859.
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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, pp. 123329-123329.
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Krishankumar, R, Ravichandran, KS, Liu, P, Kar, S & Gandomi, AH 2021, 'A decision framework under probabilistic hesitant fuzzy environment with probability estimation for multi-criteria decision making', Neural Computing and Applications, vol. 33, no. 14, pp. 8417-8433.
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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.

Kumar, A, Kim, Y, Su, X, Fukuda, H, Naidu, G, Du, F, Vigneswaran, S, Drioli, E, Hatton, TA & Lienhard, JH 2021, 'Advances and challenges in metal ion separation from water', Trends in Chemistry, vol. 3, no. 10, pp. 819-831.
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Kumar, A, Naidu, G, Fukuda, H, Du, F, Vigneswaran, S, Drioli, E & Lienhard, JH 2021, 'Metals Recovery from Seawater Desalination Brines: Technologies, Opportunities, and Challenges', ACS Sustainable Chemistry & Engineering, vol. 9, no. 23, pp. 7704-7712.
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Kundariya, N, Mohanty, SS, Varjani, S, Hao Ngo, H, W. C. Wong, J, Taherzadeh, MJ, Chang, J-S, Yong Ng, H, Kim, S-H & Bui, X-T 2021, 'A review on integrated approaches for municipal solid waste for environmental and economical relevance: Monitoring tools, technologies, and strategic innovations', Bioresource Technology, vol. 342, pp. 125982-125982.
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Kusakunniran, W, Charoenpanich, P, Samunyanoraset, P, Suksai, S, Karnjanapreechakorn, S, Wu, Q & Zhang, J 2021, 'Hybrid Learning of Vessel Segmentation in Retinal Images', ECTI Transactions on Computer and Information Technology (ECTI-CIT), vol. 15, no. 1, pp. 1-11.
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This paper aims to develop a technique of vessel segmentation in retinal images. Interpreting the segmented vessels is necessary for the automatic detection of the severe stage of the diabetic retinopathy. Thus, it is important to have the technique for segmenting vessels in an automatic way with high performance, for the sake of further analysis. In this paper, the proposed method is developed based on the double layer combining supervised and non-supervised learning aspects. The first layer is to detect the initial seeds of vessels using the supervised learning. It learns based on three types of features including green intensity, line operators, and Gabor filters. Then, the support vector machine (SVM) is applied as the classification tool. In the second layer, the segmentation results from the first layer is further revised and completed using the non-supervised learning. The morphological operations with the watershed technique are applied on the results obtained from the first layer, to remain with the segmented pixels with high confidential to be vessels. Then, these pixels are used as the initial seeds of foreground in the iterative graph cut. As the result, the more completed and comprehensive foreground (i.e. vessels) can be obtained. The proposed method is evaluated using two well-known datasets including DRIVE and STARE. The experimental results show the promising performance of the proposed method when compared with other existing methods in the literature.

Kutay, C 2021, 'Caretaking Aboriginal Australian Knowledges Online', ab-Original, vol. 4, no. 1-2, pp. 72-102.
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ABSTRACT The influence of Aboriginal Australian's Knowledges and Protocols on Australian culture has been profound and yet little acknowledged. To acknowledge the First Peoples of Australia and integrate their knowledge into the education system, we start with the First Peoples' contribution to culture and learning since invasion in Australia. We then consider contributions now to educational technologies with a focus on collectivist knowledge sharing, oral teaching, narrative teaching, peer-to-peer sharing, and truth telling. In recognition of what modern non-Indigenous cultures have lost, we are appropriating technology to share the concepts around narrative learning and sustainable practice. This uses pattern matching skills that were initially developed for sharing knowledge across different environments between Aboriginal Australian communities and provides processes for memorizing and sharing diversity. Ways of emulating these processes online is constructive in modern language reclamation, where the existing language information is scattered across many individuals, clans, and locations.

Kute, DV, Pradhan, B, Shukla, N & Alamri, A 2021, 'Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering–A Critical Review', IEEE Access, vol. 9, pp. 82300-82317.
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Kwon, S, Tomonaga, A, Lakshmi Bhai, G, Devitt, SJ & Tsai, J-S 2021, 'Gate-based superconducting quantum computing', Journal of Applied Physics, vol. 129, no. 4, pp. 041102-041102.
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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, pp. 141708-141708.
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Laccone, F, Malomo, L, Pietroni, N, Cignoni, P & Schork, T 2021, 'Integrated computational framework for the design and fabrication of bending-active structures made from flat sheet material', Structures, vol. 34, pp. 979-994.
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Laengle, S, Lobos, V, Merigó, JM, Herrera-Viedma, E, Cobo, MJ & De Baets, B 2021, 'Forty years of Fuzzy Sets and Systems: A bibliometric analysis', Fuzzy Sets and Systems, vol. 402, pp. 155-183.
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© 2020 Elsevier B.V. Fuzzy Sets and Systems is a leading international journal in computer science and applied mathematics that was created in 1978. In 2018, the journal celebrated its 40th anniversary. The aim of this study is to present a bibliometric overview of the leading trends occurring in the journal between 1978 and 2016 by analysing the most productive and influential authors, institutions and countries as well as the publication and citation structure. Additionally, this work presents a graphical visualization of the bibliographic data by using the visualization of similarities (VOS) viewer and the science mapping analysis tool (SciMAT) software. The results show the strong growth of fuzzy set theory over time and a huge diversity of publications from all over the world, especially from Europe, North America and East Asia.

Lai, W-H, Zhang, L, Yan, Z, Hua, W, Indris, S, Lei, Y, Liu, H, Wang, Y-X, Hu, Z, Liu, HK, Chou, S, Wang, G & Dou, SX 2021, 'Activating Inert Surface Pt Single Atoms via Subsurface Doping for Oxygen Reduction Reaction', Nano Letters, vol. 21, no. 19, pp. 7970-7978.
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Lalbakhsh, A, Afzal, MU, Hayat, T, Esselle, KP & Mandal, K 2021, 'All-metal wideband metasurface for near-field transformation of medium-to-high gain electromagnetic sources', Scientific Reports, vol. 11, no. 1.
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AbstractElectromagnetic (EM) metasurfaces are essential in a wide range of EM engineering applications, from incorporated into antenna designs to separate devices like radome. Near-field manipulators are a class of metasurfaces engineered to tailor an EM source’s radiation patterns by manipulating its near-field components. They can be made of all-dielectric, hybrid, or all-metal materials; however, simultaneously delivering a set of desired specifications by an all-metal structure is more challenging due to limitations of a substrate-less configuration. The existing near-field phase manipulators have at least one of the following limitations; expensive dielectric-based prototyping, subject to ray tracing approximation and conditions, narrowband performance, costly manufacturing, and polarization dependence. In contrast, we propose an all-metal wideband phase correcting structure (AWPCS) with none of these limitations and is designed based on the relative phase error extracted by post-processing the actual near-field distributions of any EM sources. Hence, it is applicable to any antennas, including those that cannot be accurately analyzed with ray-tracing, particularly for near-field analysis. To experimentally verify the wideband performance of the AWPCS, a shortened horn antenna with a large apex angle and a non-uniform near-field phase distribution is used as an EM source for the AWPCS. The measured results verify a significant improvement in the antenna’s aperture phase distribution in a large frequency band of 25%.

Lalbakhsh, A, Mohamadpour, G, Roshani, S, Ami, M, Roshani, S, Sayem, ASM, Alibakhshikenari, M & Koziel, S 2021, 'Design of a Compact Planar Transmission Line for Miniaturized Rat-Race Coupler With Harmonics Suppression', IEEE Access, vol. 9, pp. 129207-129217.
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Lan, T, Hutvagner, G, Lan, Q, Liu, T & Li, J 2021, 'Sequencing dropout-and-batch effect normalization for single-cell mRNA profiles: a survey and comparative analysis', Briefings in Bioinformatics, vol. 22, no. 4.
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Abstract Single-cell mRNA sequencing has been adopted as a powerful technique for understanding gene expression profiles at the single-cell level. However, challenges remain due to factors such as the inefficiency of mRNA molecular capture, technical noises and separate sequencing of cells in different batches. Normalization methods have been developed to ensure a relatively accurate analysis. This work presents a survey on 10 tools specifically designed for single-cell mRNA sequencing data preprocessing steps, among which 6 tools are used for dropout normalization and 4 tools are for batch effect correction. In this survey, we outline the main methodology for each of these tools, and we also compare these tools to evaluate their normalization performance on datasets which are simulated under the constraints of dropout inefficiency, batch effect or their combined effects. We found that Saver and Baynorm performed better than other methods in dropout normalization, in most cases. Beer and Batchelor performed better in the batch effect normalization, and the Saver–Beer tool combination and the Baynorm–Beer combination performed better in the mixed dropout-and-batch effect normalization. Over-normalization is a common issue occurred to these dropout normalization tools that is worth of future investigation. For the batch normalization tools, the capability of retaining heterogeneity between different groups of cells after normalization can be another direction for future improvement.

Laranjo, L, Ding, D, Heleno, B, Kocaballi, B, Quiroz, JC, Tong, HL, Chahwan, B, Neves, AL, Gabarron, E, Dao, KP, Rodrigues, D, Neves, GC, Antunes, ML, Coiera, E & Bates, DW 2021, 'Do smartphone applications and activity trackers increase physical activity in adults? Systematic review, meta-analysis and metaregression', British Journal of Sports Medicine, vol. 55, no. 8, pp. 422-432.
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ObjectiveTo determine the effectiveness of physical activity interventions involving mobile applications (apps) or trackers with automated and continuous self-monitoring and feedback.DesignSystematic review and meta-analysis.Data sourcesPubMed and seven additional databases, from 2007 to 2020.Study selectionRandomised controlled trials in adults (18–65 years old) without chronic illness, testing a mobile app or an activity tracker, with any comparison, where the main outcome was a physical activity measure. Independent screening was conducted.Data extraction and synthesisWe conducted random effects meta-analysis and all effect sizes were transformed into standardised difference in means (SDM). We conducted exploratory metaregression with continuous and discrete moderators identified as statistically significant in subgroup analyses.Main outcome measuresPhysical activity: daily step counts, min/week of moderate-to-vigorous physical activity, weekly days exercised, min/week of total physical activity, metabolic equivalents.ResultsThirty-five studies met inclusion criteria and 28 were included in the meta-analysis (n=7454 participants, 28% women). The meta-analysis showed a small-to-moderate positive effect on physical activity measures (SDM 0.350, 95% CI 0.236 to 0.465, I2=69%, T2=0.051) corresponding to 1850 steps per day (95% CI 1247 to 2457). Interventions including text-messaging and personalisation features were significantly more effective in subgroup analyses and metaregression...

Le, AT, Huang, X & Guo, YJ 2021, 'Analog Self-Interference Cancellation in Dual-Polarization Full-Duplex MIMO Systems', IEEE Communications Letters, vol. 25, no. 9, pp. 3075-3079.
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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, Tran, LC, 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, Choi, J, Dutkiewicz, E, Phung, SL & Bouzerdoum, A 2021, 'Performance Analysis of Uplink NOMA Systems With Hardware Impairments and Delay Constraints Over Composite Fading Channels', IEEE Transactions on Vehicular Technology, vol. 70, no. 7, pp. 6881-6897.
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Le, NP, Tran, LC, Huang, X, Dutkiewicz, E, Ritz, C, Phung, SL, Bouzerdoum, A, Franklin, DR & Hanzo, L 2021, 'Energy-Harvesting Aided Unmanned Aerial Vehicles for Reliable Ground User Localization and Communications Under Lognormal-Nakagami-$m$ Fading Channels.', IEEE Trans. Veh. Technol., vol. 70, no. 2, pp. 1632-1647.
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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, no. 9, 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, pp. 120309-120309.
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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, pp. 106921-106921.
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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, pp. 108087-108087.
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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.

