Publications

Angelini, M, Coupland, M, Schuck, S & Prescott, A 2020, Making school maths engaging: The Maths Inside Project, Springer, Dordrecht.

Burdon, S, Clegg, SR, Clay, J & Smithers, J 2020, Setting up Projects for Success, wsp/UTS, Sydney.

Hoang, DT, Niyato, D, Kim, DI, Huynh, NV & Gong, S 2020, Ambient Backscatter Communication Networks, Cambridge University Press.
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This is an essential tool for graduate students, researchers, engineers, developers, and entrepreneurs.

Irga, P, Allan, P, Wilkinson, S & Torpy, F 2020, Grow Up Guide: An Introduction to DIY Laneway Greening, 1st, UTS Shopfront: Centre of Social Justice and Inclusion, Sydney, Australia.
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Introduction Vertical Greening Systems (VGS) DIY friendliness Selecting your Green Wall Site Site Access Checklist for Appraising the Suitability of an Existing Wall Structure for Green Wall Retrofit Budget Installation Weight loading and External forces Waterproofing Maintenance Drainage and Irrigation Professional Consultants and Advice Plant Considerations Achievable DIY Designs Design Ratings References Disclaimers and Acknowledgment

Oberst, S, Halkon, B, Ji, J & Brown, T 2020, Vibration Engineering for a Sustainable Future: Active and Passive Noise and Vibration Control, Vol. 1, 1, Springer International Publishing, Springer Nature Switzerland AG.
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Oberst, S, Halkon, B, Ji, J & Brown, T 2020, Vibration Engineering for a Sustainable Future: Experiments, Materials and Signal Processing, Vol. 2, 1, Springer International Publishing, Springer Nature Switzerland AG.
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This volume presents the proceedings of the Asia-Pacific Vibration Conference (APVC) 2019, "Vibration Engineering for a Sustainable Future," emphasizing work devoted to numerical simulation and modelling. The APVC is one of the larger conferences held biannually with the intention to foster scientific and technical research collaboration among Asia-Pacific countries. The APVC provides a forum for researchers, practitioners, and students from, but not limited to, areas around the Asia-Pacific countries in a collegial and stimulating environment to present, discuss and disseminate recent advances and new findings on all aspects of vibration and noise, their control and utilization. All aspects of vibration, acoustics, vibration and noise control, vibration utilization, fault diagnosis and monitoring are appropriate for the conference, with the focus this year on the vibration aspects in dynamics and noise & vibration. This 18th edition of the APVC was held in November 2019 in Sydney, Australia. The previous seventeen conferences have been held in Japan (‘85, ’93, ‘07), Korea (’87, ’97, ‘13), China (’89, ’01, ’11, ‘17), Australia (’91, ‘03), Malaysia (’95, ‘05), Singapore (‘99), New Zealand (‘09) and Vietnam (‘15).

Oberst, S, Halkon, B, Ji, J & Brown, T 2020, Vibration Engineering for a Sustainable Future: Numerical and Analytical Methods to Study Dynamical Systems, Vol. 3, Springer.
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This volume presents the proceedings of the Asia-Pacific Vibration Conference (APVC) 2019, "Vibration Engineering for a Sustainable Future," emphasizing work devoted to experimental methods and verification.

van den Hoven, E & Loke, L 2020, Proceedings of the Fourteenth International Conference on Tangible, Embedded, and Embodied Interaction (TEI2020), Association for Computing Machinery, New York, NY, United States.
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Acampora, G, Pedrycz, W, Vasilakos, AV & Vitiello, A 2020, 'Preface', pp. vii-x.

Al Taee, A & Al-Jumaily, A 2020, 'Correction to: Electrogastrogram Based Medical Applications an Overview and Processing Frame Work' in Hybrid Intelligent Systems, Springer International Publishing, pp. C1-C1.
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Al-Doghman, F, Chaczko, Z & 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.

Alsawwaf, M & Chaczko, Z 2020, 'Multimodal Access Control: A Review of Emerging Mechanisms' in Klempous, R & Nikodem, J (eds), Smart Innovations in Engineering and Technology, Springer, Switzerland, pp. 183-200.
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Accurately identifying and authenticating a human without requiring proximity, yet with high reliability, is crucial for application areas such as surveillance, dynamic authentication, and proof of identity. While a significant research has been conducted in the direction of human authentication, achieving the authentication in an ambient manner, with high reliability, and security is still far from being perfect under the currently deployed methods. This is due to the requirement of a specific posture and approximation to the device in many approved and deployed methods such as fingerprint, facial recognition, and retinal scan. An ambient authentication system is highly dependent on biometric features that can accurately discriminate between one human and another. These biometric features should be insensitive to variation in posture, proximity, and aging. Extraction of a unique feature set that allows a human to be identified smoothly, even without proximity, under varying conditions is one of the main steps needed for an advancement in this field. In this paper, a review of current and emerging mechanism are provided. A proposed method will use a feature set that allows the system to achieve dynamic detection compared to the currently used technologies such as fingerprint, facial recognition and retinal scan. The proposed feature set is made up of a combination of facial minutiae and thermal contours which are extracted from the human face on-the-fly even without the subject coordination.

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

Barua, S & Braun, R 2020, 'A Markovian Approach to the Mobility Management for the D2D Communications in 5G Cellular Network System' in Smart Innovations in Engineering and Technology, Springer, Singapore, pp. 253-265.
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Device to Device communication, which is mostly known as D2D communication is considered an integral part of the next generation (5G) telecommunication network system. However, it is still a challenge how to manage the devices while they are in moving condition. Only a few papers highlighted this problem and yet to come up with a reliable solution. We already proposed a unique algorithm of mobility management for the D2D communication system in our previous papers. In this chapter, we will highlight and explain the mobility model mathematically and analytically along with the simulation of the Markovian model. Markov model, which is essentially a simplified approach of describing a system that occupies a discrete state at any point in time. We also make a bridge between our mobility algorithm and Markovian model.

Burdon, S, Kang, K & Mooney, G 2020, 'Decoding Success Factors of Innovation Culture' in Khosrow-Pour, MK-P (ed), Disruptive Technology: Concepts, Methodologies, Tools, and Applications, IGI Global, Hershey PA, USA 17033, pp. 477-490.
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The proliferation of entrepreneurship, technological and business innovations, emerging social trends and lifestyles, employment patterns, and other developments in the global context involve creative destruction that transcends geographic and political boundaries and economic sectors and industries. This creates a need for an interdisciplinary exploration of disruptive technologies, their impacts, and their implications for various stakeholders widely ranging from government agencies to major corporations to consumer groups and individuals. Disruptive Technology: Concepts, Methodologies, Tools, and Applications is a vital reference source that examines innovation, imitation, and creative destruction as critical factors and agents of socio-economic growth and progress in the context of emerging challenges and opportunities for business development and strategic advantage. Highlighting a range of topics such as IT innovation, business strategy, and sustainability, this multi-volume book is ideally designed for entrepreneurs, business executives, business professionals, academicians, and researchers interested in strategic decision making using innovations and competitiveness.

Burdon, S, Kang, K & Mooney, G 2020, 'Understanding the Key Attributes for a Successful Innovation Culture' in Khosrow-Pour, M, Clarke, S, Jennex, ME & Anttiroiko, A-V (eds), Disruptive Technology: Concepts, Methodologies, Tools, and Applications, IGI Global, Hershey PA, USA 17033, pp. 97-110.
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The proliferation of entrepreneurship, technological and business innovations, emerging social trends and lifestyles, employment patterns, and other developments in the global context involve creative destruction that transcends geographic and political boundaries and economic sectors and industries. This creates a need for an interdisciplinary exploration of disruptive technologies, their impacts, and their implications for various stakeholders widely ranging from government agencies to major corporations to consumer groups and individuals.

Castilla Rho, J, Holley, C & Castilla, JC 2020, 'Groundwater as a Common Pool Resource: Modelling, Management and the Complicity Ethic in a Non-Collective World' in Global Changes: Ethics, Politics and Environment in the Contemporary Technological World, Springer, Switzerland, pp. 89-109.
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Sustainable development of the natural resources that support our current standards of living is arguably one the biggest challenges of the Anthropocene. Institutions and policies alone however cannot guarantee that the right decisions are made. In this chapter, we argue that sound decisions must overcome the ethical dilemmas that experts face when encapsulating hypotheses of the real-world into numerical models. Using groundwater as an example, we show how the so-called complicity ethic may unfold during the process of designing management and policy interventions, and subsequently recommend eight guiding principles (a charter) that can be followed to reduce the likelihood that this complicity ethic takes place. We then introduce The Collaborative Pathway—a mediated modelling activity that synergistically blends the eight guiding principles of our ethics charter into a practical decision-making process. This approach is designed to foster community engagement, to improve the way sectoral risks and trade-offs are evaluated, and to help stakeholders understand what might drive a particular sector towards best- and worst-case outcomes. If done right and with the right tools and strategies, The Collaborative Pathway can become a useful framework to encode ethics, resilience, and sustainability in our decisions relating to the development and protection of any common-pool resource that maintains our humanity.

Cetindamar Kozanoglu, D, Lammers, T & Sick, N 2020, 'Understanding The Relationship Between Smart Cities And Entrepreneurial Ecosystems: The Case Of Sydney' in Biloria, N (ed), Data-driven Multivalence in the Built Environment, Springer, Switzerland, pp. 301-315.
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Smart city literature is overdrawn with discussions on public services such as transportation while there is a need to broaden the analysis to understand the very rich dynamics of cities. In this chapter, the goal is to focus on the rise of technology-based entrepreneurs in cities who are creating emerging digital technologies. Cities have been a popular unit of analysis for technological development and economic activities due to their high dependency on immediate local context factors. Nowadays, transformation of cities into “smart” have increased their role further – both for economic value and for technological growth. This paper aims to expand the smart city concept to an ecosystem approach where cities become hubs of digital technologies. By combining the previous literature on entrepreneurship and digital technologies within a particular urban context, this paper discusses how smart cities could be a solid base to build digital entrepreneurship ecosystems for sustainable, liveable and competitive cities. In particular, the paper provides a case study for Sydney by illustrating the interactions between smart cities and digital entrepreneurship ecosystems in practice. The chapter ends with a summary of findings and implications for both policy makers and digital entrepreneurs.

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, Chiu, C & Yu, Z 2020, 'Assessment of a Multi-Agent RFID Platform for Industrial Logistic Operations' in Klempous, R & Nikodem, J (eds), Smart Innovations in Engineering and Technology: Intelligent Engineering and Informatics, Springer Nature, Cham, Switzerland, pp. 166-182.
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The paper examines the experimental implementation of multi-agent, omni-dimensional RFID technology for logistical and industrial operations. The aim of the research is to demonstrate the processes taken to test RFID under real-world industrial scenarios using a multi-agent analytical approach. The results obtained demonstrate the level of accuracy for an omni-directional versus agent-based optimized analytical approach, that is essential in conveying the performance in industrial logistic setups. Further work into the positioning and localization of multidimensional RFID inventory scanning for commercial setups is considered for future investigation.

Chaczko, Z, Harssh, S & Alsawwaf, M 2020, 'An Overview of Multi-layer Security System Using Biometrics' in Klempous, R & Nikodem, J (eds), Smart Innovations in Engineering and Technology, Springer, Singapore, pp. 219-231.
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After years of research on biometrics, still the authentication and verification of a human being is at risk. Many challenges have been faced by the system to authenticate a human on the basis of their biometric trait. The design of this paper is such that it focuses on three main biometric techniques to extract the various features of a human body contour for authentication and verification purposes. This paper presents an approach towards voice recognition patterns, face geometry patterns and gait pattern analysis to increase the accuracy and precision of the system.

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 ACMSM25, Springer, Singapore, 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, 'Surrogate Model-Driven Evolutionary Algorithms: Theory and Applications' in Evolution in Action: Past, Present and Future, Springer International Publishing, pp. 435-451.
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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.

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

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, Springer, Singapore, pp. 269-276.
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High Performance Concrete (HPC), particularly high strength concrete mixes (60–100 MPa) containing high cementitious content and low w/b ratios (0.40–0.25) is used for some precast elements. Ultra High Performance Concrete (UHPC) up to 200 MPa containing very high cementitious content and very low w/b ratios (0.25–0.17) is also used for precast and prestressed elements in buildings and bridges. Shrinkage characteristics of HPC and UHPC differ considerably from those of conventional concrete. Due to their high cementitious content and low w/b ratios, the drying shrinkage component is significantly smaller when compared with the autogenous shrinkage component. HPC and UHPC elements, when steam-cured, undergo majority of their shrinkage within a few days of casting. In this paper, mechanisms of autogenous shrinkage of HPC and UHPC at early ages are compared under different curing conditions. Contribution of autogenous shrinkage and drying shrinkage components on the total shrinkage is discussed. At very early ages, plastic shrinkage and plastic settlement cracking and factors affecting them for HPC and UHPC are also outlined.

Guo, W, Khan, MA, Ngo, HH, Johir, MAH, Nghiem, LD & Ni, B-J 2020, 'Anaerobic membrane bioreactors—An introduction' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 1-24.
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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, pp. 411-431.
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Compliance and enforcement is a major issue for groundwater management. Yet it remains untheorised and underexamined. This chapter drills down into Australian compliance and enforcement efforts, which have been on a significant reform journey over the last two decades, oscillating between being an under resourced, low priority water reform task, to taking primacy within national and state water reform frameworks. The chapter begins by developing an analytical framework for studying groundwater compliance and enforcement. Using a case study of the state of New South Wales, the chapter examines the experiences of a government regulator and the compliance and enforcement experiences of water users. It concludes with a summary of challenges and policy implications for groundwater compliance and enforcement regimes.

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.

Irga, P, Pettit, T, Shagol, C, Kim, KJ & Torpy, F 2020, 'Phytosystems implementation: examples of application in practice' in Soreanu, G & Dumont, E (eds), From Biofiltration to Promising Options in Gaseous Fluxes Biotreatment Recent Developments, New Trends, Advances, and Opportunities, Elsevier, The Netherlands, pp. 283-289.
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Many developing and developed countries are facing the challenges of high levels of air pollution. The use of techniques to control air pollution have increasing value in contemporary communities. Active phytosystem biofiltration is an emergent technology. There are currently few botanical indoor air filtration systems that are available commercially. Although many studies and technological developments have been made on the design and operations of polluted air treatment using phytosystem processes, a comprehensive examination of these active phytosystem case studies is lacking. Examples of the current in situ implementations of phytosystem technology is presented here, including laboratory prototypes that demonstrate significant air pollution removal potential, and systems that have been proven in practice. The contents will cover both the design and the stated pollutant removal efficiency of the phytosystems described in the existing scientific literature, as well as outlining some of the existing commercial botanical biofilter phytosystems on the market.

Irga, P, Shagol, C, Kim, KJ, Pettit, T & Torpy, F 2020, 'Plant–microbe interaction within phytosystems used for air treatment' in Soreanu, G & Dumont, E (eds), From Biofiltration to Promising Options in Gaseous Fluxes Biotreatment Recent Developments, New Trends, Advances, and Opportunities, Elsevier, The Netherlands, pp. 245-262.
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The importance of plants in indoor spaces has increased in the recent decades not only for their esthetic value but for their benefits in indoor air quality improvement. The major contribution of the air purification effect provided by botanical systems is most generally attributed to microorganisms associated with the plants. In this chapter, the synergistic plant–microbe interactions for air phytoremediation, particularly of volatile organic compound removal are presented. The different functions and contributions of phyllospheric, rhizospheric, and endophytic microorganisms on botanical air purification are discussed. The manifold unknown aspects of the microbiome of indoor plants will be explored. Considerations related to harmful microorganisms associated with indoor plants also are addressed. Further, opportunities for enhancing air phytoremediation by stimulating the indoor plant–microbe interaction from simple and practical steps to advanced genetic manipulations.

Irga, PJ, Pettit, T, Shagol, CC, Kim, KJ & Torpy, FR 2020, 'Phytosystems implementation: examples of application in practice' in From Biofiltration to Promising Options in Gaseous Fluxes Biotreatment, Elsevier, pp. 283-299.
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Irga, PJ, Shagol, CC, Kim, KJ, Pettit, T & Torpy, FR 2020, 'Plant–microbe interaction within phytosystems used for air treatment' in From Biofiltration to Promising Options in Gaseous Fluxes Biotreatment, Elsevier, pp. 245-262.
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Jain, LC, Balas, VE, Tsihrintzis, GA & Sharma, DK 2020, 'Preface', p. xiii.

Kale, A & Chaczko, Z 2020, 'Learning Classifiers for Multimodal Image Detection' in Klempous, R & Nikodem, J (eds), Asia-Pacific Conference on Computer Assisted and System Engineering, Springer Nature, Cham, Switzerland, pp. 266-280.
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Learning Classifiers are essential analysis tools in today's era due to the rapid expansion in data-intensive applications. Every type of the classifier has own distinct architectural properties which allow to apply them in various application-specific requirements. Here, experimental work is performed to analyse the performance and accuracy of most commonly used learning classifiers applied within the scope of multimodal image classification problem space. The purpose of this study is to investigate the usefulness of the learning-classifiers with the multimodal datasets which are in many cases have the curse of dimensionality and may contain a very high volume of noise. Validation is conducted over multimodal datasets with the proposed classification algorithms operating in a parallel manner. Finally, results are discussed to explore possible ways to apply every learning classifier in multi-modal image classification applications.

Kim, KJ, Shagol, C, Torpy, F, Pettit, T & Irga, P 2020, 'Plant physiological mechanisms of air treatment' in Soreanu, G & Dumont, E (eds), From Biofiltration to Promising Options in Gaseous Fluxes Biotreatment Recent Developments, New Trends, Advances, and Opportunities, Elsevier, The Netherlands, pp. 219-244.
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With people in urban areas spending most of their time indoors, methods to improve indoor air quality (IAQ) have recently emerged as an important research field. The role of plants for air purification within interior spaces has thus become a developing branch within the field of phytoremediation. This chapter presents mechanisms by which plant systems improve IAQ, particularly in regards to volatile organic compound (VOCs), particulate matter (PM), and CO2 removal. Metabolic pathways whereby VOCs are degraded in plant cells are presented. Factors affecting different plant processes and therefore mechanisms of removal are discussed. The role and contribution to botanical air purification of plant-associated microorganisms, a crucial but often neglected driver of air purification, is also highlighted. Further, plant physiological aspects that influence how plants capture and retain PM are presented. The ability of plants to remove CO2 and regulate relative humidity and temperature in interior spaces is also examined. Some aspects highlighting the potential of plants in the removal of other relevant gases such as NO2 and SO2 are additionally addressed. Finally, advancements in improving plant processes for enhanced air purification capacity are explored, specifically at the molecular level.

Kim, KJ, Shagol, CC, Torpy, FR, Pettit, T & Irga, PJ 2020, 'Plant physiological mechanisms of air treatment' in From Biofiltration to Promising Options in Gaseous Fluxes Biotreatment, Elsevier, pp. 219-244.
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Lammers, T, Borkert, M & Sick, N 2020, 'Unternehmertum in der digitalen Stadt – Welche Rolle spielt die digitale Gründerszene für die Wettbewerbsfähigkeit von Smart Cities?' in Hoelzle, K, TIberius, V & Surrey, H (eds), Perspektiven des Entrepreneurships - Unternehmerische Konzepte zwischen Theorie und Praxis, Schäffer-Poeschel Verlag, Germany, pp. 461-468.
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The digital city of the future, the “Smart City”, is becoming a beacon of hope to meet the challenges of a growing world population. Cities have therefore initiated digitization strategies in order to use the potential of new technologies for themselves. In parallel A technology-driven start-up scene is emerging in cities around the world, which has to be established Companies can potentially compete. In this article we ask ourselves which criteria are used to measure the success of a digital city and to what extent the digital start-up scene is taken into account. An analysis of existing indices is carried out which are located in the context of the competitiveness of digital cities. The total of 352 evaluation criteria used cover 249 different evaluation aspects. About different Indices recurring criteria for the analysis of competitiveness were found mainly for the Features connectivity, education, transportation infrastructure, and business environment. Despite the apparently high relevance of economic aspects in the evaluation, the digital one The start-up scene as a sub-area has only rarely been considered. A direct mention of The start-up scene can only be found in two of the 26 indices examined. A specific analysis of tech start-ups is not carried out in any of the indices examined. Before the Background of the outstanding potential of digital technologies and thus of tech start-ups, This article therefore provides both argumentation and first steps, the digital start-up scene to determine the competitiveness of cities.

Lawson, J, Zucker, I & Hadgraft, R 2020, 'Students Take over as Curriculum Co-designers and Facilitators: A Case Study from Engineering' in Applied Degree Education and the Future of Work, Springer, Singapore, pp. 181-194.
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© 2020, Springer Nature Singapore Pte Ltd. The Faculty of Engineering and Information Technology (FEIT) at the University of Technology Sydney is taking courageous and creative steps in its teaching and learning. FEIT is developing landmark innovations to transform the curriculum to better engage with students and respond to the demands of a rapidly changing future workforce. For example, drawing inspiration from design studios, many programs now contain engineering studios to engage students in authentic and complex problems drawn from professional practice. This chapter reports on the first iteration of ‘Summer Studios’, an innovative subject that involved senior undergraduate students designing and facilitating studios for fellow students.

Li, JT, Zhu, XQ & Samali, B 2020, 'Bridge Operational Modal Identification Using Sparse Blind Source Separation' in ACMSM25, Springer, Singapore, 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 Advances in Dynamics of Vehicles on Roads and Tracks, Springer, Singapore, pp. 1169-1177.
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© 2020, Springer Nature Switzerland AG. Electric vehicles are commercially becoming more popular due to their high efficiency and low emission capabilities. The electrification of the powertrain allows for more efficient layouts compared to conventional vehicles, such as the use of in-wheel motors. These motors present new opportunities when it comes to vehicle stability control as they can be driven and controlled individually. This paper investigates the vehicle handling dynamics of a lightweight solar-electric vehicle in comparison to a standard sized commercial vehicle with direct yaw moment control. The research is based on the Australian Technology Networks’ Bridgestone World Solar Challenge vehicle that will participate in the 2019 competition. The vehicle is rear wheel drive, with room for a driver and one passenger. To allow for space for the solar arrays the vehicle is considerably large compared to its weight. Conventional yaw controls commonly use yaw and/or sideslip as a control variable, this paper employs dynamic curvature as a control variable. The main goal to improve the yaw rate and sideslip as a result of improving the dynamic curvature variable was achieved. The dynamic properties and control response of the two vehicles are studied via co-simulation of Simcenter Amesim™ and MATLAB Simulink.

Mataei, B, Nejad, FM, Zakeri, H & Gandomi, AH 2020, 'Computational intelligence for modeling of pavement surface characteristics' in New Materials in Civil Engineering, Elsevier, pp. 65-77.
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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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Mohebali, B, Tahmassebi, A, Meyer-Baese, A & Gandomi, AH 2020, 'Probabilistic neural networks: A brief overview of theory, implementation, and application' in Handbook of Probabilistic Models, Elsevier, The Netherlands, pp. 347-367.
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© 2020 Elsevier Inc. All rights reserved. Probabilistic neural networks (PNNs) offer a scalable alternative to the conventional back-propagation neural networks in classification problems without the need for massive forward and backward calculations that is associated with the ordinary neural networks. In addition, they can work with smaller sets of training data. However, this advantage may come at a cost of requiring large amounts of memory as the training data get larger. This chapter takes a look at the fundamental mathematics behind the modern PNNs, their application, and approaches that address some practical issues that come with them.

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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Nguyen, LN, Commault, AS, Sutherland, D, Semblante, GU, Oh, S & Nghiem, LD 2020, 'Contemporary Methods for Removal of Nonsteroidal Anti-inflammatory Drugs in Water Reclamations' in The Handbook of Environmental Chemistry.
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Global water quantity and quality are anticipated to decrease in the coming decades, as a result of both increasing global populations and the effects of climate change. Reusing and recycling water is a key part of reducing the pressure on our existing water supplies and the aquatic environment. However, the occurrence of nonsteroidal anti-inflammatory drugs (NSAIDs) in secondary, and in some tertiary, treated effluents- and sewage-impacted water bodies is one of the major obstacles for the implementation of water reuse. For several decades, NSAIDs have been extensively used for therapeutic purposes in both humans and domestic livestock. The negative effects of NSAIDs on aquatic biota are just beginning to be realized. Currently, intensive treatments are required to remove effectively NSAIDs from recycled treated effluent in order to minimize or eliminate risks to human health and aquatic environment. In this chapter, we focus the discussion on contemporary methods for NSAID removal including biological, physical, chemical, and combined process that may provide a more effective and efficient alternative.

Nguyen, QD, Khan, M, Castel, A & Kim, T 2020, 'Mitigation of Alkali–Silica Reaction in Limestone Calcined Clay Cement-Based Mortar' in Calcined Clays for Sustainable Concrete, Springer, Singapore, pp. 665-672.
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© RILEM 2020. This work aims to assess the potential of limestone calcined clay cement (LC3) in mitigating alkali–silica reaction (ASR). Two General Purpose (GP) cement substitution rates using calcined clay and limestone were tested: 20 and 30% with a ratio 2:1 by mass of calcined clay and limestone.Silica ions dissolution of rhyodacite rock used as reactive aggregate, expansion of mortar specimens using the accelerated mortar bar test (AMBT) and the initial chemical composition of the mortars pore solution were investigated. The combination of calcined clay and limestone significantly reduced the expansion of mortar bars compared to reference mortar using 100% GP cement. The reduction in mortar bar expansion correlates well with the reduction in alkalis ions concentration and pH of the pore solution of LC3 mortars compared to reference mortar. 30% OPC substituted by calcined clay and limestone seems to be able to mitigate the risk of ASR in concrete using alkali-reactive aggregate.

Nimbalkar, S, Punetha, P & Kaewunruen, S 2020, 'Performance Improvement of Ballasted Railway Tracks Using Geocells: Present State of the Art' in Geocells, Springer Singapore, pp. 277-318.
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Pande, S, Roobavannan, M, Kandasamy, J, Sivapalan, M, Hombing, D, Lyu, H & Rietveld, L 2020, 'A Socio-Hydrological Perspective on the Economics of Water Resources Development and Management' in Oxford Research Encyclopedia of Environmental Science, Oxford University Press, UK.
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Water quantity and quality crises are emerging everywhere, and other crises of a similar nature are emerging at several locations. In spite of a long history of investing in sustainable solutions for environmental preservation and improved water supply, these phenomena continue to emerge, with serious economic consequences. Water footprint studies have found it hard to change culture, that is, values, beliefs, and norms, about water use in economic production. Consumption of water-intensive products such as livestock is seen as one main reason behind our degrading environment. Culture of water use is indeed one key challenge to water resource economics and development. Based on a review of socio-hydrology and of societies going all the way back to ancient civilizations, a narrative is developed to argue that population growth, migration, technology, and institutions characterize co-evolution in any water-dependent society (i.e., a society in a water-stressed environment). Culture is proposed as an emergent property of such dynamics, with institutions being the substance of culture. Inclusive institutions, strong diversified economies, and resilient societies go hand in hand and emerge alongside the culture of water use. Inclusive institutions, in contrast to extractive institutions, are the ones where no small group of agents is able to extract all the surplus from available resources at the cost of many. Just as values and norms are informed by changing conditions resulting from population and economic growth and climate, so too are economic, technological, and institutional changes shaped by prevailing culture. However, these feedbacks occur at different scales—cultural change being slower than economic development, often leading to “lock-ins” of decisions that are conditioned by prevailing culture. Evidence-based arguments are presented, which suggest that any attempt at water policy that ignores the key role that culture plays will struggle to be effective. I...

Pardeshi, V & Nimbalkar, S 2020, 'Visual Inspection Based Maintenance Strategy on Unsealed Road Network in Australia' in Sustainable Issues in Transportation Engineering, Springer International Publishing, pp. 92-103.
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Pardeshi, V, Nimbalkar, S & Khabbaz, H 2020, 'Theoretical and experimental assessment of gravel loss on unsealed roads in Australia' in Sustainable Civil Engineering Practices, Springer, Singapore, pp. 21-29.
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© Springer Nature Singapore Pte Ltd 2020. Gravel loss is one of the major issues on unsealed roads which attract large annual maintenance. The continual process of gravel loss makes roads environmentally unsustainable. The unsealed road management faces several challenges which are: inaccuracies in behavior prediction, numerous data gathering requirements and exposure in the level of service and maintenance practices. To address these issues, the modified gravel is used on the unsealed road network in Australia. A case study is conducted to assess the gravel loss of unsealed roads. To monitor gravel loss on good quality, gravel monitoring stations are installed. Geographical information system (GIS) is used for finalizing the gravel monitoring station locations. Roughometer is used for surface assessment longitudinally. Roughness will be measured over two years at an interval of every three months. This paper discusses the gravel loss monitoring approaches, data analyses and improved material specification for gravel.

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 ...

Pettit, T, Irga, PJ, Torpy, FR, Shagol, CC & Kim, KJ 2020, 'Technological aspects of the removal of air pollutants by phytosystems' in From Biofiltration to Promising Options in Gaseous Fluxes Biotreatment, Elsevier, The Netherlands, pp. 263-281.
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To overcome the limitation of diffusion associated with the phytoremediation by potted plants, active botanical biofilters have been developed. This technology has progressed to now incorporate more than just horticultural and biotechnological tools for improving indoor air quality, and may offer a new means to reduce the concentration of indoor air pollution. However, the technological aspects related to the removal of air pollutants by phytosystems still need to be explored, including the airflow rate and flow direction, moisture and irrigation regimes, the botanical component, and growth medium selection. The comprehensive air cleaning abilities and the potential for functional reductions in the concentrations of several air pollutants, suggest that active phytosystems may become implemented at a greater rate than that at which knowledge is accumulated. To fully realize the value of botanical air biofiltration technology, considerable technical development is still required.

Phoumin, H, Kimura, F & Sandu, S 2020, 'Household-level analysis of the impacts of electricity consumption on welfare and the environment in Cambodia' in Energy Sustainability and Development in ASEAN and East Asia, Routledge, pp. 26-45.
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Rahman, MM, Mahlia, TMI & Uddin, MN 2020, 'Energy Efficiency Analysis in Building Walls in Tropical Climate Using Thermal Insulation System' in Encyclopedia of Renewable and Sustainable Materials, Elsevier, The Netherlands, pp. 255-261.
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Thermal comfort is essential in a tropical climate and air conditioning system in buildings offers better thermal comfort compared to other systems. But the air conditioning system in the building consumes a lot of energy to fulfil the thermal comfort needs in a tropical climate. An efficient thermal insulation system in the building can increase the energy efficiency by lowering the consumption for cooling systems. Thus, this research aims to study the effectiveness of different thermal insulation material on energy saving for building walls in Malaysia. The energy savings and the life-cycle cost of using ten thermal insulation materials in Malaysian building walls have been evaluated. In addition, the energy savings due to the air gap in the wall construction has been investigated for the selected insulation material. The findings indicated that the life-cycle cost and energy savings are varying from 73% to 85%/m2 wall and 85% to 92%/m2 wall respectively depending on the optimal thickness of insulation material. Among the insulation materials, styropor insulation showed better economic benefits. It was also found that 6 cm gap in the wall construction can save additional 0.64%/m2 wall life cycle cost by improving the insulation effect by retarding the heat flow into the building.

Rakhi Ajayan, A, Al-Doghman, F & Chaczko, Z 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. 1-4-118.
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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.

Ramezani, F, Naderpour, M, Taheri, J, Romanous, J & Zomaya, AY 2020, 'Task Scheduling in Cloud Environments: A Survey on Population-based Evolutionary Algorithms' in Gandomi, AH, Emrouznejad, A, Jamshidi, MM, Deb, K & Rahimi, I (eds), Evolutionary Computation in Scheduling, John Wiley & Sons, USA, pp. 213-255.
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In distributed heterogeneous computing environments, such as cloud computing, hundreds of online users may individually or collectively submit thousands of jobs anytime and anywhere to dynamic resources. Given the large number of arriving tasks resulting from dividing these bulk‐submitted jobs, and considering the amount of data being used in these tasks, optimal scheduling can become a serious problem in cloud environments – where stochastic jobs contend for limited computer and storage resources. Task scheduling optimization plays a crucial role in minimizing service response times and costs while also improving quality of service (QoS) but, in a dynamic cloud environment, this is a non‐linear multi‐objective NP‐hard problem. The main concerns with dynamic scheduling are how to adapt to the inherent uncertainty and how to cope with conflicting objectives, such as minimizing task transfer times, task execution costs, power consumption, task queue lengths, and response times. Evolutionary algorithms can solve such problems by significantly reducing the complexity of the search space to ensure acceptable runtimes for the scheduling algorithm. This chapter reviews the recent literature on models that use evolutionary algorithms, such as particle swarm optimization (PSO) and genetic algorithms (GA), to optimize task scheduling in cloud environments. The analysis is based on the type and number of objectives, as well as the efficiency and reliability of the developed models and applied algorithms....

Rudorf, M, Oberst, S, Stender, M & Hoffmann, N 2020, 'Bifurcation analysis of a doubly curved thin shell considering inertial effects' in Vibration Engineering for a Sustainable Future: Numerical and Analytical Methods to Study Dynamical Systems, Springer International Publishing, Switzerland.
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Thin-elastic structures can be found in nature as well as in many technical applications, including plant material (leaves) or insect appendages (wings) and aircraft outer bodies, optical mirrors and membranes, solar panels or satellite antennas. Numerical modelling of those structures is commonly conducted using shell elements. Especially doubly curved shells have found much attention due to their applicability in thin shell or sandwich structures used in the automotive, aerospace and space industry. In the design process it is generally assumed that these structures behave linearly, however, considering their curvature and how thin they are, large deflections easily become an issue as shown experimentally. Yet, the numerical modelling does conventionally assume that inertia e ffects can be neglected. Here we derive the equations of motion of a simply supported configuration of a doubly curved shell with 9 degrees of freedom with and without inertial coupling terms. We show by conducting a bifurcation analysis that the additional inertia e ects cannot be neglected and that care has to be taken when structures as such are being employed as appendages on real-life satellites.

Samanta, S, Singhar, SS, Gandomi, AH, Ramasubbareddy, S & Sankar, S 2020, 'A WiVi Based IoT Framework for Detection of Human Trafficking Victims Kept in Hideouts' in Lecture Notes in Computer Science, Springer International Publishing, pp. 96-107.
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Sandu, S, Yang, M, Phoumin, H & Aghdam, RF 2020, 'Energy sustainability in Asia' in Energy Sustainability and Development in ASEAN and East Asia, Routledge, pp. 147-176.
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Saputra, Y, Dinh, H, Nguyen, D, Dutkiewicz, E & Niyato, D 2020, 'Wireless Edge Caching for Mobile Social Networks' in Vu, T, Chatzinotas, S, Baştuğ, E & Quek, TQS (eds), Wireless Edge CachingModeling, Analysis, and Optimization, Cambridge University Press, UK, pp. 146-172.

Shakor, P, Nejadi, S & Gowripalan, N 2020, 'Effect of Heat Curing and E6-Glass Fibre Reinforcement Addition on Powder-Based 3DP Cement Mortar' in P. Bos, F, S. Lucas, S, J.M. Wolfs, R & A.M. Salet, T (eds), Second RILEM International Conference on Concrete and Digital Fabrication, Springer, Cham, EindhovenThe Netherlands, pp. 508-515.
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Powder-based 3D printing is one of the most promising techniques in additive manufacturing. The speed, resolution of the printed part and complicated geometries are important features in this technique and these features are usually not experienced in traditional construction techniques. This study aims to discuss the concept of using a custom-made powder (cement mortar) instead of a commercial (gypsum) powder in 3DP. Therefore, broad investigations are required to study and understand the details of the cement mortar 3D printed scaffold. This paper discovers the effect of heat-curing and addition of E6-glass fibres as reinforcement for the printed specimens. The results show that the mechanical properties of the cement mortar are improved through a heat-curing procedure. Addition of fibre reinforcement enhances powder flowability consistency and surface roughness throughout. Experiments are conducted on printed 50 mm cubic specimens, cured in an oven at different temperatures. The optimum heat-curing temperature is found to be 80 °C to achieve the highest compressive strength in cement mortar specimens. Detailed 3D laser scanning of the printed cement mortar specimens is conducted. The 3D laser scanning results found rougher surface in cement mortar when it is not reinforced with glass fibre.

Sharma, D, Sandu, S & Yang, M 2020, 'A Methodological Framework for Assessing Macroeconomic Impacts of Energy Security Improvements in Asia' in Soytas, U & Sari, R (eds), Routledge Handbook of Energy Economics, Taylor & Francis, Great Britain, pp. 446-457.
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The Asian countries have experienced significant economic growth since the late 20th century, and these trends are expected to continue in the years to come (OECD 2018). This growth will however bring with it an unprecedented demand for energy. According to the International Energy Agency (IEA), energy demand in the Asia-Pacific region is projected to increase by 1.8% per year over the period 2016–2040 – faster than the projected world average annual growth rate of 1.4% over the period. This will make the Asia-Pacific region the world’s largest energy consumer by 2040, accounting for approximately 60% of total energy consumption (IEA 2017). Such rapid growth in energy demand could lead to a tightening of energy markets, growing import dependence, and rising price volatility, thus threatening the security of energy supply and socioeconomic prosperity of the region. The intensity of this threat could be further exacerbated by the impending threat of climate change, caused by ever-increasing rates of greenhouse gases (GHG) emissions from excessive use of fossil fuels. Search is therefore on for ways to redress the energy security challenge in the region. Among various policy options under consideration to redress the challenge, a broad consensus seems to have emerged on two options, namely, diversification of fuel-mix, with reduced reliance on fossil fuels and their replacement with low-emission sources (such as, wind, solar and nuclear); and energy efficiency improvements. Several studies have been conducted to analyse the impacts of these policy options. Most of these studies have however tended to be micro-assessments, focusing on the immediate impact of specific policy measures on energy security. For example, some studies analyze measures (such as feed-in tariffs, tax exemptions, and public grants) to promote renewable energy and its impacts on fossil fuel consumption, energy prices, and emissions (Kumar 2016; Kumar, Shrestha & Abdul Salam 2013; Mofijur et al. ...

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.

Shdifat, B, Cetindamar Kozanoglu, D & Erfani, S 2020, 'Achieving the Triple Bottom Line through Big Data Analytics' in Park, SH, Gonzalez-Perez, MA & Floriani, DE (eds), The Palgrave Handbook of Corporate Sustainability in the Digital Era, Palgrave Macmillan, pp. 1-27.
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This handbook addresses the intersection between corporate sustainability and digital transformation. It analyzes the challenges and transformations required to be able to have sustainable businesses with a future orientation.

Shi, J, Brown, R, Yu, Z, Chu, L, Shi, Q & Xu, Z 2020, 'Tracking of Multiple Moving Objects Using Two Dimensional Beamforming Based on Crossed Array' in Rudas, IJ & Szakál, A (eds), Smart Innovations in Engineering and Technology: Intelligent Engineering and Informatics, Springer International Publishing, Singapore, pp. 232-242.
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Moving objects tracking is very important in intelligent systems. Two dimensional Beamforming technologies have been applied to create images, with additional information such as ranges and radial velocities. Most radars use two dimensional full array to sense the target. To achieve high resolution, with the increasing sensor number, the sampling and computation cost are large. In order to reduce the overall cost, some methods based on sparse arrays have been proposed to create radar images. The sensor number can be reduced without losing much information. In this paper, a two dimensional beamforming method based on low cost crossed array is proposed. One linear phased array transmitting beamforming is used to steer the angle of beams in one dimension while the other orthogonal linear phased array receiving beamforming is used to detect the angle of arrival in the other direction. The sampled one dimensional beamforming image combines with time of steering direction to create a two dimensional spectrum. The proposed algorithm is simulated and the results of the system is analyzed

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.

Tan, S, Chan, NJA, Collins, J, Fu, Q & Qiao, GG 2020, 'New Approaches towards the Design of Tough Amphiphilic Polymeric Co-networks' in Amphiphilic Polymer Co-networks: Synthesis, Properties, Modelling and Applications, Royal Society for Chemistry, UK, pp. 277-308.
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© The Royal Society of Chemistry 2020. Since the 1960s, polymer network hydrogels have been used for various applications. However, these applications have been restricted to non-load-bearing ones, due to the inherent mechanical weakness of common hydrogels. This weakness is attributed to the irregular crosslinking density, the high proportion of singly-attached (dangling and, therefore, elastically inactive) chains and the broadly varying distance between crosslinking points. In recent years, ground-breaking studies focusing on the development of novel approaches to fabricate near-ideal polymer network hydrogels with excellent mechanical properties have been developed. In this chapter, these new approaches will be outlined, with specific interest in amphiphilic polymer co-networks (APCNs), i.e. networks containing both hydrophilic and hydrophobic domains. The ability to generate these near-ideal networks, which often also possess 'smart' stimuli-responsive properties, would allow APCNs to be used in a wide range of advanced applications, including soft robotics, biomaterials and materials science. These new synthetic methodologies will be described in this chapter and will be separated into either fundamentally altering the network architecture and/or by employing facile and orthogonal coupling chemistries.

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.

Tekmen-Araci, Y, Francis, B, Hadgraft, R, Zucker, I, Lawson, J & Jarman, R 2020, 'Changing the Mindset of Engineering Educators to Teach Design Studios' in Lecture Notes in Educational Technology, pp. 169-179.
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© 2020, Springer Nature Singapore Pte Ltd. The Faculty of Engineering and Information Technology at the University of Technology Sydney recruited and established a ‘Learning and Teaching Design’ team. One of the primary aims of this team was to change the mindset of engineering academics to deliver a curriculum that produced More Innovative Design-Abled Students and Staff (MIDAS). To model what an inspiring pedagogy might look like, a set of short, intensive, summer school offerings were designed and delivered. These ‘MIDAS Summer Studios’ aimed to strategically shift the culture of education and to revolutionise how students learn engineering and information technology. In order to confirm that each of the MIDAS Summer Studios and their studio leaders delivered a calibrated student learning experience, a series of training workshops were delivered to ensure the language used by studio facilitators remained consistent. During these workshops, the participants were encouraged to adopt some values and principles. This study reports how these values have been effective in transforming engineering education in one Australian university.

