Publications

Ferrer-Comalat, JC, Linares-Mustarós, S, Merigó, JM & Kacprzyk, J 2020, Preface.

Irga, P 2020, Grow Up Guide, UTS Shopfront.
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Jain, LC, Balas, VE, Tsihrintzis, GA & Sharma, DK 2020, Preface.

Alsawwaf, M, Chaczko, Z & Kulbacki, M 2020, 'In Your Face: Person Identification Through Ratios of Distances Between Facial Features' in Intelligent Information and Database Systems, Springer International Publishing, pp. 527-536.
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Alzuhairi, M, Jena, R & Pradhan, B 2020, 'Geospatial Technology Applications in Environmental Disaster Management' in Sustainable Energy and Environment: An Earth System Approach, Apple Academic Press, USA, pp. 271-306.
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Geospatial technologies like GIS and GPS have grown rapidly and widely utilized in disaster management. This chapter provides an overview of the roles and applications of various geospatial technologies in landslide, oil spill, and earthquake disaster management. It explains the use of each technology in various stages of a disaster management process, for example, pre-disaster, during disaster, and post-disaster; and provides the current limitations of such technologies in reducing the impacts of these disasters. Finally, it presents examples of recent methods applied to disaster management using different geospatial technologies

Cetindamar, Lammers, T & Sick, N 2020, 'Digital technologies, competitiveness & policies: An integrative city-based policy roadmap for entrepreneurial ecosystems' in Managing Innovation in a Global and Digital World Meeting Societal Challenges and Enhancing Competitiveness, Springer Gabler, Cham, Switzerland, pp. 49-62.
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This paper offers a conceptual city-based policy roadmap for policy makers who are interested in managing their entrepreneurial ecosystems. The literature lacks specific policies bringing together both technological developments and entrepreneurial activities at city level, even though recent studies point out the role of policies for entrepreneurial ecosystems in order to build entrepreneurial economies. Given the wide opportunities arising from digital technologies for economic growth, policy makers need to identify feasible frameworks to support a digitally competitive entrepreneurial ecosystem. By combining the previous literature on entrepreneurship and digital technologies within a particular urban context, this paper describes a conceptual roadmap as a tool that might help policy makers to plan the future competitiveness of their cities.

Chaczko, Z, Al-Doghman, F & Rakhi Ajayan, A 2020, 'Policy-Based Consensus Data Aggregation for the Internet of Things' in Klempous, R & Nikodem, J (eds), Smart Innovations in Engineering and Technology, Springer Nsture, Cham, Switzerland, pp. 119-130.
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The new trend of the Internet of Things brings a whole breed of opportunities and applications. Within it, a massive amount of data coming from heterogeneous sources travel in a bidirectional way. Data aggregation is one of the most efficient ways to mitigate Big Data. However, using one type of aggregation within a network at all times is not an optimal option. Various network situations require different aggregation functions at different times. We introduce a policy-based data aggregation framework that can handle this issue by referring to a policy when executing the aggregation strategy. An agreement process is used to reach consensus about the aggregation function that is to be applied on the network (or part of it) at a specific time. Participants are to negotiate the policy terms based on the current network status and the nature of the coming requests. The framework represents a promising scope for fully automated IoT.

Chaczko, Z, Rakhi Ajayan, A & Al-Doghman, F 2020, 'Tensor Decompositions in Multimodal Big Data: Studying Multiway Behavioral Patterns' in Klempous, R & Nikodem, J (eds), Smart Innovations in Engineering and Technology, Springer Nature, Cham, Switzerland, pp. 1-296.
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Preset day cyber-physical systems (CPS) are the confluence of very large data sets, tight time constraints, and heterogeneous hardware units, ridden with latency and volume constraints, demanding newer analytic perspectives. Their system logistics can be well-defined by the data-streams’ behavioural trends across various modalities, without numerical restrictions, favouring resource-saving over methods of investigating individual component features and operations. The aim of this paper is to demonstrate how behaviour patterns and related anomalies comprehensively define a CPS. Tensor decompositions are hypothesized as the solution in the context of multimodal smart-grid-originated Big Data analysis. Tensorial data representation is demonstrated to capture the complex knowledge encompassed in these data flows. The uniqueness of this approach is highlighted in the modified multiway anomaly patterns models. In addition, higher-order data preparation schemes, design and implementation of tensorial frameworks and experimental-analysis are final outcomes.

Chaczko, Z, Zhang, W, Christopher, C, Jan, S & Su, S 2020, 'Internet of Things Is Changing Teaching of Technical Subjects at UTS' in Klempous, R & Nikodem, J (eds), Smart Innovations in Engineering and Technology, Springer Nature AG Switzerland, Gewerbestrasse 11, 6330 Cham, Switzerland, pp. 131-296.
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The Internet of Things, involving the connection of intelligent devices (other than standard, already connected products such as computers and smartphones) to the Internet, is in the process of transforming numerous areas of lives. For many years, both academia and industry prepared the background theories and implementations for what is today's IoT, keeping changing and evolving. Students of universities, preparing to be the driving forces of innovation and progress have a very strong interest in IoT and related subjects. Teaching at universities, especially technical ones is strongly affected by the need to give students the required background and show the direction for self-study. In this paper we will provide a literature review to analyze how IoT inspired changes in teaching some technical subjects at the University of Technology Sydney (UTS) by describing, the recently undertaken projects in this area, providing some case studies and drawing conclusions.

Chilwesa, M, Akbarnezhad, A, Gharehchaei, M, Castel, A, Lloyd, R & Foster, S 2020, 'Using the Double Ring Test to Assess the Effect of Type of Aggregate Used on Thermal Cracking Potential of Concrete' in Lecture Notes in Civil Engineering, pp. 337-347.
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© 2020, Springer Nature Singapore Pte Ltd. Early age thermal cracking occurs when tensile stress generated from restrained deformations in young concrete reaches its tensile strength. The risk of early age cracking is especially high for elements involving large concrete placements such as bridge piers, foundations etc. and concrete with low water-to-binder (w/c) ratio. The research in literature has focused on finding ways to mitigate cracking through mix design approaches and devising test methods to determine restraint stresses in concrete. The most commonly used test methods, such as the Rigid Cracking Frame, require special equipment and calibration to function properly. There is need for a more simple but accurate test that can measure the effects of both early-age expansion and shrinkage, as well as account for the temperature changes that occur in a mass concrete element. The double ring test, which has been used to study effects of internal curing on early age cracking, has potential to measure effects of both shrinkage and expansion. To evaluate its viability as a reliable test to characterise the early age thermal cracking potential of concrete, the double ring test was used in this study to assess the effect of type of aggregate used on early age thermal cracking of concrete subjected to temperature history similar to that experienced in an actual mass concrete structural element. Three types of locally available aggregates were investigated. Preliminary results indicate that the double ring test can be used to study effects of thermal deformations on concrete subjected to a changing temperature history, and that type of aggregate used can influence the potential of cracking.

Deng, L, Guo, W, Ngo, HH, Nguyen, LD, Nguyen, LN, Liu, Y & Bui, XT 2020, 'Aerobic membrane bioreactors for municipal wastewater treatment' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 103-128.
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Dutta, S & Gandomi, AH 2020, 'Design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels' in Handbook of Probabilistic Models, Elsevier, pp. 369-381.
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Ekechukwu, J & Lammers, T 2020, 'Digital Technology to Enhance Project Leadership Practice: The Case of Civil Construction' in George, B & Paul, J (eds), Digital Transformation in Business and Society Theory and Cases, Palgrave Macmillan, Cham, pp. 247-272.
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Digital transformation is fundamentally influencing all aspects of business and society. It can enable individuals and organizations to transcend from one way of working to another. In construction, emphasis is shifting from project management to project leadership, as professionals need assisting tools to not only aid in managing tasks and activities, but aid in leading people. As such, the adoption of digital technologies may hold the key in taking project leadership practice into the future. To date, there have been few attempts to explore the potential of digital technology as an aid for project leadership development and practice. This research therefore aims to investigate this potential in the context of the Australian civil construction industry. We review the existing body of knowledge on both industry-specific leadership demands and relevant digital technology capabilities to identify areas of improvement and to guide interviews with construction project managers. So far, literature has focused on endorsing particular leadership behaviors and/or styles, while ignoring the difficulties faced by professionals in practicing these behaviors. We find that project managers of civil contractors within Sydney understand the significance of leadership but are often overwhelmed by its complexity.

Fleck, R, Pettit, T, Douglas, A, Irga, P & Torpy, F 2020, '15 - Botanical biofiltration for reducing indoor air pollution' in Bio-Based Materials and Biotechnologies for Eco-Efficient Construction, Woodhead Publishing Limited.
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The book will be an essential reference resource for academic researchers, civil engineers, contractors working in construction works, postgraduate students and other professionals.

Gong, S, Oberst, S & Wang, X 2020, 'A Non-linear Model of Rubber Shear Springs Validated by Experiments' in Lacarbonara, W, Balachandran, B, Ma, J, Tenreiro Machado, JA & Stepan, G (eds), Nonlinear Dynamics of Structures, Systems and Devices Proceedings of the International Nonlinear Dynamics Conference (NODYCON 2019), |, Springer, pp. 319-328.
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Vibrating flip-flow screens provide an effective solution for screening highly viscous or fine materials. However, only linear theory has been applied to their design. Yet, to understand deficiencies and to improve performance an accurate model especially of the rubber shear springs equipped in screen frames is critical for its dynamics to predict, e.g. frequency- and amplitude-dependent behaviour. In this chapter, the amplitude dependency of the rubber shear spring is represented by employing a friction model in which parameters are fitted to an affine function rather constant values used for the classic Berg’s friction model; the fractional derivative model is used to describe its frequency dependency and compared to conventional dashpot and Maxwell models with its elasticity being represented by a non-linear spring. The experimentally validated results indicate that the proposed model with a non-linear spring, friction and fractional derivative model is able to more accurately describe the dynamic characteristics of a rubber shear spring compared with other models.

Gowripalan, N 2020, 'Autogenous Shrinkage of Concrete at Early Ages' in Wang, CM, Ho, JCM & Kitipornchai, S (eds), ACMSM25, pp. 269-276.
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Holley, C, Mutongwizo, T, Pucci, S & Castilla Rho, J 2020, 'Groundwater regulation, compliance and enforcement: insights on regulators, regulated actors and frameworks in New South Wales, Australia' in Rinaudo, JD, Holley, C, Barnett, S & Montginoul, M (eds), Sustainable Groundwater Management: A Comparative Analysis of French and Australian Policies and Implications to Other Countries, Springer.
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Huang, Y, Su, S & Song, R 2020, 'Voluntary intention-driven rehabilitation robots for the upper limb' in Hu, X (ed), Intelligent Biomechatronics in Neurorehabilitation, Academic Press, UK, pp. 111-130.
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Voluntary motion intention during rehabilitation is essential for patients with neurological motor disorders but who still have their own residual motor ability. The weight of the arm and limited involvement of the residual motor efforts might hinder movement performance because of patients' weak muscles. Two different kinds of control strategies are developed and applied by a cable-based upper limb rehabilitation robot for these reasons. A novel strategy for position-varying compensation of arm gravity is proposed for assisting patients whose residual motor ability is restricted by gravity. The robot can time-varyingly adjust the forces for compensating the gravity according to real-time position feedback. The performance of the strategy was assessed by recruiting seven healthy participants to track different movements in three-dimensional space. A natural integration for human–machine cooperation is built up using surface electromyography (sEMG) signals for revealing the voluntary motor intention of patients who still have their own residual motor ability. A model for continuously revealing the relationship between the sEMG signals and participants' voluntary forces is trained based on a linear state-space model. Ten healthy participants were recruited to track a rectilinear task with and without robot assistance. The results indicated that, with the proposed gravity compensation strategy, the robot might assist participants to perform higher-quality movements and improve their participation in movements. With the sEMG-based control strategy, the robot could provide appropriate assistance based on participants' voluntary motion and reveal their motor efforts. Overall, it is reasonable to choose different rehabilitation therapies for stroke patients with different motor statuses.

Jaddoa, M, Gonzalez, L, Cuthbertson, H & Al-Jumaily, A 2020, 'Multi View Face Detection in Cattle Using Infrared Thermography' in Communications in Computer and Information Science, Springer International Publishing, pp. 223-236.
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Li, JT, Zhu, XQ & Samali, B 2020, 'Bridge Operational Modal Identification Using Sparse Blind Source Separation' in Lecture Notes in Civil Engineering, pp. 911-920.
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© 2020, Springer Nature Singapore Pte Ltd. The bridge infrastructures are subjected to continuous degradation due to ageing, environmental and excess loading. Monitoring of these structures is a key part of any maintenance strategy as it can give early warning if a bridge is becoming unsafe. Most of the current approaches are using direct measurements that the sensors are installed at different specific locations on the bridge to capture the dynamic characteristics of the structure under random input, such as wind loads, traffic loads and ground motions. Based on the assumption on the white noise characteristics of the random input, structural properties of the bridge could be extracted from the vibration responses only. However, the bridge is subjected to non-stationary traffic loads, and the frequency characteristics of vibrations are varied. Especially for short-span bridges, the non-stationary traffic excitation is significant and most of the existing output-only structural identification methods could not be used to assess the bridge condition. This study proposes a blind source separation (BSS) method using short time Fourier transform (STFT) for the analysis of non-stationary measurements in time frequency (TF) domain. The proposed method is capable of source component separation from response measurement for underdetermined problems when the number of independent measurements (sensors) is less than that of source component. The proposed method is applied to a cable-stayed bridge in the field for the operational modal identification under different traffic conditions.

Lidfors Lindqvist, A & Walker, PD 2020, 'Handling Dynamics of a Lightweight Solar-Electric Vehicle with Direct Yaw Moment Control' in Lecture Notes in Mechanical Engineering, Springer International Publishing, pp. 1169-1177.
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Meena, NK, Nimbalkar, S & Fatahi, B 2020, 'Finite Element Modeling of Soil Arching in Pile Supported Embankment: 2D Approach' in Innovative Solutions for Deep Foundations and Retaining Structures, Springer International Publishing, pp. 40-50.
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Mohebali, B, Tahmassebi, A, Meyer-Baese, A & Gandomi, AH 2020, 'Probabilistic neural networks' in Handbook of Probabilistic Models, Elsevier, pp. 347-367.
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More, FJ, Chaczko, Z & Kulbacka, J 2020, 'Early Detection of Coronary Artery Diseases Using Endocrine Markers' in Intelligent Information and Database Systems, Springer International Publishing, pp. 593-601.
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Naik, G, Chai, R, Su, S, Rong, S & Nguyen, HT 2020, 'Comparison of Independence of Triceps Brachii and Biceps Brachii Between Paretic and Non-paretic Side During Different MVCs—A Case Study' in Series in BioEngineering, Springer Singapore, pp. 71-79.
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Nghiem, LD, Nguyen, LN, Phan, HV, Ngo, HH, Guo, W & Hai, F 2020, 'Aerobic membrane bioreactors and micropollutant removal' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 147-162.
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Ngo, HH, Khan, MA, Guo, W, Liu, Y, Zhang, X, Li, J & Wang, J 2020, 'Energy production in anaerobic membrane bioreactors: Opportunities and challenges' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 309-333.
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Pathak, N, Tran, VH, Phuntsho, S & Shon, HY 2020, 'Membrane bioreactors for the removal of micro-pollutants' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 231-252.
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Patmore, G, Balnave, N & Marjanovic, O 2020, 'Visualising Organisations Over Time and Space - The Visual Atlas of the Australian Cooperatives' in Contemporary Issues in Work and Organisations Actors and Institutions, Routledge, UK, pp. 143-156.
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The International Co-operative Alliance (ICA, 2018a)–the international umbrella organisation of member-owned co-operatives–defines a co-operative as‘an autonomous association of persons united voluntarily to meet their common economic, social and cultural needs and aspirations through a jointly-owned and democratically-controlled enterprise’. Five broad types, or traditions, of co-operative activity have developed internationally since the mid-nineteenth century: retail (or consumer);financial (orbanking); agricultural (including fishing, marketing, processing and supply); worker;and services. While co-operatives are governed by a set of principles, which were originally inspired by the Rochdale consumer co-operative movement, these have changed over time. The seven current principles include‘voluntary and open membership’,‘democratic member control’and‘concern for community’(ICA, 2018a). In an inter-national context, the ICA has 313 member organisations from 109 countries and co-operatives have an estimated 1.2 billion members and generate 280 million part- and full-time jobs worldwide (ICA, 2018b). There are an estimated 2,000 co-operative and mutual businesses operating in Australia with total memberships of 14.8 million (Busi-ness Council of Co-operatives and Mutuals, 2018).This chapter will examine the Visual Atlas of Australian Co-operatives History Project (VAACHP), which tries to map Australian co-operatives over time and space. It combines history with emerging visualisation approaches in information technology and draws upon skills from academics in the two areas.It first examines the background to the project drawing upon the historio-graphical literature on co-operatives particularly regarding Australia and then explores internationally the ideas that explain the formation, growth, decline and survival of co-operatives. The chapter then focuses on the project itself and concludes with some early findings, particularly regarding the dramatic growth in ...

Sharma, D, Sandu, S & Yang, M 2020, 'Analysis of Macroeconomic Impacts of Energy Security Improvements in Asia' in Soytas, U & Sari, R (eds), Routledge Handbook of Energy Economics, Taylor & Francis, Great Britain, pp. 129-150.
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This chapter analyzes the macroeconomic impacts of energy security improvements in Asia, with particular emphasis on the trade-offs that policy makers may like to consider while designing policies to redress the energy security challenge in the region. This analysis is based on estimates of impacts obtained from the application of the methodology described in Chapter 31. The cases in point for the analysis are provided by seven major Asian countries (China, India, Indonesia, Japan, Korea, Malaysia, and Thailand). This chapter is organized as follows. Section 2 analyzes the macroeconomic impacts of energy security improvements for the three scenarios, namely, Country Policy (CP), Country Aspiration (CA), and Sustainable Development (SD), as described in Chapter 31. Section 3 further extends this analysis to identify major policy trade-offs. Section 4 presents some broad conclusions of the chapter.

Song, X, Luo, W, Nguyen, LN, Ngo, HH, Guo, W & Nghiem, LD 2020, 'Anaerobic membrane bioreactors for emerging pollutants removal' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 197-218.
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Sotirova, E, Shannon, A, Atanassova, V, Atanassov, K & Bureva, V 2020, 'Interval Valued Intuitionistic Fuzzy Evaluations for Analysis of Students’ Knowledge' in Studies in Computational Intelligence, pp. 75-82.
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© Springer Nature Switzerland AG 2020. In the paper is proposed a method for evaluation of student’s knowledge obtained in the university e-learning courses. For the assessment of the student’s solution of the respective assessment units the theory of intuitionistic fuzzy and interval valued intuitionistic fuzzy sets is used. The obtained intuitionistic fuzzy estimations reflect the degree of each student’s good performances, or poor performances, for each assessment unit and for all units.

Tang, Z, Li, WG, Li, PR & Shah, SP 2020, 'Durability of Sustainable Construction Materials with Solid Wastes' in Lecture Notes in Civil Engineering, pp. 3-13.
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© 2020, Springer Nature Singapore Pte Ltd. Solid waste disposal has been emerging as a challenging issue worldwide following the enhanced economic activities and rapid urbanization. Considerable efforts have been made to use solid waste materials as partial cement replacement in concrete. This work investigates the sulfate resistance of concrete containing different solid waste materials—waste glass powder, coal gangue powder and fly ash, at various substitution levels (10, 20, and 30%). Two water to binder ratios were adopted as 0.41 and 0.54. The sulfate resistance was evaluated by exposing the prepared concrete specimens to a 5% sodium sulfate solution for a total period of 22 months and examining the mass change, compressive strength, splitting tensile strength, ultrasonic pulse velocity, mineralogical and microstructural properties. Results obtained show that the sulfate resistance of concrete can be improved substantially by the addition of solid waste materials. Additionally, waste glass powder is found to be the most effective, followed by gangue coal powder and fly ash. These studies reveal that in addition to helping solid waste management, reusing of these solid waste materials in concrete improves the resistance to sulfate attack.

Tran, VH, Pathak, N & Shon, HY 2020, 'Osmotic membrane bioreactor technology—Concept, potential and challenges' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 181-207.
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Voinov, A, Castilla Rho, J, Perez, P & Kenny, D 2020, 'Integrated Ecological Economic Modeling: What is it good for?' in Creating Wellbeing Futures: A Research and Action Agenda for Ecological Economics.
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Climate disruption, overpopulation, biodiversity loss, the threats of financial collapse, large-scale damage to our natural and social environments and eroding democracy are all becoming critically important concerns. The editors of this timely book assert that these problems are not separate, but all stem from our overreliance on an out-dated approach to economics that puts growth of production and consumption above all else. Ecological economics can help create the future that most people want – a future that is prosperous, just, equitable and sustainable. This forward-thinking book lays out an alternative approach that places the sustainable wellbeing of humans and the rest of nature as the overarching goal. Each of the book’s chapters, written by a diverse collection of scholars and practitioners, outlines a research and action agenda for how this future can look and possible actions for its realization. Sustainable Wellbeing Futures will be of value to academics and students researching environmental and ecological economics, as well as individuals interested in gaining a greater understanding of the concept of a wellbeing future and how we might act to achieve it.

Wang, W & Wu, C 2020, 'Numerical Simulation of FRP-Concrete-Steel Double-Skin Tubular Column Under Lateral Impact Loading' in Lecture Notes in Civil Engineering, pp. 467-476.
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© 2020, Springer Nature Singapore Pte Ltd. Hybrid fiber reinforced polymer (FRP)-concrete-steel double-skin tubular column (DSTC) is a new form of composite column that consists of an outer FRP tube and an inner steel tube, with inner space filled with concrete. This study presents the first numerical investigation on the behavior of hybrid DSTC under lateral impact loading using LS-DYNA. The numerical models of hybrid DSTC were first developed and then verified by using existing test results. Afterwards, parametric analyses were conducted to investigate the influences of impactor shape and impact energy on the lateral impact behavior of hybrid DSTC. The impact force-time history and mid-span deflection-time history of hybrid DSTC were extracted from the simulation results. The simulation results show that hybrid DSTC behave in a very ductile manner under lateral impact loading. The impact energy can significantly influence the impact behavior of hybrid DSTC. However, the influence of impactor shape is insignificant.

Zhang, W, Chiu, C, Szymanski, J, Chaczko, Z, Su, S & Zhou, J 2020, 'Internet of Things Is Changing Teaching of Technical Subjects at UTS' in Smart Innovations in Engineering and Technology, Springer International Publishing, pp. 131-143.
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Abbasian Dehkordi, S, Farajzadeh, K, Rezazadeh, J, Farahbakhsh, R, Sandrasegaran, K & Abbasian Dehkordi, M 2020, 'A survey on data aggregation techniques in IoT sensor networks', Wireless Networks, vol. 26, no. 2, pp. 1243-1263.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. There is a growing interest in using wireless sensor technologies in various Internet of things scenarios. Considering the huge growth of smart objects and their applications, the need to collect and analyze their product data are becoming one of the main challenges. Sensor nodes are powered by batteries, efficient operations in term of energy are critical. Toward that end, it is desirable for a sensor node to eliminate redundancies in the received data from the neighboring nodes before transferring the final data to the central station. Data aggregation is one of the influential techniques in elimination of data redundancy and improvement of energy efficiency; also it increases the lifespan of Wireless Sensor Networks. In addition, the efficient data aggregation protocol can reduce network traffic. When a specific objective takes place in a specific area, it might be detected by more than one sensor. Considering the main challenges and aspects of data aggregation in wireless sensor networks, a review on different types of data aggregation techniques and protocols are presented in this paper. The ultimate objective of this study is to make the basic foundations to develop new advanced designs based on data integration techniques and clustering that have been proposed so far. Major techniques of data integration in wireless sensor networks covering ground, underground and underwater sensor networks are presented in this paper and the applications, advantages and disadvantages of using each technique are described.

Abbasipour, M, Milimonfared, J, Heidary Yazdi, SS & Rouzbehi, K 2020, 'Power injection model of IDC-PFC for NR-based and technical constrained MT-HVDC grids power flow studies', Electric Power Systems Research, vol. 182.
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© 2020 Elsevier B.V. Insufficient control flexibility in multi-terminal HVDC (MT-HVDC) grids is an important motivation to install suitable power electronic-based DC power flow controller (PFC)s to ensure grid controllability, security, and reliability. This article proposes a new static power injection model (PIM) for those (interline) DC-PFCs to enable DC power flow (PF) studies and ease integration of the PFCs in the power system analysis softwares within the well-accepted Newton–Raphson (NR) solver-based framework. HVDC lines shunt conductances are also taken in to account in this newly developed paradigm. For this purpose, the IDC-PFC, as well as other MT-HVDC grid physical/control state variables are modified in cooperation to attain predefined control objective(s). Furthermore, a novel general routine (solution procedure) is proposed to handle several system physical/control limitations during the NR-based DC PF problem solution. Static/dynamic simulations are executed on an eight-bus test MT-HVDC grid to show/confirm the accuracy, effective performance, and excellent convergence property of the proposed IDC-PFC model, NR-based DC PF solver, and technical constraints handling routine. In this situation, it is proved that the original structure and symmetry of the admittance matrix can still be kept, and a few modifications are needed to be done in the Jacobin matrix.

Abdin, Z, Zafaranloo, A, Rafiee, A, Merida, W, Lipinski, W & Khalilpour, KR 2020, 'Hydrogen as an energy vector', RENEWABLE & SUSTAINABLE ENERGY REVIEWS, vol. 120.
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Abdo, P, Taghipour, R & Huynh, BP 2020, 'Three-Dimensional Simulation of Wind-Driven Ventilation Through a Windcatcher With Different Inlet Designs', Journal of Thermal Science and Engineering Applications, vol. 12, no. 4.
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Abstract Windcatcher is an effective natural ventilation system, and its performance depends on several factors including wind speed and wind direction. It provides a comfortable and healthy indoor environment since the introduced fresh air decreases the moisture content and reduces the pollutant concentration. Since the wind speed and its direction are generally unpredictable, it is important to use special inlet forms and exits to increase the efficiency of a windcatcher. In this study, computational fluid dynamics (CFD) modeling is implemented using ansys fluent to investigate the airflow entering a three-dimensional room through a windcatcher with different inlet designs. Three designs are studied which are a uniform inlet, a divergent inlet, and a bulging-convergent inlet. The airflow pattern with all inlets provided adequate ventilation through the room. With all the applied wind velocities (1, 2, 3, and 6 m/s) at the domain's inlet, the divergent inlet shape has captured the highest airflow through the room and provided higher average velocity at 1.2 m high enhancing the thermal comfort where most of the human occupancy occurs. With 6 m/s wind velocity, the divergent inlet has captured 2.55% more flow rate compared to the uniform inlet and 4.70% compared to the bulging-convergent inlet, and it has also provided an average velocity at 1.2 m high in the room of 7.16% higher than the uniform inlet and 8.44% higher than the bulging-convergent inlet.

Abdulkareem, SA, Augustijn, E-W, Filatova, T, Musial, K & Mustafa, YT 2020, 'Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning.', PLoS One, vol. 15, no. 1, pp. e0226483-e0226483.
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Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agent-based modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socio-environmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.

Afroz, F & Braun, R 2020, 'Energy-efficient MAC protocols for wireless sensor networks: a survey', International Journal of Sensor Networks, vol. 33, no. 3, pp. 150-173.
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Aghayarzadeh, M, Khabbaz, H, Fatahi, B & Terzaghi, S 2020, 'Interpretation of Dynamic Pile Load Testing for Open-Ended Tubular Piles Using Finite-Element Method', INTERNATIONAL JOURNAL OF GEOMECHANICS, vol. 20, no. 2.
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Ahmed, MB, Alam, MM, Zhou, JL, Xu, B, Johir, MAH, Karmakar, AK, Rahman, MS, Hossen, J, Hasan, ATMK & Moni, MA 2020, 'Advanced treatment technologies efficacies and mechanism of per- and poly-fluoroalkyl substances removal from water', Process Safety and Environmental Protection, vol. 136, pp. 1-14.
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© 2020 Institution of Chemical Engineers The increasing occurrence of chemically resistant per- and poly-fluoroalkyl substances (PFASs) in the natural environment, animal tissues and even the human body poses a significant health risk. Temporal trend studies on water, sediments, bird, fish, marine mammal and the human show that the exposure of PFAS has significantly increased over the last 20–30 years. Different physical, biological and chemical treatment processes have been investigated for PFAS removal from water. However, there is a lack of detailed understating of the mechanism of removal by different methods, especially by different advanced chemical treatment processes. This article reviews PFASs removal efficacy and mechanism by the advanced chemical treatment methods from aqueous solution. Review shows that several advanced oxidation processes (e.g., electrochemical oxidation, activated persulfate oxidation, photocatalysis, UV-induced oxidation) are successful in degrading PFASs. Moreover, defluorination treatment, some thermal and non-thermal degradation processes are also found to be prominent for the degradation of PFASs with some limitations including process costs over physical treatment (e.g., sorption), production of toxic by-products and greenhouse gases. Finally, knowledge gaps concerning the advanced chemical treatment of PFASs are discussed.

Al zahrani, S, Islam, MS, Xu, F & Saha, SC 2020, 'Thermal performance investigation in a novel corrugated plate heat exchanger', International Journal of Heat and Mass Transfer, vol. 148.
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© 2019 Compact heat exchangers have become an essential necessity for power production and multi other purposes on a daily basis. The corrugated plate heat exchangers (CPHEs) are well-known for their high thermal performance. This study proposes a unique CPHE with a simple modification that can boost its thermal performance significantly. The overall tests have been conducted on four CPHEs for two symmetric chevron angles (β) of 30°/30° and 60°/60° Two CPHEs belong to the newly CPHEs, and the other two belong to the well-known basic CPHE. Data are obtained for steady-state, single-phase (water-water), counter-current arrangements, and for Reynolds number (Re) ranges from 500 to 2500. Sophisticated mesh techniques have been adopted to develop the mesh for the plates and the fluids between the plates. An appropriate grid refinement test has been carried out for the accuracy of the numerical results. The results have been validated with benchmark experimental and numerical data. A realizable k−ε turbulence model with scalable wall treatment found to provide the most consistent and accurate prediction of the thermal performance of CPHE. The numerical results showed that the Nusselt number (Nu) and the effectiveness (ϵ) of the newly developed CPHEs are much higher than that of the basic one, which can be very useful when a heavy heat duty is required. The enhancement for Nu is up to 75% and for ϵ is up to 42%, and generally both exhibit a direct proportional relationship with Re. Based on the numerical result, a new correlation to predict Nu has been developed.

Alam, MM, Hossain, MA, Hossain, MD, Johir, MAH, Hossen, J, Rahman, MS, Zhou, JL, Hasan, ATMK, Karmakar, AK & Ahmed, MB 2020, 'The potentiality of rice husk-derived activated carbon: From synthesis to application', Processes, vol. 8, no. 2.
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© 2020 by the authors. Activated carbon (AC) has been extensively utilized as an adsorbent over the past few decades. AC has widespread applications, including the removal of different contaminants from water and wastewater, and it is also being used in capacitors, battery electrodes, catalytic supports, and gas storage materials because of its specific characteristics e.g., high surface area with electrical properties. The production of AC from naturally occurring precursors (e.g., coal, biomass, coconut shell, sugarcane bagasse, and so on) is highly interesting in terms of the material applications in chemistry; however, recently much focus has been placed on the use of agricultural wastes (e.g., rice husk) to produce AC. Rice husk (RH) is an abundant as well as cheap material which can be converted into AC for various applications. Various pollutants such as textile dyes, organic contaminants, inorganic anions, pesticides, and heavy metals can be effectively removed by RHderived AC. In addition, RH-derived AC has been applied in supercapacitors, electrodes for Li-ion batteries, catalytic support, and energy storage, among other uses. Cost-effective synthesis of AC can be an alternative for AC production. Therefore, this review mainly covers different synthetic routes and applications of AC produced from RH precursors. Different environmental, catalytic, and energy applications have been pinpointed. Furthermore, AC regeneration, desorption, and relevant environmental concerns have also been covered. Future scopes for further research and development activities are also discussed. Overall, it was found that RH-derived AC has great potential for different applications which can be further explored at real scales, i.e., for industrial applications in the future.

Alam, MM, Lu, DC & Siwakoti, Y 2020, 'Small signal analysis of dual input buck converter', International Journal of Smart Grid and Clean Energy, vol. 9, no. 1, pp. 8-16.
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© 2020 by the authors. This paper presents a small signal modelling and voltage-mode control of a pulse-width modulated (PWM) dual-input DC-DC buck converter. The control of multiple switches in a power converter is the main challenge for multipleinput converters addressed in this paper. Using the concept of linearization and perturbation depicted in circuit averaging technique, the closed-loop small signal model for multi-input DC-DC buck converter is derived. The closed loop control to output voltage transfer function is derived. A brief compensator design is introduced for a multi-input buck converter. In order to control the duty cycles of multiple switches and control the output voltage, a new variable is introduced to relate the duty cycles in the closed loop control to output voltage transfer function. The analysis and controller design are simulated in LTSpice.

Alanezi, AA, Altaee, A & Sharif, AO 2020, 'The effect of energy recovery device and feed flow rate on the energy efficiency of reverse osmosis process', Chemical Engineering Research and Design.
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Alfouneh, M, Ji, J & Luo, Q 2020, 'Damping design of harmonically excited flexible structures with graded materials to minimize sound pressure and radiation', ENGINEERING OPTIMIZATION.
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Alghamdi, K & Braun, R 2020, 'Software Defined Network (SDN) and OpenFlow Protocol in 5G Network', Communications and Network, vol. 12, no. 01, pp. 28-40.
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Al-Hadhrami, Y & Hussain, FK 2020, 'Real time dataset generation framework for intrusion detection systems in IoT', Future Generation Computer Systems, vol. 108, pp. 414-423.
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© 2020 The Internet of Things (IoT) has evolved in the last few years to become one of the hottest topics in the area of computer science research. This drastic increase in IoT applications across different disciplines, such as in health-care and smart industries, comes with a considerable security risk. This is not limited only to attacks on privacy; it can also extend to attacks on network availability and performance. Therefore, an intrusion detection system is essential to act as the first line of defense for the network. IDS systems and algorithms depend heavily on the quality of the dataset provided. Sadly, there has been a lack of work in evaluating and collecting intrusion detection system related datasets that are designed specifically for an IoT ecosystem. Most of the studies published focus on outdated and non-compatible datasets such as the KDD98 dataset. Therefore, in this paper, we aim to investigate the existing datasets and their applications for IoT environments. Then we introduce a real-time data collection framework for building a dataset for intrusion detection system evaluation and testing. The main advantages of the proposed dataset are that it contains features that are explicitly designed for the 6LoWPAN/RPL network, the most widely used protocol in the IoT environment.

Alismaili, SZ, Li, M, Shen, J, Huang, P, He, Q & Zhan, W 2020, 'Organisational-Level Assessment of Cloud Computing Adoption: Evidence from the Australian SMEs', JOURNAL OF GLOBAL INFORMATION MANAGEMENT, vol. 28, no. 2, pp. 73-89.
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Al-Jubainawi, A, Ma, Z, Guo, Y & Nghiem, LD 2020, 'Effect of regulating main governing factors on the selectivity membranes of electrodialysis used for LiCl liquid desiccant regeneration', JOURNAL OF BUILDING ENGINEERING, vol. 28.
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Almotairy, SM, Fadavi Boostani, A, Hassani, M, Wei, D & Jiang, ZY 2020, 'Effect of hot isostatic pressing on the mechanical properties of aluminium metal matrix nanocomposites produced by dual speed ball milling', Journal of Materials Research and Technology.
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© 2019 The Authors. In this study a suggested model for flake powder metallurgy were implemented and its mechanism was explained. The suggested model includes dual-speed ball milling (DSBM) to take the advantage of the low-speed and high-speed ball milling (LSBM and HSBM). The modelled process was utilised to uniformly disperse SiC nanoparticles into aluminium metal matrix to produce nanocomposites. The produced mixed powder was hot isostatically pressed. The effects of processing parameters such as stearic acid content, SiC volume content, ball milling speed and time on the microstructure and consequently tensile properties of the manufactured composites have been investigated experimentally to optimise the processing parameters bringing about the enhanced tensile properties of the fabricated composites. The results showed that the implementation of LSBM and HSBM processes can be considered as a unique strategy, i.e. the dual-speed ball milling (DSBM), for uniform dispersion of SiC nanoparticles associated with perfect bonding.

Alosime, EM, Alshahrani, AA, Nghiem, LD & Panhuis, M 2020, 'The preparation and characterization of buckypaper made from carbon nanotubes impregnated with chitosan', Polymer Composites.
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Alshahrani, AA, Algamdi, MS, Alsohaimi, IH, Nghiem, LD, Tu, KL, Al-Illwajfeh, AE & Panhuis, MIH 2020, 'The rejection of mono- and di-valent ions from aquatic environmental by MWNT/chitosan buckypaper composite membranes: Influences of chitosan concentrations', SEPARATION AND PURIFICATION TECHNOLOGY, vol. 234.
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Al-Shetwi, AQ, Hannan, MA, Jern, KP, Mansur, M & Mahlia, TMI 2020, 'Grid-connected renewable energy sources: Review of the recent integration requirements and control methods', Journal of Cleaner Production, vol. 253.
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© 2020 Elsevier Ltd The growing of renewable power generation and integration into the utility grid has started to touch on the security and stability of the power system operation. Hence, the grid integration requirements have become the major concern as renewable energy sources (RESs) such as wind and solar photovoltaic (PV) started to replace the conventional power plant slowly. In line with this, some of the new requirements and technical regulations have been established to ensure grid stability. This study aims to fill the gap and conduct an updating review of the recent integration requirements and compliance control methods regarding the penetration of renewable power plants to the power grid. The review is conducted by a comparing of the key requirements related to voltage stability, frequency stability, voltage ride-through (VRT), power quality, active and reactive power regulations towards grid stability. In order to fulfill these requirements, different control methods have been recently proposed. Accordingly, this paper compares and reviews the state-of-the-art solutions for compliance technology and control methods. Furthermore, a broad discussion on the global harmonization of the integration requirements, challenges, advantages and disadvantages is also highlighted. The rigorous review indicates that although the recent integration requirements can improve the grid operation, stability, security, and reliability, further improvements are still required with respect to protective regulations, global harmonization, and control optimization. Various recommendations for future research related to the integration and technical regulations of RESs are then presented. In sum, the insights provided by this review may aid the development of smooth and stable grid integration of RESs, help developers and researchers to develop the design and control strategies in the sense of current requirements. Additionally, assist power system operators in establishing ...

Al-Zubi, M & Sanagavarapu, A 2020, 'Implantable Biosensor Interface Platform for Monitoring of Atherosclerosis', IEEE Sensors Letters.
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The drug-eluting stents (DESs) have been considered as an effective technique to reduce the severity of atherosclerotic stenosis. Recent technological advances have opened the door for developing new and innovative smart cardiovascular stents by integrating various electronics components and sensors to improve health monitoring and diagnosis. Rupture of atherosclerotic plaque represents the major cause of cardiovascular disease (CVD) such as heart attacks due to blockages of the arterial lumen. Pentraxin-3 (PTX3) is a novel localized inflammatory biomarker associated with the development of atherosclerosis and plaque vulnerability. In this paper, we propose a biosensor interface platform for monitoring the plaque vulnerability via detection and prediction of the inflammatory biomarker (PTX3). We propose mathematical and stochastic models using molecular communication paradigm for detection of Pentraxin-3 (PTX3) molecules in the atherosclerotic arterial wall using biosensor attached to a vascular stent inside the artery. The proposed platform and models can help in the localized sensing of the atherosclerotic biomarkers for monitoring of the plaque progression and for early warning of certain disorders such as heart attack.

Arabameri, A, Blaschke, T, Pradhan, B, Pourghasemi, HR, Tiefenbacher, JP & Bui, DT 2020, 'Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study', Sensors, vol. 20, no. 2, pp. 335-335.
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Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.

Arabameri, A, Cerda, A, Pradhan, B, Tiefenbacher, JP, Lombardo, L & Bui, DT 2020, 'A methodological comparison of head-cut based gully erosion susceptibility models: Combined use of statistical and artificial intelligence', Geomorphology, pp. 107136-107136.
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Arabameri, A, Chen, W, Loche, M, Zhao, X, Li, Y, Lombardo, L, Cerda, A, Pradhan, B & Bui, DT 2020, 'Comparison of machine learning models for gully erosion susceptibility mapping', Geoscience Frontiers.
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© 2019 China University of Geosciences (Beijing) and Peking University Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory, especially in the Northern provinces. A number of studies have been recently undertaken to study this process and to predict it over space and ultimately, in a broader national effort, to limit its negative effects on local communities. We focused on the Bastam watershed where 9.3% of its surface is currently affected by gullying. Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability. However, unlike the bivariate statistical models, their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors. To cope with such weakness, we interpret preconditioning causes on the basis of a bivariate approach namely, Index of Entropy. And, we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely, Alternating Decision Tree (ADTree), Naïve-Bayes tree (NBTree), and Logistic Model Tree (LMT). We dichotomized the gully information over space into gully presence/absence conditions, which we further explored in their calibration and validation stages. Being the presence/absence information and associated factors identical, the resulting differences are only due to the algorithmic structures of the three models we chose. Such differences are not significant in terms of performances; in fact, the three models produce outstanding predictive AUC measures (ADTree ​= ​0.922; NBTree ​= ​0.939; LMT ​= ​0.944). However, the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns. This is a strong indication of what model combines best performance and mapping for any natural hazard – oriented application.

Arabameri, A, Pradhan, B, Rezaei, K, Lee, S & Sohrabi, M 2020, 'An ensemble model for landslide susceptibility mapping in a forested area', Geocarto International.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This article proposes a new methodological approach using a combination of expert knowledge-based (analytic hierarchy process, AHP), bivariate (statistical index, SI) and multivariate (linear discriminant analysis, LDA) models for landslide susceptibility mapping (LSM) in Mazandran Province, Iran. Tolerance and variance inflation factor indicators were used for assessing multi-collinearity among parameters, and three (i.e. profile curvature, soil type and topography wetness index) of 18 factors were eliminated because of multi-collinearity issues. Fifteen geo-environmental conditioning factors including elevation, slope degree, slope aspect, plan curvature, slope length, convergence index, stream power index, distance from river, drainage density, distance from road, distance from fault, lithology, rainfall, land use/landcover and normalized difference vegetation index and 321 landslide locations (testing data set, 70% of total landslides) were used for modeling. The importance of factors showed that distance to road (AHP = 0.201, LDA = 0.301) was the most important factor in landslide occurrence. The validation results using validation data set (138 landslide locations, 30% of total landslides) and area under the receiver operating characteristic curve (AUROC) showed that the ensemble models AHP-LDR (83%), AHP-SI (95%) and SI-LDR (83%) had higher prediction accuracies than the individual AHP (82%), SI (82%) and LDA (79%) models and combination of AHP and SI models along with ALOS-PALSAR remote sensing data and geographic information system (GIS) technique provide powerful tool in LSM in the study area. The results of proposed novel methodological framework can be used by decision-makers and forest engineers for forest management spatially forest roads conservation that have key importance in sustainable development in local and regional scales.

Arjmandi, M, Peyravi, M, Altaee, A, Arjmandi, A, Pourafshari Chenar, M, Jahanshahi, M & Binaeian, E 2020, 'A state-of-the-art protocol to minimize the internal concentration polarization in forward osmosis membranes', Desalination, vol. 480.
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© 2020 Elsevier B.V. The main reason for the lower water flux, than expected, in the forward osmosis (FO) process, is the internal concentration polarization (DICP). Usually, the structural parameter (S) is used as an indicator of the intensity of DICP. Small S value is desirable for the FO membrane due to the low DICP. However, due to design and construction problems, structural parameter reduction has some drawbacks. In this work, DICP reduction in FO membranes will be investigated using an approach other than structural parameter reduction. Accordingly, during the FO process, the feed solution (FS) valve is opened and closed at a constant period of time (feed valve timing (FVT)). Four types of FO membranes with different S parameters were used. The effects of the implementation of the proposed protocol on the water flux (Jw), reverse salt flux (Js), specific reverse solute flux (Js/Jw) and effective driving force were investigated. The effects of the S parameter and draw solution (DS) concentration also investigated separately. The results showed that the proposed protocol significantly increased Jw. Also, the values of Js/Jw decreased with increasing the FVT values and reached the lowest level in the practical recovery time (PRT).

Asadabadi, MR, Chang, E, Zwikael, O, Saberi, M & Sharpe, K 2020, 'Hidden fuzzy information: Requirement specification and measurement of project provider performance using the best worst method', Fuzzy Sets and Systems, vol. 383, pp. 127-145.
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© 2019 Elsevier B.V. The requirement specification process is an important part of a project and has the potential to prevent problems that may last for years after a project is delivered. Previous studies on the requirement specification process have focused on clarifying stated fuzzy terms in software requirement engineering. However, in many projects there is information that is not stated, but it is implied and can be inferred. This hidden information is usually ignored due to the assumption that ‘the provider understands what they mean/need’. This assumption is not always true. Such information, if extracted, may include fuzzy terms, namely hidden fuzzy terms (HFTs), which need specification. Therefore, these fuzzy terms have to be identified and then specified to avoid potential future consequences. This study proposes an algorithm to extract the hidden fuzzy terms, utilises a fuzzy inference system (FIS) to specify them, and applies the best worst multi-criteria decision making method (BWM) to evaluate the delivered product and measure the performance of the provider. The model is then used to examine a case from Defence Housing Australia. Such evaluation and measurement enable the project owner/manager to have a transparent basis to support decisions later in different phases of the project, and to ultimately reduce the likelihood of conflict and the receipt of an unsatisfactory product.

Ashrafian, A, Gandomi, AH, Rezaie-Balf, M & Emadi, M 2020, 'An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement', MEASUREMENT, vol. 152.
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Awan, I, Younas, M & Hussain, F 2020, 'Emerging challenges and frontiers in cloud computing', Concurrency Computation, vol. 32, no. 1.
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Azadi, M, Emrouznejad, A, Ramezani, F & Hussain, FK 2020, 'Efficiency measurement of cloud service providers using network data envelopment analysis', IEEE Transactions on Cloud Computing.
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IEEE An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements (Duan, 2017). To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, variable returns to scale (VRS), the non-oriented network slacks-based measure (SBM) model and input-oriented and output-oriented SBM models are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models.

Azeez, OS, Pradhan, B & Jena, R 2020, 'Urban tree classification using discrete-return LiDAR and an object-level local binary pattern algorithm', GEOCARTO INTERNATIONAL.
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Urban trees have the potential to mitigate some of the harm brought about by rapid urbanization and population growth, as well as serious environmental degradation (e.g. soil erosion, carbon pollution and species extirpation), in cities. This paper presents a novel urban tree extraction modelling approach that uses discrete laser scanning point clouds and object-based textural analysis to (1) develop a model characterised by four sub-models, including (a) height-based split segmentation, (b) feature extraction, (c) texture analysis and (d) classification, and (2) apply this model to classify urban trees. The canopy height model is integrated with the object-level local binary pattern algorithm (LBP) to achieve high classification accuracy. The results of each sub-model reveal that the classification of urban trees based on the height at 47.14 (high) and 2.12 m (low), respectively, while based on crown widths were highest and lowest at 22.5 and 2.55 m, respectively. Results also indicate that the proposed algorithm of urban tree modelling is effective for practical use.

Aziminezhad, M, Mardi, S, Hajikarimi, P, Moghadas Nejad, F & Gandomi, AH 2020, 'Loading rate effect on fracture behavior of fiber reinforced high strength concrete using a semi-circular bending test', Construction and Building Materials, vol. 240.
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© 2019 Elsevier Ltd Adding different types of fiber is one of the most common ways to enhance high strength concrete's mechanical behavior. In this paper, the effect of the loading rate and different type of fibers including glass, polypropylene, and steel were studied using the semi-circular bending (SCB) test method. It was evaluated that the SCB test can be used as a rapid and simple method to measure fracture properties of fiber reinforced high strength concrete (HSC) including ductility, energy absorption, and loading capacity by considering the effect of the loading rate on the parameters mentioned above. Specimens with glass fibers showed the most ductile behavior among all specimens with different types of fiber. On the other hand, steel fibers provided higher strength and higher energy absorption among the specimens. While specimens with steel fibers are highly sensitive to the loading rate in terms of peak load, this effect is not significant for specimens with glass and polypropylene fibers.

Baba, AA, Hashmi, RM, Esselle, KP, Attygalle, M & Borg, D 2020, 'A Millimeter-Wave Antenna System for Wideband Two-Dimensional Beam Steering', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Bajan, S & Hutvagner, G 2020, 'RNA-Based Therapeutics: From Antisense Oligonucleotides to miRNAs', CELLS, vol. 9, no. 1.
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Ball, J, Qiuhua, L, Jingming, H, Bingyao, L, Yu, T, Hui, L & Liping, M 2020, 'Cause Analysis for a New Type of Devastating Flash Flood', Hydrology Research: an international journal, vol. 51, no. 1, pp. 1-16.
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This work introduces an unprecedented flash flood that resulted in nine casualties in Shimen Valley, China, 2015. Through field survey and numerical simulation, the causes of the disaster are systematically analyzed, finding that the intense storm, terrain features, and the large woody debris(LWD) played important roles. The intense storm induced fast runoff and, in turn, high discharges as a result of the steep catchment surfaces and channels. The flood flushed LWD and boulders downstream until blockage occurred in a contraction section, forming a debris lake. When the debris dam broke, a dam break wave rapidly propagated to the valley mouth, washing people away. After considering the disaster-inducing factors, measures for preventing similar floods are proposed. The analysis presented herein should help others manage flash floods in mountain areas.

Banaei, M, Ahmadi, A, Gramann, K & Hatami, J 2020, 'Emotional evaluation of architectural interior forms based on personality differences using virtual reality', Frontiers of Architectural Research.
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© 2019 The Authors Recent studies have shown that architectural interior forms could impact the affective state of inhabitants. However, the direct relation of specific forms with specific affective states is difficult to determine. In addition, no systematic categorization of architectural forms and their relation to emotional states exists. The investigation of the impact of architectural features on inhabitants' emotions is further complicated by the use of two-dimensional images of forms in laboratory investigations, which cannot perceive real-world architecture. Furthermore, the interior form consists of a combination of different forms rather than only pure forms, which was considered in previous studies. This study aimed to fill these gaps by evaluating interior forms on the basis of clustering different images of built living rooms throughout history as well as their impact on emotions. This study used pleasure, arousal, and dominance ratings with an emphasis on individual differences in personality. Virtual sample rooms were created based on formal clusters of architectural forms. Results showed a relationship between forms and emotional states for different personality traits. This work provided a novel approach on the influence of architecture on emotion by considering systematic form categorization and combinations, personality differences, and a virtual reality setup.

Bao, T, Damtie, MM, Hosseinzadeh, A, Wei, W, Jin, J, Phong Vo, HN, Ye, JS, Liu, Y, Wang, XF, Yu, ZM, Chen, ZJ, Wu, K, Frost, RL & Ni, BJ 2020, 'Bentonite-supported nano zero-valent iron composite as a green catalyst for bisphenol A degradation: Preparation, performance, and mechanism of action', Journal of Environmental Management, vol. 260.
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© 2020 Elsevier Ltd Bisphenol A (BPA) is a toxic environmental pollutant commonly found in wastewater. Using non-toxic materials and eco-friendly technology to remove this pollutant from wastewater presents multiple advantages. Treatment of wastewater with clay minerals has received growing interest because of the environment friendliness of these materials. Bentonite is a 2:1 layered phyllosilicate clay mineral that can support nano-metal catalysts. It can prevent the agglomeration of nano-metal catalysts and improve their activity. In this article, a green catalytic nano zero-valent iron/bentonite composite material (NZVI@bentonite) was synthesized via liquid-phase reduction. The average size of NZVI was approximately 40–50 nm. Good dispersion and low aggregation were observed when NZVI was loaded on the surface or embedded into the nanosheets of bentonite. Degradation of BPA, a harmful contaminant widely found in wastewater at relatively high levels, by NZVI@bentonite was then investigated and compared with that by pristine NZVI through batch Fenton-like reaction experiments. Compared with pristine NZVI and bentonite alone, the NZVI@bentonite showed a higher BPA degradation ratio and offered highly effective BPA degradation up to 450 mg/g in wastewater under optimum operating conditions. Adsorption coupled with the Fenton-like reaction was responsible for BPA degradation by NZVI@bentonite. This work extends the application of NZVI@bentonite as an effective green catalyst for BPA degradation in aqueous environments.

Barthe, G, Hsu, J, Ying, M, Yu, N & Zhou, L 2020, 'Relational proofs for quantum programs', Proceedings of the ACM on Programming Languages, vol. 4, no. POPL, pp. 1-29.
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Barthe, G, Hsu, J, Ying, M, Yu, N & Zhou, L 2020, 'Relational proofs for quantum programs.', PACMPL, vol. 4, pp. 21:1-21:1.

Barzegarkhoo, R, Siwakoti, Y & Blaabjerg, F 2020, 'A New Switched-Capacitor Five-Level Inverter Suitable for Transformerless Grid-Connected Applications', IEEE Transactions on Power Electronics, pp. 1-1.
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Barzegarkhoo, R, Siwakoti, YP, Vosoughi, N & Blaabjerg, F 2020, 'Six-Switch Step-up Common-Grounded Five-Level Inverter with Switched-Capacitor Cell for Transformerless Grid-Tied PV Applications', IEEE Transactions on Industrial Electronics, pp. 1-1.
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Behera, TM, Mohapatra, SK, Samal, UC, Khan, MS, Daneshmand, M & Gandomi, AH 2020, 'I-SEP: An Improved Routing Protocol for Heterogeneous WSN for IoT-Based Environmental Monitoring', IEEE INTERNET OF THINGS JOURNAL, vol. 7, no. 1, pp. 710-717.
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Belotti, Y, McGloin, D & Weijer, C 2020, 'Chemotaxis overrides Barotaxis during Directional Decision-Making in Dictyostelium discoideum'.
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Abstract Neutrophils and dendritic cells have, besides their well characterised chemotactic movement responses, been shown to be able to detect and respond to local differences in hydraulic resistance ( barotaxis ). Furthermore, for neutrophils, it has been suggested that barotaxis overrides chemotaxis. Here, we investigate whether Dictyostelium cells also respond to hydraulic resistance or primarily to chemical gradients using an asymmetric bifurcating micro-channel. This channel design allows us to decouple hydraulic and chemical stimuli, by providing a choice between moving up a chemical gradient or down a chemical gradient into a channel with 100 times lower hydraulic resistance. Under these conditions chemotaxis always overrides barotaxis. Cells confronted by a microchannel bifurcation are observed to often partially split their leading edge and to start moving into both channels. Cells in steeper cAMP gradients, that move faster, split more readily. The decision to retract the pseudopod moving away from the cAMP source is made when the average velocity of the pseudopod moving up the cAMP gradient is 20% higher than the average velocity of the pseudopod moving down the gradient. Surprisingly, this decision threshold is independent of the steepness of the cAMP gradient and speed of movement. It indicates that a critical force imbalance threshold underlies the repolarisation decision. Significance Statement We investigate the directional ‘decision-making’ of Dictyostelium discoideum cells migrating within engineered micro-channels harbouring asymmetric bifurcations. Unlike neutrophils and immature dendritic cells, Dictyostelium cells strongly prioritise chemical over barotactic guidance cues. Cells in steeper cAMP gradients migrate at higher speeds, split their leading edges more readily when confronted with a bifurcation in the channel. The decision to retract a pseudopod pointing in an unfavourable direction occurs when a critical tension gradient between tw...

Ben, X, Gong, C, Zhang, P, Yan, R, Wu, Q & Meng, W 2020, 'Coupled Bilinear Discriminant Projection for Cross-View Gait Recognition', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 3, pp. 734-747.
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© 1991-2012 IEEE. A problem that hinders good performance of general gait recognition systems is that the appearance features of gaits are more affected-prone by views than identities, especially when the walking direction of the probe gait is different from the register gait. This problem cannot be solved by traditional projection learning methods because these methods can learn only one projection matrix, and thus for the same subject, it cannot transfer cross-view gait features into similar ones. This paper presents an innovative method to overcome this problem by aligning gait energy images (GEIs) across views with the coupled bilinear discriminant projection (CBDP). Specifically, the CBDP generates the aligned gait matrix features for two views with two sets of bilinear transformation matrices, so that the original GEIs' spatial structure information can be preserved. By iteratively maximizing the ratio of inter-class distance metric to intra-class distance metric, the CBDP can learn the optimal matrix subspace where the GEIs across views are aligned in both horizontal and vertical coordinates. Therefore, the CBDP is also able to avoid the under-sample problem. We also theoretically prove that the upper and lower bounds of the objective function sequence of the CBDP are both monotonically increasing, so the convergence of the CBDP is demonstrated. In the terms of accuracy, the comparative experiments on the CASIA (B) and OU-ISIR gait databases show that our method is superior to the state-of-the-art cross-view gait recognition methods. More impressively, encouraging performance is obtained by our method even in matching a lateral-view gait with a frontal-view gait.

Beydoun, G, Hoffmann, A, Valencia-García, R, Shen, J & Gill, A 2020, 'Towards an assessment framework of reuse: a knowledge level analysis approach', Complex and Intelligent Systems.
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Bhuiya, MMK, Rasul, M, Khan, M, Ashwath, N & Rahman, M 2020, 'Comparison of oil extraction between screw press and solvent (n-hexane) extraction technique from beauty leaf (Calophyllum inophyllum L.) feedstock', Industrial Crops and Products, vol. 144.
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© 2019 Elsevier B.V. An experimental investigation was conducted to evaluate the effects of the processing of the raw materials, such as preparation and extraction, grating (whole and grated), drying and moisture conditioning of the seed kernels on the yield of oil extraction. Both mechanical and chemical methods were used to extract oil from the beauty leaf (BL) seed kernel using a screw press expeller and n-hexane as an oil solvent, respectively. Both whole kernel (WK) and grated kernel (GK) were used in screw press technique, whereas, only GK was used in n-hexane technique. The study indicated that the kernels prepared for 14.4 % moisture content (MC) produced the highest yield of oil in both methods. The highest oil yield of 86.4 % was obtained on the basis of the weight of the kernel in n-hexane method for the GK. On the other hand, the oil yield of 78.0 and 72.1 % was achieved using a screw press technique for the GK and WK, respectively. The gas chromatography (GC) analysis was conducted to determine the fatty acid compositions of beauty leaf oil. The physico-chemical properties of the beauty leaf oil (BLO) were evaluated. A comparison of fossil energy ratio (FER) was made between n-hexane and screw press methods. The FER in-hexane method was found 4.9, whereas, in screw press method it was found 3.4 and 3.1 for the GK and WK, respectively, which indicates that n-hexane method is more efficient than screw press technique. This study provides an understanding of the basis of selecting an appropriate oil extraction technique for oil extraction users in large scale applications.

Bishop, AN & Moral, PD 2020, 'An Explicit Floquet-Type Representation of Riccati Aperiodic Exponential Semigroups', International Journal of Control.
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The article presents a rather surprising Floquet-type representation of time-varying transition matrices associated with a class of nonlinear matrix differential Riccati equations. The main difference with conventional Floquet theory comes from the fact that the underlying flow of the solution matrix is aperiodic. The monodromy matrix associated with this Floquet representation coincides with the exponential (fundamental) matrix associated with the stabilizing fixed point of the Riccati equation. The second part of this article is dedicated to the application of this representation to the stability of matrix differential Riccati equations. We provide refined global and local contraction inequalities for the Riccati exponential semigroup that depend linearly on the spectral norm of the initial condition. These refinements improve upon existing results and are a direct consequence of the Floquet-type representation, yielding what seems to be the first results of this type for this class of models.

Bordbar, M, Neshat, A, Javadi, S, Pradhan, B & Aghamohammadi, H 2020, 'Meta-heuristic algorithms in optimizing GALDIT framework: A comparative study for coastal aquifer vulnerability assessment', Journal of Hydrology, vol. 585, pp. 124768-124768.
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Bradbury, P, Wu, H, Choi, JU, Rowan, AE, Zhang, H, Poole, K, Lauko, J & Chou, J 2020, 'Modeling the Impact of Microgravity at the Cellular Level: Implications for Human Disease.', Frontiers in cell and developmental biology, vol. 8, p. 96.
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A lack of gravity experienced during space flight has been shown to have profound effects on human physiology including muscle atrophy, reductions in bone density and immune function, and endocrine disorders. At present, these physiological changes present major obstacles to long-term space missions. What is not clear is which pathophysiological disruptions reflect changes at the cellular level versus changes that occur due to the impact of weightlessness on the entire body. This review focuses on current research investigating the impact of microgravity at the cellular level including cellular morphology, proliferation, and adhesion. As direct research in space is currently cost prohibitive, we describe here the use of microgravity simulators for studies at the cellular level. Such instruments provide valuable tools for cost-effective research to better discern the impact of weightlessness on cellular function. Despite recent advances in understanding the relationship between extracellular forces and cell behavior, very little is understood about cellular biology and mechanotransduction under microgravity conditions. This review will examine recent insights into the impact of simulated microgravity on cell biology and how this technology may provide new insight into advancing our understanding of mechanically driven biology and disease.

Brodka, P, Musial, K & Jankowski, J 2020, 'Interacting Spreading Processes in Multilayer Networks: A Systematic Review', IEEE Access, vol. 8, pp. 10316-10341.
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© 2013 IEEE. The world of network science is fascinating and filled with complex phenomena that we aspire to understand. One of them is the dynamics of spreading processes over complex networked structures. Building the knowledge-base in the field where we can face more than one spreading process propagating over a network that has more than one layer is a challenging task, as the complexity comes both from the environment in which the spread happens and from characteristics and interplay of spreads' propagation. As this cross-disciplinary field bringing together computer science, network science, biology and physics has rapidly grown over the last decade, there is a need to comprehensively review the current state-of-the-art and offer to the research community a roadmap that helps to organise the future research in this area. Thus, this survey is a first attempt to present the current landscape of the multi-processes spread over multilayer networks and to suggest the potential ways forward.

Brodka, P, Musial, K & Jankowski, J 2020, 'Interacting Spreading Processes in Multilayer Networks: A Systematic Review', IEEE Access, vol. 8, pp. 10316-10341.
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Burton, GJ, Sheng, D & Airey, DW 2020, 'Critical state behaviour of an unsaturated high-plasticity clay', Geotechnique, vol. 70, no. 2, pp. 161-172.
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© 2020 ICE Publishing. All rights reserved. This paper describes a series of tests carried out to examine triaxial compression and shearing of a high-plasticity compacted clay. Reconstituted and compacted samples which were saturated are used as a basis for interpreting the unsaturated test results within a critical state soil mechanics (CSSM) framework. The shear strength behaviour of unsaturated soils have previously been found to be reasonably well captured in a CSSM framework, whereas the volume change behaviour has been more difficult to rationalise. Based on test results presented using the Bishop effective stress, the volume change behaviour during shear suggests that a unique critical state line is approached, independent of the applied suction. The normalised shearing behaviour of the compacted unsaturated soil is interpreted to be analogous to that of saturated-structured soils.

C. A. Tirado Cortes, H. Chen, D. Sturnieks, J. Garcia Marin, S. Lord & C. Lin 2020, 'Evaluating Balance Recovery Techniques for Users Wearing Head-Mounted Display in VR', IEEE Transactions on Visualization and Computer Graphics, pp. 1-1.
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Room-scale 3D position tracking allows users to explore the virtual environment by physically walking. However, since the eyesight of the user is blocked by a Head-Mounted Display (HMD), the user might lose her balance because of bumping into real-world obstacles or shifting the body weight onto virtual objects that are inexistent in the real-world. This paper investigates assistive fall recovery methods under the assumption that the onset of the fall is given. Our experiment simulated the forward loss-of-balance with a tether-release protocol. A magnetic lock attached to a counter-weight was released while the subject was in a static leaning posture and engaged in a secondary 3D object selection task. The experiment uses a two by two design that examines two assistive techniques, i.e. video-see-through and auditory warning, at two different timing, i.e. fall onset and 500ms prior to onset. The data from 17 subjects show that the video-see-through triggered 500ms before the onset of fall can effectively help users recover from falls. Surprisingly, the video-see-through at the fall onset has a significant negative impact on the fall recovery providing similar results to the baseline condition (no intervention).

Cai, G, He, X, Dong, L, Liu, S, Xu, Z, Zhao, C & Sheng, D 2020, 'The shear and tensile strength of unsaturated soils by a grain-scale investigation', GRANULAR MATTER, vol. 22, no. 1.
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Cai, Y, Pan, S, Wang, X, Chen, H, Cai, X & Zuo, M 2020, 'Measuring distance-based semantic similarity using meronymy and hyponymy relations', Neural Computing and Applications.
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© 2018, The Natural Computing Applications Forum. The assessment of semantic similarity between lexical terms plays a critical part in semantic-oriented applications for natural language processing and cognitive science. The optimization of calculation models is still a challenging issue for improving the performance of similarity measurement. In this paper, we investigate WordNet-based measures including distance-based, information-based, feature-based and hybrid. Among them, the distance-based measures are considered to have the lowest computational complexity due to simple distance calculation. However, most of existing works ignore the meronymy relation between concepts and the non-uniformity of path distances caused by various semantic relations, in which path distances are simply determined by conceptual hyponymy relation. To solve this problem, we propose a novel model to calculate the path distance between concepts, and also propose a similarity measure which nonlinearly transforms the distance to semantic similarity. In the proposed model, we assign different weights in accordance with various relations to edges that link different concepts. On basis of the distance model, we use five structure properties of WordNet for similarity measurement, which consist of multiple meanings, multiple inheritance, link type, depth and local density. Our similarity measure is compared against state-of-the-art WordNet-based measures on M&C dataset, R&G dataset and WS-353 dataset. According to experiment results, the proposed measure in this work outperforms others in terms of both Pearson and Spearman correlation coefficients, which indicates the effectiveness of our distance model. Besides, we construct six additional benchmarks to prove that the proposed measure maintains stable performance.

Cai, Z, Yang, Y, Tang, X, Li, Z, Zhang, T & Zhu, H 2020, 'Ultra-low phase noise oscillator employing mixed electric and magnetic coupling resonator', Microwave and Optical Technology Letters.
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© 2020 Wiley Periodicals, Inc. In this article, a pair of stub-loaded interdigital hairpin resonators-based filter with high group delay and improved stopband suppression is used to design an S-band ultra-low phase noise single-ended oscillator. A transmission zero closed to the edge of passband in the filter is generated by introducing strong electric and magnetic coupling, which can improve the phase noise performance of the proposed oscillator. A low phase noise figure-of-merit (FOM) that considers both insertion loss and group delay of the feedback loop is used to evaluate the overall phase noise performance of the proposed oscillator. In order to verify the concept, the proposed oscillator is designed and fabricated. The measured results show that the oscillation frequency is 2.037 GHz with the output power of 8.92 dBm while the second harmonic suppression level is about 35.66 dB. The phase noise and the FOM of the proposed oscillator at 100 kHz frequency offset are −127.95 and −199.31 dBc/Hz, respectively. According to the open literature, this is one of the best phase noise performances of the single-ended hybrid integrated oscillators oscillating at the similar frequency range.

Cao, Y & Veitch, D 2020, 'Toward Trusted Time: Remote Server Vetting and the Misfiring Heart of Internet Timing', IEEE/ACM Transactions on Networking, pp. 1-13.
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Cao, Z, Lin, C-T, Lai, K-L, Ko, L-W, King, J-T, Liao, K-K, Fuh, J-L & Wang, S-J 2020, 'Extraction of SSVEPs-Based Inherent Fuzzy Entropy Using a Wearable Headband EEG in Migraine Patients', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 28, no. 1, pp. 14-27.
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Casanovas, M, Torres-Martínez, A & Merigó, JM 2020, 'Multi-person and multi-criteria decision making with the induced probabilistic ordered weighted average distance', Soft Computing, vol. 24, no. 2, pp. 1435-1446.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. This paper presents a new approach for selecting suppliers of products or services, specifically with respect to complex decisions that require evaluating different business characteristics to ensure their suitability and to meet the conditions defined in the recruitment process. To address this type of problem, this study presents the multi-person multi-criteria induced ordered weighted average distance (MP-MC-IOWAD) operator, which is an extension of the OWA operators that includes the notion of distances to multiple criteria and expert valuations. Thus, this work introduces new distance measures that can aggregate the information with probabilistic information and consider the attitudinal character of the decision maker. Further extensions are developed using probabilities to form the induced probabilistic ordered weighted average distance (IPOWAD) operator. An example in the management of insurance policies is presented, where the selection of insurance companies is very complex and requires the consideration of subjective criteria by experts in decision making.