Lei, G, Bramerdorfer, G, Liu, C, Guo, Y & Zhu, J 2021, 'Robust Design Optimization of Electrical Machines: A Comparative Study and Space Reduction Strategy', IEEE Transactions on Energy Conversion, vol. 36, no. 1, pp. 300-313.
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Lei, G, Bramerdorfer, G, Ma, B, Guo, Y & Zhu, J 2021, 'Robust Design Optimization of Electrical Machines: Multi-Objective Approach', IEEE Transactions on Energy Conversion, vol. 36, no. 1, pp. 390-401.
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Lei, H, Chen, S, Wang, M, He, X, Jia, W & Li, S 2021, 'A New Algorithm for Sketch-Based Fashion Image Retrieval Based on Cross-Domain Transformation', Wireless Communications and Mobile Computing, vol. 2021, pp. 1-14.
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Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.

Leng, D, Zhu, Z, Xu, K, Li, Y & Liu, G 2021, 'Vibration control of jacket offshore platform through magnetorheological elastomer (MRE) based isolation system', Applied Ocean Research, vol. 114.
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Undesirable vibrations in offshore platforms due to ocean loadings may reduce platform productivity and increase the fatigue failure. This study proposes a magnetorheological elastomer (MRE) based isolation system to control the jacket platform oscillations and its effectiveness is numerically evaluated. The working principle and design method of MRE-based isolation system are proposed, and MRE materials with high magnetorheological effects are conceptually designed. Practical jacket offshore platforms are selected for case studies. Semi-active fuzzy controller (SFC) is utilized to achieve real-time non-resonance vibration control. The proposed fuzzy core is constructed conceptually by the dynamic analysis of object structure. Numerical results demonstrate that MRE isolation system with SFC significantly reduces the maximum, minimum and RMS of the deck displacement and acceleration under realistic irregular waves at different sea states. MRE system could also reduce the response spectrum peaks and present robustness under various deck's mass. The present study proves the feasibility of MRE isolation systems in the application of vibration control for marine structures.

Leng, D, Zhu, Z, Xu, K, Li, Y & Liu, G 2021, 'Vibration control of jacket offshore platform through magnetorheological elastomer (MRE) based isolation system', Applied Ocean Research, vol. 114, pp. 102779-102779.
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Leon-Castro, E, Blanco-Mesa, F, Alfaro-Garcia, V, Gil-Lafuente, AM & Merigo, JM 2021, 'Fuzzy systems and applications in innovation and sustainability', Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 1723-1726.
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Leon-Castro, E, Blanco-Mesa, F, Alfaro-Garcia, V, Gil-Lafuente, AM & Merigo, JM 2021, 'Fuzzy systems in innovation and sustainability', Computational and Mathematical Organization Theory, vol. 27, no. 4, pp. 377-383.
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Leon-Castro, E, Blanco-Mesa, FR, Gil-Lafuente, AM & Merigo Lindahl, JM 2021, 'Editorial', International Journal of Entrepreneurship and Innovation Management, vol. 25, no. 2-3, pp. 105-109.

León-Castro, E, Espinoza-Audelo, LF, Merigó, JM, Herrera-Viedma, E & Herrera, F 2021, 'Measuring volatility based on ordered weighted average operators: The case of agricultural product prices', Fuzzy Sets and Systems, vol. 422, pp. 161-176.
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León-Castro, E, Perez-Arellano, LA, Olazabal-Lugo, M & Merigó, JM 2021, 'Prioritized Induced Heavy Operators Applied to Political Modelling', International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 29, no. 04, pp. 603-620.
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This paper presents the prioritized induced heavy ordered weighted average (PIHOWA) operator. This operator combines an unbounded weighting vector, an induced vector and a prioritized vector and can be applied to the group decision-making process where the information provided by each decision maker does not have the same importance. An application of this operator is done in governmental transparency in Mexico based on the Open Government Metric (OGM). Among the main results it is possible to visualize how the relative importance of each component can generate important change in the top 10 ranking.

Li, A, Yang, B, Huo, H & Hussain, FK 2021, 'Leveraging implicit relations for recommender systems', Information Sciences, vol. 579, pp. 55-71.
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Li, B, Guo, T, Li, R, Wang, Y, Ou, Y & Chen, F 2021, 'Delay Propagation in Large Railway Networks with Data-Driven Bayesian Modeling', Transportation Research Record: Journal of the Transportation Research Board, vol. 2675, no. 11, pp. 472-485.
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Reliability and punctuality are the key evaluation criteria in railway service for both passengers and operators. Delays spanning over spatial and temporal dimensions significantly affect the reliability and punctuality level of train operation. The optimization of capacity utilization and timetable design requires the prediction of the reliability and punctuality level of train operations, which is determined by train delays and delay propagation. To predict the punctuality level of train operations, the distributions of arrival and departure delays must be estimated as realistically as possible by taking into account the complex railway network structure and different types of delays caused by route conflict and connected trips. This paper aims to predict the propagation of delays on the railway network in the Greater Sydney area by developing a conditional Bayesian model. In the model, the propagation satisfies the Markov property if one can predict future delay propagation in the network based solely on its present state just as well as one could knowing the process’s full history, so that it is independent of such historical procedures. Meanwhile, we consider the throughput estimation for the cases of delay caused by interchange line conflicts and train connection in this model. To the best of the authors’ knowledge, this is the first work of data-driven delay propagation modeling that examines both spatial and temporal dimensions under four different scenarios for railway networks. Implementation on real-world railway network operation data shows the feasibility and accuracy of the proposed model compared with traditional probability models.

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, C, Xie, H-B, Fan, X, Xu, RYD, Van Huffel, S & Mengersen, K 2021, 'Kernelized Sparse Bayesian Matrix Factorization', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 1, pp. 391-404.
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Li, C, Zhang, F, Zhang, Y, Qin, L, Zhang, W & Lin, X 2021, 'Discovering fortress-like cohesive subgraphs', Knowledge and Information Systems, vol. 63, no. 12, pp. 3217-3250.
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Li, D, Tang, M-C, Wang, Y, Hu, K-Z & Ziolkowski, RW 2021, 'Compact Differential Diplex Filtenna With Common-Mode Suppression for Highly Integrated Radio Frequency Front-Ends', IEEE Transactions on Antennas and Propagation, vol. 69, no. 11, pp. 7935-7940.
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Li, DL, Prasad, M, Liu, C-L & Lin, C-T 2021, 'Multi-View Vehicle Detection Based on Fusion Part Model With Active Learning', IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 5, pp. 3146-3157.
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IEEE Computer vision-based vehicle detection techniques are widely used in real-world applications. However, most of these techniques aim to detect only single-view vehicles, and their performances are easily affected by partial occlusion. Therefore, this paper proposes a novel multi-view vehicle detection system that uses a part model to address the partial occlusion problem and the high variance between all types of vehicles. There are three features in this paper; firstly, different from Deformable Part Model, the construction of part models in this paper is visual and can be replaced at any time. Secondly, this paper proposes some new part models for detection of vehicles according to the appearance analysis of a large number of modern vehicles by the active learning algorithm. Finally, this paper proposes the method that contains color transformation along with the Bayesian rule to filter out the background to accelerate the detection time and increase accuracy. The proposed method outperforms other methods on given dataset.

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, vol. 18, no. 4, pp. 046097-046097.
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Abstract 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, F, Li, Y, Zheng, H, Jiang, L, Gao, D, Li, C, Peng, Y, Cao, Z, Zhang, Y, Yao, D, Xu, T, Yuan, T-F & Xu, P 2021, 'Identification of the General Anesthesia Induced Loss of Consciousness by Cross Fuzzy Entropy-Based Brain Network', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 2281-2291.
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Li, F, Yi, C, Liao, Y, Jiang, Y, Si, Y, Song, L, Zhang, T, Yao, D, Zhang, Y, Cao, Z & Xu, P 2021, 'Reconfiguration of Brain Network Between Resting State and P300 Task', IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 2, pp. 383-390.
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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, Zheng, J, Zhang, Y-F, Liu, N & Jia, W 2021, 'AMDFNet: Adaptive multi-level deformable fusion network for RGB-D saliency detection', Neurocomputing, vol. 465, pp. 141-156.
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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, vol. 28, no. 10.
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Li, H, Li, J, Yu, Y & Li, Y 2021, 'Modified Adaptive Negative Stiffness Device with Variable Negative Stiffness and Geometrically Nonlinear Damping for Seismic Protection of Structures', International Journal of Structural Stability and Dynamics, pp. 2150107-2150107.
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Adaptive negative stiffness device is one of the promising seismic protection devices since it can generate seismic isolation effect through negative stiffness when it is mostly needed and achieve similar vibration mitigation as a semi-active control device. However, the adaptive negative stiffness device generally combined with linear viscous damping underpins the drawback of degrading the vibration isolation effect during the high-frequency region. In this paper, a modified adaptive negative stiffness device (MANSD) with the ability to provide both lateral negative stiffness and nonlinear damping by configuring linear springs and linear viscous dampers is proposed to address the above issue. The negative stiffness 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 effect of nonlinear damping on suppressing displacement and acceleration responses is numerically verified under different sinusoidal excitations and earthquakes with different 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, Li, Y, Wang, K, Lai, L, Xu, X, Sun, B, Yang, Z & Ding, G 2021, 'Ultra-high sensitive micro-chemo-mechanical hydrogen sensor integrated by palladium-based driver and high-performance piezoresistor', International Journal of Hydrogen Energy, vol. 46, no. 1, pp. 1434-1445.
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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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To isolate vibration at a low-frequency range and at the same time to provide sufficient loading support to the isolated structure impose a challenge in vibration isolation. Quasi-zero stiffness (QZS) vibration isolator, as a potential solution to the challenge, has been widely investigated due to its unique property of high-static & low-dynamic stiffness. This paper provides an in-depth analysis and potential optimization of a typical QZS vibration isolator to illustrate the complexity and importance of design optimization. By carefully examining the governing fundamentals of the QZS vibration isolator, a simplified approximation of force and stiffness relationship is derived to enable the characteristic analysis of the QZS vibration isolator. The explicit formulae of the amplitude-frequency response (AFR) and transmissibility of the QZS vibration isolator are obtained by employing the Harmonic Balance Method. The transmissibility curves under force excitation with different values of nonlinear coefficient, damping ratio, and amplitude of excitation are further investigated. As the result, an optimization of the structural parameter has been demonstrated using a comprehensive objective function with considering multiple dynamic characteristic parameters simultaneously. Finally, the genetic algorithm (GA) is adopted to minimise the objective function to obtain the optimal stiffness ratios under different conditions. General recommendations are provided and discussed in the end.

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, vol. 43, pp. 103145-103145.
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Li, J, Guo, J & Zhu, X 2021, 'Time-Varying Parameter Identification of Bridges Subject to Moving Vehicles Using Ridge Extraction Based on Empirical Wavelet Transform', International Journal of Structural Stability and Dynamics, vol. 21, no. 04, pp. 2150046-2150046.
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For a vehicle moving over a bridge, the vehicle-bridge interaction (VBI) embraces the time-varying modal parameters of the system. The identification of non-stationary characteristics of bridge responses due to moving vehicle load is important and remains a challenging task. A new method based on the improved empirical wavelet transform (EWT) along with ridge detection of signals in time-frequency representation (TFR) is proposed to estimate the instantaneous frequencies (IFs) of the bridge. Numerical studies are conducted using a VBI model to investigate the time-varying characteristics of the system. The effects of the measurement noise, road surface roughness and structural damage on the bridge IFs are investigated. Finally, the dynamic responses of an in-situ cable-stayed bridge subjected to a passing vehicle are analyzed to further explore the time varying characteristics of the VBI system. Numerical and experimental studies demonstrate the feasibility and effectiveness of the proposed method on the IF estimation. The identified IFs reveal important time-varying characteristics of the bridge dynamics that is significant to evaluating the actual performance of operational bridges in operation and may be used for structural health assessment.