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 Sustainable 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 ACMSM25 (Lecture Notes in Civil Engineering), Springer, Singapore, 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.

Xu, H & Ji, J 2020, 'Creation of Neimark-Sacker Bifurcation for a Three-Degree-of-Freedom Vibro-Impact System with Clearances' in Lacarbonara, W, Balachandran, B, Ma, J, Tenreiro Machado, J & Stepan, G (eds), Nonlinear Dynamics of Structures, Systems and Devices Proceedings of the First International Nonlinear Dynamics Conference (NODYCON 2019), Volume I, Springer Nature, pp. 107-115.
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The impulsive state feedback control is used to create Neimark-Sacker bifurcation for a three-degree-of-freedom vibro-impact system with clearances. The linear control gains are determined to guarantee the existence of Neimark-Sacker bifurcation by using the explicit criteria of Neimark-Sacker bifurcation without directly using eigenvalues. Differently, the nonlinear control gains are selected to determine the direction and stability of Neimark-Sacker bifurcation by using center manifold reduction theory and normal form approach. The amplitude of the created invariant cycle from Neimark-Sacker bifurcation is analytically obtained to achieve the control of the amplitude by selecting appropriate nonlinear control gains. Numerical experiments are provided to show the effectiveness of the proposed control method.

Yu, Z, Chaczko, Z & Shi, J 2020, 'Low-Cost Wireless Micro-Electro-Mechanical-Systems Accelerometers Linear Sensor Model' in Klempous, R & Nikodem, J (eds), Cham, Switzerland, Springer Nature, Cham, Switzerland, pp. 156-165.
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This chapter introduces a new linear sensor model of low-cost micro-electro-mechanical-systems accelerometers. A modelled algorithm has been developed to reduce different errors for the system. Experiments with Matlab analysis has been displayed in the paper. Transmission of an accelerometer in both cable and wireless has been calculated and displayed from 1500 samples. Linear sensor model has been applied to the experiment results to optimize the accuracy of the accelerometer data. This method is able to be used in different positions of the accelerometer. It is also used to calibrate and align the system before the system operation. Therefore, it is possible that the sensor model can be used to reduce errors in different sensor networks, such as Bluetooth, beacon and ZigBee.

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 Klempous, R & Nikodem, J (eds), Smart Innovations in Engineering and Technology: Intelligent Engineering and Informatics, Springer Nature, Cham, Switzerland, pp. 131-143.
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Zhang, W, Christopher, C, Jan, S, Chaczko, Z, Su, S & Zhou, J 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.

Abbasipour, M, Milimonfared, J, Yazdi, SSH & 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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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, Huynh, BP, Braytee, A & Taghipour, R 2020, 'An experimental investigation of the thermal effect due to discharging of phase change material in a room fitted with a windcatcher', Sustainable Cities and Society, vol. 61.
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© 2020 Elsevier Ltd This paper investigates experimentally the effect of the Phase Change Material (PCM) discharging process as a passive cooling technique on the performance of a two sided windcatcher fitted on an acrylic chamber with dimensions 1250 × 1000 × 750 mm3. Four different models with different locations of PCM are studied, and the results are compared with each other and with a fifth model with No PCM. PCM is integrated respectively at the walls of the chamber, its floor and ceiling and also within the windcatcher's inlet channel. Humidity, temperature and air velocity are monitored for each of the models studied. It is noted that with all the models containing PCM, the average humidity inside the chamber changed only slightly compared to the model with No PCM. The difference in humidity ranged between 0 and 3.88 % which indicates that the humidity variations are not significant. The model with the PCM located on the floor, ceiling and walls as well as in the windcatcher's inlet channel has shown the best performance, with a significant minimum reduction of average temperature in the chamber of about 2.75 °C (approximately 9.33 %) compared with the model with No PCM.

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.

Abdollahi, A, Pradhan, B, Shukla, N, Chakraborty, S & Alamri, A 2020, 'Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review', REMOTE SENSING, vol. 12, no. 9.
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Abdollahi, M, Ghobadian, B, Najafi, G, Hoseini, SS, Mofijur, M & Mazlan, M 2020, 'Impact of water – biodiesel – diesel nano-emulsion fuel on performance parameters and diesel engine emission', Fuel, vol. 280.
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© 2020 Elsevier Ltd The use of emulsion fuel in the engine has been reported as an effective solution to reduce the harmful emission. This experimental study examined the effects of nano-emulsion biodiesel fuel on engine efficiency, gas emission parameters and combustion parameters of a single-cylinder air-cooled diesel engine. Nano-emulsion fuel made of 5% waste cooking oil biodiesel and 5% distilled water were used to test the performance of the diesel engine. This fuel was produced using ultrasonic waves by stabilizing 5% by volume tween 80 and spans 80 surfactants in HLB8 (Hydrophilic-Lipophilic Balance). Performance parameters and pollutants emission of a diesel engine using nano-emulsion fuel were compared with emulsion fuel. This test was performed on four different engine loads (25%, 50%, 75%, and 100%) at different speeds of 1700, 2000, 2300, and 2600 rpm. Power, torque, cylinder pressure levels, and emissions including soot opacity, carbon monoxide (CO), unburned hydrocarbon (UHC), carbon dioxide (CO2), and nitrogen oxides (NOX) were measured. The test results show that diesel engine power and torque using nano-emulsion fuel improved by about 4.84% and 4.65% compared to emulsion fuel, respectively. The use of nano-emulsion fuel significantly decreased CO (~11%), UHCs (~6%), NOx (~9%) and soot opacity (~10%) emission. However, a small rise in CO2 (~7%) emission was observed. The combustion result shows that nano-emulsion fuel creates more cylinder pressure (CP) than emulsion fuel during combustion. The highest CP was recorded at 10° crank angle after the top dead centre with diesel fuel. Finally, nano-emulsion fuel can be a satisfactory alternative to diesel fuel in a diesel engine without having to change the engine.

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.

Abedin, B, Milne, D & Erfani, E 2020, 'Attraction, selection, and attrition in online health communities: Initial conversations and their association with subsequent activity levels', International Journal of Medical Informatics, vol. 141.
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© 2020 The Authors Background: The effectiveness of online health communities (OHCs) for improving outcomes for health care consumers, health professionals, and health services has already been well investigated. However, research on determinants of OHC users’ activity levels, what is associated with attrition or attraction to these communities, and the impacts of initial posts is limited. Objectives: We sought to explore topic exchanges in OHCs and determine how users’ initial posts and community reactions to them are associated with their subsequent activity levels. We also aimed to extend the theory of Attraction-Selection-Attrition for Online Communities (OCASA) to this area. Methods: We examined exchanges in a major Australian OHC for cancer patients, analyzing about 2500 messages posted over 2009–18. We developed a novel annotation scheme to examine new members’ initial posts and the community's reactions to them. Results: The annotation scheme includes five themes: informational support provision, emotional support provision, requests for help, self-reflection & disclosures, and conversational cues. Initial conversations were associated with future activity levels in terms of active posting versus non-active engagement in the community. We found that most OHC members disclosed personal reflections to bond with the community, and many actively posted to the community solely to provide informational and emotional support to others. Conclusion: Our work extends OCASA theory to bond-based contexts, presents a new annotation scheme for OHC support topics, and makes an important contribution to knowledge about the relationship between users’ activity levels and their initial posts. The findings help managers and owners understand how members use OHCs and how to encourage active participation. They also suggest how to attract new members and minimize attrition among existing members.

Abeywickrama, HV, He, Y, Dutkiewicz, E, Jayawickrama, BA & Mueck, M 2020, 'A Reinforcement Learning Approach for Fair User Coverage Using UAV Mounted Base Stations Under Energy Constraints', IEEE Open Journal of Vehicular Technology, vol. 1, pp. 67-81.
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Aboulkheyr Es, H, Zhand, S, Thiery, JP & Warkiani, ME 2020, 'Pirfenidone reduces immune-suppressive capacity of cancer-associated fibroblasts through targeting CCL17 and TNF-beta', Integrative biology : quantitative biosciences from nano to macro, vol. 12, no. 7, pp. 188-197.
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© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. Various factors in the tumor microenvironment (TME) regulate the expression of PD-L1 in carcinoma cells. The cancer-associated fibroblasts (CAFs) play a crucial role in regulating and rewiring TME to enhance their immune suppressive function and to favor the invasion of the malignant cells. Tumor progression may be retarded by targeting CAFs in the TME. Various studies highlighted the ability of targeting CAF with pirfenidone (PFD), leading to increased efficacy of chemotherapy. However, its potential for the reduction of immune-suppression capacity of CAFs remains to be elusive. Here, we assessed the effect of PFD on the expression of PD-L1 on CAF cells. Besides migration inhibitory effects of PFD on CAFs, the expression level of PD-L1 reduced in CAFs after treatment with PFD. The downstream analysis of released cytokines from CAFs showed that PFD significantly dropped the secretion of CCL17 and TNF-β, where a positive association between PFD-targeted proteins and PD-L1 was observed. These data suggest that the treatment of CAF within TME through the PFD may reduce the acquisition of CAF-mediated invasive and immune-suppressive capacity of breast carcinoma cells.

Abraham, MT, Satyam, N, Kushal, S, Rosi, A, Pradhan, B & Segoni, S 2020, 'Rainfall threshold estimation and landslide forecasting for Kalimpong, India using SIGMA model', Water (Switzerland), vol. 12, no. 4.
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© 2020 by the authors. Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta-integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develo...

Abraham, MT, Satyam, N, Pradhan, B & Alamri, AM 2020, 'Forecasting of landslides using rainfall severity and soil wetness: A probabilistic approach for Darjeeling Himalayas', Water (Switzerland), vol. 12, no. 3, pp. 1-19.
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© 2020 by the authors. Rainfall induced landslides are creating havoc in hilly areas and have become an important concern for the stakeholders and public. Many approaches have been proposed to derive rainfall thresholds to identify the critical conditions that can initiate landslides. Most of the empirical methods are defined in such a way that it does not depend upon any of the in situ conditions. Soil moisture plays a key role in the initiation of landslides as the pore pressure increase and loss in shear strength of soil result in sliding of soil mass, which in turn are termed as landslides. Hence this study focuses on a Bayesian analysis, to calculate the probability of occurrence of landslides, based on different combinations of severity of rainfall and antecedent soil moisture content. A hydrological model, called Systeme Hydrologique Europeen Transport (SHETRAN) is used for the simulation of soil moisture during the study period and event rainfall-duration (ED) thresholds of various exceedance probabilities were used to characterize the severity of a rainfall event. The approach was used to define two-dimensional Bayesian probabilities for occurrence of landslides in Kalimpong (India), which is a highly landslide susceptible zone in the Darjeeling Himalayas. The study proves the applicability of SHETRAN model for simulating moisture conditions for the study area and delivers an effective approach to enhance the prediction capability of empirical thresholds defined for the region.

Abraham, MT, Satyam, N, Pradhan, B & Alamri, AM 2020, 'Iot-based geotechnical monitoring of unstable slopes for landslide early warning in the Darjeeling Himalayas', Sensors (Switzerland), vol. 20, no. 9.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In hilly areas across the world, landslides have been an increasing menace, causing loss of lives and properties. The damages instigated by landslides in the recent past call for attention from authorities for disaster risk reduction measures. Development of an effective landslide early warning system (LEWS) is an important risk reduction approach by which the authorities and public in general can be presaged about future landslide events. The Indian Himalayas are among the most landslide-prone areas in the world, and attempts have been made to determine the rainfall thresholds for possible occurrence of landslides in the region. The established thresholds proved to be effective in predicting most of the landslide events and the major drawback observed is the increased number of false alarms. For an LEWS to be successfully operational, it is obligatory to reduce the number of false alarms using physical monitoring. Therefore, to improve the efficiency of the LEWS and to make the thresholds serviceable, the slopes are monitored using a sensor network. In this study, micro-electro-mechanical systems (MEMS)-based tilt sensors and volumetric water content sensors were used to monitor the active slopes in Chibo, in the Darjeeling Himalayas. The Internet of Things (IoT)-based network uses wireless modules for communication between individual sensors to the data logger and from the data logger to an internet database. The slopes are on the banks of mountain rivulets (jhoras) known as the sinking zones of Kalimpong. The locality is highly affected by surface displacements in the monsoon season due to incessant rains and improper drainage. Real-time field monitoring for the study area is being conducted for the first time to evaluate the applicability of tilt sensors in the region. The sensors are embedded within the soil to measure the tilting angles and moisture content at shallow depths. The slopes were monitore...

Abraham, MT, Satyam, N, Rosi, A, Pradhan, B & Segoni, S 2020, 'The Selection of rain gauges and rainfall parameters in estimating intensity-duration thresholds for landslide occurrence: Case study from Wayanad (India)', Water (Switzerland), vol. 12, no. 4.
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© 2020 by the authors. Recurring landslides in theWestern Ghats have become an important concern for authorities, considering the recent disasters that occurred during the 2018 and 2019 monsoons. Wayanad is one of the highly affected districts in Kerala State (India), where landslides have become a threat to lives and properties. Rainfall is the major factor which triggers landslides in this region, and hence, an early warning system could be developed based on empirical rainfall thresholds considering the relationship between rainfall events and their potential to initiate landslides. As an initial step in achieving this goal, a detailed study was conducted to develop a regional scale rainfall threshold for the area using intensity and duration conditions, using the landslides that occurred during the years from 2010 to 2018. Detailed analyses were conducted in order to select the most effective method for choosing a reference rain gauge and rainfall event associated with the occurrence of landslides. The study ponders the effect of the selection of rainfall parameters for this data-sparse region by considering four different approaches. First, a regional scale threshold was defined using the nearest rain gauge. The second approach was achieved by selecting the most extreme rainfall event recorded in the area, irrespective of the location of landslide and rain gauge. Third, the classical definition of intensity was modified from average intensity to peak daily intensity measured by the nearest rain gauge. In the last approach, four different local scale thresholds were defined, exploring the possibility of developing a threshold for a uniform meteo-hydro-geological condition instead of merging the data and developing a regional scale threshold. All developed thresholds were then validated and empirically compared to find the best suited approach for the study area. From the analysis, it was observed that the approach selecting the rain gauge based on the most ex...

Abu ul Fazal, M, Ferguson, S & Johnston, A 2020, 'Investigating efficient speech-based information communication: a comparison between the high-rate and the concurrent playback designs', MULTIMEDIA SYSTEMS, vol. 26, no. 5, pp. 621-630.
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Abuhilaleh, M, Li, L & Hossain, MJ 2020, 'Power management and control coordination strategy for autonomous hybrid microgrids', IET Generation, Transmission and Distribution, vol. 14, no. 1, pp. 119-130.
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© The Institution of Engineering and Technology 2019. This study presents an improved power management and control coordination strategy for autonomous hybrid microgrids (MGs). The new strategy aims to reduce the continuous operation of interlinking converters (ILCs) under all load conditions, and thereby avoids the significant power loss on the ILCs that adversely affects the operational feasibility of the hybrid MGs. The hybrid MG considered in this study consists of multiple alternating current and direct current sub-microgrids (SMGs) with different voltage levels. The hierarchal coordination of power management and control strategy for autonomous hybrid MG is introduced and analysed. The designed system includes both the primary and secondary control levels to ensure a seamless and accurate transfer of power among the SMGs. A new technique to ensure the continuous power flow among the SMGs while minimising the continuous operation of the ILCs in the islanded mode is presented with a focus on the secondary control level. Five scenarios of transferring power among SMGs are analysed using MATLAB/Simulink. The results indicate that the system's high level of flexibility in managing the power flow at different control levels can be achieved by the proposed approach.

Abuzied, SM & Pradhan, B 2020, 'Hydro-geomorphic assessment of erosion intensity and sediment yield initiated debris-flow hazards at Wadi Dahab Watershed, Egypt', Georisk.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. This study attempts to assess slope and channel erosion for modelling their implications on debris-flow occurrences in Wadi Dahab Watershed (WDW). Remote sensing and Geographic Information System (GIS) were integrated to appraise erosion rates from a hillslope and channel storage throughout WDW. A mass-wasting database was built initially for modelling hazard zones and validating the final map using a bivariate statistical analysis. An Erosion Hazard Model (EHM) was developed to evaluate the erosion intensity and sediment yield throughout WDW and to prognosticate the hazard zones due to debris-flows. The EHM was developed based on hydrological and geomorphic controls which are responsible for disintegrating bedrocks, delivering detritus downslopes, and accelerating debris through channels. Multi-source datasets, including topographic and geologic maps, climatic, satellite images, aerial photographs, and field-based datasets, were used to derive factors associated with the hydro-geomorphic processes. A spatial prediction of erosion intensity was obtained by the integration of both static and dynamic factors generated hazards in GIS platform. The erosion intensity map classifies WDW relatively to five intensity zones in which the most hazardous zones are distributed in steep sloping terrains and structurally controlled channels covered by metamorphic and clastic rocks. The erosion intensity map was correlated and tested against the debris-flows dataset which was not used during the spatial modelling process. The statistical correlation analysis has confirmed that the debris-flow locations increase exponentially in the high erosion intensity zones. The holistic integration approach provides the promising model for forecasting critical zones prone to erosion intensity and their associated hazards in WDW.

Adak, C, Chaudhuri, BB, Lin, C-T & Blumenstein, M 2020, 'Intra-Variable Handwriting Inspection Reinforced With Idiosyncrasy Analysis', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 15, pp. 3567-3579.
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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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Afshar, S, Hamilton, TJ, Davis, L, Van Schaik, A & Delic, D 2020, 'Event-Based Processing of Single Photon Avalanche Diode Sensors', IEEE Sensors Journal, vol. 20, no. 14, pp. 7677-7691.
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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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Aghdam, MM, Li, L & Zhu, J 2020, 'Comprehensive study of finite control set model predictive control algorithms for power converter control in microgrids', IET Smart Grid, vol. 3, no. 1, pp. 1-10.
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© 2020 Institution of Engineering and Technology. All rights reserved. Advances in power electronics and digital control open a new horizon in the control of power converters. Particularly, model predictive control has been developed for control applications in industrial electronics and power systems. This study presents a comprehensive study on recent achievements of model predictive control algorithms to overcome the challenges in the real-time implementation of power converter control, which is the lowest level control of hierarchical control in microgrids. The study shows that most of these alternate solutions can enhance system reliability, stability, and efficiency. The control platform devices for the real-time implementation of these algorithms are compared. The related issues are discussed and classified, respectively. Finally, a summary is provided, leading to some further research questions and future work.

Ahmad, HA, Ni, S-Q, Ahmad, S, Zhang, J, Ali, M, Ngo, HH, Guo, W, Tan, Z & Wang, Q 2020, 'Gel immobilization: A strategy to improve the performance of anaerobic ammonium oxidation (anammox) bacteria for nitrogen-rich wastewater treatment', BIORESOURCE TECHNOLOGY, vol. 313.
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Ahmed, AM, Chacon, A, Rutherford, H, Akamatsu, G, Mohammadi, A, Nishikido, F, Tashima, H, Yoshida, E, Yamaya, T, Franklin, DR, Rosenfeld, AB, Guatelli, S & Safavi-Naeini, M 2020, 'A validated Geant4 model of a whole-body PET scanner with four-layer DOI detectors', Physics in Medicine & Biology.
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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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Ahmed, MB, Johir, MAH, McLaughlan, R, Nguyen, LN, Xu, B & Nghiem, LD 2020, 'Per- and polyfluoroalkyl substances in soil and sediments: Occurrence, fate, remediation and future outlook', Science of the Total Environment, vol. 748.
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© 2020 Elsevier B.V. Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are contaminants of great concern due to their wide-spread occurrence and persistence in the environments (i.e., in water, soil and sediment) and potential toxicology even at very low concentration. The main focus of this review is on the PFASs in soil and sediments. More specifically, this review systematically examines the occurrence and toxicological effects with associated risks, fate (i.e., PFASs adsorption by soil and sediment, transportation and transformation, and bioaccumulation), and remediation practices of PFASs in soil and sediment. Various models and equations such as fugacity-based multimedia fate and hydrodynamic models are used to study the fate, transport, and transformation of PFASs. Among different remediation practices, sorption is the dominant process for the removal of PFASs from soil and sediments. Results also indicate that PFASs adsorption onto activated carbon decrease with the increase of carbon chain length in the PFASs. The longer-chain PFASs have larger partition coefficient values than shorter-chained PFASs. Sorption of PFASs to soil and sediments are mainly governed by different electrostatic interactions, hydrogen bonds formation, hydrophobic interactions, organic content in soil and sediments, and ligand exchange. Other technology such as thermal treatment might be potential in the removal of PAFSs, but need further study to elucidate a conclusion. Finally, the associated challenges and future outlook have been included.

Akbari, F, Saberi, M & Hussain, OK 2020, 'Social network structure-based framework for innovation evaluation and propagation for new product development', Service Oriented Computing and Applications, vol. 14, no. 3, pp. 189-201.
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© 2020, Springer-Verlag London Ltd., part of Springer Nature. Evaluating the innovation of a new idea before its implementation is a complicated but important phenomenon as it plays a critical role in the success of a product. The literature widely uses sentiment analysis as a technique for product designers to ascertain users’ opinion toward an idea before its implementation. However, that technique focuses only on determining the opinion of users studied. It does not assist designers in providing insights in terms of what needs to be done to propagate the popularity of the idea further to ensure its success. One framework by which this can be done is by considering social network structure and representing users as nodes of that network. In this paper, we investigate how a social network structure can be used to influence a user’s opinion among the society. Our proposed framework consists of four main components, namely data collection, sentiment extraction, budget approximation and presentation. After gathering customers’ comments in the data collection phase, the opinion of users who have expressed it is analyzed in the sentiment analysis phase. The budget approximation component then determines the cost of spreading positive opinion among the network of users, including those who have not given it. For that, influence maximization is used to compare the cost of convergence of the general opinion of society in the direction of innovation. In presentation component, the comparative information will be used by product designers to assist them in determining the viability of selecting an idea for implementation. The simulation results show that the network structure and the individuals’ positions are important factors in the acceptance of an innovation by society. This framework can be used to compare different innovative ideas and provide decision makers in organizations with informative reports as decision support materials.

Akther, N, Ali, SM, Phuntsho, S & Shon, H 2020, 'Surface modification of thin-film composite forward osmosis membranes with polyvinyl alcohol-graphene oxide composite hydrogels for antifouling properties', DESALINATION, vol. 491.
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Akther, N, Yuan, Z, Chen, Y, Lim, S, Phuntsho, S, Ghaffour, N, Matsuyama, H & Shon, H 2020, 'Influence of graphene oxide lateral size on the properties and performances of forward osmosis membrane', DESALINATION, vol. 484.
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Al zahrani, S, Islam, MS & Saha, SC 2020, 'Heat transfer augmentation in retrofitted corrugated plate heat exchanger', International Journal of Heat and Mass Transfer, vol. 161.
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© 2020 Corrugated plate heat exchangers (CPHEs) have been extensively adopted especially for systems that require high thermal efficiencies such as aerospace and gas turbine power plants. According to several factors (i.e. required heat duty), CPHEs can be optimized to meet the application requirements. However, the number of the required thermal plates (Np) could be very large (Np>40) which in turn would cause several disadvantages (i.e. sever flow maldistribution). Therefore, the present study aims to introduce an innovative modification that can boost the thermal performance of the basic CPHE which in turn would reduce the number of required plates for the same heat duty. The thermal performance of the modified CPHE has been numerically investigated by using Computational Fluid dynamics (CFD) software. The numerical data have been validated with experimental measurement from the literature. The impact of the new modification has been studied on Nusselt number (Nu), fanning friction factor (f), Stanton number (St), Turbulence kinetic energy (TKE), flow maldistribution, j factor, and quality index factor (JF). The result has been compared with previously reported data of the basic corrugated and flat PHEs. At the same mass flow rate, Nu, f, and TKE of the modified CPHE are respectively 1.3, 1.7, and 3.5 times greater than those of the basic CPHE. Moreover, JF data of the modified CPHE are 1.4, and 64 times greater than those of the basic corrugated and flat PHEs, respectively. In addition to the superior thermal performance, the present modification offers larger contact area between the plates which could boost the overall mechanical integrity of the heat exchanger. Thus, this modification could pave the way for CPHEs to be incorporated in new applications that require more compact and durable HEs. The heat transfer correlations of the modified CPHE have been developed.

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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Alajlouni, D, Bliuc, D, Tran, T, Eisman, JA, Nguyen, TV & Center, JR 2020, 'Decline in Muscle Strength and Performance Predicts Fracture Risk in Elderly Women and Men', The Journal of clinical endocrinology and metabolism, vol. 105, no. 9.
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© Endocrine Society 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. CONTEXT: Muscle strength and performance are associated with fractures. However, the contribution of their rate of decline is unclear. OBJECTIVE: To assess the independent contribution of the rate of decline in muscle strength and performance to fracture risk. DESIGN, SETTING, AND PARTICIPANTS: Community-dwelling women (n = 811) and men (n = 440) aged 60 years or older from the prospective Dubbo Osteoporosis Epidemiology Study followed from 2000 to 2018 for incident fracture. Clinical data, appendicular lean mass/height2 (ht)2, bone mineral density, quadricep strength/ht (QS), timed get-up-and-go (TGUG), 5 times repeated sit-to-stand (5xSTS), and gait speed (GS) measured biennially. Rates of decline in muscle parameters were calculated using ordinary least squares regression and fracture risk was assessed using Cox's models. MAIN OUTCOME: Incident low-trauma fracture ascertained by x-ray report. RESULTS: Apart from lean mass in women, all muscle parameters declined over time. Greater rates of decline in physical performance were associated with increased fracture risk in women (Hazard ratios [HRs] ranging from 2.1 (95% CI: 1.5-2.9) for GS to 2.7 (95% CI: 1.9-3.6) for 5xSTS, while in men only the decline in GS was associated with fracture risk (HR: 3.4 [95% CI: 1.8-6.3]). Baseline performance and strength were also associated with increased fracture risk in men (HRs ranging from 1.8 (95% CI: 1.1-3.0) for QS to 2.5 (95% CI: 1.5-4.1) for TGUG, but not in women. CONCLUSION: Rate of decline in physical performance in both genders, and baseline strength and performance in men, contributed independently to fracture risk. Sit-to-stand and GS were the tests most consistently associated with fractures. Further studies are required to determine whether muscle strength and/or performance improve the predictive accuracy of fracture prediction models.

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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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.

Alambeigi, P, Burry, J, Zhao, S & Cheng, E 2020, 'A study of human vocal effort in response to the architectural auditory environment', Architectural Science Review.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. This paper examines human auditory interaction with an architectural design hypothesized to decrease users’ vocal effort and thus enhance their speech privacy. This detailed design increased sound scattering in semi-enclosed meeting rooms within open plan offices. To achieve desirable speech intelligibility, a live sound environment is strongly recommended for meeting rooms. The research explores the hypothesis that by adding early reflections to the direct sound energy with an integrated design, the speaker as a self-listener might benefit from perceiving their own voice with more clarity. This can cause adaptive changes to subconscious vocal effort and increase the corresponding speech privacy of the space. An architecture-driven talker-quality experiment in a natural situation has been conducted in two rounds and in two different acoustic environments with 20 participants. The results implied the importance of human visual and spatial perception of privacy over auditory interaction with the environment on decreasing vocal effort. Such factors could thus be considered within the architectural design process.

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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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. Topology optimization is an effective method in the design of acoustic media. This article presents optimization for graded damping materials to minimize sound pressure at a reference point or sound power radiation under harmonic excitation. The Helmholtz integral equation is used to calculate an acoustic field to satisfy the Sommerfeld conditions. The equation of motion is solved using a unit dynamic load method. Formulations for the sound pressure or sound power radiation in an integral form are derived in terms of mutual kinetic and strain energy densities. These integrals lead to novel physical response functions for solving the proposed optimization problem to design graded damping materials. The response function derived for individual frequency is utilized to solve the multi-objective optimization problem of minimizing sound pressure at the reference point for excitations with a range of frequencies. Numerical examples are presented to verify the efficiency of the present formulations.

Alfouneh, M, Ji, J & Luo, Q 2020, 'Optimal design of multi-cellular cores for sandwich panels under harmonic excitation', COMPOSITE STRUCTURES, vol. 248.
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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.

Alhathal Alanezi, A, 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, vol. 158, pp. 12-23.
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© 2020 Institution of Chemical Engineers The energy requirements for reverse osmosis (RO) seawater desalination continue to be a major matter of debate. Previous studies have shown the dependence of optimum RO desalination energy on the RO recovery rate. However, they overlooked including the effect of Energy Recovery Device (ERD) and pretreatment on the power consumption. In this work, a computer model was used to analyze the energy requirements for RO desalination, taking into account the effect of ERD efficiencies and pretreatment. The specific power consumption (SPC) of the RO was found to increase with the increase of RO recovery rate when the ERD system was included. The optimum SPC became more dependent on the RO recovery rate when the pretreatment energy was added. The recovery for optimum desalination energy was 46%, 44%, and 40% for the RO system coupled with an ERD of 65%, 80%, and 95% efficiency, respectively. The results showed that RO process could be operated at lower recovery rate and still meet the projected desalination capacity by increasing the feed flow rate and coupling with high-efficiency ERD. A trivial decrease of the total desalination energy was achieved when the feed flow rate increased from 7 m3/h to 8 m3/h and recovery rate decreased from 46% to 44% by coupling the RO with an ERD of 95% efficiency. This suggests that the RO–ERD system can be operated at a high feed flow rate and low recovery rate without affecting the plant capacity.

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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Allison-Logan, S, Fu, Q, Sun, Y, Liu, M, Xie, J, Tang, J & Qiao, GG 2020, 'From UV to NIR: A Full-Spectrum Metal-Free Photocatalyst for Efficient Polymer Synthesis in Aqueous Conditions', Angewandte Chemie - International Edition.
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© 2020 Wiley-VCH GmbH Photo-mediation offers unparalleled spatiotemporal control over controlled radical polymerizations (CRP). Photo-induced electron/energy transfer reversible addition–fragmentation chain transfer (PET-RAFT) polymerization is particularly versatile owing to its oxygen tolerance and wide range of compatible photocatalysts. In recent years, broadband- and near-infrared (NIR)-mediated polymerizations have been of particular interest owing to their potential for solar-driven chemistry and biomedical applications. In this work, we present the first example of a novel photocatalyst for both full broadband- and NIR-mediated CRP in aqueous conditions. Well-defined polymers were synthesized in water under blue, green, red, and NIR light irradiation. Exploiting the oxygen tolerant and aqueous nature of our system, we also report PET-RAFT polymerization at the microliter scale in a mammalian cell culture medium.

Almotairy, SM, Boostani, AF, Hassani, M, Wei, D & Jiang, ZY 2020, 'Mechanical Properties of Aluminium Metal Matrix Nanocomposites Manufactured by Assisted-Flake Powder Thixoforming Process', Metals and Materials International.
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© 2019, The Korean Institute of Metals and Materials. Abstract: This study discusses the superior effect of thixoforming process on enhancing the tensile properties of aluminium matrix composite produced using flake metallurgy route. The flake metallurgy process was utilised to manufacture aluminium matrix composites followed by thixoforming process. Microstructural investigations carried out using transmission electron microscope have shown the synergic effect of thixoforming process on rendering uniform distribution of SiC nanoparticles associated with lower porosity content. X-ray diffraction characterisations have revealed the promising effect of uniform dispersion of SiC nanoparticles on restricting the grain growth of aluminium matrix within nanoscale regime (90 nm) even at high semi-solid thixoforming temperatures (575 °C). The achieved results of tensile tests have shown a profound effect of flake metallurgy of aluminium powder through dual speed ball milling. These results are higher than those achieved by low speed and high speed even with higher SiC content. This was attributed to the uniform confinement of SiC nanoparticles within the samples produced using flake-assisted forming process compared to the ones manufactured using ball milling-assisted processes. Graphic abstract: [Figure not available: see fulltext.].

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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Alsheikh, MA, Hoang, DT, Niyato, D, Leong, D, Wang, P & Han, Z 2020, 'Optimal Pricing of Internet of Things: A Machine Learning Approach', IEEE Journal on Selected Areas in Communications, vol. 38, no. 4, pp. 669-684.
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© 1983-2012 IEEE. Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition.

Al-Shetwi, AQ, Hannan, MA, Jern, KP, Mansur, M & Mahlia, TM 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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Altaee, A 2020, 'Organic Fouling in Forward Osmosis: A Comprehensive Review', Water, vol. 12, no. 5.
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Altaee, A & AlZainati, N 2020, 'Novel Thermal Desalination Brine Reject-Sewage Effluent Salinity Gradient for Power Generation and Dilution of Brine Reject', Energies, vol. 13, no. 7.
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Salinity gradient resource presents an essential role for power generated in the process of pressure-retarded osmosis (PRO). Researchers proposed several designs for coupling the PRO process with the desalination plants, particularly reverse osmosis technology for low-cost desalination but there is no study available yet on the utilization of the concentrated brine reject from a thermal desalination plant. This study evaluates the feasibility of power generation in the PRO process using thermal plant brine reject-tertiary sewage effluent (TSE) salinity gradient resource. Power generation in the PRO process was determined for several commercially available FO membranes. Water flux in Oasys Forward Osmosis membrane was more than 31 L/m2h while the average water flux in the Oasys module was 17 L/m2h. The specific power generation was higher in the thin film composite (TFC) membranes compared to the cellulose triacetate (CTA) membranes. The specific power generation for the Oasys membrane was 0.194 kWh/m3, which is 41% of the maximum Gibbs energy of the brine reject-TSE salinity gradient. However, the Hydration Technology Innovation CTA membrane extracted only 0.133 kWh/m3 or 28% of Gibbs free energy of mixing for brine reject-TSE salinity gradient. The study reveals the potential of the brine reject-TSE salinity gradient resource for power generation and the dilution of brine reject.

Altaee, A, Khalaifat, N & Zhou, J 2020, 'Assessment of wind energy potential at Yanco, New South Wales, Australia', International Journal of Industrial Electronics and Electrical Engineering, vol. 8, no. 1, pp. 26-30.

Altaee, A, Khlaifat, N, Zhou, J & Huang, Y 2020, 'A review of the key sensitive parameters on the aerodynamic performance of a horizontal wind turbine using Computational Fluid Dynamics modelling', AIMS Energy, vol. 8, no. 3, pp. 493-524.
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Renewable energy technologies are receiving much attention to replacing power plants operated by fossil and nuclear fuels. Of all the renewable technologies, wind power has been successfully implemented in several countries. There are several parameters in the aerodynamic characteristics and design of the horizontal wind turbine. This paper highlights the key sensitive parameters that affect the aerodynamic performance of the horizontal wind turbine, such as environmental conditions, blade shape, airfoil configuration and tip speed ratio. Different turbulence models applied to predict the flow around the horizontal wind turbine using Computational Fluid Dynamics modeling are reviewed. Finally, the challenges and concluding remarks for future research directions in wind turbine design are discussed.

Altaee, A, Khlaifat, N, Zhou, J & Huang, Y 2020, 'Evaluation of wind resource potential using statistical analysis of probability density functions in New South Wales, Australia', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.
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Wind energy is a vital part of Australia's energy mix. The first step in a wind power project at a particular site is to assess the wind resource potential and feasibility for wind energy production. Research on wind potential and statistical analysis has been done throughout the world. Currently, recent potential wind studies are lacking, especially in New South Wales (NSW), Australia. This study highlighted the feasibility of wind potential at four sites in NSW, namely Ballina, Merriwa, Deniliquin, and the Bega region. The type of wind speed distribution function dramatically affects the output of the available wind energy and wind turbine performance at a particular site. Therefore, the accuracy of four probability density functions was evaluated, namely Rayleigh, Weibull, Gamma, and Lognormal distributions. The outcomes showed Weibull provided the most accurate distribution. The annual average scale and shape parameters of Weibull distribution varied between 2.935-5.042 m/s and 1.137-2.096, respectively. The maximum shape and scale factors were at Deniliquin, while the minimum shape and scale factors were at Bega area. Assessment of power density indicated that Deniliquin had a marginal wind speed resource, while Ballina, Bega, and Merriwa had poor wind resources.

Altaee, A, Khlaifat, N, Zhou, J, Huang, Y & Braytee, A 2020, 'Optimization of a Small Wind Turbine for a Rural Area: A Case Study of Deniliquin, New South Wales, Australia', Energies, vol. 13.
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The performance of a wind turbine is profoundly affected by wind conditions. Small wind turbines usually achieve the demand for electricity in rural areas. The shape of the blade greatly influences the performance of the wind turbine. The present study aims to optimize the performance of a 20 kW horizontal-axis wind turbine (HAWT) under local wind conditions at Deniliquin, New South Wales, Australia. ANSYS Fluent was used to investigate the aerodynamic performance of the 20 KW HAWT. The effects of four Reynolds Averaged Navier Stokes (RANS) turbulence models on predicting the flow over the wind turbine under separation condition were examined. Transition SST model had the best agreement with NREL CER data, which was used to investigate the mechanical output at different rotational speeds and variable pitch angles. Then the aerodynamic shape of the rotor of the wind turbine was optimized to maximize the annual energy production (AEP) in the Deniliquin region. Statistical wind analysis was applied to define the Weibull function and scale parameters which were 2.096 and 5.042 m/s, respectively. HARP_Opt was enhanced with design variables concerning the shape of the blade, rated rotational speed, and pitch angle. Pitch angle remained at 0ᵒ while the rising wind speed improved rotor speed to 148.4482 rpm at rated speed. This optimization improved the AEP rate by 9.068% when compared to the original NREL design.

Alzoubi, YI & Gill, AQ 2020, 'An Empirical Investigation of Geographically Distributed Agile Development: The Agile Enterprise Architecture is a Communication Enabler', IEEE ACCESS, vol. 8, pp. 80269-80289.
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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.

Amin, BMR, Taghizadeh, S, Maric, S, Hossain, MJ & Abbas, R 2020, 'Smart Grid Security Enhancement by Using Belief Propagation', IEEE Systems Journal, pp. 1-12.
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Amin, DB, Tavakoli, J, Freeman, BJC & Costi, JJ 2020, 'Mechanisms of Failure Following Simulated Repetitive Lifting: A Clinically Relevant Biomechanical Cadaveric Study', Spine, vol. 45, no. 6, pp. 357-367.
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© 2018 Wolters Kluwer Health, Inc. All rights reserved. Study Design.A biomechanical analysis correlating internal disc strains and tissue damage during simulated repetitive lifting.Objective.To understand the failure modes during simulated safe and unsafe repetitive lifting.Summary of Background Data.Repetitive lifting has been shown to lead to lumbar disc herniation (LDH). In vitro studies have developed a qualitative understanding of the effect of repetitive loading on LDH. However, no studies have measured internal disc strains and subsequently correlated these with disc damage.Methods.Thirty human cadaver lumbar functional spinal units were subjected to an equivalent of 1 year of simulated repetitive lifting under safe and unsafe levels of compression, in combination with flexion (13-15°), and right axial rotation (2°) for 20,000 cycles or until failure. Safe or unsafe lifting were applied as a compressive load to mimic holding a 20kg weight either close to, or at arm's length, from the body, respectively. Maximum shear strains (MSS) were measured, and disc damage scores were determined in nine regions from axial post-Test magnetic resonance imaging (MRI) and macroscopic images.Results.Twenty percent of specimens in the safe lifting group failed before 20,000 cycles due to endplate failure, compared with 67% in the unsafe group. Over half of the specimens in the safe lifting group failed via either disc protrusion or LDH, compared with only 20% via protrusion in the unsafe group. Significant positive correlations were found between MRI and macroscopic damage scores in all regions (rs>0.385, P<0.049). A significant positive correlation was observed in the left lateral region for MSS versus macroscopic damage score (rs=0.486, P<0.037) and MSS versus failure mode (rs=0.724, P=0.018, only specimens with disc failure). Pfirrmann Grade 3 discs were strongly associated with subsequent LDH (P=0.003).Conclusion.Increased shear strains were observed in the contralater...

Amjadipour, M, Su, D & Iacopi, F 2020, 'Graphitic-Based Solid-State Supercapacitors: Enabling Redox Reaction by In Situ Electrochemical Treatment', BATTERIES & SUPERCAPS.
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Amjadipour, M, Su, D & Iacopi, F 2020, 'Graphitic‐Based Solid‐State Supercapacitors: Enabling Redox Reaction by In Situ Electrochemical Treatment', Batteries && Supercaps.
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Anh, TL, Le, CT, Huang, X & Guo, YJ 2020, 'Beam-Based Analog Self-Interference Cancellation in Full-Duplex MIMO Systems', IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, vol. 19, no. 4, pp. 2460-2471.
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Anshar, M & Williams, M-A 2020, 'Simplified Pain Matrix Method for Artificial Pain Activation Embedded into Robot Framework', INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS.
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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, vol. 359.
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© 2020 Elsevier B.V. A GIS-based hybrid approach for gully erosion susceptibility mapping (GESM) in the Biarjamand watershed in Iran is presented. A database comprised of 15 geo-environmental factors (GEFs) was compiled and used to predict the spatial distribution of 358 gully locations; 70% (251) of which were extracted for training and 30% (107) for validation. A Dempster-Shafer (DS) statistical model was employed to map susceptibility. Next, the results of four kernels (binary logistic, reg logistic, binary logitraw, and reg linear) of a boosted regression tree (BRT) model were combined to increase the efficiency and accuracy of the mapping. Area under receiver operating characteristics (AUROC), true skill statistic (TSS) and efficiency (E) metrics were used to rank the five validated models. The results show that elevation and distance to road play crucial roles in gullying. Integrating BRT and DS enhanced prediction accuracy. Among the four BRT kernels, binary logistic performed best (AUROC of 0.886, TSS of 0.854 and E equal to 0.880). The worst results were produced by the individual DS model (AUROC = 0.849, TSS = 0.774 and E = 0.834). The hybrid binary logistic-BRT and DS map categorized 14.50% of the study area as having very-low susceptibility, 16.99% low susceptibility, 22.77% moderate susceptibility, 24.12% high susceptibility, and 21.59% very-high susceptibility.