Cetindamar Kozanoglu, D, Lammers, T, Kocaoglu, D & Zhang, Y 2020, 'The Anniversary Tribute of PICMET: 1989-2018', IEEE Transactions on Engineering Management, pp. 1-15.
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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.

Chaczko, Z, Kulbacki, M, Gudzbeler, G, Alsawwaf, M, Thai-Chyzhykau, I & Wajs-Chaczko, P 2020, 'Exploration of Explainable AI in Context of Human-Machine Interface for the Assistive Driving System', Lecture Notes in Artificial Intelligence, vol. Part II, no. 10752.
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Chandran, D & Alammari, A 2020, 'Influence of Power Distance on Knowledge Sharing Adoption among Academic Staff in eLearning Virtual Communities in Saudi Arabia', Information Systems Frontiers.
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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, Z, Long, G, Zhou, JL & Ma, C 2020, 'Valorization of sewage sludge in the fabrication of construction and building materials: A review', Resources, Conservation and Recycling, vol. 154.
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© 2019 Elsevier B.V. With increasing amount of sewage sludge becoming an urgent and inevitable issue for every country, its applications in the production of construction and building materials provide an alternative solution for sludge disposal and resource recovery. Similar to clay and Portland cement, the main oxides in sewage sludge are SiO2 (10–25 %), Al2O3 (5–10 %) and CaO (10–30 %) which are increased in sludge ash after incineration to 25–50 %, 10–20 % and 15–30 %. Therefore, this solid waste can be utilized not only as raw material for the production of eco-cement, bricks, ceramic materials and lightweight aggregates through sintering process, but also as supplementary admixtures in cementitious materials such as pozzolanic component, fine aggregate or filling material. By critically reviewing current utilizations of sewage sludge, it is feasible to replace up to 15 % natural raw materials with sewage sludge in cement production and the manufactured eco-cement clinkers show comparable performance to traditional Portland cement. Whilst as raw feed in the fabrication of bricks, ceramic materials and lightweight aggregates, 20 % of sewage sludge substitution is acceptable to produce good quality products (within 8 % firing shrinkage and 15 % water absorption). Though high content of organic matter in raw sludge causes a decrease in mechanical strength and delay in hydration process, controlled low-strength materials offer an innovative reuse with large amount of sludge. The immobilization of heavy metals in products prevents sewage sludge causing secondary environmental pollution. Furthermore, suggestions for future research are proposed in order to strengthen the high value-added applications of sewage sludge.

Chen, H, Shen, J, Wang, L & Song, J 2020, 'A framework towards data analytics on host–pathogen protein–protein interactions', Journal of Ambient Intelligence and Humanized Computing.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. With the rapid development of high-throughput technologies, systems biology is now embracing a great opportunity made possible by the increased accumulation of data available online. Biological data analytics is considered as a critical means to contribute to a better understanding on such data through extraction of the latent features, relationships and the associated mechanisms. Therefore, it is important to evaluate how to involve data analytics from both computational and biological perspectives in practice. This paper has investigated interaction relationships in the proteomics area, which provide insights of the critical molecular processes within infection mechanisms. Specifically, we focused on host–pathogen protein–protein interactions, which represented the primary challenges associated with infectious diseases and drug design. Accordingly, a novel framework based on data analytics and machine learning techniques is detailed for analyzing these areas and we will describe the analytical results from host–pathogen protein–protein interactions (HP-PPI). Based on this framework, which serves as a pipeline solution for extracting and learning from the raw proteomics data, we have firstly evaluated several models from literature using different analytic technologies and performance measurements. An unsupervised deep learning model based on stacked denoising autoencoders, is subsequently proposed to capture higher level feature regarding the sequence information in the framework. The achieved performance indicates a superior capability of the unsupervised deep learning model in dealing with the host–pathogen protein interactions scenario among all of these models. The results will further help to enrich a theoretical and technical foundation for analyzing HP-PPI networks.

Chen, K, Xue, J, Lu, J & Qiu, X 2020, 'Improving active noise control without secondary path modeling using subband phase estimation', JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, vol. 147, no. 2, pp. 1275-1283.
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Chen, K, Yao, L, Zhang, D, Wang, X, Chang, X & Nie, F 2020, 'A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition.', IEEE Transactions on Neural Networks and Learning Systems.
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Recent years have witnessed the success of deep learning methods in human activity recognition (HAR). The longstanding shortage of labeled activity data inherently calls for a plethora of semisupervised learning methods, and one of the most challenging and common issues with semisupervised learning is the imbalanced distribution of labeled data over classes. Although the problem has long existed in broad real-world HAR applications, it is rarely explored in the literature. In this paper, we propose a semisupervised deep model for imbalanced activity recognition from multimodal wearable sensory data. We aim to address not only the challenges of multimodal sensor data (e.g., interperson variability and interclass similarity) but also the limited labeled data and class-imbalance issues simultaneously. In particular, we propose a pattern-balanced semisupervised framework to extract and preserve diverse latent patterns of activities. Furthermore, we exploit the independence of multi-modalities of sensory data and attentively identify salient regions that are indicative of human activities from inputs by our recurrent convolutional attention networks. Our experimental results demonstrate that the proposed model achieves a competitive performance compared to a multitude of state-of-the-art methods, both semisupervised and supervised ones, with 10% labeled training data. The results also show the robustness of our method over imbalanced, small training data sets.

Chen, S-L, Karmokar, DK, Qin, P-Y, Ziolkowski, RW & Guo, YJ 2020, 'Polarization-Reconfigurable Leaky-Wave Antenna With Continuous Beam Scanning Through Broadside', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 1, pp. 121-133.
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Chen, W, Li, Y, Xue, W, Shahabi, H, Li, S, Hong, H, Wang, X, Bian, H, Zhang, S, Pradhan, B & Ahmad, BB 2020, 'Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods', Science of the Total Environment, vol. 701.
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© 2019 Elsevier B.V. Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets.

Chen, W, Long, G, Yao, L & Sheng, QZ 2020, 'AMRNN: attended multi-task recurrent neural networks for dynamic illness severity prediction', World Wide Web.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Illness severity prediction (ISP) is crucial for caregivers in the intensive care unit (ICU) while saving the life of patients. Existing ISP methods fail to provide sufficient evidence for the time-critical decision making in the dynamic changing environment. Moreover, the correlated temporal features in multivariate time-series are rarely be considered in existing machine learning-based ISP models. Therefore, in this paper, we propose a novel interpretable analysis framework which simultaneously analyses organ systems differentiated based on the pathological and physiological evidence to predict illness severity of patients in ICU. It not only timely but also intuitively reflects the critical conditions of patients for caregivers. In particular, we develop a deep interpretable learning model, namely AMRNN, which is based on the Multi-task RNNs and Attention Mechanism. Physiological features of each organ system in multivariate time series are learned by a single Long-Short Term Memory unit as a dedicated task. To utilize the functional and temporal relationships among organ systems, we use a shared LSTM task to exploit correlations between different learning tasks for further performance improvement. Real-world clinical datasets (MIMIC-III) are used for conducting extensive experiments, and our method is compared with the existing state-of-the-art methods. The experimental results demonstrated that our proposed approach outperforms those methods and suggests a promising way of evidence-based decision support.

Chen, Y, Mao, Y, Liang, H, Yu, S, Wei, Y & Leng, S 2020, 'Data poison detection schemes for distributed machine learning', IEEE Access, vol. 8, pp. 7442-7454.
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© 2013 IEEE. Distributed machine learning (DML) can realize massive dataset training when no single node can work out the accurate results within an acceptable time. However, this will inevitably expose more potential targets to attackers compared with the non-distributed environment. In this paper, we classify DML into basic-DML and semi-DML. In basic-DML, the center server dispatches learning tasks to distributed machines and aggregates their learning results. While in semi-DML, the center server further devotes resources into dataset learning in addition to its duty in basic-DML. We firstly put forward a novel data poison detection scheme for basic-DML, which utilizes a cross-learning mechanism to find out the poisoned data. We prove that the proposed cross-learning mechanism would generate training loops, based on which a mathematical model is established to find the optimal number of training loops. Then, for semi-DML, we present an improved data poison detection scheme to provide better learning protection with the aid of the central resource. To efficiently utilize the system resources, an optimal resource allocation approach is developed. Simulation results show that the proposed scheme can significantly improve the accuracy of the final model by up to 20% for support vector machine and 60% for logistic regression in the basic-DML scenario. Moreover, in the semi-DML scenario, the improved data poison detection scheme with optimal resource allocation can decrease the wasted resources for 20-100%.

Chen, Y, Zhao, L, Lee, KMB, Yoo, C, Huang, S & Fitch, R 2020, 'Broadcast Your Weaknesses: Cooperative Active Pose-Graph SLAM for Multiple Robots', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2200-2207.
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© 2016 IEEE. In this letter, we propose a low-cost, high-efficiency framework for cooperative active pose-graph simultaneous localization and mapping (SLAM) for multiple robots in three-dimensional (3D) environments based on graph topology. Based on the selection of weak connections in pose graphs, this method aims to find the best trajectories for optimal information exchange to repair these weaknesses opportunistically when robots move near them. Based on tree-connectivity, which is greatly related to the D-optimality metric of the Fisher information matrix (FIM), we explore the relationship between measurement (edge) selection and pose-measurement (node-edge) selection, which often occurs in active SLAM, in terms of information increment. The measurement selection problem is formulated as a submodular optimization problem and solved by an exhaustive method using rank-1 updates. We decide which robot takes the selected measurements through a bidding framework where each robot computes its predicted cost. Finally, based on a novel continuous trajectory optimization method, these additional measurements collected by the winning robot are sent to the requesting robot to strengthen its pose graph. In simulations and experiments, we validate our approach by comparing against existing methods. Further, we demonstrate online communication based on offline planning results using two unmanned aerial vehicles (UAVs).

Chen, Z, Duan, X, Wei, W, Wang, S, Zhang, Z & Ni, B-J 2020, 'Boride-based electrocatalysts: Emerging candidates for water splitting', NANO RESEARCH.
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Cheng, D, Hao Ngo, H, Guo, W, Wang Chang, S, Duc Nguyen, D, Liu, Y, Zhang, X, Shan, X & Liu, Y 2020, 'Contribution of antibiotics to the fate of antibiotic resistance genes in anaerobic treatment processes of swine wastewater: A review', Bioresource Technology, vol. 299.
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© 2019 Elsevier Ltd Antibiotic resistance genes (ARGs) in water environment have become a global health concern. Swine wastewater is widely considered to be one of the major contributors for promoting the proliferation of ARGs in water environments. This paper comprehensively reviews and discusses the occurrence and removal of ARGs in anaerobic treatment of swine wastewater, and contributions of antibiotics to the fate of ARGs. The results reveal that ARGs’ removal is unstable during anaerobic processes, which negatively associated with the presence of antibiotics. The abundance of bacteria carrying ARGs increases with the addition of antibiotics and results in the spread of ARGs. The positive relationship was found between antibiotics and the abundance and transfer of ARGs in this review. However, it is necessary to understand the correlation among antibiotics, ARGs and microbial communities, and obtain more knowledge about controlling the dissemination of ARGs in the environment.

Cheng, D, Huu, HN, Guo, W, Chang, SW, Dinh, DN, Liu, Y, Shan, X, Long, DN & Luong, NN 2020, 'Removal process of antibiotics during anaerobic treatment of swine wastewater', BIORESOURCE TECHNOLOGY, vol. 300.
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Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Wei, Q & Wei, D 2020, 'A critical review on antibiotics and hormones in swine wastewater: Water pollution problems and control approaches.', Journal of Hazardous Materials, vol. 387.
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Swine wastewater (SW) is an important source of antibiotics and hormones (A&H) in the environment due to their large-scale application in swine industry. A&H in SW can be released into the water environment through the direct discharge of SW, effluent from SW treatment plants, and runoff and leaching from farmland polluted by swine wastes. The presence of A&H in the water environment has become an increasing global concern considering their adverse effects to the aquatic organism and human. This review critically discusses: (i) the occurrence of A&H in global water environment and their potential risks to water organisms and human; (ii) the management and technical approaches for reducing the emission of A&H in SW to the water environment. The development of antibiotic alternatives and the enhanced implementation of vaccination and biosecurity are promising management approaches to cut down the consumption of antibiotics during swine production. Through the comparison of different biological treatment technologies for removing A&H in SW, membrane-based bioprocesses have relatively higher and more stable removal efficiencies. Whereas, the combined system of bioprocesses and AOPs is expected to be a promising technology for elimination and mineralization of A&H in swine wastewater. Further study on this system is therefore necessary.

Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, X, Varjani, S & Liu, Y 2020, 'Feasibility study on a new pomelo peel derived biochar for tetracycline antibiotics removal in swine wastewater', Science of The Total Environment, vol. 720, pp. 137662-137662.
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Cheng, EJ, Prasad, M, Yang, J, Khanna, P, Chen, BH, Tao, X, Young, KY & Lin, CT 2020, 'A fast fused part-based model with new deep feature for pedestrian detection and security monitoring', Measurement: Journal of the International Measurement Confederation, vol. 151.
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© 2019 Elsevier Ltd In recent years, pedestrian detection based on computer vision has been widely used in intelligent transportation, security monitoring, assistance driving and other related applications. However, one of the remaining open challenges is that pedestrians are partially obscured and their posture changes. To address this problem, deformable part model (DPM) uses a mixture of part filters to capture variation in view point and appearance and achieves success for challenging datasets. Nevertheless, the expensive computation cost of DPM limits its ability in the real-time application. This study propose a fast fused part-based model (FFPM) for pedestrian detection to detect the pedestrians efficiently and accurately in the crowded environment. The first step of the proposed method trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. These six response feature maps are combined with full-body model to produce spatial deep features. The second step of the proposed method uses the deep features as an input to support vector machine (SVM) to detect pedestrian. A variety of strategies is introduced in the proposed model, including part-based to full-body method, spatial filtering, and multi-ratios combination. Experiment results show that the proposed FFPM method improves the computation speed of DPM and maintains the performance in detection.

Choi, Y, Naidu, G, Lee, S & Vigneswaran, S 2020, 'Recovery of sodium sulfate from seawater brine using fractional submerged membrane distillation crystallizer', Chemosphere, vol. 238.
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© 2019 Elsevier Ltd Seawater reverse osmosis (SWRO) brine contain many valuable resources. In this study, fractional-submerged membrane distillation crystallizer (F-SMDC) was used to recover sodium sulfate (Na2SO4) from SWRO brine. The concentration/temperature gradient (CG/TG) in the reactor enhanced water recovery utilizing MD and Na2SO4 crystallization via a crystallizer. Crystals were not obtained at the bottom section of the F-SMDC due to: firstly, calcium sulfate crystallization occurring on the membrane surface; and secondly, low temperature-sensitivity solubility component such as NaCl exerting a negative influence. In order to obtain supersaturation, a sulfate-rich scenario was created in the reactor through the addition of the following three components: Na2SO4, MgSO4 and (NH4)2SO4. When Na2SO4 and MgSO4 were added, a larger concentration was observed at the top section, resulting in a low concentration gradient (CG) ratio, i.e. around 1.7. Conversely, the addition of (NH4)2SO4 achieved faster Na2SO4 crystallization (VCF 1.42) at the bottom section with a greater CG ratio of more than 2.0. Total water recovery ratio of 72% and 223.73 g Na2SO4 crystals were successfully extracted from simulated SWRO brine using laboratory scale F-SMDC.

Chomsiri, T, He, XS, Nanda, P & Tan, Z 2020, 'Hybrid Tree-rule Firewall for High Speed Data Transmission', IEEE Transactions on Cloud Computing.
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Traditional firewalls employ listed rules in both configuration and process phases to regulate network traffic. However, configuring a firewall with listed rules may create rule conflicts, and slows down the firewall. To overcome this problem, we have proposed a Tree-rule firewall in our previous study. Although the Tree-rule firewall guarantees no conflicts within its rule set and operates faster than traditional firewalls, keeping track of the state of network connections using hashing functions incurs extra computational overhead. In order to reduce this overhead, we propose a hybrid Tree-rule firewall in this paper. This hybrid scheme takes advantages of both Tree-rule firewalls and traditional listed-rule firewalls. The GUIs of our Tree-rule firewalls are utilized to provide a means for users to create conflict-free firewall rules, which are organized in a tree structure and called 'tree rules'. These tree rules are later converted into listed rules that share the merit of being conflict-free. Finally, in decision making, the listed rules are used to verify against packet header information. The rules which have matched with most packets are moved up to the top positions by the core firewall. The mechanism applied in this hybrid scheme can significantly improve the functional speed of a firewall.

Choudhury, TT, Paul, SK, Rahman, HF, Jia, Z & Shukla, N 2020, 'A systematic literature review on the service supply chain: research agenda and future research directions', Production Planning & Control, pp. 1-22.
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Clegg, SR & Burdon, S 2020, 'Exploring creativity and innovation in broadcasting', Human Relations, pp. 001872671988800-001872671988800.
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We consider the emergence of design innovations in process, emerging around the form of polyarchy. This is done by using a case study of innovation conducted by a production organization’s project that was embedded in and hosted by a bureaucratic public institution, the Australian Broadcasting Corporation (ABC). The research reported here was part of a larger project comparing the BBC and ABC’s use of different modes of organization. It focused mainly on the organization designed to deliver a six-part television series, The Code. The innovative process of Scribe, the organization in question, in producing the story is a good example of idea work being instituted in a polyarchic design process. Scribe represents a new organizational design characterized by a polyarchic structure, which is soft and decentralized, with strict and relatively insuperable social and symbolic boundaries. This results in a project-based organization to coordinate collective innovation that is curated by making the writer also the creative director or showrunner. The research contributes further to exploring organizational idea work, through prioritizing creativity and innovation by an explicit positioning of a product and collaborative generative idea work.

Cong, TN, Walker, PD, Zhang, N & Ruan, J 2020, 'Efficiency improvement of a novel dual motor powertrain for plug-in hybrid electric buses', PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING.
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Cui, L, Wu, J, Pi, D, Zhang, P & Kennedy, P 2020, 'Dual Implicit Mining-Based Latent Friend Recommendation', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.

Cui, Z, Hao Ngo, H, Cheng, Z, Zhang, H, Guo, W, Meng, X, Jia, H & Wang, J 2020, 'Hysteresis effect on backwashing process in a submerged hollow fiber membrane bioreactor (MBR) applied to membrane fouling mitigation', Bioresource Technology, vol. 300.
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© 2019 Elsevier Ltd Hysteresis effect on backwashing in a submerged MBR was investigated with dead-end hollow fiber membranes. The out-of-step changes in TMP and flux is the real hysteresis effect which is common but easily overlooked. Methods of visualization and ultrasonic spectrum analysis were implemented. The results showed that fouling layer is just the culprit of hysteresis effect. Fouling level and fiber length were determined as two key factors that affect hysteresis effect by data and model derivation. Moreover, a hysteresis evaluation index “τbw” is proposed to quantify the result of TMP vs time. The relationship between influence factors and “τbw” is interactive. A linear relationship between fouling level and “τbw” was found as well as an extreme value between fiber length and “τbw”. A lower fouling level (lower backwashing flow) and optimal backwashing duration will be helpful for an effective backwashing no matter for membrane fouling control or energy cost reduce.

Curiskis, SA, Drake, B, Osborn, TR & Kennedy, PJ 2020, 'An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit', INFORMATION PROCESSING & MANAGEMENT, vol. 57, no. 2.
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Dai, P, Lu, W, Le, K & Liu, D 2020, 'Sliding Mode Impedance Control for contact intervention of an I-AUV: Simulation and experimental validation', Ocean Engineering, vol. 196.
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Dai, P, Lu, W, Le, K & Liu, D 2020, 'Sliding Mode Impedance Control for contact intervention of an I-AUV: Simulation and experimental validation'.

Dang, D, Hoang, D & Nguyen, D 2020, 'Trust-based Scheduling Framework for Big Data Processing with MapReduce', IEEE Transactions on Services Computing (TSC).
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de Koning, K & Filatova, T 2020, 'Repetitive floods intensify outmigration and climate gentrification in coastal cities', Environmental Research Letters, vol. 15, no. 3, pp. 034008-034008.
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Dikshit, A, Sarkar, R, Pradhan, B, Jena, R, Drukpa, D & Alamri, AM 2020, 'Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan', Water, vol. 12, no. 1, pp. 267-267.
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Landslides are one of the major natural disasters that Bhutan faces every year. The monsoon season in Bhutan is usually marked by heavy rainfall, which leads to multiple landslides, especially across the highways, and affects the entire transportation network of the nation. The determinations of rainfall thresholds are often used to predict the possible occurrence of landslides. A rainfall threshold was defined along Samdrup Jongkhar–Trashigang highway in eastern Bhutan using cumulated event rainfall and antecedent rainfall conditions. Threshold values were determined using the available daily rainfall and landslide data from 2014 to 2017, and validated using the 2018 dataset. The threshold determined was used to estimate temporal probability using a Poisson probability model. Finally, a landslide susceptibility map using the analytic hierarchy process was developed for the highway to identify the sections of the highway that are more susceptible to landslides. The accuracy of the model was validated using the area under the receiver operating characteristic curves. The results presented here may be regarded as a first step towards understanding of landslide hazards and development of an early warning system for a region where such studies have not previously been conducted.

Dimuro, GP, Lucca, G, Bedregal, B, Mesiar, R, Sanz, JA, Lin, CT & Bustince, H 2020, 'Generalized CF1F2-integrals: From Choquet-like aggregation to ordered directionally monotone functions', Fuzzy Sets and Systems, vol. 378, pp. 44-67.
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© 2019 Elsevier B.V. This paper introduces the theoretical framework for a generalization of CF1F2-integrals, a family of Choquet-like integrals used successfully in the aggregation process of the fuzzy reasoning mechanisms of fuzzy rule based classification systems. The proposed generalization, called by gCF1F2-integrals, is based on the so-called pseudo pre-aggregation function pairs (F1,F2), which are pairs of fusion functions satisfying a minimal set of requirements in order to guarantee that the gCF1F2-integrals to be either an aggregation function or just an ordered directionally increasing function satisfying the appropriate boundary conditions. We propose a dimension reduction of the input space, in order to deal with repeated elements in the input, avoiding ambiguities in the definition of gCF1F2-integrals. We study several properties of gCF1F2-integrals, considering different constraints for the functions F1 and F2, and state under which conditions gCF1F2-integrals present or not averaging behaviors. Several examples of gCF1F2-integrals are presented, considering different pseudo pre-aggregation function pairs, defined on, e.g., t-norms, overlap functions, copulas that are neither t-norms nor overlap functions and other functions that are not even pre-aggregation functions.

Ding, C, Sun, H-H, Zhu, H & Guo, YJ 2020, 'Achieving Wider Bandwidth with Full-Wavelength Dipoles (FWDs) for 5G Base Stations', IEEE Transactions on Antennas and Propagation, vol. 68, no. 2, pp. 1119-1127.
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Ding, W, Lin, CT, Liew, AWC, Triguero, I & Luo, W 2020, 'Current trends of granular data mining for biomedical data analysis', Information Sciences, vol. 510, pp. 341-343.
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Dinh, TH, Phung, MD & Ha, QP 2020, 'Summit Navigator: A Novel Approach for Local Maxima Extraction.', IEEE transactions on image processing : a publication of the IEEE Signal Processing Society.
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This paper presents a novel method, called the Summit Navigator, to effectively extract local maxima of an image histogram for multi-object segmentation of images. After smoothing with a moving average filter, the obtained histogram is analyzed, based on the data density and distribution to find the best observing location. An observability index for each initial peak is proposed to evaluate if it can be considered as dominant by using the calculated observing location. Recursive algorithms are then developed for peak searching and merging to remove any false detection of peaks that are located on one side of each mode. Experimental results demonstrated the advantages of the proposed approach in terms of accuracy and consistency in different reputable datasets.

Doan, S & Fatahi, B 2020, 'Analytical solution for free strain consolidation of stone column-reinforced soft ground considering spatial variation of total stress and drain resistance', COMPUTERS AND GEOTECHNICS, vol. 118.
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Dong, W, Li, W, Shen, L, Sun, Z & Sheng, D 2020, 'Piezoresistivity of smart carbon nanotubes (CNTs) reinforced cementitious composite under integrated cyclic compression and impact', Composite Structures, vol. 241.
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© 2020 Elsevier Ltd The cyclic compression and four series of fixed magnitude impact loads with an increment of 50 times were conducted alternatively on the smart carbon nanotubes (CNTs) reinforced cementitious composites, to evaluate the piezoresistive sensitivity and repeatability of composites after exposed to different drop impact energies. The results show that the impacts procedure suddenly increased in electrical resistivity due to the emerged micro-cracks and pores, and higher impact energy led to faster resistivity increase. On the other hand, when the impact is repeatedly applied, a high impact resistance of the cementitious composites could be observed, which was attributed to the dense microstructures. Moreover, instead of instable and uneven output of electrical resistivity during cyclical compression, more stable and uniform fractional changes of resistivity were achieved after exposed to impact load. However, severe nonlinearity with swift resistivity reduction of cementitious composites under low loads was observed at the beginning and the end of cyclic compression after subjected to many impacts with impact energy of 18.72 × 10−4 J/cm3. The related outcomes of smart conductive cementitious composites subjected to cyclic compression and impact will provide an insight into the stable electrical signal output and promote the applications of cement-based sensors for structural health monitoring under various loading conditions.

Dong, W, Li, W, Wang, K, Luo, Z & Sheng, D 2020, 'Self-sensing capabilities of cement-based sensor with layer-distributed conductive rubber fibres', Sensors and Actuators A: Physical, vol. 301, pp. 111763-111763.
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Dong, X, Gong, Y & Cao, L 2020, 'e-RNSP: An Efficient Method for Mining Repetition Negative Sequential Patterns.', IEEE Transactions on Cybernetics.
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Negative sequential patterns (NSPs), which capture both frequent occurring and nonoccurring behaviors, become increasingly important and sometimes play a role irreplaceable by analyzing occurring behaviors only. Repetition sequential patterns capture repetitions of patterns in different sequences as well as within a sequence and are very important to understand the repetition relations between behaviors. Though some methods are available for mining NSP and repetition positive sequential patterns (RPSPs), we have not found any methods for mining repetition NSP (RNSP). RNSP can help the analysts to further understand the repetition relationships between items and capture more comprehensive information with repetition properties. However, mining RNSP is much more difficult than mining NSP due to the intrinsic challenges of nonoccurring items. To address the above issues, we first propose a formal definition of repetition negative containment. Then, we propose a method to convert repetition negative containment to repetition positive containment, which fast calculates the repetition supports by only using the corresponding RPSP's information without rescanning databases. Finally, we propose an efficient algorithm, called e-RNSP, to mine RNSP efficiently. To the best of our knowledge, e-RNSP is the first algorithm to efficiently mine RNSP. Intensive experimental results on the first four real and synthetic datasets clearly show that e-RNSP can efficiently discover the repetition negative patterns; results on the fifth dataset prove the effectiveness of RNSP which are captured by the proposed method; and the results on the rest 16 datasets analyze the impacts of data characteristics on mining process.

Dong, Y, Fatahi, B & Khabbaz, H 2020, 'Three dimensional discrete element simulation of cylindrical cavity expansion from zero initial radius in sand', COMPUTERS AND GEOTECHNICS, vol. 117.
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Dorji, P, Kim, DI, Hong, S, Phuntsho, S & Shon, HK 2020, 'Pilot-scale membrane capacitive deionisation for effective bromide removal and high water recovery in seawater desalination', Desalination, vol. 479.
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© 2020 Although seawater desalination is becoming an important technology for freshwater production, the presence of a high concentration of bromide in the seawater presents a major challenge. Bromide is one of the major inorganic precursors for the formation of disinfection by-products such as bromate, which is highly regulated due to its toxicity and carcinogenicity. Hence, a significant reduction of bromide ions is required prior to water disinfection. In Australia, all the desalination plants have to operate a two-stage reverse osmosis system to ensure effective bromide removal, which adds significant cost to the desalination system. In this study, a pilot-scale membrane capacitive deionisation (MCDI) was investigated as a potential alternative to the 2nd stage RO in seawater desalination. Moreover, strategies to enhance water recovery in MCDI was also carried out by using lower flow rates and shorter duration during the desorption stage. In order to reduce energy consumption in MCDI, a combined short-circuit and reverse polarity desorption is introduced. The results showed that MCDI can effectively remove bromide and dissolved salt at a much lower energy consumption compared with membrane process and that MCDI can be operated to achieve high water recovery without increasing the total energy consumption.

Dou, Y, Li, Y, Zhang, C, Yue, S & Zhu, J 2020, 'Effects of Uniaxial Stress Along Different Directions on Alternating Magnetic Properties of Silicon Steel Sheets', IEEE Transactions on Magnetics, vol. 56, no. 3, pp. 1-4.
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Du, G, Xu, W, Zhu, J & Huang, N 2020, 'Effects of Design Parameters on the Multiphysics Performance of High-Speed Permanent Magnet Machines', IEEE Transactions on Industrial Electronics, vol. 67, no. 5, pp. 3472-3483.
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Du, G, Xu, W, Zhu, J & Huang, N 2020, 'Power Loss and Thermal Analysis for High-Power High-Speed Permanent Magnet Machines', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 67, no. 4, pp. 2722-2733.
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Duan, P, Mao, G, Kang, J & Huang, B 2020, 'Estimation of Link Travel Time Distribution With Limited Traffic Detectors', IEEE Transactions on Intelligent Transportation Systems, pp. 1-14.
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Motivated by the network tomography, in this paper, we present a novel methodology to estimate link travel time distributions (TTDs) using end-to-end (E2E) measurements detected by the limited traffic detectors at or near the road intersections. As it is not necessary to monitor the traffic in each link, the proposed estimator can be readily implemented in real life. The technical contributions of this paper are as follows: First, we employ the kernel density estimator (KDE) to model link travel times instead of parametric models, e.g., Gaussian distribution. It is able to capture the dynamic of link travel times that vary with the change of road conditions. The model parameters are estimated with the proposed C-shortest path algorithm, K-means-based algorithm, as well as expectation maximization (EM) algorithm. Second, to reduce the complexity of parameter estimation, we further propose a Q-opt and an X-means-based algorithm. Finally, we validate our proposed method using a dataset consisting of 3.0e + 07 GPS trajectories collected by the taxicabs in Xi'an, China. With the metrics of Kullback Leibler and Kolmogorov-Smirnov test, the experimental results show that the link TTDs obtained from our proposed model are in excellent agreement with the empirical distributions, provided that ~70% of the intersections are equipped with traffic detectors.

Elsawah, S, Filatova, T, Jakeman, AJ, Kettner, AJ, Zellner, ML, Athanasiadis, IN, Hamilton, SH, Axtell, RL, Brown, DG, Gilligan, JM, Janssen, MA, Robinson, DT, Rozenberg, J, Ullah, IIT & Lade, SJ 2020, 'Eight grand challenges in socio-environmental systems modeling', Socio-Environmental Systems Modelling, vol. 2, pp. 16226-16226.
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Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.

Every, JP, Li, L & Dorrell, DG 2020, 'Köppen-Geiger climate classification adjustment of the BRL diffuse irradiation model for Australian locations', Renewable Energy, vol. 147, pp. 2453-2469.
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© 2019 Elsevier Ltd Numerous mathematical models have been developed to estimate diffuse and direct irradiance components based on global irradiation measurements. The Boland–Ridley–Lauret (BRL) model consists of a single set of parameters for all global locations. There is scope to improve the BRL model to better match local climatic conditions. In this research, the Köppen-Geiger climate classification system is considered to develop a set of adjusted BRL models for Australian conditions. Ground-based and satellite-based irradiation data derived from the Australian Bureau of Meteorology are used to tune and test new BRL models developed at a national level and for each climate zone. Irradiation data are processed through a rigorous quality control procedure before parameter tuning. For ground-based data, a new national model results in an improvement in 96% of statistical indicators over the original BRL model while Köppen-Geiger zone adjusted models show improvement over the new national model in 72% of the statistics. For satellite-based global irradiation estimates, a new national BRL model also results in observed improvements, however, no discernible improvement is observed for Köppen-Geiger zone models.

Fang, K, Wang, X, Tomamichel, M & Berta, M 2020, 'Quantum Channel Simulation and the Channel’s Smooth Max-Information', IEEE Transactions on Information Theory, vol. 66, no. 4, pp. 2129-2140.
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Fang, Y, Huang, X, Qin, L, Zhang, Y, Zhang, W, Cheng, R & Lin, X 2020, 'A survey of community search over big graphs.', VLDB J., vol. 29, pp. 353-392.

Fang, Y, Huang, X, Qin, L, Zhang, Y, Zhang, W, Cheng, R & Lin, X 2020, 'Correction: A survey of community search over big graphs (The VLDB Journal, (2019), 10.1007/s00778-019-00556-x)', VLDB Journal.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. In the original article, the Table 1 was published with incorrect figures. The correct Table 1 is given below:(Table presented).

Fanos, AM & Pradhan, B 2020, 'A Novel Hybrid Machine Learning-Based Model for Rockfall Source Identification in Presence of Other Landslide Types Using LiDAR and GIS', Earth Systems and Environment.
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© 2019, King Abdulaziz University and Springer Nature Switzerland AG. Abstract: Rockfall is a common phenomenon in mountainous and hilly areas worldwide, including Malaysia. Rockfall source identification is a challenging task in rockfall hazard assessment. The difficulty rise when the area of interest has other landslide types with nearly similar controlling factors. Therefore, this research presented and assessed a hybrid model for rockfall source identification based on the stacking ensemble model of random forest (RF), artificial neural network, Naive Bayes (NB), and logistic regression in addition to Gaussian mixture model (GMM) using high-resolution airborne laser scanning data (LiDAR). GMM was adopted to automatically compute the thresholds of slope angle for various landslide types. Chi square was utilised to rank and select the conditioning factors for each landslide type. The best fit ensemble model (RF–NB) was then used to produce probability maps, which were used to conduct rockfall source identification in combination with the reclassified slope raster based on the thresholds obtained by the GMM. Next, landslide potential area was structured to reduce the sensitivity and the noise of the model to the variations in different conditioning factors for improving its computation performance. The accuracy assessment of the developed model indicates that the model can efficiently identify probable rockfall sources with receiver operating characteristic curve accuracies of 0.945 and 0.923 on validation and training datasets, respectively. In general, the proposed hybrid model is an effective model for rockfall source identification in the presence of other landslide types with a reasonable generalisation performance. Graphic Abstract: [Figure not available: see fulltext.].

Far, H 2020, 'Flexural Behavior of Cold-Formed Steel-Timber Composite Flooring Systems', Journal of Structural Engineering (United States), vol. 146, no. 5.
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Composite flooring systems comprising cold-formed steel beams and timber floorboards have been proposed recently for building flooring as efficient, economical, durable, and sustainable solutions. In this study, a parameterised three-dimensional (3D) finite-element model for analyzing the performance of such flooring systems has been developed and validated against experimental data reported for a specific steel/particle board (PB) flooring system in the literature. Once validated, the model was used to investigate the flexural behavior of various composite floors by replacing the PB with a range of sustainable engineered products with known mechanical properties. The results of this study provide insight into the use of various sustainable engineered board products in novel composite flooring systems and the importance of their elastic constants. It has also become apparent that the flexural behavior of the studied composite flooring systems is governed mainly by the connections between the cold-formed steel beams and the floorboards.

Farhood, H, Perry, S, Cheng, E & Kim, J 2020, 'Enhanced 3D Point Cloud from a Light Field Image', Remote Sensing, vol. 12, no. 7, pp. 1-1.
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The importance of three-dimensional (3D) point cloud technologies in the field of agriculture environmental research has increased in recent years. Obtaining dense and accurate 3D reconstructions of plants and urban areas provide useful information for remote sensing. In this paper, we propose a novel strategy for the enhancement of 3D point clouds from a single 4D light field (LF) image. Using a light field camera in this way creates an easy way for obtaining 3D point clouds from one snapshot and enabling diversity in monitoring and modelling applications for remote sensing. Considering an LF image and associated depth map as an input, we first apply histogram equalization and histogram stretching to enhance the separation between depth planes. We then apply multi-modal edge detection by using feature matching and fuzzy logic from the central sub-aperture LF image and the depth map. These two steps of depth map enhancement are significant parts of our novelty for this work. After combing the two previous steps and transforming the point–plane correspondence, we can obtain the 3D point cloud. We tested our method with synthetic and real world image databases. To verify the accuracy of our method, we compared our results with two different state-of-the-art algorithms. The results showed that our method can reliably mitigate noise and had the highest level of detail compared to other existing methods.

Farrok, O, Islam, MR, Muttaqi, KM, Sutanto, D & Zhu, J 2020, 'Design and Optimization of a Novel Dual-Port Linear Generator for Oceanic Wave Energy Conversion', IEEE Transactions on Industrial Electronics, vol. 67, no. 5, pp. 3409-3418.
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Farrokhi, F, Buchlak, QD, Sikora, M, Esmaili, N, Marsans, M, McLeod, P, Mark, J, Cox, E, Bennett, C & Carlson, J 2020, 'Investigating Risk Factors and Predicting Complications in Deep Brain Stimulation Surgery with Machine Learning Algorithms', WORLD NEUROSURGERY, vol. 134, pp. E325-E338.
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Fatahi, B, Huang, B, Yeganeh, N, Terzaghi, S & Banerjee, S 2020, 'Three-Dimensional Simulation of Seismic Slope-Foundation-Structure Interaction for Buildings Near Shallow Slopes', INTERNATIONAL JOURNAL OF GEOMECHANICS, vol. 20, no. 1.
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Feng, Q, Wu, Y, Fan, H, Yan, C, Xu, M & Yang, Y 2020, 'Cascaded Revision Network for Novel Object Captioning', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Ferro, V, Chuai, M, McGloin, D & Weijer, C 2020, 'Measurement of junctional tension in epithelial cells at the onset of primitive streak formation in the chick embryo via non-destructive optical manipulation.', Development: the journal of the Society for International Development.
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Directional cell intercalations of epithelial cells during gastrulation has in several organisms been shown to be associated with a planar cell polarity in the organisation of the actin-myosin cytoskeleton and is postulated to reflect directional tension that drives oriented cell intercalations. We have characterised and applied a recently introduced non-destructive optical manipulation technique to measure the tension in individual epithelial cell junctions of cells in various locations and orientations in the epiblast of chick embryos in the early stages of primitive streak formation. Junctional tension of mesendoderm precursors in the epiblast is higher in junctions oriented in the direction of intercalation than in junctions oriented perpendicular to the direction of intercalation and higher than in junctions of other cells in the epiblast. The kinetic data are fitted best with a simple visco-elastic Maxwell model and we find that junctional tension and to a lesser extent viscoelastic relaxation time are dependent on myosin activity.

Fis, MA & Cetindamar Kozanoglu, D 2020, 'Unlocking the Relationship between Corporate Entrepreneurship and Firm Performance', Entrepreneurship Research Journal, pp. 1-47.
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This paper explores the relationship between corporate entrepreneurship and performance by developing a comprehensive theoretical model based on Schumpeterian understanding of entrepreneurship supported with the Theory of Planned Behavior from social psychology. The model shows how organizational culture (value) triggers a chain effect through its influence on entrepreneurial orientation (attitude) and managerial support (intentions) that ultimately generate impact on corporate entrepreneurship (behavior). We test our model in an emerging economy context and present our results with implications to theory and practice.

Frost, SA, Kelly, A, Gaudin, J, Mc Evoy, L, Wilson, C, Marov, L, El Haddad, C, Center, J, Eisman, JA, Nguyen, TV & Hassett, G 2020, 'Establishing baseline absolute risk of subsequent fracture among adults presenting to hospital with a minimal-trauma-fracture', BMC MUSCULOSKELETAL DISORDERS, vol. 21, no. 1.
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Gan, YY, Ong, HC, Chen, WH, Sheen, HK, Chang, JS, Chong, CT & Ling, TC 2020, 'Microwave-assisted wet torrefaction of microalgae under various acids for coproduction of biochar and sugar', Journal of Cleaner Production, vol. 253.
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© 2020 Elsevier Ltd Wet torrefaction is a promising method to converts the high moisture microalgae to biofuel. In this study, the microalgae were torrefied in water or dilute acid solutions with the aid of microwaves irradiation. The reaction temperature and heating time were fixed at 160 °C and 10 min. The effects of sulfuric, phosphorus, and succinic acids on two different microalgae species (Chlorella vulgaris ESP-31 and FSP-E) were investigated. As a result, the disruption of the microalga FSP-E (high protein) was not notable in the acidic solution. The higher heating value of wet torrefied microalga ESP-31 (high carbohydrate) in succinic acid was enhanced up to 40% with at least 45% of energy yield. The use of 0.1 M of phosphorus acid in wet torrefaction produced the highest ash content of 1.61 and 11.60 wt% for microalgae ESP-31 and FSP-E, respectively. Thermogravimetric analysis revealed that the carbohydrate content of microalga ESP-31 has the highest degradation in sulfuric acid solution. In contrast, crystalline cellulose in the microalgae is hardly affected by the wet torrefaction process in low-temperature acidic solution. In terms of liquid product, a maximum glucose extraction of 35.39 g/L occurred with the use of 0.1 M of sulfuric acid. In conclusion, biochar produced using organic acid is desirable as solid fuel, whereas the use of sulfuric acid is more suitable to produce sugar for bioethanol production.

Gandomi, AH & Atefi, E 2020, 'Software review: the GPTIPS platform', Genetic Programming and Evolvable Machines.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. GPTIPS is a widely used genetic programming software that was developed in Matlab. The most recent version of this software, GPTIPS 2.0, provides a symbolic multi-gene regression for data analysis, in addition to traditional evolutionary algorithms. We briefly explain the GPTIPS methodology and describe its main features, including its weaknesses and strengths, and give examples of GPTIPS applications.

Gao, J, Luo, Z, Xiao, M, Gao, L & Li, P 2020, 'A NURBS-based Multi-Material Interpolation (N-MMI) for isogeometric topology optimization of structures', Applied Mathematical Modelling, vol. 81, pp. 818-843.
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© 2020 In this paper, the main intention is to propose a new Multi-material Isogeometric Topology Optimization (M-ITO) method for the optimization of multiple materials distribution, where an improved Multi-Material Interpolation model is developed using Non-Uniform Rational B-splines (NURBS), namely the “NURBS-based Multi-Material Interpolation (N-MMI)”. In the N-MMI model, three key components are involved: (1) multiple Fields of Design Variables (DVFs): NURBS basis functions with control design variables are applied to construct DVFs with the sufficient smoothness and continuity; (2) multiple Fields of Topology Variables (TVFs): each TVF is expressed by a combination of all DVFs to present the layout of a distinct material in the design domain; (3) Multi-material interpolation: the material property at each point is equal to the summation of all TVFs interpolated with constitutive elastic properties. DVFs and TVFs are in the decoupled expression and optimized in a serial evolving mechanism. This feature can ensure the constraint functions are separate and linear with respect to TVFs, which can be beneficial to lower the complexity of numerical computations and eliminate numerical troubles in the multi-material optimization. Two kinds of multi-material topology optimization problems are discussed, i.e., one with multiple volume constraints and the other with the total mass constraint. Finally, several numerical examples in 2D and 3D are provided to demonstrate the effectiveness of the M-ITO method.

Gao, K, Liu, Z, Jia, J, Liu, Z, Yisimayili, A, Qi, Z, Wu, Y & Li, S 2020, 'Study on Flame Spread Characteristics of Flame-Retardant Cables in Mine', ADVANCES IN POLYMER TECHNOLOGY, vol. 2020.
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Gao, P, Huang, Z & Yu, H 2020, 'Exploration of the Dehydrogenation Pathways of Ammonia Diborane and Diammoniate of Diborane by Molecular Dynamics Simulations Using Reactive Force Fields', JOURNAL OF PHYSICAL CHEMISTRY A, vol. 124, no. 9, pp. 1698-1704.
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Gao, S, Lu, R, Wang, X, Chou, J, Wang, N, Huai, X, Wang, C, Zhao, Y & Chen, S 2020, 'Immune response of macrophages on super-hydrophilic TiO2 nanotube arrays', Journal of Biomaterials Applications.
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© The Author(s) 2020. Macrophages are attracting increasing attention in promoting implant-mediated osteogenesis by modulating the microenvironment of the implant site. Biomaterial surface properties including topography and wettability regulate macrophage responses to influence tissue repair. The objective of our present study was to investigate the effects of hydrogenated titanium dioxide nanotube surfaces on the immune response of macrophages in vitro. Hydrogenated titanium dioxide nanotubes (TNTs) were synthesized on Ti surfaces by anodic oxidation and hydrogenation to form super-hydrophilic nanotubular surfaces. Macrophages were seeded directly onto three substrates (hydrogenated TNTs (H2-TNTs), air-annealed TNTs, and commercially pure Ti substrates) and grown under standard or lipopolysaccharide-stimulated culture conditions. Cell proliferation and viability were evaluated by Cell Counting Kit-8 and live/dead staining at 24 and 48 h. Secretion and expression of pro- and anti-inflammatory cytokines were evaluated at 24 and 48 h to determine whether the surfaces elicited differential macrophage behaviors. Gene expression of the M1/M2 surface markers of macrophages was analyzed to assess the effect of the H2-TNT surface on macrophage polarization. The results showed that hydrogenation of the TNT surface resulted in a super-hydrophilic substrate, which exhibited markedly improved wettability attributable to the formation of oxygen vacancies in the nanotubes. The H2-TNT group induced a significantly lower macrophage proliferation rate and up-regulated anti-inflammatory cytokines (interleukin-10, bone morphogenetic protein-2, and transforming growth factor-β1) irrespective of lipopolysaccharide stimulation, while alleviating the secretion of pro-inflammatory cytokines (tumor necrosis factor-α, interleukin-6, nitric oxide, and macrophage chemotactic protein-1) induced by lipopolysaccharide. Moreover, the H2-TNT surface elicited up-regulated gene expression of M2 s...

Gao, X, Zhang, T, Du, J & Guo, YJ 2020, '340 GHz Double-Sideband Mixer Based on Antenna-Coupled High-Temperature Superconducting Josephson Junction', IEEE TRANSACTIONS ON TERAHERTZ SCIENCE AND TECHNOLOGY, vol. 10, no. 1, pp. 21-31.
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Gautam, S, Lu, Y, Hassan, W, Xiao, W & Lu, DDC 2020, 'Single phase NTD PLL for fast dynamic response and operational robustness under abnormal grid condition', Electric Power Systems Research, vol. 180.
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© 2019 Elsevier B.V. Non-frequency-dependent transfer delay (NTD) approach is one of the simplest and the fastest solution for single-phase AC systems to achieve phase-locked loops (PLLs). It also exhibits immunity to error in estimated quantities in grid frequency variations. However it is susceptible to grid voltage disturbances, such as DC offset and distortions. This paper proposed an improved NTD scheme to achieve both fast dynamic response and operational robustness against abnormal grid conditions. The solution is based on a digital phase lead compensator (PLC), which is cascaded with the MAF inside the PLL control loop to enhance dynamic response and mitigate disturbance, DC offset, and harmonics. The filtering structure is also designed to be adaptive to grid frequency variations. The effectiveness of the proposed solution is verified via simulation and experimental results.

Ghadi, MJ, Azizivahed, A, Rajabi, A, Ghavidel, S, Li, L, Zhang, J, Shafie-khah, M & Catalao, JPS 2020, 'Day-Ahead Market Participation of an Active Distribution Network Equipped with Small-Scale Compressed Air Energy Storage Systems', IEEE Transactions on Smart Grid, pp. 1-1.
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Ghavidel, S, Ghadi, MJ, Azizivahed, A, Aghaei, J, Li, L & Zhang, J 2020, 'Risk-Constrained Bidding Strategy for a Joint Operation of Wind Power and CAES Aggregators', IEEE Transactions on Sustainable Energy, vol. 11, no. 1, pp. 457-466.
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© 2010-2012 IEEE. This paper proposes a coordinated strategy of a hybrid power plant (HPP), which includes a wind power aggregator and a commercial compressed air energy storage (CAES) aggregator to participate in three electricity markets (day-ahead, intraday, and balancing markets). The CAES aggregator has an extra ability which is called a simple-cycle mode operation that makes it works like a gas turbine when needed, which helps the HPP to economically handle the miscalculations of the wind power and electricity price predictions. The coordinated strategy of the HPP is formulated as a three-stage stochastic optimization problem. To control the financial risks, the conditional value-at-risk model is added to the optimization problem. Moreover, the proposed offering method is capable of submitting both bidding quantity and curves to the day-ahead market. A mixed integer linear programming formulation is written for the problem that can be easily solved by commercially available software such as GAMS. The results that were tested on a realistic-based case study located in Spain show the applicability of the suggested method to increase the joint operation profit and decrease the financial risks.

Ghobadi, R, Altaee, A, Zhou, JL, McLean, P & Yadav, S 2020, 'Copper removal from contaminated soil through electrokinetic process with reactive filter media', Chemosphere, pp. 126607-126607.
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Giubilato, R, Vayugundla, M, Schuster, MJ, Stuerzl, W, Wedler, A, Triebel, R & Debei, S 2020, 'Relocalization With Submaps: Multi-Session Mapping for Planetary Rovers Equipped With Stereo Cameras', IEEE ROBOTICS AND AUTOMATION LETTERS, vol. 5, no. 2, pp. 580-587.
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Goh, BHH, Ong, HC, Chong, CT, Chen, W-H, Leong, KY, Tan, SX & Lee, XJ 2020, 'Ultrasonic assisted oil extraction and biodiesel synthesis of Spent Coffee Ground', FUEL, vol. 261.
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Gong, S, Oberst, S & Wang, X 2020, 'An experimentally validated rubber shear spring model for vibrating flip-flow screens', Mechanical Systems and Signal Processing, vol. 139.
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© 2020 Elsevier Ltd Vibrating flip-flow screens (VFFS) provide an effective solution for screening highly moist and fine-grained minerals, and the dynamic response of the main and the floating screen frames largely accounts for a VFFS's screening performance and its processing capacity. An accurate dynamic model of the rubber shear springs inserted between the frames of the VFFS is critical for its dynamic analysis but has rarely been studied in detail. In this paper, a variance-based global sensitivity analysis is applied to actually illustrate that the rubber shear spring is the most important component for the dynamics of VFFS. Then a nonlinear rubber shear spring model is proposed to predict its amplitude and frequency dependency, which is described by a friction model and a fractional derivative viscoelastic model, respectively, and the elasticity is predicted by a nonlinear spring. The reasonability of the proposed model is verified by experimental cyclic tests of the rubber shear spring. Comparisons between the newly proposed model and other classic models, including the Generalized Maxwell model, adopted for the dynamic analysis of the VFFS are carried out, and experimental tests of an industrial VFFS's dynamic response show that dynamics of the VFFS can be better described using the proposed model than the existing models. Furthermore, the method of the global sensitivity analysis is also applied to the newly VFFS dynamic model to calculate the sensitivities of model outputs caused by the input parameters. The results reveal that the dynamic response of an operating VFFS is most sensitive to changes in the stiffness of the rubber shear spring, followed by the mass of the floating screen frames.

Govindarajan, P, Soundarapandian, RK, Gandomi, AH, Patan, R, Jayaraman, P & Manikandan, R 2020, 'Classification of stroke disease using machine learning algorithms', Neural Computing and Applications, vol. 32, pp. 817-828.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the tracking of information with respect to semantic as well as syntactic perspectives. The proposed idea is to mine patients’ symptoms from the case sheets and train the system with the acquired data. In the data collection phase, the case sheets of 507 patients were collected from Sugam Multispecialty Hospital, Kumbakonam, Tamil Nadu, India. Next, the case sheets were mined using tagging and maximum entropy methodologies, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes. Then, the processed data were fed into various machine learning algorithms such as artificial neural networks, support vector machine, boosting and bagging and random forests. Among these algorithms, artificial neural networks trained with a stochastic gradient descent algorithm outperformed the other algorithms with a higher classification accuracy of 95% and a smaller standard deviation of 14.69.

Gravina da Rocha, C, El Ghoz, HBC & Guadanhim, S 2020, 'A model for implementing product modularity in buildings design', Engineering, Construction and Architectural Management.
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© 2019, Emerald Publishing Limited. Purpose: The purpose of this paper is to examine the fundamental underpinnings of product modularity and how these can be adapted to construction and its specificities (e.g. one-off products delivered by temporary supply chains) to create a model to design modular buildings. Design/methodology/approach: This research adopts a design science research approach. Explanation I (substantive theory devising based on the analysis of an artefact ‒ a low-income housing project) is used, followed by Solution Incubation (a model to implement product modularity in buildings design). Findings: The model allows product modularity to be implemented at distinct levels (i.e. building, systems and components) at a single stage (building design), different from manufacturing where each level is considered at a distinct stage. This is in line with the project investigated: modularity was considered for house layouts, roof types and gable formats. Practical implications: The model provides a hands-on tool for practitioners to design modular buildings. The low-income project is also extensively detailed: three-dimensional models, floor plans and conceptual diagrams (outlining how fundamental underpinnings were applied at each level) are presented. There is a lack of comprehensive accounts such as the one presented here to demonstrate the application of product modularity in real-world projects. Originality/value: This paper identifies and adapts the fundamental underpinnings of product modularity to construction, and it details how these were applied to a low-income housing design at distinct levels. Based on these two endeavours, a model to implement product modularity in buildings design is proposed.

Gu, F, Niu, J, Jin, X & Yu, S 2020, 'FDFA: A fog computing assisted distributed analytics and detecting system for family activities', Peer-to-Peer Networking and Applications, vol. 13, pp. 38-52.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Researches have shown that taking parting in family activities could establish good relationships with family members. Fine-grained family activities detection is proven effective for increasing self-awareness and motivating people to modify their life styles for improved well being. Mobile health provides the possibility to solve this problem. However, with the increase of such applications, the requirements for computation, communication, and storage capability are becoming higher and higher. Fog computing, a new computing paradigm, utilizes a collaborative multitude of end-user clients or near-user edge devices to conduct a substantial amount of computing, communication, storage, and so on. In this paper, we propose FDFA, the first fog computing assisted distributed analytics and detecting system for family activities using smartphones and smart watches. Specifically, FDFA firstly uses the built-in sensors to obtain sensing data, such as the striding frequency and heart rate of the users, the sound of environment, and so forth. Then, a fog computing assisted resolution framework is proposed to efficiently detect family activities in an unobtrusive manner based on sensed data. Finally, considering the characteristics of different people, FDFA sets a personal plan for family members in doing some exercise and making continuous progress in the process of communicating. We have fully implemented FDFA on the Android platform and the extensive experimental results demonstrate that FDFA is easy to use, accurate, and appropriate for family activities with the accuracy of 79.1% and the user satisfaction degree of 82.4%. Moreover, the system can achieve more than 90% bandwidth efficiency and offer low-latency real time response with fog computing.

Guan, Q, Huang, Y, Zhong, Z, Zheng, Z, Zheng, L & Yang, Y 2020, 'Thorax disease classification with attention guided convolutional neural network', Pattern Recognition Letters, vol. 131, pp. 38-45.
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© 2019 Elsevier B.V. This paper considers the task of thorax disease diagnosis on chest X-ray (CXR) images. Most existing methods generally learn a network with global images as input. However, thorax diseases usually happen in (small) localized areas which are disease specific. Thus training CNNs using global images may be affected by the (excessive) irrelevant noisy areas. Besides, due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. For addressing the above problems, we propose to integrate the global and local cues into a three-branch attention guided convolution neural network (AG-CNN) to identify thorax diseases. An attention guided mask inference based cropping strategy is proposed to avoid noise and improve alignment in the global branch. AG-CNN also integrates the global cues to compensate the lost discriminative cues by the local branch. Specifically, we first learn a global CNN branch using global images. Then, guided by the attention heatmap generated from the global branch, we infer a mask to crop a discriminative region from the global image. The local region is used for training a local CNN branch. Lastly, we concatenate the last pooling layers of both the global and local branches for fine-tuning the fusion branch. Experiments on the ChestX-ray14 dataset demonstrate that after integrating the local cues with the global information, the average AUC scores are improved by AG-CNN.

Ha, QP, Metia, S & Phung, MD 2020, 'Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring', IEEE Sensors Journal, pp. 1-1.
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Hadgraft, RG & Kolmos, A 2020, 'Emerging learning environments in engineering education', Australasian Journal of Engineering Education.
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© 2020, © 2020 Engineers Australia. Three major challenges, sustainability, the fourth industrial revolution, and employability, will require new types of engineering programs, to help students develop skills in cross-disciplinarity, complexity, and contextual understanding. Future engineering students should be able to understand the needs for technological, sustainable solutions in context. The engineering graduates should be able to act in complex and chaotic situations. The question is how engineering institutions are responding now and how they should respond in the future. This article analyses the general responses from engineering education over the last 20 years. These responses are student-centred learning, integration of theory and practice, digital and online learning, and the definition of professional competencies. Examples are given of institutions that are already applying several of these components in the curriculum. On the long-term horizon, more personalised curriculum models are emerging based on students developing and documenting their own learning and career trajectories, as part of their lifelong learning strategy.

Hagihghi, R, Razmjou, A, Orooji, Y, Warkiani, ME & Asadnia, M 2020, 'A miniaturized piezoresistive flow sensor for real-time monitoring of intravenous infusion.', Journal of Biomedical Materials Research Part B: Applied Biomaterials, vol. 108, no. 2, pp. 568-576.
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Drug overdose (DO) is considered one of the current issues of intravenous (IV) infusion particularly resulting in serious injuries and deaths. Malfunction of infusion pumps is reported as the main cause of the drug overdose. Live monitoring and flow rate calculation by health professionals have been practicing to avoid DO. However, human errors and miscalculations are inevitable. A secondary measurement tool is required to avoid the risk of OD when infusion pump malfunctions cannot be detected immediately. Here, inspired by nature, we developed a real-time monitoring device through which an administrator can review, evaluate, and modify the IV infusion process. Our flow sensor possesses an erected polymer hair cell on a multi-layered silicon base forming from a patterned gold strained gauge layer on a piezoresistive liquid crystal polymer (LCP) membrane. Gold strain gauges on an LCP membrane have been used instead of a piezoresistive silicon membrane as the sensing element. The combination of gold strain gauges and LCP membrane provides better sensitivity than a piezoresistive silicon membrane of the same dimensions and thickness. We also miniaturized our biocompatible sensor such that it can be possible to install it inside the IV tube in contact with the liquid providing an in-suite online flow monitoring. The proposed LCP membrane sensor is compared with two commercially available IV sensors to validate its flow sensing ability. The experimental results demonstrate that the proposed sensor provides a low threshold detection limit of 5 mL/hr, which betters the performance of other commercial sensors at low flow rates.

Hakdaoui, S, Emran, A, Pradhan, B, Qninba, A, Balla, TE, Mfondoum, AHN, Lee, C-W & Alamri, AM 2020, 'Assessing the Changes in the Moisture/Dryness of Water Cavity Surfaces in Imlili Sebkha in Southwestern Morocco by Using Machine Learning Classification in Google Earth Engine', Remote Sensing, vol. 12, no. 1, pp. 131-131.
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Imlili Sebkha is a stable and flat depression in southern Morocco that is more than 10 km long and almost 3 km wide. This region is mainly sandy, but its northern part holds permanent water pockets that contain fauna and flora despite their hypersaline water. Google Earth Engine (GEE) has revolutionized land monitoring analysis by allowing the use of satellite imagery and other datasets via cloud computing technology and server-side JavaScript programming. This work highlights the potential application of GEE in processing large amounts of satellite Earth Observation (EO) Big Data for the free, long-term, and wide spatio-temporal wet/dry permanent salt water cavities and moisture monitoring of Imlili Sebkha. Optical and radar images were used to understand the functions of Imlili Sebkha in discovering underground hydrological networks. The main objective of this work was to investigate and evaluate the complementarity of optical Landsat, Sentinel-2 data, and Sentinel-1 radar data in such a desert environment. Results show that radar images are not only well suited in studying desertic areas but also in mapping the water cavities in desert wetland zones. The sensitivity of these images to the variations in the slope of the topographic surface facilitated the geological and geomorphological analyses of desert zones and helped reveal the hydrological functions of Imlili Sebkha in discovering buried underground networks.

Hanawal, M, Nguyen, D & Krunz, M 2020, 'Cognitive Networks with In-band Full-duplex Radios: Jamming Attacks and Countermeasures', IEEE Transactions on Cognitive Communications and Networking.
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Although in-band full-duplex (IBFD) radios promise to double the throughput of a wireless link, they are more vulnerable to jamming attacks than their out-of-band full-duplex (OBFD) counterparts. For two communicating OBFD nodes, a jammer needs to attack both the uplink and the downlink channels to completely break the communication link. In contrast, only one common channel needs to be jammed in the case of two IBFD nodes. Even worse, a jammer with self-interference suppression (SIS) capabilities (the underlying technique of IBFD radios) can learn the transmitters’ activity while injecting interference, allowing it to react instantly to the transmitter’s strategies. In this work, we consider a power-constrained IBFD “reactive-sweep” jammer that sweeps through the set of channels by jamming a subset of them simultaneously. We model the interactions between the IBFD radios and the jammer as a stochastic constrained zero-sum Markov game in which nodes adopt the frequency hopping (FH) technique as their strategies to counter jamming attacks. Beside the IBFD transmission-reception (TR) mode, we introduce an additional operation mode, called transmission-detection (TD), in which an IBFD radio transmits and leverages its SIS capability to detect jammers. The aim of the TD mode is to make IBFD radios more cognitive to jamming. The nodes’ optimal defense strategy that guides them when to hop and which operational mode (TD or TR) to use is then established from the equilibrium of the stochastic Markov game. We prove that this optimal policy has a threshold structure, in which IBFD nodes stay on the same channel up to a certain number of time slots before hopping. Simulation results show that our policy significantly improves the throughput of IBFD nodes under jamming attacks.

Handayani, K, Filatova, T, Krozer, Y & Anugrah, P 2020, 'Seeking for a climate change mitigation and adaptation nexus: Analysis of a long-term power system expansion', Applied Energy, vol. 262.
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© 2020 The Authors Reductions in carbon emissions have been a focus of the power sector. However, the sector itself is vulnerable to the impacts of global warming. Extreme weather events and gradual changes in climate variables can affect the reliability, cost, and environmental impacts of the energy supply. This paper analyzed the interplay between CO2 mitigation attempts and adaptations to climate change in the power sector using the Long-range Energy Alternative Planning System (LEAP) model. This paper presented a novel methodology to integrate both CO2 mitigation goals and the impacts of climate change into simulations of a power system expansion. The impacts on electricity supply and demand were quantified, based on historical climate-related impacts revealed during fieldwork and existing literature. The quantified effects, together with climate mitigation targets, were then integrated into the LEAP modeling architecture. The results showed a substantial alteration in technology composition and an increase in installed capacities driven by the joint climate mitigation–adaptation efforts when compared with the scenario without mitigation and adaptation (reference). Furthermore, an increase in CO2 emissions was observed under the mitigation-adaptation scenario compared with the mitigation only scenario, indicating that the power sector's adaptations for climate change are likely to hinder CO2 mitigation efforts. Therefore, a nexus between mitigation and adaptation should be exploited in the policy development for a low-carbon and climate-resilient power system.

Hao, Q, Jia, G, Wei, W, Vinu, A, Wang, Y, Arandiyan, H & Ni, BJ 2020, 'Graphitic carbon nitride with different dimensionalities for energy and environmental applications', Nano Research, vol. 13, no. 1, pp. 18-37.
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© 2019, Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature. As a metal-free semiconductor, graphitic carbon nitride (g-C3N4) has received extensive attention due to its high stability, nontoxicity, facile and low-cost synthesis, appropriate band gap in the visible spectral range and wide availability of resources. The dimensions of g-C3N4 can influence the regime of the confinement of electrons, and consequently, g-C3N4 with various dimensionalities shows different properties, making them available for many stimulating applications. Although there are some reviews focusing on the synthesis strategy and applications of g-C3N4, there is still a lack of comprehensive review that systemically summarises the synthesis and application of different dimensions of g-C3N4, which can provide an important theoretical and practical basis for the development of g-C3N4 with different dimensionalities and maximises their potential in diverse applications. By reviewing the latest progress of g-C3N4 studies, we aim to summarise the preparation of g-C3N4 with different dimensionalities using various structural engineering strategies, discuss the fundamental bottlenecks of currently existing methods and their solution strategies, and explore their applications in energy and environmental applications. Furthermore, it also puts forward the views on the future research direction of these unique materials. [Figure not available: see fulltext.].

Hao, Q, Liu, C, Jia, G, Wang, Y, Arandiyan, H, Wei, W & Ni, B-J 2020, 'Catalytic reduction of nitrogen to produce ammonia by bismuth-based catalysts: state of the art and future prospects', Materials Horizons.
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This review provides an up-to-date review on Bi-based nitrogen-fixation materials and future directions for the development of new Bi-based nitrogen-fixation materials under ambient conditions.

Hao, Q, Xie, C, Huang, Y, Chen, D, Liu, Y, Wei, W & Ni, B-J 2020, 'Accelerated separation of photogenerated charge carriers and enhanced photocatalytic performance of g-C3N4 by Bi2S3 nanoparticles', CHINESE JOURNAL OF CATALYSIS, vol. 41, no. 2, pp. 249-258.
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Hao, Z, Cook, K, Canning, J, Chen, H-T & Martelli, C 2020, '3-D Printed Smart Orthotic Insoles: Monitoring a Person's Gait Step by Step', IEEE Sensors Letters, vol. 4, no. 1, pp. 1-4.
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Hassan, M & Liu, D 2020, 'PPCPP: A Predator–Prey-Based Approach to Adaptive Coverage Path Planning', IEEE Transactions on Robotics, pp. 1-18.
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He, L, Lu, Z, Geng, L, Zhang, J, Li, X & Guo, X 2020, 'Environmental economic dispatch of integrated regional energy system considering integrated demand response', International Journal of Electrical Power and Energy Systems, vol. 116.
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© 2019 Elsevier Ltd To reduce the emissions of greenhouse gases and air pollutants, various low-emission measures have been taken in the power system, which are gradually intensifying the interdependency among different energy systems. Considering the carbon trading scheme and different air pollutant control technologies, this paper proposes an environmental economic dispatch model for the coordinated operation of an integrated regional energy system, which consists of a regional electricity supply network and a natural gas network, along with district energy hubs. Each energy hub contains a combined heat and power unit, a CO2-capture-based power to gas facility, a heat pump, a gas furnace and different energy storage facilities. To achieve an optimized balance between operational cost and emissions during the environmental economic dispatch of this integrated regional energy system, a price-based integrated demand response program is introduced in the energy hub. Then the proposed model is converted into a mixed-integer linear programming problem to find solutions efficiently. Case studies are carried out to demonstrate the effectiveness of proposed environmental economic dispatch model of the integrated regional energy system.