Li, J, Jin, J, Lyu, L, Yuan, D, Yang, Y, Gao, L & Shen, C 2021, 'A fast and scalable authentication scheme in IOT for smart living', Future Generation Computer Systems, vol. 117, pp. 125-137.
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Li, J, Pan, W, Liu, Q, Chen, Z, Chen, Z, Feng, X & Chen, H 2021, 'Interfacial Engineering of Bi₁₉Br₃S₂₇ Nanowires Promotes Metallic Photocatalytic CO₂ Reduction Activity under Near-Infrared Light Irradiation', Journal of the American Chemical Society, vol. 143, no. 17, pp. 6551-6559.
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Li, K, Ni, W, Tovar, E & Guizani, M 2021, 'Joint Flight Cruise Control and Data Collection in UAV-Aided Internet of Things: An Onboard Deep Reinforcement Learning Approach', IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9787-9799.
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Employing unmanned aerial vehicles (UAVs) as aerial data collectors in Internet-of-Things (IoT) networks is a promising technology for large-scale environment sensing. A key challenge in UAV-aided data collection is that UAV maneuvering gives rise to buffer overflow at the IoT node and unsuccessful transmission due to lossy airborne channels. This article formulates a joint optimization of flight cruise control and data collection schedule to minimize network data loss as a partially observable Markov decision process (POMDP), where the states of individual IoT nodes can be obscure to the UAV. The problem can be optimally solvable by reinforcement learning, but suffers from the curse of dimensionality and becomes rapidly intractable with the growth in the number of IoT nodes. In practice, a UAV-aided IoT network contains a large number of network states and actions in POMDP while the up-to-date knowledge is not available at the UAV. We propose an onboard deep Q -network-based flight resource allocation scheme (DQN-FRAS) to optimize the online flight cruise control of the UAV and data scheduling given outdated knowledge on the network states. Numerical results demonstrate that DQN-FRAS reduces the packet loss by over 51%, as compared to existing nonlearning heuristics.

Li, K, Ni, W, Tovard, E & Jamalipour, A 2021, 'Online Velocity Control and Data Capture of Drones for the Internet of Things: An Onboard Deep Reinforcement Learning Approach', IEEE Vehicular Technology Magazine, vol. 16, no. 1, pp. 49-56.
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Li, M, Cao, Z & Li, Z 2021, 'A Reinforcement Learning-Based Vehicle Platoon Control Strategy for Reducing Energy Consumption in Traffic Oscillations', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 12, pp. 5309-5322.
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The vehicle platoon will be the most dominant driving mode on future roads. To the best of our knowledge, few reinforcement learning (RL) algorithms have been applied in vehicle platoon control, which has large-scale action and state spaces. Some RL-based methods were applied to solve single-agent problems. If we need to tackle multiagent problems, we will use multiagent RL algorithms since the parameters space grows exponentially with the increasing number of agents involved. Previous multiagent RL algorithms generally may provide redundant information to agents, indicating a large amount of useless or unrelated information, which may cause to be difficult for convergence training and pattern extractions from shared information. Also, random actions usually contribute to crashes, especially at the beginning of training. In this study, a communication proximal policy optimization (CommPPO) algorithm was proposed to tackle the above issues. In specific, the CommPPO model adopts a parameter-sharing structure to allow the dynamic variation of agent numbers, which can well handle various platoon dynamics, including splitting and merging. The communication protocol of the CommPPO consists of two parts. In the state part, the widely used predecessor-leader follower typology in the platoon is adopted to transmit global and local state information to agents. In the reward part, a new reward communication channel is proposed to solve the spurious reward and ``lazy agent'' problems in some existing multiagent RLs. Moreover, a curriculum learning approach is adopted to reduce crashes and speed up training. To validate the proposed strategy for platoon control, two existing multiagent RLs and a traditional platoon control strategy were applied in the same scenarios for comparison. Results showed that the CommPPO algorithm gained more rewards and achieved the largest fuel consumption reduction (11.6%).

Li, M, Chen, Q, Wen, K, Nimbalkar, S & Dai, R 2021, 'Improved Vacuum Preloading Method Combined with Sand Sandwich Structure for Consolidation of Dredged Clay-Slurry Fill and Original Marine Soft Clay', International Journal of Geomechanics, vol. 21, no. 10, pp. 04021182-04021182.
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Li, M, Liu, Y & Guo, YJ 2021, 'Design of Sum and Difference Patterns by Optimizing Element Rotations and Positions for Linear Dipole Array', IEEE Transactions on Antennas and Propagation, vol. 69, no. 5, pp. 3027-3032.
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IEEE This communication presents a novel method of synthesizing both sum and difference patterns by optimizing the element rotations and positions for linear dipole array. The common element rotations and positions are optimized by using the particle swarm optimization (PSO) method to produce sum and difference patterns with reduced sidelobe levels (SLLs) and cross-polarization levels (XPLs), and as steep slope as possible for the difference pattern at the target direction. Such method leads to a sum-and-difference array with sparsely distributed uniform amplitude elements, thus saving many antenna elements and unequal power dividers. Three examples for synthesizing sparse rotated dipole arrays with sum and difference patterns are provided. Synthesis results show that the obtained arrays with uniform amplitudes can produce satisfactory sum and difference patterns while saving about 34.69% ~ 42.27% of the antenna elements when compared with λ/2-spaced arrays occupying the same aperture.

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, vol. 68, no. 7, pp. 3163-3169.
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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 and Concrete Composites, vol. 121, pp. 104100-104100.
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Li, P, Pan, P, Liu, P, Xu, M & Yang, Y 2021, 'Hierarchical Temporal Modeling With Mutual Distance Matching for Video Based Person Re-Identification', IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 2, pp. 503-511.
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Li, Q, Meng, S, Sang, X, Zhang, H, Wang, S, Bashir, AK, Yu, K & Tariq, U 2021, 'Dynamic Scheduling Algorithm in Cyber Mimic Defense Architecture of Volunteer Computing', ACM Transactions on Internet Technology, vol. 21, no. 3, pp. 1-33.
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Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.

Li, Q, Tian, Y, Wu, D, Gao, W, Yu, Y, Chen, X & Yang, C 2021, 'The nonlinear dynamic buckling behaviour of imperfect solar cells subjected to impact load', Thin-Walled Structures, vol. 169, pp. 108317-108317.
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Li, Q, Wang, Z, Liu, S, Li, G & Xu, G 2021, 'Causal Optimal Transport for Treatment Effect Estimation', IEEE Transactions on Neural Networks and Learning Systems, vol. PP, pp. 1-13.
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Treatment effect estimation helps answer questions, such as whether a specific treatment affects the outcome of interest. One fundamental issue in this research is to alleviate the treatment assignment bias among those treated units and controlled units. Classical causal inference methods resort to the propensity score estimation, which unfortunately tends to be misspecified when only limited overlapping exists between the treated and the controlled units. Moreover, existing supervised methods mainly consider the treatment assignment information underlying the factual space, and thus, their performance of counterfactual inference may be degraded due to overfitting of the factual results. To alleviate those issues, we build on the optimal transport theory and propose a novel causal optimal transport (CausalOT) model to estimate an individual treatment effect (ITE). With the proposed propensity measure, CausalOT can infer the counterfactual outcome by solving a novel regularized optimal transport problem, which allows the utilization of global information on observational covariates to alleviate the issue of limited overlapping. In addition, a novel counterfactual loss is designed for CausalOT to align the factual outcome distribution with the counterfactual outcome distribution. Most importantly, we prove the theoretical generalization bound for the counterfactual error of CausalOT. Empirical studies on benchmark datasets confirm that the proposed CausalOT outperforms state-of-the-art causal inference methods.

Li, R-H, Dai, Q, Qin, L, Wang, G, Xiao, X, Yu, JX & Qiao, S 2021, 'Signed Clique Search in Signed Networks: Concepts and Algorithms.', IEEE Trans. Knowl. Data Eng., vol. 33, no. 2, pp. 710-727.
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© 1989-2012 IEEE. Mining cohesive subgraphs from a network is a fundamental problem in network analysis. Most existing cohesive subgraph models are mainly tailored to unsigned networks. In this paper, we study the problem of seeking cohesive subgraphs in a signed network, in which each edge can be positive or negative, denoting friendship or conflict, respectively. We propose a novel model, called maximal (α, k)(α,k)-clique, that represents a cohesive subgraph in signed networks. Specifically, a maximal (α, k)(α,k)-clique is a clique in which every node has at most kk negative neighbors and at least ⌈ α k ⌈αk⌉ positive neighbors (α ≥q 1α≥1). We show that the problem of enumerating all maximal (α, k)(α,k)-cliques in a signed network is NP-hard. To enumerate all maximal (α, k)(α,k)-cliques efficiently, we first develop an elegant signed network reduction technique to significantly prune the signed network. Then, we present an efficient branch and bound enumeration algorithm with several carefully-designed pruning rules to enumerate all maximal (α, k) (α,k)-cliques in the reduced signed network. In addition, we also propose an efficient algorithm with three novel upper-bounding techniques to find the maximum (α, k) (α,k)-clique in a signed network. The results of extensive experiments on five large real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms.

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, vol. 8, no. 3, pp. 2213-2222.
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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, Li, Y & Li, J 2021, 'Thixotropy of magnetorheological gel composites: Experimental testing and modelling', Composites Science and Technology, vol. 214, pp. 108996-108996.
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Li, S, Zhou, X & Feng, Y 2021, 'Qubit Mapping Based on Subgraph Isomorphism and Filtered Depth-Limited Search', IEEE Transactions on Computers, vol. 70, no. 11, pp. 1777-1788.
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Li, W, Dong, W, Castel, A & Sheng, D 2021, 'Self-sensing cement-based sensors for structural health monitoring toward smart infrastructure', Journal and Proceedings of the Royal Society of New South Wales, vol. 154, pp. 24-32.
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Since its first appearance more than 100 years ago, concrete has had a significant impact on urban development — buildings, roads, bridges, ports, tunnels, railways and other structures. While traditional concrete is a structural material without any function, a new branch of concrete technology has produced smart (or intelligent) concrete, with superior self-sensing capabilities that can detect stress, strain, cracks and damage, and monitor temperature and humidity. With the incorporation of functional conductive fillers, traditional concrete can exhibit electrical conductivity with intrinsic piezoresistivity. This piezoresistivity means that the electrical resistivity of concrete is synchronously altered under applied load or environmental factors. The self-sensing electrical resistivity thus obtained can be an index or parameter to detect stress or strain changes in concrete, or cracks and damage to concrete. On the other hand, because of the relationship between electrical resistivity, temperature and humidity, self-sensing concrete can also monitor environmental factors. This smart self-sensing concrete can therefore be a promising alternative to conventional sensors for monitoring structural health and detecting traffic information from concrete roads, all of which are critical to achieving smart automation in concrete infrastructures.

Li, W, Ji, J, Huang, L & Guo, Z 2021, 'Global dynamics of a controlled discontinuous diffusive SIR epidemic system', Applied Mathematics Letters, vol. 121, pp. 107420-107420.
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Li, W, Wang, G-G & Gandomi, AH 2021, 'A Survey of Learning-Based Intelligent Optimization Algorithms', Archives of Computational Methods in Engineering, vol. 28, no. 5, pp. 3781-3799.
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Li, W, Yang, M, Long, R, He, Z, Zhang, L & Chen, F 2021, 'Assessment of greenhouse gasses and air pollutant emissions embodied in cross-province electricity trade in China', Resources, Conservation and Recycling, vol. 171, pp. 105623-105623.
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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, Guan, R, Ou, K, Fu, Q, Yang, G & Sun, Y 2021, 'Ultra-high stability and magnetic response of magnetorheological fluids based on magnetic ionic liquids and carbonyl iron fibers', Journal of Rheology, vol. 65, no. 6, pp. 1347-1359.
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Li, X, He, Y, Zhang, JA & Jing, X 2021, 'Supervised Domain Adaptation for Few-Shot Radar-Based Human Activity Recognition', IEEE Sensors Journal, vol. 21, no. 22, pp. 25880-25890.
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With the application of deep learning (DL) techniques, radar-based human activity recognition (HAR) attracts increasing attention thanks to its high accuracy and good privacy. However, training a DL model requires a large volume of data, and generally the trained model cannot be adapted to a new scenario. In this paper, we propose a supervised few-shot adversarial domain adaptation (FS-ADA) method for HAR, where only limited radar training data is collected from a new application scenario. We adopt the domain adaptation method to learn a common feature space between a pre-existing radar dataset and the newly acquired training data. We also design a multi-class discriminator network, which integrates the category classifier and the binary domain discriminator, to employ the supervised label information in the limited radar data for model training. Then, a multitask generative adversarial training mechanism is proposed to optimize FS-ADA. In this way, both domain-invariant and category-discriminative features can be extracted for HAR in a new scenario. Experimental results for two few-shot radar-based HAR tasks show that the proposed FS-ADA method is effective and outperforms state-of-the-art methods.