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, Nalivan, OA, Saha, S, Roy, J, Pradhan, B, Tiefenbacher, JP & Ngo, PTT 2020, 'Novel ensemble approaches of machine learning techniques in modeling the gully erosion susceptibility', Remote Sensing, vol. 12, no. 11.
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© 2020 by the authors. Gully erosion has become one of the major environmental issues, due to the severity of its impact in many parts of the world. Gully erosion directly and indirectly affects agriculture and infrastructural development. The Golestan Dam basin, where soil erosion and degradation are very severe problems, was selected as the study area. This research maps gully erosion susceptibility (GES) by integrating four models: maximum entropy (MaxEnt), artificial neural network (ANN), support vector machine (SVM), and general linear model (GLM). Of 1042 gully locations, 729 (70%) and 313 (30%) gully locations were used for modeling and validation purposes, respectively. Fourteen effective gully erosion conditioning factors (GECFs) were selected for spatial gully erosion modeling. Tolerance and variance inflation factors (VIFs) were used to examine the collinearity among the GECFs. The random forest (RF) model was used to assess factors' effectiveness and significance in gully erosion modeling. An ensemble of techniques can provide more accurate results than can single, standalone models. Therefore, we compared two-, three-, and four-model ensembles (ANN-SVM, GLM-ANN, GLM-MaxEnt, GLM-SVM, MaxEnt-ANN, MaxEnt-SVM, ANN-SVM-GLM, GLM-MaxEnt-ANN, GLM-MaxEnt-SVM, MaxEnt-ANN-SVM and GLM-ANN-SVM-MaxEnt) for GES modeling. The susceptibility zones of the GESMs were classified as very-low, low, medium, high, and very-high using Jenks' natural break classification method (NBM). Subsequently, the receiver operating characteristics (ROC) curve and the seed cell area index (SCAI) methods measured the reliability of the models. The success rate curve (SRC) and predication rate curve (PRC) and their area under the curve (AUC) values were obtained from the GES maps. The results show that the ANN model combined with two and three models are more accurate than the other combinations, but the ANN-SVM model had the highest accuracy. The rank of the others from best to worst accu...

Arabameri, A, Pradhan, B & Bui, DT 2020, 'Spatial modelling of gully erosion in the Ardib River Watershed using three statistical-based techniques', Catena, vol. 190.
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© 2020 Elsevier B.V. Gully erosion threatens land sustainability. Gullies trigger considerable erosion, damaging agricultural land, infrastructure and urban areas; thus, predicting and modelling gully susceptibility is of utmost concern. In particular, such a model is urgently required in semiarid areas where soil loss from gullies is high. Three predictive models are evaluated to assess gully erosion susceptibility mapping (GESM) in Semnan Province, Iran. The index of entropy (IOE), frequency ratio (FR) and certainty factor (CF) models are combined with remote sensing and geographic information system techniques to predict gully erosion. The collation of data from geographic resources identified 287 gullies in the study area. These areas were then randomly divided into 2 groups for calibration (70% or 201 gullies) and validation (30% or 86 gullies). Pairwise linear dependency amongst geoenvironmental factors was also assessed. A total of 16 factors were screened for modelling. Four performance metrics, namely, true skill statistic (TSS), area under the receiver operating characteristic (AUROC) curve, seed cell area index (SCAI) and modified SCAI (mSCAI), were used to evaluate the prediction accuracy and robustness of each model using validation datasets. Bootstrapped replicates were considered in estimating the accuracy and robustness of each model by varying gully/no-gully samples. The IOE results indicated that elevation, lithology and slope angle promoted favourable conditions for gully erosion in the study area. The results showed that the IOE model performed better than the FR and CF models for all three validation datasets (AUROCmean = 0.874 and TSSmean = 0.855). This finding was also confirmed in terms of stability and robustness (RTSS = 0.024 and RAUROC = 0.023). The SCAI and mSCAI results showed that all the models exhibited acceptable accuracy, but IOE demonstrated superior performance. Accordingly, IOE was used as the reference model for the study are...

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.

Arabameri, A, Saha, S, Chen, W, Roy, J, Pradhan, B & Bui, DT 2020, 'Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques', Journal of Hydrology, vol. 587.
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© 2020 Elsevier B.V. The present research aims to assess and judge the capability of flash flood susceptibility (FFS) models considering hybrid machine learning ensemble techniques for the FFS assessment in the Gorgan Basin in Iran. Three novel intelligence approaches, namely, bagging–functional tree (BFT), dagging–functional tree, and rotational forest–functional tree are used for modelling, with consideration to 15 flood conditioning factors (FCFs) as independent variables and 426 flood locations as dependent variables. Three threshold-dependent and -independent approaches are used to evaluate the goodness-of-fit and prediction capability of the ensemble models with a single functional tree (FT). These approaches include the area under the receiver operating characteristic curve of the success rate curve (SRC) and prediction rate curve (PRC), efficiency (E) and true skill statistics (TSS). The random forest model is used to determine the relative importance of FCFs. Elevation, stream distance and normalized difference vegetation index (NDVI) have crucial roles in the study area during flash flood occurrences. According to the results of all threshold-dependent and -independent approaches (AUC of SRC = 0.933, AUC of PRC = 0.959, E = 0.76 and TSS = 0.72), the BFT ensemble model has the greatest accuracy in terms of modelling FFS. Results also show that the performance of the FT model is enhanced by three meta-classifiers. The seed cell area index technique is also used to check model classification accuracy and reliability. Results of this technique show that all the models demonstrate good performance and reliability. However, the FFS maps prepared by machine learning ensemble techniques have excellent accuracy and reliability, as per the results of validation methods. Thus, these FFS maps can be used as a convenient tool to reduce the effect of flood in flash flood-prone areas.

Arabameri, A, Saha, S, Roy, J, Tiefenbacher, JP, Cerda, A, Biggs, T, Pradhan, B, Thi Ngo, PT & Collins, AL 2020, 'A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility', Science of the Total Environment, vol. 726.
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© 2020 Elsevier B.V. Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total of 96 locations of LS and 12 LS conditioning factors (LSCFs) were collected. Each feature in the LS inventory map (LSIM) was randomly assigned to one of four groups or folds, each comprising 25% of cases. The novel ensemble model was trained using 75% (3 folds) and validated with the remaining 25% (1 fold) in a four-fold cross-validation (CV) system, which is used to control for the effects of the random selection of the training and validation datasets. LSCFs for LS prediction were selected using the information-gain ratio and multi-collinearity test methods. Factor significance was evaluated using a random forest (RF) model. Groundwater drawdown, land use and land cover, elevation, and lithology were the most important LSCFs. Using the k-fold CV approaches, twelve LS susceptibility maps (LSSMs) were prepared as each fold employed all three models (ANN-bagging, ANN, and bagging). The LS susceptibility mapping showed that between 5.7% and 12.6% of the plain had very high LS susceptibility. All three models produced LS susceptibility maps with acceptable prediction accuracies and goodness-of-fits, but the best maps were produced by the ANN-bagging ensemble method. Overall, LS risk was highest in agricultural areas with high groundwater drawdown in the flat l...

Arabameri, A, Tiefenbacher, JP, Blaschke, T, Pradhan, B & Bui, DT 2020, 'Morphometric analysis for soil erosion susceptibility mapping using novel gis-based ensemble model', Remote Sensing, vol. 12, no. 5.
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© 2020 by the author. Licensee MDPI, Basel, Switzerland. The morphometric characteristics of the Kalvari basin were analyzed to prioritize sub-basins based on their susceptibility to erosion by water using a remote sensing-based data and a GIS. The morphometric parameters (MPs)-linear, relief, and shape-of the drainage network were calculated using data from the Advanced Land-observing Satellite (ALOS) phased-array L-type synthetic-aperture radar (PALSAR) digital elevation model (DEM) with a spatial resolution of 12.5 m. Interferometric synthetic aperture radar (InSAR) was used to generate the DEM. These parameters revealed the network's texture, morpho-tectonics, geometry, and relief characteristics. A complex proportional assessment of alternatives (COPRAS)-analytical hierarchy process (AHP) novel-ensemble multiple-criteria decision-making (MCDM) model was used to rank sub-basins and to identify the major MPs that significantly influence erosion landforms of the Kalvari drainage basin. The results show that in evolutionary terms this is a youthful landscape. Rejuvenation has influenced the erosional development of the basin, but lithology and relief, structure, and tectonics have determined the drainage patterns of the catchment. Results of the AHP model indicate that slope and drainage density influence erosion in the study area. The COPRAS-AHP ensemble model results reveal that sub-basin 1 is the most susceptible to soil erosion (SE) and that sub-basin 5 is least susceptible. The ensemble model was compared to the two individual models using the Spearman correlation coefficient test (SCCT) and the Kendall Tau correlation coefficient test (KTCCT). To evaluate the prediction accuracy of the ensemble model, its results were compared to results generated by the modified Pacific Southwest Inter-Agency Committee (MPSIAC) model in each sub-basin. Based on SCCT and KTCCT, the ensemble model was better at ranking sub-basins than the MPSIAC model, which indicated that ...

Ardalan, RB, Emamzadeh, ZN, Rasekh, H, Joshaghani, A & Samali, B 2020, 'Physical and mechanical properties of polymer modified self-compacting concrete (SCC) using natural and recycled aggregates', Journal of Sustainable Cement-Based Materials, vol. 9, no. 1, pp. 1-16.
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The study researched the effectiveness of four polymer admixtures (3%, 5%, 10%, and 15% of water weight) on the fresh and hardened properties of self-compacting concrete (SCC) cast using recycled and natural aggregates. Results show that polymer additives had positive effects on the fresh properties of SCC using recycled aggregates. Incorporating polymer additives increased the filling ability of concrete by more than four times. All polymer modified SCCs had a 100% passing ability compared to the 80% passing ability of the control samples. The compressive strength of materials at similar polymer ratios decreased by about 50% when natural aggregates were replaced with recycled aggregates. The flexural strength of SCC including recycled aggregates with 15% polymer was maintained compared to the control SCC including natural aggregates. The addition of 15% polymer to recycled aggregates concrete could improve workability and maintain flexural strength.

Arjmandi, A, Peyravi, M, Arjmandi, M & Altaee, A 2020, 'Exploring the use of cheap natural raw materials to reduce the internal concentration polarization in thin-film composite forward osmosis membranes', Chemical Engineering Journal, vol. 398.
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© 2020 Elsevier B.V. Internal concentration polarization (ICP) is a significant problem in Forward osmosis (FO) membranes, which reduces the water flux. In order to mitigate the ICP phenomenon, rice bran (RB) and wood sawdust (WSD) particles were selected as natural green pore formers and incorporated into the polyethersulfone (PES) matrix to fabricate mixed matrix membranes (MMMs). Fabricated MMMs were used as the porous support layer (SL) to make thin-film composite (TFC) FO membranes. Firstly, the water uptake experiment was performed to evaluate the water adsorption capacity of the RB and WSD particles. Furthermore, all samples were characterized by FTIR, FESEM, AFM, XPS, DLS, static contact angle (CA), and tensile strength. Also, performance tests in reverse osmosis (RO) and the FO units were performed to evaluate the fabricated membranes. The results showed that the use of RB and WSD particles dramatically reduced the structural parameter in all MMMs, resulting in lower ICP effects and high water flux. Due to the softer structure, smaller size, and more water uptake, the RB-based TFC membranes recorded better results. The TFC-RB-5 (with 5% of RB in the SL) was the best membrane with a water flux of about 65.71 L/m2.h for Caspian seawater desalination, while the FO water flux for DI water as the feed solution (FS) was 83.65 L/m2.h. The present study showed the membranes made in this study are competitive with the existing FO membranes and very cost-effective for broad applications.

Arjmandi, M, Altaee, A, Arjmandi, A, Pourafshari Chenar, M, Peyravi, M & Jahanshahi, M 2020, 'A facile and efficient approach to increase the magnetic property of MOF-5', Solid State Sciences, vol. 106.
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© 2020 Elsevier Masson SAS In this study, a facile and efficient approach to increase the magnetic property of metal-organic framework-5 (MOF-5) has been investigated. The basis of this approach is the encapsulation of cluster-oxygen composition (i.e. ZnO in MOF-5) during the synthesis process of MOF-5 to form ZnO@MOF-5 nanocrystals. Both MOF-5 and ZnO@MOF-5 were synthesized for comparison purposes, considering their magnetic property. The physicochemical properties of MOF-5 and ZnO@MOF-5 were characterized by XRD, FTIR, TGA, DLS, FESEM, and Magnetization measurements. The FTIR spectra confirmed the presence of additional ZnO molecules in the ZnO@MOF-5 structure. Results from the XRD showed that the presence of additional ZnO molecules in the ZnO@MOF-5 altered the structure of MOF-5. The TGA analysis also confirmed the presence of additional ZnO molecules in the ZnO@MOF-5 structure, indicating that the ZnO@MOF-5 contains 15.23 wt% ZnO more than MOF-5. The FESEM and DLS results showed that the average sizes of MOF-5 and ZnO@MOF-5 nanocrystals are below 100 nm, with no defined morphology. Finally, the magnetization measurements showed that the MOF-5 nanocrystals have diamagnetic properties. For ZnO@MOF-5 nanocrystals, a ferromagnetic-like character was observed in the scanned field range and the saturation value of about 2.59 × 10−3 emu/g was obtained. The success of this facile and hassle-free approach can be an important step towards enhancing the magnetic properties of MOFs.

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).

Arjmandi, M, Pourafshari Chenar, M, Altaee, A, Arjmandi, A, Peyravi, M, Jahanshahi, M & Binaeian, E 2020, 'Caspian seawater desalination and whey concentration through forward osmosis (FO)-reverse osmosis (RO) and FO-FO-RO hybrid systems: Experimental and theoretical study', Journal of Water Process Engineering, vol. 37.
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© 2020 Elsevier Ltd The current experimental and theoretical study proposes forward osmosis (FO)-reverse osmosis (RO) (FR*) and FO-FO-RO (FFR) hybrid systems to combine concentration of cheese whey and Caspian seawater desalination. The powerful TF-PMM was used as a FO membrane. The impact of sales of concentrated cheese whey on reducing Caspian seawater desalination costs was studied. The results showed that the TF-PMM has good potential for concentration of cheese whey with a water flux of about 12.62 L/m2h. In terms of permeate concentration (Cp) and at low recovery rates (RR), the choice of FR* and FFR hybrid systems with 0.3 M NaCl as the draw solution (DS) should be the priority. At high RR, although the sensitivity of the type of system is reduced, the FR* system should not be used. The total power consumption (Est) for the FR*-30 % and FR*-50 % systems are lower than that of a single RO unit. In FR* hybrid system by increasing the RRFO1, the power consumption of the FO unit and also the Est decreased. Also, in the FFR system, by increasing the concentration of the DS, while increasing the Est, the power consumption portion of the FO unit decreased. The existence of a valuable by-product in the proposed hybrid systems increases the allowable limit of the cost of required equipment and other operating costs to achieve profitability and reach the break-even point. In the meantime, the FR*-50 % system generates the most revenue, and also the quality of produced freshwater from the FFR-50 %–0.3 M system is the highest.

Arodudu, O, Holmatov, B & Voinov, A 2020, 'Ecological impacts and limits of biomass use: a critical review', CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY.
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Asad, M, Yang, J, He, J, Shamsolmoali, P & He, X 2020, 'Multi-frame feature-fusion-based model for violence detection', Visual Computer.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Human behavior detection is essential for public safety and monitoring. However, in human-based surveillance systems, it requires continuous human attention and observation, which is a difficult task. Detection of violent human behavior using autonomous surveillance systems is of critical importance for uninterrupted video surveillance. In this paper, we propose a novel method to detect fights or violent actions based on learning both the spatial and temporal features from equally spaced sequential frames of a video. Multi-level features for two sequential frames, extracted from the convolutional neural network’s top and bottom layers, are combined using the proposed feature fusion method to take into account the motion information. We also proposed Wide-Dense Residual Block to learn these combined spatial features from the two input frames. These learned features are then concatenated and fed to long short-term memory units for capturing temporal dependencies. The feature fusion method and use of additional wide-dense residual blocks enable the network to learn combined features from the input frames effectively and yields better accuracy results. Experimental results evaluated on four publicly available datasets: HockeyFight, Movies, ViolentFlow and BEHAVE show the superior performance of the proposed model in comparison with the state-of-the-art methods.

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.

Asadabadi, MR, Saberi, M, Zwikael, O & Chang, E 2020, 'Ambiguous requirements: A semi-automated approach to identify and clarify ambiguity in large-scale projects', Computers & Industrial Engineering, vol. 149, pp. 106828-106828.
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Asadniaye Fardjahromi, M, Razmjou, A, Vesey, G, Ejeian, F, Banerjee, B, Chandra Mukhopadhyay, S & Ebrahimi Warkiani, M 2020, 'Mussel inspired ZIF8 microcarriers: a new approach for large-scale production of stem cells', RSC Advances, vol. 10, no. 34, pp. 20118-20128.
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© The Royal Society of Chemistry 2020. Metal-organic frameworks (MOFs) have high porosity, large surface area, and tunable functionality and have been widely used for drug loading. The aim of this study was to exploit unique features of zeolitic imidazolate framework-8 (ZIF8) to develop an innovative composite microcarrier (MC) for human mesenchymal stem cells (hMSCs) adhesion and proliferation. ZIF8 MCs were prepared by immobilization of polydopamine/polyethyleneimine (PDA/PEI) and ZIF8 on the surface of polystyrene beads. The chemical properties of MCs such as coating stability and homogeneity were characterized by different techniques such as ATR-FTIR, XRD, EDX, SEM, and contact angle. The prepared MCs were tested using human adipose-derived mesenchymal stem cells (hADSCs) in both static and dynamic conditions, and results were compared to a commercially available MC (Star-Plus), polydopamine coated MCs and amine-functionalized MCs as a control. Results show that PDA/PEI/ZIF8 coated MCs (in short: ZIF8 MCs) provides an excellent biocompatible environment for hADSCs adhesion and growth. In conclusion, ZIF8 MCs represent suitable and low-cost support for hADSCs culture and expansion, which can maximize cell yield and viability while preserving hADSCs multipotency. The present findings have revealed this strategy has the potential for chemical and topographical modification of MCs in tissue engineering applications.

Ashok, B, Nanthagopal, K, Chyuan, OH, Le, PTK, khanolkar, K, Raje, N, Raj, A, Karthickeyan, V & Tamilvanan, A 2020, 'Multi-functional fuel additive as a combustion catalyst for diesel and biodiesel in CI engine characteristics', Fuel, vol. 278.
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© 2020 Elsevier Ltd The present research work aims at investigating the effect of newly developed multifunctional additive with diesel and Calophyllum Inophyllum biodiesel on compression ignition engine characteristics. A newly developed hydrocarbon based multifunctional fuel additive named as “Thermol-D” which comprises of various ingredients at suitable composition like surfactant, demulsifier, lubricity enhancer, dispersant, cetane improver, antioxidant and combustion catalyst. In this present study, the Thermol-D has been doped with conventional diesel and Calophyllum Inophyllum biodiesel at 0.5 ml, 1 ml and 2 ml concentrations. Moreover, the Thermol-D addition with diesel and biodiesel has shown remarkable stability at all concentrations without any phase separation issues. All the fuel comparative analysis is carried out using all the fuel samples at same operating conditions under load variation from No load to full load at constant engine speed. It has been noticed that the doping of Thermol-D with diesel and biodiesel has increased the brake thermal efficiency by 21% and 43% at 100% loading conditions due to the presence of combustion catalyst and cetane improver in the additive. The multifunctional additive presence in the fuel blends is reduced the carbon monoxide and unburnt hydrocarbon emissions by 32–36% and 20% respectively. Furthermore, the oxides of nitrogen emission has also reduced at significant rate in the range of 18–20.5% for 2% Thermol-D addition with diesel and biodiesel. The Thermol-D contains slight fraction of antioxidant and cetane improvers which has resulted in combustion temperature. All the combustion characteristics are improved by the addition of Thermol-D with diesel and biodiesel.

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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Assaduzzaman, M, Filatova, T, Coenen, F & Lovett, J 2020, 'Freedom of choice to migrate: adaptation to climate change in Bangladesh', International Journal of Sustainable Development and World Ecology, vol. 27, no. 7, pp. 652-661.
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© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Adaptation is an essential part of climate change policy. In areas where impacts are likely to be severe, migration is considered to be an adaptation option. In Bangladesh coastal areas migration due to climate change is contingent on people’s freedom of choice at individual and household level. Following Amartya Sen’s capability approach, we argue that there should be a line drawn between migrations by free choice versus forced migration. Sen’s capability approach focuses on the importance of people’s freedom of choice to act, and the ability to achieve what they consider valuable in their life. In this paper, we use an extensive empirical work engaging 22 focus groups discussions (8–12 individuals in each group) and 14 Key Informants Interviews in South-West Bangladesh to elicit how freedom of choice changes with the economic class and social status of an individual. Using these data we apply Sen’s capability approach to understand the role of the freedom of choice when considering migration as an adaptation option. We argue that the capability approach is essential in revealing a thin border between migration as a (planned) adaptation option and forced migration.

Atif, A, Richards, D, Liu, D & Bilgin, AA 2020, 'Perceived benefits and barriers of a prototype early alert system to detect engagement and support 'at-risk' students: The teacher perspective', COMPUTERS & EDUCATION, vol. 156.
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Atov, I, Chen, KC, Kamal, A & Yu, S 2020, 'Data Science and Artificial Intelligence for Communications', IEEE Communications Magazine, vol. 58, no. 1, pp. 10-11.
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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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Ayanian, N, Robuffo Giordano, P, Fitch, R, Franchi, A & Sabattini, L 2020, 'Guest editorial: special issue on multi-robot and multi-agent systems', AUTONOMOUS ROBOTS, vol. 44, no. 3-4, pp. 297-298.
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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.

Azadi, S, Aboulkheyr Es, H, Kulasinghe, A, Bordhan, P & Ebrahimi Warkiani, M 2020, 'Application of microfluidic technology in cancer research and therapy'.
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© 2020 Elsevier Inc. Cancer is a heterogeneous disease that requires a multimodal approach to diagnose, manage and treat. A better understanding of the disease biology can lead to identification of novel diagnostic/prognostic biomarkers and the discovery of the novel therapeutics with the goal of improving patient outcomes. Employing advanced technologies can facilitate this, enabling better diagnostic and treatment for cancer patients. In this regard, microfluidic technology has emerged as a promising tool in the studies of cancer, including single cancer cell analysis, modeling angiogenesis and metastasis, drug screening and liquid biopsy. Microfluidic technologies have opened new ways to study tumors in the preclinical and clinical settings. In this chapter, we highlight novel application of this technology in area of fundamental, translational and clinical cancer research.

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, Nejad, FM & 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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Azizi, N, CheshmehBeigi, HM & Rouzbehi, K 2020, 'Optimal placement of direct current power system stabiliser (DC-PSS) in multi-terminal HVDC grids', IET GENERATION TRANSMISSION & DISTRIBUTION, vol. 14, no. 12, pp. 2315-2322.
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Azizivahed, A, Arefi, A, Ghavidel Jirsaraie, S, Shafie-khah, M, Li, L, Zhang, J & Catalao, JPS 2020, 'Energy Management Strategy in Dynamic Distribution Network Reconfiguration considering Renewable Energy Resources and Storage', IEEE Transactions on Sustainable Energy, pp. 1-1.
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Azizivahed, A, Razavi, SE, Arefi, A, Jabbari Ghadi, M, Li, L, Zhang, J, Shafie-khah, M & Catalao, JPS 2020, 'Risk-Oriented Multi-Area Economic Dispatch Solution with High Penetration of Wind Power Generation and Compressed Air Energy Storage System', IEEE Transactions on Sustainable Energy, pp. 1-1.
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Baba, AA, Hashmi, RM, Esselle, KP, Attygalle, M & Borg, D 2020, 'A Millimeter-Wave Antenna System for Wideband 2-D Beam Steering', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 5, pp. 3453-3464.
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Bai, L, Yao, L, Wang, X, Kanhere, SS, Guo, B & Yu, Z 2020, 'Adversarial Multi-view Networks for Activity Recognition', Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 4, no. 2.
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© 2020 ACM. Human activity recognition (HAR) plays an irreplaceable role in various applications and has been a prosperous research topic for years. Recent studies show significant progress in feature extraction (i.e., data representation) using deep learning techniques. However, they face significant challenges in capturing multi-modal spatial-temporal patterns from the sensory data, and they commonly overlook the variants between subjects. We propose a Discriminative Adversarial MUlti-view Network (DAMUN) to address the above issues in sensor-based HAR. We first design a multi-view feature extractor to obtain representations of sensory data streams from temporal, spatial, and spatio-temporal views using convolutional networks. Then, we fuse the multi-view representations into a robust joint representation through a trainable Hadamard fusion module, and finally employ a Siamese adversarial network architecture to decrease the variants between the representations of different subjects. We have conducted extensive experiments under an iterative left-one-subject-out setting on three real-world datasets and demonstrated both the effectiveness and robustness of our approach.

Bajan, S & Hutvagner, G 2020, 'RNA-Based Therapeutics: From Antisense Oligonucleotides to miRNAs', CELLS, vol. 9, no. 1.
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Baliarsingh, SK, Vipsita, S, Gandomi, AH, Panda, A, Bakshi, S & Ramasubbareddy, S 2020, 'Analysis of high-dimensional genomic data using MapReduce based probabilistic neural network', Computer Methods and Programs in Biomedicine, vol. 195.
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© 2020 Elsevier B.V. Background: The size of genomics data has been growing rapidly over the last decade. However, the conventional data analysis techniques are incapable of processing this huge amount of data. For the efficient processing of high dimensional datasets, it is essential to develop some new parallel methods. Methods: In this work, a novel distributed method is presented using Map-Reduce (MR)-based approach. The proposed algorithm consists of MR-based Fisher score (mrFScore), MR-based ReliefF (mrRefiefF), and MR-based probabilistic neural network (mrPNN) using a weighted chaotic grey wolf optimization technique (WCGWO). Here, mrFScore, and mrRefiefF methods are introduced for feature selection (FS), and mrPNN is implemented as an effective method for microarray classification. The proper choice of smoothing parameter (σ) plays a major role in the prediction ability of the PNN which is addressed using a novel technique namely, WCGWO. The WCGWO algorithm is used to select the optimal value of σ in PNN. Results: These algorithms have been successfully implemented using the Hadoop framework. The proposed model is tested by using three large and one small microarray datasets, and a comparative analysis is carried out with the existing FS and classification techniques. The results suggest that WCGWO-mrPNN can outperform other methods for high dimensional microarray classification. Conclusion: The effectiveness of the proposed methods are compared with other existing schemes. Experimental results reveal that the proposed scheme is accurate and robust. Hence, the suggested scheme is considered to be a reliable framework for microarray data analysis. Significance: Such a method promotes the application of parallel programming using Hadoop cluster for the analysis of large-scale genomics data, particularly when the dataset is of high dimension.

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.

Ball, JE 2020, 'An Assessment of Continuous Modeling for Robust Design Flood Estimation in Urban Environments', Frontiers in Earth Science, vol. 8.
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© Copyright © 2020 Ball. Catchment management is a complex task that, over the past decade, has become increasingly important to urban communities. While there are many water related management issues, estimation of the magnitude and likelihood of flood events is one that remains a concern to many mangers of urban drainage systems. Data is an essential component of any approach for estimation of the magnitude and likelihood of design flood characteristics. This data can be obtained from catchment monitoring or catchment modeling with these data sources being complementary rather than competitive. However, the absence of monitored data in urban environments has resulted in the data being obtained predominantly from the use of catchment modeling. Numerous alternative approaches for catchment modeling have been developed; these approaches can be categorized as either single event or continuous models. The philosophical basis behind the use of a continuous modeling approach is the concept that the model predictions will replicate the data that would have been recorded if catchment monitoring were to be undertaken at that location and for the modeled catchment conditions. When using this philosophy, a modeler must determine when the predicted data suitably replicates the true data. Presented herein is an analysis of continuous and event modeling undertaken for design flood estimation in an urban catchment located in Sydney, Australia where monitored data is available to assess the utility of the catchment model. It will be shown that frequency analysis of the predicted flows from the continuous model more closely resemble the frequency analysis of the recorded data.

Balogun, AL, Yekeen, ST, Pradhan, B & Althuwaynee, OF 2020, 'Spatio-temporal analysis of oil spill impact and recovery pattern of coastal vegetation and wetland using multispectral satellite Landsat 8-OLI imagery and machine learning models', Remote Sensing, vol. 12, no. 7.
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© 2020, by the authors. Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and environmental dimensions of the coastal ecosystem. However, comprehensive assessment of oil spill impacts and selection of appropriate remediation approaches have been restricted due to reliance on laboratory experiments which offer limited area coverage and classification accuracy. Thus, this study utilizes multispectral Landsat 8-OLI remote sensing imagery and machine learning models to assess the impacts of oil spills on coastal vegetation and wetland and monitor the recovery pattern of polluted vegetation and wetland in a coastal city. The spatial extent of polluted areas was also precisely quantified for effective management of the coastal ecosystem. Using Johor, a coastal city in Malaysia as a case study, a total of 49 oil spill (ground truth) locations, 54 non-oil-spill locations and Landsat 8-OLI data were utilized for the study. The ground truth points were divided into 70% training and 30% validation parts for the classification of polluted vegetation and wetland. Sixteen different indices that have been used to monitor vegetation and wetland stress in literature were adopted for impact and recovery analysis. To eliminate similarities in spectral appearance of oil-spill-affected vegetation, wetland and other elements like burnt and dead vegetation, Support Vector Machine (SVM) and Random Forest (RF) machine learning models were used for the classification of polluted and nonpolluted vegetation and wetlands. Model optimization was performed using a random search method to improve the models' performance, and accuracy assessments confirmed the effectiveness of the two machine learning models to identify, classify and quantify the area extent of oil pollution on coastal vegetation and wetland. Considering the harmonic mean (F1), overall accuracy (OA), User's accuracy (UA), and producers' accuracy (PA), both models have high accuracies. Howeve...

Bao, T, Damtie, MM, Hosseinzadeh, A, Wei, W, Jin, J, Hoang, NPV, Ye, JS, Liu, Y, Wang, XF, Yu, ZM, Chen, ZJ, Wu, K, Frost, RL & Ni, B-J 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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Barthe, G, Hsu, J, Ying, M, Yu, N & Zhou, L 2020, 'Relational proofs for quantum programs.', Proc. ACM Program. Lang., vol. 4, pp. 21:1-21:1.

Barzegarkhoo, R, Lee, SS, Siwakoti, YP, Khan, SA & Blaabjerg, F 2020, 'Design, Control, and Analysis of a Novel Grid-Interfaced Switched-Boost Dual T-Type Five-Level Inverter With Common-Ground Concept', IEEE Transactions on Industrial Electronics, pp. 1-1.
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Barzegarkhoo, R, Siwakoti, YP & Blaabjerg, F 2020, 'A New Switched-Capacitor Five-Level Inverter Suitable for Transformerless Grid-Connected Applications', IEEE TRANSACTIONS ON POWER ELECTRONICS, vol. 35, no. 8, pp. 8140-8153.
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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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Bazaz, SR, Amiri, HA, Vasilescu, S, Mehrizi, AA, Jin, D, Miansari, M & Warkiani, ME 2020, 'Obstacle-free planar hybrid micromixer with low pressure drop', MICROFLUIDICS AND NANOFLUIDICS, vol. 24, no. 8.
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Bazaz, SR, Mashhadian, A, Ehsani, A, Saha, SC, Kruger, T & Warkiani, ME 2020, 'Computational inertial microfluidics: a review', LAB ON A CHIP, vol. 20, no. 6, pp. 1023-1048.
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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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Behmanesh, R, Rahimi, I & Gandomi, AH 2020, 'Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study', ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING.
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Behmanesh, R, Rahimi, I, Zandieh, M & Gandomi, AH 2020, 'Advanced Ant Colony Optimization in Healthcare Scheduling', pp. 37-72.
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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.

Bharill, N, Tiwari, A, Malviya, A, Patel, OP, Gupta, A, Puthal, D, Saxena, A & Prasad, M 2020, 'Fuzzy knowledge based performance analysis on big data', NEUROCOMPUTING, vol. 389, pp. 218-228.
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Bhuiya, MMK, Rasul, M, Khan, M, Ashwath, N & Mofijur, 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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Bian, Y, Wang, D, Liu, X, Yang, Q, Liu, Y, Wang, Q, Ni, BJ, Li, H & Zhang, Y 2020, 'The fate and impact of TCC in nitrifying cultures', Water Research, vol. 178.
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© 2020 Elsevier Ltd Triclocarban (TCC) is a highly effective antibacterial agent, which is widely used in a variety of applications and present at significant levels (e.g., 760 μg/L) in wastewater worldwide. However, the interaction between TCC and nitrifiers, important microbial cultures in wastewater treatment plants, has not been documented. This work therefore aimed to evaluate the fate of TCC in a nitrifying culture and its impact on nitrifiers in four long-term nitrifiers-rich reactors, which received synthetic wastewater containing 0, 0.1, 1, or 5 mg/L TCC. Experimental results showed that 36.7%–50.7% of wastewater TCC was removed by nitrifying cultures in stable operation. Mass balance analysis revealed that the removal of TCC was mainly achieved through adsorption rather than biodegradation. Adsorption kinetic analysis indicated that inhomogeneous multilayer adsorption was responsible for the removal while fourier transform infrared spectroscopy indicated that several functional groups such as hydroxyl, amide and polysaccharide seemed to be the main adsorption sites. The adsorbed TCC significantly deteriorated settleability and performance of nitrifying cultures. With an increase of influent TCC from 0 to 5 mg/L, reactor volatile suspended solids and effluent nitrate decreased from 1200 ± 90 mg/L and 300.81 ± 7.52 mg/L to 880 ± 80 and 7.35 ± 4.62 mg/L while effluent ammonium and nitrite increased from 0.41 ± 0.03 and 0.45 ± 0.23 mg/L to104.65 ± 3.46 and 182.06 ± 7.54 mg/L, respectively. TCC increased the extracellular polymeric substances of nitrifying cultures, inhibited the specific activities of nitrifiers, and altered the abundance of nitrifiers especially Nitrospira sp. In particular, TCC at environmentally relevant concentration (i.e., 0.1 mg/L) significantly inhibited NOB activity and reduced NOB population.

Binh, NTM, Binh, HTT, Van Linh, N & Yu, S 2020, 'Efficient meta-heuristic approaches in solving minimal exposure path problem for heterogeneous wireless multimedia sensor networks in internet of things', Applied Intelligence, vol. 50, no. 6, pp. 1889-1907.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. One of the well-known methods for evaluating Heterogeneous wireless multimedia sensor networks (HWMSNs) in Internet of Things have drawn attention of the research community because this type of networks possesses great advantages of both coverage and performance. One of the most fundamental issues in HWMSNs is the barrier coverage problem which evaluates the surveillance capability of the network systems, especially those designed for security purposes. Among multiple approaches to solve this issue, finding the minimal exposure path (MEP), which corresponds to the worst-case coverage of the network is the most popular and efficient way. However, the MEP problem in HWMSNs (hereinafter heterogeneous multimedia MEP or HM-MEP) is specifically complex and challenging with the unique features of the HWMSNs. Thus, the problem is then converted into numerical functional extreme with high dimension, non-differential and non-linearity. Adapting to these features, two efficient meta-heuristic algorithms, Hybrid Evolutionary Algorithm (HEA) and Gravitation Particle Swarm Optimization (GPSO) are proposed for solving the problem. The HEA is a hybrid evolutionary algorithm in combination with local search while the GPSO is a novel particle swarm optimization based on the gravity force theory. Experimental results on extensive instances indicate that the proposed algorithms are suitable for the HM-MEP problem and perform well in term of both solution accuracy and computation time compared to existing approaches.

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.

Biswas, PC, Rani, S, Hossain, MA, Islam, MR & Canning, J 2020, 'Multichannel Smartphone Spectrometer Using Combined Diffraction Orders', IEEE Sensors Letters, vol. 4, no. 9, pp. 1-4.
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Blazevski, A, Scheltema, MJ, Yuen, B, Masand, N, Nguyen, TV, Delprado, W, Shnier, R, Haynes, A-M, Cusick, T, Thompson, J & Stricker, P 2020, 'Oncological and Quality-of-life Outcomes Following Focal Irreversible Electroporation as Primary Treatment for Localised Prostate Cancer: A Biopsy-monitored Prospective Cohort.', European urology oncology.
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BACKGROUND:Focal irreversible electroporation (IRE) can be used to treat men with localised prostate cancer (PCa) with reduced impact on quality of life (QoL). OBJECTIVE:To assess oncological and functional outcomes. DESIGN, SETTING, AND PARTICIPANTS:To report on a prospective database of patients undergoing primary IRE between February 2013 and August 2018. A minimum of 12-mo follow-up was available for 123 patients. Median follow-up was 36 mo (interquartile range [IQR] 24-52 mo). A total of 112 (91%) patients had National Comprehensive Cancer Network intermediate risk and 11 (9%) had low risk. A total of 12 (9.8%) had International Society of Urological Pathology (ISUP) grade 1, 88 (71.5%) had ISUP 2, and 23 (18.7%) had ISUP 3. INTERVENTION:Focal IRE ablation of PCa lesions. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:Follow-up involved serial prostate-specific antigen (PSA), multiparametric magnetic resonance imaging (mpMRI), and transperineal template mapping biopsy (TTMB) at 12 mo. Failure-free survival (FFS) was defined as progression to whole-gland or systemic treatment or metastasis/death. Functional outcomes were assessed. RESULTS AND LIMITATIONS:Median age was 68yr (IQR 62-73yr). Median preoperative PSA was 5.7ng/ml (IQR 3.8-8.0ng/ml). On post-treatment TTMB, in-field recurrence was present in 2.7-9.8% of patients. FFS at 3yr was 96.75%, metastasis-free survival 99%, and overall survival 100%. A total of 18 patients required salvage treatment (12 had repeat IRE; six had whole-gland treatment). The negative predictive value of mpMRI was 94% and sensitivity 40% for detecting in-field residual disease 6 mo after treatment. Among patients who returned questionnaires, 80/81 (98.8%) remained pad free and 40/53 (76%) had no change in erectile function. CONCLUSIONS:Focal IRE in select patients with localised clinically significant PCa has satisfactory short-term oncological outcomes with a minimal impact on patient QoL. PATIENT SUMMARY:In this study, 123 pati...

Bobba, SS, Hamdouni, N, Pande, K, Namassivayane, K, Agrawal, A & Grattan, KT 2020, 'Design and optimization of perovskite plasmonic nano-laser for operation at room temperature', JOURNAL OF LASER APPLICATIONS, vol. 32, no. 2.
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Bommes, D, Pietroni, N & Hu, R 2020, 'Foreword to the Special Section on Shape Modeling International 2020', COMPUTERS & GRAPHICS-UK, vol. 90, pp. A4-A6.
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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.
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© 2020 Creating a reliable groundwater vulnerability map is a key solution for protecting the groundwater resources and further planning in coastal aquifers. The GALDIT framework, which is an abbreviation for six parameters of Groundwater occurrence, Aquifer hydraulic conductivity, Level of groundwater above sea level, Distance from the shoreline, Impact of the existing status of seawater intrusion, and Thickness of the aquifer, is a well-known framework for evaluating the groundwater vulnerability in coastal zones. In this study, two meta-heuristic algorithms of Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA) were proposed to optimize the weights of GALDIT framework. This framework was tested in the Gharesoo-Gorgan Rood coastal aquifer located in the north of Iran. The GALDIT index illustrated poor evaluation of vulnerability to seawater intrusion. Contrariwise, GALDIT-GWO and GALDIT-GA frameworks provided results that are more reasonable. Both vulnerability maps showed a close similarity in terms of vulnerability to seawater intrusion. The vulnerability maps demonstrated that the west and northwest parts of the study area suffer from seawater intrusion. Additionally, the values of Spearman's rank correlation coefficient between the indices of GALDIT, GALDIT-GWO and GALDIT-GA and the parameter of Cl/HCO3 were obtained as 0.31, 0.53, and 0.49 and the corresponding values for the parameter of TDS were obtained 0.45, 0.64 and 0.60, respectively. Therefore, it can be concluded that the proposed optimization models are able to provide accurate results. Furthermore, these models reduce the subjectivity and increase the capability of the GALDIT index.

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.
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Broadbent, A, Ji, Z, Song, F & Watrous, J 2020, 'Zero-knowledge proof systems for qma', SIAM Journal on Computing, vol. 49, no. 2, pp. 245-283.
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© 2020 Society for Industrial and Applied Mathematics. Prior work has established that all problems in NP admit classical zero-knowledge proof systems, and under reasonable hardness assumptions for quantum computations, these proof systems can be made secure against quantum attacks. We prove a result representing a further quantum generalization of this fact, which is that every problem in the complexity class QMA has a quantum zero-knowledge proof system. More specifically, assuming the existence of an unconditionally binding and quantum computationally concealing commitment scheme, we prove that every problem in the complexity class QMA has a quantum interactive proof system that is zero-knowledge with respect to efficient quantum computations. Our QMA proof system is sound against arbitrary quantum provers, but only requires an honest prover to perform polynomial-time quantum computations, provided that it holds a quantum witness for a given instance of the QMA problem under consideration. The proof system relies on a new variant of the QMA-complete local Hamiltonian problem in which the local terms are described by Clifford operations and standard basis measurements. We believe that the QMA-completeness of this problem may have other uses in quantum complexity.