He, S, Huang, D, Feng, X, Deng, J, Li, J & Zhu, J 2020, 'Transient potential distribution on transformer winding considering the effect of core lamination stack', AIP Advances, vol. 10, no. 1, pp. 015024-015024.
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He, X, Wu, W, Cai, G, Qi, J, Kim, JR, Zhang, D & Jiang, M 2020, 'Work-energy analysis of granular assemblies validates and calibrates a constitutive model', GRANULAR MATTER, vol. 22, no. 1.
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He, Y, Dong, X, Kang, G, Fu, Y, Yan, C & Yang, Y 2020, 'Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks.', IEEE Transactions on Cybernetics.
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Deeper and wider convolutional neural networks (CNNs) achieve superior performance but bring expensive computation cost. Accelerating such overparameterized neural network has received increased attention. A typical pruning algorithm is a three-stage pipeline, i.e., training, pruning, and retraining. Prevailing approaches fix the pruned filters to zero during retraining and, thus, significantly reduce the optimization space. Besides, they directly prune a large number of filters at first, which would cause unrecoverable information loss. To solve these problems, we propose an asymptotic soft filter pruning (ASFP) method to accelerate the inference procedure of the deep neural networks. First, we update the pruned filters during the retraining stage. As a result, the optimization space of the pruned model would not be reduced but be the same as that of the original model. In this way, the model has enough capacity to learn from the training data. Second, we prune the network asymptotically. We prune few filters at first and asymptotically prune more filters during the training procedure. With asymptotic pruning, the information of the training set would be gradually concentrated in the remaining filters, so the subsequent training and pruning process would be stable. The experiments show the effectiveness of our ASFP on image classification benchmarks. Notably, on ILSVRC-2012, our ASFP reduces more than 40% FLOPs on ResNet-50 with only 0.14% top-5 accuracy degradation, which is higher than the soft filter pruning by 8%.

He, Z, Liu, S, Zhong, Y, Chen, D, Ding, H, Wang, J, Du, G, Yang, G & Hao, Q 2020, 'Preparation of BiPO4/graphene photoelectrode and its photoelectrocatalyitic performance', CHINESE JOURNAL OF CATALYSIS, vol. 41, no. 2, pp. 302-311.
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Heidary, A, Radmanesh, H, Naghibi, SH, Samandarpour, S, Rouzbehi, K & Shariati, N 2020, 'Distribution system protection by coordinated fault current limiters', IET Energy Systems Integration.
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Heidary, A, Radmanesh, H, Rouzbehi, K & Moradi CheshmehBeigi, H 2020, 'A Multifunction High-Temperature Superconductive Power Flow Controller and Fault Current Limiter', IEEE Transactions on Applied Superconductivity, vol. 30, no. 5, pp. 1-8.
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Hendryx, M, Islam, MS, Dong, G-H & Paul, G 2020, 'Air Pollution Emissions 2008-2018 from Australian Coal Mining: Implications for Public and Occupational Health.', International Journal of Environmental Research and Public Health, vol. 17, no. 5.
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Occupational exposure limits for respirable coal dust are based on exposure during working hours, but coal miners may experience additional community-based exposures during nonworking hours. We analyzed Australia National Pollutant Inventory (NPI) data for the years 2008-2018 to estimate air pollutants (metals, nitrogen oxides, particulate matter ≤ 10 micrometers (PM10) and ≤2.5 micrometers (PM2.5)) originating from coal mines. PM10 levels from community-based air monitors in Queensland and New South Wales were also compared between mining and nonmining communities. Results indicated that tons of coal mined increased over the study period, and that levels of particulate matter, metals, and nitrogen oxides increased significantly over time as well. Coal mines accounted for 42.1% of national PM10 air emissions from NPI sites. PM2.5 from coal mines accounted for 19.5% of the national total, metals for 12.1%, and nitrogen oxides for 10.1%. Coal mining occurred in 57 different post codes; the 20 coal-mining post codes with the highest PM10 emissions were home to 160,037 people. Emissions of all studied pollutants were significantly higher from coal mining sites than from other types of NPI sites. Results from community-based air monitoring stations indicated significantly higher population PM10 exposure in coal mining communities than in nonmining communities. The health of the public at large is impacted by coal mining, but to the extent that miners also live near coal mining operations, their total exposure is underestimated by consideration of exposure only during working hours.

Hien, NT, Lan, HN, Huu, TV, Thi, DN, Thi, HVN, Thi, HHC, Tien, VN, Van, TT, Xuan, HV & Aziz, KHH 2020, 'Heterogeneous catalyst ozonation of Direct Black 22 from aqueous solution in the presence of metal slags originating from industrial solid wastes', SEPARATION AND PURIFICATION TECHNOLOGY, vol. 233.
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Hill, M, Mason, D, Monteiro Marques, T, Gama Carvalho, M & Tran, N 2020, 'Developing a virus-microRNA interactome using cytoscape', MethodsX, vol. 7.
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© 2019 It is currently difficult to determine the effect of oncogenic viruses on the global function and regulation of pathways within mammalian cells. A thorough understanding of the molecular pathways and individual genes altered by oncogenic viruses is needed for the identification of targets that can be utilised for early diagnosis, prevention, and treatment methods. We detail a logical step-by-step guide to uncover viral-protein-miRNA interactions using publically available datasets and the network building program, Cytoscape. This method may be applied to identify specific pathways that are altered in viral infection, and contribute to the oncogenic transformation of cells. To demonstrate this, we constructed a gene regulatory interactome encompassing Human Papillomavirus Type 16 (HPV16) and its control of specific miRNAs. This approach can be broadly applied to understand and map the regulatory functions of other oncogenic viruses, and determine their role in altering the cellular environment in cancer. Availability and Implementation Cytoscape (Shannon et al. (2003), Smoot et al. (2010)) is freely available at https://cytoscape.org/. • This method allows for the analysis and visualization of large datasets to generate an interactome that integrates key players of molecular biology • This approach may be applied to any oncogenic virus to map its regulatory functions, and its secondary impact on gene regulation via microRNAs.

Hlalele, TG, Naidoo, RM, Zhang, J & Bansal, RC 2020, 'Dynamic Economic Dispatch With Maximal Renewable Penetration Under Renewable Obligation', IEEE Access, vol. 8, pp. 38794-38808.
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Hoang, LP, Nguyen, TMP, Van, HT, Hoang, TKD, Vu, XH, Nguyen, TV & Ca, NX 2020, 'Cr(VI) Removal from Aqueous Solution Using a Magnetite Snail Shell', WATER AIR AND SOIL POLLUTION, vol. 231, no. 1.
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Hoang, TM, El Shafie, A, Da Costa, DB, Duong, TQ, Tuan, HD & Marshall, A 2020, 'Security and Energy Harvesting for MIMO-OFDM Networks', IEEE Transactions on Communications.
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IEEE We consider a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) network in which a source node, Alice, communicates with an energy-harvesting destination node, Bob, in the presence of a passive eavesdropper. To secure the wireless transmission, Alice generates a hybrid artificial noise (AN) in both frequency and time domains. Moreover, in order to collect more energy, Bob splits the received signal power of the cyclic prefix of each OFDM block. We then propose two non-convex optimization problems to balance both the need for security and the need for harvesting energy at Bob. While one considers maximizing the secrecy rate, the other approach aims at maximizing the harvested energy. Path-following algorithms of low computational complexity are developed and evaluated. Our numerical results show the gain of our proposed scheme and the effectiveness of our proposed algorithms.

Hoang, VT, Phung, MD, Dinh, TH & Ha, QP 2020, 'System Architecture for Real-Time Surface Inspection Using Multiple UAVs', IEEE Systems Journal, pp. 1-12.
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Hoque, MAA, Pradhan, B & Ahmed, N 2020, 'Assessing drought vulnerability using geospatial techniques in northwestern part of Bangladesh', Science of the Total Environment, vol. 705.
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© 2019 Elsevier B.V. Drought is a damaging and costly natural disaster that frequently affects many climatic regions in the world. A multi-criteria-based approach for integrated spatial drought vulnerability mapping that combines all drought categories is required to generate detailed vulnerability information for formulating drought mitigation strategies. This study presents a spatial multi-criteria integrated approach for mapping comprehensive drought vulnerability using geospatial techniques and an analytical hierarchy process (AHP). The developed approach was applied in the northwestern region of Bangladesh to justify its applicability. A total of 17 criteria under 4 drought categories, namely, meteorological, agricultural, hydrological and socio-economic, were selected. Moreover, spatial layers for each criterion were developed. AHP was used to calculate the weights for each criterion and drought types using pair-wise comparison matrices. Individual categories of drought and overall drought vulnerability maps were developed using the weighted overlay technique by integrating the corresponding criteria. The produced maps effectively defined the spatial extents and levels (e.g. normal, mild, moderate, severe and extreme) of drought vulnerability. Results demonstrated that approximately 77% of the total area of the north-western region of Bangladesh was moderately to extremely vulnerable to drought. The output of the developed approach was successfully validated using the receiver operating characteristics and area under the curve techniques. The findings suggest that the proposed approach is highly effective in mapping comprehensive drought vulnerability for formulating strong drought mitigation strategies.

Hossain, MA, Canning, J & Yu, Z 2020, 'Fluorescence-Based Determination of Olive Oil Quality Using an Endoscopic Smart Mobile Spectrofluorimeter', IEEE Sensors Journal, vol. 20, no. 8, pp. 4156-4163.
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© 2019 IEEE. The application of self-contained field-portable, internet connected spectroscopic diagnostics in food analysis using a fibre endoscopic smart fluorimeter is reported. A UV-induced fluorescence is used to measure the quality of olive oils, distinguishing between extra virgin and others within a smartphone platform. When excited at λ ∼ ~370 nm, the extra virgin olive oil fluoresces red at λ ∼ ~670 nm. Notably, other oils do not fluoresce red but rather blue, consistent with degradation of the chlorophyll in the oil. Artificial refinement employed in some of the commercial products removes the red emission providing a simple method for distinguishing extra virgin olive oil from all other oils. A smartphone endoscopic fluorimeter is designed and constructed that measures the emission band Δ λ ∼ (400-700) nm with λ ∼ ~370 nm excitation. The instrument is used to characterise the fluorescence of the oils. Photo-degradation over time for extra virgin olive oil under room lights is observed, demonstrating the origin for the decomposition of extra virgin olive oil in transparent bottles. Extra virgin olive oils are also susceptible to thermal degradation more than refined oils.

Hossain, SMG & McLaughlan, RG 2020, 'Non-equilibrium 2, 4-DCP uptake onto pine chips from aqueous solutions.', Environmental technology, pp. 1-22.
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Wide application of 2, 4-dichlorophenol (2, 4-DCP) in industry has resulted in environmental contamination of soils and groundwater. Approaches to cost-effectively remove 2, 4-DCP from water need to be found. 2, 4-DCP uptake onto pine chips from aqueous solution were evaluated in column studies under different particle sizes and flow conditions. The breakthrough curves (BTCs) showed evidence of non-equilibrium with early breakthroughs. The uptake capacity increased from 3.0 to 6.0 mg g- 1 with decreasing flow rate from 10 to 5 mL min-1 but did not show significant differences for particle sizes 1.18 and 4.75 mm at the same flow rate. The BTC for all cases could not be adequately fitted using an equilibrium model with batch derived sorption parameters. They could be better fitted by two site non-equilibrium model using parameters derived from both batch and inverse modelling. At a higher flow rate, the fraction of instantaneous sorption decreased suggesting a higher degree of non-equilibrium. Non-equilibrium processes need to be considered in the design of these types of treatment and operational systems.

Hosseinzadeh, A, Baziar, M, Alidadi, H, Zhou, JL, Altaee, A, Najafpoor, AA & Jafarpour, S 2020, 'Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions', Bioresource Technology, vol. 303.
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© 2020 Elsevier Ltd Vermicomposting is one of the best technologies for nutrient recovery from solid waste. This study aims to assess the efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models in predicting nutrient recovery from solid waste under different vermicompost treatments. Seven chemical and biological indices were studied as input variables to predict total nitrogen (TN) and total phosphorus (TP) recovery. The developed ANN and MLR models were compared by statistical analysis including R-squared (R2), Adjusted-R2, Root Mean Square Error and Absolute Average Deviation. The results showed that vermicomposting increased TN and TP proportions in final products by 1.5 and 16 times. The ANN models provided better prediction for TN and TP with R2 of 0.9983 and 0.9991 respectively, compared with MLR models with R2 of 0.834 and 0.729. TN and C/N ratio were key factors for TP and TN prediction by ANN with percentages of 17.76 and 18.33.

Hou, J, Li, B, Tong, Y, Ma, L, Ball, J, Luo, H, Liang, Q & Xia, J 2020, 'Cause analysis for a new type of devastating flash flood', Hydrology Research, vol. 51, no. 1, pp. 1-16.
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Abstract This work introduces an unprecedented flash flood that resulted in nine casualties in Shimen Valley, China, 2015. Through field survey and numerical simulation the causes of the disaster are systematically analyzed, finding that the intense storm, terrain features, and the large woody debris (LWD) played important roles. The intense storm induced fast runoff and, in turn, high discharges as a result of the steep catchment surfaces and channels. The flood flushed LWD and boulders downstream until blockage occurred in a contraction section, forming a debris lake. When the debris dam broke, a dam break wave rapidly propagated to the valley mouth, washing people away. After considering the disaster-inducing factors, measures for preventing similar floods are proposed. The analysis presented herein should help others manage flash floods in mountain areas.

Hou, Z, Tang, JF, Ferrie, C, Xiang, GY, Li, CF & Guo, GC 2020, 'Experimental realization of self-guided quantum process tomography', Physical Review A, vol. 101, no. 2.
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© 2020 American Physical Society. Characterization of quantum processes is a preliminary step necessary in the development of quantum technology. The conventional method uses standard quantum process tomography, which requires d2 input states and d4 quantum measurements for a d-dimensional Hilbert space. These experimental requirements are compounded by the complexity of processing the collected data, which can take several orders of magnitude longer than the experiment itself. In this paper we propose an alternative self-guided algorithm for quantum process tomography, tuned for the task of finding an unknown unitary process. Our algorithm is a fully automated and adaptive process characterization technique. The advantages of our algorithm are the following: it has an inherent robustness to both statistical and technical noise; it requires less space and time since there is no postprocessing of the data; it requires only a single input state and measurement; and it provides on-the-fly diagnostic information while the experiment is running. Numerical results show our algorithm achieves the same 1/n scaling as standard quantum process tomography when n uses of the unknown process are used. We also present experimental results wherein the algorithm and its advantages are realized for the task of finding an element of SU(2).

Hu, X, Zhang, X, Ngo, HH, Guo, W, Wen, H, Li, C, Zhang, Y & Ma, C 2020, 'Comparison study on the ammonium adsorption of the biochars derived from different kinds of fruit peel.', The Science of the total environment, pp. 135544-135544.
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Application of biochars to remove inorganic nitrogen (NH4+, NO2-, NH3, NO, NO2, N2O) from wastewater and agricultural fields has gained a significant interest. This study aims to investigate the relationship between ammonium sorption and physicochemical properties of biochars derived from different kinds of fruit peel. Biochars from three species of fruit peel (orange, pineapple and pitaya) were prepared at 300, 400, 500 and 600 °C with the residence time of 2 h and 4 h. Their characteristics and sorption for ammonium was evaluated. The results show a clear effect of pyrolysis conditions on physicochemical properties of biochars, including elemental composition, functional groups and pH. The maximum NH4+ adsorption capacities were associated with biochars of orange peel (4.71 mg/g) and pineapple peel (5.60 mg/g) produced at 300 °C for 2 h. The maximum NH4+ adsorption capacity of the pitaya peel biochar produced at 400 °C for 2 h was 2.65 mg/g. For all feedstocks, biochars produced at low temperatures showed better NH4+ adsorption capacity. It was found that biochars had better adsorption efficiency on ammonium at a pH of 9. Adsorption kinetics of ammonium on biochars followed the pseudo-second-order kinetic model while Langmuir isotherm model could well simulate the adsorption behavior of ammonium on biochars. The adsorption mechanism of ammonium on biochars predominantly involved surface complexation, cation exchange and electrostatic attraction. Conclusively, the fruit peel-derived biochars can be used as an alternative to conventional sorbents in water treatment.

Hu, Z, Liu, RP, Ni, W, Wen, X, Lu, Z & Dutkiewicz, E 2020, 'Analysis of Clustered Licensed-Assisted Access in Unlicensed Spectrum', IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, vol. 69, no. 1, pp. 349-360.
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Huang, L, Yang, Q, Wu, J, Huang, Y, Wu, Q & Xu, J 2020, 'Generated Data with Sparse Regularized Multi-Pseudo Label for Person Re-Identification', IEEE Signal Processing Letters, vol. 27, pp. 391-395.
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© 1994-2012 IEEE. Recently, Generative Adversarial Network (GAN) has been adopted to improve person re-identification (person re-ID) performance through data augmentation. However, directly leveraging generated data to train a re-ID model may easily lead to over-fitting issue on these extra data and decrease the generalisability of model to learn true ID-related features from real data. Inspired by the previous approach which assigns multi-pseudo labels on the generated data to reduce the risk of over-fitting, we propose to take sparse regularization into consideration. We attempt to further improve the performance of current re-ID models by using the unlabeled generated data. The proposed Sparse Regularized Multi-Pseudo Label (SRMpL) can effectively prevent the over-fitting issue when some larger weights are assigned to the generated data. Our experiments are carried out on two publicly available person re-ID datasets (e.g., Market-1501 and DukeMTMC-reID). Compared with existing unlabeled generated data re-ID solutions, our approach achieves competitive performance. Two classical re-ID models are used to verify our sparse regularization label on generated data, i.e., an ID-embedding network and a two-stream network.

Huang, ML, Lu, L, Ren, X, Cui, C & Luo, Y 2020, 'Tensor mutual information and its application', Concurrency and Computation: Practice and Experience, vol. 2020, pp. 1-10.
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Correlation analysis has long been a question of great interest in measuring the relationship among different variables and has been applied in many fields, such as dimension reduction, classification, and so on. However, current methods of correlation analysis take into account the linear relationship between multiple variables and only few works on nonlinear interaction of two variables have been considered. In this article, we first present a nonlinear analysis method of multiple (two or more) variables based on mutual information for tensor analysis (MITA). In addition, we extend the mutual‐information matrix analysis directly to MITA and show the multivariable mutual information formula based on Venn diagram. Experiments on multiview dimension reduction, including attacking internet traffic prediction, advertisement classification, and biometric structure prediction illustrate the effectiveness of the proposed method, especially in the case of low‐dimensional subspace.

Huang, Q-S, Wu, W, Wei, W, Song, L, Sun, J & Ni, B-J 2020, 'Highly-efficient Pb2+ removal from water by novel K2W4O13 nanowires: Performance, mechanisms and DFT calculation', CHEMICAL ENGINEERING JOURNAL, vol. 381.
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Huang, S, Xu, Z, Tsang, IW & Kang, Z 2020, 'Auto-weighted multi-view co-clustering with bipartite graphs', Information Sciences, vol. 512, pp. 18-30.
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© 2019 Co-clustering aims to explore coherent patterns by simultaneously clustering samples and features of data. Several co-clustering methods have been proposed in the past decades. However, in real-world applications, datasets are often with multiple modalities or composed of multiple representations (i.e., views), which provide different yet complementary information. Hence, it is essential to develop multi-view co-clustering models to solve the multi-view application problems. In this paper, a novel multi-view co-clustering method based on bipartite graphs is proposed. To make use of the duality between samples and features of multi-view data, a bipartite graph for each view is constructed such that the co-occurring structure of data can be extracted. The key point of utilizing the bipartite graphs to deal with the multi-view co-clustering task is to reasonably integrate these bipartite graphs and obtain an optimal consensus one. As for this point, the proposed method can learn an optimal weight for each bipartite graph automatically without introducing an additive parameter as previous methods do. Furthermore, an efficient algorithm is proposed to optimize this model with theoretically guaranteed convergence. Extensive experimental results on both toy data and several benchmark datasets have demonstrated the effectiveness of the proposed model.

Huang, W, Hua, W, Chen, F, Hu, M & Zhu, J 2020, 'Model Predictive Torque Control With SVM for Five-Phase PMSM Under Open-Circuit Fault Condition', IEEE Transactions on Power Electronics, vol. 35, no. 5, pp. 5531-5540.
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Huang, Y, Ng, ECY, Surawski, NC, Yam, YS, Mok, WC, Liu, CH, Zhou, JL, Organ, B & Chan, EFC 2020, 'Large eddy simulation of vehicle emissions dispersion: Implications for on-road remote sensing measurements', Environmental Pollution, vol. 259.
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© 2020 Elsevier Ltd On-road remote sensing technology measures the concentration ratios of pollutants over CO2 in the exhaust plume in half a second when a vehicle passes by a measurement site, providing a rapid, non-intrusive and economic tool for vehicle emissions monitoring and control. A key assumption in such measurement is that the emission ratios are constant for a given plume. However, there is a lack of study on this assumption, whose validity could be affected by a number of factors, especially the engine operating conditions and turbulence. To guide the development of the next-generation remote sensing system, this study is conducted to investigate the effects of various factors on the emissions dispersion process in the vehicle near-wake region and their effects on remote sensing measurement. The emissions dispersion process is modelled using Large Eddy Simulation (LES). The studied factors include the height of the remote sensing beam, vehicle speed, acceleration and side wind. The results show that the measurable CO2 and NO exhaust plumes are relatively short at 30 km/h cruising speed, indicating that a large percentage of remote sensing readings within the measurement duration (0.5 s) are below the sensor detection limit which would distort the derived emission ratio. In addition, the valid measurement region of NO/CO2 emission ratio is even shorter than the measurable plume and is at the tailpipe height. The effect of vehicle speed (30–90 km/h) on the measurable plume length is insignificant. Under deceleration condition, the length of the valid NO/CO2 measurement region is shorter than under cruising and acceleration conditions. Side winds from the far-tailpipe direction have a significant effect on remote sensing measurements. The implications of these findings are discussed and possible solutions to improve the accuracy of remote sensing measurement are proposed.

Huang, Y, Song, R, Argha, A, Savkin, AV, Celler, BG & Su, SW 2020, 'Continuous Description of Human 3D Motion Intent Through Switching Mechanism', IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, vol. 28, no. 1, pp. 277-286.
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Huang, Y, Xu, J, Wu, Q, Zhong, Y, Zhang, P & Zhang, Z 2020, 'Beyond Scalar Neuron: Adopting Vector-Neuron Capsules for Long-Term Person Re-Identification', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Huang, Y, Zhu, F, Porter, AL, Zhang, Y, Zhu, D & Guo, Y 2020, 'Exploring Technology Evolution Pathways to Facilitate Technology Management: From a Technology Life Cycle Perspective', IEEE Transactions on Engineering Management.
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IEEE Technological innovation is a dynamic process that spans the life cycle of an idea, from scientific research to production. Within this process, there are often a few key innovations that significantly impact a technology's development, and the ability to identify and trace the development of these key innovations comes with a great payoff for researchers and technology managers. In this article, we present a framework for identifying the technology's main evolutionary pathway. What is unique about this framework is that we introduce new indicators that reflect the connectivity and the modularity in the interior citation network to distinguish between the stages of a technology's development. We also show how information about a family of patents can be used to build a comprehensive patent citation network. Finally, we apply integrated approaches of main path analysis (MPA)—namely global MPA and global key-route main analysis—for extracting technological trajectories at different technological stages. We illustrate this approach with dye-sensitized solar cells (DSSCs), a low-cost solar cell belonging to the group of thin-film solar cells, contributing to the remarkable growth in the renewable energy industry. The results show how this approach can trace the main development trajectory of a research field and distinguish key technologies to help decision makers manage the technological stages of their innovation processes more effectively.

Ibrahim, IA, Hossain, MJ & Duck, BC 2020, 'An Optimized Offline Random Forests-Based Model for Ultra-Short-Term Prediction of PV Characteristics', IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 16, no. 1, pp. 202-214.
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Ibrar, I, Altaee, A, Zhou, JL, Naji, O & Khanafer, D 2020, 'Challenges and potentials of forward osmosis process in the treatment of wastewater', Critical Reviews in Environmental Science and Technology, pp. 1-45.
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Imran, K, Ullah, K, Khattak, A, Zhang, J, Pal, A, Rafique, MN & Baig, SM 2020, 'Matchmaking model for bilateral trading decisions of load serving entity', Electric Power Systems Research, vol. 183.
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© 2020 Matchmaking and bilateral negotiations are two distinct phases of practical market participants’ decision making for bilateral transactions. Agent-based models are naturally suitable for electricity markets in general and bilateral transactions in particular. This paper's contribution includes development of a novel matchmaking model that generates forward contracting power and utility curves. The matchmaking model enables a load serving entity agent to undertake its own matchmaking, to find optimal trading allocations over a range of prices, before engaging in bilateral negotiations with generation company agents. Open-source agent-based simulation platform allows combined simulation of bilateral transactions and day-ahead auction. In this research paper, matchmaking is achieved by direct-search without any organized bulletin board, broker, or matchmaker. Instead of random matchmaking, portfolio optimization based matchmaking systematically explores available electricity trading options throughout the market: local and non-local bilateral trades as well as day-ahead auctions. The matchmaking algorithm is unique because it scans all trading options over the entire range of negotiable prices. Depending on private profit-seeking goals, risk-aversion preferences and market price statistics, each load serving entity agent individually finds its matchmaking results. A set of case studies demonstrates how matchmaking model depends on transmission rights and performs for different risk aversion factors.

Islam, MS, Paul, G, Ong, HX, Young, PM, Gu, YT & Saha, SC 2020, 'A Review of Respiratory Anatomical Development, Air Flow Characterization and Particle Deposition', International Journal of Environmental Research and Public Health, vol. 17, no. 2, pp. 380-380.
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The understanding of complex inhalation and transport processes of pollutant particles through the human respiratory system is important for investigations into dosimetry and respiratory health effects in various settings, such as environmental or occupational health. The studies over the last few decades for micro- and nanoparticle transport and deposition have advanced the understanding of drug-aerosol impacts in the mouth-throat and the upper airways. However, most of the Lagrangian and Eulerian studies have utilized the non-realistic symmetric anatomical model for airflow and particle deposition predictions. Recent improvements to visualization techniques using high-resolution computed tomography (CT) data and the resultant development of three dimensional (3-D) anatomical models support the realistic representation of lung geometry. Yet, the selection of different modelling approaches to analyze the transitional flow behavior and the use of different inlet and outlet conditions provide a dissimilar prediction of particle deposition in the human lung. Moreover, incorporation of relevant physical and appropriate boundary conditions are important factors to consider for the more accurate prediction of transitional flow and particle transport in human lung. This review critically appraises currently available literature on airflow and particle transport mechanism in the lungs, as well as numerical simulations with the aim to explore processes involved. Numerical studies found that both the Euler–Lagrange (E-L) and Euler–Euler methods do not influence nanoparticle (particle diameter ≤50 nm) deposition patterns at a flow rate ≤25 L/min. Furthermore, numerical studies demonstrated that turbulence dispersion does not significantly affect nanoparticle deposition patterns. This critical review aims to develop the field and increase the state-of-the-art in human lung modelling.

Jackson, T, Shen, J, Nikolic, S & Xia, G 2020, 'Managerial factors that influence the success of knowledge management systems: A systematic literature review', Knowledge and Process Management.
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© 2020 John Wiley & Sons Ltd The purpose of this research is to remove the ambiguity that clouds the analysis of knowledge management systems (KMSs). This is because of an overall lack of consensus on how KMSs adapt to the new “knowledge explosion” embraced by the booming “Big Data” hype. In this paper, a refreshing synthesis of literature will uncover benefits and identify gaps in current knowledge. These findings will also be of benefit to researchers and industries as it allows for the holistic analysis of a KMS. This systematic literature review collected 54 papers for qualitative analysis. This analysis led to a synthesis of factors evident in the research and how they could be combined and collected as key categories. Once each factor was categorized, the future directions of research was analysed and documented. The primary factors discussed include (a) formal processes, (b) company culture, (c) top-down support, (d) motivation, (e) clear goals, and (f) quality of KMS. This research has created a baseline for the further evaluation of KMSs in the real world.

Jafarizadeh, S, Tofigh, F, Lipman, J & Abolhasan, M 2020, 'Optimizing synchronizability in networks of coupled systems', AUTOMATICA, vol. 112.
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Jena, DP & Qiu, X 2020, 'Sound transmission loss of porous materials in ducts with embedded periodic scatterers', JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, vol. 147, no. 2, pp. 978-983.
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Jena, R, Pradhan, B & Alamri, AM 2020, 'Geo-structural stability assessment of surrounding hills of Kuala Lumpur City based on rock surface discontinuity from geological survey data', ARABIAN JOURNAL OF GEOSCIENCES, vol. 13, no. 2.
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Jena, R, Pradhan, B & Beydoun, G 2020, 'Earthquake vulnerability assessment in Northern Sumatra province by using a multi-criteria decision-making model', International Journal of Disaster Risk Reduction, vol. 46.
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© 2020 Elsevier Ltd The prerequisite for earthquake risk estimation is vulnerability assessment. Therefore, estimating vulnerability is necessary to reduce future fatalities. This study aims to evaluate the earthquake vulnerability assessment (EVA) in Banda Aceh by using the multi-criteria decision-making approach through an analytical hierarchy process and VIseKriterijumska Optimizacija I Kompromisno Resenje method using a geographical information system. Banda Aceh City is located close to the Great Sumatran Fault in North Sumatra. Several factors were used to produce social vulnerability, structural vulnerability, and geotechnical vulnerability indices. Subsequently, the adopted approaches were integrated and applied to estimate the criteria weight, priority ranking, and alternatives of criterion by applying the pair-wise comparison at all levels. Finally, vulnerability layers were superimposed to estimate the earthquake vulnerability index and produce the vulnerability map. Results showed that the central part of the city exhibits high to very high vulnerability. A tiny part of the northern–central part is under severe vulnerability conditions. The consistency ratios for all three vulnerability layers were 1.9%, 4.6% and 5.5%. The consistency ratios for the final EVA was 1.9%. The developed map revealed that 3.39% of Banda Aceh City falls under very high, 11.86% high, 23.73% medium, 28.82% low, and 32.20% of very low vulnerability areas. The proposed method for the EVA provides useful information that could assist in earthquake disaster mitigation.

Jena, R, Pradhan, B, Beydoun, G, Al-Amri, A & Sofyan, H 2020, 'Seismic hazard and risk assessment: a review of state-of-the-art traditional and GIS models', ARABIAN JOURNAL OF GEOSCIENCES, vol. 13, no. 2.
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Ji, M, Hu, Z, Hou, C, Liu, H, Ngo, HH, Guo, W, Lu, S & Zhang, J 2020, 'New insights for enhancing the performance of constructed wetlands at low temperatures', Bioresource Technology, vol. 301.
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© 2019 Elsevier Ltd Constructed wetlands (CWs) have been widely utilized for various types of wastewater treatment due to their merits, including high cost-effectiveness and easy operation. However, a few intrinsic drawbacks have always restricted their application and long-term stability, especially their weak performance at temperatures under 10 °C (low temperatures) due to the deterioration of microbial assimilation and plant uptake processes. The existing modifications to improve CWs performance from the direct optimization of internal components to the indirect adjunction of external resources promoted the wastewater treatment efficiency to a certain degree, but the sustainability and sufficiency of pollutants removal remains a challenge. With the goal of optimizing CW components, the integrity of the CW ecosystem and the removal of emerging pollutants, future directions for research should include radiation plant breeding, improvements to CW ecosystems, and the combination or integration of certain treatment processes with CWs to enhance wastewater treatment effects at low temperatures.

Jin, P, Tian, Y, Lu, Y, Guo, Y, Lei, G & Zhu, J 2020, '3-D Analytical Magnetic Field Analysis of the Eddy Current Coupling With Halbach Magnets', IEEE TRANSACTIONS ON MAGNETICS, vol. 56, no. 1.
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Ju, M, Ding, C, Guo, YJ & Zhang, D 2020, 'IDGCP: Image Dehazing Based on Gamma Correction Prior', IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 29, pp. 3104-3118.
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Karimi, M, Croaker, P, Maxit, L, Robin, O, Skvortsov, A, Marburg, S & Kessissoglou, N 2020, 'A hybrid numerical approach to predict the vibrational responses of panels excited by a turbulent boundary layer', Journal of Fluids and Structures, vol. 92, pp. 102814-102814.
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Karimidastenaei, Z, Torabi Haghighi, A, Rahmati, O, Rasouli, K, Rozbeh, S, Pirnia, A, Pradhan, B & Kløve, B 2020, 'Fog-water harvesting Capability Index (FCI) mapping for a semi-humid catchment based on socio-environmental variables and using artificial intelligence algorithms.', The Science of the total environment, vol. 708, pp. 135115-135115.
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Fog is an important component of the water cycle in northern coastal regions of Iran. Having accurate tools for mapping the precise spatial distribution of fog is vital for water harvesting within integrated water resources management in this semi-humid region. In this study, environmental variables were considered in prediction mapping of areas with high concentrations of fog in the Vazroud watershed, Iran. Fog probability maps were derived from four artificial intelligence algorithms (Generalized Linear Model, Generalized Additive Model, Generalized Boosted Model, and Generalized Dissimilarity Model). Models accuracy were assessed using Receiver Operating characteristic Curve (ROC). Three social variables were also selected according to their relevance for fog suitability mapping. Finally, Fog-water harvesting Capability Index (FCI) maps were produced by multiplying fog probability by fog suitability maps. The results showed high accuracy in fog probability mapping for the study area, with all models proving capable of identifying areas with high fog concentrations in the south and southeast. For all models, the highest values of importance were obtained for sky view factor and the lowest for slope curvature. Analytic Hierarchy Process results showed the relative importance of social conditioning factors in fog suitability mapping, with the highest weight given to distance to residential area, followed by distance to livestock buildings and distance to road. Based on the fog suitability map, southeast and southern parts of the study area are most suitable for fog water harvesting. The fog spatial distribution maps obtained can increase fog water harvesting efficiency. They also indicate areas for future study with regions where fog is a critical component in the water cycle.

Karmokar, DK & Chen, S-L 2020, 'Slot antenna miniaturization by loading dumbbell-shaped slots and strips, square-shaped loops, and shorting vias', MICROWAVE AND OPTICAL TECHNOLOGY LETTERS.
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Karmokar, DK, Chen, S-L, Thalakotuna, D, Qin, P-Y, Bird, TS & Guo, YJ 2020, 'Continuous Backward-to-Forward Scanning 1-D Slot-Array Leaky-Wave Antenna With Improved Gain', IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 19, no. 1, pp. 89-93.
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Karmokar, DK, Guo, YJ, Chen, S-L & Bird, TS 2020, 'Composite Right/Left-Handed Leaky-Wave Antennas for Wide-Angle Beam Scanning With Flexibly Chosen Frequency Range', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 1, pp. 100-110.
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Khalilpour, KR & Lusis, P 2020, 'Network capacity charge for sustainability and energy equity: A model-based analysis', Applied Energy, vol. 266, pp. 114847-114847.
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Khalilpour, KR, Pace, R & Karimi, F 2020, 'Retrospective and prospective of the hydrogen supply chain: A longitudinal techno-historical analysis', International Journal of Hydrogen Energy.
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Khan, MA, Zhu, Y, Yao, Y, Zhang, P, Agrawal, A & Reece, PJ 2020, 'Impact of metal crystallinity-related morphologies on the sensing performance of plasmonic nanohole arrays.', Nanoscale.
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Plasmonic nanohole arrays for biosensing applications have attracted tremendous attention because of their flexibility in optical signature design, high multiplexing capabilities, simple optical alignment setup, and high sensitivity. The quality of the metal film, including metal crystallinity and surface roughness, plays an important role in determining the sensing performance because the interaction between free electrons in the metal and incident light is strongly influenced by the metal surface morphology. We systematically investigated the influence of metal crystallinity-related morphologies on the sensing performance of plasmonic nanohole arrays after different metal deposition processes. We utilised several non-destructive nanoscale surface characterisation techniques to perform a quantitative and comparative analysis of the Au quality of the fabricated sensor. We found empirically how the surface roughness and grain sizes influence the permittivity of the Au film and thus the sensitivity of the fabricated sensor. Finally we confirmed that the deposition conditions that provide both low surface roughness and large metal grain sizes improve the sensitivity of the plasmonic sensor.

Khan, MNH, Forouzesh, M, Siwakoti, Y, Li, L & Blaabjerg, F 2020, 'Switched Capacitor Integrated (2n+1)-Level Step-up Single-Phase Inverter', IEEE Transactions on Power Electronics, pp. 1-1.
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Khan, MNH, Forouzesh, M, Siwakoti, YP, Li, L, Kerekes, T & Blaabjerg, F 2020, 'Transformerless Inverter Topologies for Single-Phase Photovoltaic Systems: A Comparative Review', IEEE Journal of Emerging and Selected Topics in Power Electronics.
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Khan, MNH, Siwakoti, YP, Li, L & Blaabjerg, F 2020, 'H-Bridge Zero-Voltage Switch Controlled Rectifier (HB-ZVSCR) Transformerless Mid-Point-Clamped Inverter for Photovoltaic Applications', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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A single-phase transformerless mid-point clamped H-bridge zero-voltage switch-controlled rectifier inverter topology is proposed in this paper for photovoltaic (PV) systems to address the issue of common mode (CM) voltage and leakage currents. Apart from the full H-bridge inverter, the proposed voltage clamping circuit consists of two switches and a full-bridge diode which clamps the AC terminal to the DC midpoint (consisting of two DC-link capacitors) during the freewheeling period. As a result, the common mode voltage is held constant which makes it suitable for the grid-connected PV system. The operating principle and CM effect of the proposed topology are analysed and compared with the conventional topologies. This is followed by the thermal analysis and loss calculation, which shows that the proposed circuit is more efficient over the conventional topologies. Validation is carried out using MATLAB-Simulink using the PLECS toolbox followed by a scale down prototype of 1.5 kW. It is shown that the proposed inverter has the 98±1% efficiency over a wide range of loads with a peak efficiency of 98.96%, and the total harmonic distortion of the output current relatively low (≤1.8 %). The leakage current (icm) is measured for different values of parasitic capacitance that reaches a maximum of 16.65 mA for 330 nF capacitor under consideration which is well below the limit set by different safety standards

Khanzada, NK, Farid, MU, Kharraz, JA, Choi, J, Tang, CY, Nghiem, LD, Jang, A & An, AK 2020, 'Removal of organic micropollutants using advanced membrane-based water and wastewater treatment: A review', Journal of Membrane Science, vol. 598.
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© 2019 Elsevier B.V. The rising consumption of pharmaceuticals, personal care products, and endocrine disruptive compounds for healthcare purposes and improving living standards has resulted in the widespread occurrence of organic micropollutants (MPs) in water and wastewater. Conventional water/wastewater treatment plants are faced with inherent limitations in tackling these compounds, leading to difficulties in the provision of secure and safe water supplies. In this context, membrane technology has been found to be a promising method for resolving this emerging concern. To ensure the suitability of membrane-based treatment processes in full-scale applications, we first need to develop a better understanding of the behavior of MPs and the mechanisms behind their removal using advanced membrane technologies. This review provides a thorough overview of the advanced membrane-based treatment methods available for the effective removal of MPs, including reverse osmosis, nanofiltration, ultrafiltration, forward osmosis, and membrane distillation.

Khari, M, Garg, AK, Gandomi, AH, Gupta, R, Patan, R & Balusamy, B 2020, 'Securing Data in Internet of Things (IoT) Using Cryptography and Steganography Techniques', IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, vol. 50, no. 1, pp. 73-80.
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Khuat, TT & Gabrys, B 2020, 'A comparative study of general fuzzy min-max neural networks for pattern classification problems', Neurocomputing, vol. 386, pp. 110-125.
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© 2019 Elsevier B.V. General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks formed by hyperbox fuzzy sets for classification and clustering problems. Two principle algorithms are deployed to train this type of neural network, i.e., incremental learning and agglomerative learning. This paper presents a comprehensive empirical study of performance influencing factors, advantages, and drawbacks of the general fuzzy min-max neural network on pattern classification problems. The subjects of this study include (1) the impact of maximum hyperbox size, (2) the influence of the similarity threshold and measures on the agglomerative learning algorithm, (3) the effect of data presentation order, (4) comparative performance evaluation of the GFMM with other types of fuzzy min-max neural networks and prevalent machine learning algorithms. The experimental results on benchmark datasets widely used in machine learning showed overall strong and weak points of the GFMM classifier. These outcomes also informed potential research directions for this class of machine learning algorithms in the future.

Kim, D, Nguyen, LN & Oh, S 2020, 'Ecological impact of the antibiotic ciprofloxacin on microbial community of aerobic activated sludge.', Environmental geochemistry and health.
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This study investigated the effects and fate of the antibiotic ciprofloxacin (CIP) at environmentally relevant levels (50-500 µg/L) in activated sludge (AS) microbial communities under aerobic conditions. Exposure to 500 µg/L of CIP decreased species diversity by about 20% and significantly altered the phylogenetic structure of AS communities compared to those of control communities (no CIP exposure), while there were no significant changes upon exposure to 50 µg/L of CIP. Analysis of community composition revealed that exposure to 500 µg/L of CIP significantly reduced the relative abundance of Rhodobacteraceae and Nakamurellaceae by more than tenfold. These species frequently occur in AS communities across many full-scale wastewater treatment plants and are involved in key ecosystem functions (i.e., organic matter and nitrogen removal). Our analyses showed that 50-500 µg/L CIP was poorly removed in AS (about 20% removal), implying that the majority of CIP from AS processes may be released with either their effluents or waste sludge. We therefore strongly recommend further research on CIP residuals and/or post-treatment processes (e.g., anaerobic digestion) for waste streams that may cause ecological risks in receiving water bodies.

King, JT, Prasad, M, Tsai, T, Ming, YR & Lin, CT 2020, 'Influence of Time Pressure on Inhibitory Brain Control During Emergency Driving', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE It is believed that failures of people's reaction to emergencies occurred during driving are closely related to the inhibitory mechanism of brain's operations. To investigate the role of this function in emergency driving, two virtual realistic driving conditions based on stop signal task were designed and time limitation was manipulated to increase the stress in one condition. Sixteen subjects with behavioral encephalography recordings were collected and analyzed. By comparing successful and unsuccessful stop trials with event-related spectral perturbation analysis, δ and θ band power increases in frontal and central areas are correlated with driving inhibitory control of the brain. Moreover, β and ɣ band power in frontal and central areas showed more increases upon stress condition. Time pressure in driving could adjust the operation of brain's inhibition control, to benefit the people's reactive ability upon emergency.

Kishore Kumar, D, Loskot, J, Kříž, J, Bennett, N, Upadhyaya, HM, Sadhu, V, Venkata Reddy, C & Reddy, KR 2020, 'Synthesis of SnSe quantum dots by successive ionic layer adsorption and reaction (SILAR) method for efficient solar cells applications', Solar Energy, vol. 199, pp. 570-574.
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Kong, FH, Zhao, J, Zhao, L & Huang, S 2020, 'Analysis of Minima for Geodesic and Chordal Cost for a Minimal 2-D Pose-Graph SLAM Problem', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 323-330.
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© 2016 IEEE. In this letter, we show that for a minimal 2D pose-graph SLAM problem, even in the ideal case of perfect measurements and spherical covariance, using geodesic distance (in 2D, the 'wrap function') to compare angles results in multiple suboptimal local minima. We numerically estimate regions of attraction to these local minima for some examples, give evidence to show that they are of nonzero measure, and that these regions grow in size as noise is added. In contrast, under the same assumptions, we show that the chordal distance representation of angle error has a unique minimum up to periodicity. For chordal cost, we find that initial conditions failing to converge to the global minimum are far fewer, fail because of numerical issues, and do not seem to grow with noise in our examples.

Laccone, F, Malomo, L, Froli, M, Cignoni, P & Pietroni, N 2020, 'Automatic Design of Cable-Tensioned Glass Shells', Computer Graphics Forum.
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© 2019 The Authors Computer Graphics Forum © 2019 The Eurographics Association and John Wiley & Sons Ltd. We propose an optimization algorithm for the design of post-tensioned architectural shell structures, composed of triangular glass panels, in which glass has a load-bearing function. Due to its brittle nature, glass can fail when it is subject to tensile forces. Hence, we enrich the structure with a cable net, which is specifically designed to post-tension the shell, relieving the underlying glass structure from tension. We automatically derive an optimized cable layout, together with the appropriate pre-load of each cable. The method is driven by a physically based static analysis of the shell subject to its service load. We assess our approach by applying non-linear finite element analysis to several real-scale application scenarios. Such a method of cable tensioning produces glass shells that are optimized from the material usage viewpoint since they exploit the high compression strength of glass. As a result, they are lightweight and robust. Both aesthetic and static qualities are improved with respect to grid shell competitors.

Lai, S, Fan, X, Ye, Q, Tan, Z, Zhang, Y, He, X & Nanda, P 2020, 'FairEdge: A Fairness-Oriented Task Offloading Scheme for Iot Applications in Mobile Cloudlet Networks', IEEE Access, vol. 8, pp. 13516-13526.
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Lalbakhsh, A, Afzal, MU, Esselle, KP & Smith, SL 2020, 'Low-Cost Non-Uniform Metallic Lattice for Rectifying Aperture Near-Field of Electromagnetic Bandgap Resonator Antennas', IEEE Transactions on Antennas and Propagation.
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IEEE This paper addresses a critical issue, which has been overlooked, in relation to the design of Phase Correcting Structures (PCSs) for Electromagnetic Bandgap (EBG) Resonator Antennas (ERAs). All previously proposed PCSs for ERAs are made either using several expensive Radio Frequency (RF) dielectric laminates, or thick and heavy dielectric materials, contributing to very high fabrication cost, posing an industrial impediment to the application of ERAs. This paper presents a new industrialfriendly generation of PCS, in which dielectrics, known as the main cause of high manufacturing cost, are removed from the PCS configuration, introducing an All-Metallic PCS (AMPCS). Unlike existing PCSs, a hybrid topology of fully-metallic spatial phase shifters are developed for the AMPCS, resulting in an extremely lower prototyping cost as that of other state-of-theart substrate-based PCSs. The APMCS was fabricated using laser technology and tested with an ERA to verify its predicted performance. Results show that the phase uniformity of the ERA aperture has been remarkably improved, resulting in 8.4 dB improvement in the peak gain of the antenna and improved sidelobe levels (SLLs). The antenna system including APMCS has a peak gain of 19.42 dB with a 1-dB gain bandwidth of around 6%.

Lam, SC & Sandrasegaran, K 2020, 'Fractional Frequency Reuse in Multi-tier Networks: Performance Analysis and Optimization', International Journal of Wireless Information Networks, vol. 27, no. 1, pp. 164-183.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Frequency Reuse (FR) is an efficient approach to improve the network performance in a multi-cell cellular network. In this paper, we investigate the performance of heterogeneous networks utilising two well-known frequency reuse algorithms, called Strict FR and Soft FR, with a reuse factor of Δ (Δ> 1). Based on a two-phase operation of the FR algorithm, we develop a new approach to analyse cellular networks based on the Poisson Point Process (PPP) model which successfully demonstrates the impact of the network parameters (such as the number of allocated Resource Blocks (RBs) and bias factor) and FR parameters (such as Base Station (BS) transmit power) on the performance of a user and overall network. Compared to related works, we propose the following novel approaches: (i) we investigate flexible FR networks in which users have connections with the BSs which deliver the highest performance; (ii) the BS observes SINR on the data channel to classify each user into either a Cell-Edge User (CEU) or Cell-Center User (CCU); (iii) the initial state of the network is considered to establish the initial network interference when new users arrive and request connections to the BSs. In the case of a single-tier network, it is proved that our analytical approach is more accurate than previous works. In the case of a two-tier network, the analytical results indicate that compared to the 3GPP model, our proposed model not only reduces up to 40.79% and 3.8% power consumption of a BS on the data channel but also achieves 16.08% and 18.63% higher data rates in the case of Strict FR and Soft FR respectively. Furthermore, the paper presents an approach to find an optimal value of SINR thresholds and bias factor to achieve the maximum network performance.

Lama, S, Pradhan, S & Shrestha, A 2020, 'Exploration and implication of factors affecting e-tourism adoption in developing countries: a case of Nepal', Information Technology and Tourism, vol. 22, no. 1, pp. 5-32.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. E-tourism plays a pivotal role in delivering services to tourists by enhancing travel experiences. While small tourism service providers in the developed economies can efficiently take advantage of e-tourism by partnering with global online platforms such as TripAdvisor or Booking.com, small tourism service providers in developing countries often struggle to reach global markets due to factors that are unique to the country they operate in. The aim of this study, therefore, is to identify the key barriers and motivators of e-tourism adoption for small and medium tourism enterprises (SMTEs) in developing countries. Nepal is selected as the case study due to its enormous tourism potential that is plagued by typical challenges. Based on the ‘Technology, Organization and Environment’ framework and ‘e-readiness’ model, this study identified ten key factors that affect e-tourism adoption by SMTEs in Nepal. A mixed-method approach, using interviews with seven key stakeholders and a survey with 198 SMTEs, were employed for data collection and validation of the proposed factors. Finally, an e-tourism adoption model highlighting the barriers and motivators for e-tourism by SMTEs is presented. This research found that e-tourism adoption by SMTEs in Nepal is affected by environmental factors related to national infrastructure, market size, country-specific contextual factors and organizational factors comprising e-tourism awareness, ICT resources, value proposition, and top management support. This study offers implications for policy and practice towards effective e-tourism adoption in other developing countries.

Lau, AKS, Bilad, MR, Nordin, NAHM, Faungnawakij, K, Narkkun, T, Wang, DK, Mahlia, TMI & Jaafar, J 2020, 'Effect of membrane properties on tilted panel performance of microalgae biomass filtration for biofuel feedstock', RENEWABLE & SUSTAINABLE ENERGY REVIEWS, vol. 120.
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Le Gentil, C, Vidal-Calleja, T & Huang, S 2020, 'Gaussian Process Preintegration for Inertial-Aided State Estimation', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 2108-2114.
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Le, AT, Tran, LC, Huang, X & Guo, YJ 2020, 'Analog Least Mean Square Loop for Self-Interference Cancellation: A Practical Perspective', SENSORS, vol. 20, no. 1.
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Le, AT, Tran, LC, Huang, X & Guo, YJ 2020, 'Beam-Based Analog Self-Interference Cancellation in Full-Duplex MIMO Systems', IEEE Transactions on Wireless Communications, pp. 1-1.
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Lee, D, Woo, YC, Park, KH, Phuntsho, S, Tijing, LD, Yao, M, Shim, W-G & Shon, HK 2020, 'Polyvinylidene fluoride phase design by two-dimensional boron nitride enables enhanced performance and stability for seawater desalination', JOURNAL OF MEMBRANE SCIENCE, vol. 598.
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Lee, SS, Lim, CS, Siwakoti, Y & Lee, K-B 2020, 'Dual-T-Type 5-Level Cascaded Multilevel Inverter (DTT-5L-CMI) with Double Voltage Boosting Gain', IEEE Transactions on Power Electronics, pp. 1-1.
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Lee, SS, Siwakoti, YP, Lim, CS & Lee, K-B 2020, 'An Improved PWM Technique to Achieve Continuous Input Current in Common-Ground Transformerless Boost Inverter', IEEE Transactions on Circuits and Systems II: Express Briefs, pp. 1-1.
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Li, C, Zhang, Y, Zhang, Y, Mao, G & Jia, C 2020, 'Task Assignment Strategy for Platoons in Cooperative Driving', Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, vol. 42, no. 1, pp. 65-73.
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© 2020, Science Press. All right reserved. Autonomous vehicles are equipped with multiple on-board sensors to achieve self-driving functions. However, a tremendous amount of data is generated by autonomous vehicles, which significantly challenges the real-time task processing. Through multiple-vehicle cooperation, which makes the best of vehicle onboard computing resources, autonomous and cooperative driving becomes a promising candidate to solve the aforementioned problem. In this case, it is vital for autonomous and cooperative driving to form a driving platoon and allocate driving tasks efficiently. In this paper, a more general analytical model is developed based on G/G/1 queueing theory to model the topology of platoons. Next, Support Vector Machine (SVM) method is adopted to classify the “idle” and “busy” categories of the vehicles in the platoon based on their computing load and task processing capacity. Finally, based on the analysis above, an efficient task balancing strategy of platoons in autonomous and cooperative driving called Classification based Greed Balancing Strategy (C-GBS) is proposed, in order to balance the task burden among vehicles and cooperate more efficiently. Extensive simulations demonstrate that the proposed technique can reduce the processing delay of driving tasks in platoons with high computing load, which will improve the processing efficiency in autonomous vehicles.

Li, H, Li, Y & Li, J 2020, 'Negative stiffness devices for vibration isolation applications: A review', ADVANCES IN STRUCTURAL ENGINEERING.
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Li, J, Liu, M, Xue, Z, Fan, X & He, X 2020, 'RTVD: A Real-Time Volumetric Detection Scheme for DDoS in the Internet of Things', IEEE Access, vol. 8, pp. 36191-36201.
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Li, J, Zhu, X, Law, S-S & Samali, B 2020, 'Time-varying characteristics of bridges under the passage of vehicles using synchroextracting transform', Mechanical Systems and Signal Processing, vol. 140, pp. 106727-106727.
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Li, K, Liu, AX & Yu, S 2020, 'Special issue on natural computation, fuzzy systems and knowledge discovery from the ICNC&FSKD 2017', Neurocomputing.
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Li, L, Ju, N, He, C, Li, C & Sheng, D 2020, 'A computationally efficient system for assessing near-real-time instability of regional unsaturated soil slopes under rainfall', Landslides.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. The objective of this paper is to obtain an applicable assessment method and a Web-GIS-based prediction system for regional landslides. The traditional Richards function is reconstructed using the soil-water characteristic curve (SWCC) and the coordinate transformation technique. The analytical pore pressure for a slope model is derived by solving the modified Richard equation via the Green function and Fourier transformation. The obtained transient pore pressure field is then incorporated with Brakensiek’s matric suction theory, to build a conceptual model for rainfall-induced shallow landslides. The safety factor is obtained by solving the limit equilibrium equation of the conceptual model. The method is then implemented in a Web-GIS system, considering influence of slope geometry features, geology parent material, and near-real-time rainfall intensity of the study area. It is verified that this method is computationally efficient and reliable for gentle slopes and short rainfall durations. Moreover, an extensive parameter study shows that the two commonly used coefficients in the intensity-duration equation are both correlated to rainfall inter-event time via exponential functions, and rainfall event time via power functions. The primary influential factor for regional landslides is the initial water content, followed by the rainfall duration and intensity, and least by soil thickness.

Li, L, Song, K, Yeerken, S, Geng, S, Liu, D, Dai, Z, Xie, F, Zhou, X & Wang, Q 2020, 'Effect evaluation of microplastics on activated sludge nitrification and denitrification', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 707.
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Li, P, Gao, X, Wang, K, Tam, VWY & Li, W 2020, 'Hydration mechanism and early frost resistance of calcium sulfoaluminate cement concrete', Construction and Building Materials, vol. 239.
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© 2019 Elsevier Ltd This study investigated the hydration mechanism and mechanical properties of ordinary Portland cement (OPC) blended with calcium sulfoaluminate (CSA) cement. Heat evolution, hydration products, pore size distribution, and microstructure were investigated for OPC-CSA blends concrete with different contents of CSA cement. Macroscopic properties, such as internal temperature, dynamic elastic modulus, and compressive strength, are also studied through concrete subjected to early frost conditions. The results show that the OPC-CSA blended cement displayed a higher early strength and exhibited enhanced resistance to the early frost damage compared to OPC. The OPC-CSA blended cement also exhibits a higher hydration rate and a larger amount of heat of hydration than that in the OPC at the early stage. The increased heat of hydration can effectively prolong the hydration duration at sub-zero temperatures. However, incorporating CSA delayed the hydration of C3S at the late stage, thus affecting the development of compressive strength and dynamic elastic modulus. On the other hand, the hardened blended cement exhibited an higher porosity, which was corresponding to the increasing proportion of macropores (diameter over 1000 nm). If concrete directly is suffered from early frost after casting, blended cement with 20% of CSA can effectively reduce strength loss from frost damage by 100% at −5 °C, and that from frost damage by 80% at −15 °C respectively. Furthermore, when the calcium nitrite is incorporated as the antifreeze admixture with OPC-CSA blended concrete, the early stage frost resistance of concrete infrastructures can be significantly improved.

Li, Q, Wu, D, Gao, W & Tin-Loi, F 2020, 'Size-dependent instability of organic solar cell resting on Winkler–Pasternak elastic foundation based on the modified strain gradient theory', International Journal of Mechanical Sciences, vol. 177, pp. 105306-105306.
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Li, R, Wu, B, Ying, M, Sun, X & Yang, G 2020, 'Quantum Supremacy Circuit Simulation on Sunway TaihuLight', IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, vol. 31, no. 4, pp. 805-816.
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Li, W, Ji, J & Huang, L 2020, 'Global dynamic behavior of a predator–prey model under ratio-dependent state impulsive control', Applied Mathematical Modelling, vol. 77, pp. 1842-1859.
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© 2019 This paper studies the global dynamic behavior of a prey–predator model with square root functional response under ratio-dependent state impulsive control strategy. It is shown that the boundary equilibrium point of the controlled system is globally asymptotically stable. An order-k periodic orbit is obtained by employing the Brouwer's fixed point theorem. Furthermore, the critical values are determined for the existence of orbitally asymptotically stable order-1 and order-2 periodic orbits in finite time. These critical values play an important role in determining different kinds of order-k periodic orbits and can also be used for designing the control parameters to obtain the desirable dynamic behavior of the controlled prey–predator system. Moreover, it is found that the local equilibrium point is also globally asymptotically stable under the control strategy. Numerical examples are provided to validate the effectiveness and feasibility of the theoretical results.

Li, W, Ji, J, Huang, L & Wang, J 2020, 'Bifurcations and dynamics of a plant disease system under non-smooth control strategy', NONLINEAR DYNAMICS.
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Li, X, Chen, L, Ji, Y, Li, M, Dong, B, Qian, G, Zhou, J & Dai, X 2020, 'Effects of chemical pretreatments on microplastic extraction in sewage sludge and their physicochemical characteristics.', Water research, vol. 171, pp. 115379-115379.
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Sewage sludge is a primary pathway for microplastics (MPs) entering into terrestrial ecosystems. However, a standardized method to analyze MP in sludge is lacking due to its high organic matter. This study investigated the extraction efficiency of six MPs in five solid matrices, i.e. sewage sludge, cattle manure, soil, sediment and silicon dioxide. Results show lower extraction efficiency of 87.2% for MPs in sludge compared with that in other matrices, especially polyethylene terephthalate (PET) (only 27.8%). The possible reason was that the presence of extracellular polymeric substances within the sludge hinders the MPs to float. Therefore, five protocols, i.e. hydrogen peroxide (H2O2), Fenton, nitric acid (HNO3), hydrochloric acid (HCl) and sodium hydroxide (NaOH) were used to pretreat the sludge and optimize the MP extraction. The sludge pretreated by H2O2, Fenton and 1 M of acids had higher MP extraction efficiency than the raw sludge due to higher extraction of the PET. The MP extraction efficiency in the sludge first increased, and subsequently decreased with the soluble chemical oxygen demand (SCOD) content, implying that moderate dissolution of sludge organic matter is beneficial to the MP extraction. Quantitative analysis of the changes in the MP physicochemical characteristics after the pretreatments indicated that polyamide (PA) and PET are not resistant to acid and alkali treatment, respectively. Principal component analysis shows that the effect of pretreatments on the MPs follows a decreasing sequence: alkali > high concentration of acids > low concentration of acids > H2O2 and Fenton. Additionally, the susceptibility of the MPs to the pretreatments follows a decreasing sequence: PET, PA and polymethyl methacrylate (PMMA) > polystyrene (PS) > polyethylene (PE) and polypropylene (PP). The findings supply novel insights into the effect of chemical pretreatments on MP extraction in sewage sludge.

Li, X, Ji, M, Nghiem, LD, Zhao, Y, Liu, D, Yang, Y, Wang, Q, Trinh, QT, Vo, DVN, Pham, VQ & Tran, NH 2020, 'A novel red mud adsorbent for phosphorus and diclofenac removal from wastewater', Journal of Molecular Liquids, vol. 303.
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© 2019 Elsevier B.V. The ubiquitous occurrence of nutrients (e.g. phosphorus) and micropollutants (e.g. pharmaceuticals and endocrine disrupting chemicals) in wastewater and urban stormwater runoff sources may cause adverse effects on aquatic ecosystems and human health. Therefore, the removal of these pollutants from wastewater, treated effluent, and urban stormwater runoff is critically needed. In this study, a novel modified red mud with polypyrrole (RM-PPy) was successfully synthesized with improved functional groups (–OH, –N=, –NH–, N+), specific area (SBET 102.24 m2/g), and mesopore structure (i.e. average pore diameter of 3.29 nm), which are assumed to enhance the adsorptive removal of diclofenac (DCF) and phosphorus (P) in aqueous solution. The measured maximum adsorption capacity of RM-PPy towards diclofenac (195 mg/g) in single adsorbate system was higher than that (115.7 mg/g) in the binary adsorbates system (i.e. in the presence of P), indicating that the presence of pollutants such as P in water hampered the adsorptive removal of DCF. The adsorption of DCF and P was largely dependent on solution pH values. Higher adsorptive removals of DCF and P were observed at acidic conditions (pH 2–5). Adsorption isotherm of DCF and P was better fitted to Freundlich model compared to Langmuir isotherm model, suggesting multilayer coverage. Adsorption of DCF onto RM-PPy might take place via anion exchange and electrostatic interactions. For P adsorption, apart from anion exchange and electrostatic interactions, the chemical precipitation via ligand exchange between P and hydroxyl (–OH) in RM-PPy can be considered as one of the main adsorption mechanisms. Further studies on the competitive adsorption of other anionic micropollutants at environmentally relevant concentrations (ng/L–μg/L) in water samples by RM-PPy are needed to evaluate the potential application of RM-PPy for the removal of other anionic micropollutants (i.e. antibiotics) in treated wastewater and s...

Li, Y, Wang, D, Yang, G, Yuan, X, Xu, Q, Yang, Q, Liu, Y, Wang, Q, Ni, B-J, Tang, W & Jiang, L 2020, 'Enhanced dewaterability of anaerobically digested sludge by in-situ free nitrous acid treatment', WATER RESEARCH, vol. 169.
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Li, Y, Zhu, J, Zhu, L, Li, Y & Lei, G 2020, 'A Dynamic Magnetostriction Model of Grain-Oriented Sheet Steels Based on Becker-Döring Crystal Magnetization Model and Jiles-Atherton Theory of Magnetic Hysteresis', IEEE Transactions on Magnetics, vol. 56, no. 3.
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© 1965-2012 IEEE. Grain-oriented (GO) sheet steels are widely used as the core material in large power transformers. The strong magnetostriction in GO sheet steels, however, causes undesirable vibrations and acoustic noises, and is difficult to estimate in the design optimization of power transformers. This article proposes a new magnetostriction model of GO sheet steels by combining the Becker-Döring crystal model and the dynamic Jiles-Atherton hysteresis model. Incorporated in the finite-element analysis (FEA), the new model can correctly simulate the butterfly loops of magnetostriction in GO sheet steels. The effectiveness and accuracy of the proposed model are verified by the experimental measurement results on a single sheet sample in a free state with no external stress.

Li, Y, Zhu, Y, Wang, D, Yang, G, Pan, L, Wang, Q, Ni, BJ, Li, H, Yuan, X, Jiang, L & Tang, W 2020, 'Fe(II) catalyzing sodium percarbonate facilitates the dewaterability of waste activated sludge: Performance, mechanism, and implication', Water Research, vol. 174.
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© 2020 Elsevier Ltd In this work, Fe(II) catalyzing sodium percarbonate (Fe(II)/SPC) was managed to facilitate waste activated sludge (WAS) dewatering for the first time. The results showed that after WAS was treated by 20 mg/g total suspended solids (TSS) Fe(II) and 50 mg/g TSS SPC, the water content of sludge cake (WCSC) by press filtration and capillary suction time (CST) dropped from 90.8% ± 1.6% and 96.1 ± 4.0 s (the control) to 55.6% ± 1.4% and 30.1 ± 2.5 s, respectively. The mechanism investigations indicated that four intermediates or products (i.e., •OH, H2O2, Fe(II), and Fe(III)) generated in the Fe(II)/SPC process were responsible for the improved WAS dewaterability, and •OH and Fe(III) were the two major contributors. It was found that •OH collapsed and fragmented extracellular polymeric substances, damaged cell wall and permeabilized cytoplasmic membrane, and transformed conformation of the extracellular proteins secondary structure via both affecting the hydrogen bond maintaining α-helix and cracking disulfide bond in cysteine residues while Fe(III), the oxidization product of Fe(II), decreased the surface electronegativity and water-affinity surface areas of WAS flocs. As a result, the bound water release, flocculability, surface hydrophobicity, drain capability, and flowability of WAS flocs were strengthened whereas the compact surface structure, colloidal forces, network strength, gel-like structure, and apparent viscosity of WAS flocs were weakened. In addition, Fe(II)/SPC process also reduced the recalcitrant organics and fecal coliforms in sludge, which facilitated land application of dewatered sludge. The findings acquired in this work not only deepens our understanding of Fe(II)/SPC-involved WAS treatment process but also may guide engineers to develop both effective and promising strategies to better condition WAS for dewatering in the future.

Li, Z, Tao, M, Du, K, Cao, W & Wu, C 2020, 'Dynamic stress state around shallow-buried cavity under transient P wave loads in different conditions', Tunnelling and Underground Space Technology, vol. 97.
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© 2019 Elsevier Ltd The failure of underground structure is always caused by local stress redistribution resulted from transient impact loads. An analytical model is established and solved by using the complex variable function method to illustrate the dynamic stress concentration around a shallow-buried cavity under transient P wave loads. The transient wave response is attained as an integration of steady-state wave response in circular frequency domain. By adopting a Butterworth filter, the jump points in the dynamic stress concentration factor (DSCF) curve which is not in line with the overall trend is filtered out to obtain more reasonable results. The convergence and accuracy of the solutions are verified and discussed in detail. DSCF distributions of high wavenumber incidences differ from that of low wavenumber incidences significantly in amplitudes and distribution patterns. The effects of the cavity depth, incidence angle and position of wave peak on DSCF distributions are illustrated.

Lian, J-W, Ban, Y-L, Zhu, H & Guo, YJ 2020, 'Reduced-Sidelobe Multibeam Array Antenna Based on SIW Rotman Lens', IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 19, no. 1, pp. 188-192.
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Liang, Q, Wu, W, Yang, Y, Zhang, R, Peng, Y & Xu, M 2020, 'Multi-player tracking for multi-view sports videos with improved K-shortest path algorithm', Applied Sciences (Switzerland), vol. 10, no. 3.
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© 2020 by the authors. Sports analysis has recently attracted increasing research efforts in computer vision. Among them, basketball video analysis is very challenging due to severe occlusions and fast motions. As a typical tracking-by-detection method, k-shortest paths (KSP) tracking framework has been well used for multiple-person tracking. While effective and fast, the neglect of the appearance model would easily lead to identity switches, especially when two or more players are intertwined with each other. This paper addresses this problem by taking the appearance features into account based on the KSP framework. Furthermore, we also introduce a similarity measurement method that can fuse multiple appearance features together. In this paper, we select jersey color and jersey number as two example features. Experiments indicate that about 70% of jersey color and 50% of jersey number over a whole sequence would ensure our proposed method preserve the player identity better than the existing KSP tracking method.

Lin, BJ, Chen, WH, Lin, YY, Chang, JS, Farooq, A, Singh, Y, Ong, HC & Show, PL 2020, 'An evaluation of thermal characteristics of bacterium Actinobacillus succinogenes for energy use and circular bioeconomy', Bioresource Technology, vol. 301.
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© 2020 Elsevier Ltd The thermal characteristics of Actinobacillus succinogenes (AS) from pyrolysis, torrefaction, and combustion are analyzed to evaluate the potential of this biomass as a renewable fuel. AS pyrolysis can be classified into four stages, and its main decomposition zone is at 200–500 °C. The solid yield of AS after 60 min torrefaction is over 60 wt%, and the torrefaction severity index map indicates that a high torrefaction temperature with a short duration has a more profound influence on its decomposition. The Py-GC/MS analysis of AS suggests that the volatile products from 500 °C pyrolysis are similar to microalgae-derived pyrolysis bio-oils. The combustibility index (S) of AS is 4.07 × 10−7 which is much higher than that of lignite coal (0.39 × 10−7) and bituminous coal (0.18 × 10−7), and close to those of biochar and bio-oil. The obtained results are conducive to the development of microorganisms as fuel to achieve a circular bioeconomy.