Li, X, Kulandaivelu, J, O'Moore, L, Wilkie, S, Hanzic, L, Bond, PL, Yuan, Z & Jiang, G 2021, 'Synergistic effect on concrete corrosion control in sewer environment achieved by applying surface washing on calcium nitrite admixed concrete', Construction and Building Materials, vol. 302, pp. 124184-124184.
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Li, X, Kulandaivelu, J, Zhang, S, Shi, J, Sivakumar, M, Mueller, J, Luby, S, Ahmed, W, Coin, L & Jiang, G 2021, 'Data-driven estimation of COVID-19 community prevalence through wastewater-based epidemiology', Science of The Total Environment, vol. 789, pp. 147947-147947.
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Li, X, Li, M, Mei, Q, Niu, S, Wang, X, Xu, H, Dong, B, Dai, X & Zhou, JL 2021, 'Aging microplastics in wastewater pipeline networks and treatment processes: Physicochemical characteristics and Cd adsorption', Science of The Total Environment, vol. 797, pp. 148940-148940.
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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. 18, no. 6, pp. 2483-2491.
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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, pp. 045025-045025.
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Abstract A novel magnetoresistance material based on magnetorheological fluid (MRF) was developed for applications in micro-displacement sensor. The MRF samples were fabricated by dispersing carbonyl iron particles (CIP) into a magnetic ion liquid (MIL) composed of 1-methylethyl ether-3-butylimidazole cation and [Fe2Cl7]− anions. The magnetoresistance characteristics were also systematically tested. It was found that the resistance value of MRF with a CIP content of 20 vol% decreased from 125 to 24.4 KΩ when increasing the magnetic field from 0 to 0.2 T. A sensor device was developed to study the displacement sensing characteristics of MRF, and found that the sensor had a high sensitivity of 0.1 Ω μm−1 and a high resolution of 10.0 μm. The excellent performance can be attributed to the low modulus and good stability of the MIL matrix, allowing for easy change of the resistance by controlling the magnetic field or displacement. In summary, these unique characters make the present MRF a promising magnetoresistance material with potential applications in displacement sensor.

Li, X, Zhang, S, Shi, J, Luby, SP & Jiang, G 2021, 'Uncertainties in estimating SARS-CoV-2 prevalence by wastewater-based epidemiology', Chemical Engineering Journal, vol. 415, pp. 129039-129039.
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Li, Y, Buys, N, Li, Z, Li, L, Song, Q & Sun, J 2021, 'The efficacy of cognitive behavioral therapy-based interventions on patients with hypertension: A systematic review and meta-analysis', Preventive Medicine Reports, vol. 23, pp. 101477-101477.
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Li, Y, Huang, C, Ngo, HH, Yin, S, Dong, Z, Zhang, Y, Chen, Y, Lu, Y & Guo, W 2021, 'Analysis of event stratigraphy and hydrological reconstruction of low-frequency flooding: A case study on the Fenhe River, China', Journal of Hydrology, vol. 603, pp. 127083-127083.
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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, vol. 11, no. 4, pp. 1627-1627.
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This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

Li, Y, Li, Y, Zhu, J, Zhu, L & Liu, C 2021, 'Vibration Estimation in Power Transformers Based on Dynamic Magnetostriction Model and Finite-Element Analysis', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-4.
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This paper presents a modeling approach for estimating the vibration of power transformers based on a magnetostriction model and maxwell stress calculation. The magnetostriction model, accounting for the dynamic hysteresis behavior, is constructed by combining the Becker-Doring crystal magnetostriction model and J-A dynamic hysteresis model. By incorporating the proposed model into the finite-element method (FEM), both the Maxwell stress and the magnetostriction force in each mesh element can be readily obtained simultaneously. To verify the calculation method, the vibration of a three-phase transformer prototype is measured and compared with simulated results. It demonstrated that the proposed method is accurate enough to predict the vibration of power transformers.

Li, Y, Liu, Z, Yao, L & Chang, X 2021, 'Attribute-Modulated Generative Meta Learning for Zero-shot Learning', IEEE Transactions on Multimedia, pp. 1-1.
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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, pp. 130467-130467.
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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, vol. 419, pp. 126389-126389.
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Li, Y, Xue, B, Zhang, M, Zhang, L, Hou, Y, Qin, Y, Long, H, Su, QP, Wang, Y, Guan, X, Jin, Y, Cao, Y, Li, G & Sun, Y 2021, 'Transcription-coupled structural dynamics of topologically associating domains regulate replication origin efficiency', Genome Biology, vol. 22, no. 1.
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Abstract Background Metazoan cells only utilize a small subset of the potential DNA replication origins to duplicate the whole genome in each cell cycle. Origin choice is linked to cell growth, differentiation, and replication stress. Although various genetic and epigenetic signatures have been linked to the replication efficiency of origins, there is no consensus on how the selection of origins is determined. Results We apply dual-color stochastic optical reconstruction microscopy (STORM) super-resolution imaging to map the spatial distribution of origins within individual topologically associating domains (TADs). We find that multiple replication origins initiate separately at the spatial boundary of a TAD at the beginning of the S phase. Intriguingly, while both high-efficiency and low-efficiency origins are distributed homogeneously in the TAD during the G1 phase, high-efficiency origins relocate to the TAD periphery before the S phase. Origin relocalization is dependent on both transcription and CTCF-mediated chromatin structure. Further, we observe that the replication machinery protein PCNA forms immobile clusters around TADs at the G1/S transition, explaining why origins at the TAD periphery are preferentially fired. Conclusion Our work reveals a new origin selection mechanism that the replication efficiency of origins is determined by their physical distribution in the chromatin domain, which undergoes a transcription-dependent structural re-organization process. Our model explains the complex links between replication origin efficiency and many genetic and epigenetic signatures that mark active transcription. The coordination between DNA r...

Li, Y, Yin, J & Chen, L 2021, 'SEAL: Semisupervised Adversarial Active Learning on Attributed Graphs', IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 3136-3147.
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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, pp. 127775-127775.
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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, Y, Zhu, J, Li, Y, Wang, H & Zhu, L 2021, 'Modeling dynamic magnetostriction of amorphous core materials based on Jiles–Atherton theory for finite element simulations', Journal of Magnetism and Magnetic Materials, vol. 529, pp. 167854-167854.
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Li, Z, Luo, Z, Zhang, L-C & Wang, C-H 2021, 'Topological design of pentamode lattice metamaterials using a ground structure method', Materials & Design, vol. 202, pp. 109523-109523.
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Li, Z, Xie, H, Xu, G, Li, Q, Leng, M & Zhou, C 2021, 'Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information', Pattern Recognition, vol. 113, pp. 107824-107824.
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Targeting at boosting business revenue, purchase prediction based on user behavior is crucial to e-commerce. However, it is not a well-explored topic due to a lack of relevant datasets. Specifically, no public dataset provides both price and discount information varying on time, which play an essential role in the user's decision making. Besides, existing learn-to-rank methods cannot explicitly predict the purchase possibility for a specific user-item pair. In this paper, we propose a two-step graph-based model, where the graph model is applied in the first step to learn representations of both users and items over click-through data, and the second step is a classifier incorporating the price information of each transaction record. To evaluate the model performance, we propose a transaction-based framework focusing on the purchased items and their context clicks, which contain items that a user is interested in but fails to choose after comparison. Our experiments show that exploiting the price and discount information can significantly enhance prediction accuracy.

Li, Z, Yang, C, Shen, Q & Wen, S 2021, 'A Document Image Dataset for Quality Assessment', Journal of Physics: Conference Series, vol. 1828, no. 1, pp. 012033-012033.
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Abstract Mobile device plays an very important role in capturing document image. However, the quality of the captured image is influenced by many factors, such as device quality and shooting conditions. In this context, it is necessary to automatically assess the quality of captured document image. Although there has a lot of work in the filed of image quality assessment (IQA), insufficient attention has been paid to the establishment of document images dataset. Thus, we propose a large dataset of document images containing 19,943 images which are collected by mobile devices. During the process of image acquisition, many factors such as light intensity, distortion type, document material are considered. After capturing images, multiple volunteers participated in the evaluation and collection of Mean Opinion Score (MOS) of the document images. We use two no-reference image quality assessment algorithms to test the proposed dataset. The experimental results show the validity of our dataset and the reliability of MOS. The proposed dataset can be used in the field of image quality assessment and Optical Character Recognition.

Li, Z-X, Zhang, X, Shi, Y, Wu, C & Li, J 2021, 'Finite element modeling of FRP retrofitted RC column against blast loading', Composite Structures, vol. 263, pp. 113727-113727.
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© 2021 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, Z-X, 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, pp. 112519-112519.
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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.

Lian, J-W, Ban, Y-L & Guo, YJ 2021, 'Wideband Dual-Layer Huygens’ Metasurface for High-Gain Multibeam Array Antennas', IEEE Transactions on Antennas and Propagation, vol. 69, no. 11, pp. 7521-7531.
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A wideband dual-layer Huygens’ unit cell based on offset electric dipole pair (OEDP) is proposed. Different from traditional designs with a combination of electric and magnetic polarizabilities, the proposed Huygens’ unit cell employs electric polarizabilities exclusively. By doing so, it practically avoids the unbalanced resonant frequencies between two polarizabilities, thereby achieving wideband transmission. Based on the proposed unit cell, a wideband and high-gain multibeam array antenna is developed. Firstly, a Rotman lens is designed by using a substrate integrated waveguide (SIW) technology. Then a parallel-fed slot antenna array is connected to the Rotman lens to generate multiple beams. Without using a series-fed slot antenna array, the multibeam array antenna based on Rotman lens can operate within a relatively wide bandwidth (28 GHz to 32 GHz). Secondly, a wideband dual-layer Huygens’ metasurface is developed that serves as a superstrate of the multibeam array antenna for increasing the antenna gain further. A wideband and high-gain multibeam array antenna is finally realized, which is comprised of a Rotman lens, a parallel-fed slot antenna array, and a Huygens’ metasurface. To verify the performance of this design, a prototype is fabricated and its measured results are compared to the simulated counterparts.