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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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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Brzozowska, MM, Thach, T, Bliuc, D, Jorgensen, J, Talbot, M, Fenton-Lee, D, Chen, W, Hong, A, Viardot, A, White, CP, Nguyen, T, Pocock, N, Eisman, JA, Baldock, PA & Center, JR 2020, 'Roux-en-Y gastric bypass and gastric sleeve surgery result in long term bone loss', INTERNATIONAL JOURNAL OF OBESITY.
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Buchlak, QD, Esmaili, N, Leveque, JC, Bennett, C, Piccardi, M & Farrokhi, F 2020, 'Ethical thinking machines in surgery and the requirement for clinical leadership', American Journal of Surgery.
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Buchlak, QD, Esmaili, N, Leveque, JC, Farrokhi, F, Bennett, C, Piccardi, M & Sethi, RK 2020, 'Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review', Neurosurgical Review.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied. Our systematic search strategy yielded 6866 results, 70 of which met inclusion criteria. Performance statistics analyzed included area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Natural language processing (NLP) was used to model topics across the corpus and to identify keywords within surgical subspecialties. ML applications were heterogeneous. The densest cluster of studies focused on preoperative evaluation, planning, and outcome prediction in spine surgery. The main algorithms applied were NN, LR, and SVM. Input and output features varied widely and were listed to facilitate future research. The accuracy (F(2,19) = 6.56, p < 0.01) and specificity (F(2,16) = 5.57, p < 0.01) of NN, LR, and SVM differed significantly. NN algorithms demonstrated significantly higher accuracy than LR. SVM demonstrated significantly higher specificity than LR. We found no significant difference between NN, LR, and SVM AUC and sensitivity. NLP topic modeling reached maximum coherence at seven topics, which were defined by modeling approach, surgery type, and pathology themes. Keywords captured research foci within surgical domains. ML technology accurately predicts outcomes and facilitates clinical decision-making in neurosurgery. NNs frequently outperformed other algorithms on supervised learning tasks. This study identified gaps in the literature and opportunities for future neurosurgical ML research.

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).

Ca, Y, Tang, W, Li, L, Zhang, B, Zhan, L & Wang, Y 2020, 'Multi-mode adaptive local reactive power control method based on pv inverters in low voltage distribution networks', IET Generation, Transmission and Distribution, vol. 14, no. 4, pp. 542-551.
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© The Institution of Engineering and Technology 2019. Low voltage distribution networks with the high penetration of photovoltaic (PV) units are facing four types of challenges, including over-voltage issues, under-voltage issues, voltage fluctuation issues and high power losses. In order to mitigate the above issues, this study proposes a multi-mode adaptive local reactive power control method based on Q(P) characteristics. A new concept, node virtual injection active power (NVIP), is developed to take the PV active power, load active power and load reactive power as a whole and as the basis to regulate the PV reactive power. Then, an NVIP-based multi-mode local Q(P) framework is proposed to consider the four types of challenges based on four operation modes, respectively, that can be adaptively switched according to the NVIP value and the NVIP variation. In addition, a systematic parameter design for the Q(P) framework is proposed based on an optimisation model to further enhance the effectiveness of the proposed multi-mode control. The simulation results demonstrate the effectiveness of the proposed method in mitigating voltage violations and voltage fluctuations, and improving the power losses and power factor.

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, vol. 32, pp. 3521-3534.
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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, vol. 62, no. 5, pp. 1914-1919.
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Canning, J & Ziyani, A 2020, 'Chirping fiber Bragg gratings within additively manufactured polymer packages', OPTICS LETTERS, vol. 45, no. 8, pp. 2235-2238.
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Cao, DL, Hong, G & Le, AT 2020, 'Applying chemical heat storage to saving exhaust gas energy in diesel engines: Principle, design and experiment', Journal of Energy Storage, vol. 28.
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© 2020 Elsevier Ltd Thermal energy storage has become more and more important to improving the overall efficiency of energy systems by utilising the wasted energy. This study was aimed to develop a chemical heat storage (CHS) system using magnesium hydroxide (Mg(OH)2) and its dehydration and hydration reactions to recover the thermal energy wasted by the exhaust gases in internal combustion (IC) engines. Experiments were conducted on a Diesel engine (D1146TI) to estimate the percentage of exhaust gas energy stored by CHS technology in the heat storage process. In this process, at 80% engine load, 61.4% of chemical material reacted and 5.05% heat energy of exhaust gas was stored in an hour. The percentage of the stored exhaust gas energy decreased with the engine loads. In the heat output process, in one of the proposed applications, the engine intake air was heated with the stored energy by hydrating MgO when the temperature of the inner wall of the reactor (T6) increased from 45 °C to 86.4 °C. The experimental results show that at the ambient temperature 23 °C, the intake air can be heated to the temperature 5.7 °C-17.3 °C higher than the ambient temperature. The applications of CHS to heating the engine, lubricant or batteries in hybrid vehicles are proposed.

Cao, L, Yuan, G, Leung, T & Zhang, W 2020, 'Special issue on AI and FinTech: The challenge ahead', IEEE Intelligent Systems, vol. 35, no. 2, pp. 3-6.
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Cao, S, Wu, C & Wang, W 2020, 'Behavior of FRP confined UHPFRC-filled steel tube columns under axial compressive loading', Journal of Building Engineering, vol. 32.
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© 2020 Elsevier Ltd Ultra-high performance fiber-reinforced concrete (UHPFRC) has been widely investigated in recent years. This study focusses on the experimental results of FRP confined UHPFRC filled steel tube (UHPFRCFST) specimens under axial compression. In total 37 specimens, both square and circular, were prepared and tested to investigate the axial compressive behaviors of FRP confined UHPFRCFST specimens. The main investigated parameters were the FRP layers, the concrete type and the steel fiber addition. The experimental results indicate that axial load capacity of concrete filled steel tube (CFST) specimens can be effectively enhanced by the FRP confinement. However, the performance enhancement was less significant for square UHPFRCFST specimens as compared to circular UHPFRCFST specimens. Comparisons of these results demonstrate that FRP confined CFST specimens exhibit a higher load-bearing capacity in the post-peak stage than the non-wrapped CFST specimens. Moreover, prediction equations were proposed to predict the ultimate axial load capacity of FRP confined UHPFRCFST specimens, and the predicted results matched well with the experimental results.

Cao, Y & Veitch, D 2020, 'Toward Trusted Time: Remote Server Vetting and the Misfiring Heart of Internet Timing', IEEE-ACM TRANSACTIONS ON NETWORKING, vol. 28, no. 2, pp. 944-956.
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Cao, Y, Cao, Y, Guo, Z, Huang, T & Wen, S 2020, 'Global exponential synchronization of delayed memristive neural networks with reaction–diffusion terms', Neural Networks, vol. 123, pp. 70-81.
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© 2019 Elsevier Ltd This paper investigates the global exponential synchronization problem of delayed memristive neural networks (MNNs) with reaction–diffusion terms. First, by utilizing the pinning control technique, two novel kinds of control methods are introduced to achieve synchronization of delayed MNNs with reaction–diffusion terms. Then, with the help of inequality techniques, pinning control technique, the drive–response concept and Lyapunov functional method, two sufficient conditions are obtained in the form of algebraic inequalities, which can be used for ensuring the exponential synchronization of the proposed delayed MNNs with reaction–diffusion terms. Moreover, the obtained results based on algebraic inequality complement and improve the previously known results. Finally, two illustrative examples are given to support the effectiveness and validity of the obtained theoretical results.

Cao, Z, Ding, W, Wang, YK, Hussain, FK, Al-Jumaily, A & Lin, CT 2020, 'Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy', Neurocomputing, vol. 389, pp. 198-206.
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© 2019 Elsevier B.V. Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generally believed to reflect the ability of the brain to adapt to a visual stimulus environment. In this study, we explored repetitive steady-state visual evoked potential (SSVEP)-based EEG complexity by assessing multiscale inherent fuzzy entropy with relative measurements. We used a wearable EEG device with Oz and Fpz electrodes to collect EEG signals from 40 participants under the following three conditions: a resting state (closed-eyes (CE) and open-eyes (OE) stimulation with five 15-Hz CE SSVEPs and stimulation with five 20-Hz OE SSVEPs. We noted monotonic enhancement of occipital EEG relative complexity with increasing stimulus times in CE and OE conditions. The occipital EEG relative complexity was significantly higher for the fifth SSVEP than for the first SSEVP (FDR-adjusted p < 0.05). Similarly, the prefrontal EEG relative complexity tended to be significantly higher in the OE condition compared to that in the CE condition (FDR-adjusted p < 0.05). The results also indicate that multiscale inherent fuzzy entropy is superior to other competing multiscale-based entropy methods. In conclusion, EEG relative complexity increases with stimulus times, a finding that reflects the strong habituation of brain systems. These results suggest that multiscale inherent fuzzy entropy is an EEG pattern with which brain complexity can be assessed using repetitive SSVEP stimuli.

Cao, Z, Lin, 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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Carmichael, CL, Wang, J, Nguyen, T, Kolawole, O, Benyoucef, A, De Maziere, C, Milne, AR, Samuel, S, Gillinder, K, Hediyeh-zadeh, S, Vo, ANQ, Huang, Y, Knezevic, K, McInnes, WRL, Shields, BJ, Mitchell, H, Ritchie, ME, Lammens, T, Lintermans, B, Van Vlierberghe, P, Wong, NC, Haigh, K, Thoms, JAI, Toulmin, E, Curtis, DJ, Oxley, EP, Dickins, RA, Beck, D, Perkins, A, McCormack, MP, Davis, MJ, Berx, G, Zuber, J, Pimanda, JE, Kile, BT, Goossens, S & Haigh, JJ 2020, 'The EMT modulator SNAI1 contributes to AML pathogenesis via its interaction with LSD1', BLOOD, vol. 136, no. 8, pp. 957-973.
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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 & Abedin, B 2020, 'Understanding the Role of Employees in Digital Transformation: Conceptualization of Digital Literacy of Employees as a Multi-Dimensional Organizational Affordance', Journal of Enterprise Information Management, pp. 1-12.
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Abstract Purpose Much of recent academic and professional interest in exploring digital transformation and enterprise systems has focused on the technology or the organizations’ external forces, leaving internal factors, in particular employees, overlooked. The purpose of this paper is to explore digital literacy of employees as an organizational affordance to capture contextual factors within which digital technologies are situated and are used. Design/methodology/approach We used the evidence-based practice for information systems approach and undertook a systematic literature review of 30 papers coupled with brainstorming with 11 professional experts on the neglected topic of digital literacy and its assessment. Findings This paper draws upon affordance theory and develops a novel framework for conceptualization of digital literacy of employees as an organizational affordance. We do this by distinguishing digital literacy at the individual level and organizational level, and by assessing digital literacy through Information/Cognitive and Social Practice/Articulation affordances. Research Implications The current paper contributes to the notion of organizational affordances by examining the effect of interactions between employee-technology through digital literacy of employees in using digital technologies. We offer a novel conceptualization of digital literacy to improve understanding of the role of employee in digital transformation and utilization of enterprise systems. Thus, our definition of digital literacy offers an extension to the recent discussions in the IS literature regarding the actualization of affordances by bringing a lens of employees into the process. Practical Implications This paper operationalizes digital literacy at organizational and individual levels and offers managers a high-level tool to assess digital literacy of their employees. By doing so, managers can achieve the fit between employees' capabilities and digital technologies t...

Cetindamar Kozanoglu, D, Lammers, T & Zhang, Y 2020, 'Exploring the knowledge spillovers of a technology in an entrepreneurial ecosystem—The case of artificial intelligence in Sydney', Thunderbird International Business Review, vol. 62, no. 5, pp. 457-474.
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New knowledge presents opportunities for commercial value and can hence be a critical asset for entrepreneurial ecosystems. In particular, general purpose technologies are major drivers of entrepreneurship, thus, a nuanced understanding on technological knowledge and its spillovers among actors within an entrepreneurial ecosystem (EE) is warranted. Using knowledge-spillover-based strategic entrepreneurship theory, we propose to observe knowledge spillovers through the assessment of the knowledge bases of a technology in an EE. To do so, this paper proposes to use three key sources of knowledge: publications reflecting the emerging knowledge base, patents representing the realized knowledge base, and startups showing the experimental knowledge base. This paper uses secondary data sources such as Web of Science and applies the method of bibliometrics to illustrate how an assessment is carried out in practice by evaluating the artificial intelligence (AI) knowledge bases in Sydney from 2000 to 2018. The findings are summarized with an illustration of the evolution of the key actors and their activities over time in order to indicate the key strengths and weaknesses in Sydney’s AI knowledge among the different bases. Contrary to expectations from the high potential of knowledge spillovers from a general purpose digital technology such as AI, the paper shows that apparent knowledge spillovers are yet highly limited in Sydney. Even though Sydney has a strong emerging knowledge base, the realized knowledge base seems weak and the experimental knowledge base is slowly improving. That observation itself verifies the need to take strategic actions to facilitate knowledge spillovers within EEs. After the implications for theory and policy makers are discussed, suggestions for further studies are proposed.

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.

Chacon, A, James, B, Tran, L, Guatelli, S, Chartier, L, Prokopvich, D, Franklin, DR, Mohammadi, A, Nishikido, F, Iwao, Y, Akamatsu, G, Takyu, S, Tashima, H, Yamaya, T, Parodi, K, Rosenfeld, A & Safavi-Naeini, M 2020, 'Experimental investigation of the characteristics of radioactive beams for heavy ion therapy', MEDICAL PHYSICS, vol. 47, no. 7, pp. 3123-3132.
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Chaczko, Z, Klempous, R, Rozenblit, J, Adegbija, T, Chiu, C, Kluwak, K & Smutnick, C 2020, 'Biomimetic Middleware Design Principles for IoT Infrastructures', Acta Polytechnica Hungarica, vol. 17, no. 5, pp. 135-150.
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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.

Chai, J, Tsang, IW & Chen, W 2020, 'Large Margin Partial Label Machine.', IEEE transactions on neural networks and learning systems.
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Partial label learning (PLL) is a multi-class weakly supervised learning problem where each training instance is associated with a set of candidate labels but only one label is the ground truth. The main challenge of PLL is how to deal with the label ambiguities. Among various disambiguation techniques, large margin (LM)-based algorithms attract much attention due to their powerful discriminative performance. However, existing LM-based algorithms either neglect some potential candidate labels in constructing the margin or introduce auxiliary estimation of class capacities which is generally inaccurate. As a result, their generalization performances are deteriorated. To address the above-mentioned drawbacks, motivated by the optimistic superset loss, we propose an LM Partial LAbel machiNE (LM-PLANE) by extending multi-class support vector machines (SVM) to PLL. Compared with existing LM-based disambiguation algorithms, LM-PLANE considers the margin of all potential candidate labels without auxiliary estimation of class capacities. Furthermore, an efficient cutting plane (CP) method is developed to train LM-PLANE in the dual space. Theoretical insights into the effectiveness and convergence of our CP method are also presented. Extensive experiments on various PLL tasks demonstrate the superiority of LM-PLANE over existing LM based and other representative PLL algorithms in terms of classification accuracy.

Challa, S, Das, AK, Gope, P, Kumar, N, Wu, F & Vasilakos, AV 2020, 'Design and analysis of authenticated key agreement scheme in cloud-assisted cyber–physical systems', Future Generation Computer Systems, vol. 108, pp. 1267-1286.
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© 2018 Elsevier B.V. With advancements in engineering and science, the application dimensions of Cyber–Physical System (CPS) are increasing due to their improving efficiency, safety, reliability, usability and autonomy. By providing on-demand access to shared processing resources, cloud computing reduces infrastructure costs. Ensuring quality of service and information privacy and security is important in such environments. In this paper, we design a new authentication scheme related to the cloud-assisted CPS in two directions: (1) authentication between a user and a cloud server, and (2) authentication between a smart meter and a cloud server. In the former situation, any external party (user) can access the information stored in a cloud server provided that the user is legal and has the right to access information. In the later situation, a smart meter and a cloud server authentication is needed for secure communication of data stored in the cloud server. In both cases, both entities first mutually authenticate each other and only after successful authentication with the help of a trusted authority, establish a session key for their future secure communication. The proposed scheme deals with both the cases and provides high security as compared to other related works, which is shown through formal and informal security analysis. In addition, the mutual authentication using the widely-accepted Burrows–Abadi–Needham logic (BAN logic) and also formal security verification using the broadly-used Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool demonstrate further that the scheme is strong in security. Finally, the proposed scheme is shown to be efficient in terms of communication and computation costs as compared to those for other related existing schemes.

Chalmers, T, Maharaj, S, Lees, T, Lin, CT, Newton, P, Clifton-Bligh, R, McLachlan, CS, Gustin, SM & Lal, S 2020, 'Impact of acute stress on cortical electrical activity and cardiac autonomic coupling', Journal of Integrative Neuroscience, vol. 19, no. 2, pp. 239-248.
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© 2020 IMR Press Limited. All rights reserved. Assessment of heart rate variability (reflective of the cardiac autonomic nervous system) has shown some predictive power for stress. Further, the predictive power of the distinct patterns of cortical brain activity and-cardiac autonomic interactions are yet to be explored in the context of acute stress, as assessed by an electrocardiogram and electroencephalogram. The present study identified distinct patterns of neural-cardiac autonomic coupling during both resting and acute stress states. In particular, during the stress task, frontal delta waves activity was positively associated with low-frequency heart rate variability and negatively associated with high-frequency heart rate variability. Low high-frequency power is associated with stress and anxiety and reduced vagal control. A positive association between resting high-frequency heart rate variability and frontocentral gamma activity was found, with a direct inverse relationship of low-frequency heart rate variability and gamma wave coupling at rest. During the stress task, low-frequency heart rate variability was positively associated with frontal delta activity. That is, the parasympathetic nervous system is reduced during a stress task, whereas frontal delta wave activity is increased. Our findings suggest an association between cardiac parasympathetic nervous system activity and frontocentral gamma and delta activity at rest and during acute stress. This suggests that parasympathetic activity is decreased during acute stress, and this is coupled with neuronal cortical prefrontal activity. The distinct patterns of neural-cardiac coupling identified in this study provide a unique insight into the dynamic associations between brain and heart function during both resting and acute stress states.

Chamoli, U, Umali, J, Kleuskens, MWA, Chepurin, D & Diwan, AD 2020, 'Morphological characteristics of the kangaroo lumbar intervertebral discs and comparison with other animal models used in spine research.', European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, vol. 29, no. 4, pp. 652-662.
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PURPOSE:Animal models are frequently used to elucidate pathomechanism and pathophysiology of various disorders of the human intervertebral disc (IVD) and also to develop therapeutic approaches. Here we report morphological characteristics of the kangaroo lumbar IVDs and compare them with other animal models used in spine research. METHODS:Twenty-five fresh-frozen cadaveric lumbar spines (T12-S1) derived from kangaroo carcases (Macropus giganteus) of undetermined age were first scanned in a C-Arm X-ray machine. A photograph of the axial section of the disc including a calibrated metric scale was also acquired. The digital radiographs and photographs were processed in ImageJ to determine the axial and sagittal plane dimensions for the whole disc (WD) and the nucleus pulposus (NP) and the mid-sagittal disc height for all the lumbar levels. RESULTS:Our results suggest that the L6-S1 IVD in kangaroos is distinctly large compared with the upper lumbar IVDs. Based on previously published data, human lumbar IVDs are the largest of all the animal IVDs used in spine research, with camelid cervical IVDs being the closest relative in absolute dimensions (llamas: 78% in disc height, 40% in WD volume, and 38% in NP volume). Kangaroo L6-S1 IVD was approximately 51% in height, 20% in WD volume, and 20% in NP volume of the human lumbar IVD. CONCLUSIONS:We conclude that morphological similarities exist between a kangaroo and human lumbar IVD, especially with the lima bean shape in the axial plane, wedge shape in the sagittal plane, convexity at the cephalad endplates, and percentage volume occupied by the NP in the IVD. These slides can be retrieved under Electronic Supplementary Material.

Chan, NJ-A, Gu, D, Tan, S, Fu, Q, Pattison, TG, O'Connor, AJ & Qiao, GG 2020, 'Spider-silk inspired polymeric networks by harnessing the mechanical potential of beta-sheets through network guided assembly', NATURE COMMUNICATIONS, vol. 11, no. 1.
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Chandran, D & Alammari, A 2020, 'Influence of Culture on Knowledge Sharing Attitude 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.

Chandran, M, Bhadada, SK, Ebeling, PR, Gilchrist, NL, Khan, AH, Halbout, P, Lekamwasam, S, Lyubomirsky, G, Mitchell, PJ, Nguyen, TV & Tiu, KL 2020, 'IQ driving QI: the Asia Pacific Consortium on Osteoporosis (APCO): an innovative and collaborative initiative to improve osteoporosis care in the Asia Pacific', OSTEOPOROSIS INTERNATIONAL.
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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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Changani, Z, Razmjou, A, Taheri-Kafrani, A, Warkiani, ME & Asadnia, M 2020, 'Surface modification of polypropylene membrane for the removal of iodine using polydopamine chemistry', CHEMOSPHERE, vol. 249.
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Cheah, MY, Ong, HC, Zulkifli, NWM, Masjuki, HH & Salleh, A 2020, 'Physicochemical and tribological properties of microalgae oil as biolubricant for hydrogenpowered engine', INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, vol. 45, no. 42, pp. 22364-22381.
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Chehade, M, Bullock, M, Glover, A, Hutvagner, G & Sidhu, S 2020, 'Key MicroRNA's and Their Targetome in Adrenocortical Cancer.', Cancers, vol. 12, no. 8.
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Adrenocortical Carcinoma (ACC) is a rare but aggressive malignancy with poor prognosis and limited response to available systemic therapies. Although complete surgical resection gives the best chance for long-term survival, ACC has a two-year recurrence rate of 50%, which poses a therapeutic challenge. High throughput analyses focused on characterizing the molecular signature of ACC have revealed specific micro-RNAs (miRNAs) that are associated with aggressive tumor phenotypes. MiRNAs are small non-coding RNA molecules that regulate gene expression by inhibiting mRNA translation or degrading mRNA transcripts and have been generally implicated in carcinogenesis. This review summarizes the current insights into dysregulated miRNAs in ACC tumorigenesis, their known functions, and specific targetomes. In addition, we explore the possibility of particular miRNAs to be exploited as clinical biomarkers in ACC and as potential therapeutics.

Chen, B, Peng, F, Wang, H & Yu, Y 2020, 'Compound fault identification of rolling element bearing based on adaptive resonant frequency band extraction', Mechanism and Machine Theory, vol. 154.
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© 2020 The high frequency resonance (HFR) technique is regarded as a powerful tool for fault diagnosis of rolling element bearings. Different from the usage of the HFR in single fault, the determination of multiple resonant frequency bands under the compound faults and extraneous random impulses is still a challenging task. This paper develops a novel compound fault identification method based on adaptive resonant frequency band extraction. The improved redundant second generation wavelet packet transform is first presented to decompose vibration signal into various narrow bands for providing a fine separation of fault signatures. Then the squared envelope spectrum sparsity criteria is designed to quantify fault characteristics buried in narrow frequency bands. Consequently, the squared envelope spectrum sparsogram is constructed to highlight optimal resonant bands, and the compound faults can be well detected by band-pass filtering and envelope analysis. The numerical and experimental results confirm effectiveness and superiority of the proposed method, which is more sensitive to fault-related impulses and robust to extraneous interferences.

Chen, B, Yu, S, Yu, Y & Zhou, Y 2020, 'Acoustical damage detection of wind turbine blade using the improved incremental support vector data description', Renewable Energy, vol. 156, pp. 548-557.
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© 2020 Elsevier Ltd The blade is a crucial part of wind turbine for generating electricity and prone to damage due to harsh external environment. Accurate damage detection of wind turbine blade (WTB) is still a prominent challenge. This paper presents an acoustical detection method for damage identification of the WTB based on pattern recognition. In the proposed method, sound pulse extraction of the WTB is first investigated through physical method in combination with the filter and sliding window. Subsequently, the wavelet packet energy ratios of acoustic signal are introduced to characterize the discrepancy between intact and cracked sound pulses, and the support vector data description (SVDD) model is built for WTB damage detection. Besides, an improved incremental learning method is presented and employed to adaptively update the SVDD model, which aims at simplifying calculation procedure. Finally, the performance of proposed method is evaluated using experimental data collected from the WTBs with both intact and damaged conditions in commercial wind farms. It is demonstrated that proposed method has improvement in prediction accuracy compared to previous incremental SVDD models and performs the best on training time.

Chen, H, Fang, Y, Zhang, Y, Zhang, W & Wang, L 2020, 'ESPM: Efficient Spatial Pattern Matching', IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 6, pp. 1227-1233.
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© 1989-2012 IEEE. With recent advances in information technologies such as global position system and mobile internet, a huge volume of spatio-textual objects have been generated from location-based services, which enable a wide range of spatial keyword queries. Recently, researchers have proposed a novel query, called Spatial Pattern Matching (SPM), which uses a pattern to capture the user's intention. It has been demonstrated to be fundamental and useful for many real applications. Despite its usefulness, the SPM problem is computationally intractable. Existing algorithms suffer from the low efficiency issue, especially on large scale datasets. To enhance the performance of SPM, in this paper we propose a novel Efficient Spatial Pattern Matching (ESPM) algorithm, which exploits the inverted linear quadtree index and computes matched node pairs and object pairs level by level in a top-down manner. In particular, it focuses on pruning unpromising nodes and node pairs at the high levels, resulting in a large number of unpromising objects and object pairs to be pruned before accessing them from disk. We experimentally evaluate the performance of ESPM on real large datasets. Our results show that ESPM is over one order of magnitude faster than the state-of-the-art algorithm, and also uses much less I/O cost.

Chen, H, Heidari, AA, Chen, H, Wang, M, Pan, Z & Gandomi, AH 2020, 'Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies', FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, vol. 111, pp. 175-198.
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Chen, H, Li, F, Wang, L, Jin, Y, Chi, C-H, Kurgan, L, Song, J & Shen, J 2020, 'Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions.', Briefings in bioinformatics.
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In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.

Chen, H, Qi, C, Shen, L, Fu, Q, Wang, Z, Xiong, Z, Sun, Y & Liu, Y 2020, 'Tunable d-spacing of dry reduced graphene oxide nanosheets for enhancing re-dispersibility in organic solvents', Applied Surface Science, vol. 531.
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© 2020 Elsevier B.V. The reduced graphene oxide (rGO) nanosheets were modified by using polymeric nanospheres to adjust the d-spacings between dry rGO nanosheets. The experimental and computational studies show that the incorporation of polymeric nanospheres can effectively increase the d-spacing between dry rGO nanosheets, and meanwhile provide good compatibility of rGO with organic solvents. Specifically, when the d-spacing between nanosheets is greater than 14.0 nm, the dried rGO nanosheets can be well redispersed and stabilized in various organic solvents. This study thus provides a new technology that can produce dry rGO nanosheets with good re-dispersibility and stability in various organic solvents on a large-scale in a more environmental friendly manner.

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, H, Xu, J-X, Kong, Z-H, Chen, W-H & Zhang, XY 2020, 'Broadband High-Efficiency Power Amplifier With Quasi-Elliptic Low-Pass Response', IEEE ACCESS, vol. 8, pp. 52566-52574.
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Chen, J, Zhou, J, Cao, Z, Vasilakos, AV, Dong, X & Choo, KKR 2020, 'Lightweight Privacy-Preserving Training and Evaluation for Discretized Neural Networks', IEEE Internet of Things Journal, vol. 7, no. 4, pp. 2663-2678.
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© 2014 IEEE. Machine learning, particularly the neural network (NN), is extensively exploited in dizzying applications. In order to reduce the burden of computing for resource-constrained clients, a large number of historical private datasets are required to be outsourced to the semi-trusted or malicious cloud for model training and evaluation. To achieve privacy preservation, most of the existing work either exploited the technique of public key fully homomorphic encryption (FHE) resulting in considerable computational cost and ciphertext expansion, or secure multiparty computation (SMC) requiring multiple rounds of interactions between user and cloud. To address these issues, in this article, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized NNs (DiNNs) is proposed. First, we put forward an efficient single key fully homomorphic data encapsulation mechanism (SFH-DEM) without exploiting public key FHE. Based on SFH-DEM, a series of atomic calculations over the encrypted domain, including multivariate polynomial, nonlinear activation function, gradient function, and maximum operations are devised as building blocks. Furthermore, a lightweight privacy-preserving model training and evaluation scheme LPTE for DiNNs is proposed, which can also be extended to convolutional NN. Finally, we give the formal security proofs for dataset privacy, model training privacy, and model evaluation privacy under the semi-honest environment and implement the experiment on real dataset MNIST for recognizing handwritten numbers in DiNN to demonstrate the high efficiency and accuracy of our proposed LPTE.

Chen, K, Xu, W, Ding, Y, Xue, P, Sheng, P, Qiao, H, Wang, S & Yu, Y 2020, 'Mechanical and thermal properties of all-wood biocomposites through controllable dissolution of cellulose with ionic liquid', Polymers, vol. 12, no. 2.
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© 2020 by the authors. All-wood biocomposites were prepared with an efficient method. The ionic liquid of 1-butyl-3-methylimidazolium chloride (BMIMCl) was used to impregnate manchurian ash (MA) before hot-pressing, and the all-wood biocomposites were prepared by controllable dissolving and regenerating the cellulose in MA. The Fourier transform infrared analysis suggested that all the components of MA remained unchanged during the preparation. X-ray diffraction, thermogravimetric and scanning electron microscope analysis were carried out to study the process parameters of hot-pressing pressure and time on the crystallinity, thermal properties and microstructure of the all-wood biocomposites. The tensile strength of the prepared all-wood biocomposites reached its highest at 212.6 MPa and was increased by 239% compared with that of the original MA sample. The thermogravimetric analysis indicated that as the thermo-stability of the all-wood biocomposites increased, the mass of the residual carbon increased from 19.7% to 22.7% under a hot-pressing pressure of 10 MPa. This work provides a simple and promising pathway for the industrial application of high-performance and environmentally friendly all-wood biocomposites.

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, L, Zhang, N, Sun, HM, Chang, CC, Yu, S & Choo, KKR 2020, 'Secure search for encrypted personal health records from big data NoSQL databases in cloud', Computing.
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© 2019, Springer-Verlag GmbH Austria, part of Springer Nature. As the healthcare industry adopts the use of cloud to store personal health record (PHR), there is a need to ensure that we maintain the ability to perform efficient search on encrypted data (stored in the cloud). In this paper, we propose a secure searchable encryption scheme, which is designed to search on encrypted personal health records from a NoSQL database in semi-trusted cloud servers. The proposed scheme supports almost all query operations available in plaintext database environments, especially multi-dimensional, multi-keyword searches with range query. Specifically, in the proposed scheme, an Adelson-Velsky Landis (AVL) tree is utilized to construct the index, and an order-revealing encryption (ORE) algorithm is used to encrypt the AVL tree and realize range query. As document-based databases are probably the most popular NoSQL database, due to their flexibility, high efficiency, and ease of use, MongoDB, a document-based NoSQL database, is chosen to store the encrypted PHR data in our scheme. Experimental results show that the scheme can achieve secure and practical searchable encryption for PHRs. A comparison of the range query demonstrates that the time overhead of our ORE-based scheme is 25.5% shorter than that of the mOPE-based Arx (an encrypted database system) scheme.

Chen, M, Ji, J, Liu, H & Yan, F 2020, 'Periodic Oscillations in the Quorum-Sensing System with Time Delay', INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS, vol. 30, no. 9.
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Chen, M, Voinov, A, Ames, DP, Kettner, AJ, Goodall, JL, Jakeman, AJ, Barton, MC, Harpham, Q, Cuddy, SM, DeLuca, C, Yue, S, Wang, J, Zhang, F, Wen, Y & Lu, G 2020, 'Position paper: Open web-distributed integrated geographic modelling and simulation to enable broader participation and applications', EARTH-SCIENCE REVIEWS, vol. 207.
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Chen, R-H, Ong, HC & Wang, W-C 2020, 'The optimal blendings of diesel, biodiesel and gasoline with various exhaust gas recirculations for reducing NO(x)and smoke emissions from a diesel engine', INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY.
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Chen, RS, Zhu, L, Lin, JY, Wong, SW, Li, Y, Yang, Y & He, Y 2020, 'Miniaturized Full-Metal Dual-Band Filter Using Dual-Mode Circular Spiral Resonators', IEEE Microwave and Wireless Components Letters, vol. 30, no. 6, pp. 573-576.
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© 2001-2012 IEEE. A miniaturized full-metal dual-band bandpass filter using a dual-mode circular spiral resonator (CSR) in a single metal cavity is proposed. Two spirals are combined together to form a dual-mode resonator, and the transmission zero (TZ) produced by the source-load coupling can separate the two modes and achieve the desired dual-band property. Then, a second-order dual-band filter is designed based on the dual-mode resonator, and each band can be individually synthesized, designed, and tuned to achieve the desired filtering performances. In addition, each band has TZs on both sides of the passband to achieve high selectivity. The size of the second-order dual-band filter is only 0.073\lambda _{0} \times 0.055\lambda _{0} \times 0.015\lambda _{0} calculated at the center frequency of the lower band. Finally, the filter is fabricated and measured, and a good agreement is achieved between the measurement and simulation.

Chen, R-S, Zhu, L, Lin, J-Y, Wong, S-W, Yang, Y, Li, Y, Zhang, L & He, Y 2020, 'High-Efficiency and Wideband Dual-Resonance Full-Metal Cavity-Backed Slot Antenna Array', IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 19, no. 8, pp. 1360-1364.
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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.', The Science of the total environment, vol. 701, pp. 134979-134979.
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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, X, Chamoli, U, Castillo, JV, Ramakrishna, VAS & Diwan, AD 2020, 'Complication rates of different discectomy techniques for symptomatic lumbar disc herniation: a systematic review and meta-analysis', EUROPEAN SPINE JOURNAL, vol. 29, no. 7, pp. 1752-1770.
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Chen, X, Cui, J, Ni, W, Wang, X, Zhu, Y, Zhang, J & Xu, S 2020, 'DFT-s-OFDM: Enabling Flexibility in Frequency Selectivity and Multiuser Diversity for 5G', IEEE Consumer Electronics Magazine.
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IEEE The upcoming fifth-generation (5G) wireless cellular communication systems are expected to provide efficiency and productivity by ushering in flexibility of waveforms and resource allocation. Clustered discrete Fourier transform-spread-orthogonal frequency division multiplexing (DFT-s-OFDM) has been specified as the waveform for 5G, due to its flexibility in the exploitation of frequency selectivity and multiuser diversity. This article discusses the flexibility that clustered DFT-s-OFDM is able to achieve at the physical layer, and the requirements that it needs to comply with at the media access control layer. This article emphasizes on the resource allocation of clustered DFT-s-OFDM that can leverage between the flexibility and compliance requirements, and reveals that clustered DFT-s-OFDM is suited for cells with UEs closely distributed around the base stations. A new enhanced riding peak method which operates on the basis of multiple resolutions is identified to be able to balance the data rate of clustered DFT-s-OFDM and computational complexity.

Chen, X, Tang, M-C, Yi, D & Ziolkowski, RW 2020, 'Wideband, Electrically Small, Near-Field Resonant Parasitic Dipole Antenna With Stable Radiation Performance', IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 19, no. 5, pp. 826-830.
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Chen, X, Yang, L, Sun, J, Wei, W, Liu, Y & Ni, B-J 2020, 'Influences of Longitudinal Heterogeneity on Nitrous Oxide Production from Membrane-Aerated Biofilm Reactor: A Modeling Perspective.', Environmental science & technology.
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As a promising technology for sustainable nitrogen removal from wastewater, the membrane-aerated biofilm reactors (MABRs) performing autotrophic deammonification are faced with the problem of unwanted production of nitrous oxide (N2O, a potent greenhouse gas). As a common tool to study N2O production from such an MABR, the traditional one-dimensional modeling approach fails to simulate the existence of longitudinal gradients in the reactor and therefore might render N2O production significantly deviated from reality. To this end, this work aims to study the influences of key longitudinal gradients (i.e., in oxygen, liquid-phase components, and biofilm thickness) on the N2O production from a typical MABR performing autotrophic deammonification by applying a modified version of a newly developed compartmental model. Through comparing the modeling results of different reactor configurations, this work reveals that the single impact of the longitudinal gradients studied on the N2O production from the MABR follows the order: oxygen (significant) > liquid-phase components (slight) > biofilm thickness (almost none). When multiple longitudinal gradients are present, they become correlated and would jointly influence the N2O production and nitrogen removal of the MABR. The results also show the need for multispot measurements to get an accurate representation of spatial biofilm features of the MABR configuration with the membrane lumen designed/operated as a plug flow reactor. While the traditional modeling approach is acceptable to evaluate the nitrogen removal in most cases, it might overestimate or underestimate the N2O production from the MABR with at least one of the longitudinal gradients in oxygen and liquid-phase components. For such an MABR, the longitudinal heterogeneity in biofilm thickness and the number of biofilm thickness classes to be included in the model would also make a difference to the simulation results, especially the N2O production. The work also ...

Chen, Y, An, P, Huang, X, Yang, C, Liu, D & Wu, Q 2020, 'Light Field Compression Using Global Multiplane Representation and Two-Step Prediction', IEEE Signal Processing Letters, vol. 27, pp. 1135-1139.
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© 1994-2012 IEEE. Due to its spatio-angular structure, light field image allows for a wealth of post-processing techniques like digital refocusing and depth estimation. In order to compress the data of the two domains, the current proposal intends to embed the disparity-based view synthesis method into the decoder. However, predicting each view separately or in local groups means bringing more computational burden to the decoder and destroying the light field structure. Since disparity contains the relationship between all light rays in the light field, the proposed solution is to predict a disparity-based global representation as the first step. In the second step, all the views can be predicted easily based on this representation. In this letter, we use the recently proposed multiplane as the form of this global representation. The experimental results show the effectiveness of the proposed solution, and the better RD performance compared to other schemes especially under low bitrates.

Chen, Y, Huang, S & Fitch, R 2020, 'Active SLAM for Mobile Robots With Area Coverage and Obstacle Avoidance', IEEE-ASME TRANSACTIONS ON MECHATRONICS, vol. 25, no. 3, pp. 1182-1192.
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Chen, Y, Leighton, B, Zhu, H, Ke, X, Liu, S & Zhao, L 2020, 'Submap-Based Indoor Navigation System for the Fetch Robot', IEEE ACCESS, vol. 8, pp. 81479-81491.
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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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Chen, Y, Zhao, L, Zhang, Y & Huang, S 2020, 'Dense Isometric Non-Rigid Shape-From-Motion Based on Graph Optimization and Edge Selection', IEEE ROBOTICS AND AUTOMATION LETTERS, vol. 5, no. 4, pp. 5889-5896.
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Chen, Z, Duan, X, Wei, W, Wang, S & Ni, B-J 2020, 'Electrocatalysts for acidic oxygen evolution reaction: Achievements and perspectives', Nano Energy, vol. 78, pp. 105392-105392.
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Chen, Z, Duan, X, Wei, W, Wang, S & Ni, B-J 2020, 'Iridium-based nanomaterials for electrochemical water splitting', Nano Energy, vol. 78, pp. 105270-105270.
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Chen, Z, Duan, X, Wei, W, Wang, S, Zhang, Z & Ni, B-J 2020, 'Boride-based electrocatalysts: Emerging candidates for water splitting', NANO RESEARCH, vol. 13, no. 2, pp. 293-314.
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Cheng, C, Cao, Z & Xiao, F 2020, 'A generalized belief interval-valued soft set with applications in decision making', SOFT COMPUTING, vol. 24, no. 13, pp. 9339-9350.
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Cheng, D, Huu, HN, Guo, W, Chang, SW, Dinh, DN, 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.
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Cheng, D, Liu, Y, Ngo, HH, Guo, W, Chang, SW, Dinh, DN, Zhang, S, Luo, G & Liu, Y 2020, 'A review on application of enzymatic bioprocesses in animal wastewater and manure treatment', BIORESOURCE TECHNOLOGY, vol. 313.
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Cheng, D, Ngo, HH, Guo, W, Chang, SW, Dinh, DN, 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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Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Li, J, Ly, QV, Nguyen, TAH & Tran, VS 2020, 'Applying a new pomelo peel derived biochar in microbial fell cell for enhancing sulfonamide antibiotics removal in swine wastewater', Bioresource Technology.
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© 2020 Elsevier Ltd A sequential anode-cathode double-chamber microbial fuel cell (MFC) is a promising system for simultaneously removing contaminants, recovering nutrients and producing energy from swine wastewater. To improve sulfonamide antibiotics (SMs)’s removal in the continuous operating of MFC, one new pomelo peel-derived biochar was applied in the anode chamber in this study. Results demonstrated that SMs can be absorbed onto the heterogeneous surfaces of biochar through pore-filling and π-π EDA interaction. Adding biochar to a certain concentration (500 mg/L) could enhance the efficiency in removing sulfamethoxazole, sulfadiazine and sulfamethazine to 82.44–88.15%, 53.40–77.53% and 61.12–80.68%, respectively. Moreover, electricity production, COD and nutrients removal were improved by increasing the concentration of biochar. Hence, it is proved that adding biochar in MFC could effectively improve the performance of MFC in treating swine wastewater containing SMs.

Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Shan, X, Nghiem, LD & Nguyen, LN 2020, 'Removal process of antibiotics during anaerobic treatment of swine wastewater.', Bioresource technology, vol. 300, pp. 122707-122707.
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High concentrations of antibiotics in swine wastewater pose potentially serious risks to the environment, human and animal health. Identifying the mechanism for removing antibiotics during the anaerobic treatment of swine wastewater is essential for reducing the serious damage they do to the environment. In this study, batch experiments were conducted to investigate the biosorption and biodegradation of tetracycline and sulfonamide antibiotics (TCs and SMs) in anaerobic processes. Results indicated that the removal of TCs in the anaerobic reactor contributed to biosorption, while biodegradation was responsible for the SMs' removal. The adsorption of TCs fitted well with the pseudo-second kinetic mode and the Freundlich isotherm, which suggested a heterogeneous chemisorption process. Cometabolism was the main mechanism for the biodegradation of SMs and the process fitted well with the first-order kinetic model. Microbial activity in the anaerobic sludge might be curtailed due to the presence of high concentrations of SMs.

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, Lee, D, Duc, LN, Zhang, J, Liang, S, Varjani, S & Wang, J 2020, 'Performance of microbial fuel cell for treating swine wastewater containing sulfonamide antibiotics', BIORESOURCE TECHNOLOGY, vol. 311.
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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.

Cheng, Q, Nguyen, D, Dutkiewicz, E & Mueck, M 2020, 'Preserving Honest/Dishonest Users' Operational Privacy with Blind Interference Calculation in Spectrum Sharing System', IEEE Transactions on Mobile Computing.
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In this paper, we investigate the operational privacy issue of Incumbent Users (IUs) and honest/dishonest Secondary Users (SUs). For the case of IUs and honest SUs, we propose a privacy-preserving scheme for DSA by leveraging encryption and obfuscation methods (PSEO). To implement PSEO, we introduce an interference calculation scheme that allows users to calculate an interference budget without revealing operational information, referred to as the blind interference calculation scheme (BICS). BICS also reduces the computing overhead of PSEO, compared with FCC's SAS by moving interference budgeting tasks to local users and calculating it in an offline manner. To further save the overhead in calculating the interference map, we introduce a quantization method and optimize the grid sizes of the terrestrial area of interest. Additionally, for the case of IUs and dishonest SUs, we propose a "punishment and forgiveness" (PF) mechanism, which draws support from SUs' reputation scores (RSs) and reputation histories (RHs), to encourage SUs to provide truthful information. Theoretical analysis and extensive simulations show that our proposed PSEO and PF-PSEO schemes can better protect all users' operational privacy under various privacy attacks, yielding higher spectrum utilization with less online overhead, compared with state of the art approaches

Cheng, ZQ, Zhao, R, Yuan, Y, Li, F, Castel, A & Xu, T 2020, 'Ageing coefficient for early age tensile creep of blended slag and low calcium fly ash geopolymer concrete', Construction and Building Materials, vol. 262.
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© 2020 Elsevier Ltd The ageing coefficient is required in the age-adjusted effective modulus method to assess the effect of creep in concrete structures subjected to varied stress history. In this paper, experiments were carried out to calibrate the ageing coefficient for early age tensile creep of blended slag and low calcium fly ash geopolymer concrete. The development of total strains of geopolymer concrete under sustained tension, including instantaneous strain, creep strain and shrinkage strain was monitored by using the dog-bone shaped specimens. The specimens were loaded at the age of 2, 3, 4, 7, 14, 21 days, respectively. The strains of the unloaded companion specimens were monitored as well. The development of the creep coefficient φ(t,τ0) for geopolymer concrete was calculated based on the experimental results. By using the step-by-step numerical analysis, the ageing coefficient for early age tensile creep of geopolymer concrete was assessed. An ageing coefficient of 0.8 is recommended for structural design. The comparison between calculated and measured tensile strains from the restrained concrete ring test shows the validity of the proposed value of the ageing coefficient.

Cheshomi, A, Bakhtiyari, E & Khabbaz, H 2020, 'A comparison between undrained shear strength of clayey soils acquired by “PMT” and laboratory tests', Arabian Journal of Geosciences, vol. 13, no. 14.
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© 2020, Saudi Society for Geosciences. A pressuremeter test (PMT) is one of the in situ tests, which is used to evaluate deformation and strength parameters of soils for various projects, including subway projects. The limit pressure (PL) and undrained shear strength (Su) are the key parameters that are obtained directly and indirectly from the pressuremeter testing results. This research was carried out using geotechnical information obtained from a subway project in Qom city, Iran. Based on 44 PMT and uniaxial tests on very stiff to hard saturated clayey soils, a linear empirical equation between Su − PL and Su − PL* = (PL − σH) with R2 = 0.68 was proposed and it was found that σH had an insignificant effect on the proposed relationship. The effect of physical properties of soil, including plastic index (PI), liquid limit (LL), and water content (ω), was evaluated, and a multivariate equation was proposed between them. A comparison between the equations obtained in this research and those proposed by other researchers reveals that the empirical relationships between Su and PL are associated with the consistency of soils; the stiffer the soil is, the slope of relationship between Su and PL is less.

Chiang, YK, Oberst, S, Melnikov, A, Quan, L, Marburg, S, Alù, A & Powell, D 2020, 'Reconfigurable acoustic metagrating for high efficiency anomalous reflections', Physcal Review Applied, vol. 13, pp. 064067-064067.
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Recent study revealed that the scattering behaviors of bianisotropic scatterers can be controlled by an additional degree of freedom, represented as Willis coupling, which can be endowed with asymmetric wave scattering to form an acoustic metagrating for wavefront manipulation. Here, we introduce a flexible acoustic metagrating, formed by periodic arrays of properly design Willis scatterers, for anomalous reflection with nearly unitary efficiency and significantly less necessity of fine discretization. Numerical approaches to predict the wave steering efficiency of the proposed acoustic metagratings with infinite and finite length are developed, which are utilized to demonstrate the strength and flexible features of the metagratings. Results reveal that the proposed acoustic metagrating can reroute incident wave into desired direction at a large angle with nearly unitary efficiency in reflection. The numerical predictions also show that the proposed designs offer a high efficient tunable platform in controlling the steering angles and operating frequencies. To practically realize the ability of extreme angle steering and tunable characteristics of the metagratings, designed structures are fabricated and examined experimentally. The acoustic wave is successfully rerouted to the targeted reflection angles by the finite metagrating. The flexibility regarding different steering angles and operating frequencies of the proposed metagratings are also demonstrated experimentally.

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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Chu, P, Zhang, JA, Wang, X, Fang, G & Wang, D 2020, 'Semi-Persistent Resource Allocation Based on Traffic Prediction for Vehicular Communications', IEEE Transactions on Intelligent Vehicles, vol. 5, no. 2, pp. 345-355.
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Chu, P, Zhang, JA, Wang, X, Fei, Z, Fang, G & Wang, D 2020, 'Interference Characterization and Power Optimization for Automotive Radar With Directional Antenna', IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, vol. 69, no. 4, pp. 3703-3716.
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Chude-Okonkwo, UAK, Maharaj, BT, Vasilakos, AV & Malekian, R 2020, 'Information-Theoretic Model and Analysis of Molecular Signaling in Targeted Drug Delivery', IEEE Transactions on Nanobioscience, vol. 19, no. 2, pp. 270-284.
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© 2002-2011 IEEE. Targeted drug delivery (TDD) modality promises a smart localization of appropriate dose of therapeutic drugs to the targeted part of the body at reduced system toxicity. To achieve the desired goals of TDD, accurate analysis of the system is important. Recent advances in molecular communication (MC) present prospects to analyzing the TDD process using engineering concepts and tools. Specifically, the MC platform supports the abstraction of TDD process as a communication engineering problem in which the injection and transportation of drug particles in the human body and the delivery to a specific tissue or organ can be analyzed using communication engineering tools. In this paper we stand on the MC platform to present the information-theoretic model and analysis of the TDD systems. We present a modular structure of the TDD system and the probabilistic models of the MC-abstracted modules in an intuitive manner. Simulated results of information-theoretic measures such as the mutual information are employed to analyze the performance of the TDD system. Results indicate that uncertainties in drug injection/release systems, nanoparticles propagation channel and nanoreceiver systems influence the mutual information of the system, which is relative to the system's bioequivalence measure.

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, vol. 234, no. 7, pp. 1869-1882.
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Costa, E, Climent, E, Ast, S, Weller, MG, Canning, J & Rurack, K 2020, 'Development of a lateral flow test for rapid pyrethroid detection using antibody-gated indicator-releasing hybrid materials', ANALYST, vol. 145, no. 10, pp. 3490-3494.
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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, pp. 122710-122710.
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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 and Management, vol. 57, no. 2.
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© 2019 Elsevier Ltd Methods for document clustering and topic modelling in online social networks (OSNs) offer a means of categorising, annotating and making sense of large volumes of user generated content. Many techniques have been developed over the years, ranging from text mining and clustering methods to latent topic models and neural embedding approaches. However, many of these methods deliver poor results when applied to OSN data as such text is notoriously short and noisy, and often results are not comparable across studies. In this study we evaluate several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit. We benchmark four different feature representations derived from term-frequency inverse-document-frequency (tf-idf) matrices and word embedding models combined with four clustering methods, and we include a Latent Dirichlet Allocation topic model for comparison. Several different evaluation measures are used in the literature, so we provide a discussion and recommendation for the most appropriate extrinsic measures for this task. We also demonstrate the performance of the methods over data sets with different document lengths. Our results show that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures. We also demonstrate a method for interpreting the clusters with a top-words based approach using tf-idf weights combined with embedding distance measures.

Daer, S, Akther, N, Wei, Q, Shon, HK & Hasan, SW 2020, 'Influence of silica nanoparticles on the desalination performance of forward osmosis polybenzimidazole membranes', DESALINATION, vol. 491.
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Dai, M, Xiao, G, Cheng, S, Wang, D & He, X 2020, 'Structural correlation filters combined with a Gaussian particle filter for hierarchical visual tracking', Neurocomputing.
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© 2020 Visual tracking is a key problem for many computer vision applications such as human-computer interaction, intelligent medical diagnosis, navigation and traffic control management. Most of the existing tracking methods are mainly based on correlation filters. However, boundary effect, scale estimation and template updating have not been fully resolved. Herein, this paper presents a new hierarchical tracking method combining structural correlation filters with a Gaussian Particle Filter (GPF), named KCF-GPF. Weak KCF classifiers are constructed via a Lukas-Kanade (LK) method and the preliminary target location is presented as a weighted sum of these classifiers. Specially, a facile weight strategy is implemented to estimate the reliability of each weak classifier. On the basis of the preliminary target location, the GPF using features from a Convolutional Neural Network (CNN) is employed to predict the location and scale of a target. Extensive experiments with the OTB-2013 and the OTB-2015 databases demonstrate that the proposed algorithm performs favourably against state-of-the-art trackers.

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.

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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Security and privacy have become a great concern in cloud computing platforms in which users risk the leakage of their private data. The leakage can happen while the data is at rest (in storage), in processing, or on moving within a cloud or between different cloud infrastructures, e.g., from private to public clouds. This paper focuses on protecting data "in processing". For big data applications, the MapReduce framework has been proven as an efficient solution and has been widely deployed, e.g., in healthcare and business data analysis. In this article, we propose a trust-based framework for MapReduce in big data processing tasks. Specifically, we first quantify and propose to assign the sensitive values for data and trust values for map and reduce slots. We then compute the trust value of each resource employed in the big data processing tasks. Depending on the data's sensitivity level of a task, the task requires a given level of trust (i.e., higher sensitive data requires servers/slots with higher trust level). The MapReduce scheduling problem is then formulated as the maximum weighted matching problem of a bipartite graph that aims to maximize the total trust value over all possible assignments subject to various trust requirement of different tasks. The problem is known to be NP-hard. To tackle it, we observe that within a computing node (VM), slots share the same trust value granted from the secured transformation phase. This helps reduce the number of slot nodes of a weight bipartite graph. Leveraging this fact, we propose an efficient heuristic algorithm that achieves 94.7% of the optimal solution obtained via exhaustive search. Extensive simulations show that the trust-based scheduling scheme provides much higher protection for data sensitivity while ensuring good performance for big data applications.

Dang, LC, Dang, CC & Khabbaz, H 2020, 'Modelling of columns and fibre reinforced load transfer platform supported embankments', Proceedings of the Institution of Civil Engineers - Ground Improvement.
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This study proposes a novel ground modification technique utilising fibre reinforced load transfer platform (FRLTP) and deep cement mixing (DCM) columns supported (CS) embankment constructed over soft soils. An equivalent two-dimensional finite element model was developed to simulate the full geometry of a CS embankment reinforced without or with an FRLTP. A series of numerical analyses was firstly conducted on the proposed model for different improvement depths to assess the effectiveness of the introduction of FRLTP into the CS embankment system in terms of total and differential settlements, stress transfer mechanism and lateral displacement with depth. Subsequently, another extensive parametric study was conducted to further investigate the influence of the FRLTP key parameters including elastic deformation modulus, shear strength properties, and tensile strength on the embankment performance during construction and consolidation time. The numerical results show that the embankment with FRLTP can effectively diminish the total settlement and the lateral deformation of the embankment, meanwhile improve the stress concentration ratio and the embankment stability to a great extent. The findings of the extensive parametric study indicate that the FRLTP shear strength properties appear to be the most influence factors to be considered in the design procedure of a target CS-FRLTP-embankment system.

Dao, N-N, Na, W, Tran, A-T, Nguyen, DN & Cho, S 2020, 'Energy-Efficient Spectrum Sensing for IoT Devices', IEEE Systems Journal, pp. 1-9.
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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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Deepanraj, B, Senthilkumar, N, Ranjitha, J, Jayaraj, S & Ong, HC 2020, 'Biogas from food waste through anaerobic digestion: optimization with response surface methodology', BIOMASS CONVERSION AND BIOREFINERY.
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Dehkordi, MR, Seifzadeh, H, Beydoun, G & Nadimi-Shahraki, MH 2020, 'Success prediction of android applications in a novel repository using neural networks', COMPLEX & INTELLIGENT SYSTEMS, vol. 6, no. 3, pp. 573-590.
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Dehkordi, SA, Farajzadeh, K, Rezazadeh, J, Farahbakhsh, R, Sandrasegaran, K & Dehkordi, MA 2020, 'A survey on data aggregation techniques in IoT sensor networks', WIRELESS NETWORKS, vol. 26, no. 2, pp. 1243-1263.
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Delhomme, F, Hajimohammadi, A, Almeida, A, Jiang, C, Moreau, D, Gan, Y, Wang, X & Castel, A 2020, 'Physical properties of Australian hurd used as aggregate for hemp concrete', Materials Today Communications, vol. 24.
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© 2020 Elsevier Ltd The purpose of this study is to determine the key properties of Australian hemp particles which are used for manufacturing hempcrete. Hemp characteristics have a wide variability due to the influence of the environment conditions in various farmed areas. This study focuses on the measurements of the mechanical, thermal and acoustic performances of three Australian hemp: unretted hemp hurd, retted hemp hurd, and hemp fines. Hemp hurd is usually used in non-load bearing building walls, and hemp fine, which is the by-product of hemp manufacturing industry, is usually incorporated into a render. The experimental results show that the main impact of the retting process is a decrease in bulk density and leading to an improvement in thermal and acoustic properties. Without compaction, the bulk density is ranged from 97 and 118.8 kg.m−3, the max sound absorption coefficient from 0.88 and 0.99, and the thermal conductivity from 64 to 97 mW.m-1. K-1. Hemp fines have excellent thermal and acoustic properties and appear to be an efficient aggregate to produce an insulating render. Australian hemps investigated in this study have shown very similar characteristics to European hemps.

Deng, L, Guo, W, Ngo, HH, Wang, XC, Hu, Y, Chen, R, Cheng, D, Guo, S & Cao, Y 2020, 'Application of a specific membrane fouling control enhancer in membrane bioreactor for real municipal wastewater treatment: Sludge characteristics and microbial community', BIORESOURCE TECHNOLOGY, vol. 312.
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Deng, S, Ji, J, Wen, G & Xu, H 2020, 'Delay-induced novel dynamics in a hexagonal centrifugal governor system', INTERNATIONAL JOURNAL OF NON-LINEAR MECHANICS, vol. 121.
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Deutsch, FT, Khoury, SJ, Sunwoo, JB, Elliott, MS & Tran, NT 2020, 'Application of salivary noncodingmicroRNAsfor the diagnosis of oral cancers', HEAD AND NECK-JOURNAL FOR THE SCIENCES AND SPECIALTIES OF THE HEAD AND NECK, vol. 42, no. 10, pp. 3072-3083.
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Deveci, Ö & Shannon, AG 2020, 'A note on balanced incomplete block designs and projective geometry', International Journal of Mathematical Education in Science and Technology.
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© 2020 Informa UK Limited, trading as Taylor & Francis Group. This note outlines some connections between projective geometry and some designs used in clinical trials in the health sciences. The connections are not immediately obvious but they widen the scope for enrichment work at both the senior high school level and for capstone subjects at the undergraduate level.

Dharmapriya, S, Kiridena, S & Shukla, N 2020, 'Multiagent Optimization Approach to Supply Network Configuration Problems With Varied Product-Market Profiles', IEEE Transactions on Engineering Management.
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IEEE This article demonstrates the application of a novel multiagent modeling approach to support supply network configuration (SNC) decisions toward addressing several challenges reported in the literature. These challenges include: enhancing supply network (SN)-level performance in alignment with the goals of individual SN entities; addressing the issue of limited information sharing between SN entities; and sustaining competitiveness of SNs in dynamic business environments. To this end, a multistage, multiechelon SN consisting of geographically dispersed SN entities catering to distinct product-market profiles was modeled. In modeling the SNC decision problem, two types of agents, each having distinct attributes and functions, were used. The modeling approach incorporated a reverse-auctioning process to simulate the behavior of SN entities with differing individual goals collectively contributing to enhance SN-level performance, by means of setting reserve values generated through the application of a genetic algorithm. A set of Pareto-optimal SNCs catering to distinct product-market profiles was generated using Nondominated Sorting Genetic Algorithm II. Further evaluation of these SNCs against additional criteria, using a rule-based approach, allowed the selection of the most appropriate SNC to meet a broader set of conditions. The model was tested using a refrigerator SN case study drawn from the literature. The results reveal that a number of SNC decisions can be supported by the proposed model, in particular, identifying and evaluating robust SNs to suit varied product-market profiles, enhancing SC capabilities to withstand disruptions and developing contingencies to recover from disruptions.

Diao, K, Sun, X, Lei, G, Bramerdorfer, G, Guo, Y & Zhu, J 2020, 'System-level Robust Design Optimization of a Switched Reluctance Motor Drive System Considering Multiple Driving Cycles', IEEE Transactions on Energy Conversion, pp. 1-1.
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Diao, K, Sun, X, Lei, G, Guo, Y & Zhu, J 2020, 'Multiobjective System Level Optimization Method for Switched Reluctance Motor Drive Systems Using Finite-Element Model', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 67, no. 12, pp. 10055-10064.
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Dibaei, M, Zheng, X, Jiang, K, Abbas, R, Liu, S, Zhang, Y, Xiang, Y & Yu, S 2020, 'Attacks and defences on intelligent connected vehicles: a survey', Digital Communications and Networks.
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© 2020 Chongqing University of Posts and Telecommunications Intelligent vehicles are advancing at a fast speed with the improvement of automation and connectivity, which opens up new possibilities for different cyber-attacks, including in-vehicle attacks (e.g., hijacking attacks) and vehicle-to-everything communicationattacks (e.g., data theft). These problems are becoming increasingly serious with the development of 4G LTE and 5G communication technologies. Although many efforts are made to improve the resilience to cyber attacks, there are still many unsolved challenges. This paper first identifies some major security attacks on intelligent connected vehicles. Then, we investigate and summarize the available defences against these attacks and classify them into four categories: cryptography, network security, software vulnerability detection, and malware detection. Remaining challenges and future directions for preventing attacks on intelligent vehicle systems have been discussed as well.

Dikshit, A, Pradhan, B & Alamri, AM 2020, 'Short-Term Spatio-Temporal Drought Forecasting Using Random Forests Model at New South Wales, Australia', APPLIED SCIENCES-BASEL, vol. 10, no. 12.
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Dikshit, A, Pradhan, B & Alamri, AM 2020, 'Temporal Hydrological Drought Index Forecasting for New South Wales, Australia Using Machine Learning Approaches', ATMOSPHERE, vol. 11, no. 6.
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Dikshit, A, Sarkar, R, Pradhan, B, Acharya, S & Alamri, AM 2020, 'Spatial landslide risk assessment at Phuentsholing, Bhutan', Geosciences (Switzerland), vol. 10, no. 4.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Landslides are one of the most destructive and most recurring natural calamities in the Himalayan region. Their occurrence leads to immense damage to infrastructure and loss of land, human lives, and livestock. One of the most affected regions is the Bhutan Himalayas, where the majority of the landslides are rainfall-induced. The present study aims to determine the hazard and risk associated with rainfall-induced landslides for the Phuentsholing region located in the southwestern part of the Bhutan Himalayas. The work involves developing a landslide risk map using hazard and vulnerability maps utilizing landslide records from 2004 to 2014. The landslide hazard map was generated by determining spatial and temporal probabilities for the study region. The spatial probability was computed by analyzing the landslide contributing factors like geology, slope, elevation, rainfall, and vegetation based on comprehensive field study and expertise about the area. The contributing factors were divided into various classes and the percentage of landslide occurrence under each class was calculated to understand its contributing significance. Thereafter, a weighted linear combination approach was used in a GIS environment to develop the spatial probability map which was multiplied with temporal probabilities based on regional rainfall thresholds already determined for the region. Consequently, vulnerability assessment was conducted using key elements at risk (population, land use/land cover, proximity to road, proximity to stream) and the weights were provided based on expert judgment and comprehensive field study. Finally, risk was determined and the various regions in the study area were categorized as high, medium, and low risk. Such a study is necessary for low-economic countries like Bhutan which suffers from unavailability of extensive data and research. The study is conducted for a specific region but can be extend...

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.

Dikshit, A, Sarkar, R, Pradhan, B, Segoni, S & Alamri, AM 2020, 'Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review', APPLIED SCIENCES-BASEL, vol. 10, no. 7.
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Dikshit, A, Satyam, N, Pradhan, B & Kushal, S 2020, 'Estimating rainfall threshold and temporal probability for landslide occurrences in Darjeeling Himalayas', Geosciences Journal, vol. 24, no. 2, pp. 225-233.
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© 2020, The Association of Korean Geoscience Societies and Springer. The Indian Himalayan region has been severely affected by landslides causing an immense loss in terms of human lives and economic loss. The landslides are usually induced by rainfall which can be slow and continuous or heavy downpour. The incidences of landslide events in Indian Himalayas have been further aggravated due to the rapid increase in urbanization and thus its increasing impact on socio-economic aspects. There is a dire need for understanding landslide phenomena, estimating its occurrence potential and formulating strategies to minimize the damage caused by them. One of the most affected area is Kalimpong of Darjeeling Himalayas where significant studies have been conducted on zonation, threshold estimation and other related aspects. However, a comprehensive study in terms of temporal prediction for this region remains unattended. The paper deals with assessing landslide hazard using a rainfall threshold model involving daily and cumulative antecedent rainfall values for landslide events. The threshold values were determined using daily rainfall and antecedent rainfall using precipitation and landslide records for 2010–2016. The results show that 20-day antecedent rainfall provides the best fit for landslide occurrences in the region. The rainfall thresholds were further validated using rainfall and landslide data of 2017, which was not considered for threshold estimation. Finally, the results were used to determine the temporal probability for landslide incidence using a Poisson probability model. The validated results suggest that the model has the potential to be used as a preliminary early warning system.

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, A, Zhao, Y, Ngo, HH, Bai, L, Li, G, Liang, H, Ren, N & Nan, J 2020, 'Metabolic uncoupler, 3,3 ',4 ',5-tetrachlorosalicylanilide addition for sludge reduction and fouling control in a gravity-driven membrane bioreactor', FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING, vol. 14, no. 6.
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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, C-T & Pedrycz, W 2020, 'Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce.', IEEE Transactions on Cybernetics.
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Although feature selection for large data has been intensively investigated in data mining, machine learning, and pattern recognition, the challenges are not just to invent new algorithms to handle noisy and uncertain large data in applications, but rather to link the multiple relevant feature sources, structured, or unstructured, to develop an effective feature reduction method. In this paper, we propose a multiple relevant feature ensemble selection (MRFES) algorithm based on multilayer co-evolutionary consensus MapReduce (MCCM). We construct an effective MCCM model to handle feature ensemble selection of large-scale datasets with multiple relevant feature sources, and explore the unified consistency aggregation between the local solutions and global dominance solutions achieved by the co-evolutionary memeplexes, which participate in the cooperative feature ensemble selection process. This model attempts to reach a mutual decision agreement among co-evolutionary memeplexes, which calls for the need for mechanisms to detect some noncooperative co-evolutionary behaviors and achieve better Nash equilibrium resolutions. Extensive experimental comparative studies substantiate the effectiveness of MRFES to solve large-scale dataset problems with the complex noise and multiple relevant feature sources on some well-known benchmark datasets. The algorithm can greatly facilitate the selection of relevant feature subsets coming from the original feature space with better accuracy, efficiency, and interpretability. Moreover, we apply MRFES to human cerebral cortex-based classification prediction. Such successful applications are expected to significantly scale up classification prediction for large-scale and complex brain data in terms of efficiency and feasibility.

Ding, 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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Ding, W, Pedrycz, W, Triguero, I, Cao, Z & Lin, CT 2020, 'Multigranulation Super-Trust Model for Attribute Reduction', IEEE Transactions on Fuzzy Systems.
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IEEE As big data often contains a significant amount of uncertain, unstructured and imprecise data that are structurally complex and incomplete, traditional attribute reduction methods are less effective when applied to large-scale incomplete information systems to extract knowledge. Multigranular computing provides a powerful tool for use in big data analysis conducted at different levels of information granularity. In this paper, we present a novel multigranulation super-trust fuzzy-rough set-based attribute reduction (MSFAR) algorithm to support the formation of hierarchies of information granules of higher types and higher orders, which addresses newly emerging data mining problems in big data analysis. First, a multigranulation super-trust model based on the valued tolerance relation is constructed to identify the fuzzy similarity of the changing knowledge granularity with multimodality attributes. Second, an ensemble consensus compensatory scheme is adopted to calculate the multigranular trust degree based on the reputation at different granularities to create reasonable subproblems with different granulation levels. Third, an equilibrium method of multigranular-coevolution is employed to ensure a wide range of balancing of exploration and exploitation and can classify super elitists’ preferences and detect noncooperative behaviors with a global convergence ability and high search accuracy. The experimental results demonstrate that the MSFAR algorithm achieves a high performance in addressing uncertain and fuzzy attribute reduction problems with a large number of multigranularity variables.

Ding, W, Yen, GG, Cai, X & Cao, Z 2020, 'Foreword: Evolutionary data mining for big data', Swarm and Evolutionary Computation, vol. 57.
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Ding, X, Wang, Y, Xiong, R, Li, D, Tang, L, Yin, H & Zhao, L 2020, 'Persistent Stereo Visual Localization on Cross-Modal Invariant Map', IEEE Transactions on Intelligent Transportation Systems, pp. 1-13.
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Ding, Y, Fan, H, Xu, M & Yang, Y 2020, 'Adaptive Exploration for Unsupervised Person Re-identification', ACM Transactions on Multimedia Computing, Communications and Applications, vol. 16, no. 1.
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© 2020 ACM. Due to domain bias, directly deploying a deep person re-identification (re-ID) model trained on one dataset often achieves considerably poor accuracy on another dataset. In this article, we propose an Adaptive Exploration (AE) method to address the domain-shift problem for re-ID in an unsupervised manner. Specifically, in the target domain, the re-ID model is inducted to (1) maximize distances between all person images and (2) minimize distances between similar person images. In the first case, by treating each person image as an individual class, a non-parametric classifier with a feature memory is exploited to encourage person images to move far away from each other. In the second case, according to a similarity threshold, our method adaptively selects neighborhoods for each person image in the feature space. By treating these similar person images as the same class, the non-parametric classifier forces them to stay closer. However, a problem of the adaptive selection is that, when an image has too many neighborhoods, it is more likely to attract other images as its neighborhoods. As a result, a minority of images may select a large number of neighborhoods while a majority of images has only a few neighborhoods. To address this issue, we additionally integrate a balance strategy into the adaptive selection. We evaluate our methods with two protocols. The first one is called "target-only re-ID", in which only the unlabeled target data is used for training. The second one is called "domain adaptive re-ID", in which both the source data and the target data are used during training. Experimental results on large-scale re-ID datasets demonstrate the effectiveness of our method. Our code has been released at https://github.com/dyh127/Adaptive-Exploration-for-Unsupervised-Person-Re-Identification.

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.

Do, MH, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Deng, L, Chen, Z & Nguyen, TV 2020, 'Performance of mediator-less double chamber microbial fuel cell-based biosensor for measuring biological chemical oxygen.', Journal of environmental management, vol. 276, p. 111279.
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Recently, the microbial fuel cell-based biosensor has been considered as an attractive technology for measuring wastewater quality such as biochemical oxygen demand (BOD). In this study, a mediator-less double compartment MFC based biosensor utilizing carbon felt as an anode electrode and inoculated with mixed culture was developed to improve the real application of a rapid BOD detection. This study aims to: (i) establish the effect of the operating conditions (i.e., pH, external resistance, fuel feeding rate) on MFC performance; (ii) investigate the correlation between biochemical oxygen demand (BOD) and signal output, and (iii) evaluate the operational stability of the biosensor. The presented result reveals that the maximum current and power production was obtained while 100 mM NaCl and 50 mM Phosphate buffer saline was used as a catholyte solution, neutral pH condition of media and fuel feeding rate at 0.3 mL min-1. Notably, a wider range of BOD concentration up to 300 mg L -1 can be obtained with the voltage output (R2 > 0.9901). Stable and steady power was produced by running MFC in 30 days when cells operated at 1000 Ω external resistance. Our research has some competition with the previous double chamber MFC in the upper limit of BOD detection. This results might help to increase the real application of MFC based BOD biosensor in real-time measurement.

Do, MH, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Varjani, S & Kumar, M 2020, 'Microbial fuel cell-based biosensor for online monitoring wastewater quality: A critical review.', The Science of the total environment, vol. 712, pp. 135612-135612.
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Recently, the application of the microbial fuel cell (MFC)-based biosensor for rapid and real-time monitoring wastewater quality is very innovative due to its simple compact design, disposability, and cost-effectiveness. This review represents recent advances in this emerging technology for the management of wastewater quality, where the emphasis is on biochemical oxygen demand, toxicity, and other environmental applications. In addition, the main challenges of this technology are discussed, followed by proposing possible solutions to those challenges based on the existing knowledge of detection principles and signal processing. Potential future research of MFC-based biosensor has been demonstrated in this review.

Do, TTN, Wang, YK & Lin, CT 2020, 'Increase in brain effective connectivity in multitasking but not in a high-fatigue state', IEEE Transactions on Cognitive and Developmental Systems.
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IEEE Multitasking has become omnipresent in daily activities, and increased brain connectivity under high workload conditions has been reported. Moreover, the effect of fatigue on neural activity has been shown in participants performing cognitive tasks, but the effect of fatigue on different cognitive workload conditions is unclear. In this study, we investigated the effect of fatigue on changes in effective connectivity (EC) across the brain network under distinctive workload conditions. There were 133 electroencephalography (EEG) datasets collected from sixteen participants over a five-month study in which high-risk, reduced, and normal states of real-world fatigue were identified through a daily sampling system. The participants were required to perform a lane-keeping task (LKT) with/without multimodal dynamic attention-shifting (DAS) tasks. The results show that the EC magnitude is positively correlated with the increased workload in normal and reduced states. However, low EC was discovered in the high-risk state under high workload condition. To the best of our knowledge, this investigation is the first EEG-based longitudinal study of real-world fatigue under multitasking conditions. These results could be beneficial for real-life applications, and adaptive models are essential for monitoring important brain patterns under varying workload demands and fatigue states.

Doan, S & Fatahi, B 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, M, Yao, L, Wang, X, Benatallah, B, Huang, C & Ning, X 2020, 'Opinion fraud detection via neural autoencoder decision forest', PATTERN RECOGNITION LETTERS, vol. 132, pp. 21-29.
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Dong, W, Li, W, Guo, Y, He, X & Sheng, D 2020, 'Effects of silica fume on physicochemical properties and piezoresistivity of intelligent carbon black-cementitious composites', Construction and Building Materials, vol. 259.
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© 2020 Elsevier Ltd Carbon black (CB) filled cementitious composites as cement-based sensors with intrinsic piezoresistivity have the potential applications for structural health monitoring (SHM). Effect of silica fume (SF) replacement ratio on the physicochemical, mechanical and piezoresistive properties, and microstructure of CB-cementitious composite were experimentally investigated in this study. The results show that 5% or 10% replacement ratio of SF can improve the water impermeability, setting time and electrical conductivity, but decrease the fresh flowability. Cementitious composite with 10% SF exhibiteds excellent compressive and flexural strengths. Moreover, cement hydration in the acceleration stage decreased with the increase of SF content in the early stage, but the phase analysis after 28 days curing demonstrates that with the addition of SF, there are more hydrated products and less ettringite. In addition, the microstructures of cementitious composites without SF present more porous structures and CB agglomerations. In contrast, the amount of micropores or voids was significantly reduced by the addition of SF due to the physical filling effect and less CB agglomerations. In terms of piezoresistivity, SF can obviously improve the fractional changes of resistivity (FCR) under cyclic compression. With 10% SF, CB-cementitious composites as cement-based sensors exhibited excellent FCR and electrical stability, which will promote their development and application in the SHM for smart infrastructures.

Dong, W, Li, W, Luo, Z, Long, G, Vessalas, K & Sheng, D 2020, 'Structural response monitoring of concrete beam under flexural loading using smart carbon black/cement-based sensors', SMART MATERIALS AND STRUCTURES, vol. 29, no. 6.
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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, Vessalas, K & Wang, K 2020, 'Mechanical and Conductive Properties of Smart Cementitious Composites with Conductive Rubber Crumbs', ES Materials & Manufacturing.
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Dong, W, Li, W, Wang, K, Guo, Y, Sheng, D & Shah, SP 2020, 'Piezoresistivity enhancement of functional carbon black filled cement-based sensor using polypropylene fibre', Powder Technology, vol. 373, pp. 184-194.
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© 2020 Elsevier B.V. In this study, different dosages of carbon black (CB) and polypropylene (PP) were added to develop functional cementitious composites as cement-based sensors. The results show that electrical conductivity increased with the amount of PP fibres, due to the enclosed CB nanoparticles and more conductive passages. The compressive strength slightly decreased, while the flexural strength was significantly increased with the increased amount of PP fibres. The improvement is mainly achieved by the reduced CB concentration in cement matrix and the excellent tensile strength of PP fibres. Under the cyclic compression, the piezoresistivity increased by three times for 0.4 wt% PP fibres filled CB/cementitious composite, regardless of the loading rates. The flexural stress sensing efficiency was considerably lower than that of compressive stress sensing, but it increased with the amount of PP fibres. Moreover, fitting formulas were proposed and used to evaluate the self-sensing capacity, with the attempts to apply cement-based sensors for structural health monitoring.

Dong, W, Li, W, Wang, K, Han, B, Sheng, D & Shah, SP 2020, 'Investigation on physicochemical and piezoresistive properties of smart MWCNT/cementitious composite exposed to elevated temperatures', Cement and Concrete Composites, vol. 112.
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© 2020 Elsevier Ltd Piezoresistivity of smart carbon nanotube/cementitious composite has been experimentally investigated, but the piezoresistive performance had been rarely studied when exposed to elevated temperatures. In this study, the physicochemical and mechanical properties, and piezoresistive behaviours of multi-walled carbon nanotube (MWCNT) reinforced smart cementitious composite were investigated under heat treatments of elevated temperatures of 300 °C and 600 °C. The microstructures, crystal deterioration and thermal gravity relationships were characterized by scanning electron microscope (SEM), X-ray diffraction (XRD) and thermos-gravimetric (TG) analysis. The results show that the compressive strength and elastic modulus of MWCNT/cementitious composite after heat treatments gradually decreased, especially under the high temperature of 600 °C. There was a sudden growth of fractional changes of resistivity (FCR) after heat treatment. The higher temperature treatments led to more extensive sudden increase in the piezoresistivity. In the linear part of the relationship curves of FCR to the strain, the gauge factor even increased at the temperature of 300 °C. Moreover, the mechanism for the altered piezoresistivity was fundamentally explained and discussed by the MWCNT purification and destructions of MWCNT, cement matrix and agglomerations after heat treatments. Therefore, the related outcomes will promote the understanding and application of smart MWCNT/cementitious composite for structural health monitoring (SHM) under extreme environments.

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.
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Dong, W, Li, W, Wang, K, Vessalas, K & Zhang, S 2020, 'Mechanical strength and self-sensing capacity of smart cementitious composite containing conductive rubber crumbs', JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES, vol. 31, no. 10, pp. 1325-1340.
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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 2020, 'Discrete element simulation of cavity expansion in lightly cemented sands considering cementation degradation', COMPUTERS AND GEOTECHNICS, vol. 124.
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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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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.
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Downie, AS, Hancock, M, Shaheed, CA, McLachlan, AJ, Kocaballi, AB, Williams, CM, Michaleff, ZA & Maher, CG 2020, 'An Electronic Clinical Decision Support System for the Management of Low Back Pain in Community Pharmacy: Development and Mixed Methods Feasibility Study', JMIR MEDICAL INFORMATICS, vol. 8, no. 5.
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Drumond, PDP, Ball, JE, Moura, P & Lara Pinto Coelho, MM 2020, 'Are the current On-site Stormwater Detention (OSD) policies the best solution for source control stormwater management? A case study of Australian and Brazilian cities', URBAN WATER JOURNAL, vol. 17, no. 3, pp. 273-281.
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Du, G, Huang, N, He, H, Lei, G & Zhu, J 2020, 'Parameter Design for a High-Speed Permanent Magnet Machine under Multiphysics Constraints', IEEE Transactions on Energy Conversion, pp. 1-1.
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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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© 1982-2012 IEEE. For high-speed permanent magnet machines (HSPMMs), the permanent magnet (PM) is more likely to suffer irreversible demagnetization because the heat dissipation is serious in the HSPMMs, especially for the high-power machines. This paper focuses on the comprehensive research results on the power loss and thermal characteristic for a high-power HSPMM. First, the power loss at the rated load is investigated by finite-element analysis. Then, the temperature distribution of four cooling schemes is compared by the electromagnetic-thermal iteration calculation. The effect of different parameters on thermal behavior is obtained to reduce rotor temperature, which includes an examination of the axial flow duct, cooling medium, sleeve thickness, and sleeve thermal conductivity. Finally, an improved loss separation method is employed to obtain the loss distribution from the measured total loss, and the comprehensive experiments are implemented based on one HSPMM prototype (800 kW, 15 000 rpm) to verify the related theoretical analysis.

Du, X, Lu, Z & Wu, D 2020, 'An intelligent recognition model for dynamic air traffic decision-making', Knowledge-Based Systems, vol. 199.
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© 2019 Air traffic flow management system (ATFMS) is becoming increasingly important due to the rapid growth of air traffic and serious flight delay nowadays. To aware the air traffic flow density and identify the heat airspace in terminal areas of large hub airports is essential for an ATFMS. Due to numerous parameters in air traffic flow, traditional methods based on one single parameter fail to reflect the true complexity relationship between these parameters. This study aims to develop an intelligent air traffic flow heat airspace recognition model using advanced data science technique for establishing a real-time cloud map in the terminal airspace of airports, which attempts to use machine learning models to represent the complex relationship among these parameters. In the proposed intelligent recognition model, high dimensions of parameters (basic parameters, additional parameters and time parameters) are processed to achieve a comprehensive and accurate situation awareness for support dynamic air traffic decision-making. An aircraft trajectories points clustering method is developed to generate a 4D heat airspace map. The basic parameters and time parameters are used to identify the heat airspaces; the changes of additional parameters which influence the heat airspaces are identified and analyzed by use of grid graphs of flight trajectories; probability fitting graphs are used to verify accuracy of 4D results in order to support air traffic decision-making. A case study on Beijing International Airport (PEK) is conducted to test our model and has obtained two main research findings: there are two areas of PEK that have the high density and there are hot peaks at two different heights; flight trajectories and speed of trajectories also effect on the heat airspace. The study realizes that the proposed 4D heat airspace model is better for detailed and accurate information construction, expression of spatial changes, and visualization of multiple parameters of...

Du, X, Yin, H, Chen, L, Wang, Y, Yang, Y & Zhou, X 2020, 'Personalized Video Recommendation Using Rich Contents from Videos', IEEE Transactions on Knowledge and Data Engineering.
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IEEE Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods.