Lin, C-T, King, J-T, Chuang, C-H, Ding, W, Chuang, W-Y, Liao, L-D & Wang, Y-K 2020, 'Exploring the Brain Responses to Driving Fatigue Through Simultaneous EEG and fNIRS Measurements.', International Journal of Neural Systems, vol. 30, no. 1.
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Fatigue is one problem with driving as it can lead to difficulties with sustaining attention, behavioral lapses, and a tendency to ignore vital information or operations. In this research, we explore multimodal physiological phenomena in response to driving fatigue through simultaneous functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) recordings with the aim of investigating the relationships between hemodynamic and electrical features and driving performance. Sixteen subjects participated in an event-related lane-deviation driving task while measuring their brain dynamics through fNIRS and EEGs. Three performance groups, classified as Optimal, Suboptimal, and Poor, were defined for comparison. From our analysis, we find that tonic variations occur before a deviation, and phasic variations occur afterward. The tonic results show an increased concentration of oxygenated hemoglobin (HbO2) and power changes in the EEG theta, alpha, and beta bands. Both dynamics are significantly correlated with deteriorated driving performance. The phasic EEG results demonstrate event-related desynchronization associated with the onset of steering vehicle in all power bands. The concentration of phasic HbO2 decreased as performance worsened. Further, the negative correlations between tonic EEG delta and alpha power and HbO2 oscillations suggest that activations in HbO2 are related to mental fatigue. In summary, combined hemodynamic and electrodynamic activities can provide complete knowledge of the brain's responses as evidence of state changes during fatigue driving.

Lin, C-T, Yu, Y-H, King, J-T, Liu, C-H & Liao, L-D 2020, 'Augmented Wire-Embedded Silicon-Based Dry-Contact Sensors for Electroencephalography Signal Measurements', IEEE Sensors Journal, vol. 20, no. 7, pp. 3831-3837.
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Lin, J, Sun, G, Cui, T, Shen, J, Xu, D, Beydoun, G, Yu, P, Pritchard, D, Li, L & Chen, S 2020, 'From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning', World Wide Web.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.

Lin, W & Ziolkowski, RW 2020, 'Electrically Small Huygens CP Rectenna With a Driven Loop Element Maximizes Its Wireless Power Transfer Efficiency', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 1, pp. 540-545.
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Liu, A, Lu, J & Zhang, G 2020, 'Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Liu, C, Zhu, L, He, X & Chen, J 2020, 'Enabling Privacy-Preserving Shortest Distance Queries on Encrypted Graph Data', IEEE Transactions on Dependable and Secure Computing.
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IEEE When coming to perform shortest distance queries on encrypted graph data outsourced in external storage infrastructure such as cloud, a significant challenge is how to compute the shortest distance in an accurate, efficient and secure way. This issue is addressed by a recent work, which makes use of somewhat homomorphic encryption (SWHE) to encrypt distance values output by a 2-hop cover labeling (2HCL) scheme. However, it may import large errors and even yield negative results. Besides, SWHE would be too time-consuming for normal clients. In this paper, we propose GENOA, a novel Graph ENcryption scheme for shOrtest distAnce queries. GENOA employs only efficient symmetric-key primitives while significantly enhances the accuracy compared to the prior work. As a reasonable trade-off, it additionally reveals the order information among queried distance values in the 2HCL index. We theoretically prove the accuracy and security of GENOA under rigorous cryptographic model. Detailed experiments on eight real-world graphs demonstrate that GENOA is efficient and can produce almost exact results.

Liu, C, Zowghi, D & Talaei-Khoei, A 2020, 'An empirical study of the antecedents of data completeness in electronic medical records', International Journal of Information Management, vol. 50, pp. 155-170.
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© 2019 Elsevier Ltd There is a body of research that highlights the role of data management to improve the quality of data, which in return improves organizational performance. The literature in data management has indicated the five theoretical constructs used to understand the factors influencing data quality, including top management support, capability on the regulation and process management, business-IT alignment, staff participation, and integration of information systems. However, it is unclear how these theoretical constructs can be utilized to understand the antecedents of data completeness as a dimension of data quality. Following that stream of research, the current paper examines the factors influencing data completeness in electronic medical records (EMR). The scope of this study is by only surveying medical professionals at healthcare settings in northern Nevada. The empirical results reveal that resources should be added as one of the antecedents of data completeness in EMR.

Liu, F, Zhang, G & Lu, J 2020, 'Heterogeneous Domain Adaptation: An Unsupervised Approach', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Liu, K, Li, Q, Wu, C, Li, X & Li, J 2020, 'Optimization of spherical cartridge blasting mode in one-step raise excavation using pre-split blasting', International Journal of Rock Mechanics and Mining Sciences, vol. 126.
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© 2019 Elsevier Ltd In one-step raise excavation, spherical cartridge blasting mode is easy to be implemented due to minimal requirement of the hole-deviation. Nevertheless, it may induce cumulative damage to the country rock. This study firstly uses a validated rock model (Johnson-Holmquist model) to simulate the damage evolution process of a raise by spherical cartridge blasting mode in LS-DYNA software. Then a field test is carried out to examine the numerical results of spherical cartridge blasting mode. Both the numerical and test results indicate that because of highly confined rock mass and restricted free face in deep raise, a large charge is required in spherical cartridge blasting mode which leads to extensive damage on the wall of the raise. In order to solve such a problem, the pre-split blasting technique is developed to optimize spherical cartridge blasting mode. According to the subsequent numerical results, the improved spherical cartridge blasting mode is successfully applied in another filling raise. This study provides an effective solution to the difficulties that are encountered in one-step raise excavation by spherical cartridge blasting mode.

Liu, P, Tang, Q, Su, S, Hu, J & Yu, Y 2020, 'Modeling and Analysis of the Two-Dimensional Axisymmetric Acoustofluidic Fields in the Probe-Type and Substrate-Type Ultrasonic Micro/Nano Manipulation Systems', MICROMACHINES, vol. 11, no. 1.
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Liu, Q, Liufu, K, Cui, Z, Li, J, Fang, J & Li, Q 2020, 'Multiobjective optimization of perforated square CFRP tubes for crashworthiness', Thin-Walled Structures, vol. 149.
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© 2020 Open holes and cutouts have been widely used in composite structures for various engineering purposes. However, perforation could largely alter the structural responses and crashing behavior, thereby creating significant challenge in composite design. This study carried out a parametric study and design optimization on the perforation parameters for the perforated carbon fiber reinforced plastic (CFRP) square tubes to improve the structural crashworthiness. First, a finite element model was established for perforated square tube through experimental validation. Second, the support vector regression (SVR) surrogate models of the crashworthiness indices were established with respect to the perforation design variables following a parametric study. It was found that the radius of the hole had the most significant effect on crashworthiness performance of the CFRP tube, followed by the height-to-length ratio and the offset-to-width ratio of the hole. Finally, the multiobjective optimization was performed to optimize the perforated parameters by integrating the surrogate modeling technique and multiobjective grey wolf optimizer (MOGWO). The optimal design enabled to enhance 100.47% in the specific energy absorption (SEA) (specifically from 29.61 J/g to 59.36 J/g) only with a minor sacrifice of 4.62% in the first peak load (Fmax) (from 81.22 kN to 84.97 kN).

Liu, T, Zhang, W, Yuwono, M, Zhang, M, Ueland, M, Forbes, SL & Su, SW 2020, 'A data-driven meat freshness monitoring and evaluation method using rapid centroid estimation and hidden Markov models', Sensors and Actuators, B: Chemical, vol. 311.
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© 2020 Elsevier B.V. The food industry needs a fast and stable method to monitor and evaluate the freshness of meat products. This paper proposes the use of a metal-oxide-semiconductor (MOS) sensor-based electronic nose with a data-driven approach, hidden Markov models (HMMs), to detect meat spoilage. The data-driven approach we proposed include: (i) A training algorithm using segmental rapid centroid estimation (RCE) to increase the overall stability of the HMM optimisation algorithm; (ii) A monitoring algorithm using a single HMM trained only by fresh samples to track the change of meat freshness in real-time when the storage condition is not specified; (iii) An HMM-based decoding algorithm to cluster the freshness level when the whole life-span data of meat stored in a specific storage condition is available. Then, HMM for each freshness level is trained and applied in parallel as freshness evaluation models to classify the tested meat samples. In freshness monitoring, case studies on fish, beef and chicken demonstrate the effectiveness and potential of this method. In freshness evaluation, we observed sensitivity and specificity of 96.32% ± 2.83% and 99.07% ± 0.69%, 99.09% ± 2.40% and 99.82% ± 0.48%, as well as, 99.35% ± 0.27% and 98.31% ± 0.71% respectively for fish, beef and chicken, which warrants further investigation.

Liu, W, Chang, X, Chen, L, Phung, D, Zhang, X, Yang, Y & Hauptmann, AG 2020, 'Pair-based uncertainty and diversity promoting early active learning for person re-identification', ACM Transactions on Intelligent Systems and Technology, vol. 11, no. 2.
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© 2020 Association for Computing Machinery. The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and practical problem called Early Active Learning, which is applied to the early stage of experiments when there is no pre-labeled sample available as references for human annotating. Previous early active learning methods suffer from two limitations for Re-ID. First, these instance-based algorithms select instances rather than pairs, which can result in missing optimal pairs for Re-ID. Second, most of these methods only consider the representativeness of instances, which can result in selecting less diverse and less informative pairs. To overcome these limitations, we propose a novel pair-based active learning for Re-ID. Our algorithm selects pairs instead of instances from the entire dataset for annotation. Besides representativeness, we further take into account the uncertainty and the diversity in terms of pairwise relations. Therefore, our algorithm can produce the most representative, informative, and diverse pairs for Re-ID data annotation. Extensive experimental results on five benchmark Re-ID datasets have demonstrated the superiority of the proposed pair-based early active learning algorithm.

Liu, X, He, D, Wu, Y, Xu, Q, Wang, D, Yang, Q, Liu, Y, Ni, BJ, Wang, Q & Li, X 2020, 'Freezing in the presence of nitrite pretreatment enhances hydrogen production from dark fermentation of waste activated sludge', Journal of Cleaner Production, vol. 248.
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© 2019 Elsevier Ltd Due to the poor biodegradability of released organics and the rapid consumption of hydrogen, hydrogen production from the untreated waste activated sludge (WAS) and/or inocula is still limited. In this study, it was found that the dark fermentative hydrogen production was largely enhanced from WAS pretreated by freezing in the presence of nitrite. With an increase of nitrite addition from 100 to 400 mg NO2−-N/L during freezing pretreatment (−5 °C for 4 h), the maximal hydrogen yield increased from 7.96 to 19.40 mL/g VS (volatile solids), which was 5.5–13.4 times of that in the control (without freezing and nitrite addition). Mechanism explorations revealed that the proposed pretreatment not only accelerated the disintegration of sludge but also promoted the proportion of biodegradable organics released, thereby provided more bio-available substrates for subsequent hydrogen production. Proposed pretreatment severely suppressed the sludge microorganisms responding to homoacetogenesis (−32.1%), methanogenesis (−58.4%), and sulfate-reducing process (−51.5%), inhibited the consumption of hydrogen. Moreover, there was more acetic and butyric (76% versus 57.5%) but less propionic acid (22.6% versus 13.4%) in this pretreated fermenter, which was in correspondence with the theory of fermentation type affecting hydrogen production. Long-term fermentation experiments indicated that the proposed pretreatment boosted the [FeFe]-hydrogenase activities while suppressed the activities of carbon monoxide dehydrogenase, coenzyme F420, and adenylyl sulfate reductase.

Liu, X, Huang, X, Wu, Y, Xu, Q, Du, M, Wang, D, Yang, Q, Liu, Y, Ni, BJ, Yang, G, Yang, F & Wang, Q 2020, 'Activation of nitrite by freezing process for anaerobic digestion enhancement of waste activated sludge: Performance and mechanisms', Chemical Engineering Journal, vol. 387.
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© 2020 Elsevier B.V. Nitrite-based pretreatment was demonstrated to effectively improve anaerobic digestion of waste activated sludge. It was found in this work that the freezing activated nitrite pretreatment could further enhance the performances. With the increase of nitrite addition from 0 to 600 mg NO2−-N/L during freezing process, the biochemical methane potential of pretreated-sludge gradually increased from 191.3 ± 8.0 to 233.2 ± 10.6 mL per gram volatile solid (VS), while only 178.6 ± 7.3 mL/g VS was obtained in the raw sludge. Mechanism explorations revealed that the freezing activated nitrite pretreatment remarkably facilitated the disintegration of sludge. Excitation emission matrix and fluorescence regional integration analyses further revealed that nitrite addition during freezing process promoted the proportion of biodegradable organics released, thereby providing more bio-available substrates for subsequent anaerobic digestion. Freezing condition induced reactive derivatives from nitrite (e.g., free nitrite acid, NO2[rad], N2O3) were assumed to be the major contributors to the enhanced sludge disintegration and recalcitrant organics (e.g., humic acid-like substances) degradation. It was also found that 600 mg NO2−-N/L addition activated by freezing pretreatment produced an anaerobically digested sludge with an improved dewaterability, as indicated by the decrease of the specific resistance to filterability and moisture content of dewatered cake. Moreover, 600 mg NO2−-N/L addition activated by freezing pretreatment and subsequent anaerobic digestion largely inactivated the pathogens to the levels below Class A biosolids requirements. Considering that nitrite can be in-situ produced in wastewater treatment plants through nitritation of the digestion liquid, this nitrite-based freezing process for sludge pretreatment was environmental-friendly and economically attractive.

Liu, X, Song, W, Musial, K, Zhao, X, Zuo, W & Yang, B 2020, 'Semi-supervised stochastic blockmodel for structure analysis of signed networks', Knowledge-Based Systems.
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© 2020 Elsevier B.V. Finding hidden structural patterns is a critical problem for all types of networks, including signed networks. Among all of the methods for structural analysis of complex network, stochastic blockmodel (SBM) is an important research tool because it is flexible and can generate networks with many different types of structures. However, most existing SBM learning methods for signed networks are unsupervised, leading to poor performance in terms of finding hidden structural patterns, especially when handling noisy and sparse networks. Learning SBM in a semi-supervised way is a promising avenue for overcoming the above difficulty. In this type of model, a small number of labelled nodes and a large number of unlabelled nodes, coupled with their network structures, are simultaneously used to train SBM. We propose a novel semi-supervised signed stochastic blockmodel and its learning algorithm based on variational Bayesian inference, with the goal of discovering both assortative (the nodes connect more densely in same clusters than that in different clusters) and disassortative (the nodes link more sparsely in same clusters than that in different clusters) structures from signed networks. The proposed model is validated through a number of experiments wherein it compared with the state-of-the-art methods using both synthetic and real-world data. The carefully designed tests, allowing to account for different scenarios, show our method outperforms other approaches existing in this space. It is especially relevant in the case of noisy and sparse networks as they constitute the majority of the real-world networks.

Liu, Y, Lan, C, Blumenstein, M & Li, J 2020, 'Bi-level error correction for PacBio long reads', IEEE/ACM Transactions on Computational Biology and Bioinformatics.
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IEEE The latest sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines can generate long reads at the length of thousands of nucleic bases which is much longer than the reads at the length of hundreds generated by Illumina machines. However, these long reads are prone to much higher error rates, for example 15%, making downstream analysis and applications very difficult. Error correction is a process to improve the quality of sequencing data. Hybrid correction strategies have been recently proposed to combine Illumina reads of low error rates to fix sequencing errors in the noisy long reads with good performance. In this paper, we propose a new method named Bicolor, a bi-level framework of hybrid error correction for further improving the quality of PacBio long reads. At the first level, our method uses a de Bruijn graph-based error correction idea to search paths in pairs of solid < formula > < tex > $k$ < /tex > < /formula > -mers iteratively with an increasing length of < formula > < tex > $k$ < /tex > < /formula > -mer. At the second level, we combine the processed results under different parameters from the first level. In particular, a multiple sequence alignment algorithm is used to align those similar long reads, followed by a voting algorithm which determines the final base at each position of the reads. We compare the superior performance of Bicolor with three state-of-the-art methods on three real data sets. Results demonstrate that Bicolor always achieves the highest identity ratio. Bicolor also achieves a higher alignment ratio ( < formula > < tex > $ & #x003E; 1.3\%$ < /tex > < /formula > ) and a higher number of aligned reads than the current methods on two data sets. On the third data set, our method is closely competitive to the current methods in terms of number of aligned reads and genome coverage. The C++ source codes of our algorithm are freely available at https://github.com/yuansliu/Bicolor.

Liu, Y, Luo, G, Ngo, HH, Guo, W & Zhang, S 2020, 'Advances in thermostable laccase and its current application in lignin-first biorefinery: A review.', Bioresource technology, pp. 122511-122511.
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As the most abundant aromatic polymers on the Earth, lignin has great potential to produce biofuels and aromatic chemicals due to their high carbon content and low oxygen content. Lignin-first biorefinery methods have attracted increasing attention recently for their high-value of aromatic chemicals, and high biofuels productivity from lignocellulosic wastes. Thermostable laccase has proven to be an excellent alternative catalyst in degrading lignin for its versatile catalytic abilities under industrial conditions and pollution-free by-products. Thermostable laccases can be found in native extreme environments or modified by biologically based technologies such as gene recombination expression and enzyme direct evolution. This review demonstrated thermostable laccases and their application in lignin degradation. Future research should focus more on the investigation of the reaction of thermostable laccases with lignin substrates.

Liu, Y, Ngo, HH, Guo, W, Wang, D, Peng, L, Wei, W & Ni, B-J 2020, 'Impact of coexistence of sludge flocs on nitrous oxide production in a granule-based nitrification system: A model-based evaluation', WATER RESEARCH, vol. 170.
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Liu, Y, Yang, Y, Han, F, Liu, QH & Guo, YJ 2020, 'Improved Beam-Scannable Ultra-Wideband Sparse Antenna Arrays by Iterative Convex Optimization Based on Raised Power Series Representation', IEEE Transactions on Antennas and Propagation.
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IEEE A novel method is presented to design beam-scannable ultra-wideband (UWB) sparse arrays. A concept of design frequency is introduced which transforms the beam-scannable UWB array design to the problem of synthesizing a broadside-beam array at single-frequency. The raised power series (RPS) representation with appropriate parameter selection is adopted to generate initial element positions, and then an iterative convex optimization is applied to successively optimize the element positions for further sidelobe level (SLL) reduction. Multiple constraints for controlling the first-order Taylor expansion accuracy, the minimum element spacing and the array aperture are incorporated in the iterative convex optimization to obtain stable and practical synthesis results. Several examples for synthesizing UWB arrays with different frequency bands, beam scanning ranges and element counts, are conducted to validate the effectiveness and advantages of the proposed method. It is shown that the proposed method achieves much lower SLLs than those by the original RPS method for all test cases, and it also significantly outperforms some conventional stochastic optimization methods for large UWB array cases.

Liu, Z, Cao, J, Tan, Y, Xiao, Q & Prasad, M 2020, 'Planning Above the API Clouds Before Flying Above the Clouds: A Real-Time Personalized Air Travel Planning Approach', International Journal of Parallel Programming, vol. 48, no. 1, pp. 137-156.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. The rapid growth of the airline industry has resulted in the availability of a large number of flights, however this can also create a paralyzing problem. Flight information on all airlines across the world can be obtained via the Internet. Today, passengers trend to be interested in user personalized service. How to effectively find a passenger’s most preferred air travel plan, which might include multiple transfers from millions of possible choices with certain constraints, such as time and price, is a critical challenge. This paper presents an efficient air travel planning approach, which can find a number of air travel plans by invoking the APIs offered by airline companies. At the same time, these plans also best match the customer’s preference based on an analysis of historical orders. An algorithm to extract user preference features is introduced and heuristic rules to speed up the K path search process under constraints are presented. The experiment results show that the proposed model finds optimal air travel plans efficiently on a real-world dataset.

Liu, Z, Xiao, F, Lin, C-T, Kang, BH & Cao, Z 2020, 'A Generalized Golden Rule Representative Value for Multiple-Criteria Decision Analysis', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-12.
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Loganathan, P, Gradzielski, M, Bustamante, H & Vigneswaran, S 2020, 'Progress, challenges, and opportunities in enhancing NOM flocculation using chemically modified chitosan: A review towards future development', Environmental Science: Water Research and Technology, vol. 6, no. 1, pp. 45-61.
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© 2019 The Royal Society of Chemistry. Natural organic matter (NOM) occurs ubiquitously in water bodies and this can greatly affect feed or raw water quality (taste, colour, odour, bacterial growth). Furthermore, it reduces the performance of the coagulation-based water treatment process. Because the NOM content and its chemical complexity are increasing throughout the world, the removal of NOM from water has become a major challenge in supplying the required amounts of good quality water. The coagulation-flocculation process is widely used for purifying urban water supplies. However, it is not always sufficiently successful in removing the augmented NOM in the feed water, mostly because the polyelectrolytes currently used as coagulants/flocculants cannot effectively interact with all the NOM components consisting of different functional groups, molecular weights, charges, and hydrophobicity. Within the class of polyelectrolytes, chitosan (Cs), which is produced by the deacetylation of abundantly available chitin, has been tested in removing NOM. The effectiveness of Cs can be further improved by chemically modifying the abundant free amino and hydroxyl groups along the Cs chain backbone. This will provide new functional groups that can increase the positive charges, molecular weight, and allow for solubility over a wider pH range, as well as introduce tailored groups which interact in an optimised way with NOM, thereby reducing the solubility of the formed complexes. This paper critically reviews the chemistry of the formed polyelectrolyte/NOM complexes and provides information on how this can be taken advantage of, to identify modified chemical structures of Cs to improve NOM removal in water treatment strategies.

Lomax, BA, Conti, M, Khan, N, Bennett, NS, Ganin, AY & Symes, MD 2020, 'Proving the viability of an electrochemical process for the simultaneous extraction of oxygen and production of metal alloys from lunar regolith', PLANETARY AND SPACE SCIENCE, vol. 180.
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Long, Z, Meng, H, Li, T & Li, S 2020, 'Compact geometric representation of qualitative directional knowledge', Knowledge-Based Systems.
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© 2020 Elsevier B.V. To effectively and efficiently deal with large-scale spatial data is critical for applications in the age of information technology. Compact representation of spatial knowledge is one of the emerging research techniques that contribute to this capability. In this article, we consider the problem of compactly representing qualitative directional relations between extended objects, modelled in the Cardinal Direction Calculus (CDC) of Goyal and Egenhofer. For a large dataset of regions, this approach first constructs a simplified geometry for each region, which preserves CDC relations between regions, and then represents each simplified geometry compactly, so that the storage size is small while retrieving CDC relations from the representation is still reasonably fast. More specifically, the method called necessary cut is used to construct simple geometries, and the two methods, viz. the polygon representation and the rectangle representation, are devised to compactly represent the constructed geometries in cubic time w.r.t. the size of the corresponding simple geometry. Theoretical analyses demonstrate that the two representations, especially the rectangle representation, are promising to have small storage size. Moreover, our empirical evaluations on real-world datasets show that, for each dataset the new approach can produce a rectangle representation that has dominant performance against the state of the art techniques in reducing the storage size of the relations, while the average efficiency of retrieving CDC relations based on the rectangle representation is about the same as the fastest method in the literature.

Lu, J, Liu, A, Song, Y & Zhang, G 2020, 'Data-driven decision support under concept drift in streamed big data', COMPLEX & INTELLIGENT SYSTEMS.
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Data-driven decision-making ( D3 M) is often confronted by the problem of uncertainty or unknown dynamics in streaming data. To provide real-time accurate decision solutions, the systems have to promptly address changes in data distribution in streaming data—a phenomenon known as concept drift. Past data patterns may not be relevant to new data when a data stream experiences significant drift, thus to continue using models based on past data will lead to poor prediction and poor decision outcomes. This position paper discusses the basic framework and prevailing techniques in streaming type big data and concept drift for D3 M. The study first establishes a technical framework for real-time D3 M under concept drift and details the characteristics of high-volume streaming data. The main methodologies and approaches for detecting concept drift and supporting D3 M are highlighted and presented. Lastly, further research directions, related methods and procedures for using streaming data to support decision-making in concept drift environments are identified. We hope the observations in this paper could support researchers and professionals to better understand the fundamentals and research directions of D3 M in streamed big data environments.

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

Lu, Z, Geng, L, Huo, G, Zhao, H, Yao, W, Li, G, Guo, X & Zhang, J 2020, 'A novel hybrid multi-objective bacterial colony chemotaxis algorithm', Soft Computing, vol. 24, pp. 2013-2032.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Abstract: In this article, a novel hybrid multi-objective bacterial colony chemotaxis (HMOBCC) algorithm is proposed to solve multi-objective optimization problems. A mechanism of particle swarm optimization is introduced to multi-objective bacterial colony chemotaxis (MOBCC) algorithm to improve the performance of MOBCC algorithm. Also, three other techniques, including dynamic reverse learning operator, external archive multiplying operator and adaptive diversity maintenance operator, are further applied to improve the diversity and convergence of the algorithm. The proposed algorithm is validated using 12 benchmark problems, and three performance measures are implemented for 5 benchmark problems to compare its performance with existing popular algorithms such as MOBCC, multi-objective bacterial colony chemotaxis based on grid algorithm, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective evolutionary algorithm based on decomposition. The results show that the proposed HMOBCC is very effective against existing algorithms. Graphical abstract: The graphical abstract of this study.[Figure not available: see fulltext.].

Lyu, B, Hoang, DT & Yang, Z 2020, 'Backscatter then Forward: A Relaying Scheme for Batteryless IoT Networks', IEEE Wireless Communications Letters.
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IEEE In this paper, we introduce a novel relaying scheme together with a joint energy beamforming (EB) and time allocation optimization to meet requirements about energy efficiency and hardware constraints of batteryless IoT networks. First, we propose an intelligent relaying scheme using RF-powered gateways as relay nodes to deliver information from batteryless IoT devices to a hybrid access point (HAP). The HAP can also transfer energy to the gateways and batteryless devices using EB techniques. The energy from HAP will be then used to supply power for gateways and as a communications means to transmit data for batteryless devices. We then formulate a sum-rate maximization problem by jointly optimizing the EB vectors, time scheduling, and power allocation. Since the optimization problem is non-convex, we exploit EB characteristics for data backscattering and employ variable substitutions and semidefinite relaxation techniques to transform it into a convex one. After that, a low-complexity method is proposed to obtain the optimal solution in a closed-form. Simulation results confirm that the proposed scheme can achieve significant sum-rate gain.

M. Vanaki, S, Holmes, D, Saha, SC, Chen, J, Brown, RJ & Jayathilake, PG 2020, 'Muco-ciliary clearance: A review of modelling techniques', Journal of Biomechanics, vol. 99.
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© 2019 Elsevier Ltd The airways of the human respiratory system are covered by a protective layer, which is known as airway surface liquid (ASL). This layer consists of two relatively distinct sub-layers; a mucus layer (ML), and a periciliary liquid layer (PCL). In addition, the airways are lined with a dense mat of hair-like structures, called cilia, which beat back and forth in a co-ordinated manner and mainly propel the mucus layer. Such interaction between the cilia and mucus is called ‘muco-ciliary clearance’ (MCC) which is essential to clear the respiratory airways from the inhaled toxic particles deposited on the mucus. The complex nature of lung clearance mechanisms limit the ability to conduct experiments to investigate micro-scale physiological phenomena. As such, modelling techniques are commonly implemented to investigate the effects of biological parameters on the lung muco-ciliary clearance. In the present work, modelling techniques of cilia-ASL interactions – including continuum cilia modelling and discrete cilia modelling – are reviewed and the numerical procedures and level of complexity related to each technique are explained. This is followed by a detailed analysis of the airway surface liquid modelling approaches. In addition, findings of numerical investigations related to the effects of various parameters such as ciliary beat frequency (CBF), mucus rheology, metachronal waves of cilia, surface tension at the PCL-mucus interface, ciliary length, ciliary density, and airway surface liquid depth on the bronchial and tracheal ASL transport are reviewed. This review also explains how these biological parameters can alter the internal power required to perform ciliary beating. Lastly, the main limitations of current numerical works are discussed and significant research directions are brought forward that may be considered in future models to better understand this complex human biological system and its vital clearance mechanism.

Ma, B, Zheng, J, Lei, G, Zhu, J, Jin, P & Guo, Y 2020, 'Topology Optimization of Ferromagnetic Components in Electrical Machines', IEEE Transactions on Energy Conversion.
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IEEE This paper presents the topology optimization of ferromagnetic components in electrical machines by using the density method. Explicit expressions of machine performance based on the principle of electromechanical energy conversion are derived and incorporated in the finite element model. Because the gradient-based algorithm is employed for efficient optimization, the performance sensitivities with respect to the design variables are derived subsequently. An optimization framework is then proposed to optimize effectively the electrical machine performance, such as the flux linkage, back electromotive force, torque profile including torque density and torque ripples. Two design examples are reported to confirm the feasibility and effectiveness of the presented topology optimization approach. The accuracy of optimal solutions is verified by using the commercial electromagnetic analysis software ANSYS MAXWELL.

Ma, J, Liang, P, Yu, W, Chen, C, Guo, X, Wu, J & Jiang, J 2020, 'Infrared and visible image fusion via detail preserving adversarial learning', Information Fusion, vol. 54, pp. 85-98.
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© 2019 Elsevier B.V. TargefTablets can be detected easily from the background of infrared images due to their significantly discriminative thermal radiations, while visible images contain textural details with high spatial resolution which are beneficial to the enhancement of target recognition. Therefore, fused images with abundant detail information and effective target areas are desirable. In this paper, we propose an end-to-end model for infrared and visible image fusion based on detail preserving adversarial learning. It is able to overcome the limitations of the manual and complicated design of activity-level measurement and fusion rules in traditional fusion methods. Considering the specific information of infrared and visible images, we design two loss functions including the detail loss and target edge-enhancement loss to improve the quality of detail information and sharpen the edge of infrared targets under the framework of generative adversarial network. Our approach enables the fused image to simultaneously retain the thermal radiation with sharpening infrared target boundaries in the infrared image and the abundant textural details in the visible image. Experiments conducted on publicly available datasets demonstrate the superiority of our strategy over the state-of-the-art methods in both objective metrics and visual impressions. In particular, our results look like enhanced infrared images with clearly highlighted and edge-sharpened targets as well as abundant detail information.

Mahlia, TMI, Syazmi, ZAHS, Mofijur, M, Abas, AEP, Bilad, MR, Ong, HC & Silitonga, AS 2020, 'Patent landscape review on biodiesel production: Technology updates', RENEWABLE & SUSTAINABLE ENERGY REVIEWS, vol. 118.
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Mahmoudi, T, Pirpour Tazehkand, A, Pourhassan-Moghaddam, M, Alizadeh-Ghodsi, M, Ding, L, Baradaran, B, Razavi Bazaz, S, Jin, D & Ebrahimi Warkiani, M 2020, 'PCR-free paper-based nanobiosensing platform for visual detection of telomerase activity via gold enhancement', Microchemical Journal, vol. 154.
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© 2020 Elsevier B.V. Telomerase activity has been demonstrated in a wide variety of most solid tumors and considered as a well-known cancer biomarker. The commonly utilized method for its detection is polymerase chain reaction (PCR)-based telomeric repeat amplification protocol (TRAP). However, the TRAP technique suffers from false-negative results caused by the failure of PCR step. Moreover, it requires advanced equipment with a tedious and time-consuming procedure. Herein, we presented a portable nitrocellulose paper-based nanobiosensing platform for ultrafast and equipment-free detection of telomerase activity based on a simple colorimetric assay that enabled naked-eye visualization of the color change in response to enzyme activity. In this platform, hybridization was initially performed between telomere complementary oligonucleotide immobilized on gold nanoparticles (GNPs) and telomerase elongated biotinylated probe. Thereafter, the assembly was attached on activated paper strip via avidin-biotin interaction. The signal amplification was carried out by enlargement of the attached GNPs on the paper strip, forming tightly compact rod-shaped submicron structures of gold representing a visual color formation. Thanks to significant sensitivity enhancement, the color change was occurred for down to 6 cells, which can be easily observed by the naked eye. Due to the desired aspects of the developed assay including PCR-free, low cost, simple, and high sensitivity, it can be used for evaluation of telomerase activity in cell extracts for future clinical applications. Furthermore, this design has the ability to be easily integrated into lab-on-chip devices for point-of-care telomerase sensing.

Mahmud, K, Rahman, MS, Ravishankar, J, Hossain, MJ & Guerrero, JM 2020, 'Real-Time Load and Ancillary Support for a Remote Island Power System Using Electric Boats', IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1516-1528.
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© 2005-2012 IEEE. Powering small islands with reliable, affordable, and green electricity is a big challenge due to their dispersed geographical location with limited number of consumers and the heavy dependence on fossil fuels. This paper aims to address this challenge of reducing dependency on fossil fuel generators by providing an easy and feasible solution using available and accessible energy resources. The proposed method utilizes the bidirectional energy transfer mechanism available in electric boats (EBs) to support the consumers' power demand. It proposes a new real-time load-support (RTLS) system with a coordinated control using EBs, community generators, and battery energy storage systems. It analyzes the management of the intermittent source-dependent small-scale grid in real time, under various weather, load, and battery state-of-charge conditions. The RTLS system coordinates the customers' load demand with the available EBs, photovoltaics, and battery storage to provide efficient load support and to regulate the bus voltage and frequency. The efficacy of the proposed system is validated both computationally in a real network and in a laboratory setup. It is found that this novel system can substantially reduce the grid load demand and maintain the power quality under various load/source uncertainties and fault conditions. The system robustness is also evaluated considering undesirable conditions, such as severe three-phase faults and sudden EB disconnections. The performance of the proposed method is compared with that of the day-ahead load management approach to validate its effectiveness under various scenarios.

Makhdoom, I, Zhou, I, Abolhasan, M, Lipman, J & Ni, W 2020, 'PrivySharing: A blockchain-based framework for privacy-preserving and secure data sharing in smart cities', COMPUTERS & SECURITY, vol. 88.
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Manikandan, R, Patan, R, Gandomi, AH, Sivanesan, P & Kalyanaraman, H 2020, 'Hash polynomial two factor decision tree using IoT for smart health care scheduling', EXPERT SYSTEMS WITH APPLICATIONS, vol. 141.
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Marin, JG, Baba, AA, Hesselbarth, J, Hashmi, RM & Esselle, KP 2020, 'Millimeter-Wave Low-Loss Multifeed Superstrate Based Antenna', IEEE Transactions on Antennas and Propagation.
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IEEE A low-loss multifeed antenna with controlled polarization and beam-steering is presented. A low-profile resonant cavity antenna is fed by multiple spherical dielectric resonators, demonstrating its multifeed capabilities. Each of the dielectric resonators are themselves fed from four different sides by microstrip resonators on a planar circuit board. The beam pointing direction and polarization of the antenna are controlled by the signal phases at the respective input ports. Several antennas with varying feed topology are investigated, showing the versatility of this method. A 16-way 34GHz prototype is built and measured, demonstrating high overall efficiency (considering power combining loss and antenna loss) of 80%.

Martínez-López, FJ, Merigó, JM, Gázquez-Abad, JC & Ruiz-Real, JL 2020, 'Industrial marketing management: Bibliometric overview since its foundation', Industrial Marketing Management, vol. 84, pp. 19-38.
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© 2019 Elsevier Inc. Industrial Marketing Management (IMM) is an outstanding journal in the field of business-to-business marketing. This paper focuses on this journal, with an extensive bibliometric analysis of IMM from its foundation in 1971 to 2017, the last year analyzed in this study. It identifies, among others, the annual evolution of publications, the most influential countries, the most relevant authors, the most prominent institutions supporting research, as well as the citations of IMM papers in major marketing, but also other, business and management journals. To do so, this research uses the Web of Science Core Collection and Scopus databases, and analyzes a wide range of bibliometric indicators, including the total number of publications and citations, citations per paper, the h-index, m-value and citation thresholds, and also develops a graphical analysis of the bibliographical material using the visualization of similarities (VOS) viewer software. Finally, by applying a cluster analysis by fractional accounting, this research identifies trends and proposes future topics and research lines, such as: trust, innovation, performance, relationship marketing, the future role of new technologies in industrial marketing research, online marketing and corporate image.

Mastio, E, Chew, E & Dovey, KA 2020, 'The learning organization as a context for value co-creation', The Learning Organization, vol. ahead-of-print, no. ahead-of-print.
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Purpose This paper aims to explore the relationship between the concept of the learning organization and that of the co-creation of value. Design/methodology/approach The paper is conceptual in nature and draws on data from a case study of a small highly innovative Australian company. Findings The authors show that, from a value co-creation perspective, the learning organization can be viewed as an open, collaborative, social/economic actor engaged in social/economic activities with other interdependent actors (organizations or stakeholders) in a network or ecosystem of actors to serve its mission/purpose and the well-being of the ecosystem. Research limitations/implications As a conceptual paper, the authors rely primarily on previous research as the basis for the argument. The implications of the findings are that, as value co-creation practices are founded upon the generation and leveraging of specific intangible capital resources, more research located in alternative research paradigms is required. Practical implications There are important implications for organizational leadership in that the practices that underpin value co-creation require the leadership to be able to work constructively with multiple forms of systemic and agentic power. Social implications In increasingly turbulent and hyper-competitive global operational contexts, sustainable value creation is becoming recognized as a ...

Maxit, L, Guasch, O, Meyer, V & Karimi, M 2020, 'Noise radiated from a periodically stiffened cylindrical shell excited by a turbulent boundary layer', Journal of Sound and Vibration, pp. 115016-115016.
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Maxit, L, Karimi, M, Meyer, V & Kessissoglou, N 2020, 'Vibroacoustic responses of a heavy fluid loaded cylindrical shell excited by a turbulent boundary layer', Journal of Fluids and Structures.
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Mazaheri, H, Ong, HC, Masjuki, HH, Arslan, A, Chong, WT & Amini, Z 2020, 'Friction and wear characteristics of rice bran oil based biodiesel using calcium oxide catalyst derived from Chicoreus Brunneus shell', Energy Sources, Part A: Recovery, Utilization and Environmental Effects.
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© 2019, © 2019 Taylor & Francis Group, LLC. In this study, an investigation has been carried out to examine the tribological features of Rice bran oil (RBO) based biodiesel as an agent to improve lubricity. RBOB was successfully generated by the means of transesterification and the studied fuels were pure biodiesel (RBOB100), 10% (RBOB10), 30% (RBOB30), and 50% (RBOB50) of biodiesel combined with diesel and pure diesel (RBOB0). Examination in agreement with the ASTM D 4172 method was carried out under following conditions for all examined fuels: a constant load of 40 kg, a steady sliding speed of 1200 rpm, a constant temperature of 75°C and a reaction time of 3600 s. Frictional torque was recorded on line in the course of wear examination. Optical microscopy was implemented to investigate the wear scars of tested balls. Result showed that friction diminished with the decrease of biodiesel concentration. Moreover, formation of wear scars increased with increasing the biodiesel concentration. It could be concluded that lubricity decreases due to increasing the biodiesel concentration. Surface morphology analysis showed that pure biodiesel and diesel formed adhesive wears. However, all the wears formed by biodiesel-diesel blends were fallen into the abrasive wear group.

Mehmood, A, Zameer, A, Chaudhary, NI, Ling, SH & Raja, MAZ 2020, 'Design of meta-heuristic computing paradigms for Hammerstein identification systems in electrically stimulated muscle models', NEURAL COMPUTING & APPLICATIONS.
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Melnikov, A, Maeder, M, Friedrich, N, Pozhanka, Y, Oberst, S, Wollmann, A, Scheffler, M, Powell, D & Marburg, S 2020, 'Acoustic metamaterial capsule for reduction of stage machinery noise', Journal of the Acoustical Society of America, vol. 147, pp. 1491-1491.
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Noise mitigation of stage machinery can be quite demanding and requires innovative solutions. In this work we propose an acoustic metamaterial capsule to reduce the noise emission of several stage machinery drive trains, while still allowing the ventilation required for cooling. The metamaterial capsule consists of c-shape meta atoms, which have a simple structure that facilitates manufacturing. We design, simulate, manufacture, and experimentally validate two different metamaterial capsules, which utilize an ultra-sparse and air-permeable reflective meta-grating. Both designs demonstrate transmission loss peaks that effectively suppress gear mesh noise or other narrow band noise sources. The ventilation by natural convection was numerically verified, and was shown to give adequate cooling, whereas a conventional sound capsule would lead to overheating. The noise spectra of three common stage machinery drive trains are numerically modelled, enabling us to design meta-gratings and determine their noise suppression performance. The results fulfill the stringent stage machinery noise limits, highlighting the benefit of using metamaterial capsule of simple c-shape structure.

Meng, Q, Wu, C, Hao, H, Li, J, Wu, P, Yang, Y & Wang, Z 2020, 'Steel fibre reinforced alkali-activated geopolymer concrete slabs subjected to natural gas explosion in buried utility tunnel', Construction and Building Materials, vol. 246.
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© 2020 Elsevier Ltd Accidental gas explosions in the buried utility tunnels around the world have caused massive losses in economy and human lives. The buried utility tunnel with adequate blast resistance capacity is therefore required to withstand the possible accidental gas explosions. In this study, a novel construction material, alkali-activated steel fibre reinforced geopolymer composite is introduced and the blast resistance capacity of slabs made of this material is studied in a full-scale buried utility tunnel. Fly ash and S95 grade ground granulated blast-furnace slag powder (GGBS) were used as the major binders in this geopolymer concrete. The plain geopolymer concrete had a compressive strength of 61 MPa and the steel fibre reinforced geopolymer concrete had a compressive strength of 74 MPa. The elastic modulus of the plain geopolymer concrete was found to be lower than the conventional C30 concrete. The methane gas explosion test was conducted in a full-scale (12 m × 1.8 m × 0.6 m) tunnel segment to investigate the structural performance of selected slab specimen (1.8 m × 0.4 m × 0.09 m). The test results and numerical simulations of structural responses subjected to methane gas explosion are presented. The results indicate the fibre reinforced geopolymer concrete slab has good capacity to resist methane gas explosion load.

Metia, S, Ha, QP, Duc, HN & Scorgie, Y 2020, 'Urban air pollution estimation using unscented Kalman filtered inverse modeling with scaled monitoring data', Sustainable Cities and Society, vol. 54.
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© 2019 The increasing rate of urbanization requires effective and reliable techniques for air quality monitoring and control. For this, the Air Pollution Model and Chemical Transport Model (TAPM-CTM) has been developed and used in Australia with emissions inventory data, synoptic data and terrain data used as its input parameters. Since large uncertainties exist in the emissions inventory (EI), further refinements and improvements are required for accurate air quality prediction. This study evaluates the performance of urban air quality forecasting, using TAPM-CTM, and improves accuracy of air pollution estimation by using a two-stage optimization technique to upgrade EI with validation from monitoring data. The first stage is based on statistical analysis for EI correction and the second stage is based on the unscented Kalman filter (UKF) to take into account the spatio-temporal distributions of air pollutant levels utilizing a Matérn covariance function. The predicted nitrogen monoxide (NO) and nitrogen dioxide (NO2) concentrations with a priori emissions are first compared with observations at monitoring stations in the New South Wales (NSW). Ozone (O3) is also considered since at the ground level it represents a major air pollutant affecting human health and the environment. In the second stage, with the improved EI, TAPM-CTM model errors are reduced further by using the UKF to calibrate EI. Results obtained show effectiveness of the proposed technique, which is promising for air quality inverse modeling, an important aspect of air pollution control in smart cities to achieve environmental sustainability.

Minh, HD, Huu, HN, Guo, W, Chang, SW, Dinh, DN, Liu, Y, Varjani, S & Kumar, M 2020, 'Microbial fuel cell-based biosensor for online monitoring wastewater quality: A critical review', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 712.
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Moayedi, H, Mehrabi, M, Bui, DT, Pradhan, B & Foong, LK 2020, 'Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility', Journal of Environmental Management, vol. 260.
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© 2019 Elsevier Ltd Forests are important dynamic systems which are widely affected by fire worldwide. Due to the complexity and non-linearity of the forest fire problem, employing hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threat. Three fuzzy-metaheuristic ensembles, based on adaptive neuro-fuzzy inference systems (ANFIS) incorporated with genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) evolutionary algorithms are used to produce the forest fire susceptibility map (FFSM) of a fire-prone region in Iran. A sensitivity analysis is also executed to evaluate the effectiveness of the proposed ensembles in terms of time and complexity. The results revealed that all models produce FFSMs with acceptable accuracy. However, the superiority of the GA-ANFIS was shown in both recognizing the pattern (AUROCtrain = 0.912 and Error = 0.1277) and predicting unseen fire events (AUROCtest = 0.850 and Error = 0.1638). The optimized structures of the proposed GA-ANFIS and PSO-ANFIS ensembles could be good alternatives to traditional forest fire predictive models, and their FFSMs can be promisingly used for future planning and decision making in the proposed area.

Moon, DH, Chung, WJ, Chang, SW, Lee, SM, Kim, SS, Jeung, JH, Ro, YH, Ahn, JY, Guo, W, Ngo, HH & Dinh, DN 2020, 'Fabrication and characterization of Ni-Ce-Zr ternary disk-shaped catalyst and its application for low-temperature CO2 methanation', FUEL, vol. 260.
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Mortazavi, M, Sharafi, P, Kildashti, K & Samali, B 2020, 'Prefabricated hybrid steel wall panels for mid-rise construction in seismic regions', Journal of Building Engineering, vol. 27, pp. 100942-100942.
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Mudassar, M, Zhai, Y, Liao, L & Shen, J 2020, 'A Decentralized Latency-Aware Task Allocation and Group Formation Approach With Fault Tolerance for IoT Applications', IEEE Access, vol. 8, pp. 49212-49223.
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Mueller, M, Zolfaghari, R, Briggs, A, Furtado, H, Booth, J, Keall, P, Nguyen, D, O'Brien, R & Shieh, C-C 2020, 'The first prospective implementation of markerless lung target tracking in an experimental quality assurance procedure on a standard linear accelerator.', Physics in medicine and biology, vol. 65, no. 2, pp. 025008-025008.
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The ability to track tumour motion without implanted markers on a standard linear accelerator (linac) could enable wide access to real-time adaptive radiotherapy for cancer patients. We previously have retrospectively validated a method for 3D markerless target tracking using intra-fractional kilovoltage (kV) projections acquired on a standard linac. This paper presents the first prospective implementation of markerless lung target tracking on a standard linac and its quality assurance (QA) procedure. The workflow and the algorithm developed to track the 3D target position during volumetric modulated arc therapy treatment delivery were optimised. The linac was operated in clinical QA mode, while kV projections were streamed to a dedicated computer using a frame-grabber software. The markerless target tracking accuracy and precision were measured in a lung phantom experiment under the following conditions: static localisation of seven distinct positions, dynamic localisation of five patient-measured motion traces, and dynamic localisation with treatment interruption. The QA guidelines were developed following the AAPM Task Group 147 report with the requirement that the tracking margin components, the margins required to account for tracking errors, did not exceed 5 mm in any direction. The mean tracking error ranged from 0.0 to 0.9 mm (left-right), -0.6 to  -0.1 mm (superior-inferior) and  -0.7 to 0.1 mm (anterior-posterior) over the three tests. Larger errors were found in cases with large left-right or anterior-posterior and small superior-inferior motion. The tracking margin components did not exceed 5 mm in any direction and ranged from 0.4 to 3.2 mm (left-right), 0.7 to 1.6 mm (superior-inferior) and 0.8 to 1.5 mm (anterior-posterior). This study presents the first prospective implementation of markerless lung target tracking on a standard linac and provides a QA procedure for its safe clinical implementation, potentially enabling real-time adaptive radiother...

Mujtaba, MA, Muk Cho, H, Masjuki, HH, Kalam, MA, Ong, HC, Gul, M, Harith, MH & Yusoff, MNAM 2020, 'Critical review on sesame seed oil and its methyl ester on cold flow and oxidation stability', Energy Reports, vol. 6, pp. 40-54.
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© 2019 The demand for renewable energy is steadily increasing due to rapid population growth and economic development worldwide. An additional reason is that fossil fuel reserves are limited, and this situation results in their non-uniform availability globally. Furthermore, the attitudes of the society, energy policies and technology choices are constantly changing. Thus, renewable energy resources are now considered good alternatives to fossil fuels. In the meantime, liquid energy, such as methyl ester from locally produced vegetable oils, is well accepted by many countries, even though it is currently being blended up to 20% with petroleum fuels. Recently, the industrialisation of biodiesel is a major problem because of its poor cold flow properties and oxidative stability. Vegetable oils are also being blended in an appropriate proportion before transesterification to obtain the desired properties in biodiesel. Similarly, poor cold flow properties and oxidative stability can be improved by choosing suitable vegetable oils for making blends. Amongst all available vegetable oils, sesame seed oil (SSO) has unique cold flow properties and oxidation stability, particularly because of naturally occurring antioxidants and preservatives, which enhance the stability of oil towards rancidity. Therefore, SSO can be used as a potential feedstock for blending with other vegetable oils to enhance the overall cold flow and oxidation stability properties. This overview summarises sesame cultivation, SSO production, the physicochemical properties of SSO and its potential as an alternative renewable fuel source. In this review, the physicochemical properties of sesame biodiesel are compared with those of biodiesel derived from other vegetable oils. Results show that blending SSO with palm oil before transesterification will successfully improve the cold flow properties and oxidation stability of palm methyl ester (biodiesel).

Munasinghe, N & Paul, G 2020, 'Ultrasonic-based Sensor Fusion Approach to Measure Flow Rate in Partially Filled Pipes', IEEE Sensors Journal.
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Flow rate measurement in pipes is essential for many applications. Thus, there have been a variety of flow meters developed that incorporate different technologies. However, a typical limitation in flow meters is that the pipe must be full in order to get an accurate flow reading. In many cases, this is not possible for practical reasons. When the pipe is full, ultrasonic flow meters can calculate the flow rate using known properties of the pipe and fluid, namely the cross-section, propagation path and fluid sound velocity. However, when the pipe is only partially filled, the propagation path is unknown which leads to an inability to calculate the correct flow rate. This paper presents a cost-effective sensor fusion approach to extend the capabilities of transit time ultrasonic flow meters to handle such scenarios. The approach determines the propagation path using capacitance-based level sensing, combined with fluid velocities ascertained via an ultrasonic sensor, leading to a significantly more accurate estimation of flow rates. Experiments in low flow rate situations validated the efficacy of the proposed model, with a 92% reduction of mean error in the lowest water height when compared to a conventional ultrasonic flow meter.

Nagasubramanian, G, Sakthivel, RK, Patan, R, Gandomi, AH, Sankayya, M & Balusamy, B 2020, 'Securing e-health records using keyless signature infrastructure blockchain technology in the cloud', NEURAL COMPUTING & APPLICATIONS, vol. 32, no. 3, pp. 639-647.
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Naidu, G, Tijing, L, Johir, MAH, Shon, H & Vigneswaran, S 2020, 'Hybrid membrane distillation: Resource, nutrient and energy recovery', JOURNAL OF MEMBRANE SCIENCE, vol. 599.
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Naji, O, Al-Juboori, RA, Bowtell, L, Alpatova, A & Ghaffour, N 2020, 'Direct contact ultrasound for fouling control and flux enhancement in air-gap membrane distillation.', Ultrasonics sonochemistry, vol. 61, pp. 104816-104816.
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Air Gap Membrane distillation (AGMD) is a thermally driven separation process capable of treating challenging water types, but its low productivity is a major drawback. Membrane fouling is a common problem in many membrane treatment systems, which exacerbates AGMD's low overall productivity. In this study, we investigated the direct application of low-power ultrasound (8-23 W), as an in-line cleaning and performance boosting technique for AGMD. Two different highly saline feedwaters, namely natural groundwater (3970 μS/cm) and RO reject stream water (12760 μS/cm) were treated using Polytetrafluoroethylene (PTFE) and polyvinylidene fluoride (PVDF) membranes. Theoretical calculations and experimental investigations are presented, showing that the applied ultrasonic power range only produced acoustic streaming effects that enhanced cleaning and mass transfer. Attenuated Total Reflection Fourier-Transform Infrared Spectroscopy (ATR FT-IR) analysis showed that ultrasound was capable of effectively removing silica and calcium scaling. Ultrasound application on a fouled membrane resulted in a 100% increase in the permeate flux. Cleaning effects accounted for around 30-50% of this increase and the remainder was attributed to mass transfer improvements. Contaminant rejection percentages were consistently high for all treatments (>99%), indicating that ultrasound did not deteriorate the membrane structure. Scanning Electron Microscopy (SEM) analysis of the membrane surface was used to confirm this observation. The images of the membrane surface demonstrated that ultrasound successfully cleaned the previously fouled membrane, with no signs of structural damage. The results of this study highlight the efficient and effective application of direct low power ultrasound for improving AGMD performance.

Namadmalan, A, Rouzbehi, K, Manuel Escano, J & Bordons, C 2020, 'Dual-Active Bridge Series Resonant Electric Vehicle Charger: A Self-Tuning Method', ELECTRONICS, vol. 9, no. 2.
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Nan, Y, Huang, X & Guo, YJ 2020, 'A Millimeter-Wave GCW-SAR Based on Deramp-on-Receive and Piecewise Constant Doppler Imaging', IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 1, pp. 680-690.
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© 2019 IEEE. A novel generalized continuous-wave synthetic aperture radar (GCW-SAR) based on deramp-on-receive operating in millimeter-wave frequency is proposed in this article. With deramp-on-receive, the receiver sampling rate is drastically reduced, and the downsampled 1-D raw data can be obtained from the received beat signal. Further adopting piecewise constant Doppler (PCD) imaging in the digital domain, a GCW-SAR image can be easily reconstructed by using the existing frequency-modulated continuous-wave (FMCW) radar system. The effects of deramp-on-receive in PCD imaging are analyzed accordingly. The short wavelength of the millimeter-wave carrier used in the proposed GCW-SAR enables high azimuth resolution as well as a short synthetic aperture, which, in turn, significantly reduces the imaging computational complexity. Simulation and experimental results confirm the advantages of the proposed GCW-SAR.

Nan, Y, Huang, X & Guo, YJ 2020, 'Piecewise Constant Doppler Algorithm: Performance Analysis, Further Simplification and Motion Compensation', IEEE Transactions on Aerospace and Electronic Systems.
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IEEE The piecewise constant Doppler (PCD) algorithm is a novel radar imaging process recently proposed for the generalized continuous wave synthetic aperture radar (GCW-SAR). This paper presents a detailed theoretical analysis on the PCD algorithm's performance and proposes a further complexityreduced PCD algorithm with motion compensation (MOCO) suitable for practical applications. Firstly, the difference between conventional SAR imaging and PCD imaging, i.e., the zeroth order versus the first order slant range approximation, is revealed. Exact ambiguity function expressions of the PCD imaging in range and azimuth directions respectively are then derived. An error function of the PCD imaging as compared with the ideal matched filtering method is further defined and shown to be a function of an image quality factor which can be used to quantify the PCD imaging performance. Finally, a faster and more flexible imaging process, called decimated PCD algorithm, is proposed, by which the image azimuth spacing can be easily extended and hence the computational complexity can be significantly reduced. The decimated PCD implementation incorporated with the MOCO is developed for practical GCWSAR applications and its imaging error lower-bounded by the PCD imaging error function is analyzed accordingly. Simulation and experimental results validate the theoretical analysis of the PCD imaging and show that the decimated PCD algorithm can achieve a high imaging quality at low cost.

Nanda, P, Fan, X & He, X 2020, 'BuildSenSys: Reusing Building Sensing Data for Traffic Prediction with Cross-domain Learning', IEEE Transactions on Mobile Computing.
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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...

Naseer, A, Rani, M, Naz, S, Razzak, MI, Imran, M & Xu, G 2020, 'Refining Parkinson’s neurological disorder identification through deep transfer learning', Neural Computing and Applications, vol. 32, no. 3, pp. 839-854.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work.

Nasir, AA, Tuan, HD, Duong, TQ & Hanzo, L 2020, 'Transmitter-Side Wireless Information- and Power-Transfer in Massive MIMO Systems', IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, vol. 69, no. 2, pp. 2322-2326.
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Natarajan, S, Vairavasundaram, S, Natarajan, S & Gandomi, AH 2020, 'Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data', Expert Systems with Applications, vol. 149.
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© 2020 The web contains a huge volume of data, and it's populating every moment to the point that human beings cannot deal with the vast amount of data manually or via traditional tools. Hence an advanced tool is required to filter such massive data and mine the valuable information. Recommender systems are among the most excellent tools for such a purpose in which collaborative filtering is widely used. Collaborative filtering (CF) has been extensively utilized to offer personalized recommendations in electronic business and social network websites. In that, matrix factorization is an efficient technique; however, it depends on past transactions of the users. Hence, there will be a data sparsity problem. Another issue with the collaborative filtering method is the cold start issue, which is due to the deficient information about new entities. A novel method is proposed to overcome the data sparsity and the cold start problem in CF. For cold start issue, Recommender System with Linked Open Data (RS-LOD) model is designed and for data sparsity problem, Matrix Factorization model with Linked Open Data is developed (MF-LOD). A LOD knowledge base “DBpedia” is used to find enough information about new entities for a cold start issue, and an improvement is made on the matrix factorization model to handle data sparsity. Experiments were done on Netflix and MovieLens datasets show that our proposed techniques are superior to other existing methods, which mean recommendation accuracy is improved.

Nguyen, LN, Commault, AS, Kahlke, T, Ralph, PJ, Semblante, GU, Johir, MAH & Nghiem, LD 2020, 'Genome sequencing as a new window into the microbial community of membrane bioreactors - A critical review.', Science of the Total Environment, vol. 704, pp. 135279-135279.
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Recent developed sequencing techniques have resulted in a new and unprecedented way to study biological wastewater treatment, in which most organisms are uncultivable. This review provides (i) an insight on state-of-the-art sequencing techniques and their limitations; (ii) a critical assessment of the microbial community in biological reactor and biofouling layer in a membrane bioreactor (MBR). The data from high-throughput sequencing has been used to infer microbial growth conditions and metabolisms of microorganisms present in MBRs at the time of sampling. These data shed new insight to two fundamental questions about a microbial community in the MBR process namely the microbial composition (who are they?) and the functions of each specific microbial assemblage (what are their function?). The results to date also highlight the complexity of the microbial community growing on MBRs. Environmental conditions are dynamic and diverse, and can influence the diversity and structural dynamics of any given microbial community for wastewater treatment. The benefits of understanding the structure of microbial communities on three major aspects of the MBR process (i.e. nutrient removal, biofouling control, and micropollutant removal) were symmetrically delineated. This review also indicates that the deployment of microbial community analysis for a practical engineering context, in terms of process design and system optimization, can be further realized.

Nguyen, LN, Truong, MV, Nguyen, AQ, Johir, MAH, Commault, AS, Ralph, PJ, Semblante, GU & Nghiem, LD 2020, 'A sequential membrane bioreactor followed by a membrane microalgal reactor for nutrient removal and algal biomass production', Environmental Science: Water Research and Technology, vol. 6, no. 1, pp. 189-196.
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© 2019 The Royal Society of Chemistry. A hybrid process combining a single compartment aerobic membrane bioreactor (MBR) and a membrane microalgal reactor (MMR) was evaluated for nutrient removal and microalgal biomass production. When operated without biomass extraction, the microalgal biomass in the MMR reached 920 mg L-1 on day 18 and then collapsed, rendering nutrient removal ineffective. Stable operation of the MMR was achieved by regular biomass extraction (i.e. 1/30 of the microalgal biomass in the reactor daily). The biomass production at steady state was approximately 26 g m-3 d-1. The NO3- and PO43- uptake values by microalgae were 4.0 ± 1.1 and 1.5 ± 0.9 g m-3 d-1, respectively. A facile flocculation and separation technique capable of recovering 98% microalgal biomass was demonstrated. Although the hybrid process can significantly enhance nutrient removal and biomass production, further research is needed to intensify the microalgal growth rate. At the current microalgal growth rate, a large MMR volume (37 times that of the MBR) is necessary for synchronous operation.

Nguyen, LV, Hu, G & Spanos, CJ 2020, 'Efficient Sensor Deployments for Spatio-Temporal Environmental Monitoring', IEEE Transactions on Systems Man and Cybernetics: Systems.
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This paper addresses the problem of efficiently deploying sensors in spatial environments, e.g., buildings, for the purposes of monitoring spatio-temporal environmental phenomena. By modeling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality-cost function of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is proven not to be affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment optimization problem is then solved by a practical and feasible polynomial algorithm, where its solutions are theoretically proven to be guaranteed. The proposed method is also theoretically and experimentally compared with the existing works. The effectiveness of the proposed algorithm is demonstrated by implementation in a real tested space in a university building, where the obtained results are highly promising.

Nguyen, TKL, Ngo, HH, Guo, WS, Chang, SW, Nguyen, DD, Nghiem, LD & Nguyen, TV 2020, 'A critical review on life cycle assessment and plant-wide models towards emission control strategies for greenhouse gas from wastewater treatment plants', Journal of Environmental Management, vol. 264.
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© 2020 Elsevier Ltd For decades, there has been a strong interest in mitigating greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs). Numerous models were developed to measure the emissions and propose the quantification. Existing studies looked at the relationship between GHG emissions and operational cost (OCI), which is one of the most important indicators for decision-makers. Other parameters that can influence the control strategies include the effluent quality (EQI) and total environmental impacts. Plant-wide models are reliable methods to examine the OCI, EQI and GHG emissions while Life cycle assessment (LCA) works to assess the potential environmental impacts. A combined LCA and plant-wide model proved to be a valuable tool evaluating and comparing strategies for the best performance of WWTPs. For this study involving a WWTP, the benchmark model is used while LCA is the decision tool to find the most suitable treatment strategy. LCA adds extra criteria that complement the existing criteria provided by such models. Complementing the cost/performance criteria is proposed for plant-wide models, including environmental evaluation, based on LCA, which provides an overall better assessment of WWTPs. It can capture both the dynamic effects and potential environmental impacts. This study provides an overview of the integration between plant-wide models and LCA.

Nguyen, TTQ, Loganathan, P, Nguyen, TV & Vigneswaran, S 2020, 'Removing arsenic from water with an original and modified natural manganese oxide ore: batch kinetic and equilibrium adsorption studies', Environmental Science and Pollution Research, vol. 27, no. 5, pp. 5490-5502.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Arsenic contamination of drinking water is a serious water quality problem in many parts of the world. In this study, a low-cost manganese oxide ore from Vietnam (Vietnamese manganese oxide (VMO)) was firstly evaluated for its performance in arsenate (As(V)) removal from water. This material contains both Mn (25.6%) and Fe (16.1%) mainly in the form of cryptomelane and goethite minerals. At the initial As(V) concentration of 0.5 mg/L, the adsorption capacity of original VMO determined using the Langmuir model was 0.11 mg/g. The modified VMOs produced by coating VMO with iron oxide (Fea-VMO) and zirconium oxide (Zra-VMO) at 110 °C and 550 °C achieved the highest As(V) adsorption capacity when compared to three other methods of VMO modifications. Langmuir maximum adsorption capacities of Fea-VMO and Zra-VMO at pH 7.0 were 2.19 mg/g and 1.94 mg/g, respectively, nearly twenty times higher than that of the original VMO. Batch equilibrium adsorption data fitted well to the Langmuir, Freundlich, and Temkin models and batch kinetics adsorption data to pseudo-first order, pseudo-second order, and Elovich models. The increase of pH progressively from 3 to 10 reduced As(V) adsorption with a maximum reduction of 50–60% at pH 10 for both original and modified VMOs. The co-existing oxyanions considerably weakened the As(V) removal efficiency because they competed with As(V) anions. The competition order was PO43− > SiO32− > CO32− > SO42−. The characteristics of the original and modified VMOs evaluated using SEM, FTIR, XRD, XRF, surface area, and zeta potential explained the As(V) adsorption behaviour.

Nguyen, XC, Tran, TCP, Hoang, VH, Nguyen, TP, Chang, SW, Nguyen, DD, Guo, W, Kumar, A, La, DD & Bach, QV 2020, 'Combined biochar vertical flow and free-water surface constructed wetland system for dormitory sewage treatment and reuse', Science of the Total Environment, vol. 713.
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© 2020 Elsevier B.V. A two-stage treatment system that included vertical flow (VF) and free-water surface (FWS) constructed wetlands was investigated for the dual purposes of sewage treatment and reuse. The VF included four layers (biochar, sand, gravel, and sandy soil), and the FWS was installed after the VF and used as a polishing tank. Two types of local plants, namely Colocasia esculenta and Canna indica, were planted in the VF and FWS, respectively. The system operated for approximately six months, and the experimental period was categorized into four stages that corresponded to changes in the hydraulic loading rate (HLR) (0.02–0.12 m/d). The removal efficiencies for total suspended solids (TSS), chemical oxygen demand (COD), biological oxygen demand (BOD5), ammonia (NH4-N), and total coliform (Tcol) were 71 ± 11%, 73 ± 13%, 79 ± 11%, 91 ± 3%, and 70 ± 20%, respectively. At HLRs of 0.04–0.06 m/d, the COD and BOD5 levels satisfied Vietnam's irrigation standards, with removable rates of 64% and 88%, respectively, and the TSS and Tcol levels satisfied Vietnam's standards for potable water. Furthermore, the NO3-N levels satisfied the reuse limits, whereas the NH4-N levels exceeded the reuse standards. At high HLRs (e.g., 0.12 m/d), all the effluent parameters, except Tcol and NO3-N, exceeded the standards.

Ni, B-J, Zeng, S, Wei, W, Dai, X & Sun, J 2020, 'Impact of roxithromycin on waste activated sludge anaerobic digestion: Methane production, carbon transformation and antibiotic resistance genes', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 703.
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Ni, Z, Zhang, JA, Yang, K, Gao, F & An, J 2020, 'Estimation of Multiple Angle-of-Arrivals with Localized Hybrid Subarrays for Millimeter Wave Systems', IEEE Transactions on Communications, vol. 68, no. 3, pp. 1897-1910.
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IEEE Angle of Arrival (AoA) estimation with localized hybrid arrays is challenging in millimeter-wave (mmWave) communication systems. Most existing solutions quantize AoAs into limited values with relatively low accuracy. This paper presents a multi-AoA estimation scheme which is capable of estimating multiple AoAs from multiple users with low complexity. Specifically, we design a path filter via combining the received signals for each subarray. Each path filter enables a certain range of AoAs to pass through while suppressing the rest. Then we can use low-complexity cross-correlation operations to obtain continuous AoA estimates. Association of paths to users is further achieved by a follow-up pseudo-random codes based correlation operation. The scheme is first presented for a narrowband system and then extended to wideband with frequency selectivity. We also introduce new metrics and derive the lower bound of mean square error for evaluating the accuracy of AoA estimates, as conventional metrics face difficulties in the presence of multiple closely located AoAs. Extensive simulation results are provided and validate the effectiveness of the proposed multi-AoA estimation scheme.

Niamir, L, Ivanova, O, Filatova, T, Voinov, A & Bressers, H 2020, 'Demand-side solutions for climate mitigation: Bottom-up drivers of household energy behavior change in the Netherlands and Spain', Energy Research and Social Science, vol. 62.
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© 2019 The Authors Households are responsible for 70% of CO2 emissions (directly and indirectly). While households as agents of change increasingly become a crucial element in energy transitions, bottom-up mechanisms facilitating behavioral change are not fully understood. A scientific understanding of individual energy use, requires eliciting factors that trigger or inhibit changes in energy behavior. This paper explores individual energy consumption practices and behavioral aspects that affect them. We quantitatively study the determinants of three energy actions: (1) investments in house insulation, solar panels and/or energy-efficient appliances, (2) conservation of energy by changing energy-use habits like switching off unused devices or adjusting house temperature, and (3) switching to green(er) electricity sources. To address this goal, we conduct a comprehensive survey among households (N = 1790) in two EU regions: Overijssel, the Netherlands and Navarre, Spain. We use probit regression to estimate how behavioral factors, households’ socioeconomic characteristics and structural attributes of dwellings influence energy related actions. Our analysis demonstrates that awareness and personal and social norms are as important as monetary factors. Moreover, education and structural dwelling factors significantly affect households’ actions. These results have implications for governmental policies aimed at reducing residential CO2 footprints and facilitating demand-side solutions in a transition to low-carbon economy.