Liao, J, Zhou, J, Song, Y, Liu, B, Chen, Y, Wang, F, Chen, C, Lin, J, Chen, X, Lu, J & Jin, D 2021, 'Preselectable Optical Fingerprints of Heterogeneous Upconversion Nanoparticles', Nano Letters, vol. 21, no. 18, pp. 7659-7668.
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Liao, J, Zhou, J, Song, Y, Liu, B, Lu, J & Jin, D 2021, 'Optical Fingerprint Classification of Single Upconversion Nanoparticles by Deep Learning', The Journal of Physical Chemistry Letters, vol. 12, no. 41, pp. 10242-10248.
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Liao, T, Lei, Z, Zhu, T, Zeng, S, Li, Y & Yuan, C 2021, 'Deep Metric Learning for K Nearest Neighbor Classication', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Liberal, I & Ziolkowski, RW 2021, 'Nonperturbative decay dynamics in metamaterial waveguides', Applied Physics Letters, vol. 118, no. 11, pp. 111103-111103.
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Lim, JHK, Gan, YY, Ong, HC, Lau, BF, Chen, W-H, Chong, CT, Ling, TC & Klemeš, JJ 2021, 'Utilization of microalgae for bio-jet fuel production in the aviation sector: Challenges and perspective', Renewable and Sustainable Energy Reviews, vol. 149, pp. 111396-111396.
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Lin, C-T & Do, T-TN 2021, 'Direct-Sense Brain–Computer Interfaces and Wearable Computers', IEEE Transactions on Systems, Man, and 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, vol. 51, no. 10, pp. 4959-4967.
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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, Jiang, W-L, Chen, S-F, Huang, K-C & Liao, L-D 2021, 'Design of a Wearable Eye-Movement Detection System Based on Electrooculography Signals and Its Experimental Validation', Biosensors, vol. 11, no. 9, pp. 343-343.
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In the assistive research area, human–computer interface (HCI) technology is used to help people with disabilities by conveying their intentions and thoughts to the outside world. Many HCI systems based on eye movement have been proposed to assist people with disabilities. However, due to the complexity of the necessary algorithms and the difficulty of hardware implementation, there are few general-purpose designs that consider practicality and stability in real life. Therefore, to solve these limitations and problems, an HCI system based on electrooculography (EOG) is proposed in this study. The proposed classification algorithm provides eye-state detection, including the fixation, saccade, and blinking states. Moreover, this algorithm can distinguish among ten kinds of saccade movements (i.e., up, down, left, right, farther left, farther right, up-left, down-left, up-right, and down-right). In addition, we developed an HCI system based on an eye-movement classification algorithm. This system provides an eye-dialing interface that can be used to improve the lives of people with disabilities. The results illustrate the good performance of the proposed classification algorithm. Moreover, the EOG-based system, which can detect ten different eye-movement features, can be utilized in real-life 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, vol. 15.
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Many studies have reported that exercise can influence cognitive performance. But advancing our understanding of the interrelations between psychology and physiology in sports neuroscience requires the study of real-time brain dynamics during exercise in the field. Electroencephalography (EEG) is one of the most powerful brain imaging technologies. However, the limited portability and long preparation time of traditional wet-sensor systems largely limits their use to laboratory settings. Wireless dry-sensor systems are emerging with much greater potential for practical application in sports. Hence, in this paper, we use the BR8 wireless dry-sensor EEG system to measure P300 brain dynamics while cycling at various intensities. The preparation time was mostly less than 2 min as BR8 system’s dry sensors were able to attain the required skin-sensor interface impedance, enabling its operation without any skin preparation or application of conductive gel. Ten participants performed four sessions of a 3 min rapid serial visual presentation (RSVP) task while resting and while cycling. These four sessions were pre-CE (RSVP only), low-CE (RSVP in 40–50% of max heart rate), vigorous-CE (RSVP in 71–85% of max heart rate) and post-CE (RSVP only). The recorded brain signals demonstrate that the P300 amplitudes, observed at the Pz channel, for the target and non-target responses were significantly different in all four sessions. The results also show decreased reaction times to the visual attention task during vigorous exercise, enriching our understanding of the ways in which exercise can enhance cognitive performance. Even though only a single channel was evaluated in this study, the quality and reliability of the measurement using these dry sensor-based EEG systems is clearly demonstrated by our results. Further, the smooth implementation of the experiment with a dry system and the success of the data analysis demonstrate that wireless dry EEG devices can open avenue...

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, vol. 2021, pp. 1-10.
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For emergency or intensive-care units (ICUs), patients with unclear consciousness or unstable hemodynamics often require aggressive monitoring by multiple monitors. Complicated pipelines or lines increase the burden on patients and inconvenience for medical personnel. Currently, many commercial devices provide related functionalities. However, most devices measure only one biological signal, which can increase the budget for users and cause difficulty in remote integration. In this study, we develop a wearable device that integrates electrocardiography (ECG), electroencephalography (EEG), and blood oxygen machines for medical applications with the hope that it can be applied in the future. We develop an integrated multiple-biosignal recording system based on a modular design. The developed system monitors and records EEG, ECG, and peripheral oxygen saturation (SpO2) signals for health purposes simultaneously in a single setting. We use a logic level converter to connect the developed EEG module (BR8), ECG module, and SpO2 module to a microcontroller (Arduino). The modular data are then smoothly encoded and decoded through consistent overhead byte stuffing (COBS). This developed system has passed simulation tests and exhibited proper functioning of all modules and subsystems. In the future, the functionalities of the proposed system can be expanded with additional modules to support various emergency or ICU applications.

Lin, J-Y, Yang, Y, Wong, S-W & 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, vol. 69, no. 6, pp. 3084-3092.
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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, Q, Zhu, Y, Lu, H, Shi, K & Niu, Z 2021, 'Improving University Faculty Evaluations via multi-view Knowledge Graph', Future Generation Computer Systems, vol. 117, pp. 181-192.
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Lin, W & Ziolkowski, RW 2021, 'Compact, Highly Efficient Huygens Antenna Array With Low Sidelobe and Backlobe Levels', IEEE Transactions on Antennas and Propagation, vol. 69, no. 10, pp. 6401-6409.
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Lin, W & Ziolkowski, RW 2021, 'Electrically Small, Single-Substrate Huygens Dipole Rectenna for Ultracompact Wireless Power Transfer Applications', IEEE Transactions on Antennas and Propagation, vol. 69, no. 2, pp. 1130-1134.
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Lin, W & Ziolkowski, RW 2021, 'Theoretical Analysis of Beam-Steerable, Broadside-Radiating Huygens Dipole Antenna Arrays and Experimental Verification of an Ultrathin Prototype for Wirelessly Powered IoT Applications', IEEE Open Journal of Antennas and Propagation, vol. 2, pp. 954-967.
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Lin, X, Far, H & Zhang, X 2021, 'Shear Capacity Analysis of Welded Steel I-Girders with Corrugated Webs based on First Yield', International Journal of Steel Structures, vol. 21, no. 3, pp. 1053-1062.
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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, vol. 39, no. 4, pp. 919-933.
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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, vol. 20, no. 7, pp. 4631-4645.
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Linares-Mustarós, S, Ferrer-Comalat, JC, Corominas-Coll, D & Merigó, JM 2021, 'The weighted average multiexperton', Information Sciences, vol. 557, pp. 355-372.
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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, L, Yelland, N, Hatzigianni, M & Dickson-Deane, C 2021, 'Toward a conceptualization of the internet of toys', Australasian Journal of Early Childhood, vol. 46, no. 3, pp. 249-262.
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The Internet of Things is reshaping many households’ digital landscape and influencing children’s play and learning, especially in the form of toys that are named the Internet of Toys (IoToys). IoToys may generate a significant influence on children’s growth. While increasing attention is drawn to the IoToys, confusion around their conceptualization and use is evident. Without a thorough understanding of what the IoToys are, the progress of meaningful research on this topic will be greatly hindered. We, thus, conducted a systematic review to determine existing definitions of the IoToys using seven major databases over the past 20 years. After analyzing the definitions identified, we found that the previous definitions neglected the significance of defining “toys” in their work. The review led to a discussion around how to understand “toys” and then a more precise conceptualization of the IoToys, based on which implications for future research are offered.

Liu, A, Lu, J & Zhang, G 2021, 'Concept Drift Detection via Equal Intensity k-Means Space Partitioning', IEEE Transactions on Cybernetics, vol. 51, no. 6, pp. 3198-3211.
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The data stream poses additional challenges to statistical classification tasks because distributions of the training and target samples may differ as time passes. Such a distribution change in streaming data is called concept drift. Numerous histogram-based distribution change detection methods have been proposed to detect drift. Most histograms are developed on the grid-based or tree-based space partitioning algorithms which makes the space partitions arbitrary, unexplainable, and may cause drift blind spots. There is a need to improve the drift detection accuracy for the histogram-based methods with the unsupervised setting. To address this problem, we propose a cluster-based histogram, called equal intensity k-means space partitioning (EI-kMeans). In addition, a heuristic method to improve the sensitivity of drift detection is introduced. The fundamental idea of improving the sensitivity is to minimize the risk of creating partitions in distribution offset regions. Pearson's chi-square test is used as the statistical hypothesis test so that the test statistics remain independent of the sample distribution. The number of bins and their shapes, which strongly influence the ability to detect drift, are determined dynamically from the sample based on an asymptotic constraint in the chi-square test. Accordingly, three algorithms are developed to implement concept drift detection, including a greedy centroids initialization algorithm, a cluster amplify-shrink algorithm, and a drift detection algorithm. For drift adaptation, we recommend retraining the learner if a drift is detected. The results of experiments on the synthetic and real-world datasets demonstrate the advantages of EI-kMeans and show its efficacy in detecting concept drift.

Liu, A, Lu, J & Zhang, G 2021, 'Concept Drift Detection: Dealing With Missing Values via Fuzzy Distance Estimations', IEEE Transactions on Fuzzy Systems, vol. 29, no. 11, pp. 3219-3233.
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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, Wang, F, Chen, C & McGloin, D 2021, 'Single-Pixel Diffuser Camera', IEEE Photonics Journal, vol. 13, no. 6, pp. 1-5.
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Liu, B, Wang, F, Chen, C, Dong, F & McGloin, D 2021, 'Self-evolving ghost imaging', Optica, vol. 8, no. 10, pp. 1340-1340.
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Ghost imaging captures 2D images with a point detector instead of an array sensor. It could therefore solve the challenge of building cameras in wave bands where sensors are difficult and expensive to produce and could open up more routine THz, near-infrared, lifetime, and hyperspectral imaging simply by using single-pixel detectors. Traditionally, ghost imaging retrieves the image of an object offline by correlating measured light intensities with pre-designed illuminating patterns. Here we present a “self-evolving” ghost imaging (SEGI) strategy for imaging objects bypassing offline post-processing. It also offers the capability to image objects in turbid media. By inspecting the optical feedback, we evaluate the illumination patterns by a cost function and generate offspring illumination patterns that mimic the object’s image, bypassing the reconstruction process. At the initial evolving state, the object’s “genetic information” is stored in the patterns. At the following imaging stage, the object’s image ( 48 × 48 p i x e l s ) can be updated at a 40 Hz imaging rate. We numerically and experimentally demonstrate this concept for static and moving objects. The frame-memory effect between the self-evolving illumination patterns provided by the genetic algorithm enables SEGI imaging through turbid media. We further demons...

Liu, C, Bano, M, Zowghi, D & Kearney, M 2021, 'Analysing user reviews of inquiry-based learning apps in science education.', Comput. Educ., vol. 164, pp. 104119-104119.
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© 2020 Elsevier Ltd The science education community is increasingly valuing the use of mobile apps in inquiry-based learning (IBL) to improve learner’ attitudes and their understanding of science concepts. Although there exists a body of research on mobile apps used for IBL in science education, limited attention has been paid to linking apps' features with their pedagogical affordances. Our study addresses this research gap by evaluating science mobile learning apps with respect to IBL pedagogy. Nine functional features of apps that support educational aspects of inquiry-based pedagogy are identified from user reviews, including: fingertip interaction, graphics visualisation, informative materials, location-based services, offline access, search by question, timeline scrolling, user tutorials, and zoom control. The information contained in the version history of the apps is analysed and four educational aspects of IBL supported by the nine functional features are identified as: motivation, conceptualisation, exploration, and conclusion. We have further compared the evolution of the functional features of apps to the educational aspects of inquiry-based pedagogy identified from different versions of apps. The findings of this study show the trend of updated functional features that support IBL and inform practitioners seeking to improve their use of mobile apps to support students' learning in science. We conclude by proposing areas of future research in this field.

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, Liu, Q, Wang, S, Wang, Y, Lei, G, Guo, Y & Zhu, J 2021, 'A novel flux switching claw pole machine with soft magnetic composite cores', International Journal of Applied Electromagnetics and Mechanics, vol. 67, no. 2, pp. 183-203.
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This paper proposes a novel flux switching claw pole machine (FSCPM) with soft magnetic composite (SMC) cores. The proposed FSCPM holds advantages of the conventional flux switching permanent magnet machine (FSPMM) and claw pole machine (CPM) with SMC cores. As permanent magnets are installed between the stator claw pole teeth, FSCPM has good flux concentrating ability, and the air gap flux density can be significantly improved. The torque coefficient of FSCPM is relatively high due to the applied claw pole teeth and global winding. FSCPM is mechanically robust because there are no windings or PMs on its rotor. Moreover, the core loss of FSCPM is relatively low for the SMC material has lower core loss at high frequency compared with silicon steels. The topology and operational principle of FSCPM are explained first. Several main dimensions of the machine are optimized to achieve better performance, based on 3D finite element method (FEM). Furthermore, the rotor skewing technology is adopted to reduce the cogging torque and torque ripple.