Dun, MD, Mannan, A, Rigby, CJ, Butler, S, Toop, HD, Beck, D, Connerty, P, Sillar, J, Kahl, RGS, Duchatel, RJ, Germon, Z, Faulkner, S, Chi, M, Skerrett-Byrne, D, Murray, HC, Flanagan, H, Almazi, JG, Hondermarck, H, Nixon, B, De Iuliis, G, Chamberlain, J, Alvaro, F, de Bock, CE, Morris, JC, Enjeti, AK & Verrills, NM 2020, 'Shwachman–Bodian–Diamond syndrome (SBDS) protein is a direct inhibitor of protein phosphatase 2A (PP2A) activity and overexpressed in acute myeloid leukaemia', Leukemia.
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Duong, HC, Ansari, AJ, Cao, HT, Nguyen, NC, Do, KU & Nghiem, LD 2020, 'Membrane distillation regeneration of liquid desiccant solution for air-conditioning: Insights into polarisation effects and mass transfer', Environmental Technology and Innovation, vol. 19.
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© 2020 Membrane distillation (MD) embodies ideal attributes for the regeneration of liquid desiccant solutions used in air-conditioning systems. The MD process has been experimentally proven technically viable for the regeneration of liquid desiccant solutions; however, it suffers severely from temperature and concentration polarisation effects. In this study, for the first time a descriptive mass and heat transfer (DMHT) model is developed to quantitatively describe the mass transfer and the negative impacts of temperature and concentration polarisation during the MD regeneration of the LiCl desiccant solution. The simulation results demonstrate significant reduction in water flux along the membrane due to decreasing mass transfer coefficient (Cm) and transmembrane water vapour pressure gradient (ΔPm). Over the length of the membrane leaf of 0.145 m, water flux reduces by 31% from 11.0 to 7.6 L/m2⋅h. The temperature and concentration polarisation effects cause a substantial decline in the process driving force - ΔPm is only two thirds of the water vapour pressure difference between the bulk feed and distillate (ΔPb). Temperature polarisation is the predominant cause of the reduction in ΔPm compared with ΔPb; however, the negative impact of concentration polarisation is also notable. Finally, amongst the key operating conditions, the inlet feed temperature and concentration exert the most profound influence on the temperature and concentration polarisation during the DCMD regeneration of the hyper saline LiCl solution.

Duong, HC, Ansari, AJ, Hailemariam, RH, Woo, YC, Pham, TM, Ngo, LT, Dao, DT & Nghiem, LD 2020, 'Membrane Distillation for Strategic Water Treatment Applications: Opportunities, Challenges, and Current Status', Current Pollution Reports, vol. 6, no. 3, pp. 173-187.
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© 2020, Springer Nature Switzerland AG. Purpose of Review: Membrane distillation (MD) has been known as a promising water treatment process for many years. However, despite its advantages, MD has never been able to compete with other processes for industrial water treatment and supply. Instead, it has been orientated towards several unique strategic water treatment applications. This review aims to uncover the opportunities and technical challenges pertinent to the MD process and the current status of its strategic water treatment applications most notably including decentralised small-scale desalination for fresh water provision in remote areas, hybridisation with forward osmosis (FO) for treatment of challenging polluted waters, regeneration of liquid desiccant solutions for air conditioning, and treatment of acid effluents for beneficial reuse. Recent Findings: Pilot and small-scale MD systems have been demonstrated for decentralised desalination using various renewable energy sources to supply fresh water in remote, rural areas and on ships where other desalination processes are inefficient or unfeasible. For this strategic desalination application, MD is technically viable, but more works on configuration modification and process optimisation are required to reduce the process energy consumption and water production costs. For the three other strategic applications, the technical viability of the MD process has been proved by extensive lab-scale researches, but its economic feasibility is still questionable due to the lack of large-scale evaluation and the uncertain costs of MD systems. Summary: The orientation of MD towards strategic water treatment applications is clear. However, huge efforts are required to facilitate these applications at commercial and full scale.

Dutta, S & Gandomi, AH 2020, 'Bilevel Data-Driven Modeling Framework for High-Dimensional Structural Optimization under Uncertainty Problems', Journal of Structural Engineering, vol. 146, no. 11, pp. 04020245-04020245.
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Ekanayake, UGM, Seo, DH, Faershteyn, K, O'Mullane, AP, Shon, H, MacLeod, J, Golberg, D & Ostrikov, K 2020, 'Atmospheric-pressure plasma seawater desalination: Clean energy, agriculture, and resource recovery nexus for a blue planet', Sustainable Materials and Technologies, vol. 25.
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© 2020 Elsevier B.V. Water connects every aspect of life. Only 4% of the world's water is fresh water, as most water sources have different degrees of salinity. As a result, billions of people face water scarcity, which is a global challenge. Desalination technologies that separate fresh water from solvated salt ions in saline water are attracting major attention. However, conventional desalination processes including thermally and pressure driven processes are highly energy intensive. To address this issue we demonstrate that the atmospheric-pressure plasma (APP) treatment of saline water can be a new potential alternative low-energy and green desalination route. Valuable salts are recovered by direct salt crystal precipitation within a short plasma processing time. During desalination and salt precipitation, plasma activated desalinated water (PADW) is generated and can be used for clean energy processes such as water electrolysis and sustainable agriculture by enhanced plant seed germination. In addition, functional nanomaterials can be extracted from the precipitated salt. The PADW exhibited a low salinity of 5.6 mS/cm with a low pH value of 2.1. The unique intrinsic PADW chemistries enabled electrochemical water splitting for both the hydrogen evolution reaction (HER) at a Pt electrode and the oxygen evolution reaction (OER) at a RuO2 electrode. Moreover, the feasibility of using PADW in sustainable agriculture was demonstrated by enhancing mungbean seed germination using tap water mixed with PADW. At optimum mix concentration, both seed germination rates and germination percentages increased. Finally, we demonstrated the feasibility of synthesizing high-value 2D nanomaterials exemplified by Mg(OH)2 nanosheets via a single step thermal process using the salt precipitated from the seawater by the plasma process. Combined with straightforward use of renewable electricity to generate APPs, this study reveals the plasma potential for sustainable recovery of clea...

Eldosouky, AM, Pham, LT, Mohmed, H & Pradhan, B 2020, 'A comparative study of THG, AS, TA, Theta, TDX and LTHG techniques for improving source boundaries detection of magnetic data using synthetic models: A case study from G. Um Monqul, North Eastern Desert, Egypt', Journal of African Earth Sciences, vol. 170.
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© 2020 Elsevier Ltd The boundaries detection techniques have a great role in enhancing and interpreting the geologic features of magnetic data. In the literature, several filters (THG, AS, TA, NTilt, Theta, TDX, TAHG, LTHG) for identifying the boundaries of the magnetic sources have been suggested. These methods are generally performed based on gradients (vertical and horizontal) of the potential field. This paper presents a comparative investigation of different boundary detection filters including THG (total horizontal gradient), AS (analytical signal), TA (tilt angle), Theta (Cos θ), TDX (horizontal tilt angle), and LTHG (Logistic function of the THG). The effect of each filter was examined on two synthetic magnetic data sets. Moreover, the filters are also applied to a real magnetic data set from the Gabal (G) Um Monqul, North Eastern Desert (NED) of Egypt. The obtained results were correlated with known geologic structures of the study area. From the comparison between several applied methods, the horizontal boundaries of geologic sources obtained by the LTHG were found to be sharper and clearer than other ones. The results confirm that the LTHG method is an effective filter for interpreting aeromagnetic data qualitatively and can be applied for enhancing the source edges of different potential field datasets.

Elgharabawy, A, Mukesh, P & CT, L 2020, 'Preference Neural Network', Transaction of Neural network and learning system.
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This paper proposes a preference neural network (PNN) to address the problem of indifference preferences orders with new activation function. PNN also solves the Multi-label ranking problem, where labels may have indifference preference orders or subgroups are equally ranked. PNN follows a multi-layer feedforward architecture with fully connected neurons. Each neuron contains a novel smooth stairstep activation function based on the number of preference orders. PNN inputs represent data features and output neurons represent label indexes. The proposed PNN is evaluated using new preference mining dataset that contains repeated label values which have not experimented before. PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.

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.

Escano, JM, Ridao-Olivar, MA, Ierardi, C, Sanchez, AJ & Rouzbehi, K 2020, 'Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components', IEEE Sensors Journal, vol. 20, no. 19, pp. 11454-11462.
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Eskandari, M, Li, L, Moradi, MH, Wang, F & Blaabjerg, F 2020, 'A Control System for Stable Operation of Autonomous Networked Microgrids', IEEE Transactions on Power Delivery, vol. 35, no. 4, pp. 1633-1647.
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The interaction of droop controllers through power network is high in networked microgrids (NMGs) due to the low X/R ratio of the power lines impedance and lack of inertia in converter-based NMGs, which has raised stability concerns. On the other hand, inaccurate reactive power sharing and poor power quality due to the voltage and frequency deviations still remain as noticeable issues in NMGs. In this paper, a novel fuzzy consensus protocol is proposed to improve the droop controller performance in power sharing by incorporating the X/R ratio of the power lines impedance into droop loops. Power quality is also enhanced by restoring the average voltage profile based on a new consensus protocol, which is designed to be in coordination with reactive power sharing. In order to guarantee stability of the closed-loop system, linear matrix inequality method is adopted to determine the consensus signal coefficients as structured static output feedback gains. To this end, a novel small-signal model is proposed for NMGs to be adopted in the design process, by which the cross-coupling as well as interaction of droop controllers through the power network is properly realized. The numerical results in MATLAB/SIMULINK prove the effectiveness and accuracy of the proposed method.

Esselle, KP 2020, 'Call for IEEE AP-S Distinguished Lecturer Nominations', IEEE Antennas and Propagation Magazine, vol. 62, no. 3, pp. 127-127.
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Esselle, KP 2020, 'CALL FOR IEEE AP-S DISTINGUISHED LECTURER NOMINATIONS NOMINATION DEADLINE: JULY 31,2020', IEEE ANTENNAS AND PROPAGATION MAGAZINE, vol. 62, no. 3, pp. 127-127.
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Esselle, KP 2020, 'Distinguished Lectures on Radio Astronomy and Training the Next Generation', IEEE ANTENNAS AND PROPAGATION MAGAZINE, vol. 62, no. 4, pp. 140-+.
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Esselle, KP 2020, 'Meet the New Distinguished Lecturers for 2020?2022 [Distinguished Lecturers]', IEEE Antennas and Propagation Magazine, vol. 62, no. 3, pp. 120-123.
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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.

Fachrunnisa, O & Hussain, FK 2020, 'A methodology for creating sustainable communities based on dynamic factors in virtual environments', International Journal of Electronic Business, vol. 15, no. 2, pp. 133-159.
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Copyright © 2020 Inderscience Enterprises Ltd. A virtual community is one of communities that exist in an internet economy; however, little research has been conducted on how to make it sustainable. We propose a methodology for creating sustainable virtual communities which depends on the community’s respond to the dynamic factors in its environment such as number of members, shared contents and interaction rules. The methodology proposes the use of iterative negotiation and a panel of expert agents to assess the quality of service (QoS) delivered. This QoS assessment is based on an interaction agreement between the community members and expert agent as the administrator’s representative. The administrators use this QoS assessment to determine whether an individual’s membership will be renewed or terminated after a certain period of time. We present a metric to measure the sustainability index and demonstrate the validity of the methodology by engineering a prototype setup and running simulations under various operational conditions.

Fahmideh, M & Beydoun, G 2020, 'Big data analytics architecture design.', CoRR, vol. abs/2004.08021.

Fahmideh, M & Beydoun, G 2020, 'Reusing empirical knowledge during cloud computing adoption.', CoRR, vol. abs/2004.11268.

Fahmideh, M & Zowghi, D 2020, 'An exploration of IoT platform development', Information Systems, vol. 87.
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© 2019 Elsevier Ltd IoT (Internet of Things) platforms are key enablers for smart city initiatives, targeting the improvement of citizens’ quality of life and economic growth. As IoT platforms are dynamic, proactive, and heterogeneous socio-technical artefacts, systematic approaches are required for their development. Limited surveys have exclusively explored how IoT platforms are developed and maintained from the perspective of information system development process lifecycle. In this paper, we present a detailed analysis of 63 approaches. This is accomplished by proposing an evaluation framework as a cornerstone to highlight the characteristics, strengths, and weaknesses of these approaches. The survey results not only provide insights of empirical findings, recommendations, and mechanisms for the development of quality aware IoT platforms, but also identify important issues and gaps that need to be addressed.

Fahmideh, M, Beydoun, G & Low, G 2020, 'Experiential probabilistic assessment of cloud services.', CoRR, vol. abs/2004.10858.

Fahmideh, M, Daneshgar, F, Beydoun, G & Rabhi, FA 2020, 'Challenges in migrating legacy software systems to the cloud an empirical study.', CoRR, vol. abs/2004.10724.

Fahmideh, M, Low, G, Beydoun, G & Daneshgar, F 2020, 'Cloud Migration Process A Survey Evaluation Framework and Open Challenges.', CoRR, vol. abs/2004.10725.

Fan, H, Zhu, L, Yang, Y & Wu, F 2020, 'Recurrent Attention Network with Reinforced Generator for Visual Dialog', ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 16, no. 3, pp. 1-16.
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Fan, K, Bao, Z, Liu, M, Vasilakos, AV & Shi, W 2020, 'Dredas: Decentralized, reliable and efficient remote outsourced data auditing scheme with blockchain smart contract for industrial IoT', Future Generation Computer Systems.
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© 2019 Elsevier B.V. The development of cloud computing and the Internet of things (IOT) attracts more and more enterprises to outsource the data from their Industrial Internet of things (IIOT) to cloud servers in order to save operating costs and improve efficiency. However, in this environment, protecting the security and privacy of data storage is an important challenge for IIOT and cloud server provider (CSP). Data auditing could allow data owner discover malicious behaviors of CSP which destroy their outsourced data. The public auditing authorizes the trusted third part auditor (TPA) to audit the owner's outsourced data and frees owner from regular tasks. However, the public auditing using TPA is considered a centralized auditing, and the TPA is assumed totally honest, but it is difficult to find a reliable auditing organization. In this paper, a novel decentralized auditing smart contract in Ethereum is proposed. By replacing the TPA with a designed smart contract, a decentralized auditing scheme (Dredas) is proposed, where anyone can obtain the auditing result from Ethereum without worrying about semi-honest TPA. Compared with traditional auditing, apart from being able to perform traditional auditing functions, Dredas has three important benefits over previous work. First, the random values of challenge are more secure. Dredas chooses the current blockchain nonce as a random seed to prevent any party forging random values. Secondly, in order to achieve a safe, regular, proactive auditing, the protocol writes the auditing rules into the blockchain, and uses the number blocks on the Ethereum as the security timestamp. Finally, data owner, user and CSP must pay some ether for smart contract as deposit. This way not only inhibits the malicious behavior of these three parties, but also makes it more reasonable in real life. We implement Dredas to show that the computation costs are reasonable and efficient.

Fan, X, Xiang, C, Chen, X, Song, X, Yang, P, Gong, L, Nanda, P & 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...

Fan, X, Xiang, C, Gong, L, He, X, Qu, Y, Amirgholipour, S, Xi, Y, Nanda, P & He, X 2020, 'Deep learning for intelligent traffic sensing and prediction: recentadvances and future challenges', CCF Transactions on Pervasive Computing and Interaction.
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With the emerging concepts of smart cities and intelligent transportation systems, accurate traffic sensing and prediction have become critically important to support urban management and traffic control. In recent years, the rapid uptake of the Internet of Vehicles and the rising pervasiveness of mobile services have produced unprecedented amounts of data to serve traffic sensing and prediction applications. However, it is significantly challenging to fulfill the computation demands by the big traffic data with ever-increasing complexity and diversity. Deep learning, with its powerful capabilities in representation learning and multi-level abstractions, has recently become the most effective approach in many intelligent sensing systems. In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.

Fang, C, Rajabzadeh, S, Zhang, P, Liu, W, Kato, N, Shon, HK & Matsuyama, H 2020, 'Controlling spherulitic structures at surface and sub-layer of hollow fiber membranes prepared using nucleation agents via triple-orifice spinneret in TIPS process', Journal of Membrane Science, vol. 609.
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© 2020 Elsevier B.V. In this study, the growth of a spherulitic structure at the surface and sub-layer of the polyvinylidene fluoride (PVDF) hollow fiber membrane was controlled by co-extruding nucleation agent (NA) solutions as the outermost layer of a triple-orifice spinneret in the thermally induced phase separation (TIPS) process. Using this method, the spherulites were tailored to be a variety of morphologies having different combinations with the bi-continuous network at the membrane surface and sub-layer, while the bulk membrane structure remained almost unchanged. These typically tailored spherulitic structures dramatically enhanced the surface roughness, hydrophobicity, liquid entry pressure, and permeation stability of the TIPS-prepared PVDF hollow fiber membranes. The experimental results from the pressure-driven filtration and the direct contact membrane distillation demonstrated great potential of the tailored membranes to achieve water treatment and seawater desalination. The electrostatic interaction between NA and PVDF played a key role in tailoring the special membrane spherulite structures, providing new possibilities for engineering TIPS-prepared hollow fiber membranes with desirable structures and performances to address current and future water shortages.

Fang, J, Wu, C, Rabczuk, T, Wu, C, Sun, G & Li, Q 2020, 'Phase field fracture in elasto-plastic solids: a length-scale insensitive model for quasi-brittle materials', COMPUTATIONAL MECHANICS.
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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, L, Li, Y, Yun, X, Wen, Z, Ji, S, Meng, W, Cao, Z & Tanveer, M 2020, 'THP: A Novel Authentication Scheme to Prevent Multiple Attacks in SDN-Based IoT Network', IEEE Internet of Things Journal, vol. 7, no. 7, pp. 5745-5759.
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© 2014 IEEE. SDN has provided significant convenience for network providers and operators in cloud computing. Such a great advantage is extending to the Internet of Things network. However, it also increases the risk if the security of an SDN network is compromised. For example, if the network operator's permission is illegally obtained by a hacker, he/she can control the entry of the SDN network. Therefore, an effective authentication scheme is needed to fit various application scenarios with high-security requirements. In this article, we design, implement, and evaluate a new authentication scheme called the hidden pattern (THP), which combines graphics password and digital challenge value to prevent multiple types of authentication attacks at the same time. We examined THP in the perspectives of both security and usability, with a total number of 694 participants in 63 days. Our evaluation shows that THP can provide better performance than the existing schemes in terms of security and usability.

Fang, L, Zhang, X, Sood, K, Wang, Y & Yu, S 2020, 'Reliability-aware virtual network function placement in carrier networks', Journal of Network and Computer Applications, vol. 154.
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© 2020 Network Function Virtualization (NFV) is a promising technology that implements Virtual Network Function (VNF) with software on general servers. Traffic needs to go through a set of ordered VNFs, which is called a Service Function Chain (SFC). Rational deployment of VNFs can reduce costs and increase profits for network operators. However, during the deployment of the VNFs, how to guarantee the reliability of SFC requirements while optimizing network resource cost is still an open problem. To this end, we study the problem of reliability-aware VNF placement in carrier networks. In this paper, we firstly redefine the reliability of SFC, which is the product of the reliability of all nodes and physical links in SFC. On this basis, we propose two reliability protection mechanisms: the All-Nodes Protection Mechanism (ANPM) and the Single-Node Protection Mechanism (SNPM). Following this, for each protection mechanism, we formulate the problem as an Integer Linear Programming (ILP) model. Due to the problem complexity, we propose a heuristic algorithm based on Dynamic Programming and Lagrangian Relaxation for each protection mechanism. With extensive simulations using real world topologies, our results show that compared with the benchmark algorithm and ANPM, SNPM can save up to 33.34% and 26.76% network resource cost on average respectively while guaranteeing the reliability requirement of SFC requests, indicating that SNPM performs better than ANPM and has better application potential in carrier networks.

Fang, X, Sheng, QZ, Wang, X, Zhang, W, NGU, AHH & Yang, J 2020, 'From Appearance to Essence: Comparing Truth Discovery Methods without Using Ground Truth', ACM Transactions on Intelligent Systems and Technology.

Fang, Y, Huang, X, Qin, L, Zhang, Y, Zhang, W, Cheng, R & Lin, X 2020, 'A survey of community search over big graphs', The VLDB Journal, vol. 29, no. 1, 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.

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).

Fang, Z, Hu, J, Lu, Y & Ni, W 2020, 'Three-User Cooperative NOMA Transmission', IEEE Wireless Communications Letters, vol. 9, no. 4, pp. 465-469.
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© 2012 IEEE. This letter presents a new downlink cooperative non-orthogonal multiple access (NOMA) transmission scheme to serve three users within only two time slots. The scheme involves a base station (BS), two direct-link users and one indirect-link user. The BS sends superposed signals to the two direct-link users which decode and forward the signals to the indirect-link user in an alternating fashion. Closed-form expression is derived for the sum-rate of the proposed scheme and a simple expression of sum-rate is also derived in the high signal-to-noise ratio (SNR) region. In the presence of strong inter-user interference (IUI), the proposed scheme can be applied by having each direct-link user to detect the IUI first and then cancel the IUI to decode the signals destined for the indirect-link user and itself. Simulation results show that the scheme with configurable decoding orders at the users is able to achieve a higher sum-rate than existing orthogonal multiple access (OMA) based alternatives.

Fang, Z, Lu, J, Liu, F, Xuan, J & Zhang, G 2020, 'Open Set Domain Adaptation: Theoretical Bound and Algorithm.', IEEE transactions on neural networks and learning systems, vol. PP.
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The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled (target) domain--the basic strategy being to mitigate the effects of discrepancies between the two distributions. Most existing algorithms can only handle unsupervised closed set domain adaptation (UCSDA), i.e., where the source and target domains are assumed to share the same label set. In this article, we target a more challenging but realistic setting: unsupervised open set domain adaptation (UOSDA), where the target domain has unknown classes that are not found in the source domain. This is the first study to provide learning bound for open set domain adaptation, which we do by theoretically investigating the risk of the target classifier on unknown classes. The proposed learning bound has a special term, namely, open set difference, which reflects the risk of the target classifier on unknown classes. Furthermore, we present a novel and theoretically guided unsupervised algorithm for open set domain adaptation, called distribution alignment with open difference (DAOD), which is based on regularizing this open set difference bound. The experiments on several benchmark data sets show the superior performance of the proposed UOSDA method compared with the state-of-the-art methods in the literature.

Fani, A, Golroo, A, Ali Mirhassani, S & Gandomi, AH 2020, 'Pavement maintenance and rehabilitation planning optimisation under budget and pavement deterioration uncertainty', INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING.
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Fanos, AM, Pradhan, B, Alamri, A & Lee, CW 2020, 'Machine learning-based and 3d kinematic models for rockfall hazard assessment using LiDAR Data and GIS', Remote Sensing, vol. 12, no. 11.
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© 2020 by the authors. Rockfall is one of the most hazardous phenomena in mountainous and hilly regions with high and steep terrain. Such incidents can cause massive damage to people, properties, and infrastructure. Therefore, proper rockfall hazard assessment methods are required to save lives and provide a guide for the development of an area. The aim of this research is to develop a method for rockfall hazard assessment at two different scales (regional and local). A high-resolution airborne laser scanning (ALS) technique was utilized to derive an accurate digital terrain model (DTM); next, a terrestrial laser scanner (TLS) was used to capture the topography of the two most critical areas within the study area. A staking machine-learning model based on different classifiers, namely logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (KNN), was optimized and employed to determine rockfall probability by utilizing various rockfall conditioning factors. A developed 3D rockfall kinematic model was used to obtain rockfall trajectories, velocity, frequency, bouncing height, kinetic energy, and impact location. Next, a spatial model combined with a fuzzy analytical hierarchy process (fuzzy-AHP) integrated in the Geographic Information System (GIS) was developed to assess rockfall hazard in two different areas in Ipoh, Malaysia. Additionally, mitigation processes were suggested and assessed to provide a comprehensive information for urban planning management. The results show that, the stacking random forest-k-nearest neighbor (RF-KNN) model is the best hybrid model compared to other tested models with an accuracy of 89%, 86%, and 87% based on training, validation, and cross-validation datasets, respectively. The three-dimension rockfall kinematic model was calibrated with an accuracy of 93% and 95% for the two study areas and subsequently the rockfall trajectories and their characteristic...

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.

Faramarzi, A, Heidarinejad, M, Mirjalili, S & Gandomi, AH 2020, 'Marine Predators Algorithm: A nature-inspired metaheuristic', EXPERT SYSTEMS WITH APPLICATIONS, vol. 152.
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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.
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BACKGROUND:Deep brain stimulation (DBS) surgery is an option for patients experiencing medically resistant neurologic symptoms. DBS complications are rare; finding significant predictors requires a large number of surgeries. Machine learning algorithms may be used to effectively predict these outcomes. The aims of this study were to 1) investigate preoperative clinical risk factors and 2) build machine learning models to predict adverse outcomes. METHODS:This multicenter registry collected clinical and demographic characteristics of patients undergoing DBS surgery (n = 501) and tabulated occurrence of complications. Logistic regression was used to evaluate risk factors. Supervised learning algorithms were trained and validated on 70% and 30%, respectively, of both oversampled and original registry data. Performance was evaluated using area under the receiver operating characteristics curve (AUC), sensitivity, specificity, and accuracy. RESULTS:Logistic regression showed that the risk of complication was related to the operating institution in which the surgery was performed (odds ratio [OR] = 0.44, confidence interval [CI] = 0.25-0.78), body mass index (OR = 0.94, CI = 0.89-0.99), and diabetes (OR = 2.33, CI = 1.18-4.60). Patients with diabetes were almost 3× more likely to return to the operating room (OR = 2.78, CI = 1.31-5.88). Patients with a history of smoking were 4× more likely to experience postoperative infection (OR = 4.20, CI = 1.21-14.61). Supervised learning algorithms demonstrated high discrimination performance when predicting any complication (AUC = 0.86), a complication within 12 months (AUC = 0.91), return to the operating room (AUC = 0.88), and infection (AUC = 0.97). Age, body mass index, procedure side, gender, and a diagnosis of Parkinson disease were influential features. CONCLUSIONS:Multiple significant complication risk factors were identified, and supervised learning algorithms effectively predicted adverse outcomes in DBS surgery.

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, F, Li, K-C, Shen, J, Zhou, Q & Yang, X 2020, 'Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification', IEEE ACCESS, vol. 8, pp. 69979-69996.
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Feng, L, Huang, Y, Tsang, IW, Gupta, A, Tang, K, Tan, KC & Ong, Y-S 2020, 'Towards Faster Vehicle Routing by Transferring Knowledge From Customer Representation', IEEE Transactions on Intelligent Transportation Systems, pp. 1-14.
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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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Feng, Y & Ying, M 2020, 'Quantum Hoare logic with classical variables.', CoRR, vol. abs/2008.06812.

Feng, Y, Wang, Q, Wu, D, Gao, W & Tin-Loi, F 2020, 'Stochastic nonlocal damage analysis by a machine learning approach', Computer Methods in Applied Mechanics and Engineering, vol. 372.
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© 2020 Elsevier B.V. A machine learning aided stochastic nonlocal damage analysis framework is proposed for quasi-brittle materials. The uncertain system parameters, including the material properties and loading actions, have been incorporated and analysed within a unified safety assessment framework against various working conditions. A three-dimensional integral-type nonlocal damage model through finite element method (FEM) has been adopted. For the purpose of investigating the probabilistic damage analysis problems, a freshly established machine learning approach, namely the capped-extended-support vector regression method (C-X-SVR), is proposed to eliminate the influences of random outliers in the first step, then establish the relationship between the uncertain systemic inputs and structural responses. Such that the training robustness and computational adaptability of the proposed regression model can be reinforced. Moreover, the proposed approach is competent of efficiently predicting the statistical information (i.e., means, standard deviations, probability density functions and cumulative density functions) of structural behaviours under continuous information update of the uncertain working condition from mercurial environment. One real-life experimental validation and two numerical investigations are implemented to further verify the effectiveness and efficiency of the uncertainty quantification framework against probabilistic damage analysis.

Feng, Y, Wu, D, Liu, L, Gao, W & Tin-Loi, F 2020, 'Safety assessment for functionally graded structures with material nonlinearity', Structural Safety, vol. 86.
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© 2020 Elsevier Ltd A machine learning aided reliability assessment framework is presented for functionally graded material (FGM) structures under plane strain/stress conditions with the consideration of elastoplasticity. The material nonlinearity of the FGM is modelled through the implementation of the Tamura-Tomota-Ozawa (TTO) model. For safety evaluation of FGM structures, the volume fraction of FGM has been modelled through spatially dependent uncertainty as random field for the concerned composite. In order to solve the complex stochastic elastoplastic problem, a further developed machine learning aided technique called the extended support vector regression (X-SVR) with a generalized Dirichlet feature mapping function has been introduced and then, the corresponding probabilistic features, including the statistical moments, probability density functions (PDFs), and cumulative distribution functions (CDFs), of the concerned structural responses can be effectively established for assessing the reliability of FGM structures. Moreover, the proposed approach is competent to deliver critical information regarding the uncertain system inputs which can be beneficial for subsequent safety assessment and structural designs for the FGM. Two test functions and two numerical examples have been adopted to visualise the accuracy, stability and capability of the proposed safety assessment framework for FGM structures.

Ferrari, A, Spoletini, P, Bano, M & Zowghi, D 2020, 'SaPeerandReverseSaPeer: teaching requirements elicitation interviews with role-playing and role reversal', REQUIREMENTS ENGINEERING.
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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.

Fleck, R, Gill, RL, Pettit, T, Irga, PJ, Williams, NLR, Seymour, JR & Torpy, FR 2020, 'Characterisation of fungal and bacterial dynamics in an active green wall used for indoor air pollutant removal', BUILDING AND ENVIRONMENT, vol. 179.
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Fraietta, A, Bown, O, Ferguson, S, Gillespie, S & Bray, L 2020, 'Rapid composition for networked devices: Happybrackets', Computer Music Journal, vol. 43, no. 2-3, pp. 89-108.
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© 2020 Massachusetts Institute of Technology. This article introduces an open-source Java-based programming environment for creative coding ofagglomerative systems using Internet-of-Things (IoT) technologies. Our software originally focused on digital signalprocessing of audio—including synthesis, sampling, granular sample playback, and a suite of basic effects—butcomposers now use it to interface with sensors and peripherals through general-purpose input/output and externalnetworked systems. This article examines and addresses the strategies required to integrate novel embedded musicalinterfaces and creative coding paradigms through an IoT infrastructure. These include: The use of advanced toolingfeatures of a professional integrated development environment as a composition or performance interface rather thanjust as a compiler; techniques to create media works using features such as autodetection of sensors; seamless andserverless communication among devices on the network; and uploading, updating, and running of new compositionsto the device without interruption.Furthermore, we examined the difficulties many novice programmers experience when learning to write code,and we developed strategies to address these difficulties without restricting the potential available in the codingenvironment. We also examined and developed methods to monitor and debug devices over the network, allowingartists and programmers to set and retrieve current variable values to or from these devices during the performanceand composition stages. Finally, we describe three types of art work that demonstrate how the software, calledHappyBrackets, is being used in live-coding and dance performances, in interactive sound installations, and as anadvanced composition and performance tool for multimedia works.

Frederiks, ER, Romanach, LM, Berry, A & Toscas, P 2020, 'Making energy surveys more impactful: Testing material and non-monetary response strategies', Energy Research and Social Science, vol. 63.
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© 2019 Elsevier Ltd Recent years have seen a growth in energy research that integrates social and behavioural sciences. A core component of this work involves collecting human data, commonly via surveys and field experiments. But there are often barriers to recruiting large and representative samples of participants, with sampling bias and non-response error posing threats to validity. Identifying cost-effective ways to increase participation in energy research is therefore important for strengthening the rigor, utility and generalisability of studies in this area. To this end, the current study harnesses an experimental design to test pathways for making energy surveys more impactful – specifically by improving response rates and times, lowering sampling bias, and enhancing overall cost-effectiveness. As part of a postal survey on household energy use in Australia, a set of randomised controlled trials were conducted to test the impact of four strategies: incentives, an envelope message, a handwritten sticky note, and a reminder postcard. A 3 x 2 x 2 x 2 factorial design was applied to assess both individual and interactive effects. While material incentives in the form of an upfront token gift and prize draw were ineffective in improving response relative to the control survey, results revealed that a handwritten sticky note expressing upfront thanks for participating – designed to serve as an intrinsically motivating attentional cue – improved both the rate and timeliness of response. Three combinations of strategies yielded significantly higher response rates than the control, but they were more expensive on a ‘dollar cost per response’ basis. Implications for research and practice are discussed.

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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Fry, CV, Cai, X, Zhang, Y & Wagner, CS 2020, 'Consolidation in a crisis: Patterns of international collaboration in early COVID-19 research', PLoS ONE, vol. 15, no. 7 July.
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© 2020 Fry et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This paper seeks to understand whether a catastrophic and urgent event, such as the first months of the COVID-19 pandemic, accelerates or reverses trends in international collaboration, especially in and between China and the United States. A review of research articles produced in the first months of the COVID-19 pandemic shows that COVID-19 research had smaller teams and involved fewer nations than pre-COVID-19 coronavirus research. The United States and China were, and continue to be in the pandemic era, at the center of the global network in coronavirus related research, while developing countries are relatively absent from early research activities in the COVID-19 period. Not only are China and the United States at the center of the global network of coronavirus research, but they strengthen their bilateral research relationship during COVID-19, producing more than 4.9% of all global articles together, in contrast to 3.6% before the pandemic. In addition, in the COVID-19 period, joined by the United Kingdom, China and the United States continued their roles as the largest contributors to, and home to the main funders of, coronavirus related research. These findings suggest that the global COVID-19 pandemic shifted the geographic loci of coronavirus research, as well as the structure of scientific teams, narrowing team membership and favoring elite structures. These findings raise further questions over the decisions that scientists face in the formation of teams to maximize a speed, skill trade-off. Policy implications are discussed.

Fu, L, Lin, P, Vasilakos, AV & Wang, S 2020, 'An overview of recent multi-view clustering', Neurocomputing, vol. 402, pp. 148-161.
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© 2020 Elsevier B.V. With the widespread deployment of sensors and the Internet-of-Things, multi-view data has become more common and publicly available. Compared to traditional data that describes objects from single perspective, multi-view data is semantically richer, more useful, however more complex. Since traditional clustering algorithms cannot handle such data, multi-view clustering has become a research hotspot. In this paper, we review some of the latest multi-view clustering algorithms, which are reasonably divided into three categories. To evaluate their performance, we perform extensive experiments on seven real-world data sets. Three mainstream metrics are used, including clustering accuracy, normalized mutual information and purity. Based on the experimental results and a large number of literature reading, we also discuss existing problems in current multi-view clustering and point out possible research directions in the future. This research provides some insights for researchers in related fields and may further promote the development of multi-view clustering algorithms.

Gai, K, Guo, J, Zhu, L & Yu, S 2020, 'Blockchain Meets Cloud Computing: A Survey', IEEE Communications Surveys and Tutorials, vol. 22, no. 3, pp. 2009-2030.
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© 1998-2012 IEEE. Blockchain technology has been deemed to be an ideal choice for strengthening existing computing systems in varied manners. As one of the network-enabled technologies, cloud computing has been broadly adopted in the industry through numerous cloud service models. Fusing blockchain technology with existing cloud systems has a great potential in both functionality/performance enhancement and security/privacy improvement. The question remains on how blockchain technology inserts into current deployed cloud solutions and enables the reengineering of cloud datacenter. This survey addresses this issue and investigates recent efforts in the technical fusion of blockchain and clouds. Three technical dimensions roughly are covered in this work. First, we concern the service model and review an emerging cloud-relevant blockchain service model, Blockchain-as-a-Service (BaaS); second, security is considered a key technical dimension in this work and both access control and searchable encryption schemes are assessed; finally, we examine the performance of cloud datacenter with supports/participance of blockchain from hardware and software perspectives. Main findings of this survey will be theoretical supports for future reference of blockchain-enabled reengineering of cloud datacenter.

Gan, YY, Ong, HC, Chen, W-H, Sheen, H-K, Chang, J-S, 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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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.

Gandomi, AH & Deb, K 2020, 'Implicit constraints handling for efficient search of feasible solutions', COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, vol. 363.
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Gandomi, AH, Emrouznejad, A & Rahimi, I 2020, 'Evolutionary Computation in Scheduling', pp. 1-10.
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Gao, G, Yu, Y, Xie, J, Yang, J, Yang, M & Zhang, J 2020, 'Constructing multilayer locality-constrained matrix regression framework for noise robust face super-resolution', Pattern Recognition.
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© 2020 Representation learning methods have attracted considerable attention for learning-based face super-resolution in recent years. Conventional methods perform local models learning on low-resolution (LR) manifold and face reconstruction on high-resolution (HR) manifold respectively, leading to unsatisfactory reconstruction performance when the acquired LR face images are severely degraded (e.g., noisy, blurred). To tackle this issue, this paper proposes an efficient multilayer locality-constrained matrix regression (MLCMR) framework to learn the representation of the input LR patch and meanwhile preserve the manifold of the original HR space. Particularly, MLCMR uses nuclear norm regularization to capture the structural characteristic of the representation residual and applies an adaptive neighborhood selection scheme to find the HR patches that are compatible with its neighbors. Also, MLCMR iteratively applies the manifold structure of the desired HR space to induce the representation weights learning in the LR space, aims at reducing the inconsistency gap between different manifolds. Experimental results on widely used FEI database and real-world faces have demonstrated that compared with several state-of-the-art face super-resolution approaches, our proposed approach has the capability of obtaining better results both in objective metrics and visual quality.

Gao, H, Yin, Y & Hussain, W 2020, 'The Ubiquitous Internet of Things in Electricity (IOTE): Computational-Intelligence-based Optimization, Security Control, and Fault Diagnosis', IAENG International Journal of Computer Science, vol. 47, no. 3, pp. 565-566.

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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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, K, Qi, Z, Jia, J, Li, S, Liu, Z & Liu, Z 2020, 'Investigation of coupled control of gas accumulation and spontaneous combustion in the goaf of coal mine', AIP ADVANCES, vol. 10, no. 4.
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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, vol. 34, no. 9, pp. 1239-1253.
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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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© 2011-2012 IEEE. Wireless communication and sensing are moving from microwave, millimeter-wave into the terahertz (THz) frequency regime to meet the fast growing demand of ultrahigh data-rate communications and super resolution imaging. Faced with severe atmospheric absorption attenuation and the lack of power efficient transmitting source at the higher band, ultrasensitive and cost-effective receiver frontend technology is required for advanced THz wireless systems. To date, the most sensitive heterodyne mixers, the key components of frontend receiver systems, are based on low-temperature superconducting materials that operate at liquid helium (4.2 K) temperature range, requiring expensive and bulky cryogenic cooling systems thus hindering them from commercial applications such as wireless communications and sensing. In this article, we present a 340 GHz double-sideband fundamental mixer based on thin-film antenna-coupled high-temperature superconducting (HTS) Josephson junction that operates at a much higher temperature range attainable with smaller and cheaper cryocoolers. Based on our innovative work in terms of advanced device circuit and on-chip antenna designs, accurate parametric simulation analyses, and Josephson junction parameter optimizations, the reported mixer exhibits a measured noise temperature of 470 and 780 K at operating temperatures of 20 and 40 K respectively at 340 GHz, a performance significantly higher than any HTS THz mixers reported to date.

Garcia-Rincon, J, Gatsios, E, Rayner, JL, McLaughlan, RG & Davis, GB 2020, 'Laser-induced fluorescence logging as a high-resolution characterisation tool to assess LNAPL mobility', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 725.
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Gautam, S, Lu, DDC, Xiao, W & Lu, Y 2020, 'Realisation of RPS from electrical home appliances in a smart home energy management system', IET Smart Grid, vol. 3, no. 1, pp. 11-21.
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© 2020 Institution of Engineering and Technology. All rights reserved. With the increasing integration of photovoltaic power generation in the low-voltage distribution network, the grid voltage regulation becomes critical, which demands support from different resources. This study presents the feasibility study of home appliance to be applied for appliance to grid mode of operation. The analysis includes the amendments in topology and control to support the concept of supportive platform provided by smart home and smart grid. Home appliances are then proposed as new distributed reactive sources, which are utilised to resolve the issue of voltage regulation, as well as produce reactive power locally for voltage stability. This study discusses the technical transitions in current home appliance to accommodate auxiliary functionality of grid reactive power support (RPS) and how it can fit in the home energy management system architecture to provide the required RPS.

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.

Geekiyanage, N, Sauret, E, Saha, S, Flower, R & Gu, Y 2020, 'Modelling of Red Blood Cell Morphological and Deformability Changes during In-Vitro Storage', APPLIED SCIENCES-BASEL, vol. 10, no. 9.
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Geekiyanage, NM, Sauret, E, Saha, SC, Flower, RL & Gu, YT 2020, 'Deformation behaviour of stomatocyte, discocyte and echinocyte red blood cell morphologies during optical tweezers stretching', BIOMECHANICS AND MODELING IN MECHANOBIOLOGY.
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Geissinger, A, Laurell, C, Öberg, C, Sandström, C, Sick, N & Suseno, Y 2020, 'Social media analytics for knowledge acquisition of market and non-market perceptions in the sharing economy', Journal of Knowledge Management.
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© 2020, Emerald Publishing Limited. Purpose: Using the case of Foodora, this paper aims to assess the impact of technological innovation of an emerging actor in the sharing economy through stakeholders’ perceptions in the market and non-market domains. Design/methodology/approach: Using a methodological approach called social media analytics (SMA) to explore the case of Foodora, 3,250 user-generated contents in social media are systematically gathered, coded and analysed. Findings: The findings indicate that, while Foodora appears to be a viable provider in the marketplace, there is mounting public concern about the working conditions of its employees. In the market domain, Foodora manages its status as an online delivery platform and provider well, but at the same time, it struggles with its position in the non-market sphere, suggesting that the firm is vulnerable to regulatory change. These insights highlight the importance of simultaneously exploring and balancing market and non-market perceptions when assessing the impact of disruptive innovation. Originality/value: This study offers originality by providing an integrative approach to consider both the market and non-market domains. It is also novel in its use of SMA as a tool for knowledge acquisition and management to evaluate the impact of emerging technologies in the sharing economy.