Niamir, L, Kiesewetter, G, Wagner, F, Schöpp, W, Filatova, T, Voinov, A & Bressers, H 2020, 'Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions', Climatic Change.
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© 2019, The Author(s). In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally imp...

Niu, F, Zhao, S, Qiu, X & Zhang, D 2020, 'A note on wind velocity and pressure spectra inside compact spherical porous microphone windscreens', JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, vol. 147, no. 1, pp. EL43-EL49.
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Niu, K, Guo, J, Pan, Y, Gao, X, Peng, X, Li, N & Li, H 2020, 'Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data', COMPLEXITY, vol. 2020.
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Niu, T, Wang, J, Lu, H, Yang, W & Du, P 2020, 'Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting', Expert Systems with Applications, vol. 148.
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© 2020 Intelligent financial forecasting modeling plays an important role in facilitating investment-related decision-making activities in financial markets. However, accurate multivariate financial time series forecasting remains a challenge due to its complex nonlinear pattern. Aiming to fill the gap in the field, a novel forecasting framework, based on a two-stage feature selection model, deep learning model, and error correction model, is presented in this study, aiming at effectively capturing the nonlinearity inherent in multivariate financial time series. Concretely, the proposed two-stage feature selection model is utilized to determine the optimal feature set to further improve the generalization of the proposed deep learning model based on three deep learning units. Meanwhile, the error correction model is used to correct the forecasts and improve the accuracy further. To validate the performance of the forecasting framework, the case studies and the corresponding sensitivity analysis are carried out, consequently demonstrating its superiority, as compared to 16 benchmarks considered.

Nobakht, M, Sui, Y, Seneviratne, A & Hu, W 2020, 'PGFIT: Static permission analysis of health and fitness apps in IoT programming frameworks', Journal of Network and Computer Applications, vol. 152.
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© 2020 Popular Internet of Things (IoT) programming frameworks, such as Google Fit, enable third-party developers to build apps that store and retrieve user data from a variety of data sources such as wearable devices. Most of these apps, particularly those that are health and fitness-related, collect potentially sensitive personal data and send it to cloud servers. Analogous to Android OS, IoT programming frameworks often follow similar permission model; third-party apps on IoT platforms prompt users to grant the apps the access to their private data stored on cloud servers of IoT programming frameworks. Most users have a poor understanding of why these permissions are being asked. This can often lead to unnecessary permissions being granted, which in turn grant these apps with excessive privileges. Over-privileged apps might not be harmful to users when they are used as designed, however, they can potentially be exploited by a malicious actor in a cyber security attack. This is of particular concern with health and fitness apps, which may be exploited to leak highly sensitive personal information. This paper presents PGFIT, a static permission analysis tool that precisely and efficiently identifies privilege escalation in third-party apps built on top of a popular IoT programming framework, Google Fit. PGFIT extracts the set of requested permission scopes and the set of used data types in Google Fit-enabled apps to determine whether the requested permission scopes are actually necessary. PGFIT performs graph reachability analysis on inter-procedural control flow graph. PGFIT serves as a quality assurance tool for developers and a privacy checker for app users. We evaluated PGFIT using a set of 20 popular Google Fit-enabled apps downloaded from Google Play. Our tool successfully identified the unnecessary permission scopes granted in our data set apps and found that 6 (30%) of the 20 apps are over-privileged.

Nosouhi, MR, Sood, K, Yu, S, Grobler, M & Zhang, J 2020, 'PASPORT: A Secure and Private Location Proof Generation and Verification Framework', IEEE Transactions on Computational Social Systems.
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IEEE Recently, there has been a rapid growth in location-based systems and applications in which users submit their location information to service providers in order to gain access to a service, resource, or reward. We have seen that in these applications, dishonest users have an incentive to cheat on their location. Unfortunately, no effective protection mechanism has been adopted by service providers against these fake location submissions. This is a critical issue that causes severe consequences for these applications. Motivated by this, we propose the Privacy-Aware and Secure Proof Of pRoximiTy (PASPORT) scheme in this article to address the problem. Using PASPORT, users submit a location proof (LP) to service providers to prove that their submitted location is true. PASPORT has a decentralized architecture designed for ad hoc scenarios in which mobile users can act as witnesses and generate LPs for each other. It provides user privacy protection as well as security properties, such as unforgeability and nontransferability of LPs. Furthermore, the PASPORT scheme is resilient to prover-prover collusions and significantly reduces the success probability of Prover-Witness collusion attacks. To further make the proximity checking process private, we propose P-TREAD, a privacy-aware distance bounding protocol and integrate it into PASPORT. To validate our model, we implement a prototype of the proposed scheme on the Android platform. Extensive experiments indicate that the proposed method can efficiently protect location-based applications against fake submissions.

Noushini, A, Castel, A, Aldred, J & Rawal, A 2020, 'Chloride diffusion resistance and chloride binding capacity of fly ash-based geopolymer concrete', Cement and Concrete Composites, vol. 105.
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© 2019 This study evaluated the chloride diffusion resistance of low-calcium fly ash-based geopolymer concrete through electrical and bulk diffusion techniques. The geopolymer concretes were prepared using 12 different heat curing conditions; three temperatures of 60, 75 and 90 °C and four curing durations of 8, 12, 18 and 24 h, as well as ambient curing. The mechanical and transport properties and microstructural characteristics of the geopolymer concretes were examined. NT BUILD 492 chloride migration and ASTM C1556 bulk diffusion tests were carried out. Results showed that the chloride diffusion resistance and the chloride binding capacity of fly ash-based geopolymer concrete is very low. The fly ash-based geopolymer concrete appears to be suitable for applications where there are little or no chloride-related durability concerns.

Nuruzzaman, M, Ren, J, Liu, Y, Rahman, MM, Shon, HK & Naidu, R 2020, 'Hollow Porous Silica Nanosphere with Single Large Pore Opening for Pesticide Loading and Delivery', ACS APPLIED NANO MATERIALS, vol. 3, no. 1, pp. 105-113.
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Organ, B, Huang, Y, Zhou, JL, Yam, YS, Mok, WC & Chan, EFC 2020, 'Simulation of engine faults and their impact on emissions and vehicle performance for a liquefied petroleum gas taxi', Science of the Total Environment, vol. 716.
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© 2020 Elsevier B.V. The deterioration of emissions control systems in a spark ignition engine is predominantly a gradual process of wear and tear occurring as vehicles accumulate mileage. As new innovations in engine and emissions technology have been progressively introduced to meet lower emissions targets, the impact of gradual deterioration of hardware has become more challenging to identify and quantify in the repair industry. When a pioneering emissions control programme utilising remote sensing to detect high emitting gasoline and liquefied petroleum gas (LPG) vehicles was to be introduced in Hong Kong, it became apparent the repair industry needed specialised training to assist with identifying the types of failures which would lead to high vehicle emissions. To identify the impact of hardware deterioration and failures, a Toyota Crown Comfort LPG taxi was used to demonstrate simulated failures of engine hardware systems to measure their impact on emissions, fuel consumption and drivability using a chassis dynamometer. This novel study simulated a broad range of deterioration and failures covering the intake, fuel supply, ignition, and exhaust systems. The results of the study showed significant THC and CO increases of up to 317% (0.604 g/km) and 782% (5.351 g/km) respectively for a simulated oxygen sensor high voltage fault and a sticky mixture control valve. The largest increase in NOx emissions was for restricted main fuel supply in the LPG vapouriser, producing an increase of 282% (1.41 g/km). Fuel consumption varied with increases of up to 15.5%. Drivability was impacted with poor idle from a number of faults and especially by a worn throttlebody which produced rough acceleration characteristics as well. This study clearly highlights the importance of having properly maintained emissions and engine hardware systems to achieve optimal fuel economy and compliant emissions levels, which could be reproduced in other regions for prescribed emissions regul...

Otte, M, Sofge, D & Fitch, R 2020, 'Guest editorial: Special issue on robot communication challenges: real-world problems, systems, and methods', Autonomous Robots, vol. 44, no. 1.
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Ou, T, Wang, D, Xin, Z, Tan, J, Wu, C, Guo, Q & Zhang, Y 2020, 'Full-scale tests on the mechanical behaviour of a continuously welded stainless steel roof under wind excitation', Thin-Walled Structures, vol. 150.
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© 2020 Elsevier Ltd The wind uplift performance of the continuously welded stainless steel roof (CWSSR) system adopted in the Zhaoqing New District Sports (ZNDS) Center of China is investigated in this study. To determine the optimal welding program and examine the mechanical properties of the continuously welded stainless steel joints, uniaxial tensile testing is first conducted on 27 specimens with tension-shear and tension-bending types. Two CWSSR specimens, one that is square-shaped with a horizontal layout and one that is rectangular-shaped with an inclination layout of 10.71°, are further tested under dynamic and static ultimate wind uplift loadings to explore the wind uplift capacity. All specimens are full-size, and the corresponding materials, structural details and construction technologies are kept the same as the actual building to ensure the authenticity of the testing investigations. The testing results indicate that the integrated and sealed CWSSR system has a clear force transmission mechanism and a remarkable wind resistance performance. The welded joints achieve the best performance, and the mechanical behaviours are equivalent to those of the base material under the continuously welded conditions including an electric current of 65 A and a moving velocity of 750 mm/s. An excellent dynamic wind suction performance is achieved under 5000 five-level cumulative loading cycles with a maximum pressure of 5400 Pa. The static ultimate pressure reaches 9400 Pa for the square specimen and 10,400 Pa for the rectangular specimen. Damage observations show that no tearing or rupture failures are observed for the CWSSR system. The investigation results contribute the most to the safe design of the ZNDS Center and are expected to provide guidelines for future applications of the CWSSR system.

Ouyang, D, Yuan, L, Qin, L, Chang, L, Zhang, Y & Lin, X 2020, 'Efficient Shortest Path Index Maintenance on Dynamic Road Networks with Theoretical Guarantees.', PVLDB, vol. 13, pp. 602-615.

Pan, C, Bao, Y & Xu, B 2020, 'Seasonal variation of antibiotics in surface water of Pudong New Area of Shanghai, China and the occurrence in typical wastewater sources', CHEMOSPHERE, vol. 239.
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Pathak, N, Phuntsho, S, Van, HT, Johir, MAH, Ghaffour, N, Leiknes, T, Fujioka, T & Shon, HK 2020, 'Simultaneous nitrification-denitrification using baffled osmotic membrane bioreactor-microfiltration hybrid system at different oxic-anoxic conditions for wastewater treatment', JOURNAL OF ENVIRONMENTAL MANAGEMENT, vol. 253.
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Paull, NJ, Krix, D, Irga, PJ & Torpy, FR 2020, 'Airborne particulate matter accumulation on common green wall plants.', International journal of phytoremediation, pp. 1-13.
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In order to better design greening systems for effective particulate matter (PM) removal, it is important to understand the impact leaf traits have on PM deposition. There are however, inconsistences amongst the leaf traits that have previously been correlated with PM accumulation. The aim of this paper was to identify vegetation characteristics of green wall plants that were associated with the accumulation of particulate matter. To determine patterns associated with different leaf morphologies, eleven common ornamental plant species were sampled across 15 sites, over a 6 month duration. PM deposition was determined gravimetrically and its associated size fractions determined microscopically. Linear mixed models were used to identify statistical patterns relating to differences in PM deposition across plant species. PM deposition and the relative frequencies of particle size fractions were found to be statistically different among species, sites and months. Green wall plants were shown to be effective at PM accumulation as all of the assessed plant species had equivalent PM removal efficiency, with minimal evidence of influential leaf characteristics that could enhance PM removal.

Pei, T, Zhang, J, Li, L & Hao, X 2020, 'A fault locating method for PV arrays based on improved voltage sensor placement', Solar Energy, vol. 201, pp. 279-297.
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© 2020 International Solar Energy Society Solar photovoltaic (PV) power generation has been widely used because of its environment-friendly advantages. However, operational faults in PV arrays have always been one of the critical factors affecting the PV system's power-generation efficiency and life cycle. This paper proposes a fault locating strategy, which does not need the current sensor in every PV string and can also minimize the number of voltage sensors between strings, to accurately locate the faulty PV modules in a PV array. A variety of faults including open circuit, short circuit, degradation and partial shading faults are considered, and a universal method is proposed in this paper to locate the faulty modules under these faults. In this method, the voltage sensors are deployed through differentiating the parity of the number of strings for the PV array, while the fault locating rules are formulated under each of the afore-mentioned faults, respectively. Compared with existing fault locating methods, the proposed locating technique is shown to be effective in the application to PV arrays with any size and capacity, and it can accurately locate each faulty module for those faults, especially for degradation and partial shading faults. Thus, it is helpful in PV array dynamic reconfiguration and maintenance cost reduction.

Pham, D, Nguyen, T, Le, H, Nguyen, L, Thai, N, Phan, T, Pham, H, Duong, A & Bui, L 2020, 'Adaptive neural network based dynamic surface control for uncertain dual arm robots', International Journal of Dynamics and Control.
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The paper discusses an adaptive strategy to effectively control nonlinear manipulation motions of a dual arm robot (DAR) under system uncertainties including parameter variations, actuator nonlinearities and external disturbances. It is proposed that the control scheme is first derived from the dynamic surface control (DSC) method, which allows the robot's end-effectors to robustly track the desired trajectories. Moreover, since exactly determining the DAR system's dynamics is impractical due to the system uncertainties, the uncertain system parameters are then proposed to be adaptively estimated by the use of the radial basis function network (RBFN). The adaptation mechanism is derived from the Lyapunov theory, which theoretically guarantees stability of the closed-loop control system. The effectiveness of the proposed RBFN-DSC approach is demonstrated by implementing the algorithm in a synthetic environment with realistic parameters, where the obtained results are highly promising.

Pham, M, Hoang, DB & Chaczko, Z 2020, 'Congestion-Aware and Energy-Aware Virtual Network Embedding', IEEE/ACM Transactions on Networking, pp. 1-14.
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Pham, TT, Ngo, HH, Tran, VS & Nguyen, MK 2020, 'Removal of As (V) from the aqueous solution by a modified granular ferric hydroxide adsorbent', Science of the Total Environment, vol. 706.
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© 2019 A novel adsorbent was prepared in granular form from iron (III) hydroxide and other additives to remove arsenate (As (V)) from aqueous solution. Adsorption of As (V) onto the adsorbent in batch experiments was analyzed to understand the adsorption mechanism, affecting factors, and adsorption isotherms. The optimal working conditions for the developed adsorbent were at pH 3, 30 °C and 50 g/L. The adsorption of arsenate onto the adsorbent occurred rapidly in the first 10 min and reached equilibrium in 2 h. The Langmuir isotherm was found to be best fitted the adsorption. The pre- and post-adsorption adsorbents were characterized by SEM, BET, FTIR, XRD, and Zeta potential techniques. Experimental results clearly demonstrated the potential impact of elemental composition, crystallinity, surface morphology, and other physico-chemical properties of the adsorbent on the adsorption performance variety. The experimental results with the pilot scale treatment system revealed that the adsorbent can be applied successfully and lead to a very efficient drinking water treatment system, at a competitive cost compared to the water market in Hanoi, Vietnam.

Pileggi, SF 2020, 'Is the World Becoming a Better or a Worse Place?A Data-Driven Analysis', Sustainability, vol. 12, no. 1.
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Is the World becoming a better or a worse place to live? In this paper, we propose a tool that can help to answer the question by combining a number of global indicators belonging to multiple categories. The proliferation of statistical data about various aspects of the World performance may suggest that it should be “easy” to evaluate the overall success of human enterprise on this planet. Moreover, it also points out the intrinsic importance in the selection of indicators. However, people have different values, biases, and preferences about the importance of various indicators, making it almost impossible to find an objective answer to this question. To address the variety and the heterogeneity of available indicators and world views, we present the analysis of global World performance as a multi-criteria decision problem, making sure that the assessment method remains as transparent as possible. By dynamically selecting a set of indicators of interest, defining the weights that we attach to various indicators and specifying the desired trends associated with each indicator, we make the assessment adaptive to individual values. We also try to deal with the inherent bias that may exist in the set of indicators that are chosen. As a study case, from various data sets that are openly available online, we have selected several that are most relevant and easy to interpret in the context of the question in the title of the paper. We demonstrate how the choice of personal preferences, or weights, can strongly change the result. Our method also provides analysis of the weights space, showing how results for particular value sets compare to the average and extreme (optimistic and pessimistic) combinations of weights that may be chosen by users.

Poostchi, H & Piccardi, M 2020, 'BiLSTM-SSVM: Training the BiLSTM with a Structured Hinge Loss for Named-Entity Recognition', IEEE Transactions on Big Data, pp. 1-1.
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Popovic, M, Vidal-Calleja, T, Hitz, G, Chung, JJ, Sa, I, Siegwart, R & Nieto, J 2020, 'An informative path planning framework for UAV-based terrain monitoring', AUTONOMOUS ROBOTS.
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Pradeepkumar, A, Amjadipour, M, Mishra, N, Liu, C, Fuhrer, MS, Bendavid, A, Isa, F, Zielinski, M, Sirikumara, HI, Jayasekara, T, Gaskill, DK & Iacopi, F 2020, 'p-Type Epitaxial Graphene on Cubic Silicon Carbide on Silicon for Integrated Silicon Technologies', ACS APPLIED NANO MATERIALS, vol. 3, no. 1, pp. 830-841.
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Pradhan, S & Qiu, X 2020, 'A 5-stage active control method with online secondary path modelling using decorrelated control signal', Applied Acoustics, vol. 164.
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© 2020 Elsevier Ltd Time-varying primary and secondary paths in active control systems degrade the noise control performance. In this paper, a practical method is proposed for active control systems with online secondary path modelling using the decorrelated control signal. The proposed method consists of 5 stages, i.e., primary path estimation, controller initialization, secondary path estimation, primary and secondary path changing detection, and active control operation, where the speed of the secondary path modelling is improved by incorporating decorrelation filters. The simulation results demonstrate the proposed method is capable of tracking the changes in both primary and secondary paths, remodelling the secondary path, and maintaining the noise reduction performance and stability of the system when both primary and secondary paths change.

Prakash, S, Joshi, S, Bhatia, T, Sharma, S, Samadhiya, D, Shah, RR, Kaiwartya, O & Prasad, M 2020, 'Characteristic of enterprise collaboration system and its implementation issues in business management', International Journal of Business Intelligence and Data Mining, vol. 16, no. 1, pp. 49-65.
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© 2020 Inderscience Enterprises Ltd. Collaboration is an extremely useful area for the most of the enterprise systems particularly within Web 2.0 and Enterprise 2.0. The collaboration provides help in enterprise collaboration system (ECS) to achieve the desired goal by unifying completed tasks of employees or people working on a similar or the same task. Thus, the collaboration systems have witnessed significant attention. The ECS provides consistent and off-the-shelf support to processes and managements within organisations. Management techniques of the ECS may be useful to a community which manages ECS systems for collaboration. In this context, this paper focuses on enterprise collaboration system and answers critical questions related to ECS including: 1) what does collaboration really means for an enterprise system; 2) how can the collaboration help to improve internal processes and management of the system; 3) how it is helpful to improve interactions with customers and partners?

Pramanik, BK, Long, DN & Hai, FI 2020, 'Extraction of strategically important elements from brines: Constraints and opportunities', WATER RESEARCH, vol. 168.
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Priharsari, D, Abedin, B & Mastio, E 2020, 'Value co-creation in firm sponsored online communities', Internet Research, vol. ahead-of-print, no. ahead-of-print.
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Punetha, P, Nimbalkar, S & Khabbaz, H 2020, 'Evaluation of additional confinement for three-dimensional geoinclusions under general stress state', Canadian Geotechnical Journal, vol. 57, no. 3, pp. 453-461.
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Three-dimensional cellular geoinclusions (e.g., geocells, scrap tires) offer all-around confinement to the granular infill materials, thus improving their strength and stiffness. The accurate evaluation of extra confinement offered by these geoinclusions is essential for predicting their performance in the field. The existing models to evaluate the additional confinement are based on either a plane-strain or axisymmetric stress state. However, these geoinclusions are more likely to be subjected to the three-dimensional stresses in actual practice. This note proposes a semi-empirical model to evaluate the additional confinement provided by cellular geoinclusions under the three-dimensional stress state. The proposed model is successfully validated against the experimental data. A parametric study is conducted to investigate the influence of input parameters on additional confinement. Results reveal that the simplification of the three-dimensional stress state into axisymmetric or plane-strain condition has resulted in inaccurate and unreliable results. The extra confinement offered by the geoinclusion shows substantial variation along the intermediate and minor principal stress directions depending on the intermediate principal stress, infill soil, and geoinclusion properties. The magnitude of additional confinement increases with an increase in the geoinclusion modulus. The findings are crucial for accurate assessment of the in situ performance of three-dimensional cellular geoinclusions.

Qi, H, Chen, Y, Zhang, N, Zhang, B, Wang, D & Tan, B 2020, 'Improvement of both handling stability and ride comfort of a vehicle via coupled hydraulically interconnected suspension and electronic controlled air spring', Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering, vol. 234, no. 2-3, pp. 552-571.
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© IMechE 2019. The trade-off between handling stability and ride comfort is a disadvantage for the bus fitted with passive suspension due to its high center of gravity and heavy load. A novel suspension configuration with both hydraulically interconnected suspension and electronic controlled air spring is created to handle this conflicting requirement. The proposed whole vehicle system model has three subsystems: a 9-degree-of-freedom vehicle multi-body model, hydraulically interconnected suspension model, and electronic controlled air spring. The electronic controlled air spring comprises an air spring and an auxiliary air chamber, and its height can be adjusted by a fuzzy controller. Then, analytical work is performed to evaluate the handling stability and ride comfort of the vehicle with different suspension configurations under various maneuvers and suspension height modes. Finally, the vehicle on-road test is conducted to experimentally validate the proposed models. Both analytical and experimental results indicate that the vehicle fitted with hydraulically interconnected suspension and electronic controlled air spring can obtain high performance for both handling stability and ride comfort.

Qiao, Y, Sun, X & Yu, N 2020, 'Local Equivalence of Multipartite entanglement', IEEE Journal on Selected Areas in Communications.
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IEEE Let R be an invariant polynomial ring of a reductive group acting on a vector space, and let d be the minimum integer such that R is generated by those polynomials in R of degree no more than d. To upper bound such d is a long standing open problem since the very initial study of the invariant theory in the 19th century. Motivated by its significant role in characterizing multipartite entanglement, we study the invariant polynomial rings of local unitary groups – the direct product of unitary groups acting on the tensor product of Hilbert spaces, and local general linear groups – the direct product of general linear groups acting on the tensor product of Hilbert spaces. For these two group actions, we prove explicit upper bounds on the degrees needed to generate the corresponding invariant polynomial rings. On the other hand, systematic methods are provided to construct all homogeneous polynomials that are invariant under these two groups for any fixed degree. Thus, our results can be regarded as a complete characterization of the invariant polynomial rings. As an interesting application, we show that multipartite entanglement is additive in the sense that two multipartite states are local unitary equivalent if and only if r-copies of them are local unitary equivalent for some r.

Qin, C, Andrew Zhang, J, Huang, X & Jay Guo, Y 2020, 'Virtual-Subarray-Based Angle-of-Arrival Estimation in Analog Antenna Arrays', IEEE Wireless Communications Letters, vol. 9, no. 2, pp. 194-197.
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© 2012 IEEE. Angle-of-arrival (AoA) estimation is a challenging problem for analog antenna arrays. Typical algorithms are based on beam scanning which can be time-consuming. In this letter, we propose a virtual-subarray based recursive AoA estimation scheme that can get an AoA estimate from every two measurements and recursively improve the performance by updating beamforming weights with soft probability-based information. Simulation results validate the high efficiency of the proposed scheme, demonstrating its superiority for initial AoA estimation in analog arrays.

Qu, X, Yu, Y, Zhou, M, Lin, C-T & Wang, X 2020, 'Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach', APPLIED ENERGY, vol. 257.
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Raghunathan, K, Soundarapandian, RK, Gandomi, AH, Ramachandran, M, Patan, R & Madda, RB 2020, 'Duo-Stage Decision: A Framework for Filling Missing Values, Consistency Check, and Repair of Decision Matrices in Multicriteria Group Decision Making', IEEE Transactions on Engineering Management.
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IEEE With high uncertainty and vagueness in the decision-making process, maintaining consistency in the decision matrix is an open challenge. Previous studies on the intuitionistic fuzzy (IF) theory focused on the consistency of preference relation but ignored consistency of the decision matrix. In this article, efforts are made to propose a new duo-stage decision framework in the context of IF set to better circumvent the challenge. Often, decision makers (DMs) hesitate to provide certain values in the decision matrix that are filled randomly, resulting in inaccuracies in the decision-making process. To alleviate this issue, a new systematic procedure is developed that sensibly fills the missing data in the first stage. Following the first stage, consistency of the decision matrix is determined by extending Cronbach's alpha coefficient to IF context. Furthermore, efforts are made to repair inconsistent decision matrix iteratively. In the second stage, a new aggregation operator is presented for aggregation of DMs’ preferences. Also, a new mathematical model is proposed for criteria weight estimation, and a procedure is developed for ranking objects. The practical use of the proposed framework is demonstrated using a numerical example, and the strengths and weaknesses of the framework are investigated.

Rahman, ML, Zhang, JA, Huang, X, Guo, YJ & Heath Jr, RW 2020, 'Framework for a Perceptive Mobile Network using Joint Communication and Radar Sensing', IEEE Transactions on Aerospace and Electronic Systems, pp. 1-1.
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Rajabi, A, Eskandari, M, Ghadi, MJ, Li, L, Zhang, J & Siano, P 2020, 'A comparative study of clustering techniques for electrical load pattern segmentation', Renewable and Sustainable Energy Reviews.
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© 2019 Elsevier Ltd Smart meters have been widely deployed in power networks since the last decade. This trend has resulted in an enormous volume of data being collected from the electricity customers. To gain benefits for various stakeholders in power systems, proper data mining techniques, such as clustering, need to be employed to extract the underlying patterns from energy consumptions. In this paper, a comparative study of different techniques for load pattern clustering is carried out. Different parameters of the methods that affect the clustering results are evaluated and the clustering algorithms are compared for two data sets. In addition, the two suitable and commonly used data size reduction techniques and feature definition/extraction methods for load pattern clustering are analysed. Furthermore, the existing studies on clustering of electricity customers are reviewed and the main results are highlighted. Finally, the future trends and major applications of clustering consumption patterns are outlined to inform industry practitioners and academic researchers to optimize smart meter operational use and effectiveness.

Rana, HK, Akhtar, MR, Islam, MB, Ahmed, MB, Lió, P, Huq, F, Quinn, JMW & Moni, MA 2020, 'Machine Learning and Bioinformatics Models to Identify Pathways that Mediate Influences of Welding Fumes on Cancer Progression', Scientific Reports, vol. 10, no. 1.
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© 2020, The Author(s). Welding generates and releases fumes that are hazardous to human health. Welding fumes (WFs) are a complex mix of metallic oxides, fluorides and silicates that can cause or exacerbate health problems in exposed individuals. In particular, WF inhalation over an extended period carries an increased risk of cancer, but how WFs may influence cancer behaviour or growth is unclear. To address this issue we employed a quantitative analytical framework to identify the gene expression effects of WFs that may affect the subsequent behaviour of the cancers. We examined datasets of transcript analyses made using microarray studies of WF-exposed tissues and of cancers, including datasets from colorectal cancer (CC), prostate cancer (PC), lung cancer (LC) and gastric cancer (GC). We constructed gene-disease association networks, identified signaling and ontological pathways, clustered protein-protein interaction network using multilayer network topology, and analyzed survival function of the significant genes using Cox proportional hazards (Cox PH) model and product-limit (PL) estimator. We observed that WF exposure causes altered expression of many genes (36, 13, 25 and 17 respectively) whose expression are also altered in CC, PC, LC and GC. Gene-disease association networks, signaling and ontological pathways, protein-protein interaction network, and survival functions of the significant genes suggest ways that WFs may influence the progression of CC, PC, LC and GC. This quantitative analytical framework has identified potentially novel mechanisms by which tissue WF exposure may lead to gene expression changes in tissue gene expression that affect cancer behaviour and, thus, cancer progression, growth or establishment.

Raoufi, MA, Razavi Bazaz, S, Niazmand, H, Rouhi, O, Asadnia, M, Razmjou, A & Ebrahimi Warkiani, M 2020, 'Fabrication of unconventional inertial microfluidic channels using wax 3D printing.', Soft matter, vol. 16, no. 10, pp. 2448-2459.
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Inertial microfluidics has emerged over the past decade as a powerful tool to accurately control cells and microparticles for diverse biological and medical applications. Many approaches have been proposed to date in order to increase the efficiency and accuracy of inertial microfluidic systems. However, the effects of channel cross-section and solution properties (Newtonian or non-Newtonian) have not been fully explored, primarily due to limitations in current microfabrication methods. In this study, we overcome many of these limitations using wax 3D printing technology and soft lithography through a novel workflow, which eliminates the need for the use of silicon lithography and polydimethylsiloxane (PDMS) bonding. We have shown that by adding dummy structures to reinforce the main channels, optimizing the gap between the dummy and main structures, and dissolving the support wax on a PDMS slab to minimize the additional handling steps, one can make various non-conventional microchannels. These substantially improve upon previous wax printed microfluidic devices where the working area falls into the realm of macrofluidics rather than microfluidics. Results revealed a surface roughness of 1.75 μm for the printed channels, which does not affect the performance of inertial microfluidic devices used in this study. Channels with complex cross-sections were fabricated and then analyzed to investigate the effects of viscoelasticity and superposition on the lateral migration of the particles. Finally, as a proof of concept, microcarriers were separated from human mesenchymal stem cells using an optimized channel with maximum cell-holding capacity, demonstrating the suitability of these microchannels in the bioprocessing industry.

Rasouli, H & Fatahi, B 2020, 'Geofoam blocks to protect buried pipelines subjected to strike-slip fault rupture', Geotextiles and Geomembranes.
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© 2019 Elsevier Ltd This paper proposes using geofoam blocks to improve the safety of buried steel pipelines under permanent ground deformation due to strike-slip fault rupture. Since these geofoam blocks are deformable, they can compress during fault rupture and thus reduce the pressure imposed on the pipeline by the surrounding soil. This means that the pipe can sustain a higher level of tectonic deformations. For the pipeline system adopted in this study, the geofoam blocks consist of two 1 m thick blocks at each side and another on the top of the pipeline. The effectiveness of this configuration is then assessed in comparison to the conventional buried pipeline by three dimensional numerical simulations that consider the interaction between soil and structure and the impact of critical parameters such as the pipeline-fault trace crossing angle, geofoam blocks thickness and the internal pressure of the pipeline. The results indicated that the geofoam blocks reduced the axial tensile strain of non-pressurised pipeline from the unacceptable 4.16% to the safe level of 0.75% when the crossing angle was 135°. In addition, geofoam blocks successfully decreased the maximum ovalisation parameter and compressive strain of the non-pressurised pipeline from 0.237 and −25.8% to 0.065 and −0.47%, respectively when the crossing angle was 65°.

Rasouli, H, Fatahi, B & Nimbalkar, S 2020, 'Liquefaction and Post-liquefaction Assessment of Lightly Cemented Sands', Canadian Geotechnical Journal.
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Rathee, M, Kumar, S, Gandomi, AH, Dilip, K, Balusamy, B & Patan, R 2020, 'Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks', IEEE Transactions on Engineering Management.
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IEEE Existing routing protocols for wireless sensor networks (WSNs) focus primarily either on energy efficiency, quality of service (QoS), or security issues. However, a more holistic view of WSNs is needed, as many applications require both QoS and security guarantees along with the requirement of prolonging the lifetime of the network. The limited energy capacity of sensor nodes forces a tradeoff to be made between network lifetime, QoS, and security. To address these issues, an ant colony optimization based QoS aware energy balancing secure routing (QEBSR) algorithm for WSNs is proposed in this article. Improved heuristics for calculating the end-to-end delay of transmission and the trust factor of the nodes on the routing path are proposed. The proposed algorithm is compared with two existing algorithms: distributed energy balanced routing and energy efficient routing with node compromised resistance. Simulation results show that the proposed QEBSR algorithm performed comparatively better than the other two algorithms.

Razavi Bazaz, S, Mashhadian, A, Ehsani, A, Saha, SC, Krüger, T & Ebrahimi Warkiani, M 2020, 'Computational inertial microfluidics: a review.', Lab on a chip, vol. 20, no. 6, pp. 1023-1048.
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Since the discovery of inertial focusing in 1961, numerous theories have been put forward to explain the migration of particles in inertial flows, but a complete understanding is still lacking. Recently, computational approaches have been utilized to obtain better insights into the underlying physics. In particular, fundamental aspects of particle focusing inside straight and curved microchannels have been explored in detail to determine the dependence of focusing behavior on particle size, channel shape, and flow Reynolds number. In this review, we differentiate between the models developed for inertial particle motion on the basis of whether they are semi-analytical, Navier-Stokes-based, or built on the lattice Boltzmann method. This review provides a blueprint for the consideration of numerical solutions for modeling of inertial particle motion, whether deformable or rigid, spherical or non-spherical, and whether suspended in Newtonian or non-Newtonian fluids. In each section, we provide the general equations used to solve particle motion, followed by a tutorial appendix and specified sections to engage the reader with details of the numerical studies. Finally, we address the challenges ahead in the modeling of inertial particle microfluidics for future investigators.

Razzak, I, Saris, RA, Blumenstein, M & Xu, G 2020, 'Integrating joint feature selection into subspace learning: A formulation of 2DPCA for outliers robust feature selection', Neural Networks, vol. 121, pp. 441-451.
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© 2019 Elsevier Ltd Since the principal component analysis and its variants are sensitive to outliers that affect their performance and applicability in real world, several variants have been proposed to improve the robustness. However, most of the existing methods are still sensitive to outliers and are unable to select useful features. To overcome the issue of sensitivity of PCA against outliers, in this paper, we introduce two-dimensional outliers-robust principal component analysis (ORPCA) by imposing the joint constraints on the objective function. ORPCA relaxes the orthogonal constraints and penalizes the regression coefficient, thus, it selects important features and ignores the same features that exist in other principal components. It is commonly known that square Frobenius norm is sensitive to outliers. To overcome this issue, we have devised an alternative way to derive objective function. Experimental results on four publicly available benchmark datasets show the effectiveness of joint feature selection and provide better performance as compared to state-of-the-art dimensionality-reduction methods.

Ren, LF, Ngo, HH, Bu, C, Ge, C, Ni, SQ, Shao, J & He, Y 2020, 'Novel external extractive membrane bioreactor (EMBR) using electrospun polydimethylsiloxane/polymethyl methacrylate membrane for phenol-laden saline wastewater', Chemical Engineering Journal, vol. 383.
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© 2019 Elsevier B.V. Phenol-laden saline wastewaters can adversely affect water, groundwater, soil, organisms and ecosystems. Given that frequently-used biodegradation process is generally inhibited by salinity, this work aims to solve the problem through a novel configuration of external extractive membrane bioreactor (EMBR) for the objective of simultaneous phenol permeation, salt rejection and biodegradation. Contact angles of 160.9 ± 2.2° (water) and 0.0° (phenol) were observed on the electrospun polydimethylsiloxane/polymethyl methacrylate (PDMS/PMMA) membrane, suggesting this superhydrophobic/superorganophilic membrane was suitable for separating phenol from water-soluble salt. Phenol ranging from 14.1 ± 2.7 to 290.7 ± 10.4 mg/L (stages 1 to 8) was continuously permeated and completely biodegraded in external EMBR under a hydraulic retention time (HRT) of 24 h, which corresponded with detoxification performance improving from 6.3% to 70.5%. After phenol exposure of 8 stages, Proteobacteria and Saccharibacteria became the main phyla for microorganisms. Enumeration of functional genes (phe, amoA, narG, nirS) confirmed that phenol was mainly consumed by denitrifiers and other heterotrophs as the sole carbon and energy source via oxidation and ring cleavage. As bacterial responses, these genes’ proliferation was promoted under low phenol concentrations but inhibited under high phenol concentrations. Meanwhile, results of extracellular polymeric substances revealed that protein was the key substance in toxicity resistance, phenol adsorption and transfer.

Ren, Y, Huu, HN, Guo, W, Wang, D, Peng, L, Ni, B-J, Wei, W & Liu, Y 2020, 'New perspectives on microbial communities and biological nitrogen removal processes in wastewater treatment systems', BIORESOURCE TECHNOLOGY, vol. 297.
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Richards, D, Ranjbartabar, H, Bilgin, A & Kutay, C 2020, 'First Impressions Count! The Role of the Human’s Emotional State on Rapport Established with an Empathic versus Neutral Virtual Therapist', IEEE Transactions on Affective Computing, pp. 1-3.
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Intelligent virtual agents are being endowed with empathic behaviours to perform roles such as virtual therapists. Studies often evaluate the level of rapport established, but do not measure the therapeutic benefit and the relative advantage of empathic versus neutral behaviours. We have created two virtual (empathic/neutral) therapists. Our experiment with 63 participants consisted of one within-subjects (empathic/neutral) and one between-subjects (order) factors. Regardless of the virtual therapist used, improvements in baseline emotion were reported after the first interaction (time one) and further improvement after the second interaction (time two). Our study reveals that if the human initially expresses strong emotional feeling for a problem they are facing, rapport will be higher for the empathic therapist and the level of rapport established at the first meeting will persist regardless of whether the second encounter used empathic or neutral dialogue. Conversely, participants experiencing low emotional feeling reported greater rapport with the neutral therapist, and that level of rapport persisted in the second encounter with the alternative therapist. This study shows that an empathic agent will not necessarily build more rapport or deliver better emotional outcomes than a neutral agent. Further studies are needed to determine when tailoring and complex behaviours are justified.

Rouf, RA, Jahan, N, Alam, KCA, Sultan, AA, Saha, BB & Saha, SC 2020, 'Improved cooling capacity of a solar heat driven adsorption chiller', CASE STUDIES IN THERMAL ENGINEERING, vol. 17.
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Rzhevskiy, AS, Bazaz, SR, Ding, L, Kapitannikova, A, Sayyadi, N, Campbell, D, Walsh, B, Gillatt, D, Warkiani, ME & Zvyagin, AV 2020, 'Rapid and label-free isolation of tumour cells from the urine of patients with localised prostate cancer using inertial microfluidics', Cancers, vol. 12, no. 1.
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. During the last decade, isolation of circulating tumour cells via blood liquid biopsy of prostate cancer (PCa) has attracted significant attention as an alternative, or substitute, to conventional diagnostic tests. However, it was previously determined that localised forms of PCa shed a small number of cancer cells into the bloodstream, and a large volume of blood is required just for a single test, which is impractical. To address this issue, urine has been used as an alternative to blood for liquid biopsy as a truly non-invasive, patient-friendly test. To this end, we developed a spiral microfluidic chip capable of isolating PCa cells from the urine of PCa patients. Potential clinical utility of the chip was demonstrated using anti-Glypican-1 (GPC-1) antibody as a model of the primary antibody in immunofluorescent assay for identification and detection of the collected tumour cells. The microchannel device was first evaluated using DU-145 cells in a diluted Dulbecco’s phosphate-buffered saline sample, where it demonstrated >85 (±6) % efficiency. The microchannel proved to be functional in at least 79% of cases for capturing GPC1+ putative tumour cells from the urine of patients with localised PCa. More importantly, a correlation was found between the amount of the captured GPC1+ cells and crucial diagnostic and prognostic parameter of localised PCa—Gleason score. Thus, the technique demonstrated promise for further assessment of its diagnostic value in PCa detection, diagnosis, and prognosis.

Saki, M, Abolhasan, M & Lipman, J 2020, 'A Novel Approach for Big Data Classification and Transportation in Rail Networks', IEEE Transactions on Intelligent Transportation Systems, pp. 1-11.
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Salek, F, Anshu, A, Hsieh, M-H, Jain, R & Fonollosa, JR 2020, 'One-Shot Capacity Bounds on the Simultaneous Transmission of Classical and Quantum Information', IEEE Transactions on Information Theory, vol. 66, no. 4, pp. 2141-2164.
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Samaei, SM, Gato-Trinidad, S & Altaee, A 2020, 'Performance evaluation of reverse osmosis process in the post-treatment of mining wastewaters: Case study of Costerfield mining operations, Victoria, Australia', Journal of Water Process Engineering, vol. 34.
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© 2019 Elsevier Ltd Reverse Osmosis (RO) membrane has been used for treatment and purification of industrial wastewaters including those from the mining industry before being discharged to receiving body or reuse for applications that are fit for purpose. This study evaluates the performance of Reverse Osmosis (RO) plant as a post-treatment process in mining operations in Victoria, Australia. The data analysis shows that the RO unit significantly improves the quality of the final permeate before discharged to surface waters. Considering average rejection efficiency for the entire evaluated period, turbidity, total dissolved solids (TDS), Antimony, Arsenic, Nickel, Zinc and Iron concentrations are reduced by 85 %, 96 %, 95 %, 66 %, 82 %, 48 % and 10 %, respectively in the RO permeate compared to the feed water. Although the quality of the RO permeate was in a desirable condition in most days of the evaluated years, TDS concentrations on the October 11 and 20,2016 and November 14, 2017 were higher than the limits specified by Environmental Protection Authority (EPA) Victoria. Anomalies regarding antimony levels in RO permeate occurred in September and November 2016 as well as August 2017 due to inconsistency in the RO feed quality. This resulted in fouling of RO membranes and contributed to discharge non-compliance with EPA licence conditions on TDS and antimony. Discharge to waterways was suspended over the period when TDS and antimony contents were above the EPA guidelines. Changes in the pre-treatment reduced the turbidity of the feed water and improved the performance of the RO system to comply with the discharge guidelines.

Samavati, M, Essam, D, Nehring, M & Sarker, R 2020, 'Production planning and scheduling in mining scenarios under IPCC mining systems', COMPUTERS & OPERATIONS RESEARCH, vol. 115.
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Sameen, MI, Pradhan, B & Lee, S 2020, 'Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment', Catena, vol. 186.
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© 2019 Elsevier B.V. This study developed a deep learning based technique for the assessment of landslide susceptibility through a one-dimensional convolutional network (1D-CNN) and Bayesian optimisation in Southern Yangyang Province, South Korea. A total of 219 slide inventories and 17 slide conditioning variables were obtained for modelling. The data showed a complex scenario. Some past slides have spread over steep lands, while others have spread through flat terrain. Random forest (RF) served to keep only important factors for further analysis as a pre-processing measure. To select CNN hyperparameters, Bayesian optimization was used. Three methods contributed to overcoming the overfitting issue owing to small training data in our research. The selection of key factors by RF helped first of all to reduce information dimensionality. Second, the CNN model with 1D convolutions was intended to considerably decrease the number of its parameters. Third, a high rate of drop-out (0.66) helped reduce the CNN parameters. Overall accuracy, area under the receiver operating characteristics curve (AUROC) and 5-fold cross-validation were used to evaluate the models. CNN performance was compared to ANN and SVM. CNN achieved the highest accuracy on testing dataset (83.11%) and AUROC (0.880, 0.893, using testing and 5-fold CV, respectively). Bayesian optimization enhanced CNN accuracy by~3% (compared with default configuration). CNN could outperform ANN and SVM owing to its complicated architecture and handling of spatial correlations through convolution and pooling operations. In complex situations where some variables make a non-linear contribution to the occurrence of landslides, the method suggested could thus help develop landslide susceptibility maps.

Sameen, MI, Pradhan, B, Bui, DT & Alamri, AM 2020, 'Systematic sample subdividing strategy for training landslide susceptibility models', Catena, vol. 187.
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© 2019 Elsevier B.V. Current practice in choosing training samples for landslide susceptibility modelling (LSM) is to randomly subdivide inventory information into training and testing samples. Where inventory data differ in distribution, the selection of training samples by a random process may cause inefficient training of machine learning (ML)/statistical models. A systematic technique may, however, produce efficient training samples that well represent the entire inventory data. This is particularly true when inventory information is scarce. This research proposed a systemic strategy to deal with this problem based on the fundamental distribution of probabilities (i.e. Hellinger) and a novel graphical representation of information contained in inventory data (i.e. inventory information curve, IIC). This graphical representation illustrates the relative increase in available information with the growth of the training sample size. Experiments on a selected dataset over the Cameron Highlands, Malaysia were conducted to validate the proposed methods. The dataset contained 104 landslide inventories and 7 landslide-conditioning factors (i.e. altitude, slope, aspect, land use, distance from the stream, distance from the road and distance from lineament) derived from a LiDAR-based digital elevation model and thematic maps acquired from government authorities. In addition, three ML/statistical models, namely, k-nearest neighbour (KNN), support vector machine (SVM) and decision tree (DT), were utilised to assess the proposed sampling strategy for LSM. The impacts of model's hyperparameters, noise and outliers on the performance of the models and the shape of IICs were also investigated and discussed. To evaluate the proposed method further, it was compared with other standard methods such as random sampling (RS), stratified RS (SRS) and cross-validation (CV). The evaluations were based on the area under the receiving characteristic curves. The results show that IICs a...

Sameen, MI, Sarkar, R, Pradhan, B, Drukpa, D, Alamri, AM & Park, HJ 2020, 'Landslide spatial modelling using unsupervised factor optimisation and regularised greedy forests', Computers and Geosciences, vol. 134.
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© 2019 Elsevier Ltd This study evaluates the contribution of an unsupervised factor optimisation based on sparse autoencoders (SAEs) to spatial landslide modelling with regularised greedy forests (RGFs). A total of 952 landslides were identified by field surveys, equally divided and used for training and testing of the proposed model. Ten conditioning factors related to landslides, including geo-morphometrical (i.e. altitude, slope, aspect, curvature, slope length, topographic wetness index and sediment transport index) and geo-environmental (i.e. lithology, nearness to roads and nearness to streams), were used to investigate the spatial relationships between the variables and landslides. 1The steps of the modelling were twofold. First, the factors were optimised by SAE to reduce information redundancy and correlation in the data. Second, RGF was used to create landslide susceptibility maps with the optimised feature representations. The area under the receiver operating characteristic curve (AUROC) was used to assess the predictive ability of the proposed models. Experimental results show that the proposed SAE–RGF outperforms the RGF and random forest (RF) models in terms of prediction rate and is less sensitive to overfitting and underfitting. The highest prediction rate (AUROC = 0.892) was obtained with only seven features by the SAE–RGF model, which is better than the two other methods (RGF and RF). The unsupervised factor optimisation approach not only reduces computation time but also improves the prediction accuracy of tree-based models, including RGF. The generated landslide susceptibility maps can be implemented to mitigate landslide hazards and to designate land use by stakeholders (e.g. planners and engineers).

Saputra, Y, Dinh, H, Nguyen, D & Dutkiewicz, E 2020, 'A Novel Mobile Edge Network Architecture with Joint Caching-Delivering and Horizontal Cooperation', IEEE Transactions on Mobile Computing.
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Sathik, J, Bhatnagar, K, Silwakoti, Y, Yang, Y, Bassi, H, Rawa, M, N, S & Blaabjerg, F 2020, 'Switched-capacitor multilevel inverter with self-voltage-balancing for high-frequency power distribution system', IET Power Electronics.
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Satya, A, Harimawan, A, Haryani, GS, Johir, MAH, Vigneswaran, S, Ngo, HH & Setiadi, T 2020, 'Batch study of cadmium biosorption by carbon dioxide enriched aphanothece sp. dried biomass', Water (Switzerland), vol. 12, no. 1.
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© 2020 by the authors. The conventional method for cadmium removal in aqueous solutions (1-100 mg/L) is ineffective and inefficient. Therefore, a batch biosorption reactor using a local freshwater microalga (originating from an urban lake, namely, Situ Rawa Kalong-Depok) as dried biosorbent was tested. Biosorbent made from three kinds of cyanobacterium Aphanothece sp. cultivars (A0, A8, and A15) were used to eliminate cadmium (Cd2+) ions in aqueous solution (1-7 mg/L). The biosorbents were harvested from a photobioreactor system enriched with carbon dioxide gas of 0.04% (atmospheric), 8%, and 15% under continuous light illumination of about 5700-6000 lux for 14 d of cultivation. Produced dried biosorbents had Brunauer-Emmet-Teller (BET) surface area ranges of 0.571-1.846 m2/g. Biosorption of Cd2+ was pH and concentration dependent. Sorption was spontaneous (G =-8.39 to-10.88 kJ/mol), exothermic (H=-41.85 to-49.16 kJ/mol), and decreased randomness (S =-0.102 to-0.126 kJ/molK) on the interface between solid and liquid phases when the process was completed. The kinetic sorption data fitted best to the pseudo-second-order model (k2 = 2.79 x 10-2, 3.96 x 10-2, and 4.54 x 10-2 g/mgmin). The dried biosorbents of A0, A8, and A15, after modeling with the Langmuir and Dubinin-Radushkevich isotherm models, indicated that cadmium binding occurred through chemisorption (qmax, D-R = 9.74 x 10-4, 4.79 x 10-3, and 9.12 x 10-3 mol/g and mean free energy of 8.45, 11.18, and 11.18 kJ/mol) on the monolayer and homogenous surface (qmax, Langmuir of 12.24, 36.90, and 60.24 mg/g). In addition, the results of SEM, EDX, and FTIR showed that there were at least nine functional groups that interacted with Cd2+ (led to bond formation) after biosorption through cation exchange mechanisms, and morphologically the surfaces changed after biosorption. Biosorbent A15 indicated the best resilient features over three cycles of sorption-desorption using 1 M HCl as the desorbing eluent. These biosorb...

Shaham, S, Ding, M, Liu, B, Dang, S, Lin, Z & Li, J 2020, 'Privacy Preserving Location Data Publishing: A Machine Learning Approach', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Shakor, P, Nejadi, S & Paul, G 2020, 'Investigation into the effect of delays between printed layers on the mechanical strength of inkjet 3DP mortar', Manufacturing Letters, vol. 23, pp. 19-22.
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© 2019 Currently, additive manufacturing have enabled to fabricate the three-dimensional models. 3D-Printing technique is a multipurpose process for producing structural members using a sequential layering approach. The “feature quality” of 3DP specimens can be improved by optimising the build constraints. In this paper, a mortar mix powder-base has been prepared that consists of cementitious materials. Experiments are conducted to investigate the effects of different delays in printing time on the mechanical properties of the scaffolds. It has been shown that the compressive stress and strength of printed specimens with a delay of 200 ms were greater than specimens with other delay values.

Shakor, P, Nejadi, S, Paul, G & Sanjayan, J 2020, 'Dimensional accuracy, flowability, wettability, and porosity in inkjet 3DP for gypsum and cement mortar materials', AUTOMATION IN CONSTRUCTION, vol. 110.
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Shakor, P, Nejadi, S, Sutjipto, S, Paul, G & Gowripalan, N 2020, 'Effects of deposition velocity in the presence/absence of E6-glass fibre on extrusion-based 3D printed mortar', Additive Manufacturing, vol. 32.
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© 2020 Additive Manufacturing (AM) technologies are widely used in various fields of industry and research. Continual research has enabled AM technologies to be considered as a feasible substitute for certain applications in the construction industry, particularly given the advances in the use of glass fibre reinforced mortar. An investigation of the resulting mechanical properties of various mortar mixes extruded using a robotic arm is presented. The nozzle paths were projected via ‘spline’ interpolation to obtain the desired trajectory and deposition velocity in the reference frame of the manipulator. Along each path, various mortar mixes, with and without chopped glass fibre, were deposited at different velocities. Tests were conducted to determine their mechanical performance when incorporated in printed structures with different layers (1, 2, 4 and 6 layers). The results are compared with those of conventional cast-in-place mortar. In this study, the mixes consist of ordinary Portland cement, fine sand, chopped glass fibres (6 mm) and chemical admixtures, which are used to print prismatic- and cubic-shaped specimens. Mechanical strength tests were performed on the printed specimens to evaluate the behaviour of the materials in the presence and absence of glass fibre. Robot end-effector velocity tests were performed to examine the printability and extrudability of the mortar mixes. Finally, horizontal and vertical line printing tests were used to determine the workability, buildability and uniformity of the mortar mix and to monitor the fibre flow directions in the printed specimens. The results show that printed specimens with glass fibre have enhanced compressive strength compared with specimens without glass fibre.

Shan, Y, Hu, J, Liu, M, Zhu, J & Guerrero, JM 2020, 'Model Predictive Voltage and Power Control of Islanded PV-Battery Microgrids with Washout-Filter-Based Power Sharing Strategy', IEEE Transactions on Power Electronics, vol. 35, no. 2, pp. 1227-1238.
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© 1986-2012 IEEE. This paper proposes a new control strategy of microgrids for the improved voltage quality. In the existing control techniques, the droop control is commonly adopted as a decentralized power sharing method at the cost of voltage deviations. Besides, the conventional cascaded control featuring relatively slow dynamic response shows difficulties in handling the fluctuation of renewable energy outputs, leading to further voltage quality deterioration. In this paper, an advanced model predictive power control strategy by considering the battery constraints is proposed for the bidirectional dc-dc converters to smooth the solar photovoltaic (PV) outputs and stabilize the dc-bus voltages. A model predictive voltage control scheme taking into account the voltage changing trend is then developed to control the distributed inverters to improve the output ac voltages. Furthermore, a washout-filter-based power sharing approach with the plug-and-play capability is adopted to achieve a proper load sharing among parallel inverters and mitigate the voltage deviation. The proposed control strategy is numerically simulated in MATLAB/Simulink and experimentally verified by hardware-in-the-loop tests under the condition of fluctuating PV outputs and variable power demands. (This paper is accompanied by a video showing the hardware-in-the-loop test.)

Shanmugam, S, Ngo, HH & Wu, YR 2020, 'Advanced CRISPR/Cas-based genome editing tools for microbial biofuels production: A review', Renewable Energy.
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© 2019 Elsevier Ltd With rapid progress in the fields of synthetic biology and metabolic engineering, there are possible applications to generate a wide range of advanced biofuels with maximized yield and productivity to achieve a more sustainable bioprocess with reduced carbon footprints. Among the diverse molecular biology tools, clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins (CRISPR-Cas) technology stands out with potential targeted genome editing, exhibiting a more precise and accurate gene knock-out and knock-in system better than its predecessors, for example zinc finger nucleases (ZFN) and transcription activator-like effector nucleases (TALEN). There are reports involved in the advanced microbial genome engineering tools for the biofuels production; however, there is lack of a comprehensive review about the CRISPR-Cas based-techniques in improved biofuel production along with the strategies to reduce the off-target effect that ensures the success and safety of this method. Therefore, in this review we attempt to systematically comment on the mechanism of CRISPR-Cas and its application to microbial biofuels production. This includes bioethanol, biobutanol as well as other hydrocarbons that sequentially follow various suggestions on enhancing the efficiency of targeting genes. The role of inducible on/off genetic circuits in response to environmental stimuli in the regulation of targeted genome editing (TGE) by minimizing metabolic burden and maximizing fermentation efficiency is also discussed. The relevant stringent regulatory demands to ensure minimal off-target cleavage with maximum efficiency coupled with complete biosafety of this technology are considered. It can be concluded that the recent development of CRISPR-Cas technology should open a new avenue in creating microbial biorefineries for potentially enhanced biofuel production.

Sharifi, S, Abrishami, S & Gandomi, AH 2020, 'Consolidation assessment using Multi Expression Programming', Applied Soft Computing Journal, vol. 86.
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© 2019 Elsevier B.V. In this study, new approximate solutions for consolidation have been developed in order to hasten the calculations. These solutions include two groups of equations, one can be used to calculate the average degree of consolidation and the other one for computing the time factor (inverse functions). Considering the complicated nature of consolidation, an evolutionary computation technique called Multi-Expression Programming was applied to generate several non-piecewise models which are accurate and straightforward enough for different purposes for calculating the degree of consolidation for each depth and its average as well for the whole soil layer. The parametric study was also performed to investigate the impact of each input parameter on the predicted consolidation degree of developed models for each depth. Moreover, the results of the consolidation test carried out on four different clays attained from the literature showed the proper performance of the proposed models.

Shen, F, Zhu, H, Zhu, G, Shi, Q & Schulte, J 2020, 'Enhanced negative-index collimation by overlapped second band of square lattice sonic crystals', Chinese Journal of Physics, vol. 63, pp. 201-206.
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© 2019 Acoustic wave collimation and enhancement are systematically investigated in the overlapped second and third bands of a two-dimensional square lattice sonic crystals by analyzing the band structure, equifrequency contour and field distribution. We find the collimation stems from a small negative-index of the second band, and an additional concave entrance can improve the collimation performance in the far field region. But for the third band, negative, zero, positive refraction and wave splitting may occur at the same time due to the simultaneous excitation of multiple Bloch modes, which ultimately destroy the collimation beam of the second band in the near field region. This work provides an insight into the collimation properties of the overlapped bands of sonic crystals with potential application in acoustic directional emission and wave manipulation.

Shi, Y, Hoang, DT, Duong, TQ, Poor, HV & Savkin, A 2020, 'PMU Placement Optimization for Efficient State Estimation in Smart Grid', IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, vol. 38, no. 1, pp. 71-83.
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Shi, Z, Pan, Q & Xu, M 2020, 'LSTM-Cubic A*-based auxiliary decision support system in air traffic management', Neurocomputing.
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© 2019 Airports, pilots and geographical environment contribute a lot to stable and safe air traffic, especially during the climbing and descending phases. Accidents occur frequently in these two phases, even small errors can lead to serious consequences. Modern equipment has made great progress in monitoring the airspace. However, when encountering strong external interference or blind zones, potential risks will be raised. With the assistance of newly developed technologies and equipment, flight trajectories can be recorded at high frequency, which enhances capabilities of Air Traffic Management systems. A four-dimensional flight trajectory prediction model is put forward in this paper. Combined with sliding windows, Long Short-Term Memory network maintains the long-term features and manages to predict accurate trajectories. Massive data contributes a lot for forecasting, but cannot guarantee better decisions. Decision-making is closely related to threat assessment. Taking the geographical environment into consideration, the static threat is involved in the current work. The experimental scenario is set to be an aircraft takes off from Hong Kong International Airport, which is located close to mountains. Characteristics of terrains are introduced to our system by digital elevation model. The proposed cubic A* search algorithm provides reasonable path by considering the ultimate dynamic performance of an aircraft. Threat is assessed during the path planning process. Finally, the auxiliary decision support system is developed based on ArcGIS 10.0, to graphically provide the intuitive and quick assistance. Multiple sets of experimental results indicate that this system is able to provide timely decision support.

Shi, Z, Sun, X, Lei, G, Yang, Z, Guo, Y & Zhu, J 2020, 'Analysis and Optimization of Radial Force of Permanent-Magnet Synchronous Hub Motors', IEEE TRANSACTIONS ON MAGNETICS, vol. 56, no. 2.
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Shirvani, F, Beydoun, G, Perez, P, Scott, W & Campbell, P 2020, 'An Architecture Framework Approach for Complex Transport Projects', INFORMATION SYSTEMS FRONTIERS.
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Shirvani, F, Beydoun, G, Perez, P, Scott, W & Campbell, P 2020, 'An Architecture Framework Approach for Complex Transport Projects (vol 56, pg 741, 2020)', INFORMATION SYSTEMS FRONTIERS.
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Shrestha, J, Bazaz, SR, Es, HA, Azari, DY, Thierry, B, Warkiani, ME & Ghadiri, M 2020, 'Lung-on-a-chip: the future of respiratory disease models and pharmacological studies', CRITICAL REVIEWS IN BIOTECHNOLOGY, vol. 40, no. 2, pp. 213-230.
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Shukla, N, Dunbar, M, Belieres, S, Amirghasemi, M, Perez, P & Mishra, N 2020, 'A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network', pp. 1-19.
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Shukla, N, Merigó, JM, Lammers, T & Miranda, L 2020, 'Half a century of computer methods and programs in biomedicine: A bibliometric analysis from 1970 to 2017.', Computer methods and programs in biomedicine, vol. 183, pp. 105075-105075.
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BACKGROUND AND OBJECTIVE:Computer Methods and Programs in Biomedicine (CMPB) is a leading international journal that presents developments about computing methods and their application in biomedical research. The journal published its first issue in 1970. In 2020, the journal celebrates the 50th anniversary. Motivated by this event, this article presents a bibliometric analysis of the publications of the journal during this period (1970-2017). METHODS:The objective is to identify the leading trends occurring in the journal by analysing the most cited papers, keywords, authors, institutions and countries. For doing so, the study uses the Web of Science Core Collection database. Additionally, the work presents a graphical mapping of the bibliographic information by using the visualization of similarities (VOS) viewer software. This is done to analyze bibliographic coupling, co-citation and co-occurrence of keywords. RESULTS:CMPB is identified as a leading and core journal for biomedical researchers. The journal is strongly connected to IEEE Transactions on Biomedical Engineering and IEEE Transactions on Medical Imaging. Paper from Wang, Jacques, Zheng (published in 1995) is its most cited document. The top author in this journal is James Geoffrey Chase and the top contributing institution is Uppsala U (Sweden). Most of the papers in CMPB are from the USA followed by the UK and Italy. China and Taiwan are the only Asian countries to appear in the top 10 publishing in CMPB. A keyword co-occurrences analysis revealed strong co-occurrences for classification, picture archiving and communication system (PACS), heart rate variability, survival analysis and simulation. Keywords analysis for the last decade revealed that machine learning for a variety of healthcare problems (including image processing and analysis) dominated other research fields in CMPB. CONCLUSIONS:It can be concluded that CMPB is a world-renowned publication outlet for biomedical researchers which has b...

Siami, M, Naderpour, M & Lu, J 2020, 'A Mobile Telematics Pattern Recognition Framework for Driving Behavior Extraction', IEEE Transactions on Intelligent Transportation Systems, pp. 1-14.
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Silitonga, AS, Shamsuddin, AH, Mahlia, TMI, Milano, J, Kusumo, F, Siswantoro, J, Dharma, S, Sebayang, AH, Masjuki, HH & Ong, HC 2020, 'Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization', Renewable Energy, vol. 146, pp. 1278-1291.
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© 2019 Elsevier Ltd In this study, microwave irradiation-assisted transesterification was used to produce Ceiba pentandra biodiesel, which accelerates the rate of reaction and temperature within a shorter period. The improvement of biodiesel production requires a reliable model that accurately reflects the effects of input variables on output variables. In this study, an extreme learning machine integrated with cuckoo search algorithm was developed to predict and optimize the process parameters. This model will be useful for virtual experimentations in order to enhance biodiesel research and development. The optimum parameters of the microwave irradiation-assisted transesterification process conditions were obtained as follows: (1) methanol/oil ratio: 60%, (2) potassium hydroxide catalyst concentration: 0.84%(w/w), (3) stirring speed: 800 rpm, and (4) reaction time: 388 s. The corresponding Ceiba pentandra biodiesel yield was 96.19%. Three independent experiments were conducted using the optimum process parameters and the average biodiesel yield was found to be 95.42%. In conclusion, microwave irradiation-assisted transesterification is an effective method for biodiesel production because it is more energy-efficient, which will reduce the overall cost of biodiesel production.

Singh, K, Afzal, MU, Kovaleva, M & Esselle, KP 2020, 'Controlling the Most Significant Grating Lobes in Two-Dimensional Beam-Steering Systems With Phase-Gradient Metasurfaces', IEEE Transactions on Antennas and Propagation, vol. 68, no. 3, pp. 1389-1401.
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Singh, P, Raghav, V, Padhmashali, V, Paul, G, Islam, MS & Saha, SC 2020, 'Airflow and Particle Transport Prediction through Stenosis Airways', INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, vol. 17, no. 3.
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Sohaib, O, Hussain, W, Asif, M, Ahmad, M & Mazzara, M 2020, 'A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption', IEEE Access, vol. 8, no. 1, pp. 13138-13150.
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Song, B, Wang, X, Ni, W, Song, Y, Liu, RP, Jiang, G-P & Guo, YJ 2020, 'Reliability Analysis of Large-Scale Adaptive Weighted Networks', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 15, pp. 651-665.
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Song, Y, Lu, J, Lu, H & Zhang, G 2020, 'Fuzzy Clustering-Based Adaptive Regression for Drifting Data Streams', IEEE Transactions on Fuzzy Systems, vol. 28, no. 3, pp. 544-557.
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Sornalingam, K, McDonagh, A, Canning, J, Cook, K, Johir, MAH, Zhou, JL & Ahmed, MB 2020, 'Photocatalysis of 17α-ethynylestradiol and estriol in water using engineered immersible optical fibres and light emitting diodes', Journal of Water Process Engineering, vol. 33.
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© 2019 Elsevier Ltd This research aims to promote photocatalysis of endocrine disrupting chemicals (EDCs) in water. Two reactor setups with (i) modified air-clad optical fibres and (ii) waterproof LED strips were utilised to transmit light to photocatalysts P25 TiO2 and gold-modified TiO2 (Au-TiO2). The performances to photodegrade 17α-ethynylestradiol (EE2) and estriol (E3) under Cool White and UVA high efficacy LEDs were examined. Au-TiO2 showed superior photocatalytic activity for EE2 removal over P25 TiO2. The pseudo first-order rate constants for EE2 photocatalysis under UVA were 0.55 h−1 and 0.89 h−1 for TiO2 and Au-TiO2, respectively. E3 was effectively degraded by Au-TiO2 in the immersible LED strip reactor (0.13 h−1).

Sritharan, K, Chamoli, U, Kuan, J & Diwan, AD 2020, 'Assessment of degenerative cervical stenosis on T2-weighted MR imaging: sensitivity to change and reliability of mid-sagittal and axial plane metrics.', Spinal cord.
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STUDY DESIGN:A retrospective cross-sectional study. OBJECTIVE:To assess the sensitivity to change and reliability of various mid-sagittal and axial plane metrics in the assessment of patients with single-level degenerative cervical stenosis on T2-weighted MR imaging. SETTING:A diagnostic MR imaging facility in Sydney (Australia). METHODS:We retrospectively reviewed T2-weighted MR images of 85 consecutive patients (48 M and 37 F) with single-level degenerative cervical stenosis. Canal compromise and cord compression were evaluated using three mid-sagittal plane metrics (M1, M2, and M3) and two axial plane metrics (M4 and M5), at the level of stenosis and nonstenotic cephalad and caudal levels (controls). Sensitivity to change (SC) for each metric was evaluated as the percentage deviation of the measured value from the estimated normal value based on cephalad and caudal controls. Reliability for each metric was evaluated using intraclass correlation coefficients. RESULTS:Degenerative cervical stenosis showed a bimodal distribution peaking at C5-6 (n = 32) and C3-4 (n = 29) levels. The changes in the canal and cord geometry along the rostrocaudal axis were inconsistent. Across all individual subjects (reflecting a range of stenosis severity), M3 (-32.87% ± 10.60%) was more sensitive to change compared with M1 (16.64% ± 16.48%) and M2 (-23.95% ± 11.12%). Similarly, M4 (-24.62% ± 12.17%) was more sensitive to change compared with M5 (-6.71% ± 11.08%). The level of reliability was "moderate" to "excellent" for mid-sagittal plane measurements, and "poor" to "excellent" for axial plane measurements. CONCLUSION:Changes in canal dimensions in the mid-sagittal plane and cord shape in the axial plane are sensitive indicators of degenerative cervical stenosis on T2-weighted MR images.