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, pp. 1-5.
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Liu, C, Zowghi, D, Kearney, M & Bano, M 2021, 'Inquiry-based mobile learning in secondary school science education: A systematic review.', J. Comput. Assist. Learn., vol. 37, no. 1, pp. 1-23.
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Recent years have seen a growing call for inquiry‐based learning in science education, and mobile technologies are perceived as increasingly valuable tools to support this approach. However, there is a lack of understanding of mobile technology‐supported inquiry‐based learning (mIBL) in secondary science education. More evidence‐based, nuanced insights are needed into how using mobile technologies might facilitate students' engagement with various levels of inquiry and enhance their science learning. We, therefore, conducted a robust systematic literature review (SLR) of the research articles on mIBL in secondary school science education that have been published from 2000 to 2019. We reviewed and analysed 31 empirical studies (34 articles) to explore the types of mIBL, and the benefits and constraints of mIBL in secondary school science education. The findings of this SLR suggest new research areas for further exploration and provide implications for science teachers' selection, use and design of mIBL approaches in their teaching.

Liu, D, Chen, Y, Tran, TT & Zhang, G 2021, 'Facile and rapid assembly of high-performance tannic acid thin-film nanofiltration membranes via Fe3+ intermediated regulation and coordination', Separation and Purification Technology, vol. 260, pp. 118228-118228.
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Liu, D, Wu, Q, Huang, Y, Huang, X & An, P 2021, 'Learning from EPI-Volume-Stack for Light Field image angular super-resolution', Signal Processing: Image Communication, vol. 97, pp. 116353-116353.
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Liu, D, Xu, X, Du, Y, Liao, J, Wen, S, Dong, X, Jin, Y, Liu, L, Jin, D, Capobianco, JA & Shen, D 2021, 'Reconstructing the Surface Structure of NaREF₄ Upconversion Nanocrystals with a Novel K⁺ Treatment', Chemistry of Materials, vol. 33, no. 7, pp. 2548-2556.
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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 pollutants', Surface Innovations, vol. 9, no. 4, pp. 222-230.
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Carbon- and oxygen-doped hexagonal boron nitrides (BCNOs) with good chemical stability and photoresponsiveness to visible light are found to be promising metal-free catalysts for degradation of Rhodamine B (RhB). By doping with heteroatoms of carbon and oxygen, insulating hexagonal boron nitride was transformed into semiconducting BCNO. The BCNO photocatalyst presents photodegradation performance towards RhB, with degradation rates up to 1.39 h−1 (0.05 wt% catalyst loading). The active species involved in the photoreaction were demonstrated to be superoxide anion radical (˙O2 −) and holes (h+), as opposed to ˙OH in the most studied titanium dioxide. The stability of BCNO in highly acidic environments was exploited for catalyst regeneration, as is necessary after long-term use and poisoning. This work demonstrates that BCNO is a promising low-cost and metal-free photocatalyst for environmental pollution remediation.

Liu, F, Zhang, G & Lu, J 2021, 'Multisource Heterogeneous Unsupervised Domain Adaptation via Fuzzy Relation Neural Networks', IEEE Transactions on Fuzzy Systems, vol. 29, no. 11, pp. 3308-3322.
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Liu, H, Lai, W-H, Yang, H-L, Zhu, Y-F, Lei, Y-J, Zhao, L, Peng, J, Wang, Y-X, Chou, S-L & Liu, H-K 2021, 'Efficient separators with fast Li-ion transfer and high polysulfide entrapment for superior lithium-sulfur batteries', Chemical Engineering Journal, vol. 408, pp. 127348-127348.
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Liu, H, Lai, W-H, Yang, Q, Lei, Y, Wu, C, Wang, N, Wang, Y-X, Chou, S-L, Liu, HK & Dou, SX 2021, 'Understanding Sulfur Redox Mechanisms in Different Electrolytes for Room-Temperature Na–S Batteries', Nano-Micro Letters, vol. 13, no. 1.
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Abstract This work reports influence of two different electrolytes, carbonate ester and ether electrolytes, on the sulfur redox reactions in room-temperature Na–S batteries. Two sulfur cathodes with different S loading ratio and status are investigated. A sulfur-rich composite with most sulfur dispersed on the surface of a carbon host can realize a high loading ratio (72% S). In contrast, a confined sulfur sample can encapsulate S into the pores of the carbon host with a low loading ratio (44% S). In carbonate ester electrolyte, only the sulfur trapped in porous structures is active via ‘solid–solid’ behavior during cycling. The S cathode with high surface sulfur shows poor reversible capacity because of the severe side reactions between the surface polysulfides and the carbonate ester solvents. To improve the capacity of the sulfur-rich cathode, ether electrolyte with NaNO3 additive is explored to realize a ‘solid–liquid’ sulfur redox process and confine the shuttle effect of the dissolved polysulfides. As a result, the sulfur-rich cathode achieved high reversible capacity (483 mAh g−1), corresponding to a specific energy of 362 Wh kg−1 after 200 cycles, shedding light on the use of ether electrolyte for high-loading sulfur cathode.

Liu, H, Li, X, Zhang, Z, Nghiem, LD, Gao, L & Wang, Q 2021, 'Semi-continuous anaerobic digestion of secondary sludge with free ammonia pretreatment: Focusing on volatile solids destruction, dewaterability, pathogen removal and its implications', Water Research, vol. 202, pp. 117481-117481.
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Liu, H, Wang, Z, Nghiem, LD, Gao, L, Zamyadi, A, Zhang, Z, Sun, J & Wang, Q 2021, 'Solid-Embedded Microplastics from Sewage Sludge to Agricultural Soils: Detection, Occurrence, and Impacts', ACS ES&T Water, vol. 1, no. 6, pp. 1322-1333.
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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 μm SiGe BiCMOS', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 68, no. 6, pp. 1847-1851.
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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, vol. 143, no. 1.
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Abstract This paper studies the bipartite consensus problem of a swarm of robots whose dynamics are formulated by Lagrangian equations. Two distributed bipartite consensus control protocols are proposed for a swarm of robots without a leader or with a virtual leader. For the nonleader case, the networked Lagrangian system can reach static bipartite consensus under the control protocol developed, and the final convergent states can be explicitly determined by the specific structure of the Laplacian matrix associated with the cooperative–competitive network topology. For the virtual leader case, all the followers can track the leader's state in a bipartite formation to realize bipartite tracking consensus. Finally, the simulation results are given to verify the theoretical results.

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, vol. 8, no. 9, pp. 7546-7561.
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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, Li, J, Liu, Z, Xu, S, Liu, K, Su, Y, Fang, J & Chen, G 2021, 'Projectile impact resistance of fibre-reinforced geopolymer-based ultra-high performance concrete (G-UHPC)', Construction and Building Materials, vol. 290, pp. 123189-123189.
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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, pp. 112983-112983.
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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, K, Wu, C, Li, X, Liu, J, Tao, M, Fang, J & Xu, S 2021, 'The influences of cooling regimes on fire resistance of ultra-high performance concrete under static-dynamic coupled loads', Journal of Building Engineering, vol. 44, pp. 103336-103336.
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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, vol. 6, no. 2, pp. 3679-3686.
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Liu, L, Guo, Y, Lei, G & Zhu, JG 2021, 'Iron Loss Calculation for High-Speed Permanent Magnet Machines Considering Rotating Magnetic Field and Thermal Effects', IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-5.
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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, M, Xie, K, Nothling, MD, Zu, L, Zhao, S, Harvie, DJE, Fu, Q, Webley, PA & Qiao, GG 2021, 'Ultrapermeable Composite Membranes Enhanced Via Doping with Amorphous MOF Nanosheets', ACS Central Science, vol. 7, no. 4, pp. 671-680.
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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, vol. 70, no. 5, pp. 4419-4434.
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Liu, P, Xu, M, Sun, J & Wen, S 2021, 'Cluster Synchronization of Multiple Fractional-Order Recurrent Neural Networks With Time-Varying Delays', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Liu, Q, Do, TDT & Cao, L 2021, 'Answer Keyword Generation for Community Question Answering by Multiaspect Gamma–Poisson Matrix Completion', IEEE Intelligent Systems, vol. 36, no. 4, pp. 35-47.
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IEEE Community question answering (CQA) recommends appropriate answers to existing and new questions. Such answer recommendation is challenging since CQA data is often sparse and decentralized and lacks sufficient information to generate suitable answers to existing questions. Matching answers to new questions is more challenging in modeling Q/A sparsity, generating answers to cold-start/novel questions, and integrating metadata about Q/A into models, etc. This paper addresses these issues by a novel statistical model to automatically generate answer keywords in CQA with multi-aspect Gamma-Poisson matrix completion (MAGIC). MAGIC is the first trial in CQA to model multiple aspects of Q/A sentence information in CQA by involving Q/A metadata, Q/A sparsity, and both lexical and semantic Q/A information in a hierarchical Gamma-Poisson model. MAGIC can efficiently generate answer keywords for both existing and new questions against nonnegative matrix factorization (MF), probability MF, and relevant Poisson factorization models w.r.t. recommending appropriate and informative answer keywords.

Liu, Q, Geng, X, Huang, H, Qin, T, Lu, J & Jiang, D 2021, 'MGRC: An End-to-End Multigranularity Reading Comprehension Model for Question Answering', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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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, vol. 29, no. 8, pp. 2132-2144.
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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, vol. 555, pp. 410-423.
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Liu, S, Wang, S, Liu, X, Gandomi, AH, Daneshmand, M, Muhammad, K & De Albuquerque, VHC 2021, 'Human Memory Update Strategy: A Multi-Layer Template Update Mechanism for Remote Visual Monitoring', IEEE Transactions on Multimedia, vol. 23, pp. 2188-2198.
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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, T, Lu, J, Yan, Z & Zhang, G 2021, 'Statistical generalization performance guarantee for meta-learning with data dependent prior', Neurocomputing, vol. 465, pp. 391-405.
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Meta-learning aims to leverage experience from previous tasks to achieve an effective and fast adaptation ability when encountering new tasks. However, it is unclear how the generalization property applies to new tasks. Probably approximately correct (PAC) Bayes bound theory provides a theoretical framework to analyze the generalization performance for meta-learning with an explicit numerical generalization error upper bound. A tighter upper bound may achieve better generalization performance. However, for the PAC-Bayes meta-learning bound, the prior distribution is selected randomly which results in poor generalization performance. In this paper, we derive three novel generalization error upper bounds for meta-learning based on the PAC-Bayes relative entropy bound. Furthermore, in order to avoid randomly prior distribution, based on the empirical risk minimization (ERM) method, a data-dependent prior for the PAC-Bayes meta-learning bound algorithm is developed and the sample complexity and computational complexity are analyzed. The experiments illustrate that the proposed three PAC-Bayes bounds for meta-learning achieve a competitive generalization guarantee, and the extended PAC-Bayes bound with a data-dependent prior can achieve rapid convergence ability.

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.', VLDB J., vol. 30, pp. 215-235.
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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, Chen, Z, Tian, K, Zhu, F, Hao, D, Cheng, D, Wei, W, Zhang, L & Ni, B-J 2021, 'Fe³⁺ Promoted the Photocatalytic Defluorination of Perfluorooctanoic Acid (PFOA) over In₂O₃', ACS ES&T Water, vol. 1, no. 11, pp. 2431-2439.
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Liu, X, Fu, Q, Liu, Z, Zeng, T, Du, M, He, D, Lu, Q, Ni, B-J & Wang, D 2021, 'Alkaline pre-fermentation for anaerobic digestion of polyacrylamide flocculated sludge: Simultaneously enhancing methane production and polyacrylamide degradation', Chemical Engineering Journal, vol. 425, pp. 131407-131407.
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Liu, X, Ren, Z, Ngo, HH, He, X, Desmond, P & Ding, A 2021, 'Membrane technology for rainwater treatment and reuse: A mini review', Water Cycle, vol. 2, pp. 51-63.
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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, pp. 116645-116645.
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Liu, X, Xu, B, Duan, X, Hao, Q, Wei, W, Wang, S & Ni, B-J 2021, 'Facile preparation of hydrophilic In₂O₃ nanospheres and rods with improved performances for photocatalytic degradation of PFOA', Environmental Science: Nano, vol. 8, no. 4, pp. 1010-1018.
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This study used metal–organic-framework (MOF) derived In₂O₃ for the photocatalytic degradation of PFOA for the first time. MOF derived In₂O₃ demonstrated significantly enhanced performance for PFOA decomposition compared to commercial In₂O₃.