Gentile, C, Kesteven, S, Wu, J, Davies, MJ, Bursill, C, Feneley, M & Figtree, G 2020, 'Endothelial nitric oxide synthase plays a protective role in endothelial cells and cardiomyocytes against myocardial infarction', Journal of Molecular and Cellular Cardiology, vol. 140, pp. 31-31.
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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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Ghasemi, M, Davoudkhani, IF, Akbari, E, Rahimnejad, A, Ghavidel, S & Li, L 2020, 'A novel and effective optimization algorithm for global optimization and its engineering applications: Turbulent Flow of Water-based Optimization (TFWO)', Engineering Applications of Artificial Intelligence, vol. 92.
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© 2020 Elsevier Ltd In this study we present a new and effective grouping optimization algorithm (namely, the Turbulent Flow of Water-based Optimization (TFWO)), inspired from a nature search phenomenon, i.e. whirlpools created in turbulent flow of water, for global real-world optimization problems. In the proposed algorithm, the problem of selecting control parameters is eliminated, the convergence power is increased and the algorithm have a fixed structure. The proposed algorithm is used to find the global solutions of real-parameter benchmark functions with different dimensions. Besides, in order to further investigate the effectiveness of TFWO, it was used to solve various types of nonlinear Economic Load Dispatch (ELD) optimization problems in power systems and Reliability–RedundancyAllocation Optimization (RRAO) for the overspeed protection system of a gas turbine, as two real-world engineering optimization problems. The results of TFWO are compared with other algorithms, which provide evidence for efficient performance with superior solution quality of the proposed TFWO algorithm in solving a great range of real-parameter benchmark and real-world engineering problems. Also, the results prove the competitive performance and robustness of TFWO algorithm compared to other state-of-the-art optimization algorithms in this study. The source codes of the TFWO algorithm are publicly available at https://github.com/ebrahimakbary/TFWO.

Ghasemkhani, N, Vayghan, SS, Abdollahi, A, Pradhan, B & Alamri, A 2020, 'Urban Development Modeling Using Integrated Fuzzy Systems, Ordered Weighted Averaging (OWA), and Geospatial Techniques', SUSTAINABILITY, vol. 12, no. 3.
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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, vol. 252.
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Ghobadi, R, Altaee, A, Zhou, JL, McLean, P, Ganbat, N & Li, D 2020, 'Enhanced copper removal from contaminated kaolinite soil by electrokinetic process using compost reactive filter media', Journal of Hazardous Materials, pp. 123891-123891.
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Ghorbani, F, Fathi, F, Aghebati-Maleki, L, Abolhasan, R, Rikhtegar, R, Dolatabadi, JEN, Babaloo, Z, Khalilzadeh, B, Warkiani, ME, Sharifzadeh, Z, Rashidi, M-R & Yousefi, M 2020, 'Kinetic and thermodynamic study of c-Met interaction with single chain fragment variable (scFv) antibodies using phage based surface plasmon resonance.', Eur J Pharm Sci, vol. 150, pp. 105362-105362.
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Mesenchymal epithelial transition factor (c-Met) has been recently regarded as an attractive target for the treatment of cancer. Our previous study showed that c-Met-specific single chain fragment variables (scFvs) can be considered as a promising therapy for cancer, however, their molecular interaction with c-Met protein have not been assessed. Accordingly, in the current study we aim to evaluate the kinetic and thermodynamic properties of c-Met interaction with these scFvs as anticancer agents by means of surface plasmon resonance (SPR) technique. Phage-scFvs were immobilized on the 11-mercaptoundecanoic acid gold chips after carboxylic groups activation by N-ethyl-N-(3-diethylaminopropyl) carbodiimide/N-hydroxysuccinimide and, then the c-Met binding to each scFvs (ES1, ES2, and ES3) at different concentrations (ranging from 20 to 665 μM) was explored. Kinetic studies revealed that ES1 has the highest affinity (KD = 3.36 × 10-8) toward its target at 25°C. Calculation of thermodynamic parameters also showed positive values for enthalpy and entropy changes, which was representative of hydrophobic forces between c-Met and ES1. Furthermore, the positive value of Gibbs free energy indicated that c-Met binding to ES1 was enthalpy-driven. Taken together, we concluded that produced ES1 can be applied as promising scFv-based therapy for diagnosis or targeting of c-Met in various cancers.

Ghosh, A, Islam, MS & Saha, SC 2020, 'Targeted drug delivery of magnetic nano-particle in the specific lung region', Computation, vol. 8, no. 1.
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© 2020 by the authors. Aerosolized drug inhalation plays an important role in the treatment of respiratory diseases. All of the published in silico, in vivo, and in vitro studies have improved the knowledge of aerosol delivery in the human respiratory system. However, aerosolized magnetic nano-particle (MNP) transport and deposition (TD) for the specific position of the human lung are still unavailable in the literature. Therefore, this study is aimed to provide an understanding of the magnetic nano-particle TD in the targeted region by imposing an external magnetic field for the development of future therapeutics. Uniform aerosolized nano-particle TD in the specific position of the lung airways will be modelled by adopting turbulence k-ω low Reynolds number simulation. The Euler-Lagrange (E-L) approach and the magneto hydrodynamics (MHD) model are incorporated in the ANSYS fluent (18.0) solver to investigate the targeted nano-particle TD. The human physical activity conditions of sleeping, resting, light activity and fast breathing are considered in this study. The aerosolized drug particles are navigated to the targeted position under the influence of external magnetic force (EMF), which is applied in two different positions of the two-generation lung airways. A numerical particle tracing model is also developed to predict the magnetic drug targeting behavior in the lung. The numerical results reveal that nano-particle deposition efficiency (DE) in two different magnetic field position is different for various physical activities, which could be helpful for targeted drug delivery to a specific region of the lung after extensive clinical trials. This process will also be cost-effective and will minimize unwanted side effects due to systemic drug distribution in the lung.

Ghosh, S, Das, A, Hembram, TK, Saha, S, Pradhan, B & Alamri, AM 2020, 'Impact of COVID-19 Induced Lockdown on Environmental Quality in Four Indian Megacities Using Landsat 8 OLI and TIRS-Derived Data and Mamdani Fuzzy Logic Modelling Approach', SUSTAINABILITY, vol. 12, no. 13.
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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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Gluth, GJG, Arbi, K, Bernal, SA, Bondar, D, Castel, A, Chithiraputhiran, S, Dehghan, A, Dombrowski-Daube, K, Dubey, A, Ducman, V, Peterson, K, Pipilikaki, P, Valcke, SLA, Ye, G, Zuo, Y & Provis, JL 2020, 'RILEM TC 247-DTA round robin test: carbonation and chloride penetration testing of alkali-activated concretes', MATERIALS AND STRUCTURES, vol. 53, no. 1.
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Goh, BHH, Chong, CT, Ge, Y, Ong, HC, Ng, JH, Tian, B, Ashokkumar, V, Lim, S, Seljak, T & Józsa, V 2020, 'Progress in utilisation of waste cooking oil for sustainable biodiesel and biojet fuel production', Energy Conversion and Management, vol. 223.
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© 2020 Elsevier Ltd The increase in human consumption of plant and animal oils has led to the rise in waste cooking oil (WCO) production. Instead of disposing the used cooking oil as waste, recent technological advance has enabled the use of WCO as a sustainable feedstock for biofuels production, thereby maximising the value of biowastes via energy recovery while concomitantly solving the disposal issue. The current regulatory frameworks for WCO collection and recycling practices imposed by major WCO producing countries are reviewed, followed by the overview of the progress in biodiesel conversion techniques, along with novel methods to improve the feasibility for upscaling. The factors which influence the efficiency of the reactions such as properties of feedstock, heterogenous catalytic processes, cost effectiveness and selectivity of reaction product are discussed. Ultrasonic-assisted transesterification is found to be the least energy intensive method for producing biodiesel. The production of bio-jet fuels from WCO, while scarce, provide diversity in waste utilisation if problems such as carbon chain length, requirements of bio-jet fuel properties, extreme reaction conditions and effectiveness of selected catalyst-support system can be solved. Technoeconomic studies revealed that WCO biofuels is financially viable with benefit of mitigating carbon emissions, provided that the price gap between the produced fuel and commercial fuels, sufficient supply of WCO and variation in the oil properties are addressed. This review shows that WCO is a biowaste with high potential for advanced transportation fuel production for ground and aviation industries. The advancement in fuel production technology and relevant policies would accelerate the application of sustainable WCO biofuels.

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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Gokuldas, S, Banerjee, S & Nimbalkar, SS 2020, 'Effects of Tunneling-Induced Ground Movements on Stability of Piled Raft Foundation: Three-Dimensional Finite-Element Approach', INTERNATIONAL JOURNAL OF GEOMECHANICS, vol. 20, no. 8.
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Gomes, SDC, Zhou, JL, Li, W & Qu, F 2020, 'Recycling of raw water treatment sludge in cementitious composites: effects on heat evolution, compressive strength and microstructure', Resources, Conservation and Recycling, vol. 161.
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© 2020 Elsevier B.V. Water treatment sludge (WTS) is produced daily and represents a globally significant solid waste stream. The application of this sludge as construction materials has been studied although most studies have modified the sludge before its incorporation, hence involving significant energy consumption. This study aims to use raw sludge as a novel cementitious material, by determining the effects of sludge addition on the composition and performance of cementitious composites. Important aspects such as the physicochemical interaction of the raw sludge with the Portland cement, the heat evolution of the cement paste and the compressive strength of the composite cement were carefully studied. The results show that for 1-2% of WTS addition, the compressive strength and heat evolution of the cement paste was well maintained being close to the reference specimen after 28 days of curing. However, for sludge addition above 5%, a delay in the hydration reaction was observed, together with about 25% reduction in compressive strength at 28 days of curing. The mineralogical and thermal analysis showed decreasing portlandite content and increasing calcite in the WTS-amended composites. Scanning electron microscope analysis demonstrated that the addition of sludge induced more porous and weak surface structures compared to the reference specimen.

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.

Gong, Y, Zhang, L, Yu, K & Liu, R 2020, 'Exploring Uplink Achievable Rate for HPO MIMO Through Quasi-Monte Carlo and Variance Reduction Techniques', IEEE ACCESS, vol. 8, pp. 75874-75883.
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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.

Gu, X, Li, J & Li, Y 2020, 'Experimental realisation of the real-time controlled smart magnetorheological elastomer seismic isolation system with shake table', Structural Control and Health Monitoring, vol. 27, no. 1.
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© 2019 John Wiley & Sons, Ltd. Traditional base isolation protects structures against severe seismic events by providing a designated lateral flexibility at the base level of the structures. Due to its inherent passive nature, in the design process, compromises have to be made among performance of different design targets (displacements, interstorey drifts, accelerations, etc.). In addition, as the working principle, the effectiveness of a base isolation relies on the degree of “decoupling” between ground excitation and superstructure. However, a higher degree of decoupling compromises the stability of the structures. In other words, for a base solation system, it possesses inherent conflicts between the effectiveness of the isolation and the lateral stability of the structure. A concept of new smart base isolation system is proposed, in which real-time controllable decoupling for a base isolation structure is achieved by employing magnetorheological elastomer (MRE) base isolators. With controllable lateral stiffness, the smart base isolation system can achieve an optimal decoupling by instantly shifting the structure's natural frequencies to a nonresonant region. This paper aims at experimentally proving and validating this innovative concept, including designing a three-storey shear building model equipped with MRE base isolators, demonstrating the feasibility and evaluating the performance of the proposed system by a series of shake table testing. The comprehensive experimental design and results of shake table testing have concept-proved the proposed smart MRE base isolation system for future development in practical applications.

Guan, J, Fang, W & Ying, M 2020, 'Robustness Verification of Quantum Machine Learning.', CoRR, vol. abs/2008.07230.

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.

Guertler, MR & Sick, N 2020, 'Exploring the enabling effects of project management for SMEs in adopting open innovation – A framework for partner search and selection in open innovation projects', International Journal of Project Management.
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© 2020 Open Innovation (OI) facilitates a multitude of innovation opportunities through allowing access to a broad variety of external partners, expertise and knowledge. Although OI has been established in academia and the corporate world, implementation by SMEs remains a formidable challenge, especially concerning the identification and selection of suitable OI partners. Given methodical support for such an endeavour is currently lacking, this article investigates how project management can support OI projects. Based on evidence from an exploratory multi-case study with four SMEs, this article develops a Situational Open Innovation framework that provides methodical support for SMEs in leveraging the complementarities between OI and project management towards effective partner search and selection. The findings illustrate how sensing capabilities for OI opportunities can benefit from systematic problem and stakeholder analyses as they allow for identifying and focussing on the most relevant innovation tasks and partners.

Guertler, MR, Kriz, A & Sick, N 2020, 'Encouraging and enabling action research in innovation management', R & D MANAGEMENT, vol. 50, no. 3, pp. 380-395.
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Guo, A, Wang, B, Lyu, C, Li, W, Wu, Y, Zhu, L, Bi, R, Huang, C, Li, JJ & Du, Y 2020, 'Consistent apparent Young's modulus of human embryonic stem cells and derived cell types stabilized by substrate stiffness regulation promotes lineage specificity maintenance.', Cell regeneration (London, England), vol. 9, no. 1, p. 15.
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BACKGROUND:Apparent Young's modulus (AYM), which reflects the fundamental mechanical property of live cells measured by atomic force microscopy and is determined by substrate stiffness regulated cytoskeletal organization, has been investigated as potential indicators of cell fate in specific cell types. However, applying biophysical cues, such as modulating the substrate stiffness, to regulate AYM and thereby reflect and/or control stem cell lineage specificity for downstream applications, remains a primary challenge during in vitro stem cell expansion. Moreover, substrate stiffness could modulate cell heterogeneity in the single-cell stage and contribute to cell fate regulation, yet the indicative link between AYM and cell fate determination during in vitro dynamic cell expansion (from single-cell stage to multi-cell stage) has not been established. RESULTS:Here, we show that the AYM of cells changed dynamically during passaging and proliferation on substrates with different stiffness. Moreover, the same change in substrate stiffness caused different patterns of AYM change in epithelial and mesenchymal cell types. Embryonic stem cells and their derived progenitor cells exhibited distinguishing AYM changes in response to different substrate stiffness that had significant effects on their maintenance of pluripotency and/or lineage-specific characteristics. On substrates that were too rigid or too soft, fluctuations in AYM occurred during cell passaging and proliferation that led to a loss in lineage specificity. On a substrate with 'optimal' stiffness (i.e., 3.5 kPa), the AYM was maintained at a constant level that was consistent with the parental cells during passaging and proliferation and led to preservation of lineage specificity. The effects of substrate stiffness on AYM and downstream cell fate were correlated with intracellular cytoskeletal organization and nuclear/cytoplasmic localization of YAP. CONCLUSIONS:In summary, this study suggests that optimal sub...

Guo, K, Guo, Y & Li, J 2020, 'Decoupling Control Analysis of a Flux Reversal Linear Rotary Permanent Magnet Actuator', Journal of Electrical Engineering and Technology, vol. 15, no. 4, pp. 1693-1704.
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© 2020, The Korean Institute of Electrical Engineers. In order to meet the requirement of industrial production, a control system of a novel flux reversal linear rotary permanent magnet actuator (FR-LRPMA) is developed in this paper, and the electromagnetic and motion decoupled process is analyzed in detail. Firstly, the control equation of FR-LRPMA is derived in the nine phase stator coordinate system and dual dq-axis mover coordinate system. Then the control model of FR-LRPMA is built in MATLAB/Simulink using improved space vector pulse width modulation (SVPWM), and the linear, rotary and spiral motions of the proposed actuator are achieved. Finally, the prototype of the actuator is manufactured, and the experiment platform of FR-LRPMA is set up to measure the cogging torque and linear detent force, validating the simulation results. It is concluded that the control system of FR-LRPMA can be converted into two independent single freedom motors by dual dq transformation. The improved SVPWM is effective in the control system, and the method can be used to solve the electromagnetic and motion decoupling problems.

Guo, Y, Karimi, F, Fu, Q, Qiao, GG & Zhang, H 2020, 'Reduced administration frequency for the treatment of fungal keratitis: a sustained natamycin release from a micellar solution', EXPERT OPINION ON DRUG DELIVERY, vol. 17, no. 3, pp. 407-421.
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Guo, Z, Kang, Y, Hu, Z, Liang, S, Xie, H, Ngo, HH & Zhang, J 2020, 'Removal pathways of benzofluoranthene in a constructed wetland amended with metallic ions embedded carbon', BIORESOURCE TECHNOLOGY, vol. 311.
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Gupta, A, Agrawal, RK, Kirar, JS, Andreu-Perez, J, Ding, W-P, Lin, C-T & Prasad, M 2020, 'On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI', IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-13.
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Gupta, A, Agrawal, RK, Kirar, JS, Kaur, B, Ding, W, Lin, CT, Andreu-Perez, J & Prasad, M 2020, 'A hierarchical meta-model for multi-class mental task based brain-computer interfaces', Neurocomputing, vol. 389, pp. 207-217.
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© 2019 In the last few years, many research works have been suggested on Brain-Computer Interface (BCI), which assists severely physically disabled persons to communicate directly with the help of electroencephalogram (EEG) signal, generated by the thought process of the brain. Thought generation inside the brain is a dynamic process, and plenty thoughts occur within a small time window. Thus, there is a need for a BCI device that can distinguish these various ideas simultaneously. In this research work, our previous binary-class mental task classification has been extended to the multi-class mental task problem. The present work proposed a novel feature construction scheme for multi mental task classification. In the proposed method, features are extracted in two phases. In the first step, the wavelet transform is used to decompose EEG signal. In the second phase, each feature component obtained is represented compactly using eight parameters (statistical and uncertainty measures). After that, a set of relevant and non-redundant features is selected using linear regression, a multivariate feature selection approach. Finally, optimal decision tree based support vector machine (ODT-SVM) classifier is used for multi mental task classification. The performance of the proposed method is evaluated on the publicly available dataset for 3-class, 4-class, and 5-class mental task classification. Experimental results are compared with existing methods, and it is observed that the proposed plan provides better classification accuracy in comparison to the existing methods for 3-class, 4-class, and 5-class mental task classification. The efficacy of the proposed method encourages that the proposed method may be helpful in developing BCI devices for multi-class classification.

Hadgraft, RG & Kolmos, A 2020, 'Emerging learning environments in engineering education', Australasian Journal of Engineering Education, vol. 25, no. 1, pp. 3-16.
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© 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.

Halkon, B & Rothberg, S 2020, 'Establishing correction solutions for Scanning Laser Doppler Vibrometer measurements affected by sensor head vibration'.

Hamidi, BA, Hosseini, SA & Hayati, H 2020, 'Forced torsional vibration of nanobeam via nonlocal strain gradient theory and surface energy effects under moving harmonic torque', WAVES IN RANDOM AND COMPLEX MEDIA.
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Hamidi, BA, Hosseini, SA, Hayati, H & Hassannejad, R 2020, 'Forced axial vibration of micro and nanobeam under axial harmonic moving and constant distributed forces via nonlocal strain gradient theory', MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES.
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Han, C, Li, W, Liu, HK, Dou, S & Wang, J 2020, 'Principals and strategies for constructing a highly reversible zinc metal anode in aqueous batteries', NANO ENERGY, vol. 74.
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Han, P, Kimura, F & Sandu, S 2020, 'Household-level analysis of the impacts of electricity consumption on welfare and the environment in Cambodia: Empirical evidence and policy implications', Economic Modelling.
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© 2019 Cambodia's biomass consumption is the most dominant energy source at residential sector, and its use is mainly for cooking and heating which could affect health due to indoor air pollution. The biomass is mainly sourced from wood cutting and forest-encroachment that could impact the environment due to reduction of forest at considerable scale. By using the data 2015 of Cambodia Socio-Economic Survey, the study investigates the impacts of electricity consumption on household welfare, such as earnings and the school performance of children in the households, and further to investigate its impacts on the environment. The study found that household's access to electricity with ability to spend on electricity consumption contributes to the positive household welfare effects and environment via a reduction of biomass consumption, and the more household spends on biomass, the more they are prone to sickness of lung problem. The study also confirmed the important role of human capital formation for the positive impact on the welfare and the environment. These findings lead to policy implications that would improve affordable access to electricity to ensure that all households can use electricity for their basic needs and productivity, and also to reduce the negative effects on environment.

Han, R, Liu, F, Wang, X, Huang, M, Li, W, Yamauchi, Y, Sun, X & Huang, Z 2020, 'Functionalised hexagonal boron nitride for energy conversion and storage', JOURNAL OF MATERIALS CHEMISTRY A, vol. 8, no. 29, pp. 14384-14399.
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Han, Y, Deng, Y, Cao, Z & Lin, CT 2020, 'An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Multicriteria decision-making process explicitly evaluates multiple conflicting criteria in decision making. The conventional decision-making approaches assumed that each agent is independent, but the reality is that each agent aims to maximize personal benefit which causes a negative influence on other agents’ behaviors in a real-world competitive environment. In our study, we proposed an interval-valued Pythagorean prioritized operator-based game theoretical framework to mitigate the cross-influence problem. The proposed framework considers both prioritized levels among various criteria and decision makers within five stages. Notably, the interval-valued Pythagorean fuzzy sets are supposed to express the uncertainty of experts, and the game theories are applied to optimize the combination of strategies in interactive situations. Additionally, we also provided illustrative examples to address the application of our proposed framework. In summary, we provided a human-inspired framework to represent the behavior of group decision making in the interactive environment, which is potential to simulate the process of realistic humans thinking.

Hanawal, M, Nguyen, D & Krunz, M 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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Hannan, MA, Abd Ali, J, Lipu, MSH, Mohamed, A, Ker, PJ, Mahlia, TMI, Mansor, M, Hussain, A, Muttaqi, KM & Dong, ZY 2020, 'Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement', NATURE COMMUNICATIONS, vol. 11, no. 1.
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Hannan, MA, Begum, RA, Al-Shetwi, AQ, Ker, PJ, Al Mamun, MA, Hussain, A, Basri, H & Mahlia, TMI 2020, 'Waste collection route optimisation model for linking cost saving and emission reduction to achieve sustainable development goals', Sustainable Cities and Society, vol. 62.
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© 2020 Elsevier Ltd Developing an efficient, cost-effective and environmentally friendly solution for solid waste collection (SWC) and transportation system remains a major challenge for municipalities. Waste collection encompasses the largest part of the total budget in current waste management systems. SWC is a crosscutting issue that can be directly or indirectly linked to 10 of the 17 United Nations’ sustainable development goals (SDGs). This study aims to develop an SWC route optimisation model to improve collection efficiency, save collection costs and reduce emissions by considering fixed routing optimisation (FRO) with static data and variable routing optimisation (VRO) with real-time data. To realise the optimisation, a mixed-integer linear programming model utilising FRO and VRO was developed. Results show that VRO improved the collection efficiency by 26.08 % when the minimum filled-up level for collection was 70 %. Moreover, VRO achieved 44.44 % cost savings and 17.60 % carbon emission reduction at 70 % filled level. The proposed system achieved the targeted goals and demonstrated the feasibility of an optimisation model for the waste management sector to build a sustainable smart city. The findings of this study can be used to strengthen efforts towards the achievement of the SDGs related to solid waste collection and management.

Hannan, MA, Lipu, MSH, Hussain, A, Ker, PJ, Mahlia, TM, Mansor, M, Ayob, A, Saad, MH & Dong, ZY 2020, 'Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques', SCIENTIFIC REPORTS, vol. 10, no. 1.
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Hao, H, Niu, J, Xue, B, Su, QP, Liu, M, Yang, J, Qin, J, Zhao, S, Wu, C & Sun, Y 2020, 'Golgi-associated microtubules are fast cargo tracks and required for persistent cell migration', EMBO REPORTS, vol. 21, no. 3.
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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, HT & Martelli, C 2020, '3-D printed smart orthotic insoles: Monitoring a person's gait step by step', IEEE Sensors Letters, vol. 4, no. 1.
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© 2017 IEEE. This article reports a 3-D printing intelligent insole gait monitoring system based on an embedded fiber Bragg grating (FBG). The smart insole combines 3-D printing technology and FBG sensors providing high sensitivity and end-point low cost. Results using pressure points measured by four FBGs are sufficient to differentiate foot loads and gait types.

Hasan, SU, Hassan, HA, Scott, MJ, Siwakoti, YP, Town, G & Blaabjerg, F 2020, 'Common-Ground Transformerless Inverter with Virtual DC Bus Concept for Single-Phase PV Systems', IEEJ Journal of Industry Applications, vol. 9, no. 5, pp. 538-548.
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Hashem Zadeh, SM, Mehryan, SAM, Islam, MS & Ghalambaz, M 2020, 'Irreversibility analysis of thermally driven flow of a water-based suspension with dispersed nano-sized capsules of phase change material', International Journal of Heat and Mass Transfer, vol. 155, pp. 119796-119796.
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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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Hassanzadeh-Barforoushi, A, Warkiani, ME, Gallego-Ortega, D, Liu, G & Barber, T 2020, 'Capillary-assisted microfluidic biosensing platform captures single cell secretion dynamics in nanoliter compartments', BIOSENSORS & BIOELECTRONICS, vol. 155.
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Hayat, T, Afzal, MU, Ahmed, F, Zhang, S, Esselle, KP & Vardaxoglou, Y 2020, 'Low-Cost Ultrawideband High-Gain Compact Resonant Cavity Antenna', IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, vol. 19, no. 7, pp. 1271-1275.
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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 & ENERGY SYSTEMS, vol. 116.
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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.
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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, X, Xu, H, Sabetamal, H & Sheng, D 2020, 'Machine learning aided stochastic reliability analysis of spatially variable slopes', Computers and Geotechnics, vol. 126.
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© 2020 This paper presents machine learning aided stochastic reliability analysis of spatially variable slopes, which significantly reduces the computational efforts and gives a complete statistical description of the factor of safety with promising accuracy compared with traditional methods. Within this framework, a small number of traditional random finite-element simulations are conducted. The samples of the random fields and the calculated factor of safety are, respectively, treated as training input and output data, and are fed into machine learning algorithms to find mathematical models to replace finite-element simulations. Two powerful machine learning algorithms used are the neural networks and the support-vector regression with their associated learning strategies. Several slopes are examined including stratified slopes with 3 or 4 layers described by 4 or 6 random fields. It is found that with 200 to 300 finite-element simulations (finished in about 5 ~ 8 h), the machine-learning generated model can predict the factor of safety accurately, and a stochastic analysis of 105 samples takes several minutes. However, the same traditional analysis would require hundreds of days of computation.

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, vol. 50, no. 8, pp. 3594-3604.
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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%.

Heidary, A, Radmanesh, H, Bakhshi, A, Samandarpour, S, Rouzbehi, K & Shariati, N 2020, 'Compound ferroresonance overvoltage and fault current limiter for power system protection', IET Energy Systems Integration.
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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, vol. 2, no. 1, pp. 59-65.
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Heidary, A, Radmanesh, H, Rouzbehi, K & CheshmehBeigi, HM 2020, 'A Multifunction High-Temperature Superconductive Power Flow Controller and Fault Current Limiter', IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY, vol. 30, no. 5.
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Heidary, A, Radmanesh, H, Rouzbehi, K, Mehrizi-Sani, A & Gharehpetian, GB 2020, 'Inductive fault current limiters: A review', Electric Power Systems Research, vol. 187, pp. 106499-106499.
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Heizmann, H, Ahuja, S & Mastio, E 2020, 'Stuck in Defensive Professionalism: Undermining Organizational Change in an Intellectual Property Law Firm', Journal of Professions and Organization, vol. 7, no. 2, pp. 117-133.

Helwani, Z, Ramli, M, Rusyana, A, Marlina, M, Fatra, W, Idroes, GM, Suhendra, R, Ashwie, V, Mahlia, TMI & Idroes, R 2020, 'Alternative Briquette Material Made from Palm Stem Biomass Mediated by Glycerol Crude of Biodiesel Byproducts as a Natural Adhesive', PROCESSES, vol. 8, no. 7.
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Helwani, Z, Ramli, M, Saputra, E, Bahruddin, B, Yolanda, D, Fatra, W, Idroes, GM, Muslem, M, Mahlia, TMI & Idroes, R 2020, 'Impregnation of CaO from Eggshell Waste with Magnetite as a Solid Catalyst (Fe3O4/CaO) for Transesterification of Palm Oil Off-Grade', CATALYSTS, vol. 10, no. 2.
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Hendryx, M, Islam, MS, Dong, GH & 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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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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.

Hirsimaki, C, Outram, JG, Millar, GJ & Altaee, A 2020, 'Process simulation of High pH reverse osmosis systems to facilitate reuse of coal seam gas associated Water', Journal of Environmental Chemical Engineering, pp. 104122-104122.
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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, CN, Wang, F-M, Kim, KD, Thanh, TP, Juan, H-Y, Nguyen, PN, Hwai, CO & Su, C-H 2020, 'Microwave-Assisted Noncatalytic Esterification of Fatty Acid for Biodiesel Production: A Kinetic Study', ENERGIES, vol. 13, no. 9.
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Hoang, DT, Nguyen, DN, Alsheikh, MA, Gong, S, Dutkiewicz, E, Niyato, D & Han, Z 2020, '"Borrowing Arrows with Thatched Boats": The Art of Defeating Reactive Jammers in IoT Networks', IEEE Wireless Communications Magazine, vol. 27, no. 3, pp. 79-87.
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In this article, we introduce a novel deception strategy inspired by the "Borrowing Arrows with Thatched Boats" strategy, one of the most famous military tactics in history, in order to defeat reactive jamming attacks for low-power IoT networks. Our proposed strategy allows resource-constrained IoT devices to be able to defeat powerful reactive jammers by leveraging their own jamming signals. More specifically, by stimulating the jammer to attack the channel through transmitting fake transmissions, the IoT system can not only undermine the jammer's power, but also harvest energy or utilize jamming signals as a communication means to transmit data through using RF energy harvesting and ambient backscatter techniques, respectively. Furthermore, we develop a low-cost deep reinforcement learning framework that enables the hardware-constrained IoT device to quickly obtain an optimal defense policy without requiring any information about the jammer in advance. Simulation results reveal that our proposed framework can not only be very effective in defeating reactive jamming attacks, but also leverage a jammer's power to enhance system performance for the IoT network.

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, NPV, Huu, HN, Guo, W, Thi, MHN, Li, J, Liang, H, Deng, L, Chen, Z & Thi, AHN 2020, 'Poly?and perfluoroalkyl substances in water and wastewater: A comprehensive review from sources to remediation', JOURNAL OF WATER PROCESS ENGINEERING, vol. 36.
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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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Hong, L, Ju, S, Yang, Y, Zheng, J, Xia, G, Huang, Z, Liu, X & Yu, X 2020, 'Hollow-shell structured porous CoSe2 microspheres encapsulated by MXene nanosheets for advanced lithium storage', SUSTAINABLE ENERGY & FUELS, vol. 4, no. 5, pp. 2352-2362.
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Ho-Pham, LT, Doan, MC, Van, LH & Nguyen, T 2020, 'Development of a model for identification of individuals with high risk of osteoporosis', ARCHIVES OF OSTEOPOROSIS, vol. 15, no. 1.
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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.

Horry, MJ, Chakraborty, S, Paul, M, Ulhaq, A, Pradhan, B, Saha, M & Shukla, N 2020, 'COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data', IEEE ACCESS, vol. 8, pp. 149808-149824.
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Hosen, ASMS, Singh, S, Sharma, PK, Rahman, MS, Ra, I-H, Cho, GH & Puthal, D 2020, 'A QoS-Aware Data Collection Protocol for LLNs in Fog-Enabled Internet of Things', IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, vol. 17, no. 1, pp. 430-444.
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Hoshyar, AN, Samali, B, Liyanapathirana, R, Houshyar, AN & Yu, Y 2020, 'Structural damage detection and localization using a hybrid method and artificial intelligence techniques', Structural Health Monitoring.
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© The Author(s) 2019. In this article, an intelligent scheme for structural damage detection and localization is introduced by implementing a hybrid method using the Hilbert–Huang transform and the wavelet transform. First, the second derivatives of the Discrete Laplacian are computed on Hilbert spectrum parameters at each frequency coordinate, and then, in order to highlight the influence of damage on signals, the data are rescaled and weighted with respect to the variance to adjust the differences in amplitude at different scales. Afterwards, the anti-symmetric extension is applied to deal with the boundary distortion phenomenon. A two-dimensional map is created using the multi two-dimensional discrete wavelet transform. This generates the coefficient matrices of level 2 approximation and horizontal, vertical and diagonal details. Horizontal detail coefficients are used to localize damages due to its sensitiveness to any perturbation. Finally, the validity of the algorithm corresponding to various damage states, the single state damage and multiple state damage, is examined through experimental analysis. The results indicate that the proposed framework can effectively localize cracks on concrete and reinforced concrete beams and can provide reliable crack localization in the presence of noise up to 5% more than the expected noise. In addition, the detection problem is mapped to machine learning tasks (support vector machine, k-nearest neighbours and ensemble methods) to automate the damage detection process. The quality of the models is evaluated and validated using the features extracted from the horizontal detail coefficients. The numerical results show that the ensemble models outperform the other models with respect to accuracy, prediction speed and training time.

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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Hossain, MI, Eager, D & Walker, P 2020, 'Greyhound racing ideal trajectory path generation for straight to bend based on jerk rate minimization', Scientific Reports, vol. 10, no. 1.
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This paper presents methods for modelling and designing an ideal path trajectory between straight and bend track path segments for racing greyhounds. To do this, we numerically generate clothoid and algebraic curve segments for racing quadrupeds using a sequential vector transformation method as well as using a helper equation for approaching ideal clothoid segments that would respect greyhound kinematic parameters and boundary conditions of the track. Further, we look into the limitations of using a clothoid curve for racing dog track path design and propose a smooth composite curve for track transition design which roughly maintains G3 curvature continuity for smooth jerk to overcome limitations of a clothoid transition. Finally, we show results from race data modelling and past injury data, which provide a strong indication of clothoid curve segments improving the dynamics and safety of racing greyhounds while reducing injuries.

Hossain, SI, Gandhi, NS, Hughes, ZE & Saha, S 2020, 'The role of SP-B1-25 peptides in lung surfactant monolayers exposed to gold nanoparticles', Physical Chemistry Chemical Physics.
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Lung surfactant (LS) monolayers that continuously expand and compress during breathing cycles, act as the first line barrier for inhaled nanoparticles. It is known that nanoparticles which adsorb to the...

Hossain, SM, Park, H, Kang, H-J, Kim, JB, Tijing, L, Rhee, I, Jun, Y-S, Shon, HK & Kim, J-H 2020, 'Preparation and Characterization of Photoactive Anatase TiO2 from Algae Bloomed Surface Water', CATALYSTS, vol. 10, no. 4.
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Hossain, SMG & McLaughlan, RG 2020, 'Non-equilibrium 2, 4-DCP uptake onto pine chips from aqueous solutions', ENVIRONMENTAL TECHNOLOGY.
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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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Hosseinzadeh, A, Zhou, JL, Altaee, A, Baziar, M & Li, D 2020, 'Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system', Bioresource Technology, vol. 316.
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© 2020 Elsevier Ltd This study aims to analyze and model cathodic H2 recovery (rcat), coulombic efficiency (CE) with inputs of voltage, electrical conductivity (EC) and anode potential, and H2 production rate and total energy recovery with inputs of rcat and CE in a microbial electrolysis cell using artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) procedures. Both ANN and ANFIS models demonstrated great goodness of fit for rcat, CE, H2 production rate and total energy recovery prediction with high R2 values. The sum square error values for rcat (0.0017), CE (0.0163), H2 production rate (0.1062) and total energy recovery (0.0136) in ANN models were slightly higher than those in ANFIS models at 0.0005, 0.0091, 0.1247 and 0.0148 respectively. Sensitivity analysis by ANN models demonstrated that voltage, EC, rcat and rcat were the most effective factors for rcat, CE, H2 production rate and total energy recovery, respectively.

Hosseinzadeh, A, Zhou, JL, Altaee, A, Baziar, M & Li, X 2020, 'Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network', BIORESOURCE TECHNOLOGY, vol. 310.
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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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© 2020 The Authors. 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, S, Ni, W, Zhao, S, Cheng, B, Chen, S & Chen, J 2020, 'Decentralized Real-Time Optimization of Voltage Reconfigurable Cloud Computing Data Center', IEEE Transactions on Green Communications and Networking, vol. 4, no. 2, pp. 577-592.
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© 2017 IEEE. Dynamic Voltage and Frequency Scaling, and Adaptive Body Biasing are increasingly adopted hardware techniques to improve energy efficiency of multi-core servers by adjusting reconfigurable supply and body bias voltages. Existing algorithms cannot fulfill the potential of the techniques because random variations of workload and background traffic can lead to coupling of voltage configurations over time and hinder effective real-time reconfigurations. This paper proposes a new approach which enables multi-core servers to optimize in real-time their configurations under random traffic variations. The approach asymptotically minimizes the time-averaged energy consumption of cloud computing while maintaining platform stability in a fully decentralized fashion. Lyapunov optimization is employed to decouple and separately optimize the voltage configuration, inter- and intra-server offloading schedules among servers and over time. The voltage configuration which is non-convex is proved to increasingly exhibit convexity with growing workloads. The optimality loss from the non-convexity asymptotically diminishes. Simulations show our approach dramatically reduces the power if the cloud is lightly loaded, or converts the power to processing capacity otherwise. Embraced by theoretical breakthroughs, the approach can potentially revolutionize cloud computing.

Hou, S, Ni, W, Zhao, S, Cheng, B, Chen, S & Chen, J 2020, 'Frequency-Reconfigurable Cloud Versus Fog Computing: An Energy-Efficiency Aspect', IEEE Transactions on Green Communications and Networking, vol. 4, no. 1, pp. 221-235.
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© 2017 IEEE. Cloud and fog computing are two emerging Internet-based collaborative technologies for big data analytics. An interesting question arising is whether the two technologies can resonate with significant gains of energy efficiency, especially in the case where advanced cloud platforms with Dynamic Voltage and Frequency Scaling (DVFS) are considered. This paper answers the question by formulating the optimization of a cloud with and without the assistance of fog, and deriving asymptotically optimal distributed solutions for the two cases. We also identify the critical condition under which fog computing helps the cloud to reduce the time-averaged queue lengths. The condition depends on the configurations of the fog, and the configurations of the connections between the fog and cloud. Extensive simulations exhibit good consistency with our analysis of the conditional benefits of fog computing. Evident from experimental datasets, the proposed fog-assisted cloud platform is able to increase the time-averaged energy efficiency by about 32.2%, and decrease the time-averaged queue length by around 37.0%, compared to a fog-coordinated counterpart where fog nodes only dispatch data and do not process the data.

Hou, Z, Tang, J-F, Ferrie, C, Xiang, G-Y, Li, C-F & Guo, G-C 2020, 'Experimental realization of self-guided quantum process tomography', PHYSICAL REVIEW A, vol. 101, no. 2.
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Hu, C, Liu, X, Lu, J & Wang, CH 2020, 'Distributionally robust optimization for power trading of waste-to-energy plants under uncertainty', Applied Energy, vol. 276.
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© 2020 Waste-to-energy (WTE) plants are operated worldwide to address the management of municipal solid waste. Against this background, an increasing number of WTE plants serve as combined heat and power (CHP) producers that supply heat to the heating systems in local districts and trade electricity in the regional power markets. This paper studies a short-term operation planning problem of determining effective power trading strategies for WTE CHP plants that participate in day-ahead markets. A two-stage distributionally robust optimization (DRO) model is developed with the consideration of uncertain electricity prices, waste supply, and district heating demand. These different kinds of uncertainty are captured by an ambiguity set that contains a collection of possible probability distributions of the uncertain parameters. The two-stage DRO model seeks to ascertain a power trading strategy that maximizes the expected profit of a WTE CHP plant on a regular operating day under the worst-case distribution in the ambiguity set. As the DRO model is intractable, a solution method based on linear decision rule techniques is designed to reformulate the model as a tractable robust linear program. To test the applicability of the DRO model, a case study with real-world data is conducted. The computational results show that the two-stage DRO model can facilitate a WTE CHP plant in obtaining economical and robust power trading strategies for regular operating days in a day-ahead market. Furthermore, the impacts of the parameters in the ambiguity set on deriving robust power trading strategies for WTE CHP plants are investigated.

Hu, C, Liu, X, Lu, J & Wang, CH 2020, 'Operations scheduling of waste-to-energy plants under uncertainty', Journal of Cleaner Production, vol. 253.
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© 2020 Elsevier Ltd Waste-to-energy (WTE) technologies provide effective solutions to the compelling challenges of waste management and the energy crisis globally. Many WTE plants utilize the combined heat and power (CHP) operation mode where both electricity and heat can be generated simultaneously. Thus, these WTE CHP plants can supply heat to the local district heating systems and trade power in the electricity markets. As such plants have the responsibilities of treating waste and of fulfilling the allocated district heating demand, necessary operational tasks such as preventive maintenance actions for the production units should be scheduled and performed periodically to ensure their continuous and reliable operations. This paper studies the scheduling of operational tasks in WTE CHP plants that participate in electricity markets and are connected to district heating networks. Firstly, we formulate a two-stage robust optimization model considering the uncertainty of electricity market prices, heat demand, and waste supply. The objective is to derive the robust optimal schedule that maximizes the worst-case operating profit of a WTE CHP plant under uncertainty. Subsequently, we design a constraint generation algorithm for the two-stage robust optimization model. Finally, a case study of scheduling preventive maintenance tasks is conducted for the production units of a WTE CHP plant over a 30-day horizon. The robust schedule thus derived is evaluated by Monte Carlo simulation tests and further compared to the deterministic schedule generated without the consideration of uncertainty. The simulation results show that the robust schedule enables an average profit of 877021.21€ to be attained for the plant over the scheduling horizon. Moreover, it improves the robustness of its deterministic counterpart from 68.4% to 98.8% with an increase of only 0.3% of the operating profit of the plant. In addition, a comprehensive sensitivity analysis is performed to investiga...