Su, D, Vidal-Calleja, T & Miro, JV 2020, 'Asynchronous microphone arrays calibration and sound source tracking', Autonomous Robots.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. In this paper, we proposed an optimisation method to solve the problem of sound source localisation and calibration of an asynchronous microphone array. This method is based on the graph-based formulation of the simultaneous localisation and mapping problem. In this formulation, a moving sound source is considered to be observed from a static microphone array. Traditional approaches for sound source localisation rely on the well-known geometrical information of the array and synchronous readings of the audio signals. Recent work relaxed these two requirements by estimating the temporal offset between pair of microphones based on the assumption that the clock timing of each microphone is exactly the same. This assumption requires the sound cards to be identically manufactured, which in practice is not possible to achieve. Hereby an approach is proposed to jointly estimate the array geometrical information, time offset and clock difference/drift rate of each microphone together with the location of a moving sound source. In addition, an observability analysis of the system is performed to investigate the most suitable configuration for sound source localisation. Simulation and experimental results are presented, which prove the effectiveness of the proposed methodology.

Sun, G, Tian, Y, Wang, R, Fang, J & Li, Q 2020, 'Parallelized multiobjective efficient global optimization algorithm and its applications', STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, vol. 61, no. 2, pp. 763-786.
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Sun, H-H, Ding, C, Zhu, H & Guo, YJ 2020, 'Dual-Polarized Multi-Resonance Antennas With Broad Bandwidths and Compact Sizes for Base Station Applications', IEEE Open Journal of Antennas and Propagation, vol. 1, pp. 11-19.
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Sun, S, Ding, H, Mei, L, Chen, Y, Hao, Q, Chen, W, Xu, Z & Chen, D 2020, 'Construction of SiO2-TiO2/g-C3N4 composite photocatalyst for hydrogen production and pollutantdegradation: Insight into the effect of SiO2', Chinese Chemical Letters.
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Sun, X, Cao, J, Lei, G, Guo, Y & Zhu, J 2020, 'Speed Sensorless Control for Permanent Magnet Synchronous Motors Based on Finite Position Set', IEEE Transactions on Industrial Electronics, vol. 67, no. 7, pp. 6089-6100.
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Sun, X, Diao, K, Lei, G, Guo, Y & Zhu, J 2020, 'Real-Time HIL Emulation for a Segmented-Rotor Switched Reluctance Motor Using a New Magnetic Equivalent Circuit', IEEE TRANSACTIONS ON POWER ELECTRONICS, vol. 35, no. 4, pp. 3841-3849.
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Sun, X, Hu, C, Lei, G, Guo, Y & Zhu, J 2020, 'State Feedback Control for a PM Hub Motor Based on Gray Wolf Optimization Algorithm', IEEE TRANSACTIONS ON POWER ELECTRONICS, vol. 35, no. 1, pp. 1136-1146.
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Surawski, NC, Macdonald, LM, Baldock, JA, Sullivan, AL, Roxburgh, SH & Polglase, PJ 2020, 'Exploring how fire spread mode shapes the composition of pyrogenic carbon from burning forest litter fuels in a combustion wind tunnel', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 698.
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Taccola, G, Gad, P, Culaclii, S, Ichiyama, RM, Liu, W & Edgerton, VR 2020, 'Using EMG to deliver lumbar dynamic electrical stimulation to facilitate cortico-spinal excitability.', Brain Stimulation, vol. 13, no. 1, pp. 20-34.
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BACKGROUND:Potentiation of synaptic activity in spinal networks is reflected in the magnitude of modulation of motor responses evoked by spinal and cortical input. After spinal cord injury, motor evoked responses can be facilitated by pairing cortical and peripheral nerve stimuli. OBJECTIVE:To facilitate synaptic potentiation of cortico-spinal input with epidural electrical stimulation, we designed a novel neuromodulation method called dynamic stimulation (DS), using patterns derived from hind limb EMG signal during stepping. METHODS:DS was applied dorsally to the lumbar enlargement through a high-density epidural array composed of independent platinum-based micro-electrodes. RESULTS:In fully anesthetized intact adult rats, at the interface array/spinal cord, the temporal and spatial features of DS neuromodulation affected the entire lumbosacral network, particularly the most rostral and caudal segments covered by the array. DS induced a transient (at least 1 min) increase in spinal cord excitability and, compared to tonic stimulation, generated a more robust potentiation of the motor output evoked by single pulses applied to the spinal cord. When sub-threshold pulses were selectively applied to a cortical motor area, EMG responses from the contralateral leg were facilitated by the delivery of DS to the lumbosacral cord. Finally, based on motor-evoked responses, DS was linked to a greater amplitude of motor output shortly after a calibrated spinal cord contusion. CONCLUSION:Compared to traditional tonic waveforms, DS amplifies both spinal and cortico-spinal input aimed at spinal networks, thus significantly increasing the potential and accelerating the rate of functional recovery after a severe spinal lesion.

Tahmoorian, F & Khabbaz, H 2020, 'Performance comparison of a MSW settlement prediction model in Tehran landfill', Journal of Environmental Management, vol. 254.
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© 2019 The municipal solid waste (MSW) landfills experience a large post-closure settlement over time. Waste settlement significantly impairs utilities, structures, and the other facilities constructed on top of a landfill. This study presents the settlement mechanisms and the methods of estimating MSW landfill settlements. Since the waste materials exhibit engineering properties which vary depending on many factors such as the location, time, climate, this study also presents the data related to the landfill characteristics, waste composition, waste moisture content, and other physical and chemical properties of waste. In addition, this paper discusses the findings of a settlement investigation conducted at a municipal solid waste landfill in Tehran. In this research, based on the collected field data and data obtained from the available literature, a technical management tool for MSW closed landfills has been developed using MATLAB, which aims to predict time dependent settlement under self-weight and surcharge loads in landfills considering various related parameters, leachate, gas generation, and moisture distribution, coefficients of compression, whilst it calculates different properties of wastes, and determines the landfill slope stability under various conditions. This user-friendly program captures the variation of the model parameters with time. The results of the verification process indicate that the results from the technical management tool have been in a very good agreement with the measured field settlement data, collected from Tehran landfill. Moreover, the results of sensitivity analysis of the model in regard to variation of input parameters indicate that there are two prominent characteristics, having significant impacts on the overall landfill settlement. These characteristics are the landfill height and the compressibility parameters. The outcomes of this study can improve the confidence for design and construction on MSW landfills. It may r...

Tan, Q, Fan, K, Tao, K, Zhao, L & Cai, M 2020, 'A two-degree-of-freedom string-driven rotor for efficient energy harvesting from ultra-low frequency excitations', Energy, pp. 117107-117107.
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Tang, Q, Yang, J, Jia, W, He, X, Zhang, Q & Liu, H 2020, 'A GMS-Guided Approach for 2D Feature Correspondence Selection', IEEE Access, vol. 8, pp. 36919-36929.
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Tang, Z, Li, W, Tam, VWY & Luo, Z 2020, 'Investigation on dynamic mechanical properties of fly ash/slag-based geopolymeric recycled aggregate concrete', Composites Part B: Engineering, vol. 185.
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© 2020 Elsevier Ltd By harnessing the benefits from both construction and demolition waste recycling and geopolymer binders, geopolymeric recycled aggregate concrete (GRAC) can contribute to the green and eco-friendly construction material products. In this study, the compressive behavior of GRAC based on fly ash and slag was experimentally investigated under both quasi-static and dynamic loadings. Quasi-static compressive tests were performed by using a high-force servo-hydraulic test system, while dynamic compressive tests were carried out by using a Ø80-mm split Hopkinson pressure bar (SHPB) apparatus. The compressive properties of GRAC under dynamic loading, including stress-strain curves, energy absorption capability, and failure modes were obtained and compared with those under quasi-static loading. The results show that the compressive properties of GRAC exhibit a strong strain rate dependency. Although the recycled aggregate replacement decreases the quasi-static compressive strength, it exhibits a slight effect on the compressive strength at high strain rates. The dynamic increase factor (DIF) for compressive strength exhibits an significant increasing trend with the recycled aggregate replacement. On the other hand, the incorporation of slag increases the quasi-static compressive strength, dynamic compressive strength, and DIF. As for the energy absorption capacity, a minor enhancement is achieved with the recycled aggregate replacement, while a significant improvement is identified after the inclusion of slag. Empirical DIF formulae for compressive strength of GRAC are proposed, in which the DIF increases approximately linearly with the strain rate in a logarithmic manner.

Tawadros, P, Awadallah, M, Walker, P & Zhang, N 2020, 'Using a low-cost bluetooth torque sensor for vehicle jerk and transient torque measurement', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 234, no. 2-3, pp. 423-437.
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© IMechE 2019. This paper presents the use and development of a specific wireless torque measurement system that is used to obtain the transient torque performance of vehicle transmissions. The torque sensor is strain-based, using surface-mounted strain gauges on a prop shaft. The gauges are connected to a compact printed circuit board, which is clamped to the shaft next to the strain gauges using a three-dimensional printed housing. The printed circuit board contains an amplifier, low-pass filter, analog-to-digital converter, microcontroller and bluetooth transceiver. The printed housing is impact resistant carbon-reinforced nylon and securely retains the printed circuit board and the battery powering the device. The transmitted torque data are received by a transceiver, which is interfaced to a PC through an RS-232 connection. NI LabVIEW is used to process, display and save data. The wireless torque sensor was installed to the Unit Under Test at the output shaft of the five-speed manual transmission. The Unit Under Test was installed on a dynamometer for verification purposes and the transient torque was recorded under various operational conditions. The transient output torque of the manual transmission is measured and compared with results obtained from simulations performed under similar operating conditions. The two sets of transient responses show a good correlation with each other and hence demonstrate that the torque sensor meets the major design specifications. The data obtained will be used to enhance the fidelity of the software model.

Telikani, A & Gandomi, AH 2020, 'Cost-sensitive stacked auto-encoders for intrusion detection in the Internet of Things', Internet of Things, pp. 100122-100122.
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Telikani, A, Gandomi, AH, Shahbahrami, A & Dehkordi, MN 2020, 'Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony', EXPERT SYSTEMS WITH APPLICATIONS, vol. 144.
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Teng, J, Kou, J, Yan, X, Zhang, S & Sheng, D 2020, 'Parameterization of soil freezing characteristic curve for unsaturated soils', Cold Regions Science and Technology, vol. 170.
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© 2019 The soil freezing characteristic curve (SFCC) describes the relationship between the temperature and unfrozen water content in a soil. The SFCC is indispensable in modelling the hydro-mechanical behaviour of frozen soils, but is less understood for the unsaturated soils. A series of SFCC tests of unsaturated silica sand, silt and red clay are preformed based on a newly developed nuclear magnetic resonance (NMR) apparatus, which can precisely control the sample temperature in the magnetic field. The experimental results show that the measured SFCC varies significantly for different initial water contents, and that a lower initial water content leads to a slower increase in unfrozen water content, proving that the SFCC is closely related to the initial unsaturated state. It is found that the thawing curve is better to represent the SFCC, in contrast the freezing curve is significantly affected by the supercooling phenomenon. A new parameterization of the SFCC is presented for unsaturated soils by combining the Clapeyron equation and the model for Soil Water Characteristic Curve (SWCC). A number of test results from the literature and this study are used to validate the new SFCC model. By inputting the parameters for the SWCC and initial state into the proposed model, the predicted SFCC can agree well with the measured results. The new model has a theoretical basis and simple form and is applicable to both saturated and unsaturated soils.

Thanh, HT, Li, J & Zhang, YX 2020, 'Numerical simulation of self-consolidating engineered cementitious composite flow with the V-funnel and U-box', Construction and Building Materials, vol. 236.
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© 2019 Elsevier Ltd A 3D numerical model is developed in this paper to simulate the flow of self-consolidating engineered cementitious composites (SC-ECC) through the V-funnel test and U-box test. The synthetic fibres utilized in SC-ECC are considered as flexible fibres and modelled as separated particles, virtually connected by a drag force between two adjacent fibre particles. The fibre particles and mortar particles possess similar continuum properties in the domain of SC-ECC. The constitutive equations of the flow of SC-ECC are solved using the mesh-free, weakly compressible smoothed particle hydrodynamics (WCSPH). The V-funnel model focuses on the flow rate characteristic and the correlation of the dispersion of synthetic fibres with the plastic viscosity of fresh mixtures. The U-box simulation is to confirm the self-consolidating capability and the passing ability of SC-ECC flow through reinforcing bars. The simulation results of the discharge time in the V-funnel model and the self-levelling index in the U-box model agree well with experimental results from the literature, which provide convincing evidence for the correctness and effectiveness of the developed model. The proposed model thus is capable of providing insight into the flow behaviour of SC-ECC in terms of filling, passing abilities and the dispersion/orientation of flexible synthetic fibres.

Thi, HN, Hai, NT, Hai, AV, Minh, VT, Tien, VN, Loganathan, P, Vigneswaran, S, Tuan, MN, Van, TT, Duc, LV & Thi, HHN 2020, 'Laterite as a low-cost adsorbent in a sustainable decentralized filtration system to remove arsenic from groundwater in Vietnam', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 699.
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Thiyagarajan, K, Kodagoda, S, Ranasinghe, R, Vitanage, D & Iori, G 2020, 'Robust Sensor Suite Combined with Predictive Analytics Enabled Anomaly Detection Model for Smart Monitoring of Concrete Sewer Pipe Surface Moisture Conditions', IEEE Sensors Journal.
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Thu, TN, Huu, HN, Guo, W & Wang, XC 2020, 'A new model framework for sponge city implementation: Emerging challenges and future developments', JOURNAL OF ENVIRONMENTAL MANAGEMENT, vol. 253.
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Thuy, C-V, Suraski, N, Ristovski, Z, Yuan, C-S, Stevanovic, S, Rahman, SMA, Hossain, FM, Guo, Y, Rainey, T & Brown, RJ 2020, 'The effect of diesel fuel sulphur and vanadium on engine performance and emissions', FUEL, vol. 261.
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Tian, Y, Yang, H, Mo, W, Zhou, S, Zhang, N & Walker, PD 2020, 'Optimal coordinating gearshift control of a two-speed transmission for battery electric vehicles', Mechanical Systems and Signal Processing, vol. 136, p. 106521.

Tijing, LD, Dizon, JRC, Ibrahim, I, Nisay, ARN, Shon, HK & Advincula, RC 2020, '3D printing for membrane separation, desalination and water treatment', Applied Materials Today, vol. 18, pp. 1-22.
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© 2019 Elsevier Ltd Additive manufacturing or commonly known as 3D printing is driving innovation in many industries and academic research including the water resource sector. The capability of 3D printing to fabricate complex objects in a fast and cost-effective manner makes it highly desirable over conventional manufacturing processes. Recent years have seen a rapid increase in research using 3D printing for membrane separation, desalination and water purification applications, potentially revolutionizing this field. This review focuses on recent advancements in 3D-printed materials and methods for water-related applications including developments in module spacers, novel filtration and desalination membranes, adsorbents, water remediation, solar steam generation materials, catalysis, etc. The emergence of new 3D printers with higher printing resolution, better efficiency, faster speed, and wider material applicability has garnered more interest and can potentially reshape research and development in this field. The promising potential, challenges and future prospects of 3D printing, additive manufacturing, and materials for water resource and treatment-related applications are all discussed in this review.

To, VHP, Nguyen, TV, Bustamante, H & Vigneswaran, S 2020, 'Effects of extracellular polymeric substance fractions on polyacrylamide demand and dewatering performance of digested sludges', Separation and Purification Technology, vol. 239.
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© 2020 Elsevier B.V. High polymer demand in sludge conditioning is an intractable aspect of the water industry. This study investigated the effects of extracellular polymeric substances (EPS) fractions on polyacrylamide demand for conditioning and dewatering performance. Specifically, it examined aerobically and anaerobically digested sludges from seven full-scale wastewater treatment plants (WWTPs). Our study successfully quantified the contributions of soluble EPS to polyacrylamide demand during conditioning and explained the role of tightly bound EPS (TB-EPS) in determining the digested sludges’ dewatering performance. Results show that the concentrations of soluble EPS in the sludges varied between 92 and 1148 mg/L. Experimental results also demonstrated that between 25% and 80% of polyacrylamides used for conditioning were wasted in “parasitic” reactions with soluble EPS. The residual cationic polyacrylamide left in solution, after the parasitic reactions, was substantial and varied between 35 and 254 mg/L. Despite this outcome, the zeta potential values of dewatered sludge cakes remained negative, i.e. between −24 and −35 mV. These indicated that the residual soluble cationic polyacrylamides would not have been absorbed on the negatively charged sludge particles. This explained the relatively poor performance of the dewatering stage in the treatment plants studied. Furthermore the results suggested the TB-EPS attached to the sludge particles would be responsible for the poor dewatering. We postulated that the TB-EPS would gelify and immobilize the water surrounding the sludge particles. Our study suggested that new and more effective polymers for conditioning are needed to both: (i) reduce polymer demand; and (ii) improve the dewatering performance.

Tofigh, F, Amiri, M, Shariati, N, Lipman, J & Abolhasan, M 2020, 'Crowd Estimation Using Electromagnetic Wave Power-Level Measurements: A Proof of Concept', IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 784-792.
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Tofigh, F, Amiri, M, Shariati, N, Lipman, J & Abolhasan, M 2020, 'Polarization-Insensitive Metamaterial Absorber for Crowd Estimation Based on Electromagnetic Energy Measurements', IEEE Transactions on Antennas and Propagation, vol. 68, no. 3, pp. 1458-1467.
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© 2020 IEEE. Noninvasive crowd estimation has remained a challenging issue among researchers. Methods such as image analysis and Wi-Fi/Bluetooth probing can always be used to identify and track people. Lately, authors have introduced a noninvasive method for crowd estimation based on ambient RF energy measurements. In this article, a polarization-insensitive multilayer metamaterial absorber is introduced to measure the variation in the available RF energy levels for crowd estimation purposes. The proposed dual-band absorber is designed to absorb and transfer the maximum of the available Wi-Fi energy to a lumped element to enable proper and accurate measurements. To evaluate the design, the proposed structure is fabricated as an array, and its performance is tested, proving perfect absorption at the desired frequencies, 2.4 and 5 GHz.

Torghabeh, AK, Pradhan, B & Jahandari, A 2020, 'Assessment of geochemical and sedimentological characteristics of atmospheric dust in Shiraz, southwest Iran', Geoscience Frontiers.
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© 2019 China University of Geosciences (Beijing) and Peking University Geogenic dust is commonly believed to be one of the most important environmental problems in the Middle East. The present study investigated the geochemical characteristics of atmospheric dust particles in Shiraz City (south of Iran). Atmospheric dust samples were collected through a dry collector method by using glass trays at 10 location sites in May 2018. Elemental composition was analysed through inductively coupled plasma optical emission spectrometry. Meteorological data showed that the dustiest days were usually in spring and summer, particularly in April. X-ray diffraction analysis of atmospheric dust samples indicated that the mineralogical composition of atmospheric dust was calcite ​+ ​dolomite (24%)>palygorskite (18%)>quartz (14%)>muscovite (13%)>albite (11%)>kaolinite (7%)>gypsum (7%)>zircon ​= ​anatase (3%). The high occurrence of palygorskite (16%–23%) could serve as a tracer of the source areas of dust storms from the desert of Iraq and Saudi Arabia to the South of Iran. Scanning electron microscopy indicated that the sizes of the collected dust varied from 50 ​μm to 0.8 ​μm, but 10 ​μm was the predominant size. The atmospheric dust collected had prismatic trigonal–rhombohedral crystals and semi-rounded irregular shapes. Moreover, diatoms were detected in several samples, suggesting that emissions from dry-bed lakes, such as Hoor Al-Azim Wetland (located in the southwest of Iran), also contributed to the dust load. Backward trajectory simulations were performed at the date of sampling by using the NOAA HYSPLIT model. Results showed that the sources of atmospheric dust in the studied area were the eastern area of Iraq, eastern desert of Saudi Arabia, Kuwait and Khuzestan Province. The Ca/Al ratio of the collected samples (1.14) was different from the upper continental crust (UCC) value (UCC ​= ​0.37), whereas Mg/Al (0.29), K/Al (0.22) and Ti/Al (0.07) ratios were close to the UC...

Trinh, VT, Nguyen, TMP, Van, HT, Hoang, LP, Nguyen, TV, Ha, LT, Vu, XH, Pham, TT, Nguyen, TN, Quang, NV & Nguyen, XC 2020, 'Phosphate Adsorption by Silver Nanoparticles-Loaded Activated Carbon derived from Tea Residue', Scientific Reports, vol. 10, no. 1.
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© 2020, The Author(s). This study presents the removal of phosphate from aqueous solution using a new silver nanoparticles-loaded tea activated carbon (AgNPs-TAC) material. In order to reduce costs, the tea activated carbon was produced from tea residue. Batch adsorption experiments were conducted to evaluate the effects of impregnation ratio of AgNPs and TAC, pH solution, contact time, initial phosphate concentration and dose of AgNPs-AC on removing phosphate from aqueous solution. Results show that the best conditions for phosphate adsorption occurred at the impregnation ratio AgNPs/TAC of 3% w/w, pH 3, and contact time lasting 150 min. The maximum adsorption capacity of phosphate on AgNPs-TAC determined by the Langmuir model was 13.62 mg/g at an initial phosphate concentration of 30 mg/L. The adsorption isotherm of phosphate on AgNPs-TAC fits well with both the Langmuir and Sips models. The adsorption kinetics data were also described well by the pseudo-first-order and pseudo-second-order models with high correlation coefficients of 0.978 and 0.966, respectively. The adsorption process was controlled by chemisorption through complexes and ligand exchange mechanisms. This study suggests that AgNPs-TAC is a promising, low cost adsorbent for phosphate removal from aqueous solution.

Ubando, AT, Chen, W-H, Show, P-L & Ong, HC 2020, 'Kinetic and thermodynamic analysis of iron oxide reduction by graphite for CO2 mitigation in chemical-looping combustion', INTERNATIONAL JOURNAL OF ENERGY RESEARCH.
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Ulapane, N, Thiyagarajan, K, Hunt, D & Miro, JV 2020, 'Quantifying the Relative Thickness of Conductive Ferromagnetic Materials Using Detector Coil-Based Pulsed Eddy Current Sensors', JOVE-JOURNAL OF VISUALIZED EXPERIMENTS, no. 155.
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Usman, M, Jan, MA, He, X & Chen, J 2020, 'A Mobile Multimedia Data Collection Scheme for Secured Wireless Multimedia Sensor Networks', IEEE Transactions on Network Science and Engineering.
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IEEE Wireless Multimedia Sensor Networks (WMSNs) produce an enormous amount of big multimedia data. Due to large size, Multimedia Sensor Nodes (MSNs) cannot store generated multimedia data for a long time. In this scenario, mobile sinks can be utilized for data collection. However, due to vulnerable nature of wireless networks, there is a need for an efficient security scheme to authenticate both MSNs and mobile sinks. In this paper, we propose a scheme to protect an underlying WMSN during mobile multimedia data collection. The proposed scheme is a two-layer scheme. At the first layer, all MSNs are distributed into small clusters, where each cluster is represented by a single Cluster Head (CH). At the second layer, all CHs verify identities of mobile sinks before sharing multimedia data. Authentication at both layers ensures a secure data exchange. We evaluate the performance of proposed scheme through extensive simulation results. The simulation results prove that the proposed scheme performs better as compared to existing state-of-the-art approaches in terms of resilience and handshake duration. The proposed scheme is also analyzed in terms of authentication rate, data freshness, and packet delivery ratio, and has shown a better performance.

Vakhshouri, B, Nejadi, S & Erkmen, E 2020, 'Advances in numerical analysis of creep effect in time-dependent deflection of light-weight concrete slabs', Mechanics of Advanced Materials and Structures.
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© 2018, © 2018 Taylor & Francis Group, LLC. Using a wide range of creep models, the experimental results of long-term deflection of lightweight concrete slabs subjected to two levels of early-age loading are investigated. Different creep models give considerably different estimation of the experimental deflection of slabs. The included factors in each creep model to simulate the experimental creep behavior of the concrete, and loading level on the slabs are the main causes of different results. Among the investigated models, the BP1 and FIBMC-2010 models including the aggregate type and concrete density is shown to be in good agreement with the experimental data in both loading levels.

Van, HT, Lim, S, Park, MJ, Han, DS, Phuntsho, S, Park, H, Matsuyama, H & Shon, HK 2020, 'Fouling and performance of outer selective hollow fiber membrane in osmotic membrane bioreactor: Cross flow and air scouring effects', BIORESOURCE TECHNOLOGY, vol. 295.
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Van, KLH, Chai, R & Nguyen, HT 2020, 'A Telepresence Wheelchair with 360-Degree Vision Using WebRTC', APPLIED SCIENCES-BASEL, vol. 10, no. 1.
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Varjani, S, Joshi, R, Srivastava, VK, Ngo, HH & Guo, W 2020, 'Treatment of wastewater from petroleum industry: current practices and perspectives.', Environmental science and pollution research international.
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Petroleum industry is one of the fastest growing industries, and it significantly contributes to economic growth in developing countries like India. The wastewater from a petroleum industry consist a wide variety of pollutants like petroleum hydrocarbons, mercaptans, oil and grease, phenol, ammonia, sulfide, and other organic compounds. All these compounds are present as very complex form in discharged water of petroleum industry, which are harmful for environment directly or indirectly. Some of the techniques used to treat oily waste/wastewater are membrane technology, photocatalytic degradation, advanced oxidation process, electrochemical catalysis, etc. In this review paper, we aim to discuss past and present scenario of using various treatment technologies for treatment of petroleum industry waste/wastewater. The treatment of petroleum industry wastewater involves physical, chemical, and biological processes. This review also provides scientific literature on knowledge gaps and future research directions to evaluate the effect(s) of various treatment technologies available.

Vazquez, S, Acuna, P, Aguilera, RP, Pou, J, Leon, JI & Franquelo, LG 2020, 'DC-Link Voltage Balancing Strategy Based on Optimal Switching Sequences Model Predictive Control for Single-Phase H-NPC Converters', IEEE Transactions on Industrial Electronics, pp. 1-1.
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Vo, HNP, Ngo, HH, Guo, W, Liu, Y, Woong Chang, S, Nguyen, DD, Zhang, X, Liang, H & Xue, S 2020, 'Selective carbon sources and salinities enhance enzymes and extracellular polymeric substances extrusion of Chlorella sp. for potential co-metabolism', Bioresource Technology, vol. 303.
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© 2020 Elsevier Ltd This study investigated the extracellular polymeric substance (EPS) and enzyme extrusion of Chlorella sp. using seven carbon sources and two salinities for potential pollutant co-metabolism. Results indicated that the levels of biomass, EPS and enzymes of microalgae cultured with glucose and saccharose outcompeted other carbon sources. For pigment production, glycine received the highest chlorophyll and carotene, up to 10 mg/L. The EPS reached 30 mg/L, having doubled the amount of protein than carbohydrate. For superoxide dismutase and peroxidase enzymes, the highest concentrations were beyond 60 U/ml and 6 nmol/d.ml, respectively. This amount could be potentially used for degrading 40% ciprofloxacin of concentration 2000 µg/L. When increasing salinity from 0.1% to 3.5%, the concentrations of pigment, EPS and enzymes rose 3 to 30 times. These results highlighted that certain carbon sources and salinities could induce Chlorella sp. to produce EPS and enzymes for pollutant co-metabolism and also for revenue-raising potential.

Vu, HP, Nguyen, LN, Lesage, G & Nghiem, LD 2020, 'Synergistic effect of dual flocculation between inorganic salts and chitosan on harvesting microalgae Chlorella vulgaris', Environmental Technology and Innovation, vol. 17.
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© 2020 Elsevier B.V. The flocculation efficiency of microalgae Chlorella vulgaris for subsequent harvesting was investigated using single flocculants of inorganic salts, synthetic polymer, chitosan and dual flocculants of inorganic salts and chitosan. Synthetic polymer (FlopamTM) could achieve over 90% optical density removal (OD680removal) at a low flocculant dose (20 to 40 mg polymer per litre of algal suspension) through the bridging mechanism and charge neutralisation. Inorganic salts (i.e. ferric chloride and aluminium sulphate) and chitosan individually resulted in low flocculation efficiency (<90%) despite high dose (i.e. 160 to 200 mg per litre of algal suspension). The dual flocculation combining ferric chloride or aluminium sulphate with chitosan induced synergistic effects, resulting in >80% flocculation efficiency, significantly higher than the sum of each individual flocculation. The improvement in flocculation efficiency was 57 and 24% respectively for ferric chloride/chitosan and aluminium sulphate/chitosan. Charge neutralisation of microalgal cells by ferric chloride or aluminium sulphate combined with bridging by chitosan produced the synergy.

Wan, G, Du, B, Pan, S & Wu, J 2020, 'Adaptive knowledge subgraph ensemble for robust and trustworthy knowledge graph completion', World Wide Web.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Knowledge graph (KG) embedding approaches are widely used to infer underlying missing facts based on intrinsic structure information. However, the presence of noisy facts in automatically extracted or crowdsourcing KGs significantly reduces the reliability of various embedding learners. In this paper, we thoroughly study the underlying reasons for the performance drop in dealing with noisy knowledge graphs, and we propose an ensemble framework, Adaptive Knowledge Subgraph Ensemble (AKSE), to enhance the robustness and trust of knowledge graph completion. By employing an effective knowledge subgraph extraction approach to re-sample the sub-components from the original knowledge graph, AKSE generates different representations for learning diversified base learners (e.g., TransE and DistMult), which substantially alleviates the noise effect of KG embedding. All embedding learners are integrated into a unified framework to reduce generalization errors via our simple or adaptive weighting schemes, where the weight is allocated based on each individual learner’s prediction capacity. Experimental results show that the robustness of our ensemble framework outperforms exiting knowledge graph embedding approaches on manually injected noise as well as inherent noisy extracted KGs.

Wan, Z, Gao, Z, Shim, B, Yang, K, Mao, G & Alouini, MS 2020, 'Compressive Sensing Based Channel Estimation for Millimeter-Wave Full-Dimensional MIMO with Lens-Array', IEEE Transactions on Vehicular Technology, vol. 69, no. 2, pp. 2337-2342.
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IEEE Channel estimation (CE) for millimeter-wave (mmWave) lens-array suffers from prohibitive training overhead, whereas the state-of-the-art solutions require an extra complicated radio frequency phase shift network. By contrast, lens-array using antenna switching network (ASN) simplifies the hardware, but the associated CE is a challenging task due to the constraint imposed by ASN. This paper proposes a compressive sensing (CS)-based CE solution for full-dimensional (FD) lens-array, where the mmWave channel sparsity is exploited. Specifically, we first propose an approach of pilot training under the more severe haraware constraint imposed by ASN, and formulate the associated CE of lens-array as a CS problem. Then, a redundant dictionary is tailored for FD lens-array to combat the power leakage caused by the continuous angles of multipath components. Further, we design the baseband pilot signals to minimize the total mutual coherence of the measurement matrix based on CS theory for more reliable CE performance. Our solution provides a framework for applying CS techniques to lens-array using simple and practical ASN. Simulation results demonstrate the effectiveness of the proposed scheme.

Wang, B, Li, T, Yan, Z, Zhang, G & Lu, J 2020, 'DeepPIPE: A distribution-free uncertainty quantification approach for time series forecasting', Neurocomputing.
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© 2020 Time series forecasting is a challenging task as the underlying data generating process is dynamic, nonlinear, and uncertain. Deep learning such as LSTM and auto-encoder can learn representations automatically and has attracted considerable attention in time series forecasting. However, current approaches mainly focus on point estimation, which leads to the inability to quantify uncertainty. Meantime, existing deep uncertainty quantification methods suffer from various limitations in practice. To this end, this paper presents a novel end-to-end framework called deep prediction interval and point estimation (DeepPIPE) that simultaneously performs multi-step point estimation and uncertainty quantification for time series forecasting. The merits of this approach are threefold: first, it requires no prior assumption on the distribution of data noise; second, it utilizes a novel hybrid loss function that improves the accuracy and stability of forecasting; third, it is only optimized by back-propagation algorithm, which is time friendly and easy to be implemented. Experimental results demonstrate that the proposed approach achieves state-of-the-art performance on three real-world datasets.

Wang, G, Lu, J, Choi, KS & Zhang, G 2020, 'A Transfer-Based Additive LS-SVM Classifier for Handling Missing Data', IEEE Transactions on Cybernetics, vol. 50, no. 2, pp. 739-752.
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IEEE The performance of a classifier might greatly deteriorate due to missing data. Many different techniques to handle this problem have been developed. In this paper, we solve the problem of missing data using a novel transfer learning perspective and show that when an additive least squares support vector machine (LS-SVM) is adopted, model transfer learning can be used to enhance the classification performance on incomplete training datasets. A novel transfer-based additive LS-SVM classifier is accordingly proposed. This method also simultaneously determines the influence of classification errors caused by each incomplete sample using a fast leave-one-out cross validation strategy, as an alternative way to clean the training data to further improve the data quality. The proposed method has been applied to seven public datasets. The experimental results indicate that the proposed method achieves at least comparable, if not better, performance than case deletion, mean imputation, and k-nearest neighbor imputation methods, followed by the standard LS-SVM and support vector machine classifiers. Moreover, a case study on a community healthcare dataset using the proposed method is presented in detail, which particularly highlights the contributions and benefits of the proposed method to this real-world application.

Wang, H, Wu, J, Zhu, X, Chen, Y & Zhang, C 2020, 'Time-Variant Graph Classification', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE Graphs are commonly used to represent objects, such as images and text, for pattern classification. In a dynamic world, an object may continuously evolve over time, and so does the graph extracted from the underlying object. These changes in graph structure with respect to the temporal order present a new representation of the graph, in which an object corresponds to a set of time-variant graphs. In this paper, we formulate a novel time-variant graph classification task and propose a new graph feature, called a graph-shapelet pattern, for learning and classifying time-variant graphs. Graph-shapelet patterns are compact and discriminative graph transformation subsequences. A graph-shapelet pattern can be regarded as a graphical extension of a shapelet--a class of discriminative features designed for vector-based temporal data classification. To discover graph-shapelet patterns, we propose to convert a time-variant graph sequence into time-series data and use the discovered shapelets to find graph transformation subsequences as graph-shapelet patterns. By converting each graph-shapelet pattern into a unique tokenized graph transformation sequence, we can measure the similarity between two graph-shapelet patterns and therefore classify time-variant graphs. Experiments on both synthetic and real-world data demonstrate the superior performance of the proposed algorithms.

Wang, J, Li, H, Lu, H, Yang, H & Wang, C 2020, 'Integrating offline logistics and online system to recycle e-bicycle battery in China', Journal of Cleaner Production, vol. 247.
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© 2019 Elsevier Ltd E-bicycles are powered by batteries including lithium-ion, lead–acid, and others. The reuse of waste batteries shows promise for grid-scale storage. The New National Standard for e-bicycles is to be introduced in China, that might result in the country becoming the largest source of battery waste in the world. If the waste batteries are not recycled appropriately, it will cause significant heavy metal pollution, which will in turn, pose a serious threat to the ecological environment and human health. This paper discusses the current status of recycling of e-bicycle batteries in China and reviews the current recycling approaches. We developed a waste e-bicycle battery recycling system based on “Internet+” to solve the dilemma of recycling end-of-life batteries; this system has three subsystems: offline reverse logistics recovery system, online network recycling system, and traceability management system. In particular, the participation of consumers and government, reward-penalty mechanism, “Internet +” development, and other strategies are considered to improve recycling systems throughout life cycle of the products. The proposed recycling system can increase the waste battery recycling rate by 2.59% under the reward-penalty mechanism, and reduce carbon dioxide emissions by 58%, which is conducive to promoting sustainable development.

Wang, J, Niu, T, Lu, HY, Yang, W & Du, P 2020, 'A Novel Framework of Reservoir Computing for Deterministic and Probabilistic Wind Power Forecasting', IEEE Transactions on Sustainable Energy, vol. 11, no. 1, pp. 337-349.
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Wang, J, Tang, L, Zhao, L, Hu, G, Song, R & Xu, K 2020, 'Equivalent circuit representation of a vortex‐induced vibration‐based energy harvester using a semi‐empirical lumped parameter approach', International Journal of Energy Research.
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Small‐scale wind energy harvesting from vortex‐induced vibrations (VIV) has been introduced in recent years as a renewable power source for microelectronics and wireless sensors. Previous studies have focused on modeling and optimizing the VIV‐based piezoelectric energy harvester (VIVPEH) structures and simplified the complicated interface circuits as pure resistors with an alternating current (AC) output. In practice, an AC output is required to be transformed into a direct current (DC) followed by further regulations before being used for real applications. Incorporating the rectification and regulation, traditional theoretical and numerical models will become extremely cumbersome and even impossible. To address this issue, this work proposes an equivalent circuit model (ECM) for a typical VIVPEH. The Scanlan‐Ehsan aerodynamic force model is employed to describe the fluid‐structure interaction. Wind tunnel experiments are carried out to validate the derived model. The performances of the VIVPEH with AC and DC interface circuits are subsequently analyzed and compared to understand the influences of these circuits on the operational wind speed bandwidth, power output, vibration amplitude, and electrical damping.

Wang, K, Lin, X, Qin, L, Zhang, W & Zhang, Y 2020, 'Efficient Bitruss Decomposition for Large-scale Bipartite Graphs.', CoRR, vol. abs/2001.06111.

Wang, L, Yu, Z, Xiong, F, Yang, D, Pan, S & Yan, Z 2020, 'Influence Spread in Geo-Social Networks: A Multiobjective Optimization Perspective.', IEEE Transactions on Cybernetics.
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As an emerging social dynamic system, geo-social network can be used to facilitate viral marketing through the wide spread of targeted advertising. However, unlike traditional influence spread problem, the heterogeneous spatial distribution has to incorporated into geo-social network environment. Moreover, from the perspective of business managers, it is indispensable to balance the tradeoff between the objective of influence spread maximization and objective of promotion cost minimization. Therefore, these two goals need to be seamlessly combined and optimized jointly. In this paper, considering the requirements of real-world applications, we develop a multiobjective optimization-based influence spread framework for geo-social networks, revealing the full view of Pareto-optimal solutions for decision makers. Based on the reverse influence sampling (RIS) model, we propose a similarity matching-based RIS sampling method to accommodate diverse users, and then transform our original problem into a weighted coverage problem. Subsequently, to solve this problem, we propose a greedy-based incrementally approximation approach and heuristic-based particle swarm optimization approach. Extensive experiments on two real-world geo-social networks clearly validate the effectiveness and efficiency of our proposed approaches.

Wang, P, Lin, Z & Qiu, X 2020, 'Influence of interaural cross-correlation coefficient and loudness level on auditory source width at different frequency', APPLIED ACOUSTICS, vol. 162.
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Wang, Q, Huang, Y, Jia, W, He, X, Blumenstein, M, Lyu, S & Lu, Y 2020, 'FACLSTM: ConvLSTM with focused attention for scene text recognition', Science China Information Sciences, vol. 63, no. 2.
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© 2020, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature. Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM (FC-LSTM) has played a critical role. Owing to the limitation of FC-LSTM, existing methods have to convert 2-D feature maps into 1-D sequential feature vectors, resulting in severe damages of the valuable spatial and structural information of text images. In this paper, we argue that scene text recognition is essentially a spatiotemporal prediction problem for its 2-D image inputs, and propose a convolution LSTM (ConvLSTM)-based scene text recognizer, namely, FACLSTM, i.e., focused attention ConvLSTM, where the spatial correlation of pixels is fully leveraged when performing sequential prediction with LSTM. Particularly, the attention mechanism is properly incorporated into an efficient ConvLSTM structure via the convolutional operations and additional character center masks are generated to help focus attention on right feature areas. The experimental results on benchmark datasets IIIT5K, SVT and CUTE demonstrate that our proposed FACLSTM performs competitively on the regular, low-resolution and noisy text images, and outperforms the state-of-the-art approaches on the curved text images with large margins.

Wang, Q, Li, Q, Wu, D, Yu, Y, Tin-Loi, F, Ma, J & Gao, W 2020, 'Machine learning aided static structural reliability analysis for functionally graded frame structures', Applied Mathematical Modelling, vol. 78, pp. 792-815.
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© 2019 A novel machine learning aided structural reliability analysis for functionally graded frame structures against static loading is proposed. The uncertain system parameters, which include the material properties, dimensions of structural members, applied loads, as well as the degree of gradation of the functionally graded material (FGM), can be incorporated within a unified structural reliability analysis framework. A 3D finite element method (FEM) for static analysis of bar-type engineering structures involving FGM is presented. By extending the traditional support vector regression (SVR) method, a new kernel-based machine learning technique, namely the extended support vector regression (X-SVR), is proposed for modelling the underpinned relationship between the structural behaviours and the uncertain system inputs. The proposed structural reliability analysis inherits the advantages of the traditional sampling method (i.e., Monte-Carlo Simulation) on providing the information regarding the statistical characteristics (i.e., mean, standard deviations, probability density functions and cumulative distribution functions etc.) of any concerned structural outputs, but with significantly reduced computational efforts. Five numerical examples are investigated to illustrate the accuracy, applicability, and computational efficiency of the proposed computational scheme.

Wang, Q, Lv, R, Rene, ER, Qi, X, Hao, Q, Du, Y, Zhao, C, Xu, F & Kong, Q 2020, 'Characterization of microbial community and resistance gene (CzcA) shifts in up-flow constructed wetlands-microbial fuel cell treating Zn (II) contaminated wastewater', BIORESOURCE TECHNOLOGY, vol. 302.
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Wang, S & Cao, L 2020, 'Inferring Implicit Rules by Learning Explicit and Hidden Item Dependency', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE Revealing complex relations between entities (e.g., items within or between transactions) is of great significance for business optimization, prediction, and decision making. Such relations include not only co-occurrence-based explicit relations but also nonco-occurrence-based implicit ones. Explicit relations have been substantially studied by rule mining-based approaches, including association rule mining and causal rule discovery. In contrast, implicit relations have received much less attention but could be more actionable. In this paper, we focus on the implicit relations between items which rarely or never co-occur while each of them co-occurs with other identical items (link items) with a high probability. A framework integrates both explicit and hidden item dependencies and a corresponding efficient algorithm IRRMiner captures such implicit relations with implicit rule inference. Experimental results show that IRRMiner not only infers implicit rules of various sizes consisting of both frequent and infrequent items effectively, it also runs at least four times faster than IARMiner, a typical indirect association rule mining algorithm which can only mine size-2 indirect association rules between frequent items. IRRMiner is applied to make recommendations and shows that the identified implicit rules can increase recommendation reliability.

Wang, S & Mao, G 2020, 'Fundamental limits of missing traffic data estimation in urban networks', IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 1191-1203.
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© 2000-2011 IEEE. Traffic data estimation plays an important role because traffic data often suffers from missing data problems, caused by a variety of reasons, i.e., temporary deployment of sensors, sensor malfunction and communication failure. Existing research on missing data estimation has mostly focused on using data-driven or model-driven models to estimate the missing data, and there is a lack of study on the achievable estimation accuracy and the conditions to achieve accurate missing data estimation. In this paper, we investigate the fundamental limits of missing traffic data estimation accuracy in urban networks using the spatial-temporal random effects model. We derive the squared flow error bound (SFEB) for the cases of the Fisher matrix being a singular and non-singular matrix, respectively. We show that the sufficient and necessary condition of the existence of an unbiased estimator is that the number of missing points is less than or equal to the rank of the Fisher matrix. For the case that no unbiased estimator can be found, we derive an inequality for the SPEB and show that the SFEB is readily determined by the covariance matrix of the unknown (missing) parameter vector, flow correlation between the unknown and the available data, and the sensor locations. Furthermore, we develop an optimal spatial-temporal Kriging estimator which is efficient in both cases where the causal relationship among available data points exists or does not exist. Our theoretical findings can be used to develop a sensor location optimization strategy to minimize the SFEB.

Wang, S, Wang, Y, Liu, C, Lei, G, Zhu, J & Guo, Y 2020, 'Detent Force Minimization of a Tubular Flux-Switching Permanent Magnet Motor Using Un-Equal Width Stator Slots Based on Taguchi Method', IEEE Transactions on Applied Superconductivity, vol. 30, no. 4, pp. 1-5.
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Wang, W, Zhang, Y, Sui, Y, Wan, Y, Zhao, Z, Wu, J, Yu, P & Xu, G 2020, 'Reinforcement-Learning-Guided Source Code Summarization via Hierarchical Attention', IEEE Transactions on Software Engineering, pp. 1-1.
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Wang, X, Gu, B, Ren, Y, Ye, W, Yu, S, Xiang, Y & Gao, L 2020, 'A Fog-Based Recommender System', IEEE Internet of Things Journal, vol. 7, no. 2, pp. 1048-1060.
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© 2014 IEEE. Fog computing is an emergent computing paradigm that extends the cloud paradigm. With the explosive growth of smart devices and mobile users, cloud computing no longer matches the requirements of the Internet of Things (IoT) era. Fog computing is a promising solution to satisfying these new requirements, such as low latency, uninterrupted service, and location awareness. As a typical new computing paradigm and network architecture, fog computing raises new challenges, such as privacy, data management, data analytics, information overload, and participatory sensing. In this article, we present a fog-based hybrid recommender system to address the issue of information overload in fog computing. Our proposed system not only abstracts useful information from the fog environment but can also be considered as an optimization tool due to its ability to provide suggestions to improve system performance. In particular, we demonstrate that the proposed system provides personalized and localized recommendations to users, and also advise the system itself to precache the content to optimize the storage capacity of the fog server.

Wang, X, Yang, Y, Liu, H, Ren, J, Xu, S, Wang, S & Yu, S 2020, 'Efficient measurement of round-trip link delays in software-defined networks', Journal of Network and Computer Applications, vol. 150.
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© 2019 Elsevier Ltd Round-trip link delay is an important indicator for network performance optimization and troubleshooting. The Software-Defined Networking (SDN) paradigm, which provides flexible and centralized control capability, paves the way for efficient round-trip link delay measurement. In this paper, we study the round-trip link delay measurement problem in SDN networks. We propose an efficient measurement scheme, which infers round-trip link delays from end-to-end delay of some measurement paths implemented with few flow rules in each SDN switch. Furthermore, to reduce measurement cost and meet measurement constraint, we address the Monitor Placement and Link Assignment (MPLA) problem involved in the measurement scheme. Specifically, we formulate the MPLA problem as a Mixed Integer Linear Programming (MILP) problem, prove that it is NP-hard, and propose an efficient algorithm called MPLA Algorithm based on Biding Strategy (MPLAA-BS) to solve the problem. The extensive simulation results on real network topologies reveal that the proposed scheme can efficiently and accurately measure round-trip link delays in SDN networks, and the MPLAA-BS can find feasible and resource-efficient solutions for the MPLA problem.

Wang, X, Zha, X, Ni, W, Liu, RP, Guo, YJ, Niu, X & Zheng, K 2020, 'Game Theoretic Suppression of Forged Messages in Online Social Networks', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-11.
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Wang, Y, Sayyadi, N, Zheng, X, Woods, TA, Leif, RC, Shi, B, Graves, SW, Piper, JA & Lu, Y 2020, 'Time-resolved microfluidic flow cytometer for decoding luminescence lifetimes in the microsecond region.', Lab on a Chip: miniaturisation for chemistry, physics, biology, materials science and bioengineering.
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Time-resolved luminescence detection using long-lived probes with lifetimes in the microsecond region have shown great potential in ultrasensitive and multiplexed bioanalysis. In flow cytometry, however, the long lifetime poses a significant challenge to measure wherein the detection window is often too short to determine the decay characteristics. Here we report a time-resolved microfluidic flow cytometer (tr-mFCM) incorporating an acoustic-focusing chip, which allows slowing down of the flow while providing the same detection conditions for every target, achieving accurate lifetime measurement free of autofluorescence interference. Through configuration of the flow velocity and detection aperture with respect to the time-gating sequence, a multi-cycle luminescence decay profile is captured for every event under maximum excitation and detection efficiency. A custom fitting algorithm is then developed to resolve europium-stained polymer microspheres as well as leukemia cells against abundant fluorescent particles, achieving counting efficiency approaching 100% and lifetime CVs (coefficient of variation) around 2-6%. We further demonstrate lifetime-multiplexed detection of prostate and bladder cancer cells stained with different europium probes. Our acoustic-focusing tr-mFCM offers a practical technique for rapid screening of biofluidic samples containing multiple cell types, especially in resource-limited environments such as regional and/or underdeveloped areas as well as for point-of-care applications.

Wang, Y, Sun, H, Huang, S & Song, Y 2020, 'Description of stability for linear time-invariant systems based on the first curvature', Mathematical Methods in the Applied Sciences, vol. 43, no. 2, pp. 486-511.
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© 2019 John Wiley & Sons, Ltd. This paper focuses on using the first curvature κ(t) of trajectory to describe the stability of linear time-invariant system. We extend the results for two and three-dimensional systems (Wang, Sun, Song et al, arXiv:1808.00290) to n-dimensional systems. We prove that for a system (Formula presented.), (a) if there exists a measurable set whose Lebesgue measure is greater than zero, such that (Formula presented.) or (Formula presented.) does not exist for any initial value in this set, then the zero solution of the system is stable; (b) if the matrix A is invertible, and there exists a measurable set whose Lebesgue measure is greater than zero, such that (Formula presented.) for any initial value in this set, then the zero solution of the system is asymptotically stable.

Wang, Y, Wang, D, Yi, N, Li, Y, Ni, B-J, Wang, Q, Wang, H & Li, X 2020, 'Insights into the toxicity of troclocarban to anaerobic digestion: Sludge characteristics and methane production', JOURNAL OF HAZARDOUS MATERIALS, vol. 385.
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Wang, Z, Xu, M, Ye, N, Xiao, F, Ruchuan, WN & Huang, H 2020, 'Computer Vision-assisted 3D Object Localization via COTS RFID Devices and a Monocular Camera', IEEE Transactions on Mobile Computing.
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IEEE In most RFID localization systems, acquiring a reader antenna's position at each sampling time is challenging, especially for those antenna-carrying robot or drone systems with unpredictable trajectories. In this paper, we present RF-MVO that fuses RFID and computer vision for stationary RFID localization in 3D space by attaching a light-weight 2D monocular camera to two reader antennas in parallel. Firstly, the existing monocular visual odometry only recovers a camera/antenna trajectory in the camera view from 2D images. By combining it with RF phase, we design a model to estimate a scale factor for real-world trajectory transformation, along with spatial directions of an RFID tag relative to a virtual antenna array due to the mobility of each antenna. Then we propose a novel RFID localization algorithm that does not require exhaustively searching all possible positions within the pre-specified region. Secondly, to speed up the searching process and improve localization accuracy, we propose a coarse-to-fine optimization algorithm. Thirdly, we introduce the concept of horizontal dilution of precision (HDOP) to measure the confidence level of localization results. Our experiments demonstrate the effectiveness of proposed algorithms and show RF-MVO can achieve 6.23 cm localization error.

Wei, W, Guo, W, Ngo, HH, Mannina, G, Wang, D, Chen, X, Liu, Y, Peng, L & Ni, BJ 2020, 'Enhanced high-quality biomethane production from anaerobic digestion of primary sludge by corn stover biochar', Bioresource Technology, vol. 306.
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© 2020 Elsevier Ltd This study conducted batch and continuous tests to reveal the feasibility of corn stover biochar on improving anaerobic digestion of primary sludge (PS). Dosing biochar (1.82, 2.55 and 3.06 g/g Total Solids (TS)) in digester improved methane content increasing from 67.5% to 81.3–87.3% and enhanced methane production by 8.6–17.8%. Model analysis indicated that biochar accelerated PS hydrolysis and enhanced methane potential of PS. The mechanistic studies showed that biochar enhanced process stability provided by strong buffering capacity and alleviated NH3 inhibition. In continuous test over 116 days, the volatile solids (VS) destruction in the biochar-dosed digester increased by 14.9%, resulting in a 14% reduction in the volume of digestate for disposal. Biochar changed microbial community in an expected direction for anaerobic digestion. This work suggests that biochar technology would apply to co-digestion of WAS and PS to maximize the energy recovery and sludge reduction from the two sludge streams.

Wei, W, Liu, X, Wu, L, Wang, D, Bao, T & Ni, BJ 2020, 'Sludge Incineration Bottom Ash Enhances Anaerobic Digestion of Primary Sludge toward Highly Efficient Sludge Anaerobic Codigestion', ACS Sustainable Chemistry and Engineering, vol. 8, no. 7, pp. 3005-3012.
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Copyright © 2020 American Chemical Society. In wastewater treatment plants (WWTPs), two main sludge streams, i.e., primary sludge (PS) and waste activated sludge (WAS), are generally mixed for anaerobic codigestion. However, the methane production is usually restricted by the poor and slow biodegradability of PS, and an effective approach for its efficient codigestion with WAS is still lacking due to its highly different sludge properties from those of WAS. Herein, we reported a novel strategy through using the sludge incineration bottom ash to enhance the anaerobic digestion of PS and its codigestion with WAS. Biochemical methane potential (BMP) test results showed that ash additive at 0.6-1.2 g/g-dry matter (DM) significantly enhanced PS anaerobic digestion, identified by an up to 18.2% increase in specific methane production. This was accompanied by a significantly improved dewaterability in the digestate. The transformations of metabolic intermediates revealed that the ash additive accelerated the hydrolysis and acidogenesis processes, which were also supported by the increased hydrolysis rate (k) of PS determined through kinetic modeling. Ash additive was then experimentally demonstrated to be effective in enhancing the anaerobic codigestion of PS and WAS, with the increased volatile solids (VS) destruction being approximately 19.8%, representing a reduction of digestate volume by 12.6%. The novel strategy proposed in this study provides a new paradigm of an integrated sludge-control by sludge to bring significant economic benefits to wastewater treatment and sludge disposal.

Wei, W, Wu, L, Liu, X, Chen, Z, Hao, Q, Wang, D, Liu, Y, Peng, L & Ni, BJ 2020, 'How does synthetic musks affect methane production from the anaerobic digestion of waste activated sludge?', Science of the Total Environment, vol. 713.
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© 2020 Elsevier B.V. The increasing use of synthetic musks has led to a large amount of synthetic musks retaining in waste activated sludge (WAS) via wastewater treatment, thereby entering anaerobic digester. However, the potential effects of synthetic musks on WAS anaerobic digestion remain unknown. Herein, this study selected the dominant galaxolide (HHCB) in WAS as the typical synthetic musks and experimentally evaluated the long-term effects on WAS anaerobic digestion using continuous lab-scale anaerobic digesters as well as the mechanisms involved. The results demonstrated that the increased HHCB levels (i.e., 90, 150 and 200 mg/kg-dw) resulted in the decreased methane production, with the methane production at 200 mg/kg-dw being only 80.5 ± 0.1% of the control. Supporting the methane production data, volatile solids (VS) destruction decreased by 18.6 ± 0.9%, which increased 6.8% of volume waste sludge for transfer and disposal. Correspondingly, the microbial community was shifted in the direction against anaerobic digestion. By modeling based on biochemical methane potential tests and investigating the key stages involved in anaerobic digestion, it was found that although the HHCB showed little impacts on the solubilization, WAS hydrolysis-acidification steps was inhibited by HHCB with the decreased hydrolysis rate and methane production potential, thereby causing the deteriorated performance of WAS anaerobic digestion.

Wen, S, Liu, W, Yang, Y, Zhou, P, Guo, Z, Yan, Z, Chen, Y & Huang, T 2020, 'Multilabel Image Classification via Feature/Label Co-Projection', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-10.
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Wilson, KJ, Alabd, R, Abolhasan, M, Safavi-Naeini, M & Franklin, DR 2020, 'Optimisation of monolithic nanocomposite and transparent ceramic scintillation detectors for positron emission tomography', Scientific Reports, vol. 10, no. 1.
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© 2020, The Author(s). High-resolution arrays of discrete monocrystalline scintillators used for gamma photon coincidence detection in PET are costly and complex to fabricate, and exhibit intrinsically non-uniform sensitivity with respect to emission angle. Nanocomposites and transparent ceramics are two alternative classes of scintillator materials which can be formed into large monolithic structures, and which, when coupled to optical photodetector arrays, may offer a pathway to low cost, high-sensitivity, high-resolution PET. However, due to their high optical attenuation and scattering relative to monocrystalline scintillators, these materials exhibit an inherent trade-off between detection sensitivity and the number of scintillation photons which reach the optical photodetectors. In this work, a method for optimising scintillator thickness to maximise the probability of locating the point of interaction of 511 keV photons in a monolithic scintillator within a specified error bound is proposed and evaluated for five nanocomposite materials (LaBr3:Ce-polystyrene, Gd2O3-polyvinyl toluene, LaF3:Ce-polystyrene, LaF3:Ce-oleic acid and YAG:Ce-polystyrene) and four ceramics (GAGG:Ce, GLuGAG:Ce, GYGAG:Ce and LuAG:Pr). LaF3:Ce-polystyrene and GLuGAG:Ce were the best-performing nanocomposite and ceramic materials, respectively, with maximum sensitivities of 48.8% and 67.8% for 5 mm localisation accuracy with scintillator thicknesses of 42.6 mm and 27.5 mm, respectively.

Wu, C, Fang, J, Zhang, Z, Entezari, A, Sun, G, Swain, MV & Li, Q 2020, 'Fracture modeling of brittle biomaterials by the phase-field method', Engineering Fracture Mechanics, vol. 224.
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© 2019 Elsevier Ltd Biomaterials have been extensively used in prosthetic applications for their proven biocompatibility and osseointegration characteristics. Nevertheless, one of the critical issues of some synthetic biomaterials is brittleness prone to experience fracture failure due to low tensile strength and low fracture toughness. This study aims to employ a recently-developed phase-field model to simulate the crack propagation in brittle biomaterials. Unlike discrete fracture modeling methods, the phase-field approach allows simulating crack path in a continuous manner, thereby avoiding remeshing that may not be trivial for complicated fracture surfaces and facilitate iterative procedure commonly required for structural optimization. The phase-field model is formulated to treat the fracture path as a localized region of diffusive damage that can be described in terms of a phase-field function, in which the discreteness in cracked materials is assumed to be smeared. In this study, three representative case studies from the biomedical context, namely a zirconia-based dental bridge (or namely fixed partial denture (FPD)), a ceramic tissue scaffold and an analog saw-bone femur, are employed as illustrative examples. The phase-field modeling results are compared with the in-house experimental tests, demonstrating the effectiveness of the phase-field technique for predicting brittle fracture failure in several typical biomedical case scenarios. The phase-field model provides a useful tool for the computational fracture analysis and design optimization of other brittle biomaterials.

Wu, C, Zheng, K, Fang, J, Steven, GP & Li, Q 2020, 'Time-dependent topology optimization of bone plates considering bone remodeling', COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, vol. 359.
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Wu, K, Ni, W, Zhang, JA, Liu, RP & Guo, YJ 2020, 'Refinement of Optimal Interpolation Factor for DFT Interpolated Frequency Estimator', IEEE Communications Letters, pp. 1-1.
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Wu, K, Ni, W, Zhang, JA, Liu, RP & Guo, YJ 2020, 'Secrecy Rate Analysis for Millimeter-Wave Lens Antenna Array Transmission', IEEE COMMUNICATIONS LETTERS, vol. 24, no. 2, pp. 272-276.
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Wu, L, Falque, R, Perez-Puchalt, V, Liu, L, Pietroni, N & Vidal-Calleja, T 2020, 'Skeleton-Based Conditionally Independent Gaussian Process Implicit Surfaces for Fusion in Sparse to Dense 3D Reconstruction', IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1532-1539.
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Wu, L, Liu, Q, Zhou, W, Mao, G, Huang, J & Huang, H 2020, 'A Semantic Web-Based Recommendation Framework of Educational Resources in E-Learning', Technology Knowledge and Learning.
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© 2018, Springer Nature B.V. A big challenge in educational resources construction is the intelligent and personalized resource recommendation for learners. This paper proposes a semantic recommendation framework of educational resources based on semantic web and pedagogics. In this framework, a domain ontology is constructed to describe the knowledge structure of the domain. All the resources and user portfolio are described with ontology technology and resource description framework to support semantic inference. Based on the semantic resource organization, we made a set of reasoning rules based on pedagogics. These rules are made from the synthesis of the type of the knowledge, the internal structure of knowledge and learner’s learning performance. A case study was implemented on the course “theory and practice of database”. In this case, learners are recommended different learning materials according to the different knowledge structure and different learning performance. Three typical learning modes are proposed to describe the personalized learning experience. This framework can be used as a guide for teachers and resource designers.

Wu, L, Peng, L, Wei, W, Wang, D & Ni, B-J 2020, 'Nitrous oxide production from wastewater treatment: The potential as energy resource rather than potent greenhouse gas', JOURNAL OF HAZARDOUS MATERIALS, vol. 387.
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Wu, S-L, Sun, J, Chen, X, Wei, W, Song, L, Dai, X & Ni, B-J 2020, 'Unveiling the mechanisms of medium-chain fatty acid production from waste activated sludge alkaline fermentation liquor through physiological, thermodynamic and metagenomic investigations', WATER RESEARCH, vol. 169.
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Wu, T, Feng, Z, Wu, C, Lei, G, Guo, Y, Zhu, J & Wang, X 2020, 'Multiobjective Optimization of a Tubular Coreless LPMSM Based on Adaptive Multiobjective Black Hole Algorithm', IEEE Transactions on Industrial Electronics, vol. 67, no. 5, pp. 3901-3910.
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© 1982-2012 IEEE. In most multiobjective optimization problems of electrical machines, the weighted function method is used to convert them into single-objective optimization problems. This paper applies a kind of new multiobjective evolutionary algorithms (MOEAs), called adaptive multiobjective black hole (AMOBH) algorithms, to achieve effective multiobjective optimization of a tubular coreless linear permanent magnet synchronous motor (LPMSM). To reduce the computation cost of the MOEAs, a one-layer analytical model (AM) is presented for the tubular coreless LPMSM in this paper. The accuracy of the simplified one-layer AM is verified by comparisons with multilayer AM and finite element analysis (FEA) under different structure parameters. It is found that the simplified AM has good accuracy and can decrease the computation cost significantly. AMOBH algorithm is subsequently introduced. The optimal Pareto front with regard to thrust, copper loss, and permanent magnet volume are analyzed, and more diversified optimization results are provided. The final Pareto solution can be selected directly by practical physical values according to the application requirements. Finally, a prototype is fabricated for the selected design; its experimental results are provided and compared with those of the FEA results.

Wu, Y, Fang, J, Cheng, Z, He, Y & Li, W 2020, 'Crashworthiness of tailored-property multi-cell tubular structures under axial crushing and lateral bending', Thin-Walled Structures, vol. 149.
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© 2020 Elsevier Ltd Thin-walled structures have been widely used as energy-absorbing components, which can be probably subjected to multiple loading conditions in real life, such as axial crushing and lateral bending. Most of the existing literature solely focuses on the pure axial crushing or lateral bending. In this paper, a novel tailored-property multi-cell tubular structure is proposed, where the material's ultimate strength at the corner region is increased to accommodate both the axial crushing and lateral bending conditions. Finite element (FE) models were developed and validated through experimental results. The FE models were used to investigate the crashworthiness performances of the tailored-property multi-cell tubes under axial crushing and lateral bending. Under both axial crushing and lateral bending, it was found that the tailored-property multi-cell tubes exhibited noticeable advantages over the corresponding traditional tubes. The tailoring ratio and thickness had a significant influence on the crashworthiness performance of the tailored-property multi-cell tubes. Moreover, the well-designed tailored-property multi-cell tubes could exhibit the progressive deformation mode under axial crushing. Furthermore, a theoretical model for the tailored-property multi-cell tubes under axial crushing was developed based on the Superfolding Element (SFE) Method. The results showed that the theoretical solutions were in good agreement with the finite element analysis results. The findings of this paper have the potential for energy absorption applications under different loading conditions.

Wu, Y, Jiang, L & Yang, Y 2020, 'Revisiting EmbodiedQA: A Simple Baseline and Beyond', IEEE Transactions on Image Processing, vol. 29, pp. 3984-3992.
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Wu, Z, Pan, S, Chen, F, Long, G, Zhang, C & Yu, PS 2020, 'A Comprehensive Survey on Graph Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-21.
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Wu, Z, Tang, M-C, Li, M & Ziolkowski, RW 2020, 'Ultralow-Profile, Electrically Small, Pattern-Reconfigurable Metamaterial-Inspired Huygens Dipole Antenna', IEEE Transactions on Antennas and Propagation, vol. 68, no. 3, pp. 1238-1248.
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Wu, Z, Wang, R, Li, Q, Lian, X, Xu, G, Chen, E & Liu, X 2020, 'A location privacy-preserving system based on query range cover-up for location-based services', IEEE Transactions on Vehicular Technology, pp. 1-1.
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Xi, Y, Zheng, J, He, X, Jia, W, Li, H, Xie, Y, Feng, M & Li, X 2020, 'Beyond context: Exploring semantic similarity for small object detection in crowded scenes', Pattern Recognition Letters.
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© 2019 Small object detection in crowded scene aims to find those tiny targets with very limited resolution from crowded scenes. Due to very little information available on tiny objects, it is often not suitable to detect them merely based on the information presented inside their bounding boxes, resulting low accuracy. In this paper, we propose to exploit the semantic similarity among all predicted objects’ candidates to boost the performance of detectors when handling tiny objects. For this purpose, we construct a pairwise constraint to depict such semantic similarity and propose a new framework based on Discriminative Learning and Graph-Cut techniques. Experiments conducted on three widely used benchmark datasets demonstrate the improvement over the state-of-the-art approaches gained by applying this idea.

Xiao, G, Zheng, Z, Jiang, B & Sui, Y 2020, 'An Empirical Study of Regression Bug Chains in Linux', IEEE Transactions on Reliability.
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Xiao, T, Qiu, X & Halkon, B 2020, 'Ultra-broadband active noise cancellation at the ears via optical microphones', Scientific Reports.
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High frequency noise has generally been difficult to be cancelled actively at a person's ears, particularly for active headrest systems aiming to free the listener from noise cancellation headphones. One of the main challenges is to measure the noise precisely at the ears. Here we demonstrate a new error sensing methodology with an optical microphone arrangement for active noise cancellation (ANC). It can measure the noise accurately for ANC without any obstructions at the listener's ears. The demonstrated system, or virtual ANC headphone as we call it, is shown to provide more than 10 dB attenuation for ultra-broadband noise - up to 6000 Hz - inside the ears in a complex sound field. The bandwidth of the controllable noise significantly exceeds the results from the state-of-the-art system, which is below 1000 Hz. The proposed method leads to the next generation of personal hearing protection system and can open up a whole new area of sound control research.

Xiao, Y, Pei, Q, Yao, L, Yu, S, Bai, L & Wang, X 2020, 'An enhanced probabilistic fairness-aware group recommendation by incorporating social activeness', Journal of Network and Computer Applications, pp. 102579-102579.
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Xie, A & Li, S 2020, 'On constructing the largest and smallest uninorms on bounded lattices', Fuzzy Sets and Systems, vol. 386, pp. 95-104.
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© 2019 Elsevier B.V. Uninorms on the unit interval are a common extension of triangular norms (t-norms) and triangular conorms (t-conorms). As important aggregation operators, uninorms play a very important role in fuzzy logic and expert systems. Recently, several researchers have studied constructions of uninorms on more general bounded lattices. In particular, Çaylı (2019) gave two methods for constructing uninorms on a bounded lattice L with e∈L∖{0,1}, which is based on a t-norm Te on [0,e] and a t-conorms Se on [e,1] that satisfy strict boundary conditions. In this paper, we propose two new methods for constructing uninorms on bounded lattices. Our constructed uninorms are indeed the largest and the smallest among all uninorms on L that have the same restrictions Te and Se on [0,e] and, respectively, [e,1]. Moreover, our constructions does not require the boundary condition, and thus completely solved an open problem raised by Çaylı.

Xie, H & Veitch, D 2020, 'Nested saturation control of multiple vector integrators and its application to motion control of UAVs', International Journal of Robust and Nonlinear Control, vol. 30, no. 1, pp. 246-265.
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This paper presents two nested input saturation control schemes for a special class of multiple vector integrators with bounded additive disturbances. The considered systems originate from the motion control of rotary‐wing unmanned aerial vehicles (UAVs). The first scheme is based on a feedforward form, which requires state transformation and can be applied to stabilize arbitrary order vector integrator systems. The second scheme is constructed with original state variables using a new approach, applied here to double vector integrator systems. The capability of handling external disturbance using the two schemes is also analyzed. The two schemes are applied to design motion controllers for rotary‐wing UAVs and simulation results are provided to show the performances of two controllers.