Liu, X, Yang, B, Chen, H, Musial, K, Chen, H, Li, Y & Zuo, W 2021, 'A Scalable Redefined Stochastic Blockmodel', ACM Transactions on Knowledge Discovery from Data, vol. 15, no. 3, pp. 1-28.
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Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

Liu, X, Yang, B, Song, W, Musial, K, Zuo, W, Chen, H & Yin, H 2021, 'A block-based generative model for attributed network embedding', World Wide Web, vol. 24, no. 5, pp. 1439-1464.
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Liu, X, Zheng, G, Luo, Q, Li, Q & Sun, G 2021, 'Fatigue behavior of carbon fibre reinforced plastic and aluminum single-lap adhesive joints after the transverse pre-impact', International Journal of Fatigue, vol. 144, pp. 105973-105973.
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Liu, Y, Bai, J, Zheng, J, Liao, H, Ren, Y & Guo, YJ 2021, 'Efficient Shaped Pattern Synthesis for Time Modulated Antenna Arrays Including Mutual Coupling by Differential Evolution Integrated With FFT via Least-Square Active Element Pattern Expansion', IEEE Transactions on Antennas and Propagation, vol. 69, no. 7, pp. 4223-4228.
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IEEE The fast Fourier transform (FFT) via the least-square active element pattern expansion (LSAEPE) is generalized to speed up the computation of array patterns including mutual coupling and platform effect for time-modulated antenna arrays (TMAAs) at the central and sideband frequencies. By integrating the LSAEPE-FFT with differential evolution algorithm (DEA), the resulting DEA-LSAEPE-FFT method can realize efficient shaped pattern synthesis with accurate control of mainlobe shape, sidelobe level (SLL) and sideband level (SBL). Two examples of synthesizing different shaped patterns for different TMAAs mounted on a nonuniform platform or with metal scatters are conducted to validate the effectiveness and robustness of the proposed method. Synthesis results show that the proposed method has much better accuracy performance than the conventional DEA-FFT while costing much less CPU time than that of using DEA combined with direct summation.

Liu, Y, Li, H, Li, Y, Xu, X, Yang, Z & Ding, G 2021, 'Optimization of the Discrete Structure in a Pressure Sensor Based on a Multiple-Contact Mechanism to Improve Sensitivity and Nonlinearity', IEEE Sensors Journal, vol. 21, no. 19, pp. 21259-21267.
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Liu, Y, Ma, C, Zhang, X, Ngo, HH, Guo, W, Zhang, M & Zhang, D 2021, 'Role of structural characteristics of MoS2 nanosheets on Pb2＋ removal in aqueous solution', Environmental Technology & Innovation, vol. 22, pp. 101385-101385.
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Liu, Y, Yang, Y, Wu, P, Ma, X, Li, M, Xu, K-D & Guo, YJ 2021, 'Synthesis of Multibeam Sparse Circular-Arc Antenna Arrays Employing Refined Extended Alternating Convex Optimization', IEEE Transactions on Antennas and Propagation, vol. 69, no. 1, pp. 566-571.
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IEEE A refined extended alternating convex optimization (REACO) method is presented to synthesize multibeam sparse circular-arc antenna arrays with minimum element spacing control by considering real antenna array structure characteristics. This method consists of initial step and a few refining steps. At the initial step, an initial array with dense elements distributed on a circular-arc is considered, and its array manifold vector is described by rotating a simulated isolated element pattern (IEP) without considering element mutual coupling. The collective excitation coefficient vector (CECV) and its energy bound are introduced for each element, and consequently the common element positions for generating desired multibeam patterns can be found by minimizing the number of active CECVs under multiple constraints. This minimization problem is further formulated as performing a sequence of alternating convex optimization (ACO) in which the CECV and an auxiliary weighting vector are alternately chosen as the optimization variables, so that the mimimum element spacing constraint can be easily dealt with. Once the initial optimization step is finished, a few refining steps are performed in which the element positions and excitations are successively updated in each step by renewing the array manifold vector through rotating the simulated nearby active element patterns (AEPs) of the antenna array obtained at the previous step. In such a way, the mutual coupling can be incorporated into the multibeam sparse array synthesis. An example of synthesizing a sparse circular-arc conformal array with 23 beams covering the space from–63.25° to 63.25° is conducted to validate the effectiveness and advantage of the proposed method.

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, vol. 12, no. 1.
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AbstractSub-diffraction limited localization of fluorescent emitters is a key goal of microscopy imaging. Here, we report that single upconversion nanoparticles, containing multiple emission centres with random orientations, can generate a series of unique, bright and position-sensitive patterns in the spatial domain when placed on top of a mirror. Supported by our numerical simulation, we attribute this effect to the sum of each single emitter’s interference with its own mirror image. As a result, this configuration generates a series of sophisticated far-field point spread functions (PSFs), e.g. in Gaussian, doughnut and archery target shapes, strongly dependent on the phase difference between the emitter and its image. In this way, the axial locations of nanoparticles are transferred into far-field patterns. We demonstrate a real-time distance sensing technology with a localization accuracy of 2.8 nm, according to the atomic force microscope (AFM) characterization values, smaller than 1/350 of the excitation wavelength.

Liu, Z, Gao, Y, Yang, J, Xu, X, Fang, J, Duan, Y & Ma, C 2021, 'Transfer path analysis and its application to diagnosis for low-frequency transient vibration in the automotive door slamming event', Measurement, vol. 183, pp. 109896-109896.
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Liu, Z, Liu, J, Pei, Q, Yu, H, Li, C & Wu, C 2021, 'Seismic response of tunnel near fault fracture zone under incident SV waves', Underground Space, vol. 6, no. 6, pp. 695-708.
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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, vol. 51, no. 5, pp. 3193-3204.
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Liu, Z, Yang, M, Cheng, J, Wu, D & Tan, J 2021, 'Meta-model based stochastic isogeometric analysis of composite plates', International Journal of Mechanical Sciences, vol. 194, pp. 106194-106194.
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Liu, Z, Yao, L, Wang, X, Monaghan, JJM, Schaette, R, He, Z & McAlpine, D 2021, 'Generalizable Sample-Efficient Siamese Autoencoder for Tinnitus Diagnosis in Listeners With Subjective Tinnitus', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1452-1461.
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Logan, J, Kennedy, PJ & Catchpoole, D 2021, 'The Untapped Social Impact of Artificial Intelligence for Breast Cancer Screening in Developing Countries: A Critical Commentary of DeepMind', Innovations in Digital Health, Diagnostics, and Biomarkers, vol. 1, no. 2, pp. 29-32.
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Lotfi, I, Niyato, D, Sun, S, Dinh, HT, Li, Y & Kim, DI 2021, 'Protecting Multi-Function Wireless Systems From Jammers With Backscatter Assistance: An Intelligent Strategy', IEEE Transactions on Vehicular Technology, vol. 70, no. 11, pp. 11812-11826.
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Lowe, D, Wilkinson, T, Willey, K, Kadi, A, Goldfinch, T & Lim, TJ 2021, 'Educating the Evolving Engineer: Lessons From the University of Sydney', IEEE Potentials, vol. 40, no. 2, pp. 7-12.
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Lu, H, Zhu, Y, Yuan, Y, Gong, W, Li, J, Shi, K, Lv, Y, Niu, Z & Wang, F-Y 2021, 'Social Signal-Driven Knowledge Automation: A Focus on Social Transportation', IEEE Transactions on Computational Social Systems, vol. 8, no. 3, pp. 737-753.
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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, vol. 70, no. 6, pp. 5537-5550.
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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, W, Yu, R, Wang, S, Wang, C, Jian, P & Huang, H 2021, 'Sentence Semantic Matching Based on 3D CNN for Human–Robot Language Interaction', ACM Transactions on Internet Technology, vol. 21, no. 4, pp. 1-24.
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The development of cognitive robotics brings an attractive scenario where humans and robots cooperate to accomplish specific tasks. To facilitate this scenario, cognitive robots are expected to have the ability to interact with humans with natural language, which depends on natural language understanding ( NLU ) technologies. As one core task in NLU, sentence semantic matching ( SSM ) has widely existed in various interaction scenarios. Recently, deep learning–based methods for SSM have become predominant due to their outstanding performance. However, each sentence consists of a sequence of words, and it is usually viewed as one-dimensional ( 1D ) text, leading to the existing available neural models being restricted into 1D sequential networks. A few researches attempt to explore the potential of 2D or 3D neural models in text representation. However, it is hard for their works to capture the complex features in texts, and thus the achieved performance improvement is quite limited. To tackle this challenge, we devise a novel 3D CNN-based SSM ( 3DSSM ) method for human–robot language interaction. Specifically, first, a specific architecture called feature cube network is designed to transform a 1D sentence into a multi-dimensional representation named as semantic feature cube. Then, a 3D CNN module is employed to learn a semantic representation for the semantic feature cube by capturing both the local features embedded in word representations and the sequential information among successive words in a sentence. Given a pair of sentences, their representations are concatenate...

Lu, W, Zhang, Y, Wang, S, Huang, H, Liu, Q & Luo, S 2021, 'Concept Representation by Learning Explicit and Implicit Concept Couplings', IEEE Intelligent Systems, vol. 36, no. 1, pp. 6-15.
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IEEE Generating the precise semantic representation of a word/concept is a fundamental task in natural language processing. Recent studies which incorporate semantic knowledge into word embedding have shown their potential in improving the semantic representation of a concept. However, existing approaches only achieved limited performance improvement as they usually (1) model a word's semantics from some explicit aspects while ignoring the intrinsic aspects of the word, (2) treat semantic knowledge as a supplement of word embeddings, and (3) consider partial relations between concepts while ignoring rich coupling relations between them, such as explicit concept co-occurrences in descriptive texts in a corpus as well as concept hyperlink relations in a knowledge network, and implicit couplings between the explicit relations. In human consciousness, concepts are associated with various coupling relations, which inspires us to capture as many concept couplings as possible for building a better concept representation. We thus propose a neural coupled concept representation (CoupledCR) framework and its instantiation: a coupled concept embedding (CCE) model. CCE first learns two types of explicit couplings from concept cooccurrences and hyperlink relations respectively, and then learns a type of high-level implicit couplings between these two types of explicit couplings. Extensive experimental results on real-world datasets show that CCE significantly outperforms state-of-the-art semantic representation methods.

Lu, X, Liu, L, Nie, L, Chang, X & Zhang, H 2021, 'Semantic-Driven Interpretable Deep Multi-Modal Hashing for Large-Scale Multimedia Retrieval', IEEE Transactions on Multimedia, vol. 23, pp. 4541-4554.
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Lu, Z, Chen, Y, Gu, Y, He, L & Zhang, J 2021, 'Total load energy supply capability and security level classification of integrated power and natural gas systems considering N ‐1 contingency of power system', International Transactions on Electrical Energy Systems, vol. 31, no. 5.
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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 & Energy Systems, vol. 130, pp. 106980-106980.
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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.

Luo, H, Wang, P, Chen, H & Xu, M 2021, 'Object Detection Method Based on Shallow Feature Fusion and Semantic Information Enhancement', IEEE Sensors Journal, vol. 21, no. 19, pp. 21839-21851.
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Luo, L, Jiang, Z, Wei, D & Jia, F 2021, 'A study of influence of hydraulic pressure on micro-hydromechanical deep drawing considering size effects and surface roughness', Wear, vol. 477, pp. 203803-203803.
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Luo, L, Wei, D, Zu, G & Jiang, Z 2021, 'Influence of blank holder-die gap on micro-deep drawing of SUS304 cups', International Journal of Mechanical Sciences, vol. 191, pp. 106065-106065.
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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, vol. 12, no. 1, pp. 15-29.
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Luo, Y, Liu, P, Zheng, L, Guan, T, Yu, J & Yang, Y 2021, 'Category-Level Adversarial Adaptation for Semantic Segmentation using Purified Features', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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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 and Concrete Composites, vol. 117, pp. 103883-103883.
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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, pp. 122690-122690.
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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, pp. 106392-106392.
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Luong, NC, Lu, X, Hoang, DT, Niyato, D & Kim, DI 2021, 'Radio Resource Management in Joint Radar and Communication: A Comprehensive Survey', IEEE Communications Surveys & Tutorials, vol. 23, no. 2, pp. 780-814.
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Luu, HM, Walsum, T, Franklin, D, Pham, PC, Vu, LD, Moelker, A, Staring, M, VanHoang, X, Niessen, W & Trung, NL 2021, 'Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion', Medical Physics, vol. 48, no. 6, pp. 2877-2890.
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Lv, Y, Miao, J, Liang, J, Chen, L & Qian, Y 2021, 'BIC-based node order learning for improving Bayesian network structure learning', Frontiers of Computer Science, vol. 15, no. 6.
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Lyu, B, Ramezani, P, Hoang, DT, Gong, S, Yang, Z & Jamalipour, A 2021, 'Optimized Energy and Information Relaying in Self-Sustainable IRS-Empowered WPCN', IEEE Transactions on Communications, vol. 69, no. 1, pp. 619-633.
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Lyu, J, Bi, X, Banerjee, S, Huang, Z, Leung, FHF, Lee, TT-Y, Yang, D-D, Zheng, Y-P & Ling, SH 2021, 'Dual-task ultrasound spine transverse vertebrae segmentation network with contour regularization', Computerized Medical Imaging and Graphics, vol. 89, pp. 101896-101896.
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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, pp. 101847-101847.
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Lyu, X, Ren, C, Ni, W, Tian, H, Cui, Q & Liu, RP 2021, 'Online Learning of Optimal Proactive Schedule Based on Outdated Knowledge for Energy Harvesting Powered Internet-of-Things', IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 1248-1262.
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Lyu, X, Ren, C, Ni, W, Tian, H, Liu, RP & Tao, X 2021, 'Distributed Online Learning of Cooperative Caching in Edge Cloud', IEEE Transactions on Mobile Computing, vol. 20, no. 8, pp. 2550-2562.
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Lyu, X, Zhang, L, Zhang, T, Li, B, Li, H & Yu, Y 2021, 'Prediction and Analysis of Ultimate Bearing Capacity of Square CFST Long Column under Eccentric Compression after Acid Rain Corrosion', Materials, vol. 14, no. 10, pp. 2568-2568.
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This paper adopts the method of steel tube wall thickness and strength reduction to simulate corrosion damage. The numerical model of the square concrete-filled steel tube long column (SCFST-LC) under eccentric compression after acid rain corrosion is established in the finite element software, ABAQUS. The reliability and accuracy of the model are verified by comparing it with published relevant experimental results. The failure mode, load-deformation curve, and ultimate compressive load were analysed. Following that, the impacts of section size, yield strength of the steel tube, axial compressive strength of concrete, steel ratio, slenderness ratio, and load eccentricity on its ultimate compressive load are comprehensively investigated. The results demonstrate that the ultimate compressive load of the SCFST-LC decreases significantly with the increase in corrosion rate. The corrosion rate increases from 10 to 40%, and the ultimate bearing capacity decreases by 37.6%. Its ultimate bearing capacity can be enhanced due to the increase in section size, material strength, and steel ratio. In contrast, the ascending slenderness ratio and load eccentricity has harmful effects on the ultimate compressive load of the specimens. Finally, a simplified formula for the axial compressive load of the SCFST-LC under eccentric compression after acid rain corrosion is proposed. The calculation accuracy is high and the deviation of the results is basically within 15%, which is in good agreement with the numerical simulation results.

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, pp. 1-9.
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Ma, M, Liu, Y, Wei, Y, Hao, D, Wei, W & Ni, B-J 2021, 'A facile oxygen vacancy and bandgap control of Bi(OH)SO4·H2O for achieving enhanced photocatalytic remediation', Journal of Environmental Management, vol. 294, pp. 113046-113046.
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Ma, Y, Wu, N, Zhang, JA, Li, B & Hanzo, L 2021, 'Parametric Bilinear Iterative Generalized Approximate Message Passing Reception of FTN Multi-Carrier Signaling', IEEE Transactions on Communications, vol. 69, no. 12, pp. 8443-8458.
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A low-complexity parametric bilinear generalized approximate message passing (PBiGAMP)-based receiver is conceived for multi-carrier faster-than-Nyquist (MFTN) signaling over frequency-selective fading channels. To mitigate the inherent ill-conditioning problem of MFTN signaling, we construct a segment-based frequency-domain received signal model in the form of a block circulant linear transition matrix, which can be efficiently calculated by applying a two dimensional fast Fourier transform. Based on the eigenvalue decomposition of the block circulant matrices, we can diagonalize the covariance matrix of the complex-valued colored noise process imposed by the associated two dimensional non-orthogonal matched filtering. Building on this model, a PBiGAMP-based parametric joint channel estimation and equalization (JCEE) algorithm is proposed for MFTN systems. In this algorithm, we introduce a pair of additive terms for characterizing the interferences arising from adjacent segments and employ the exact discrete a priori probabilities of the transmitted symbols for improving the bit error rate (BER) performance. To further enhance the system’s robustness in the presence of ill-conditioned matrices, we develop a refined PBiGAMP-based JCEE algorithm by introducing a series of scaled identity matrices. Moreover, the proposed PBiGAMP-based JCEE algorithms may be readily decomposed into GAMP-based equalization algorithms, when the channel state information is perfectly known. The overall complexity of the proposed algorithms only increases logarithmically with the total number of transmitted symbols. Our simulation results demonstrate the benefits of the proposed PBiGAMP-based iterative message passing receiver conceived for MFTN signaling.

Ma, Z, Xuan, J, Wang, YG, Li, M & Liò, P 2021, 'Path integral based convolution and pooling for graph neural networks*', Journal of Statistical Mechanics: Theory and Experiment, vol. 2021, no. 12, pp. 124011-124011.
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AbstractGraph neural networks (GNNs) extend the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and pooling is key to success. Borrowing ideas from physics, we propose path integral-based GNNs (PAN) for classification and regression tasks on graphs. Specifically, we consider a convolution operation that involves every path linking the message sender and receiver with learnable weights depending on the path length, which corresponds to the maximal entropy random walk. It generalizes the graph Laplacian to a new transition matrix that we call the maximal entropy transition (MET) matrix derived from a path integral formalism. Importantly, the diagonal entries of the MET matrix are directly related to the subgraph centrality, thus leading to a natural and adaptive pooling mechanism. PAN provides a versatile framework that can be tailored for different graph data with varying sizes and structures. We can view most existing GNN architectures as special cases of PAN. Experimental results show that PAN achieves state-of-the-art performance on various graph classification/regression tasks, including a new benchmark dataset from statistical mechanics that we propose to boost applications of GNN in physical sciences.

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, vol. 9.
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Modeling is essential for modern science, and science-based policies are directly affected by the reliability of model outputs. Artificial intelligence has improved the accuracy and capability of model simulations, but often at the expense of a rational understanding of the systems involved. The lack of transparency in black box models, artificial intelligence based ones among them, can potentially affect the trust in science driven policy making. Here, we suggest that a broader discussion is needed to address the implications of black box approaches on the reliability of scientific advice used for policy making. We argue that participatory methods can bridge the gap between increasingly complex scientific methods and the people affected by their interpretations

Mafi, R, Javankhoshdel, S, Cami, B, Jamshidi Chenari, R & Gandomi, AH 2021, 'Surface altering optimisation in slope stability analysis with non-circular failure for random limit equilibrium method', Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, vol. 15, no. 4, pp. 260-286.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. In limit equilibrium slope stability analysis, surface altering optimisation (SAO) is a novel approach to minimise the factor of safety for a given slip surface using spline curves in 2D. It is a local search algorithm that when combined with a global search method, can form a powerful hybrid optimisation technique used in slope stability analysis. Probabilistic analysis of a slope with spatial variability is a computationally intensive example that would demonstrate the accuracy and speed of optimisation techniques. In this paper, the probabilistic analysis results of three different slopes with both complicated and straightforward geometries are presented, and the application of SAO in spatial variability analysis using random limit equilibrium method (RLEM) is demonstrated. It was found that SAO combined with a global search method provides fairly accurate results and yields curtailed computational effort.

Mah, D, Chamoli, U & Smith, GCS 2021, 'Usefulness of computed tomography based three-dimensional reconstructions to assess the critical shoulder angle', World Journal of Orthopedics, vol. 12, no. 5, pp. 301-309.
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Mahmood, AH, Babaee, M, Foster, SJ & Castel, A 2021, 'Continuous Monitoring of the Early-Age Properties of Activated GGBFS with Alkaline Solutions of Different Concentrations', Journal of Materials in Civil Engineering, vol. 33, no. 12.
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Mahmood, AH, Foster, SJ & Castel, A 2021, 'Effects of mixing duration on engineering properties of geopolymer concrete', Construction and Building Materials, vol. 303, pp. 124449-124449.
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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, pp. 141920-141920.
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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.

Makhdoom, I, Hayawi, K, Kaosar, M, Mathew, SS & Ho, P-H 2021, 'D2Gen: A Decentralized Device Genome Based Integrity Verification Mechanism for Collaborative Intrusion Detection Systems', IEEE Access, vol. 9, pp. 137260-137280.
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Malik, M, Malik, MK, Mehmood, K & Makhdoom, I 2021, 'Automatic speech recognition: a survey', Multimedia Tools and Applications, vol. 80, no. 6, pp. 9411-9457.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Recently great strides have been made in the field of automatic speech recognition (ASR) by using various deep learning techniques. In this study, we present a thorough comparison between cutting-edged techniques currently being used in this area, with a special focus on the various deep learning methods. This study explores different feature extraction methods, state-of-the-art classification models, and vis-a-vis their impact on an ASR. As deep learning techniques are very data-dependent different speech datasets that are available online are also discussed in detail. In the end, the various online toolkits, resources, and language models that can be helpful in the formulation of an ASR are also proffered. In this study, we captured every aspect that can impact the performance of an ASR. Hence, we speculate that this work is a good starting point for academics interested in ASR research.

Mallick, SK, Das, P, Maity, B, Rudra, S, Pramanik, M, Pradhan, B & Sahana, M 2021, 'Understanding future urban growth, urban resilience and sustainable development of small cities using prediction-adaptation-resilience (PAR) approach', Sustainable Cities and Society, vol. 74, pp. 103196-103196.
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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', The 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, Dohaney, J, O’Hanlon, T, Pickering, J, Walker, S, Maclean, F & Smith, TD 2021, 'From problem-based learning to practice-based education: a framework for shaping future engineers', European Journal of Engineering Education, vol. 46, no. 1, pp. 27-47.
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Mann, RL 2021, 'Simulating quantum computations with Tutte polynomials', npj Quantum Information, vol. 7, no. 1.
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AbstractWe establish a classical heuristic algorithm for exactly computing quantum probability amplitudes. Our algorithm is based on mapping output probability amplitudes of quantum circuits to evaluations of the Tutte polynomial of graphic matroids. The algorithm evaluates the Tutte polynomial recursively using the deletion–contraction property while attempting to exploit structural properties of the matroid. We consider several variations of our algorithm and present experimental results comparing their performance on two classes of random quantum circuits. Further, we obtain an explicit form for Clifford circuit amplitudes in terms of matroid invariants and an alternative efficient classical algorithm for computing the output probability amplitudes of Clifford circuits.

Mann, RL & Helmuth, T 2021, 'Efficient algorithms for approximating quantum partition functions', Journal of Mathematical Physics, vol. 62, no. 2, pp. 022201-022201.
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Mansoor, A & Bano, M 2021, 'Decolonial pedagogy and English literary studies: problematics in a Pakistani context', Compare: A Journal of Comparative and International Education, vol. 51, no. 4, pp. 512-528.
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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, Y-Z, Yang, J-X & Ji, J-C 2021, 'Theoretical and experimental study of surface texturing with laser machining', Advances in Manufacturing, vol. 9, no. 4, pp. 538-557.
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Mao, Z, Zhao, L, Huang, S, Fan, Y & Pui-Wai Lee, A 2021, 'Direct 3D ultrasound fusion for transesophageal echocardiography', Computers in Biology and Medicine, vol. 134, pp. 104502-104502.
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Marjanovic, O, Cecez-Kecmanovic, D & Vidgen, R 2021, 'Algorithmic pollution: Making the invisible visible', Journal of Information Technology, vol. 36, no. 4, pp. 391-408.
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