Xiong, F, Shen, W, Chen, H, Pan, S, Wang, X & Yan, Z 2020, 'Exploiting Implicit Influence From Information Propagation for Social Recommendation.', IEEE transactions on cybernetics.
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Social recommender systems have attracted a lot of attention from academia and industry. On social media, users' ratings and reviews can be observed by all users, and have implicit influence on their future ratings. When these users make subsequent decisions about an item, they may be affected by existing ratings on the item. Thus, implicit influence propagates among the users who rated the same items, and it has significant impact on users' ratings. However, implicit influence propagation and its effect on recommendation rarely have been studied. In this article, we propose an information propagation-based social recommendation method (SoInp) and model the implicit user influence from the perspective of information propagation. The implicit influence is inferred from ratings on the same items. We investigate the concrete effect of implicit user influence in the propagation process and introduce it into recommender systems. Furthermore, we incorporate the implicit user influence and explicit trust information in the matrix factorization framework. To demonstrate the performance, we conduct comprehensive experiments on real-world datasets to compare the proposed method with the state-of-the-art models. The results indicate that SoInp makes notable improvements in rating prediction.

Xiong, F, Wang, X, Pan, S, Yang, H, Wang, H & Zhang, C 2020, 'Social Recommendation With Evolutionary Opinion Dynamics', IEEE Transactions on Systems Man and Cybernetics: Systems.
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IEEE When users in online social networks make a decision, they are often affected by their neighbors. Social recommendation models utilize social information to reveal the impact of neighbors on user preferences, and this impact is often described by the linear superposition of neighbor preferences or by global trust propagation. Further exploration needs to be undertaken to determine whether the influence pattern of other users from online interaction behaviors is adequately described. In this paper, we introduce evolutionary opinion dynamics from the field of statistical physics into recommender systems, characterizing the impact of other users. We propose an opinion dynamic model by evolutionary game theory. To describe online user interactions, we define the strategies during an interaction between two users, and present the payoff for each strategy in terms of errors of estimated ratings. Therefore, user behaviors are associated with their preferences and ratings. In addition, we measure user influence according to their topological roles in the social network. We incorporate evolutionary opinion dynamics and user influence into the recommendation framework for the prediction of unknown ratings. Experiment results on two real-world datasets demonstrate that our method outperforms state-of the-art models in terms of accuracy, and it also performs well for cold-start users. Our method reduces the divergence of user preferences, in accordance with online opinion interactions. Furthermore, our method has approximate computational complexity with matrix factorization, and results in less computation than state-of-the-art models. Our method is quite general, and indicates that studies in social physics, statistics, and other research fields may be involved in recommendation to improve the performance.

Xu, B, Ahmed, MB, Zhou, JL & Altaee, A 2020, 'Visible and UV photocatalysis of aqueous perfluorooctanoic acid by TiO2 and peroxymonosulfate: Process kinetics and mechanistic insights', CHEMOSPHERE, vol. 243.
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Xu, B, Zhou, JL, Altaee, A, Ahmed, MB, Johir, MAH, Ren, J & Li, X 2020, 'Improved photocatalysis of perfluorooctanoic acid in water and wastewater by Ga2O3/UV system assisted by peroxymonosulfate', CHEMOSPHERE, vol. 239.
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Xu, D, Shi, Y, Tsang, IW, Ong, Y-S, Gong, C & Shen, X 2020, 'Survey on Multi-Output Learning.', IEEE transactions on neural networks and learning systems.
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The aim of multi-output learning is to simultaneously predict multiple outputs given an input. It is an important learning problem for decision-making since making decisions in the real world often involves multiple complex factors and criteria. In recent times, an increasing number of research studies have focused on ways to predict multiple outputs at once. Such efforts have transpired in different forms according to the particular multi-output learning problem under study. Classic cases of multi-output learning include multi-label learning, multi-dimensional learning, multi-target regression, and others. From our survey of the topic, we were struck by a lack in studies that generalize the different forms of multi-output learning into a common framework. This article fills that gap with a comprehensive review and analysis of the multi-output learning paradigm. In particular, we characterize the four Vs of multi-output learning, i.e., volume, velocity, variety, and veracity, and the ways in which the four Vs both benefit and bring challenges to multi-output learning by taking inspiration from big data. We analyze the life cycle of output labeling, present the main mathematical definitions of multi-output learning, and examine the field's key challenges and corresponding solutions as found in the literature. Several model evaluation metrics and popular data repositories are also discussed. Last but not least, we highlight some emerging challenges with multi-output learning from the perspective of the four Vs as potential research directions worthy of further studies.

Xu, D-S, Xu, X-Y, Li, W & Fatahi, B 2020, 'Field experiments on laterally loaded piles for an offshore wind farm', MARINE STRUCTURES, vol. 69.
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Xu, J-X, Zhang, XY, Li, H-Y, Yang, Y, Dutkiewicz, E & Xue, Q 2020, 'Ultracompact Multichannel Bandpass Filter Based on Trimode Dielectric-Loaded Cavities', IEEE Transactions on Microwave Theory and Techniques, pp. 1-10.
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Xu, K, Wang, F, Wang, H, Wang, Y & Zhang, Y 2020, 'Mitigating the Impact of Data Sampling on Social Media Analysis and Mining', IEEE Transactions on Computational Social Systems.
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IEEE The last decade has witnessed the explosive growth of online social media in users and contents. Due to the unprecedented scale and the cascading power of the underlying social networks, social media has created a new paradigm for sharing information, broadcasting breaking news, and reporting real-time events by any user from anywhere at any time. Many popular social media sites including Twitter provide streaming data services by standard APIs to the broad researcher and developer communities. Given the sheer data volume, rapid velocity, and feature variety of online social media, these sites often supply only a sampled set of streaming data, rather than the full data set to reduce the resource cost of computations, storage, and network bandwidth. In light of the substantial impact of sampling in Twitter data stream, this article explores a combination of spectral clustering, locality-sensitive hashing (LSH), latent Dirichlet allocation (LDA) topic modeling, and differential equation modeling to mitigate the impact of sampling on social media data analysis, in particular on detecting real-world events and predicting information diffusion. Our extensive experiments demonstrate that our proposed method is able to detect effectively the real-time emerging events and predict accurately the cascading pattern of these events from the 1% sampled Twitter data stream. To the best of our knowledge, this article is the first effort to introduce a systematic methodology to study and mitigate the impact of data sampling on social media analysis and mining.

Xu, K-D, Zhang, F, Guo, Y, Ye, L & Liu, Y 2020, 'Spoof Surface Plasmon Polaritons Based on Balanced Coplanar Stripline Waveguides', IEEE PHOTONICS TECHNOLOGY LETTERS, vol. 32, no. 1, pp. 55-58.
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Xu, Q, Liu, X, Yang, G, Wang, D, Wu, Y, Li, Y, Huang, X, Fu, Q, Wang, Q, Liu, Y, Li, X & Yang, Q 2020, 'Norfloxacin-induced effect on enhanced biological phosphorus removal from wastewater after long-term exposure', Journal of Hazardous Materials, vol. 392.
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© 2020 Elsevier B.V. In this study, long-term experiments were performed under synthetic wastewater conditions to evaluated the potential impacts of norfloxacin (NOR) (10, 100 and 500 μg/L) on enhanced biological phosphorus removal (EBPR). Experimental result showed that long-term exposure to 10 μg/L NOR induced negligible effects on phosphorus removal. The presence of 100 μg/L NOR slightly decreased phosphorus removal efficiency to 94.41 ± 1.59 %. However, when NOR level further increased to 500 μg/L, phosphorus removal efficiency was significantly decreased from 97.96 ± 0.8 5% (control) to 82.33 ± 3.07 %. The mechanism study revealed that the presence of 500 μg/L NOR inhibited anaerobic phosphorus release and acetate uptake as well as aerobic phosphorus uptake during long-term exposure. It was also found that 500 μg/L NOR exposure suppressed the activity of key enzymes related to phosphorus removal but promoted the transformations of intracellular polyhydroxyalkanoate and glycogen. Microbial analysis revealed that that the presence of 500 μg/L NOR reduced the abundances of polyphosphate accumulating organisms but increased glycogen accumulating organisms, as compared the control.

Xu, W, Ebraheem, AK, Liu, Y, Zhu, J, Hussien, MG & Elbabo Mohammed, OM 2020, 'An MRAS Speed Observer Based on Control Winding Flux for Sensorless Control of Stand-Alone BDFIGs', IEEE Transactions on Power Electronics, vol. 35, no. 7, pp. 7271-7281.
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Xu, X, Veitch, D, Li, Y & Vucetic, B 2020, 'Minimum Cost Reconfigurable Network Template Design with Guaranteed QoS', IEEE Transactions on Communications, vol. 68, no. 2, pp. 1013-1024.
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© 2019 IEEE. Conventional networks are based on layered protocols with intensive cross-layer interactions and complex signal processing at every node, making it difficult to meet the ultra-low latency requirement of mission critical applications in future communication systems. In this paper, we address this issue by proposing the concept of network template, which allows data to flow through it at the transmission symbol level, with minimal node processing. This is achieved by carefully calibrating the inter-connecting links among the nodes and pre-calculating the routing/network coding actions for each node, according to a set of preconfigured flows. In this paper, we focus on the minimum cost network template design to minimize the connections within the template, while ensuring that all the pre-defined configurations are feasible with the guaranteed throughput, latency and reliability. We show that the minimum cost network template design problem is difficult to solve optimally in general. We thus propose an efficient greedy algorithm to find a close-to-optimal solution. Simulation results show that the construction cost of the templates obtained by the proposed algorithm is very close to a lower bound. Furthermore, the construction cost increases only slightly with the number of pre-defined configurations, which confirms the flexibility of the network template design.

Xu, X, Zhang, X, Khan, M, Dou, W, Xue, S & Yu, S 2020, 'A balanced virtual machine scheduling method for energy-performance trade-offs in cyber-physical cloud systems', Future Generation Computer Systems, vol. 105, pp. 789-799.
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© 2017 Elsevier B.V. The cloud computing scheme promises many salient features such as on-demand resource provisioning to users, and it therefore has drawn significant attention from the cyber-physical systems (CPS). An increasing number of CPS have been deployed in cloud platforms, and to accommodate numerous CPS applications, cloud datacenters often consist of a huge number of physical computation and storage nodes, and the number is still increasing. As a result, the electricity power consumption in cloud datacenters is considerable, currently accounting for about 1.3% of the worldwide electricity. How to reduce the energy consumption of datacenters is an economically beneficial but challenging problem. Optimizing virtual machine (VM) scheduling in datacenters by live VM migration is an appealing method to save energy consumption. However, it is still a challenge to conduct VM scheduling in an energy-efficient and performance-guaranteed manner, since VM migration can suffer from severe performance degradation while saving energy. In this paper, we propose a balanced VM scheduling method to achieve trade-offs between energy and performance in cyber-physical cloud systems. Specifically, the problem is formulated via a joint optimization model, and a balanced VM scheduling method is proposed accordingly to determine which VMs and where should be migrated, aiming at both reducing energy consumption and mitigating performance degradation. Both analytical and simulation results demonstrate the effectiveness and efficiency of our method.

Yadav, S, Saleem, H, Ibrar, I, Naji, O, Hawari, AA, Alanezi, AA, Zaidi, SJ, Altaee, A & Zhou, J 2020, 'Recent developments in forward osmosis membranes using carbon-based nanomaterials', Desalination, vol. 482.
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© 2020 Elsevier B.V. Contamination and industrial development are among the reasons for water quality deterioration beyond treatability by conventional processes. Unfortunately, conventional water and wastewater treatment technologies are not always capable of handling industrial wastewaters, and hence more advanced treatment technologies are required. The new trend of osmotically driven membrane technologies has demonstrated an exceptional efficiency for water purification and treatment including seawater desalination. Compared to pressure-driven membrane processes, forward osmosis (FO) technology, as a standalone process, is more energy-efficient, and less prone to membrane fouling than its predecessor reverse osmosis (RO) technology. However, forward osmosis suffers a severe concentration polarization that is acting on both sides of the membrane and results in a sharp decline in water flux. A thinner support layer has been recommended to lessen the concentration polarization impact in the FO process but a very thin support layer compromises the membrane mechanical strength. Recently, researchers have applied different carbon-based nanomaterials to enhance water flux, fouling propensity, and mechanical strength of the FO membrane. This work reviews advancement in the FO membrane fabrication using carbon nanomaterials to improve the membrane characteristics. Despite a large number of laboratory experiments, carbon-based nanomaterials in the FO membrane are still at the early-stage of laboratory investigation and no commercial products are available yet. The study also reviews the main challenges that limit the application of carbon-based nanomaterials for FO membranes.

Yan, H, Teng, JD, Zhang, S & Sheng, DC 2020, 'Model for tortuosity of soil considering geometric arrangement of particles', Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, vol. 42, no. 1, pp. 195-200.
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© 2020, Editorial Office of Chinese Journal of Geotechnical Engineering. All right reserved. Tortuosity is an important parameter in studying the permeability of soil, which reflects the flow path of fluid in soil particles. In literatures, the tortuosity is considered to be solely related to the porosity of soil. However, the expressions for the tortuosity vary greatly in the previous studies. It may be caused by some other unconsidered factors. The physical model for fluid passing through square particles in laminar flow state is established in this study. The results show that the proposed model agrees well with the numerical simulation results and the experimental results in the literatures. Finally, the influences of particle arrangement and distribution on the tortuosity are analyzed, and the value region of the tortuosity is determined. It is concluded that the tortuosity of particles relates to the porosity of soil and the distribution of particles.

Yan, X, Sun, J, Kenjiahan, A, Dai, X, Ni, B-J & Yuan, Z 2020, 'Rapid and strong biocidal effect of ferrate on sulfidogenic and methanogenic sewer biofilms', WATER RESEARCH, vol. 169.
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Yan, Y, Tan, M, Tsang, I, Yang, Y, Shi, Q & Zhang, C 2020, 'Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis and Case Study', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Matrix factorization has been widely applied to various applications. With the fast development of storage and internet technologies, we have been witnessing a rapid increase of data. In this paper, we propose new algorithms for matrix factorization with the emphasis on efficiency. In addition, most existing methods of matrix factorization only consider a general smooth least square loss. Differently, many real-world applications have distinctive characteristics. As a result, different losses should be used accordingly. Therefore, it is beneficial to design new matrix factorization algorithms that are able to deal with both smooth and non-smooth losses. To this end, one needs to analyze the characteristics of target data and use the most appropriate loss based on the analysis. We particularly study two representative cases of low-rank matrix recovery, i.e., collaborative filtering for recommendation and high dynamic range imaging. To solve these two problems, we respectively propose a stage-wise matrix factorization algorithm by exploiting manifold optimization techniques. From our theoretical analysis, they are both are provably guaranteed to converge to a stationary point. Extensive experiments on recommender systems and high dynamic range imaging demonstrate the satisfactory performance and efficiency of our proposed method on large-scale real data.

Yang, G, Zhang, N, Yang, J, Fu, Q, Wang, Y, Wang, D, Tang, L, Xia, J, Liu, X, Li, X, Yang, Q, Liu, Y, Wang, Q & Ni, B-J 2020, 'Interaction between perfluorooctanoic acid and aerobic granular sludge', WATER RESEARCH, vol. 169.
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Yang, J, Liu, X, Liu, X, Xu, Q, Wang, W, Wang, D, Yang, G, Fu, Q, Kang, Z, Yang, Q, Liu, Y, Wang, Q & Ni, B-J 2020, 'Enhanced dark fermentative hydrogen production from waste activated sludge by combining potassium ferrate with alkaline pretreatment.', The Science of the total environment, vol. 707, pp. 136105-136105.
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Alkaline pretreatment was demonstrated to be effective in the enhancement of hydrogen production. However, the sludge solubilization rate of alkaline pretreatment is still limited. This study reports a new strategy of K2FeO4 + pH 9.5 for sludge mesophilic anaerobic fermentation. Experimental results showed that the combination of K2FeO4/pH 9.5 pretreatment had a greater hydrogen yield than the individual K2FeO4 and pH 9.5. The maximum hydrogen yield was 19.2 mL per gram volatile suspended solids (VSS) under the optimal condition (0.02 g per gram total suspended solids K2FeO4 + pH 9.5). Kinetic analysis showed that the highest hydrogen production potential of 19.9 mL/g VSS was obtained in the combined reactor, which well fitted the first-order kinetic model (R2 = 0.9925). Besides, the fermentation type was mainly acetic and butyric in the combined reactor, which contributed to hydrogen production. Further analyses showed that the combined pretreatment reduced hydrogen sulfide yield, providing an environmentally friendly method to sludge treatment.

Yang, LX, Li, P, Yang, X, Xiang, Y, Jiang, F & Zhou, W 2020, 'Effective Quarantine and Recovery Scheme Against Advanced Persistent Threat', IEEE Transactions on Systems, Man, and Cybernetics: Systems.
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IEEE Advanced persistent threat (APT) for cyber espionage poses a great threat to modern organizations. In order to mitigate the impact of APT on an organization, all the compromised systems in the organization must be quarantined and recovered in a timely and effective way. This article focuses on the problem of customizing a dynamic quarantine and recovery (QAR) scheme for an organization so that the APT impact is minimized. Based on a novel node-level epidemic model characterizing the effect of the QAR scheme on the expected state of the underlying network, we estimate the expected impact of APT under a QAR scheme. On this basis, we model the original problem as an optimal control problem. By use of optimal control theory, we derive the optimality system for the optimal control problem and thereby introduce the concept of normal potential optimal (NPO) control. Next, through comparative experiments, we find that the NPO control outperforms a set of heuristic controls. Hence, the QAR scheme associated with the NPO control is satisfactory in terms of the effectiveness of defending against APT. Finally, we examine the effect of some factors on the expected APT impact under the NPO control. This article would be helpful to the defense against APT for cyber espionage.

Yang, M & Sharma, D 2020, 'The Spatiality and Temporality of Electricity Reform: A Comparative and Critical Institutional Perspective', ENERGY RESEARCH & SOCIAL SCIENCE, vol. 60.
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Yang, M, Xu, X, Chen, S & Zhu, L 2020, 'Blockchain-based Solution for Managing Renewable-based Microgrids', IEEE Blockchain Technical Briefs.
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Yang, Y, Liu, Y, Fang, X, Miao, W, Chen, X, Sun, J, Ni, B-J & Mao, S 2020, 'Heterogeneous Electro-Fenton catalysis with HKUST-1-derived Cu@C decorated in 3D graphene network', CHEMOSPHERE, vol. 243.
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Yang, Y, Zang, Y, Hu, Y, Wang, XC & Ngo, HH 2020, 'Upflow anaerobic dynamic membrane bioreactor (AnDMBR) for wastewater treatment at room temperature and short HRTs: Process characteristics and practical applicability', Chemical Engineering Journal, vol. 383.
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© 2019 Elsevier B.V. An upflow anaerobic dynamic membrane bioreactor (AnDMBR) was set up for real domestic wastewater treatment at room temperature (20–25 °C) and short hydraulic retention time (HRT = 8 h, 4 h, 2 h, and 1 h). Following continuous operation for 93 days with stepwise decreased HRT, stable average chemical oxygen demand (COD) removal was achieved (between 77.3% and 70.6%) when HRT was reduced from 8 h to 4 h, then 2 h with flux varying from 22.5 to 90 L/m2·h. At these three HRTs, the rate of increase in trans-membrane pressure (TMP) was 0.4, 0.38, and 0.57 kPa/d, and average methane (CH4) production was 0.12, 0.10, and 0.08 L/g CODremoved, respectively. Furthermore, decreasing the HRT to 1 h resulted in less COD being removed (60.4%) and lower CH4 production (0.05 L/g CODremoved) as well as a faster rate of TMP increase (2.11 kPa/d). Various analytical methods were applied to characterize the morphology and composition of the dynamic membrane (DM) layers. Organic components analysis revealed that, with reduced HRT, there were apparent increases in soluble microbial products in the liquid phase and accumulation of tryptophan protein-like substances and aromatic protein-like substances in the DM layer, especially when the HRT was shortened to 1 h. Whilst the upflow AnDMBR proved applicable to wastewater treatment at room temperature with short HRTs, 2 h could be the HRT limit for maintaining stable operation.

Yao, M, Tijing, LD, Naidu, G, Kim, S-H, Matsuyama, H, Fane, AG & Shon, HK 2020, 'A review of membrane wettability for the treatment of saline water deploying membrane distillation', DESALINATION, vol. 479.
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Yao, Z & Li, W 2020, 'Microstructure and thermal analysis of APS nano PYSZ coated aluminum alloy piston', JOURNAL OF ALLOYS AND COMPOUNDS, vol. 812.
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Ye, K, Ji, JC & Brown, T 2020, 'Design of a quasi-zero stiffness isolation system for supporting different loads', JOURNAL OF SOUND AND VIBRATION, vol. 471.
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Ye, SQ, Mao, XY, Ding, H, Ji, JC & Chen, LQ 2020, 'Nonlinear vibrations of a slightly curved beam with nonlinear boundary conditions', International Journal of Mechanical Sciences, vol. 168.
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© 2019 The existing studies of nonlinear vibration of elastic structures are usually focused on straight structures with homogeneous linear boundaries. Differently, this paper investigates the nonlinear transverse vibrations of a slightly curved beam with nonlinear boundary conditions. By using the generalized Hamilton's principle, the governing equation with geometric nonlinearity is obtained for the dynamics of the curved beam. A method of dealing with nonlinear boundaries is proposed, which is considered as a nonlinear concentrated force at the boundary. The normal modes and natural frequencies of the curved beam are determined using two different hypothetical modes based on the derived system. The harmonic balance method in combination with the pseudo arc-length method is employed to obtain the primary resonance response and 1/2 super-harmonic resonance response of the slightly curved beam. It is found that the initial curvature plays a significant role in the characteristics of the nonlinear vibrations of the curved beam. With an increase of the initial curvature, the nonlinear characteristics of softening and hardening types can coexist in the steady-state amplitude-frequency response. Moreover, the results show that the initial curvature can induce 1/2 super-harmonic resonance. Furthermore, it is also found that the nonlinear boundary has a significant influence on the nonlinear vibration of the curved structure. Therefore, the obtained results provide useful information for further studying the nonlinear vibrations of the curved beam with nonlinear and time-dependent boundary conditions.

Yi, JH, Xing, LN, Wang, GG, Dong, J, Vasilakos, AV, Alavi, AH & Wang, L 2020, 'Behavior of crossover operators in NSGA-III for large-scale optimization problems', Information Sciences, vol. 509, pp. 470-487.
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© 2018 Elsevier Inc. Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usually meet the requirements for online data processing because of their high computational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algorithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable computational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simulated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the concept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.

Yin, S, Wen, G, Ji, J & Xu, H 2020, 'Novel two-parameter dynamics of impact oscillators near degenerate grazing points', INTERNATIONAL JOURNAL OF NON-LINEAR MECHANICS, vol. 120.
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Yong, B, Shen, J, Liu, X, Li, F, Chen, H & Zhou, Q 2020, 'An intelligent blockchain-based system for safe vaccine supply and supervision', International Journal of Information Management.
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© 2019 Elsevier Ltd Immunization is an indispensable mechanism for preventing infectious diseases in modern society, and vaccine safety is closely related to public health and national security. However, issues such as vaccine expiration and vaccine record fraud are still widespread in vaccine supply chains. Therefore, an effective management system for the supervision of vaccine supply chains is urgently required. As the next generation of core technology after the Internet, blockchain is designed to build trust mechanisms that can change current information management methods. Meanwhile, the development of machine learning technologies provides additional ways to analyze the data in information management systems. The main objective of this study is to develop a “vaccine blockchain” system based on blockchain and machine learning technologies. This vaccine blockchain system is designed to support vaccine traceability and smart contract functions, and can be used to address the problems of vaccine expiration and vaccine record fraud. Additionally, the use of machine learning models can provide valuable recommendations to immunization practitioners and recipients, allowing them to choose better immunization methods and vaccines.

Youssry, A, Chapman, RJ, Peruzzo, A, Ferrie, C & Tomamichel, M 2020, 'Modeling and control of a reconfigurable photonic circuit using deep learning', Quantum Science and Technology, vol. 5, no. 2, pp. 025001-025001.
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Yu, G, Wang, X, Yu, K, Ni, W, Zhang, JA & Liu, RP 2020, 'Survey: Sharding in Blockchains', IEEE Access, vol. 8, pp. 14155-14181.
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© 2013 IEEE. The Blockchain technology, featured with its decentralized tamper-resistance based on a Peer-to-Peer network, has been widely applied in financial applications, and even further been extended to industrial applications. However, the weak scalability of traditional Blockchain technology severely affects the wide adoption due to the well-known trillema of decentralization-security-scalability in Blockchains. In regards to this issue, a number of solutions have been proposed, targeting to boost the scalability while preserving the decentralization and security. They range from modifying the on-chain data structure and consensus algorithms to adding the off-chain technologies. Therein, one of the most practical methods to achieve horizontal scalability along with the increasing network size is sharding, by partitioning network into multiple shards so that the overhead of duplicating communication, storage, and computation in each full node can be avoided. This paper presents a survey focusing on sharding in Blockchains in a systematic and comprehensive way. We provide detailed comparison and quantitative evaluation of major sharding mechanisms, along with our insights analyzing the features and restrictions of the existing solutions. We also provide theoretical upper-bound of the throughput for each considered sharding mechanism. The remaining challenges and future research directions are also reviewed.

Yu, G, Zha, X, Wang, X, Ni, W, Yu, K, Yu, P, Zhang, JA, Liu, RP & Guo, YJ 2020, 'Enabling Attribute Revocation for Fine-Grained Access Control in Blockchain-IoT Systems', IEEE Transactions on Engineering Management, pp. 1-18.
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Yu, H, Lu, J & Zhang, G 2020, 'Online Topology Learning by a Gaussian Membership-Based Self-Organizing Incremental Neural Network', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Yu, H, Zhang, T & Jia, W 2020, 'Shared subspace least squares multi-label linear discriminant analysis', Applied Intelligence, vol. 50, no. 3, pp. 939-950.
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© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Multi-label linear discriminant analysis (MLDA) has been explored for multi-label dimension reduction. However, MLDA involves dense matrices eigen-decomposition which is known to be computationally expensive for large-scale problems. In this paper, we show that the formulation of MLDA can be equivalently casted as a least squares problem so as to significantly reduce the computation burden and scale to the data collections with higher dimension. Further, it is also found that appealing regularization techniques can be incorporated into the least-squares model to boost generalization accuracy. Experimental results on several popular multi-label benchmarks not only verify the established equivalence relationship, but also demonstrate the effectiveness and efficiency of our proposed algorithms.

Yu, J, Ji, J, Miao, Z & Zhou, J 2020, 'Region-based flocking control for networked robotic systems with communication delays', EUROPEAN JOURNAL OF CONTROL, vol. 52, pp. 78-86.
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Yu, Y, Wu, D & Gao, W 2020, 'Stochastic chemo-physical-mechanical degradation analysis on hydrated cement under acidic environments', Applied Mathematical Modelling, vol. 78, pp. 75-97.
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© 2019 Elsevier Inc. The accumulation of material degradation under contact with aggressive aqueous environments could lead to reduced structural reliability. In terms of hydrated cementitious materials, such interactions often result in the chemo-physical-mechanical (CPM) degradation, which represents a multiphysics process of high non-linearity and complexity. By further considering the inevitable uncertainties associated with both the materials and the serving conditions, solving such a process requires novel probabilistic approaches. This paper presents a stochastic chemo-physical-mechanical (SCPM) degradation analysis on the hydrated cement under acidic environment. The SCPM analysis consists of modelling the stochastic chemophysical degradation by finite element method, and assessing the mechanical deterioration through analytical micromechanics. The proposed modelling framework couples the conventional Monte Carlo Simulation with a novel support vector regression algorithm. The present method is able to not only address the detailed degradation mechanisms, but also ensure low computational costs for an accurate SCPM degradation assessment.

Yuan, B, Zou, D, Jin, H, Yu, S & Yang, LT 2020, 'HostWatcher: Protecting hosts in cloud data centers through software-defined networking', Future Generation Computer Systems, vol. 105, pp. 964-972.
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© 2017 Elsevier B.V. Cloud has become a dominant computing platform, and cloud data centers have been widely deployed all over the world. Naturally, cloud data centers become the targets of cyber attacks due to the feature of publicity. In addition, the price of renting resources from cloud constantly gets cheaper and cheaper. Therefore, attackers can rent hosts from cloud data centers to initiate attacks with rather low cost. As a result, hosts in a cloud center could be either victims or attackers. However, most existing researches only treat the hosts as the targets or the sources of attacks, either protecting the hosts from being attacked or identifying the malicious hosts, which is insufficient to protect the cloud data centers comprehensively. In this paper, we hire the novel techniques of SDN to protect the cloud data centers in both directions. Aiming at mitigating DDoS attacks, we propose HostWatcher, a system that watches and protects every host in cloud data center. HostWatcher leverages the advantages of SDN techniques and distributed processing. Caching and round-robin-resending scheme is introduced to the proposed system. Our goal is to protect the hosts comprehensively with QoS guarantee. The extensive experiments show that HostWatcher can effectively mitigate the DDoS attacks that target the hosts. Meanwhile, HostWatcher can also significantly limit the packet rate of hosts that are controlled by attackers. Also, the comprehensive evaluations show that the overheads of our system are trivial, and that our system is practical to implement and deploy in the cloud data centers.

Yuan, J, Mostaan, A, Yang, Y, P. Siwakoti, Y & Blaabjerg, F 2020, 'A Modified Y-Source DC/DC Converter with High Voltage-Gains and Low Switch Stresses', IEEE Transactions on Power Electronics, pp. 1-1.
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Yuan, W, Wu, N, Zhang, A, Huang, X, Li, Y & Hanzo, L 2020, 'Iterative Receiver Design for FTN Signaling Aided Sparse Code Multiple Access', IEEE Transactions on Wireless Communications, vol. 19, no. 2, pp. 915-928.
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© 2002-2012 IEEE. The sparse code multiple access (SCMA) is a promising candidate for bandwidth-efficient next generation wireless communications, since it can support more users than the number of resource elements. On the same note, faster-than-Nyquist (FTN) signaling can also be used to improve the spectral efficiency. Hence in this paper, we consider a combined uplink FTN-SCMA system in which the data symbols corresponding to a user are further packed using FTN signaling. As a result, a higher spectral efficiency is achieved at the cost of introducing intentional inter-symbol interference (ISI). To perform joint channel estimation and detection, we design a low complexity iterative receiver based on the factor graph framework. In addition, to reduce the signaling overhead and transmission latency of our SCMA system, we intrinsically amalgamate it with grant-free scheme. Consequently, the active and inactive users should be distinguished. To address this problem, we extend the aforementioned receiver and develop a new algorithm for jointly estimating the channel state information, detecting the user activity and for performs data detection. In order to further reduce the complexity, an energy minimization based approximation is employed for restricting the user state to Gaussian. Finally, a hybrid message passing algorithm is conceived. Our Simulation results show that the FTN-SCMA system relying on the proposed receiver design has a higher throughput than conventional SCMA scheme at a negligible performance loss.

Yuan, X, Feng, Z, Ni, W, Liu, RP, Zhang, JA & Xu, W 2020, 'Secrecy Performance of Terrestrial Radio Links Under Collaborative Aerial Eavesdropping', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 15, pp. 604-619.
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Zambetta, F, Raffe, W, Tamassia, M, Mueller, FF, Li, X, Quinten, N, Patibanda, R, Dang, D & Satterley, J 2020, 'Reducing perceived waiting time in theme park queues via an augmented reality game', ACM Transactions on Computer-Human Interaction, vol. 27, no. 1.
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© 2020 Association for Computing Machinery. Theme parks visits can be very playful events for families, however, waiting in the ride's queues can often be the cause of great frustration. We developed a novel augmented reality game to be played in the theme park's queue, and an in-the-wild study with X participants using log data and interviews demonstrated that every minute playing was perceived to the same extent of about 5 minutes of not playing the game. We articulate a design space for researchers and strategies for game designers aiming to reduce perceived waiting time in queues. With our work, we hope to extend how we use games in everyday life to make our lives more playful.

Zdankowski, P, Trusiak, M, McGloin, D & Swedlow, JR 2020, 'Numerically Enhanced Stimulated Emission Depletion Microscopy with Adaptive Optics for Deep-Tissue Super-Resolved Imaging.', ACS Nano, vol. 14, no. 1, pp. 394-405.
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In stimulated emission depletion (STED) nanoscopy, the major origin of decreased signal-to-noise ratio within images can be attributed to sample photobleaching and strong optical aberrations. This is due to STED utilizing both a high-power depletion laser (increasing risk of photodamage), while the depletion beam is very sensitive to sample-induced aberrations. Here we demonstrate a custom-built STED microscope with automated aberration correction that is capable of 3D super-resolution imaging through thick, highly aberrating tissue. We introduce and investigate a state of the art image denoising method by block-matching and collaborative filtering (BM3D) to numerically enhance fine object details otherwise mixed with noise and further enhance the image quality. Numerical denoising provides an increase in the final effective resolution of the STED imaging of 31% using the well established Fourier ring correlation metric. Results achieved through the combination of aberration correction and tailored image processing are experimentally validated through super-resolved 3D imaging of axons in differentiated induced pluripotent stem cells growing under an 80 µm thick layer of tissue with lateral and axial resolution of 204 nm and 310 nm, respectively.

Zeng, J, Lv, T, Lin, Z, Liu, RP, Mei, J, Ni, W & Guo, YJ 2020, 'Achieving Ultra-Reliable and Low Latency Communications in IoT by FD-SCMA', IEEE Internet of Things Journal, pp. 1-1.
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Zeng, J, Lv, T, Liu, RP, Su, X, Guo, YJ & Beaulieu, NC 2020, 'Enabling Ultrareliable and Low-Latency Communications Under Shadow Fading by Massive MU-MIMO', IEEE INTERNET OF THINGS JOURNAL, vol. 7, no. 1, pp. 234-246.
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Zhang, C, Gao, X, Hsieh, M-H, Hang, H & Tao, D 2020, 'Matrix Infinitely Divisible Series: Tail Inequalities and Their Applications', IEEE Transactions on Information Theory, vol. 66, no. 2, pp. 1099-1117.
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Zhang, C, Qin, M, Zou, H & Qiu, X 2020, 'Secondary source and error sensing strategies for the active control of sound transmission through a small opening', JOURNAL OF SOUND AND VIBRATION, vol. 464.
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Zhang, F, Li, C, Zhang, Y, Qin, L & Zhang, W 2020, 'Finding Critical Users in Social Communities: The Collapsed Core and Truss Problems', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 1, pp. 78-91.
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IEEE In social networks, the leave of critical users may significantly break network engagement, i.e., lead a large number of other users to drop out. A popular model to measure social network engagement is k-core, the maximal subgraph in which every vertex has at least k neighbors. To identify critical users, we propose the collapsed k-core problem: given a graph G, a positive integer k and a budget b, we aim to find b vertices in G such that the deletion of the b vertices leads to the smallest k-core. We prove the problem is NP-hard and inapproximate. An efficient algorithm is proposed, which significantly reduces the number of candidate vertices. We also study the user leave towards the model of k-truss which further considers tie strength by conducting additional computation w.r.t. k-core. We prove the corresponding collapsed k-truss problem is also NP-hard and inapproximate. An efficient algorithm is proposed to solve the problem. The advantages and disadvantages of the two proposed models are experimentally compared. Comprehensive experiments on 9 real-life social networks demonstrate the effectiveness and efficiency of our proposed methods.

Zhang, H, Zhu, X & Yao, S 2020, 'Nonlinear dynamic analysis method for large-scale single-layer lattice domes with uncertain-but-bounded parameters', Engineering Structures, vol. 203.
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© 2019 Elsevier Ltd Currently, nonlinear dynamic analysis for large-scale single-layer domes is commonly performed using deterministic numerical methods. However, in practical engineering cases, complex large-scale single-layer domes have many uncertain parameters that cannot be considered using deterministic methods. Therefore, there is a growing awareness that classical deterministic methods need to be extended towards the introduction of the uncertain aspects in dynamic analysis, and a non-deterministic analysis method for large-scale spatial structures is required. In this paper, a new method is presented by introducing uncertainties into the nonlinear dynamic analysis for large-scale single-layer lattice domes. The method accounts for uncertainties in material properties, structural imperfections, loads, and damping with bounds. The focus is on the treatment of uncertainty in damping and the adopted geometric shape, which is different from that of conventional approaches. Finite element dynamic analyses for sample structures with multiple sources of uncertainty subjected to dynamic loads are performed. Results show that the variability of the variables with an associated uncertainty imposes significant negative effects on the dynamic properties, dynamic demands, and safety of a dome. Uncertain damping in a structure plays the most important role in determining structural performance. The numerical results reveal the differences between conventional analysis methods with deterministic parameters used in previous practical applications and the uncertain analysis method. Finally, a parametric study is performed, and the impacts of sample size on statistical dynamic demands, single uncertain source on structural failure, and single uncertain source on damping coefficients are discussed.

Zhang, J, Ding, F, Zhang, N, Chen, S & Zhang, B 2020, 'A New SSUKF Observer for Sliding Mode Force Tracking H∞ Control of Electrohydraulic Active Suspension', Asian Journal of Control.
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© 2018 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd Compared to passive and semi-active suspensions, active suspensions have the capacity to overcome the tradeoff design of vehicle ride comfort and handling performance. In this paper, a new control system with modified spherical simplex UKF (SSUKF) observer and sliding mode force tracker is proposed for electrohydraulic active suspensions. The estimated states obtained from the SSUKF observer are used to derive the mode energy of body motion and design the target demanded forces for suspension actuators. A sliding mode controller is presented to drive electrohydraulic actuators to track the demanded forces. Based on a robust H∞ control method, a new control strategy depending on body mode energy is proposed to restrict body motion. The performance comparisons between passive and active suspensions under three typical excitations are presented, and the obtained results indicate that the proposed control system can significantly depress body motion and decrease the mode energy to improve ride comfort and also effectively enhance the road hold abilities.

Zhang, L, Luo, M, Liu, J, Chang, X, Yang, Y & Hauptmann, AG 2020, 'Deep Top-k Ranking for Image-Sentence Matching', IEEE Transactions on Multimedia, vol. 22, no. 3, pp. 775-785.
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© 1999-2012 IEEE. Image-sentence matching is a challenging task for the heterogeneity-gap between different modalities. Ranking-based methods have achieved excellent performance in this task in past decades. Given an image query, these methods typically assume that the correct matched image-sentence pair must rank before all other mismatched ones. However, this assumption may be too strict and prone to the overfitting problem, especially when some sentences in a massive database are similar and confusable with one another. In this paper, we relax the traditional ranking loss and propose a novel deep multi-modal network with a top-k ranking loss to mitigate the data ambiguity problem. With this strategy, query results will not be penalized unless the index of ground truth is outside the range of top-k query results. Considering the non-smoothness and non-convexity of the initial top-k ranking loss, we exploit a tight convex upper bound to approximate the loss and then utilize the traditional back-propagation algorithm to optimize the deep multi-modal network. Finally, we apply the method on three benchmark datasets, namely, Flickr8k, Flickr30k, and MSCOCO. Empirical results on metrics R@K (K = 1, 5, 10) show that our method achieves comparable performance in comparison to state-of-the-art methods.

Zhang, L, Wu, Y, Li, B, Zhang, B & Zhang, N 2020, 'A novel manoeuvre stability controller based on vehicle state prediction and intellectual braking torque distribution', Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 234, no. 1, pp. 136-151.
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© IMechE 2019. This paper proposes an innovative hierarchical direct yaw moment control strategy consisting of upper, middle and lower controllers. In the upper layer, a linear quadratic regulator metric based on current side-slip angle and predicted yaw rate is established to generate the controlled yaw moment. The middle layer determines the actuating tyre forces and allocates the required longitudinal forces for each tyre according to the current tyre–road contact condition. Furthermore, the desired longitudinal slip ratios for each tyre are calculated in the middle layer. Finally, a suitable brake pressure is achieved by the sliding mode controller in the lower layer. The simulation results of sine with dwell and double lane change verify the effectiveness of the proposed method. Compared with a traditional direct yaw moment control strategy that preferentially brakes the priority wheel, the proposed novel strategy is able to keep the longitudinal force of the tyre working in a linear region and has better robustness response when the tyre–road contact condition encounters sudden change.

Zhang, P, Xu, J, Wu, Q, Huang, Y & Zhanga, J 2020, 'Top-Push Constrained Modality-Adaptive Dictionary Learning for Cross-Modality Person Re-Identification', IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1.
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Zhang, R, Wu, L, Yang, Y, Wu, W, Chen, Y & Xu, M 2020, 'Multi-camera multi-player tracking with deep player identification in sports video', Pattern Recognition, vol. 102.
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© 2020 Identity switches caused by inter-object interactions remain a critical problem for multi-player tracking in real-world sports video analysis. Existing approaches utilizing the appearance model is difficult to associate detections and preserve identities due to the similar appearance of players in the same team. Instead of the appearance model, we propose a distinguishable deep representation for player identity in this paper. A robust multi-player tracker incorporating with deep player identification is further developed to produce identity-coherent trajectories. The framework consists of three parts: (1) the core component, a Deep Player Identification (DeepPlayer) model that provides an adequate discriminative feature through the coarse-to-fine jersey number recognition and the pose-guided partial feature embedding; (2) an Individual Probability Occupancy Map (IPOM) model for players 3D localization with ID; and (3) a K-Shortest Path with ID (KSP-ID) model that links nodes in the flow graph by a proposed player ID correlation coefficient. With the distinguishable identity, the performance of tracking is improved. Experiment results illustrate that our framework handles the identity switches effectively, and outperforms state-of-the-art trackers on the sports video benchmarks.

Zhang, S, Ly, QV, Nghiem, LD, Wang, J, Li, J & Hu, Y 2020, 'Optimization and organic fouling behavior of zwitterion-modified thin-film composite polyamide membrane for water reclamation: A comprehensive study', Journal of Membrane Science, vol. 596.
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© 2019 Membrane fouling can hinder the widespread application of thin film composite (TFC) reverse osmosis (RO) for water treatment. This study evaluated a novel zwitterion-grafted TFC RO as a mean to address organic fouling for water reclamation. The membrane exhibited the best permeability at the grafting condition of 45 °C in 1 h. This modified membrane consistently possessed improved antifouling ability irrespective of organic foulants. Among individual foulants, surfactant Dodecyl Trimethyl Ammonium Chloride (DTAC) posed the worst fouling potential due to its low molecular weight and positive charge, whereas fouling induced by other substances were relatively analogous and minor. In the mixture of DTAC and proteins, the former played a key role in governing the membrane fouling. While, their interplay affected membrane fouling, the fouling extent varied upon the membrane materials. The extended Derijaguin, Landau, Verwey and Overbeek (xDLVO) theory was unable to fully describe the interactions between surfactant foulants and the membrane materials. The complementary use of quartz crystal microbalance with dissipation (QCM-D), otherwise, concurred the fouling potential and gave the plausible interpretation for fouling mechanisms by providing insightful information of foulant layer on the polyamide-coated sensor. This study provided critical insights of organic foulants’ behavior on TFC RO membrane and offered the promising industrial implication of the novel membrane.

Zhang, T, Zhu, T, Xiong, P, Huo, H, Tari, Z & Zhou, W 2020, 'Correlated Differential Privacy: Feature Selection in Machine Learning', IEEE Transactions on Industrial Informatics, pp. 1-1.
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Zhang, W, Xiong, J, Gui, L, Liu, B, Qiu, M & Shi, Z 2020, 'Distributed Caching Mechanism for Popular Services Distribution in Converged Overlay Networks', IEEE Transactions on Broadcasting.
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IEEE With the proliferation of portable devices, the exponential growth of the global mobile traffic brings great challenges to the traditional communication networks and the traditional wireless communication technologies. In this context, converged networks and cache-based data offloading have drawn more and more attention based on the strong correlation of services. This paper proposes a novel popular services pushing and caching scheme by using converged overlay networks. The most popular services are pushed by terrestrial broadcasting networks. And they are cached in n router-nodes with limited cache sizes. Each router-node only interconnects with its neighbor nodes. Users are served through the router's WiFi link. If the services requested are cached in the routers, the user can be immediately responded; otherwise, the requests can be responded through the link from cellular stations to the router. In the proposed scheme, the cache size of the router, the maximum number of requests each router can serve, and the whole-time delay are limited. Three node-selecting and dynamic programming algorithms are adopted to maximize the equivalent throughput. Analytical and numerical results demonstrate that the proposed scheme is very effective.

Zhang, X, Song, Z, Ngo, HH, Guo, W, Zhang, Z, Liu, Y, Zhang, D & Long, Z 2020, 'Impacts of typical pharmaceuticals and personal care products on the performance and microbial community of a sponge-based moving bed biofilm reactor', BIORESOURCE TECHNOLOGY, vol. 295.
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Zhang, X, Zhang, Y, Ngo, HH, Guo, W, Wen, H, Zhang, D, Li, C & Qi, L 2020, 'Characterization and sulfonamide antibiotics adsorption capacity of spent coffee grounds based biochar and hydrochar', Science of the Total Environment, vol. 716.
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© 2020 A large amount of spent coffee grounds is produced as a processing waste each year during making the coffee beverage. Sulfonamide antibiotics (SAs) are frequently detected in the environment and cause pollution problems. In this study, biochar (BC) and hydrochar (HC) were derived from spent coffee grounds through pyrolysis and hydrothermal carbonization, respectively. Their characteristics and sulfonamide antibiotics adsorption were investigated and compared with reference to adsorption capacity, adsorption isotherm and kinetics. Results showed BC possessed more carbonization and less oxygen-containing functional groups than HC when checked by Elemental Analysis, X-ray diffraction, X-ray photoelectron spectrometry and Fourier transform infrared. These groups affected the adsorption of sulfonamide antibiotics and adsorption mechanism. The maximum adsorption capacities of BC for sulfadiazine (SDZ) and sulfamethoxazole (SMX) were 121.5 μg/g and 130.1 μg/g at 25 °C with the initial antibiotic concentration of 500 μg/L, respectively. Meanwhile the maximum adsorption capacities of HC were 82.2 μg/g and 85.7 μg/g, respectively. Moreover, the adsorption mechanism for SAs adsorbed onto BC may be dominated by π-π electron donor-acceptor interactions, yet the SAs adsorption to HC may be attributed to hydrogen bonds. Further analysis of the adsorption isotherms and kinetics, found that physical and chemical interactions were involved in the SAs adsorption onto BC and HC. Overall, results suggested that: firstly, pyrolysis was an effective thermochemical conversion of spent coffee grounds; and secondly, BC was the more promising adsorbent for removing sulfonamide antibiotics.

Zhang, XY & Xu, J-X 2020, 'Multifunctional Filtering Circuits: 3D Multifunctional Filtering Circuits Based on High-Q Dielectric Resonators and Coaxial Resonators', IEEE Microwave Magazine, vol. 21, no. 3, pp. 50-68.
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Zhang, Y, Ji, J & Ma, B 2020, 'Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network', Measurement: Journal of the International Measurement Confederation, vol. 156.
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© 2020 Elsevier Ltd In order to denoise the raw signal and fuse multiple sources of information for the fault diagnosis of reciprocating compressor, this paper proposes a novel convolutional deep belief network-based method and employs a novel framework fusing multi-source information to improve the performance of fault diagnosis. Firstly, signals from different sensors of the RC are input into an auto-denoising network, namely, ensemble empirical model decomposition-convolutional deep belief network, to denoise the signal and to extract more robust features by the unsupervised learning. Secondly, the extracted features of each source are input into multiple Gaussian process classifiers which are adopted as the members of probabilistic committee machine (PCM) to calculate the probabilities that each fault occurs. Finally, these probabilities are combined with an optimized weight to make a committee decision on fault type. The proposed method combines the information from multiple sources and enhances the robustness of fault diagnosis. Data from an industrial plant were collected to verify the proposed method. The obtained results demonstrate that the proposed method can effectively diagnose the RC faults with the accuracy rate of up to 91.89%. Furthermore, a comparison of the proposed method with the other methods illustrates the superiority of the proposed method for the diagnosis of RC faults.

Zhang, Y, Ji, J & Ma, B 2020, 'Reciprocating compressor fault diagnosis using an optimized convolutional deep belief network', Journal of Vibration and Control.
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© The Author(s) 2020. This article proposes an optimized convolutional deep belief network for fault diagnosis of reciprocating compressors. Sparse filtering is first used to compress raw signal into compact time series by refining the most representative information and to reduce the computational burden. Then, the proposed convolutional deep belief network is adopted to learn the unsupervised features of the compressed signal without the need of feature extraction by human effort. To improve the generalization ability of the network, an optimized probabilistic pooling out is proposed in this article to replace the standard one in the pooling layer of the convolutional deep belief network. Finally, the unsupervised features calculated by the optimized convolutional deep belief network are fed as the input of the softmax regression classifier for fault identification. Four types of vibration signals reflecting different operating conditions are collected from the industry to validate the effectiveness of the proposed method. The obtained results demonstrate that the proposed convolutional deep belief network method can achieve a higher classification accuracy rate of up to 91% for fault diagnosis than the traditional methods and accomplish the fault diagnosis of reciprocating compressor effectively.

Zhang, Y, Sui, Y, Pan, S, Zheng, Z, Ning, B, Tsang, I & Zhou, W 2020, 'Familial Clustering For Weakly-labeled Android Malware Using Hybrid Representation Learning', IEEE Transactions on Information Forensics and Security.
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IEEE Labeling malware or malware clustering is important for identifying new security threats, triaging and building reference datasets. The state-of-the-art Android malware clustering approaches rely heavily on the raw labels from commercial AntiVirus (AV) vendors, which causes misclustering for a substantial number of weakly-labeled malware due to the inconsistent, incomplete and overly generic labels reported by these closed-source AV engines, whose capabilities vary greatly and whose internal mechanisms are opaque (i.e., intermediate detection results are unavailable for clustering). The raw labels are thus often used as the only important source of information for clustering. To address the limitations of the existing approaches, this paper presents ANDRE, a new ANDroid Hybrid REpresentation Learning approach to clustering weakly-labeled Android malware by preserving heterogeneous information from multiple sources (including the results of static code analysis, the metainformation of an app, and the raw-labels of the AV vendors) to jointly learn a hybrid representation for accurate clustering. The learned representation is then fed into our outlieraware clustering to partition the weakly-labeled malware into known and unknown families. The malware whose malicious behaviours are close to those of the existing families on the network, are further classified using a three-layer Deep Neural Network (DNN). The unknown malware are clustered using a standard density-based clustering algorithm. We have evaluated our approach using 5,416 ground-truth malware from Drebin and 9,000 malware from VIRUSSHARE (uploaded between Mar. 2017 and Feb. 2018), consisting of 3324 weakly-labeled malware. The evaluation shows that ANDRE effectively clusters weaklylabeled malware which cannot be clustered by the state-of-theart approaches, while achieving comparable accuracy with those approaches for clustering ground-truth samples.

Zhao, D, Duan, S, Yan, Z & Alippi, C 2020, 'Advances in deep neural information processing', Neurocomputing.
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Zhao, E, Wu, C, Wang, S, Hu, J & Wang, W 2020, 'Seepage dissolution effect prediction on aging deformation of concrete dams by coupled chemo-mechanical model', Construction and Building Materials, vol. 237.
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© 2019 Elsevier Ltd Concrete dams undergo a seepage dissolution process because of huge reservoir water pressure and concrete permeability, and such dissolution weakens the mechanical properties of concrete and produces a certain amount of irreversible aging deformation. This study proposes a coupled chemo-mechanical model to predict the seepage dissolution effect on aging deformation of concrete dams. The interactions between elastic–plastic, chemical damage and mechanical damage are jointly explored combining with on-site inspection of long-term service performance of a concrete gravity dam firstly. Then an aging model of the seepage dissolution damage is digitized, and a novel model on chemo-mechanical coupled effect is put forward by introducing an overall chemo-mechanical damage scalar. The validity of the model is proved by a case study on the gravity dam through finite element simulation on its seepage dissolution and comparison with monitoring data. Finally, these methods are applied into an ultra-high arch dam to quantitatively calculate the irreversible aging deformation with the increase of the seepage dissolution degree. And the annual maximum aging deformation of the arch dam increases 0.65 mm after 100 years. The results indicate that the proposed model can effectively predict the aging deformation caused by the seepage dissolution during long-term operation of concrete dams.

Zhao, J, Zhang, H & Andrew Zhang, J 2020, 'Generalized Maximum Correntropy Algorithm with Affine Projection for Robust Filtering Under Impulsive-Noise Environments', Signal Processing, pp. 107524-107524.
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Zhao, J, Zhang, H & Zhang, JA 2020, 'Gaussian kernel adaptive filters with adaptive kernel bandwidth', Signal Processing, vol. 166.
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© 2019 Gaussian kernel adaptive filters (GKAFs) have been successfully applied in functional approximation. The kernel bandwidth for GKAFs not only impacts on the smoothness of function approximation and the locality of training samples, but also affects the convergence rate and testing accuracy. However, in practice, it is hard to predesign an optimal one. In this paper, for practical applications, we propose a novel framework for kernel bandwidth adaptation in sparsification case. In this framework, we consider the latest K kernel bandwidths as free parameters, and sequentially update them using a gradient decent method to minimize the instantaneous squared error. Furthermore, we apply the proposed method to the quantized kernel least mean square (QKLMS) algorithm, and conduct convergence analysis for the algorithm. Extensive simulation results are provided and validate the superiority of our method compared to some state-of-the-art algorithms.

Zhao, L 2020, 'Synchronization extension using a bistable galloping oscillator for enhanced power generation from concurrent wind and base vibration', Applied Physics Letters, vol. 116, no. 5.
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© 2020 Author(s). This Letter proposes a compact bistable galloping oscillator for achieving enhanced power generation from concurrent wind and base vibration. The harvester consists of a D-shaped bluff body attached to a piezoelectric cantilever, with magnetic interaction introduced between the bluff body and a fixed windward prism. Both theoretical analysis and experiment demonstrate the remarkably broadened synchronization bandwidth for concurrent energy harvesting. In the experiment, the voltage steadily increases from 26.6 V at 8.5 Hz to 40.7 V at 12 Hz, achieving a 10 times wider bandwidth than the linear galloping harvester.

Zhao, S, Dou, P, Song, J, Nghiem, LD, Li, XM & He, T 2020, 'Direct preparation of dialysate from tap water via osmotic dilution', Journal of Membrane Science, vol. 598.
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© 2019 Elsevier B.V. Preparation of dialysate for hemodialysis (or dialysis) requires dilution of the dialysis concentrate with purified water. Present practice contains two steps: first to purify water, and then water is transported to clinic to mix with the dialysate concentrate before treatment. As a new forward osmosis dialysis hybrid process, based on osmotic dilution, is evaluated for decentralized health care systems. A commercial cellulose triacetate (CTA) and a tailor-made thin film composite (TFC) polyamide FO membranes were examined. The rejection of salts in tap water by the FO membranes was investigated, and the real rejections of various ions as a function of permeate flux were well described by using a irreversible thermodynamic model. The hollow fiber TFC FO membrane showed higher water flux and lower reverse salt flux than the CTA membrane in diluting process. Both steric hindrance and electrostatic repulsion explained the rejection behavior of the membranes to the ions. Higher rejections of anions were obtained than cations, which was attributed to the anions selection characteristics of the membranes. No obvious foulings or scalings were observed in a relatively long time osmotic dilution process over 5 repeated cycles. The stable, high efficient osmotic dilution process in hemodialysis holds a strong promise in reducing the consumption of purified water or even eliminating the water purification step. This work provides a potentially new platform hemodialysis which can be portable and implementable away from major hospitals and major clinics.

Zheng, D, Zhang, H, Zhang, JA, Zheng, W & Su, SW 2020, 'Stability of asynchronous switched systems with sequence-based average dwell time approaches', Journal of the Franklin Institute, vol. 357, no. 4, pp. 2149-2166.
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© 2019 The Franklin Institute This paper studies the stability problem of asynchronous switched systems and proposes novel sequence-based average dwell time approaches. Both continuous-time and discrete-time systems are considered. The proposed approaches exploit the switching sequences of subsystems which were seldom utilized in the literature. More specifically, our approaches exploit the differences between different switching sequences, including the maximal asynchronous switching time, the energy changing degree at switching times, and the increasing speed of energy functions in asynchronous time intervals. As a result, the proposed approaches can reduce the threshold value of average dwell time significantly. We also propose an approach to counterbalance the increasing of energy functions in asynchronous time intervals by prolonging the preceding rather than subsequent subsystem. Numerical results demonstrate that the proposed approaches can improve the performance significantly in comparison with a well-known method.

Zheng, J, He, X, Li, Y, Zhao, B, Ye, F, Gao, C, Li, M, Li, X & E, S 2020, 'Viscoelastic and magnetically aligned flaky Fe-based magnetorheological elastomer film for wide-bandwidth electromagnetic wave absorption', Industrial & Engineering Chemistry Research.
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Zheng, J, Ji, J, Yin, S & Tong, V-C 2020, 'Internal loads and contact pressure distributions on the main shaft bearing in a modern gearless wind turbine', TRIBOLOGY INTERNATIONAL, vol. 141.
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Zheng, L, Price, WE, He, T & Nghiem, LD 2020, 'Simultaneous cooling and provision of make-up water by forward osmosis for post-combustion CO2 capture', Desalination, vol. 476.
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© 2019 This study evaluates the feasibility of forward osmosis (FO) for simultaneous post-combustion CO2 capture and make-up water provision using seawater and treated effluent as the feed. Three amine-based CO2 adsorbents (glycine, sodium glycinate, and monoethanolamine (MEA)) were used as draw solutes. A non-linear relationship between concentration of these adsorbents and conductivity (thus osmotic pressure) was observed. Glycine showed higher water flux and lower reverse salt flux and specific reverse salt flux than sodium glycinate and MEA in both membrane orientations, thus was selected for further investigations. A higher water flux but with the considerable flux decline were observed when active layer faced draw solution. In addition, temperature increase in draw solution could alter thermodynamic properties of glycine, resulting in an increase of reverse salt flux. Water flux increase was also observed due to diminishing concentrative internal concentration polarisation. Changes in water flux were insignificant when active layer faced feed solution even as temperature increased. Temperature increase was likely to aggravate the severity of dilutive internal concentration polarisation and offset the growth of osmotic pressure. Seawater could also be a potential source for simultaneous cooling and providing the make-up water, although the water flux was lower compared to treated effluent.

Zheng, Y, Hu, R, Fung, SF, Yu, C, Long, G, Guo, T & Pan, S 2020, 'Clustering social audiences in business information networks', Pattern Recognition, vol. 100.
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© 2019 Elsevier Ltd Business information networks involve diverse users and rich content and have emerged as important platforms for enabling business intelligence and business decision making. A key step in an organizations business intelligence process is to cluster users with similar interests into social audiences and discover the roles they play within a business network. In this article, we propose a novel machine-learning approach, called CBIN, that co-clusters business information networks to discover and understand these audiences. The CBIN framework is based on co-factorization. The audience clusters are discovered from a combination of network structures and rich contextual information, such as node interactions and node-content correlations. Since what defines an audience cluster is data-driven, plus they often overlap, pre-determining the number of clusters is usually very difficult. Therefore, we have based CBIN on an overlapping clustering paradigm with a hold-out strategy to discover the optimal number of clusters given the underlying data. Experiments validate an outstanding performance by CBIN compared to other state-of-the-art algorithms on 13 real-world enterprise datasets.

Zhong, J, Kirby, R & Qiu, X 2020, 'A non-paraxial model for the audio sound behind a non-baffled parametric array loudspeaker', JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, vol. 147, no. 3, pp. 1577-1580.
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Zhong, Z, Zheng, L, Luo, Z, Li, S & Yang, Y 2020, 'Learning to Adapt Invariance in Memory for Person Re-identification', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1.
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Zhou, I, Lipman, J, Abolhasan, M, Shariati, N & Lamb, DW 2020, 'Frost monitoring cyber-physical system: a survey on prediction and active protection methods', IEEE Internet of Things Journal, pp. 1-1.
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Zhou, L, Ying, S, Yu, N & Ying, M 2020, 'Strassen's theorem for quantum couplings', THEORETICAL COMPUTER SCIENCE, vol. 802, pp. 67-76.
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Zhou, L, Ying, S, Yu, N & Ying, M 2020, 'Strassen's theorem for quantum couplings', Theoretical Computer Science, vol. 802, pp. 67-76.
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© 2019 Elsevier B.V. Strassen's theorem for probabilistic couplings is a fundamental theorem in probability theory that can be used to bound the probability of an event in a distribution by the probability of an event in another distribution coupled with the first. It has been widely applied in computer science for analysis of random algorithms, machine learning and verification of security and privacy protocols. We extend the coupling techniques in probability theory to quantum systems. A quantum generalisation of the notion of lifting, a coupling under certain constraints, is introduced. Several interesting examples and basic properties of quantum couplings and liftings are presented. Finally, a quantum extension of Strassen's theorem is established.

Zhou, Q, Yong, B, Lv, Q, Shen, J & Wang, X 2020, 'Deep Autoencoder for Mass Spectrometry Feature Learning and Cancer Detection', IEEE Access, vol. 8, pp. 45156-45166.
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Zhou, S, Walker, P & Zhang, N 2020, 'Parametric design and regenerative braking control of a parallel hydraulic hybrid vehicle', Mechanism and Machine Theory, vol. 146, p. 103714.

Zhou, X, Jin, W, Wang, Q, Guo, S, Tu, R, Han, SF, Chen, C, Xie, G, Qu, F & Wang, Q 2020, 'Enhancement of productivity of Chlorella pyrenoidosa lipids for biodiesel using co-culture with ammonia-oxidizing bacteria in municipal wastewater', Renewable Energy, vol. 151, pp. 598-603.
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© 2019 Elsevier Ltd As one of the most promising renewable energy, microalgal biodiesel has been widely studied worldwide. However, the low-efficiency of conventional microalgae cultivation procedures restrict the development of microalgae biodiesel production. Microalgal-bacterial symbiosis could both enhance the growth of algal-bacterial culture and promote the removal and conversion of wastewater nutrients. In this study, three strains of high-efficient heterotrophic ammonia-oxidizing bacteria JN1, FN3, and FN5 were screened from municipal wastewater treatment system with over 80% degradation rates of 50 mg/L ammonia-nitrogen (NH3–N) in 24 h. Among them, FN5, belonging to Kluyvera sp., had the optimum effect on enhancing growth of oil-rich microalga Chlorella pyrenoidosa. In stationary phase, the biomass and lipid content of Chlorella pyrenoidosa was14.8% and 13.6% higher than the blank control tests without FN5. In contrast, JN1 and FN3 failed to enhance the growth of Chlorella pyrenoidosa. After the cultivation of Chlorella pyrenoidosa-FN5 consortia in municipal wastewater, the degradation rate of NH3–N was up to 91% while the content of microalgae biomass and lipid attained 0.35 g/L and 39.0%. The Saturated fatty acids (SFAs), Monounsaturated fatty acids (MUFAs), and Polyunsaturated fatty acids (PUFAs) were 43.9, 37.1 and 19.0%, respectively, which had the potential for biodiesel production after pretreatment.

Zhou, X, Li, S & Feng, Y 2020, 'Quantum Circuit Transformation Based on Simulated Annealing and Heuristic Search', IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
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IEEE Quantum algorithm design usually assumes access to a perfect quantum computer with ideal properties like full connectivity, noise-freedom and arbitrarily long coherence time. In Noisy Intermediate-Scale Quantum (NISQ) devices, however, the number of qubits is highly limited and quantum operation error and qubit coherence are not negligible. Besides, the connectivity of physical qubits in a quantum processing unit (QPU) is also strictly constrained. Thereby, additional operations like SWAP gates have to be inserted to satisfy this constraint while preserving the functionality of the original circuit. This process is known as quantum circuit transformation. Adding additional gates will increase both the size and depth of a quantum circuit and therefore cause further decay of the performance of a quantum circuit. Thus it is crucial to minimize the number of added gates. In this paper, we propose an efficient method to solve this problem. We first choose by using simulated annealing an initial mapping which fits well with the input circuit and then, with the help of a heuristic cost function, stepwise apply the best selected SWAP gates until all quantum gates in the circuit can be executed. Our algorithm runs in time polynomial in all parameters including the size and the qubit number of the input circuit, and the qubit number in the QPU. Its space complexity is quadratic to the number of edges in the QPU. Experimental results on extensive realistic circuits confirm that the proposed method is efficient and the number of added gates of our algorithm is, on average, only 57% of that of state-of-the-art algorithms on IBM Q20 (Tokyo), the most recent IBM quantum device.

Zhu, F, Lu, J, Lin, A & Zhang, G 2020, 'A Pareto-smoothing method for causal inference using generalized Pareto distribution', Neurocomputing, vol. 378, pp. 142-152.
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© 2019 Elsevier B.V. Causal inference aims to estimate the treatment effect of an intervention on the target outcome variable and has received great attention across fields ranging from economics and statistics to machine learning. Observational causal inference is challenging because the pre-treatment variables may influence both the treatment and the outcome, resulting in confounding bias. The classic inverse propensity weighting (IPW) estimator is theoretically able to eliminate the confounding bias. However, in observational studies, the propensity scores used in the IPW estimator must be estimated from finite observational data and may be subject to extreme values, leading to the problem of highly variable importance weights, which consequently makes the estimated causal effect unstable or even misleading. In this paper, by reframing the IPW estimator in the importance sampling framework, we propose a Pareto-smoothing method to tackle this problem. The generalized Pareto distribution (GPD) from extreme value theory is used to fit the upper tail of the estimated importance weights and to replace them using the order statistics of the fitted GPD. To validate the performance of the new method, we conducted extensive experiments on simulated and semi-simulated datasets. Compared with two existing methods for importance weight stabilization, i.e., weight truncation and self-normalization, the proposed method generally achieves better performance in settings with a small sample size and high-dimensional covariates. Its application on a real-world heath dataset indicates its utility in estimating causal effects for program evaluation.

Zhu, H, Zhu, X, Yang, Y & Sun, Y 2020, 'Design of miniaturized on-chip bandpass filters using inverting-coupled inductors in (Bi)-CMOS Technology', IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 2, pp. 647-657.
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© 2004-2012 IEEE. In this work, a new type of miniaturized on-chip resonator using coupled-inductor structure is presented. The impact on resonances of the structure due to the use of non- inverting- and inverting-coupled configuration is extensively investigated. It has been found that using the inverting-coupled structure, a stronger resonance can be generated, which is ideally suitable for device miniaturization. To fully understand the working mechanism of the resonator and use it effectively for bandpass filter (BPF) design, simplified LC equivalent-circuit models and detailed theoretical analysis are provided. To further demonstrate the proposed concept is useful in practice, not only a 1st-order BPF, but also another two 2nd-order BPFs are designed and fabricated in a standard 0.13-μm (Bi)-CMOS technology. All of them are designed to have a centre frequency around 15 GHz. Their physical dimensions are 0.13 × 0.25 mm2, 0.26 × 0.25 mm2, 0.24 × 0.22 mm2, respectively. Good agreements between simulation and measurement have been obtained, which verify that the presented design approach is suitable for miniaturized on-chip passive design.

Zhu, J, Yang, Y, McGloin, D, Unnithan, RR, Li, S, Liao, S & Xue, Q 2020, '3-D Printed Planar Dielectric Linear-to-Circular Polarization Conversion and Beam Shaping Lenses Using Coding Polarizer', IEEE Transactions on Antennas and Propagation, pp. 1-1.
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Zhu, Q, Qiu, X, Coleman, P & Burnett, I 2020, 'A comparison between two modal domain methods for personal audio reproduction', JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, vol. 147, no. 1, pp. 161-173.
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Zhu, T, Xiong, P, Li, G, Zhou, W & Yu, PS 2020, 'Differentially private model publishing in cyber physical systems', Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications.
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© 2018 Elsevier B.V. With the development of Cyber Physical Systems, privacy issues become an important topics in the past few years. It is worthwhile to apply differential privacy, one of the most influential privacy definitions, in cyber physical system. However, as the essential idea of differential privacy is to release query results rather than entire datasets, a large volume of noise has to be introduced. To provide high quality services we need to decrease the correlation between large sets of queries, while to predict on newly entered queries. This paper transfers the data publishing problem in cyber physical systems to a machine learning problem, in which a prediction model will be shared with clients. The predict model is used to answer current submitted queries and predict results for newly entered queries from the public.

Zhuang, Y, Sun, Y, Huang, Y, Teng, Q, He, B, Chen, W & Qian, Y 2020, 'Investigation of water injection benefits on downsized boosted direct injection spark ignition engine', FUEL, vol. 264.
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Ziaei, M, Panahi, M, Fanaei, MA, Rafiee, A & Khalilpour, KR 2020, 'Maximizing the profitability of integrated Fischer-Tropsch GTL process with ammonia and urea synthesis using response surface methodology', JOURNAL OF CO2 UTILIZATION, vol. 35, pp. 14-27.
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Ziolkowski, RW & Engheta, N 2020, 'Metamaterials: Two Decades Past and Into Their Electromagnetics Future and Beyond', IEEE Transactions on Antennas and Propagation, vol. 68, no. 3, pp. 1232-1237.
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Zuo, X, Ye, W, Yang, Y, Zheng, R, Vidal‐Calleja, T, Huang, G & Liu, Y 2020, 'Multimodal localization: Stereo over LiDAR map', Journal of Field Robotics.
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Zuo, Y, Wu, Q, Fang, Y, An, P, Huang, L & Chen, Z 2020, 'Multi-Scale Frequency Reconstruction for Guided Depth Map Super-Resolution via Deep Residual Network', IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 2, pp. 297-306.
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© 1991-2012 IEEE. The depth maps obtained by the consumer-level sensors are always noisy in the low-resolution (LR) domain. Existing methods for the guided depth super-resolution, which are based on the pre-defined local and global models, perform well in general cases (e.g., joint bilateral filter and Markov random field). However, such model-based methods may fail to describe the potential relationship between RGB-D image pairs. To solve this problem, this paper proposes a data-driven approach based on the deep convolutional neural network with global and local residual learning. It progressively upsamples the LR depth map guided by the high-resolution intensity image in multiple scales. A global residual learning is adopted to learn the difference between the ground truth and the coarsely upsampled depth map, and the local residual learning is introduced in each scale-dependent reconstruction sub-network. This scheme can restore the depth structure from coarse to fine via multi-scale frequency synthesis. In addition, batch normalization layers are used to improve the performance of depth map denoising. Our method is evaluated in noise-free and noisy cases. A comprehensive comparison against 17 state-of-the-art methods is carried out. The experimental results show that the proposed method has faster convergence speed as well as improved performances based on the qualitative and quantitative evaluations.

Al-Hadhrami, Y & Hussain, FK 2020, 'A Machine Learning Architecture Towards Detecting Denial of Service Attack in IoT', Advances in Intelligent Systems and Computing, pp. 417-429.
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© 2020, Springer Nature Switzerland AG. Internet of thing is part of our everyday life nowadays. Where millions of devices contented to the internet to collect and share data. Although IoT devices are evolving quickly to the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators have been also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. With the rapid development of the Internet of Things in different areas, IoT is facing difficulty in securing overall availability of the network due to its heterogeneous nature. There are many types of vulnerability in IoT that can be mitigated with further research, however, in this paper, we have concentrated on distributed denial of Service attack (DDoS) on IoT. In this paper, we propose a machine learning architecture to detect DDoS attacks in IoT networks. The architecture collects IoT network traffic and analyzes the traffic through passing to machine learning model for attack detection. We propose the use of real-time data collection tool to dynamically monitor the network.

Al-Hadhrami, Y, Al-Hadhrami, N & Hussain, FK 2020, 'Data exportation framework for IoT simulation based devices', Advances in Intelligent Systems and Computing, pp. 212-222.
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© Springer Nature Switzerland AG 2020. Internet of things (IoT) is part of everyday life nowadays. Millions of devices are connected to the internet to collect and share data. Although IoT devices are evolving quickly in the consumer market where smart devices and sensors are becoming one of the main components of many households, IoT sensors and actuators are also heavily used in the industry where thousands of devices are used to collect and share data for different purposes. A need for an IoT simulation tool is necessary for development purposes and testing before deployments. One of the widely used tools among IoT researchers is the open-source tool Cooja simulator. Cooja has limitations—one is the lack of a way to export collected data as a data set for further processing. Therefore, this study introduces an extension tool to present and export the data into different forms.

Almansor, EH & Hussain, FK 2020, 'Survey on Intelligent Chatbots: State-of-the-Art and Future Research Directions', Advances in Intelligent Systems and Computing, pp. 534-543.
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© 2020, Springer Nature Switzerland AG. Human-computer interaction (HCI) is an area of interest which plays a major role in understanding the interaction between humans and machines. Dialogue systems or conversational systems including chatbots, voice control interfaces and personal assistants are examples of HCI application that have been developed to interact with users using natural language. Chatbots can help customers find useful information for their needs. Thus, numerous organizations are using chatbots to automate their customer service. Thus, the needs for using artificial intelligence has been increasing due to the needs of automated services. However, devolving smart bots that can respond at the human level is challenging. In this paper, we survey the state-of-art chatbot approaches from based on the ability to generate appropriate responses perspective. After summarizing the review from this aspect, we identify the research issues and challenges in chatbots. The findings of this research will highlight directions for future work.

Almansor, EH, Al-Ani, A & Hussain, FK 2020, 'Transferring Informal Text in Arabic as Low Resource Languages: State-of-the-Art and Future Research Directions', Advances in Intelligent Systems and Computing, pp. 176-187.
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© 2020, Springer Nature Switzerland AG. Rapid growth in internet technology lead to increase the usage of social media platforms which make communication between users easier. Through the communication users used their daily languages which considered as non-standard language. The non-slandered text contains lots of noise, such as abbreviations, slang which used more in English languages and dialect words which are widely used in Arabic language. These texts face challenging using any natural language processing tools. Therefore, these texts need to be treated and transferred to be similar to their standard form. According to that the normalization and translation approach have been used to transfer the informal text. However, using these approach need large label or parallel datasets. While high resource languages such as English have enough parallel datasets, low resource languages such as Arabic is lack of enough parallel dataset. Therefore, in this paper we focus on the Arabic and Arabic dialects as a low resource language in the era of transferring non-stander text using normalization and translation approach.

Al-Tae, AA & Al-Jumaily, A 2020, 'Electrogastrogram Based Medical Applications an Overview and Processing Frame Work', Advances in Intelligent Systems and Computing, pp. 511-520.
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© 2020, Springer Nature Switzerland AG. Surface Electrogastrogram (sEGG) examination is a noninvasive method based on recording the electrical signals of stomach muscles that can be used for investigation of a stomach wave propagation. sEGG records by placing electrodes on the abdominal skin. The recording of gastric dysrhythmias is an important tool for the clinician when patients have symptoms that suggest gastric dysfunction such as unexplained nausea, bloating, postprandial fullness, and early satiety. On the other hand, these upper gastrointestinal (GI) symptoms are nonspecific, and diseases or disorders of other organ such as esophagus, gallbladder, small bowel, colon, and non-GI diseases also can be considered. The progress of the EGG based application is very slow as EGG still suffers from several limitations. The aim of this study is to review the Electrogastrogram detection, analyzing methods and its application to enter the clinical world with recommendations for future study.

Amirbagheri, K, Merigó, JM & Yang, JB 2020, 'A bibliometric analysis of leading countries in supply chain management research', Advances in Intelligent Systems and Computing, pp. 182-192.
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© 2020, Springer Nature Switzerland AG. Supply chain management as a newly comer discipline has attracted many attentions of the scholars to do an investigation based on its prominent level of importance for the economy and its influence on the management of the organizations. So, the key point is to understand the trends among the countries throughout the time to have a powerful insight about this issue. To this end, this work does a comprehensive analysis from 1990 to 2017. The purpose of this study is to analyze the leading countries and understand thoroughly their trends during the time. The work has dedicated to three sections. In the first one the countries have studied globally to give a comprehensive overview to academics. Next, the performance of the countries is studied in three periods to understand better the changes of each during the time. Finally, some individual journals and groups of journals are also investigated. The results show that the USA is the leader of the countries while China has experienced an enormous growth and it is predictable that with this trend can reach to the top of the list.

Bravyi, S, Gosset, D, Koenig, R & Tomamichel, M 2019, 'Quantum Advantage with Noisy Shallow Circuits in 3D', Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS, 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS), IEEE Computer Society Press, Baltimore, MD, USA, USA, pp. 995-999.
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© 2019 IEEE. Prior work has shown that there exists a relation problem which can be solved with certainty by a constant-depth quantum circuit composed of geometrically local gates in two dimensions, but cannot be solved with high probability by any classical constant depth circuit composed of bounded fan-in gates. Here we provide two extensions of this result. Firstly, we show that a separation in computational power persists even when the constant-depth quantum circuit is restricted to geometrically local gates in one dimension. The corresponding quantum algorithm is the simplest we know of which achieves a quantum advantage of this type. Our second, main result, is that a separation persists even if the shallow quantum circuit is corrupted by noise. We construct a relation problem which can be solved with near certainty using a noisy constant-depth quantum circuit composed of geometrically local gates in three dimensions, provided the noise rate is below a certain constant threshold value. On the other hand, the problem cannot be solved with high probability by a noise-free classical circuit of constant depth. A key component of the proof is a quantum error-correcting code which admits constant-depth logical Clifford gates and single-shot logical state preparation. We show that the surface code meets these criteria.

Cetindamar Kozanoglu, D, Shdifat, B & Erfani, S 2020, 'Assessing Big Data Analytics Capability and Sustainability in Supply Chains', Hawaii International Conference on System Sciences, Hawaii, USA, pp. 208-217.
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Chen, Z & Zhu, X 2019, 'Integration of mm-wave Antennas on Fan-Out Wafer Level Packaging (FOWLP) for Automotive Radar Applications', 2019 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA), 2019 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA), IEEE, Chengdu, China.
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The integration of mm-wave antennas on fan-out wafer level packaging (FOWLP) for automotive radar applications is presented in this paper. The size of the package is 12 x 15 x 0.45 mm 3 . The package includes seven antenna arrays realized with three redistribution layers (RDL). Three patch antenna arrays are used as transmitting antennas and four patch antenna arrays are used as receiving antennas. The simulation results show that the proposed antenna achieves a 10-dB input impedance bandwidth of 5.5 GHz and a maximal peak realized gain of 9.9 dBi at 77.6 GHz. The angle resolution of the MIMO radar can be improved to 9.6°.

Cheng, Q, Shi, Z, Nguyen, D & Dutkiewicz, E 2020, 'Non-cooperative OFDM Spectrum Sensing Using Deep Learning', IEEE International Conference on Computing, Networking and Communications (ICNC), Hawaii.
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Cheng, T, Lu, DD-C & Siwakoti, Y 2019, 'Electro-Thermal Modeling of a Boost Converter Considering Device Self-heating', 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), IEEE, Singapore.
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Thermal management is one of the most critical aspects of the design of high power density converters. Recently, significant efforts and progress have been made in developing electro-thermal models for power semiconductor devices as they are very sensitive to temperature changes. Passive components such as inductors and capacitors are also investigated, since they are temperature-dependent. However, most published work focuses on electro-thermal model of a single power device or a module only instead of a whole converter, which is more realistic in terms of converter design. Hence, in this work, a datasheet informed electro-thermal model is proposed for a boost converter. It is achieved by adding additional behavior models to the existing electrical model of each power device to reflect the temperature incurred electrical behavioral change. Loss model and RC network are used to estimate the temperature change. This forms a power loss and temperature feedback loop. The advantages of this work are ease of integration with existing electrical models in the SPICE library, and elimination of the complicated physical properties of the power devices, but fully utilizes the device datasheet information and mathematical method.

Dawson, NJ, Rizoiu, M-A, Johnston, B & Williams, M-A 1970, 'Adaptively selecting occupations to detect skill shortages from online job ads', IEEE International Conference on Big Data (IEEE Big Data 2019), IEEE International Conference on Big Data, Los Angeles, CA, USA, pp. 1-7.
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Labour demand and skill shortages have historically been difficult to assess given the high costs of conducting representative surveys and the inherent delays of these indicators. This is particularly consequential for fast developing skills and occupations, such as those relating to Data Science and Analytics (DSA). This paper develops a data-driven solution to detecting skill shortages from online job advertisements (ads) data. We first propose a method to generate sets of highly similar skills based on a set of seed skills from job ads. This provides researchers with a novel method to adaptively select occupations based on granular skills data. Next, we apply this adaptive skills similarity technique to a dataset of over 6.7 million Australian job ads in order to identify occupations with the highest proportions of DSA skills. This uncovers 306,577 DSA job ads across 23 occupational classes from 2012-2019. Finally, we propose five variables for detecting skill shortages from online job ads: (1) posting frequency; (2) salary levels; (3) education requirements; (4) experience demands; and (5) job ad posting predictability. This contributes further evidence to the goal of detecting skills shortages in real-time. In conducting this analysis, we also find strong evidence of skills shortages in Australia for highly technical DSA skills and occupations. These results provide insights to Data Science researchers, educators, and policy-makers from other advanced economies about the types of skills that should be cultivated to meet growing DSA labour demands in the future.

Dong, X & Yang, Y 2019, 'One-Shot Neural Architecture Search via Self-Evaluated Template Network', 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Seoul, Korea (South), Korea.
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Duong, TQ, Nguyen, LD, Tuan, HD & Hanzo, L 2019, 'Learning-Aided Realtime Performance Optimisation of Cognitive UAV-Assisted Disaster Communication', 2019 IEEE Global Communications Conference (GLOBECOM), 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA.
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In this work, we propose efficient optimisation methods for relay-assisted unmanned aerial vehicles (UAVs) in cognitive radio networks (CRNs) to cope with the network destruction in the event of a natural disaster. Our model considers real- time optimisation in embedded UAV-CRN communication involved in recovering wireless communication services. Particularly, by conceiving advanced optimisation techniques and training deep neural networks, our solutions become capable of supporting real-time applications in disaster recovery scenarios. Our algorithms impose low computational complexity, hence, have a low execution time in solving real- time optimisation problems. Numerical results demonstrate the benefits of our approaches proposed for UAV-CRN.

Eager, D & Hayati, H 2020, 'Understanding greyhound race track risk factors', GRV On-Track Veterinarians Conference, Melbourne, Australia.
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Fan, H & Yang, Y 2020, 'Person Tube Retrieval via Language Description', New York.

Fang, Y, Huang, X, Qin, L, Zhang, Y, Zhang, W, Cheng, R & Lin, X 2020, 'A survey of community search over big graphs', VLDB Journal, pp. 353-392.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. With the rapid development of information technologies, various big graphs are prevalent in many real applications (e.g., social media and knowledge bases). An important component of these graphs is the network community. Essentially, a community is a group of vertices which are densely connected internally. Community retrieval can be used in many real applications, such as event organization, friend recommendation, and so on. Consequently, how to efficiently find high-quality communities from big graphs is an important research topic in the era of big data. Recently, a large group of research works, called community search, have been proposed. They aim to provide efficient solutions for searching high-quality communities from large networks in real time. Nevertheless, these works focus on different types of graphs and formulate communities in different manners, and thus, it is desirable to have a comprehensive review of these works. In this survey, we conduct a thorough review of existing community search works. Moreover, we analyze and compare the quality of communities under their models, and the performance of different solutions. Furthermore, we point out new research directions. This survey does not only help researchers to have better understanding of existing community search solutions, but also provides practitioners a better judgment on choosing the proper solutions.

Fei, X, Li, K, Yu, S & Li, K 2019, 'An Economical and High-Quality Encryption Scheme for Cloud Servers with GPUs', 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), IEEE, Gold Coast, Australia.
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Motivated by cloud servers undertaking heavy encryption of outsourced data for diverse devices, an Economical and High-Quality Encryption Scheme is proposed to alleviate the burden of energy consumption of the servers meanwhile to keep high-quality services. The objective of the scheme is to minimize the cost that combines economy and service quality. For achieving this objective, a two-phase scoring mechanism is proposed. And then based on the above methods and the scoring mechanism, an algorithm achieving the scheme is designed. To evaluate the scheme, some experiments are performed on a heterogeneous platform. The experimental results show that the encryption algorithm can save energy consumption by 47.8% and slightly improve delay rate by 0.93/10000 on average compared with the original one.

Gil-Doménech, D, Berbegal-Mirabent, J & Merigó, JM 2020, 'STEM education: A bibliometric overview', Advances in Intelligent Systems and Computing, pp. 193-205.
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© 2020, Springer Nature Switzerland AG. In the recent years, STEM (science, technology, engineering and mathematics) education has received increasing attention, and many calls for a fundamental change in this field have emerged. In this context, the analysis of the evolution and development of this scientific domain results crucial. In order to do so, the present study presents a bibliometric overview of the academic research developed in STEM education over the last years. The work uses the Web of Science database and a wide range of bibliometric indicators including the number of publications and citations, the h-index, and citation thresholds. The article also develops a graphical visualisation of the bibliographic data using the visualisation of similarities (VOS) viewer software. Results indicate that the amount of works addressing this topic has substantially increased in the recent years, although the number of citations has not experienced a similar growth rate. Also, when analysing journals and research areas, it can be deduced that the research on STEM education is diverse. The bibliometric analysis performed provides a rigorous and comprehensive view of research on STEM education that might be useful for those researchers interested in advancing future knowledge in this area.

Hadgraft, RG, Francis, B, Fitch, R, Halkon, B & Brown, T 2020, 'Renewing mechanical and mechatronics programs using studios', SEFI 47th Annual Conference: Varietas Delectat... Complexity is the New Normality, Proceedings, pp. 511-522.
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© 2020 SEFI 47th Annual Conference: Varietas Delectat... Complexity is the New Normality, Proceedings. All rights reserved. In a world of rapid change, engineering programs need to adapt to be relevant. This paper addresses the renewal processes for mechanical and mechatronics engineering programs at a large university of technology. The paper sits within a wider curriculum change movement, including all engineering and IT programs at this university. Several meetings have been held over the last 3 years with both industry panels and with academic staff and students to understand the nature of the problem. Using a design-thinking approach, we have explored: global trends, the nature of engineering work and projects, the capabilities required by engineers, and the kinds of capabilities that graduates need to operate confidently in this new world of work. There is a clear need for graduates to be more operational as they move from study to work. Consequently, a major focus on experiential learning is emerging as the key delivery vehicle for new kinds of graduates including projects, studios, and internships. These forms of learning are supported by ready access to online materials as required. A central thread is personalisation of the student learning experience through learning contracts and portfolios. There has been constant demand for change in engineering education for at least the last 20 years. Making change happen, however, is another matter. We are in the fortunate position at this university to have high level support from the Chancellery and the Dean to move our engineering programs to be more relevant to the future. This paper describes the process for engaging our academics, students and industry supporters in that process and will be of interest to many who are grappling with similar transitions.

Halkon, B, Cheong, I, Visser, G, Walker, P & Oberst, S 2020, 'An experimental assessment of torsional and package vibration in an industrial engine-compressor system', Vibrations in Rotating Machinery 12, Liverpool.

Hassan, W, Hasan, R, Lu, DD-C, Xiao, W & Soon, JL 2019, 'Performance Enhancement of High Step-up DC-DC Converter to Attain High Efficiency and Low Voltage Stress', 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), IEEE, Singapore.
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This study proposes a new high voltage gain and high-efficiency dc-dc converter to interface renewable energy resources into dc nanogrid. The proposed topology is formed by a coupled inductor to achieve high voltage gain and low stress on the active switch. The switch voltage stress is significantly low compared to the output voltage. Thus, efficiency is improved by utilizing a low voltage rating MOSFET. Furthermore, the utilization of couple inductor eliminated the reverse recovery losses of diodes. The converter consists of the least number of components that decrease the overall system cost. The steady-state operation and analysis of the proposed converter are discussed comprehensively. The experimental performance is verified by building and testing a prototype in the laboratory. The experimental results prove the consistency with the theoretical analysis. The converter depicts a peak efficiency of 97.10% in the laboratory.

Ian Zhou, Negin.Shariati & Shariati Moghadam, N 1970, 'A Blockchain-based File-sharing System for Academic Paper Review', IEEE International Conference on Signal Processing and Communication Systems, ICSPCS, Australia.
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Ikram, MA, Sharma, N, Raza, M & Hussain, FK 2020, 'Dynamic Ranking System of Cloud SaaS Based on Consumer Preferences - Find SaaS M2NFCP', Advances in Intelligent Systems and Computing, pp. 1000-1010.
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© 2020, Springer Nature Switzerland AG. Software as a Service (SaaS) is a type of software application that runs and operates over a cloud computing infrastructure. SaaS has grown more dramatically compared to other cloud services delivery models (i.e. PaaS and IaaS) in terms of the number of available services. This rapid growth in SaaS brings a lot of challenges for consumers in selecting the optimum services. The aim of this article is to propose a ranking system for SaaS based on consumer’s preferences called Find SaaS M2NFCP. The proposed ranking system is based on measuring the shortest distance to the minimum and maximum of the selected consumer’s non-functional preferences. In addition, linguistic terms are taken into account to weight the most important non-functional preferences. The proposed system is evaluated against traditional SaaS ranking systems using data collected from online CRM SaaS and achieved improved results.

Kalhori, H, Halkon, B, Abbasnejad, B, Li, B & Shooshtari, A 2019, 'Nonlinear Vibration of an Electrostatically Excited Capacitive Microplate', Springer, 2020, The 18th Asia-Pacific Vibration Conference, University of Technology Sydney UTS, Sydney, Australia.
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Khan, S & Hussain, FK 2020, 'A soa based sla negotiation and formulation architecture for personalized service delivery in sdn', Advances in Intelligent Systems and Computing, pp. 108-119.
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© Springer Nature Switzerland AG 2020. Supporting end-to-end personalized Quality of Services (QoS) delivery in existing network architecture is an ongoing issue. Software Defined Networking (SDN) model has emerged in response to the limitations of traditional network. Integrating Software Defined Network (SDN) architecture with Service Oriented Architecture (SOA) brings new concept for future service oriented delivery in SDN services. Researchers from both academic and industry are working to resolve the QoS limitations of service delivery, however; most of the proposed solutions are application oriented and unable to provide a reliable personalized QoS delivery in future service oriented SDN. This research propose a reliable Service Level Agreement (SLA) oriented Service Negotiation framework that would be able to provide reputation based personalized service delivery and assist in QoS management in SDN for informed decision making. Moreover, potential benefits of the proposed framework are also discussed in this paper in social, scientific and business aspects.

Kong, Q, Rizoiu, MA & Xie, L 2020, 'Modeling information cascades with self-exciting processes via generalized epidemic models', WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 286-294.
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© 2020 Association for Computing Machinery. Epidemic models and self-exciting processes are two types of models used to describe diffusion phenomena online and offline. These models were originally developed in different scientific communities, and their commonalities are under-explored. This work establishes, for the first time, a general connection between the two model classes via three new mathematical components. The first is a generalized version of stochastic Susceptible-Infected-Recovered (SIR) model with arbitrary recovery time distributions; the second is the relationship between the (latent and arbitrary) recovery time distribution, recovery hazard function, and the infection kernel of self-exciting processes; the third includes methods for simulating, fitting, evaluating and predicting the generalized process. On three large Twitter diffusion datasets, we conduct goodness-of-fit tests and holdout log-likelihood evaluation of self-exciting processes with three infection kernels — exponential, power-law and Tsallis Q-exponential. We show that the modeling performance of the infection kernels varies with respect to the temporal structures of diffusions, and also with respect to user behavior, such as the likelihood of being bots. We further improve the prediction of popularity by combining two models that are identified as complementary by the goodness-of-fit tests.

Li, D, Tang, M-C, Wang, Y, Hu, K-Z, Li, M & Ziolkowski, RW 2019, 'Compact, Low-Profile, Planar Filtering Antennas Based on A Novel Coupling Feeding Scheme', 2019 IEEE 6th International Symposium on Electromagnetic Compatibility (ISEMC), 2019 IEEE 6th International Symposium on Electromagnetic Compatibility (ISEMC), IEEE, Nanjing, China.
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The compact, low-profile linearly polarized (LP) and circularly polarized (CP) filtering antennas based on a novel feed scheme are presented. The LP design consists of a patch and an inverted-h-shaped coupling feed probe, which can introduce two radiation nulls at the lower and the upper band edges to realize a quasi-elliptic bandpass filtering response, respectively. This LP design with simple feed structure is also extended to realize the CP function by adding an extra T -shaped feedline operating as a dual-mode resonator, while maintaining the excellent filtering performance and further enhancing the impedance bandwidth. For demonstration, two prototypes of the LP and CP designs were measured. The measured results agree well with the simulated values. In addition, good performance characteristics, i.e., flat passband gain response, excellent roll-off rate, and high out-of-band suppression level, could be well achieved.

Li, HY, Xu, JX, Zhang, XY & Yang, Y 2019, 'Design of Low-Loss Single-Pole Single-Throw and Single-Pole Double-Throw Filtering Switches Using Coaxial Resonators', 2019 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2019 - Proceedings, International Conference on Microwave and Millimeter Wave Technology, IEEE, Guangzhou, China.
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© 2019 IEEE. In this paper, a single-pole single-throw (SPST) filtering switch and a single-pole double-throw (SPDT) filtering switch are designed using coaxial resonators. The ON- and OFF-states are achieved by controlling the PIN diodes embedded in the coaxial filter structures. For the SPST filtering switch, four PIN diodes are all turned off to achieve the ON-state, functioning as same as a conventional four-order coaxial filter, where the cross-coupling is employed to realize high-selectivity filtering responses. When the four PIN diodes are turned on, the SPDT switch works in the OFF-state. Capacitors are connected to the coaxial resonators to change the resonant frequencies of the resonators, thus the passband cannot be formed, resulting in a high isolation between the two ports. For demonstration, a SPST filtering switch is fabricated. Measured results show a low insertion loss of less than 1 dB and a high isolation of better than 60 dB. The excellent performance makes it attractive in wireless systems. Moreover, a SPDT filtering switch is also proposed. Compared with the state-of-the-art works using DR-based resonators, the proposed design shows a more compact size.

Li, J, Zhu, X & Samali, B 2018, 'Bridge operational modal identification using sparse blind source separation', ACMSM25 Proceedings of the 25th Australasian Conference on Mechanics of Structures and Materials, Australasian Conference on the Mechanics of Structures and Materials, Springer, Brisbane, Australia, pp. 911-920.
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The bridge infrastructures are subjected to continuous degradation due to ageing, environmental and excess loading. Monitoring of these structures is a key part of any maintenance strategy as it can give early warning if a bridge is becoming unsafe. Most of the current approaches are using direct measurements that the sensors are installed at different specific locations on the bridge to capture the dynamic characteristics of the structure under random input, such as wind loads, traffic loads and ground motions. Based on the assumption on the white noise characteristics of the random input, structural properties of the bridge could be extracted from the vibration responses only. However, the bridge is subjected to non-stationary traffic loads, and the frequency characteristics of vibrations are varied. Especially for short-span bridges, the non-stationary traffic excitation is significant and most of the existing output-only structural identification methods could not be used to assess the bridge condition. This study proposes a blind source separation (BSS) method using short time Fourier transform (STFT) for the analysis of non-stationary measurements in time frequency (TF) domain. The proposed method is capable of source component separation from response measurement for underdetermined problems when the number of independent measurements (sensors) is less than that of source component. The proposed method is applied to a cable-stayed bridge in the field for the operational modal identification under different traffic conditions.

Lin, W & Ziolkowski, RW 2019, 'Internet-of-Things Sensors Wirelessly Powered by Electrically Small Huygens Dipole Rectenna', 2019 International Symposium on Antennas and Propagation, ISAP 2019 - Proceedings, 2019 International Symposium on Antennas and Propagation (ISAP), Xi'an, China, China.
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© 2019 Antenna Branch of Chinese Institute of Electronics. Internet-of-Things (IoT) sensors are expected to be ubiquitous in a future smart and sustainable society. Due to the exponential growth of device numbers, powering IoT devices by far field wireless power transfer (WPT) is becoming a necessary trend. This paper introduces two IoT sensors (light and temperature) that are wirelessly powered by a highly compact and efficient electrically small Huygens dipole rectenna. The entire system seamlessly integrates two subsystems. One is the metamaterial-inspired electrically small Huygens antenna that organically combines capacitively loaded loop (CLL) and Egyptian axe dipole (EAD) radiators together. The other is the sensor-augmented rectifier in which the output DC voltage varies as a function of the sensor impedance. The developed wirelessly powered systems can sense light or temperature levels and, when attached to an alarm, can send a warning signal if a threshold value is exceeded. Their prototypes were fabricated and tested. The measured results agree very well with their simulated values. The prototypes are highly compact and electrically small (ka = 0.73). They represent ideal candidates for many emerging IoT wireless sensor applications.

Lin, Z, Lv, T, Zhang, JA & Liu, RP 2019, '3D wideband mmwave localization for 5G massive MIMO systems', 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings, IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA.
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© 2019 IEEE. This paper proposes a novel 3D localization method for wideband mmWave massive MIMO systems. A high dimensional linear interpolation (HDLI)-based preprocessing is first proposed to transform the frequency-associated dynamical array response vectors into the common counterparts at the reference frequency. Through this method, the received data in all frequency bands can be processed jointly, and thus the high temporal resolution provided by wideband mmWave systems can be fully exploited for position estimation. To reduce the computational complexity in the process of the parameter estimation, we then present a wideband beamspace (WBS)-based parameter estimation algorithm to estimate the angle and delay in the low-dimensional beamspace. By exploiting the quasi- optical propagation at the mmWave frequencies, a novel positioning scheme is also designed to determine the 3D location of the target. According to our analysis and simulation results, the proposed method is capable of achieving significantly reduced computational complexity, while maintaining high localization accuracy.

Liu, B, Zhu, T, Wang, K, Zhou, H, Ding, M & Zhou, W 2019, 'Protecting Privacy-Sensitive Locations in Trajectories with Correlated Positions', 2019 IEEE Global Communications Conference (GLOBECOM), 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA.
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The location privacy issue has become a critical research topic recently. The existing solutions do not solve one typical problem in practice: people may only want to protect certain privacy-sensitive locations among a group of temporal and spatial correlated points in a trajectory. As an effort towards this issue, we analyze the impact of space-time relationship on location privacy preservation. In addition, we propose new privacy definitions to better evaluate the privacy level and prove that the target location's privacy can be enhanced by randomizing its time and space related points. Moreover, under the constraint of the total noise power, the problem of obfuscating a location in a temporal and spatial correlated trajectory is formulated as finding the best noise allocation vector which can achieve the highest privacy level. This problem is solved by our proposed location privacy preserving method which applies differential privacy scheme on a series of points with noise budget allocation. Lastly, the performance of the proposed scheme is evaluated by simulations.

Luu, V-H, Dao, M-S, Nguyen, TN-T, Perry, S & Zettsu, K 2019, 'Semi-supervised Convolutional Neural Networks for Flood Mapping using Multi-modal Remote Sensing Data', 2019 6th NAFOSTED Conference on Information and Computer Science, Hanoi, Vietnam.
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When floods hit populated areas, quick detection of flooded areas is crucial for initial response by local government, residents, and volunteers. Space-borne polarimetric synthetic aperture radar (PolSAR) is an authoritative data sources for flood mapping since it can be acquired immediately after a disaster even at night time or cloudy weather. Conventionally, a lot of domain-specific heuristic knowledge has been applied for PolSAR flood mapping, but their performance still suffers from confusing pixels caused by irregular reflections of radar waves. Optical images are another data source that can be used to detect flooded areas due to their high spectral correlation with the open water surface. However, they are often affected by day, night, or severe weather conditions (i.e., cloud). This paper presents a convolution neural network (CNN) based multimodal approach utilizing the advantages of both PolSAR and optical images for flood mapping. First, reference training data is retrieved from optical images by manual annotation. Since clouds may appear in the optical image, only areas with a clear view of flooded or non-flooded are annotated. Then, a semisupervised polarimetric-features-aided CNN is utilized for flood mapping using PolSAR data. The proposed model not only can handle the issue of learning with incomplete ground truth but also can leverage a large portion of unlabelled pixels for learning. Moreover, our model takes the advantages of expert knowledge on scattering interpretation to incorporate polarimetric-features as the input. Experiments results are given for the flood event that occurred in Sendai, Japan, on 12th March 2011. The experiments show that our framework can map flooded area with high accuracy (F1 = 96:12) and outperform conventional flood mapping methods.

Majbouri Yazdi, K, Majbouri Yazdi, A, Khodayi, S, Hou, J, Zhou, W, Saedy, S & Rostami, M 2019, 'Improving Recommender Systems Accuracy in Social Networks Using Popularity', 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), IEEE, Gold Coast, Australia, pp. 304-310.
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With the rapid advancement of World Wide Web, people can share their knowledge and information via online tools such as sharing systems and ecommerce applications. Many approaches have been proposed to process and organize information. Recommender systems are good successful examples of such tools in providing personalized suggestions. The main purpose of a recommender system is to identify and introduce desired items of a user among many other options (e.g. music, movies, books, news and etc). The goal of our proposed method is to provide a recommender system based on information diffusion and popularity in social networks. By adding popularity, similarity and users' trusts a more efficient system is proposed. This approach makes an improvement in tackling the issues and defects of the previous methods such as prediction accuracy and coverage. The evaluation of the simulated proposed method on MovieLens and Epinions datasets shows that it provides more accurate recommendations in comparison to other approaches.

Marynowsky, W, Ferguson, S, Fraietta, A & Bown, O 2020, ''The Ghosts of Roller Disco', a Choreographed, Interactive Performance for Robotic Roller Skates', Proceedings of the Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction, TEI '20: Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction, ACM.
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Masood, A & Al-Jumaily, A 2020, 'Orientation Sensitive Fuzzy C Means Based Fast Level Set Evolution for Segmentation of Histopathological Images to Detect Skin Cancer', Advances in Intelligent Systems and Computing, pp. 501-510.
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© 2020, Springer Nature Switzerland AG. Malignant Melanoma in one of the most deadly skin cancer. In latest cancer diagnosis, pathologists examine biopsies for analysing cell morphology and tissue distribution for making diagnostic assessments. However, this process is quite subjective and has considerable variability. Automated computational diagnostic tools based on quantitative measures can help the diagnosis done by pathologists. The first and foremost step in automated histopathological image analysis is to properly segment the tissue structures such as nest, irregular distribution and melanocytic cells which indicate some disorder or potential cancer. This paper presents a novel technique for automatic segmentation of histopathological skin images, without user intervention. It is based on an innovative approach for utilizing the concepts of clustering and level set evolution. Firstly, the image is pre-processed to enhance the differentiating structural details. Then a novel orientation sensitive Fuzzy C mean clustering is used to generate the initial coarse segmentation and to calculate the controlling parameters for level set evolution. Later, refined fast level set based algorithm is used to finalize the segmentation process. Experimental analysis on a database of 150 histopathological images display the accuracy of the proposed method for detecting the melanocytic areas of the image, with true detection rate of 87.66% and Dice similarity coefficient of 0.88, when segmentation results are compared with images marked by expert pathologists.

Mulet-Forteza, C, Mauleón-Méndez, E, Merigó, JM & Genovart-Balaguer, J 2020, 'Leading universities in tourism and hospitality research: A bibliometric overview', Advances in Intelligent Systems and Computing, pp. 142-152.
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© 2020, Springer Nature Switzerland AG. This study presents an overview of the most influential and productive institutions around the world in the tourism, leisure and hospitality field. The research uses a bibliometric approach to examine all the journals indexed in the Web of Science database in these fields. Results show a strong influence of the USA institutions, although institutions from other countries also achieve an important and significant position in these fields. Results also consider the temporal evolution of the leading universities and the most productive institutions in the tourism, leisure and hospitality field.

Munasinghe, N, Woods, M, Miles, L & Paul, G 2019, '3-D Printed Strain Sensor for Structural Health Monitoring', IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and the IEEE International Conference on Robotics, Automation and Mechatronics (RAM), IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and the IEEE International Conference on Robotics, Automation and Mechatronics (RAM), IEEE, Bangkok.
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Additive manufacturing, or 3D printing, is evolving from a technology that can only aid rapid prototyping, to one that can be used to directly manufacture large-scale, real-world equipment. Gravity Separation Spirals (GSS) are vital to the mining industry for separating mineral-rich slurry into its different density components. In order to overcome inherent drawbacks of the traditional mould base manufacturing methods, including significant tooling costs, limited customisation and worker exposure to hazardous materials, a 3D printer is under development to directly print spirals. By embedding small Internet of Things (IoT) sensors inside the GSS, it is possible to remotely determine the operation conditions, predict faults, and use collected data to optimise production output. This work presents a 3D printed strain sensor, which can be directly printed into the GSS. This approach uses a carbon-based conductive filament to print a strain gauge on top of a Polylactic Acid (PLA) base material. Printed sensors have been tested using an Instron E10000 testing machine with an optical extensometer to improve accuracy. Testing was conducted by both loading and unloading conditions to understand the effect of hysteresis. Test results show a near-linear relationship between strain and measured resistance, and show a 6.05% increase in resistance after the test, which indicates minor hysteresis. Moreover, the impact of viscoelastic behaviour is identified, where the resistance response lags the strain. Results from both conductive and non-conductive material show the impact of the conductive carbon upon the tensile strength, which will help to inform future decisions about sensor placement.

Naji, M, Braytee, A, Anaissi, A, Sianaki, OA & Al-Ani, A 2020, 'Optimizing the Waiting Time for Airport Security Screening Using Multiple Queues and Servers', Advances in Intelligent Systems and Computing, pp. 496-507.
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© 2020, Springer Nature Switzerland AG. Airport security screening processes are essential to ensure the safety of passengers and the aviation industry. Security at airports has improved noticeably in recent years through the utilisation of state-of-the-art technologies and highly trained security officers. However, maintaining a high level of security can be costly to operate and implement and can cause delays for passengers and airlines. In optimising a security process it is essential to strike a balance between time delays, security and reduced operation cost. This paper uses queueing theory as a method to study the impact of queue formation and the size of the security area on the average waiting time for the case of multi-lane parallel servers. An experiment is conducted to validate the proposed approach.

Nguyen, H, Nguyen, D, Dinh, H, Dutkiewicz, E, Mueck, M & Srikanteswara, S 2020, 'Defeating Jamming Attacks with Ambient Backscatter Communications', IEEE International Conference on Computing, Networking and Communications (ICNC), Hawaii.
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Pan, P, Yan, Y, Yang, T & Yang, Y 2020, 'Learning Discriminators as Energy Networks in Adversarial Learning', New York.

Pelchen, T, Mathieson, L & Lister, R 2020, 'On the evidence for a learning hierarchy in data structures exams', ACE 2020 - Proceedings of the 22nd Australasian Computing Education Conference, Held in conjunction with Australasian Computer Science Week, pp. 122-131.
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© 2020 Association for Computing Machinery. ACM ISBN 978-1-4503-7686-0/20/02...$15.00 Several previous research studies have found a relationship between the ability of novices to trace and explain code, and the ability to write code. Harrington and Cheng refer to that relationship as the Learning Hierarchy. However, almost all of those studies examined students at the end of their first semester of learning to program (i.e. CS1). This paper is only the third paper to describe a study of explain in plain English questions on students at the end of an introductory data structures course. The preceding two papers reached contradictory conclusions. Corney et al. presented results consistent with the Learning Hierarchy identified in the CS1 studies. However, Harrington and Cheng presented results for data structures students suggesting that the hierarchy reversed by the time students had progressed to the level of learning about data structures; that is, tracing and explaining were skills that followed writing. In our study of data structures students, we present results that are consistent with the Learning Hierarchy derived from the CS1 students. We believe that the reversal identified by Harrington and Cheng can occur, but only as a consequence of a mismatch in the relative difficulty of tracing, explaining and writing questions.

Prestigiacomo, R, Hadgraft, R, Lockyer, L, Knight, S, van den Hoven, E, Martinez-Maldonado, R & Hunter, J 2020, 'Learning-centred Translucence: An Approach to Understand How Teachers Talk About Classroom Data', The 10th international Learning Analytics & Knowledge Conference, Frankfurt, Germany.
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Teachers are increasingly being encouraged to embrace evidence- based practices to improve their teaching. Learning analytics (LA) offer great promise in supporting these practices by providing evidence for teachers and learners to make informed decisions and transform the educational experience. However, LA limitations and their uptake by educators are also coming under critical scrutiny. This is in part due to the lack of involvement of teachers and learners in the design of LA tools to understand existing educational practices. In this paper, we propose a human-centred approach to generate understanding ofteachers’ data needs through the lens of three key principles of translucence: visibility, awareness and accountability. We illustrate our approach through a participatory design sprint to identify how teachers talk about classroom data. We describe teachers’ perspectives on the evidence they need for making better-informed decisions and discuss the implications of our approach for the design of human- centred LA in the next years.

Rehman, J, Sohaib, O, Hawryszkiewycz, I & Soomro, AM 2020, 'Developing Intellectual Capital in Professional Service Firms Using High Performance Work Practices as Toolkit', https://scholarspace.manoa.hawaii.edu/bitstream/10125/64355/0493.pdf, 53rd Hawaii International Conference on System Sciences, Hawaii, pp. 4983-4992.
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Ren, L, Wang, F, Li, L, Liu, X & Zhang, Y 2019, 'Design Optimization of Distributed PV Power Station Based on the Efficiency Modelling and Analysis', 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), IEEE, Singapore.
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Based on the large amount of data from a 3 MW distributed photovoltaic power station and a module-level experimental platform, the mathematical model of the performance ratio varying with the irradiation is established in this paper. The performance ratio value of the power station is 0.59, which is far below the engineering experience value. Therefore, the inverter efficiency, photovoltaic module efficiency and photovoltaic array efficiency are analyzed individually, and the focus is laid on the loss of performance ratio that caused by the voltage mismatch between the photovoltaic array and the maximum power point tracking of the inverter. In order to improve the performance ratio, the number of modules in series are designed and optimized. The research results are conducive to guide the optimal design of distributed photovoltaic power plants and significantly increase the power generation capacity.

Schmitt, J, Hahn, F & Deuse, J 2019, 'Practical Framework for Advanced Quality-based Process Control in Interlinked Manufacturing Processes', 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2019 IEEE International Conference on Industrial Engineering and Engineering Management, IEEE, Macau.
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As the economic manufacturing of high-quality products becomes an increasingly crucial competitive factor, corresponding quality assurance measures are gaining a growing interest. Even though research interest and industrial demand are both high, there is a large gap between methological approaches and practical applicability that needs to be closed. In this paper we therefore present a practical framework for advanced quality-based process control (AQPC) in interlinked manufacturing processes. Machine learning algorithms are used to predict the expected product quality based on recorded process parameters. That information then serves as an input for the derivation of optimal control decisions. Therefore, we formulate a mathematical optimization model including different options such as order reassignment and process parameter adaption to determine an optimal set of control decisions. We then break down the optimization into a gradual procedure that allows an application-specific integration into manufacturing.

Shafiei, S, Mihaita, A-S, Nguyen, H, Bentley, C & Cai, C 2020, 'Short-Term Traffic Prediction Under Non-Recurrent Incident Conditions Integrating Data-Driven Models and Traffic Simulation', Transportation Research Board 99th Annual Meeting, Washington D.C..
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Predicting the traffic condition in urban networks is a priority for all traffic management centers around the world. This becomes very challenging especially when the network is affected by traffic incidents which vary in both time and space. Although data-driven machine learning (ML) modeling can be considered as an ideal tool for short-term traffic predictions, its performance is severely degraded when little historical traffic information is available under non-recurrent incident conditions. This paper addresses this challenge by integrating both data-driven and traffic simulation modeling. Instead of directly predicting the traffic states using limited historical data, we apply data-driven models to reinforce the traffic microsimulation. More explicitly, we employ ML models to predict origin-destination (OD) demand flows based on historical day-to-day demand flows. The traffic simulation uses the freshly reported incident information and the predicted OD demand flows obtained from ML models to forecast the future traffic states under non-recurrent incident conditions. Since accurate OD flows cannot directly measured in large-scale areas, we propose an OD demand rolling-horizon estimation problem to estimate demand flows based on the most recent measured link volumes. Results show that Decision Tree method outperforms other ML models in OD demand flow prediction. Finally, we showcase the capability of the proposed data-driven enforced traffic simulation platform for incident impact analysis in a real –life subnetwork from Sydney, Australia.

Shu, Y & Xu, G 2019, 'Emotion Recognition From Music Enhanced By Domain Knowledge', PACIFIC RIM INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, Yanuca Island, Cuvu, Fiji.
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Music elements have been widely used to influence the audiences’ emotional experience by its music grammar. However, these domain knowledge, has not been thoroughly explored as music grammar for music emotion analyses in previous work. In this paper, we propose a novel method to analyze music emotion via utilizing the domain knowledge of music elements. Specifically, we first summarize the domain knowledge of music elements and infer probabilistic dependencies between different main musical elements and emotions from the summarized music theory. Then, we transfer the domain knowledge to constraints, and formulate affective music analysis as a constrained optimization problem. Experimental results on the Music in 2015 database and the AMG1608 database demonstrate that the proposed music content analyses method outperforms the state-of-the-art performance prediction methods.

Sofela, S, Sahloul, S, Kwon, T, Han, J, Warkiani, ME & Song, YA 2020, 'Synchronization of C. Elegans through high-throughput separation of eggs in a spiral microfluidic chip', 21st International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2017, pp. 1100-1101.
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© 17CBMS-0001. In this work, we report a high-throughput sorting of C. elegans embryos from a mixed-age worm population in a spiral microfluidic chip for synchronization. Using inertial forces in the spiral chip, C. elegans eggs could efficiently be focused along the inner channel wall and separated out with high sorting efficiency and purity.

Sood, K, Pokhrel, SR, Karmakar, K, Vardharajan, V & Yu, S 2019, 'SDN-Capable IoT Last-Miles: Design Challenges', 2019 IEEE Global Communications Conference (GLOBECOM), 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA.
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We propose to redesign SDN control in IoT last-miles so as to extend the capability from edge routers to devices (\emph{end-node things enabled with SDN capabilities}). Our approach put forward existing and new challenges that are impossible to be resolved using the seminal approaches directly. The main challenges we identify are: scalability of sensor nodes/things, maintaining the security of the system, and fulfilling the Quality of Service (QoS) requirement of all IoT applications. Firstly, we elaborate and discuss the aforementioned critical and fundamental challenges that require immediate investigations. Secondly, we propose a policy-driven framework for secure routing and conduct performance modeling and analysis. Further, in the QoS context, we have proposed an intent-based flow offloading scheme to meet the flow-specific QoS requirements. More importantly, we have developed an analysis by modeling TCP-based flows over WiFi, thus forming the required SDN-IoT network, by using mathematics as a tool for reasoning our challenges. With new insights from our analysis, the feasibility of the proposed approach is validated using factors such as path set-up time in SDN-IoT networks, SDN controller/devices throughputs, packets losses and response time of the controller.

Stannus, G, Irga, P & Torpy, F 2020, 'How do we make Work Plants the new House Plants?', Healthy Plants, Healthy People. Greenlife Industry Australia biennial national conference, Perth, WA, Australia.
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Tang, T, Wu, H, Bao, W, Yang, P, Yuan, D & Zhou, BB 2019, 'New Parallel Algorithms for All Pairwise Computation on Large HPC Clusters', 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), IEEE, Gold Coast, Australia.
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All pairwise computation is defined as performing computation between every pair of the elements in a given dataset. It is often a necessary first step in a number of bioinformatics applications. Many of such applications require multiple terabytes of main memory and take multiple peta floating point operations to complete the computation. Therefore, large HPC clusters are needed to tackle these large-scale computational problems. Conventionally designed parallel algorithms using data partitioning may have a scalability issue, i.e., for a given problem of fixed size the efficiency may decrease if the number of compute nodes is increased (Amdahl's law). In this paper we introduce a new method for parallel algorithm design. Using this method we first design an efficient one-dimensional (1D) ring algorithm and then a two-dimensional (2D) algorithm based on the 1D ring for all pairwise computation. When increasing the compute nodes, instead of reducing the block size, we make multiple copies of the original data blocks in the 1D ring and distribute them across the added compute nodes in the other dimension. By properly organizing the compute nodes the communication overhead can be reduced to a minimum in this two-dimensional setting. Experiments on a Cray XC40 HPC supercomputer show that our new algorithms are very efficient and scalable for large-scale all pairwise computation on large HPC clusters.

Tello, AMD & Abolhasan, M 2019, 'SDN Controllers Scalability and Performance Study', 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, Malaysia.
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Software Defined Networks (SDN) is a networking approach that decouples the intelligent control plane from networking devices and establishes a separate entity called ”controller” that rule the behaviour of the data plane on physical networking devices. Due to the rapid evolution and growth of SDN controllers in the market, this paper aims to present an extensive study on performance and scalability of different open source SDN controllers available in the existing literature. This work covers previous studies and expands them with updated information and official benchmarking methodologies. The study provides a framework based on the standards recommended by the IETF (Internet Engineering Task Force) and it will serve as a guideline to the SDN community to benchmark different SDN controllers.

Thiyagarajan, K, Kodagoda, S, Ulapane, N & Prasad, M 2020, 'A Temporal Forecasting Driven Approach Using Facebook’s Prophet Method for Anomaly Detection in Sewer Air Temperature Sensor System', 2020 15th IEEE Conference on Industrial Electronics and Applications, Kristiansand, Norway.
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Torres-Martínez, A, Gil-Lafuente, AM & Merigó, JM 2020, 'The OWA operator with boxplot method in time series', Advances in Intelligent Systems and Computing, pp. 13-21.
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© 2020, Springer Nature Switzerland AG. This paper presents an aggregation model for time series with the OWA operator, using as arguments representative values calculated from the quantiles of a data set and particularly with the values of the boxplot graph. The application of the OWA operator in this model allows forecasting time series including the optimistic or pessimistic attitude of the analysts, through the weighting vector. To illustrate the boxplot-OWA operator model, results are presented with data from the IBEX 35 for different periods of time in 2017.

Ubaid, A, Dong, F & Hussain, FK 2020, 'Framework for Feature Selection in Health Assessment Systems', Advances in Intelligent Systems and Computing, pp. 313-324.
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© 2020, Springer Nature Switzerland AG. Anomaly detection in health assessment systems has gained much attention in the recent past. Various feature selection techniques have been proposed for successful anomaly detection. However, these methods do not cater for the need to select features in health assessment systems. Most of the present techniques are data dependent and do not offer an option for incorporating domain information. This paper proposes a novel domain knowledge-driven feature selection framework named domain-driven selective wrapping (DSW) that can help in the selection of a correlated feature subset. The proposed framework uses an expert’s domain knowledge for the selection of subsets. The framework uses a custom-designed logic-driven anomaly detection block (LDAB) as a wrapper. The experiment results show that the proposed framework is able to select feature subsets more efficiently than traditional sequential selection methods and is very successful in detecting anomalies.

Ulapane, N, Thiyagarajan, K & Kodagoda, S 2020, 'Hyper-Parameter Initialization for Squared Exponential Kernel-based Gaussian Process Regression', 2020 15th IEEE Conference on Industrial Electronics and Applications, IEEE, Kristiansand, Norway.
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Ulapane, N, Thiyagarajan, K & Kodagoda, S 2020, 'System Identification of Static Nonlinear Elements: A Unified Approach of Active Learning, Over-fit Avoidance, and Model Structure Determination', 15th IEEE Conference on Industrial Electronics and Applications, IEEE, Kristiansand, Norway.
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Ulapane, N, Wickramanayake, S & Kodagoda, S 2019, 'Pulsed Eddy Current Sensing for Condition Assessment of Reinforced Concrete', 14th IEEE Conference on Industrial Electronics and Applications, China.
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Vicedo, P, Gil, H, Oltra-Badenes, R & Merigó, JM 2020, 'Critical success factors on ERP implementations: A bibliometric analysis', Advances in Intelligent Systems and Computing, pp. 169-181.
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© 2020, Springer Nature Switzerland AG. This work presents a bibliometric analysis of the influential authors, institutions, papers and countries on the field of Enterprise Resource Planning implementations and their Critical Success Factors based on the Web of Science database with 301 articles belonging to 86 Universities and Institutions from 48 different countries. The research has been conducted from 1999 to end of 2017.

Wang, D, Zhang, W, Yu, S & He, H 2019, 'RLS-VNE: Repeatable Large-Scale Virtual Network Embedding over Substrate Nodes', 2019 IEEE Global Communications Conference (GLOBECOM), 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA.
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Embedding multiple virtual networks (VNs) on a shared substrate network (SN), known as virtual network embedding (VNE), is a challenging problem in cloud platforms. VNE methods can provide strategies to deploy VNs onto SN resources. However, as the scale of VN greatly increases, traditional VNE methods are time-consuming and waste link resource. Meanwhile, traditional VNE methods assign each virtual node of the same VN to different substrate nodes, whereas it is hard to provide larger scale SN to provision the VN. In order to efficiently embed large-scale VNs, multiple virtual nodes from the same VN need to share the same substrate node. We therefore model a repeatable large-scale virtual network embedding (RLSVNE) problem in this study, provisioning large-scale VNs, and propose a heuristic method (Rlsvne) to handle RLS-VNE. Rlsvne pre-processes the VN topology before embedding. In the pre-processing stage, the VN topology is processed through graph coarsening, partitioning, and uncoarsening. After the pre-processing, Rlsvne accomplishes an embedding stage with a topology- aware repeatable embedding solution. 1,000 and 10,000-scale VNE experiments are conducted to demonstrate our Rlsvne. The evaluation results demonstrate that our Rlsvne outperforms three modified heuristics. Rlsvne shows improved performance in reducing substrate cost and fully utilizing substrate resources, achieving high acceptance ratio and revenue values.

Wang, H, Deng, C, Ma, F & Yang, Y 2020, 'Context Modulated Dynamic Networks for Actor and Action Video Segmentation with Language Queries', New York.

Wang, X, Wu, Y, Zhu, L & Yang, Y 2020, 'Symbiotic Attention with Privileged Information for Egocentric Action Recognition', The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI.

Wedyan, M, Al-Jumaily, A & Crippa, A 2020, 'Early Diagnose of Autism Spectrum Disorder Using Machine Learning Based on Simple Upper Limb Movements', Advances in Intelligent Systems and Computing, pp. 491-500.
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© 2020, Springer Nature Switzerland AG. The importance of early diagnosis of autism that leads to early intervention such thing shall increase the results of treating it. The Autism Spectrum Disorder (ASD) affects the children activities and caused difficulties in interaction, impairments in communication, delayed speech, and weak eye contact. These activities used as the base for ASD diagnosis decision. Children move their upper limb before some of the other activities. Moving upper limb can be based for ASD diagnosis decision for autistic children. Such paper examines diagnosing the ASD that depends on motioning the children’s upper-limb aged between two and four years based on executing specific procedures and machine learning. The approach that such study utilized is both (LDA) Linear Discriminant Analysis in order to elicit the features and (SVM) Support Vector Machines for classifying thirty children such study selected fifteen autistic children out of fifteen non-autistic children by testing the collected data that are collected from doing an easy task. The results of such study have accomplished an optimal sortation accuracy of 100% and the average accuracy of 93.8%. Such outcomes provide more proof of simple brachium motioning that might be utilized in sorting poor performance of autistic children precisely.

Williams, PT, Hill, J, Malecki, C, Kirby, R & Abom, M 2020, 'Hybrid dissipative/reactive silencer predictions with comparison to measurement', Euronoise 2015, pp. 2243-2248.
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Copyright © (2015) by EAA-NAG-ABAV, ISSN 2226-5147 All rights reserved Gas Turbines predominantly generate a broadband noise spectrum, although strong tones are also present, often at relatively low frequencies. Dissipative silencers are commonly used to reduce noise within gas turbine systems, however they are inefficient at removing low-frequency noise and this leads to silencers which are overdesigned at higher frequencies. Therefore, by introducing new techniques designed specifically to target low-frequencies it is proposed that the overall cost of the silencer may be decreased. Reactive elements are known to be successful at targeting low frequencies but their non-acoustic limitations, including problems with minimising pressure drop, have traditionally prevented their use in gas turbine systems. This paper presents a new silencer design that combines dissipative and reactive silencer elements in order to target low frequency tonal noise, whilst at the same time minimising static pressure drop. This so-called hybrid silencer is designed using advanced finite element modelling techniques and new prototype designs are then tested in the laboratory. Comparison between prediction and experiment shows good agreement over a wide frequency range, which demonstrates the validity of the modelling approach. Results demonstrate that attaching a carefully designed reactive element to a dissipative element can deliver a large increase in performance at low frequencies. This improvement in performance is characterised by a resonance "peak", which is designed to cover an octave band surrounding the target frequency in order to accommodate uncertainties in the noise output of a gas turbine. Furthermore, it is found that additional peaks occur at higher harmonics, which further increases the performance of the hybrid silencer beyond the plane wave region of the inlet duct, delivering additional and significant improvements in performance for the new hybrid silencer when com...

Xiao, T, Qiu, X & Halkon, B 2019, 'Using a Laser Doppler Vibrometer to Estimate Sound Pressure in Air', Springer, The 18th Asia-Pacific Vibration Conference, Sydney.
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Xiao, Y, Xiao, L, Zhang, H, Yu, S & Poor, HV 2019, 'Privacy Aware Recommendation: Reinforcement Learning Based User Profile Perturbation', 2019 IEEE Global Communications Conference (GLOBECOM), IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA.
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User profile release in recommendation systems can apply the user profile perturbation technique to protect user privacy, in which each user sends a perturbed user profile such as the a list of clicked items to receive a recommendation service from a server. The perturbation policy such as the privacy budget determines the recommendation quality and the privacy level, while its optimization usually depends on the known attack model, which is rarely known by the users. In this paper, we propose a reinforcement learning based user profile perturbation scheme that applies differential privacy to protect user privacy for recommendation systems. According to reinforcement learning, the privacy budget to perturb the released user profile depends on the features of the actual user profiles and the released user profiles, and the estimated user privacy level. This scheme enables a user to optimize his or her perturbation policy in terms of both the user privacy level and the received recommendation quality without being aware of the attack model. We evaluate the computational complexity of this scheme and analyze a case study, a privacy aware movie recommendation system. Simulation results show that this scheme improves user privacy protection for a given level of recommendation quality compared with a benchmark profile perturbation scheme.

Xie, F, Yan, J & Shen, J 2020, 'A Bandwidth and Latency Based Replica Selection Mechanism for Data-Intensive Workflow Applications in the Multi-Cloud Environment', ACM International Conference Proceeding Series.
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© 2020 ACM. Data-intensive workflow applications need effective data management strategies to handle large amount of data generated. To fit this goal, data replication is one of the most significant strategies to balance the data access load and improve the quality of services. The replica selection strategy can help users decide which replica is the most suitable to access. This paper proposes a bandwidth and latency based replica selection mechanism (namely, BLRS) to avoid potential network overloading issues and increase the number of concurrent-running instances in the multi-cloud environment. The simulation results show that our BLRS mechanism can significantly increase the number of concurrent-running workflow instances, in comparison with the least response time replica selection, on the traditional three-replica placement basis.

Yang, B, Wen, D, Qin, L, Zhang, Y, Wang, X & Lin, X 2020, 'Fully Dynamic Depth-First Search in Directed Graphs', Proceedings of the VLDB Endowment, 46th International Conference on Very Large Data Bases, VLDB Endowment, Tokyo, Japan, pp. 142-154.
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Depth-first search (DFS) is a fundamental and important algorithm in graph analysis. It is the basis of many graph algorithms such as computing strongly connected components, testing planarity, and detecting biconnected components. The result of a DFS is normally shown as a DFS-Tree. Given the frequent updates in many real-world graphs (e.g., social networks and communication networks), we study the problem of DFS-Tree maintenance in dynamic directed graphs. \other{In the literature, most works focus on the DFS-Tree maintenance problem in undirected graphs and directed acyclic graphs.} However, their methods cannot easily be applied in the case of general directed graphs. Motivated by this, we propose a framework and corresponding algorithms for both edge insertion and deletion in general directed graphs. We further give several optimizations to speed up the algorithms. We conduct extensive experiments on 12 real-world datasets to show the efficiency of our proposed algorithms.

Yao, Q, Lu, D & Lei, G 2019, 'Battery Impedance Measurement Using Fast Square Current Perturbation', 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC), IEEE, Singapore.
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This paper proposes a simple but accurate battery impedance measurement method. Unlike other complicated and expensive strategies, the proposed method only need a MOSFET and a resistor. A square current perturbation will be generated by the proposed circuitry and a voltage response across the tested battery can be obtained. Bases on the voltage and current signals, Discrete Fourier transform is used to calculate the impedance at fundamental and odd harmonic frequencies. The feasibility of the proposed method has been verified by simulation and experiments. The error of the proposed method is less than 5% at 80% SoC (state of charge) compared with the conventional small AC (alternating current) signal injection method.

Zhai, X, Zhou, W, Fei, G, Hu, H, Qu, Y & Hu, G 2020, 'Null Model and Community Structure in Heterogeneous Networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 151-163.
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© 2020, Springer Nature Switzerland AG. Finding different types of communities has become a research hot spot in network science. Plenty of the real-world systems containing different types of objects and relationships can be perfectly described as the heterogeneous networks. However, most of the current research on community detection is applied for the homogeneous networks, while there is no effective function to quantify the quality of the community structure in heterogeneous networks. In this paper, we first propose the null model with the same heterogeneous node degree distribution of the original heterogeneous networks. The probability of there being an edge between two nodes is given to build the modularity function of the heterogeneous networks. Based on our modularity function, a fast algorithm of community detection is proposed for the large scale heterogeneous networks. We use the algorithm to detect the communities in the real-world twitter event networks. The experimental results show that our method perform better than other exciting algorithms and demonstrate that the modularity function of the heterogeneous networks is an effective parameter that can be used to quantify the quality of the community structure in heterogeneous networks.

Zhang, G, Li, H & Wenger, F 2020, 'Object Detection and 3D Estimation via an FMCW Radar Using A Fully Convolutional Network', the 45th International Conference on Acoustics, Speech, and Signal Processing, Spain.
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Zhang, G, Niwa, K & Kleijn, WB 2020, 'Projected Weight Regularization To Improve Neural Network Generalization', the 45th International Conference on Acoustics, Speech, and Signal Processing, Spain.
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Zhang, J, Chen, B, Yu, S & Deng, H 2019, 'PEFL: A Privacy-Enhanced Federated Learning Scheme for Big Data Analytics', 2019 IEEE Global Communications Conference (GLOBECOM), 2019 IEEE Global Communications Conference, IEEE, Waikoloa, HI, USA.
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Federated learning has emerged as a promising solution for big data analytics, which jointly trains a global model across multiple mobile devices. However, participants' sensitive data information may be leaked to an untrusted server through uploaded gradient vectors. To address this problem, we propose a privacy-enhanced federated learning (PEFL) scheme to protect the gradients over an untrusted server. This is mainly enabled by encrypting participants' local gradients with Paillier homomorphic cryptosystem. In order to reduce the computation costs of the cryptosystem, we utilize the distributed selective stochastic gradient descent (DSSGD) method in the local training phase to achieve the distributed encryption. Moreover, the encrypted gradients can be further used for secure sum aggregation at the server side. In this way, the untrusted server can only learn the aggregated statistics for all the participants' updates, while each individual's private information will be well-protected. For the security analysis, we theoretically prove that our scheme is secure under several cryptographic hard problems. Exhaustive experimental results demonstrate that PEFL has low computation costs while reaching high accuracy in the settings of federated learning.

Zhang, M, Li, H & Su, S 2019, 'High dimensional Bayesian optimization via supervised dimension reduction', IJCAI International Joint Conference on Artificial Intelligence, IJCAI International Joint Conference on Artificial Intelligence, Macao, China, pp. 4292-4298.
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© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. Bayesian optimization (BO) has been broadly applied to computational expensive problems, but it is still challenging to extend BO to high dimensions. Existing works are usually under strict assumption of an additive or a linear embedding structure for objective functions. This paper directly introduces a supervised dimension reduction method, Sliced Inverse Regression (SIR), to high dimensional Bayesian optimization, which could effectively learn the intrinsic sub-structure of objective function during the optimization. Furthermore, a kernel trick is developed to reduce computational complexity and learn nonlinear subset of the unknowing function when applying SIR to extremely high dimensional BO. We present several computational benefits and derive theoretical regret bounds of our algorithm. Extensive experiments on synthetic examples and two real applications demonstrate the superiority of our algorithms for high dimensional Bayesian optimization.

Zhang, X, Lu, W, Zhang, G, Li, F & Wang, S 2020, 'Chinese Sentence Semantic Matching Based on Multi-Granularity Fusion Model', The 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), singapore.

Zhao, C, Wu, Y, Li, J, Nie, J, Meng, X & Yu, Y 2019, 'Sinusoidal pressure generator contraction section simulation analysis', Proceedings - 2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2019, 2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM), Shanghai, China, pp. 6-11.
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© 2019 IEEE. The key to dynamic pressure calibration is the generation of sinusoidal pressure signals. This paper uses a liquid-gas two-phase piston sinusoidal pressure generator for generating higher frequency pressure signals. The design of the contraction section has a large effect on the sinusoidal pressure generator. The flow field characteristics of the contraction section were simulated by Fluent simulation software. We analyze the frequency response, exit velocity distribution and axial pressure distribution of different contraction curves, then select the appropriate contraction curve and verify it by experiment. The simulation work in this paper provides a reference for the design of liquid-gas two-phase high-frequency sinusoidal pressure generator.

Zhong, Y, Huang, Y, Dutkiewicz, E, Wu, Q & Jiang, T 2020, 'Differential Evolution FPA-SVM for Target Classification in Foliage Environment Using Device-Free Sensing', Lecture Notes in Electrical Engineering, pp. 553-560.
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© 2020, Springer Nature Singapore Pte Ltd. Target classification in foliage environment is a challenging task in realistic due to the high-clutter background and unsettled weather. To detect a particular target, e.g., human, under such an environment, is an indispensable technique with significant application value. Traditional method such as computer vision techniques is hardly leveraged since the working condition is limited. Therefore, in this paper, we attempt to tackle human detection by using the radio frequency (RF) signal with a device-free sensing. To this end, we propose a differential evolution flower pollination algorithm support vector machine (DEFPA-SVM) approach to detect human among other targets, e.g., iron cupboard and wooden board. This task can be formally described as a target classification problem. In our experiment, the proposed DEFPA-SVM can effectively attain the best performance compared to other classical multi-target classification models and achieve a faster convergent speed than the traditional FPA-SVM.

Zhong, Z, Zheng, L, Kang, G, Li, S & Yang, Y 2020, 'Random Erasing Data Augmentation', New York.

Zhu, L, Sevilla-Lara, L, Tran, D, Feiszli, M, Yang, Y & Wang, H 2020, 'FASTER Recurrent Networks for Video Classification', The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI.

Zhu, M, Pan, P, Chen, W & Yang, Y 2020, 'EEMEFN: Low-Light Image Enhancement via Edge-Enhanced Multi-Exposure Fusion Network', New York.

Irga, P, Allan, P, Wilkinson, S, Torpy, F, Julia, Y, Douglas, A & Pettit, T 2020, 'It's Time to Grow Up', Hort Innovation.

Pearce, C, Guertler, M & Harvard, H 2020, 'The next wave (3D printing at UTS)', Manufacturers' Monthly.
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Media article about additive manufacturing/3D printing at UTS and possibilities of engaging with FEIT, Rapido and ProtoSpace.

Tomamichel, M, Bravyi, S, Gosset, D & Koenig, R 2020, 'An information-theoretic treatment of quantum dichotomies'.

Tomamichel, M, Bravyi, S, Gosset, D & Koenig, R 2020, 'Quantum advantage with noisy shallow circuits in 3D.'.