Hu, G, Wang, J, Qiao, H, Zhao, L, Li, Z & Tang, L 2020, 'An experimental study of a two‐degree‐of‐freedom galloping energy harvester', International Journal of Energy Research.
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Hu, S, Chen, X, Ni, W, Wang, X & Hossain, E 2020, 'Modeling and Analysis of Energy Harvesting and Smart Grid-Powered Wireless Communication Networks: A Contemporary Survey', IEEE Transactions on Green Communications and Networking, vol. 4, no. 2, pp. 461-496.
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© 2017 IEEE. The advancements in smart power grid and the advocation of 'green communications' have inspired the wireless communication networks to harness energy from ambient environments and operate in an energy-efficient manner for economic and ecological benefits. This article presents a contemporary review of recent breakthroughs on the utilization, redistribution, trading and planning of energy harvested in future wireless networks interoperating with smart grids. This article starts with classical models of renewable energy harvesting technologies. We embark on constrained operation and optimization of different energy harvesting wireless systems, such as point-to-point, multipoint-to-point, multipoint-to-multipoint, multi-hop, and multi-cell systems. We also review wireless power and information transfer technologies which provide a special implementation of energy harvesting wireless communications. A significant part of the article is devoted to the redistribution of redundant (unused) energy harvested within cellular networks, the energy planning under dynamic pricing when smart grids are in place, and two-way energy trading between cellular networks and smart grids. Applications of different optimization tools, such as convex optimization, Lagrangian dual-based method, subgradient method, and Lyapunov-based online optimization, are compared. This article also collates the potential applications of energy harvesting techniques in emerging (or upcoming) 5G/B5G communication systems. It is revealed that an effective redistribution and two-way trading of energy can significantly reduce the electricity bills of wireless service providers and decrease the consumption of brown energy. A list of interesting research directions are provided, requiring further investigation.

Hu, W, Ding, F, Zhang, J, Zhang, B, Zhang, N & Qin, A 2020, 'Robust adaptive backstepping sliding mode control for motion mode decoupling of two-axle vehicles with active kinetic dynamic suspension systems', International Journal of Robust and Nonlinear Control, vol. 30, no. 8, pp. 3110-3133.
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© 2020 John Wiley & Sons, Ltd. A mode decoupling control strategy is proposed for the active Kinetic Dynamic Suspension Systems (KDSS) with electrohydrostatic actuator (EHA) to improve the roll and warp mode performances. A matrix transfer method is employed to derive the modes of body and wheel station motions for full vehicle with active KDSS. The additional mode stiffness produced by the active KDSS is obtained and quantitatively described with the typical physical parameters. A new hierarchical feedback control strategy is proposed for the active KDSS to improve the roll and warp motion performances and simultaneously accounting for nonlinear dynamics of the actuators with hydraulic uncertainties. H∞ static output-feedback control is employed to obtain the desirable mode forces, and a new projection-based adaptive backstepping sliding mode tracking controller is designed for EHA to deal with address the nonlinearity and parameters uncertainty. This controller is used to realize the desirable pressure difference of EHA required from the target mode forces. Numerical simulations are presented to compare the roll and warp performances between the active KDSS, conventional spring-damper suspension, and suspension with antiroll bar under typical excitation conditions. The evaluation indices are normalized and compared with radar chart. The obtained results illustrate that the proposed active KDSS with proposed controller does not produce additional warp motion for vehicle body, and has achieved more reasonable tire force distribution among wheel stations, the roll stability, road holding, and significantly improved ride comfort simultaneously.

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, Y, Zang, Y, Yang, Y, Duan, A, Wang, XC, Ngo, HH, Li, YY & Du, R 2020, 'Zero-valent iron addition in anaerobic dynamic membrane bioreactors for preconcentrated wastewater treatment: Performance and impact', Science of the Total Environment, vol. 742.
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© 2020 Elsevier B.V. Wastewater preconcentration to capture abundant organics is promising for facilitating subsequent anaerobic digestion (AD) to recover bioenergy, however research efforts are still needed to verify the effectiveness of such an emerging strategy as carbon capture plus AD. Therefore, lab-scale anaerobic dynamic membrane bioreactors (AnDMBRs) without and with the addition of zero-valent iron (ZVI) (i.e., AnDMBR1 versus AnDMBR2) were developed for preconcentrated domestic wastewater (PDW) treatment, and the impact of ZVI addition on process performance and associated mechanisms were investigated. The stepwise addition of ZVI from 2 to 4 to 6 g/L improved the treatment performance as COD removal slightly increased and TP removal and methane production were enhanced by 53.3%–62.9% and 22.6%–31.3%, respectively, in consecutive operational phases. However, the average increasing rate of the transmembrane pressure (TMP) in AnDMBR2 (0.18 kPa/d) was obviously higher than that in AnDMBR1 (0.05 kPa/d), indicating an unfavorable impact of dosing ZVI on the dynamic membrane (DM) filtration performance. ZVI that has transformed to iron ions (mainly Fe2+) can behave as a coagulant, electron donor or inorganic foulant, thus enabling the excellent removal of dissolved phosphorous, enhancing the enrichment and activities of specific methanogens and causing the formation of a compact DM layer. Morphological, componential, and microbial community analyses provided new insights into the functional mechanisms of ZVI added to membrane-assisted anaerobic digesters, indicating that ZVI has the potential to improve bioenergy production and resource recovery, while optimizing the ZVI dosage should be considered to alleviate membrane fouling.

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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Hua, J, Wang, G, Huang, ML, Hua, S & Yang, S 2020, 'A Visual Approach for the SARS (Severe Acute Respiratory Syndrome) Outbreak Data Analysis', International Journal of Environmental Research and Public Health, vol. 17, no. 11, pp. 1-16.
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Virus outbreaks are threats to humanity, and coronaviruses are the latest of many epidemics in the last few decades in the world. SARS-CoV (Severe Acute Respiratory Syndrome Associated Coronavirus) is a member of the coronavirus family, so its study is useful for relevant virus data research. In this work, we conduct a proposed approach that is non-medical/clinical, generate graphs from five features of the SARS outbreak data in five countries and regions, and offer insights from a visual analysis perspective. The results show that prevention measures such as quarantine are the most common control policies used, and areas with strict measures did have fewer peak period days; for instance, Hong Kong handled the outbreak better than other areas. Data conflict issues found with this approach are discussed as well. Visual analysis is also proved to be a useful technique to present the SARS outbreak data at this stage; furthermore, we are proceeding to apply a similar methodology with more features to future COVID-19 research from a visual analysis perfective.

Hua, W, Sui, Y, Wan, Y, Liu, G & Xu, G 2020, 'FCCA: Hybrid Code Representation for Functional Clone Detection Using Attention Networks', IEEE Transactions on Reliability, pp. 1-15.
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Huang, C, Yao, L, Wang, X, Benatallah, B & Sheng, QZ 2020, 'Software Expert Discovery via Knowledge Domain Embeddings in a Collaborative Network', Pattern Recognition Letters, vol. 130, pp. 46-53.
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Huang, H, Savkin, AV & Ni, W 2020, 'Energy-efficient 3D navigation of a solar-powered UAV for secure communication in the presence of eavesdroppers and no-fly zones', Energies, vol. 13, no. 6.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Unmanned Aerial Vehicles (UAVs) have been regarded as a promising means to reshape future wireless communication systems. In this paper, we consider how to plan the trajectory of a solar-powered UAV under a cloudy condition to secure the communication between the UAV and a target ground node against multiple eavesdroppers. We propose a new 3D UAV trajectory optimization model by taking into account the UAV energy consumption, solar power harvesting, eavesdropping and no-fly zone avoidance. A Rapidly-exploring Random Tree (RRT) method is developed to construct the UAV trajectory. Computer simulations and comparisons with a baseline method demonstrate that the proposed method is able to produce trajectories to ensure the valid wireless communication link with the ground node and prevent eavesdropping.

Huang, L, Liu, F & Zhang, Y 2020, 'Overlapping Community Discovery for Identifying Key Research Themes', IEEE Transactions on Engineering Management, pp. 1-13.
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Huang, L, Liu, Z, Wu, C & Liang, J 2020, 'Interaction between a tunnel and alluvial valley under plane SV waves of earthquakes by IBIEM', European Journal of Environmental and Civil Engineering.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This paper investigates the dynamic interaction between a lined tunnel and an alluvial valley under plane SV waves with the indirect boundary integral equation method (IBIEM). The impact of different parameters on the displacement of sedimentary valley and the dynamic stress concentration factors (DSCF) of the lining inner and outer walls are studied. The dynamic response of a tunnel embedded in an alluvial valley is considerably different from that of a tunnel not embedded in an alluvial valley. Numerical results indicate that under the same frequency, different buried depths can change the distribution of displacement in the sedimentary area. The deeper the tunnel is buried, the more resonance points the response spectrum curve has. In general, the DSCF on the inner surfaces of the tunnels embedded in the sedimentary valley is obviously greater than that of the tunnels in the half space. However, the DSCF on the outer surfaces of the former is smaller than that of the latter. The results reported here will provide a quantitative basis to the security assessment and seismic design of lined tunnels.

Huang, L, 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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Huang, L, Zhang, G & Yu, S 2020, 'A Data Storage and Sharing Scheme for Cyber-Physical-Social Systems', IEEE Access, vol. 8, pp. 31471-31480.
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© 2013 IEEE. Cyber-Physical-Social System (CPSS) provides users secure and high-quality mobile service applications to share and exchange data in the cyberspace and physical world. With the explosive growth of data, it is necessary to introduce cloud storage service, which allows devices frequently resort to the cloud for data storage and sharing, into CPSS. In this paper, we propose a data storage and sharing scheme for CPSS with the help of cloud storage service. Since data integrity assurance is an inevitable problem in cloud storage, we first design a secure and efficient data storage scheme based on the technology of public auditing and bilinear map, which also ensures the security of the verification. In order to meet the real-time and reliability requirements of the CPSS, the rewards of timeliness incentive and effectiveness incentive are considered in the scheme. Secondly, based on the proposed storage scheme and ElGamal encryption, we propose a lightweight access model for users to access the final data processed by cloud server. We formally prove the security of the proposed scheme, and conduct performance evaluation to validate its high efficiency. The experimental results show that the proposed scheme has lower overheads in communication and access as compared to the technique CDS.

Huang, M, Liu, A, Xiong, NN, Wang, T & Vasilakos, AV 2020, 'An effective service-oriented networking management architecture for 5G-enabled internet of things', Computer Networks, vol. 173.
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© 2020 The development of a 5G-enabled Internet of Things has led to a dramatic increase in network traffic load, which has presented tremendous challenges to network management. In this paper, a service-oriented network architecture is proposed to support the effective management of 5G-enabled IoT systems. This architecture effectively reduces the traffic load and simplifies network management by introducing a service aggregation and caching (SAaC) scheme. Specifically, SAaC first breaks through the data-centric network architecture by converting data into services. Then, SAaC significantly reduces traffic load and energy consumption by service aggregation. Finally, SAaC introduces service caching, and each content router caches new services locally after aggregating received services so that user requests are handled at the network layer. Experimental results demonstrate that compared with traditional solutions, the SAaC scheme improves the request response time by 20.52%–56.09%, reduces the traffic load by 10.85%–37.67%, and reduces energy consumption by more than 50%.

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, P, Huang, Y, Li, JJ, Li, Y, Luo, K, Tang, G, Tang, L, Wu, Y-L, Yang, Z & Yu, B 2020, 'Outstanding Reviewers for Biomaterials Science in 2019', BIOMATERIALS SCIENCE, vol. 8, no. 9, pp. 2343-2343.
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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 & Zhu, J 2020, 'Enhanced Model Predictive Torque Control of Fault-Tolerant Five-Phase Permanent Magnet Synchronous Motor With Harmonic Restraint and Voltage Preselection', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 67, no. 8, pp. 6259-6269.
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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, Mok, W-C, Yam, Y-S, Zhou, JL, Surawski, NC, Organ, B, Chan, EFC, Mofijur, M, Mahlia, TMI & Ong, HC 2020, 'Evaluating in-use vehicle emissions using air quality monitoring stations and on-road remote sensing systems', SCIENCE OF THE TOTAL ENVIRONMENT, vol. 740.
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Huang, Y, Ng, ECY, Surawski, NC, Yam, Y-S, Mok, W-C, Liu, C-H, 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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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, Surawski, NC, Yam, Y-S, Lee, CKC, Zhou, JL, Organ, B & Chan, EFC 2020, 'Re-evaluating effectiveness of vehicle emission control programmes targeting high-emitters', NATURE SUSTAINABILITY.
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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, Yu, Y, Yam, YS, Zhou, JL, Lei, C, Organ, B, Zhuang, Y, Mok, WC & Chan, EFC 2020, 'Statistical evaluation of on-road vehicle emissions measurement using a dual remote sensing technique', Environmental Pollution, vol. 267.
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© 2020 Elsevier Ltd On-road remote sensing (RS) is a rapid, non-intrusive and economical tool to monitor and control the emissions of in-use vehicles, and currently is gaining popularity globally. However, a majority of studies used a single RS technique, which may bias the measurements since RS only captures a snapshot of vehicle emissions. This study aimed to use a unique dual RS technique to assess the characteristics of on-road vehicle emissions. The results show that instantaneous vehicle emissions are highly dynamic under real-world driving conditions. The two emission factors measured by the dual RS technique show little correlation, even under the same driving condition. This indicates that using the single RS technique may be insufficient to accurately represent the emission level of a vehicle based on one measurement. To increase the accuracy of identifying high-emitting vehicles, using the dual RS technique is essential. Despite little correlation, the dual RS technique measures the same average emission factors as the single RS technique does when a large number of measurements are available. Statistical analysis shows that both RS systems demonstrate the same Gamma distribution with ≥200 measurements, leading to converged mean emission factors for a given vehicle group. These findings point to the need for a minimum sample size of 200 RS measurements in order to generate reliable emission factors for on-road vehicles. In summary, this study suggests that using the single or dual RS technique will depend on the purpose of applications. Both techniques have the same accuracy in calculating average emission factors when sufficient measurements are available, while the dual RS technique is more accurate in identifying high-emitters based on one measurement only.

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.

Huang, Z, Wang, S, Dewhurst, RD, Ignat'ev, NV, Finze, M & Braunschweig, H 2020, 'Bor in energiebezogenen Prozessen und Anwendungen', Angewandte Chemie, vol. 132, no. 23, pp. 8882-8900.
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Huang, Z, Wang, S, Dewhurst, RD, Ignat'ev, NV, Finze, M & Braunschweig, H 2020, 'Boron: Its role in energy related research and applications.', Angewandte Chemie (International ed. in English).
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Boron's uniqueposition in the periodic table, i.e. at the apex of the line separating metals and nonmetals, makes it highly versatile in chemical reactions and applications. Contemporary demand for renewable and clean energy and energy-efficient products has seen boron playing key roles in energy-related research, from activating and synthesizing energy-rich small molecules, to storing chemical and electrical energy, to converting electrical energy to light. These applications are fundamentally associated with boron's unique characteristics, such as its electron-deficiency and the availability of an unoccupied p orbital, which allow the formation of a myriad of compounds with great tunability in chemical and physical properties. For example, boron's ability to achieve a full octet of electrons with four covalent bonds and a negative charge has led to the synthesis of a wide variety of borate anions of high chemical and electrochemical stability, in particular the useful family of weakly coordinating anions. This review summarizes recent advances in the study of boron compounds for energy-related research and applications, driven by modern demands and enabled by breakthroughs in the synthesis and understanding of boron chemistry.

Huo, X, Liu, H, Luo, Q, Sun, G & Li, Q 2020, 'On low-velocity impact response of foam-core sandwich panels', INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES, vol. 181.
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Hussain, T, Muhammad, K, Ullah, A, Cao, Z, Baik, SW & de Albuquerque, VHC 2020, 'Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM', IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 16, no. 1, pp. 77-86.
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Hussain, W, Sohaib, O, Naderpour, M & Gao, H 2020, 'Cloud Marginal Resource Allocation: A Decision Support Model', Mobile Networks and Applications, vol. 25, no. 4, pp. 1418-1433.
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Huynh, TV, Wilkinson, RH, Lech, M & Cheng, E 2020, 'A Hybrid Neural Network for Graph-Based Human Pose Estimation From 2D Images', IEEE ACCESS, vol. 8, pp. 52830-52840.
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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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Ibrahim, IA, Hossain, MJ, Duck, BC & Fell, CJ 2020, 'An Adaptive Wind-Driven Optimization Algorithm for Extracting the Parameters of a Single-Diode PV Cell Model', IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, vol. 11, no. 2, pp. 1054-1066.
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Ibrahim, IA, Hossain, MJ, Duck, BC & Nadarajah, M 2020, 'An improved wind driven optimization algorithm for parameters identification of a triple-diode photovoltaic cell model', ENERGY CONVERSION AND MANAGEMENT, vol. 213.
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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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Ibrar, I, Yadav, S, Altaee, A, Hawari, A, Nguyen, V & Zhou, J 2020, 'A novel empirical method for predicting concentration polarization in forward osmosis for single and multicomponent draw solutions', Desalination, vol. 494.
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© 2020 Elsevier B.V. Concentration polarization is one of the inherent problems in forward osmosis membrane process. A quantitative evaluation of concentration polarization is therefore vital to understand its impact on the performance of the forward osmosis. Limited data in the literature exists for the diffusion coefficient of mixed electrolyte or multicomponent solutions, which makes the calculation of mass transfer coefficient and solute resistance to diffusion in forward osmosis complicated. Therefore, an empirical method based on a limited set of well-defined experiments for evaluating and predicting the concentration polarization, water flux, and reverse solute flux is presented for single and mixed, or multi-ions draw solutions. The proposed method does not rely on the hydrodynamic conditions and flow regime in the system and provides an approach to measure and predict concentration polarization, water flux, and reverse salt flux when the diffusion coefficient of a feed solution (FS) or draw solution (DS) is challenging to determine. The developed numerical method is two steps method to measure internal and external concentration polarization using different concentrations of the draw and feed solutions. Experimental work was carried out with a single, and highly soluble sodium chloride (NaCl) DS and a mixture of NaCl and magnesium sulphate (MgSO4) were used as a selected multicomponent DS. The results showed a 95% to 99% agreement with the experimental data.

Ibrar, I, Yadav, S, Altaee, A, Samal, AK, Zhou, JL, Nguyen, TV & Ganbat, N 2020, 'Treatment of biologically treated landfill leachate with forward osmosis: Investigating membrane performance and cleaning protocols', Science of the Total Environment, vol. 744.
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© 2020 Elsevier B.V. This study presents systematic investigations to evaluate the performance, rejection rate, fouling, cleaning protocols and impact of physical and chemical cleaning strategies on the performance of commercial cellulose triacetate (CTA) membrane. The treatment of landfill leachate (LFL) solution was performed in the active layer facing feed solution and support layer facing the draw solution (AL-FS mode), and active layer facing the draw solution and support layer facing the feed solution (AL-DS mode). Compared to the AL-FS mode, a higher flux for AL-DS mode was achieved, but membrane fouling was more severe in the latter. In both membrane orientations, the rejection rate of the FO membrane to heavy ions and contaminants in the wastewater was between 93 and 99%. Physical and chemical cleaning strategies were investigated to recover the performance of the FO membrane and to study the impact of cleaning methods on the membrane rejection rate. Physical cleaning with hot water at 35 °C and osmotic backwashing with 1.5 M NaCl demonstrated excellent water flux recovery compared to chemical cleaning. In the chemical cleaning, an optimal concentration of 3% hydrogen peroxide was determined for 100% flux recovery of the fouled membrane. However, slight membrane damage was achieved at this concentration on the active layer side. Alkaline cleaning at pH 11 was more effective than acid cleaning at pH 4, although both protocols compromised the membrane rejection rate for some toxic ions. A comparison of the membrane long-term performance found that cleaning with osmotic backwashing and hot water were effective methods to restore water flux without comprising the membrane rejection rate. Overall, it was found that physical cleaning protocols are superior to chemical cleaning protocols for forward osmosis membrane fouled by landfill leachate wastewater.

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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Imran, K, Zhang, J, Pal, A, Khattak, A, Ullah, K & Baig, SM 2020, 'Bilateral negotiations for electricity market by adaptive agent-tracking strategy', ELECTRIC POWER SYSTEMS RESEARCH, vol. 186.
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Indraratn, B, Korkitsuntornsan, W & Thanh, TN 2020, 'Influence of Kaolin content on the cyclic loading response of railway subgrade', TRANSPORTATION GEOTECHNICS, vol. 22.
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Indraratna, B, Medawela, S, Rowe, RK, Thamwattana, N & Heitor, A 2020, 'Biogeochemical Clogging of Permeable Reactive Barriers in Acid-Sulfate Soil Floodplain', Journal of Geotechnical and Geoenvironmental Engineering, vol. 146, no. 5.
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© 2020 American Society of Civil Engineers. Column experiments that investigate the use of calcitic limestone as a potential material for permeable reactive barriers (PRBs), as well as its clogging behavior, are conducted under conditions that involve continuous acidic flow containing Al, Fe, and acidophilic bacteria. Results show that nonhomogenous biogeochemical clogging occurred toward the outlet, resulting in a 45% reduction of hydraulic conductivity at the inlet and 10% reduction at the outlet after the bicarbonate buffering period. A mathematical model developed to capture the reductions in longevity is presented. The model, which considers the effects of time-varying porosity, hydraulic conductivity, and head at a particular point on the horizontal flow path, is used for assessing the effect of coupled clogging in a calcitic porous medium.

Indraratna, B, Ngo, T, Bessa Ferreira, F, Rujikiatkamjorn, C & Shahkolahi, A 2020, 'Laboratory examination of ballast deformation and degradation under impact loads with synthetic inclusions', Transportation Geotechnics, vol. 25.
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© 2020 Elsevier Ltd This paper presents a laboratory study on alleviating the deformation and degradation (breakage) of ballast subjected to impact loads using geogrids and rubber mats. A series of drop hammer impact tests are carried out to determine how well the geogrid, under-ballast mat (UBM) or under-sleeper pad (USP) can attenuate impact loads and mitigate ballast degradation. Geogrids to be placed at different locations in a ballast assembly, in combination either with a UBM or a USP are tested. Laboratory test results prove that the inclusion of rubber mats and geogrids decrease the dynamic impact loads transferred to the ballast aggregates and subsequently decrease the degradation (breakage) and deformation of ballast. The tensile strength of geogrids and subgrade stiffness are found to considerably influence the performance of geogrid-reinforced ballast under impact loading conditions. The measured impact forces and accelerations of ballast with and without an artificial inclusion show that rubber mats definitely reduce track vibration (acceleration) and the subsequent deformation and breakage of ballast. These inclusions will not only increase safety and passenger comfort they will also lead to more economical and efficient track designs.

Indraratna, B, Singh, M & Nguyen, TT 2020, 'The mechanism and effects of subgrade fluidisation under ballasted railway tracks', Railway Engineering Science, vol. 28, no. 2, pp. 113-128.
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© 2020, The Author(s). The rapid growth in railway infrastructure and the construction of high-speed heavy-haul rail network, especially on ground that is basically unsuitable, poses challenges for geotechnical engineers because a large part of the money invested in the development of railway lines is often spent on track maintenance. In fact around the world, the mud pumping of subgrade fines is one of the common reasons why track performance deteriorates and track stability is hindered. This article presents a series of laboratory tests to examine following aspects of mud pumping: (1) the mechanisms of subgrade fluidisation under undrained condition, (2) the effects of mud pumping on the engineering characteristics of ballast, and (3) the use of vertical drains to stabilize subgrade under cyclic loads. The undrained cyclic triaxial testing on vulnerable soft subgrade was performed by varying the cyclic stress ratio (CSR) from 0.2 to 1.0 and the loading frequency f from 1.0 to 5.0 Hz. It is seen from the test results that for a specimen compacted at an initial dry density of 1790 kg/m3, the top portion of the specimen fluidises at CSR = 0.5, irrespective of the applied loading frequency. Under cyclic railway loading, the internal redistribution of water at the top of the subgrade layer softens the soil and also reduces its stiffness. In response to these problems, this paper explains how the inclusion of vertical drains in soft subgrade will help to prevent mud pumping by alleviating the build-up of excess pore pressures under moving train loads.

Indraratna, B, Trung, N & Rujikiatkamjorn, C 2020, 'Performance of Ballast Influenced by Deformation and Degradation: Laboratory Testing and Numerical Modeling', INTERNATIONAL JOURNAL OF GEOMECHANICS, vol. 20, no. 1.
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Iqbal, S, Parker, LM, Everest-Dass, AV, Moh, ESX, Sayyadi, N, Hutchinson, MR & Packer, NH 2020, 'Lipopolysaccharide and Morphine-3-Glucuronide-Induced Immune Signalling Increases the Expression of Polysialic Acid in PC12 Cells.', Molecular neurobiology, vol. 57, no. 2, pp. 964-975.
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Polysialic acid (polySia), a long homopolymer of 2,8-linked sialic acids, is abundant in the embryonic brain and is restricted largely in adult brain to regions that exhibit neurogenesis and structural plasticity. In the central nervous system (CNS), polySia is highly important for cell-cell interactions, differentiation, migration and cytokine responses, which are critical neuronal functions regulating intercellular interactions that underlie immune signalling in the CNS. In recent reports, a metabolite of morphine, morphine-3-glucuronide (M3G), has been shown to cause immune signalling in the CNS. In this study, we compared the effects of neurite growth factor (NGF), lipopolysaccharide (LPS) and M3G exposure on the expression of polySia in PC12 cells using immunocytochemistry and Western blot analysis. PolySia was also extracted from stimulated cell proteins by endo-neuraminidase digestion and quantitated using fluorescent labelling followed by HPLC analysis. PolySia expression was significantly increased following NGF, M3G or LPS stimulation when compared with unstimulated cells or cells exposed to the TLR4 antagonist LPS-RS. Additionally, we analyzed the effects of test agent exposure on cell migration and the oxidative stress response of these cells in the presence and absence of polySia expression on their cell surface. We observed an increase in oxidative stress in cells without polySia as well as following M3G or LPS stimulation. Our study provides evidence that polySia expression in neuronal-like PC12 cells is influenced by M3G and LPS exposure alike, suggestive of a role of TLR4 in triggering these events.

Islam, M, Nadarajah, M & Hossain, MJ 2020, 'A Grid-Support Strategy with PV Units to Boost Short-Term Voltage Stability under Asymmetrical Faults', IEEE Transactions on Power Systems, vol. 35, no. 2, pp. 1120-1131.
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© 1969-2012 IEEE. Modern grid codes demand the integration of voltage support capability with photo-voltaic (PV) generators to ensure a secure operation of power systems. On the other hand, short-term voltage instability (STVI) of distribution networks (DNs) is one of the key issues to be addressed. It can occur due to high proportion of induction motor loads. However, the existing literature lacks a thorough analysis of short-term voltage stability (STVS) of DNs following an asymmetrical fault. Furthermore, an effective voltage-support strategy for PV units, which can improve the STVS while mitigating the excessive voltage swell, is essential for secure operation. Hence, firstly this paper thoroughly investigates the STVS of DNs subjected to asymmetrical faults. It is perceived that voltage support through conventional methods can increase the risk of STVI and excessive voltage swell. Secondly, a new voltage-support strategy is proposed based on the negative sequence voltage at point of common coupling (PCC) aimed at mitigating the STVI and regulating the phase voltages within the margins. The key features of the proposed strategy are, fast and accurate estimation of the network's impedance at PCC is not required and it can be easily re-designed considering the network's dynamic behaviors. The efficacy of the proposed method is validated on two IEEE benchmark test systems, and the results demonstrate the effectiveness in improving the STVS and alleviating over voltage issues.

Islam, M, Saha, SC, Yarlagadda, PKDV & Karim, A 2020, 'A tool to minimize the need of Monte Carlo ray tracing code for 3D finite volume modelling of a standard parabolic trough collector receiver under a realistic solar flux profile', Energy Science and Engineering, vol. 8, no. 9, pp. 3087-3102.
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© 2020 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd. The energy collection element of a parabolic trough collector includes a selective coated metallic receiver tube inside an evacuated glass tube. Perpendicularly incident sun light on the parabolic trough mirror aperture is concentrated on the receiver tube highly nonuniformly along its circular direction. This solar energy is collected as thermal energy circulating a suitable heat transfer fluid (HTF) through the tube. This conjugate heat transfer phenomenon under nonuniform heat flux boundary condition is computationally studied applying 3D finite volume (FV) modelling technique of computational fluid dynamics coupled with Monte Carlo ray tracing (MCRT) optical data. The MCRT model simulates the actual flux profile around the receiver tube. Apart from a FV model, this coupled study requires expertise in, and access to, a suitable MCRT code. A combination of polynomial correlations and user-defined function (UDF) is introduced in this article in order to minimize the need of MCRT codes from subsequent FV modelling of the receiver tube of the Luz Solar 2 (LS2) collector. The correlations are developed from a verified 3D MCRT model, which is equivalent to the local irradiation data as a function of receiver circular location. The UDF includes two algorithms: one to develop solar flux profile from the correlations around the receiver, and the other to calculate heat loss from the receiver. Interpreting the UDF into ANSYS Fluent, a 3D FV model of the LS2 receiver is developed and validated with experimental results. The effectiveness of the UDF as an alternative to MCRT code is verified. The FV model is capable to investigate the heat transfer characteristics of the LS2 collector receiver at different solar irradiation level, optical properties of the collector components, glass tube conditions, HTFs, inserts or swirl generators, collector len...

Islam, MR, Lu, H, Islam, MR, Hossain, MJ & Li, L 2020, 'An IoT- Based Decision Support Tool for Improving the Performance of Smart Grids Connected with Distributed Energy Sources and Electric Vehicles', IEEE Transactions on Industry Applications, vol. 56, no. 4, pp. 4552-4562.
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© 1972-2012 IEEE. The growing penetration of distributed energy sources (DES), such as photovoltaic (PV) solar power, battery energy systems and electric vehicles (EVs) into low voltage distribution networks is creating serious challenges for distribution network operators. Uncertain nature of these DES and EV charging is a key factor to cause unbalance, which degrade network performance in terms of energy loss, voltage unbalance, and voltage profile of the distribution network, etc. Some methods were proposed to mitigate such negative impact of these uncertain DES and EV charging from both centralized and decentralized approaches by controlling charging or discharging power of EVs. However, these methods involve all active EVs to participate in coordination and this causes significant inconvenience to EV owners along with requirements of complex communication infrastructure and huge data processing overhead. This article proposes an Internet of Things -based centralized control strategy to coordinate EV and DES distribution by using the differential evolution (DE) optimization algorithm. The obtained results show that the proposed control strategy can improve network performance (voltage imbalance, neutral current, energy loss, and node voltage) significantly. In addition, the control strategy is less demanding on communication infrastructure and convenient for EV owners as well as having a lighter data processing overhead.

Islam, MS, Gu, Y, Farkas, A, Paul, G & Saha, SC 2020, 'Helium-Oxygen Mixture Model for Particle Transport in CT-Based Upper Airways', INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, vol. 17, no. 10.
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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, vol. 27, no. 2, pp. 77-92.
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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.

Jafari, M, Malekjamshidi, Z, Zhu, J & Khooban, MH 2020, 'Novel Predictive Fuzzy Logic-Based Energy Management System for Grid-connected and Off-grid Operation of Residential Smart Micro-grids', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 8, no. 2, pp. 1391-1404.
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IEEE In this paper, a novel energy management system with two operating horizons is proposed for a residential micro-grid application. The micro-grid utilises the energies of a photovoltaic (PV), a fuel cell and a battery bank to supply the local loads through a combination of electric and magnetic buses. The proposed micro-grid operates in a large number of grid-connected and off-grid operation modes. The energy management system includes a long-term data prediction unit based on a 2D dynamic programming and a short-term fuzzy controller. The long-term prediction unit is designed to determine the appropriate variation range of the battery state of charge and fuel cell state of hydrogen. The efficiency performance of the micro-grid components, predicted energy generation and demand, energy cost and the system constraints are taken into account. The resultant data then is sent to the short-term fuzzy controller which determines the operation mode of the micro-grid based on the real-time condition of the micro-grid elements. A prototype of the proposed micro-grid including the energy management system is developed, and experimental tests are conducted for three different energy management scenarios. The proposed management technique is validated through energy distribution and cost analysis.

Jafarizadeh, S, Tofigh, F, Lipman, J & Abolhasan, M 2020, 'Optimizing synchronizability in networks of coupled systems', AUTOMATICA, vol. 112.
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Jahangoshai Rezaee, M, Yousefi, S, Eshkevari, M, Valipour, M & Saberi, M 2020, 'Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA', Stochastic Environmental Research and Risk Assessment.
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Jamborsalamati, P, Hossain, MJ, Taghizadeh, S, Konstantinou, G, Manbachi, M & Dehghanian, P 2020, 'Enhancing Power Grid Resilience Through an IEC61850-Based EV-Assisted Load Restoration', IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 16, no. 3, pp. 1799-1810.
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Jamil, S, Loganathan, P, Kandasamy, J, Listowski, A, McDonald, JA, Khan, SJ & Vigneswaran, S 2020, 'Removal of organic matter from wastewater reverse osmosis concentrate using granular activated carbon and anion exchange resin adsorbent columns in sequence', Chemosphere, vol. 261.
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© 2020 Elsevier Ltd Reverse osmosis concentrate (ROC) generated as a waste stream during reverse osmosis treatment of reclaimed wastewater, presents significant disposal challenges. This is because it causes environmental pollution when it is disposed to lands and natural water bodies. A long-term dynamic adsorption experiment was conducted by passing ROC from a wastewater reclamation plant, firstly through a granular activated carbon (GAC) column, and subsequently through an anion exchange resin (Purolite) column, for the removal of two major ROC pollutants, namely dissolved organic carbon (DOC) and microorganic pollutants (MOP). GAC removed most of the smaller-sized low molecular weight neutrals and building block fractions as well as the hydrophobic fraction of DOC with much less removal by the subsequent Purolite column. In contrast, the humics fraction was less well removed by the GAC column; however, Purolite column removed all that was remaining of this fraction. This study demonstrated that combining adsorbents having different affinities towards a variety of DOC fractions constitute an effective method of taking advantage of their different properties and achieving larger DOC removals. Almost 100% of all 17 MOPs were removed by the GAC column, even after 2880 bed volumes of continuous use. This contrasted with the DOC fractions’ removal which was much lower.

Jayaraman, S, Ramachandran, M, Patan, R, Daneshmand, M & Gandomi, AH 2020, 'Fuzzy Deep Neural Learning Based on Goodman and Kruskal's Gamma for Search Engine Optimization', IEEE Transactions on Big Data, pp. 1-1.
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Jayasuriya, C, Indraratna, B & Ferreira, FB 2020, 'The Use of Under Sleeper Pads to Improve the Performance of Rail Tracks', Indian Geotechnical Journal, vol. 50, no. 2, pp. 204-212.
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© 2020, Indian Geotechnical Society. In recent years, with the growing demand for both passenger and freight mobility, faster and heavier rail traffic has been the norm rather than the exception in many countries. As a result, track geometry and the safety of ballasted rail tracks have been adversely affected, leading to exacerbated maintenance costs. Increased stresses in granular foundation induce progressive track degradation, which can result in excessive vertical and lateral deformation, ballast and subballast fouling and impeded drainage. These effects tend to be more severe at specific locations, such as bridges, level crossings and tunnels (i.e. over stiff subgrade). Finding an economical strategy to mitigate ballast degradation has been a challenging task for practitioners, and the inclusion of energy-absorbing rubber pads underneath the sleepers (under sleeper pads—USPs) to minimise track damage is an attractive solution. This paper presents a laboratory study conducted at the University of Wollongong to investigate the use of USPs as resilient elements in ballasted rail tracks involving a stiff subgrade. Test results have shown a significant improvement in track performance resulting from the use of USPs, whereby it is demonstrated that ballast damage induced by the applied cyclic loads can be reduced due to the favourable damping characteristics of these rubber pads. A significant attenuation in particle breakage was observed along with a reduction in both the vertical settlement and lateral movement of the ballast layer, thereby suggesting that USPs can be an effective means of improving the stability and serviceability of the track system.

Jayawickrama, B & Huss, F 2020, 'Generation of independent rayleigh faders for discrete signal', IEEE Communications Letters, vol. 24, no. 6, pp. 1155-1158.
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© 1997-2012 IEEE. Generating Rayleigh fading is a well investigated subject. However, most existing methods are fundamentally based on a sum-of-sinusoids, hence have a high computational complexity and become impractical when modelling massive MIMO systems. In this letter we present a novel recursive complex number multiplication method to generate Rayleigh fading for discrete signals. The computational complexity of the method is 75-94% less than all prominent sum-of-sinusoid methods. It also preserves the theoretically expected Bessel autocorrelation of a fading channel, zero cross-correlation between different fading channels and the power spectral density.

Jayawickreme, N, Atefi, E, Jayawickreme, E, Qin, J & Gandomi, AH 2020, 'Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka', INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, vol. 17, no. 8.
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Jena, R & Pradhan, B 2020, 'A Model for Visual Assessment of Fault Plane Solutions and Active Tectonics Analysis Using the Global Centroid Moment Tensor Catalog', Earth Systems and Environment, vol. 4, no. 1, pp. 197-211.
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© 2019, King Abdulaziz University and Springer Nature Switzerland AG. In this study, individual fault plane solutions are developed using various methods to improve the understanding of active tectonics on a regional scale. The comparative analysis of a focal mechanism solution (FMS) has not elicited the attention of researchers. Therefore, this study aims (1) to visually analyze the fault plane solution for 20 local faults that are responsible for all the earthquakes that occurred using visualization techniques such as: fault parameters, the linked Bingham method, the ad hoc pressure (P) axis and tension (T) axis method, and the moment tensor method; (2) to identify the best method for FMS; and (3) to understand the active tectonics of a fault population. A comparative analysis of the models is systematically documented to improve the understanding of the methods. An analysis of the overall fault mechanism is conducted for the analytic determination of fault movement using fault population data from the Global Centroid Moment Tensor catalog. The approach used in this work is a newly designed method for analyzing the reliability of various techniques for fault mechanism and overall fault movement research. Findings show that for the fault mechanism analysis, the P and T axes method and the moment tensor method are better than the fault plane solution from the fault parameters and the linked Bingham method based on the input parameters, output information, model outfit, and accuracy. The moment tensor method is one of the best approaches for analyzing fault mechanism because the errors in the nine components used as input data for the modeling are negligible. Meanwhile, the P and T axes method is one of the best techniques for the overall analysis of fault movement. P and T dihedral analysis using Kamb contouring is modeled. It indicates that the overall mechanisms of compression and dilation are features at the NW–SE and E–W directions, respectively. This compreh...

Jena, R & Pradhan, B 2020, 'Integrated ANN-cross-validation and AHP-TOPSIS model to improve earthquake risk assessment', International Journal of Disaster Risk Reduction, vol. 50.
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© 2020 Elsevier Ltd The current study presents a novel combination of artificial neural network cross-validation (fourfold ANN-CV) with a hybrid analytic hierarchy process-Technique for Order of Preference by Similarity to Ideal Solution (AHP-TOPSIS) method to improve the earthquake risk assessment (ERA) and applied it to Aceh, Indonesia, to test the model. Recent studies have suggested that neural networks improve probability mapping in a city scale. The network architecture design with probability index remains unexplored in earthquake-based probability studies. This study explored and specified the major indicators needed to improve the predictive accuracy in probability mapping. First, probability mapping was conducted and used for hazard assessment in the next step. Second, a vulnerability map was created based on social and structural factors. Finally, hazard and vulnerability indices were multiplied to produce the ERA, and the population and areas under risk were calculated. Results show that the proposed model achieves 85.4% accuracy, and its consistency ratio is 0.06. Risk varies from very high to high in the city center, approximately covering an area of 23% (14.82 km2) and a total population of 54,695. The model's performance changes on the basis of the input parameters, indicating the selection and importance of input layers on network architecture selection. The proposed model is found to generalize better results than traditional and some existing probabilistic models. The proposed model is simple and transferable to other regions by localizing the input parameters that contribute to earthquake risk mitigation and prevention planning.

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 & Alamri, AM 2020, 'Susceptibility to Seismic Amplification and Earthquake Probability Estimation Using Recurrent Neural Network (RNN) Model in Odisha, India', APPLIED SCIENCES-BASEL, vol. 10, no. 15.
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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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Jena, R, Pradhan, B, Al-amri, A, Lee, CW & Park, HJ 2020, 'Earthquake probability assessment for the indian subcontinent using deep learning', Sensors (Switzerland), vol. 20, no. 16, pp. 1-24.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Earthquake prediction is a popular topic among earth scientists; however, this task is challenging and exhibits uncertainty therefore, probability assessment is indispensable in the current period. During the last decades, the volume of seismic data has increased exponentially, adding scalability issues to probability assessment models. Several machine learning methods, such as deep learning, have been applied to large-scale images, video, and text processing; however, they have been rarely utilized in earthquake probability assessment. Therefore, the present research leveraged advances in deep learning techniques to generate scalable earthquake probability mapping. To achieve this objective, this research used a convolutional neural network (CNN). Nine indicators, namely, proximity to faults, fault density, lithology with an amplification factor value, slope angle, elevation, magnitude density, epicenter density, distance from the epicenter, and peak ground acceleration (PGA) density, served as inputs. Meanwhile, 0 and 1 were used as outputs corresponding to non-earthquake and earthquake parameters, respectively. The proposed classification model was tested at the country level on datasets gathered to update the probability map for the Indian subcontinent using statistical measures, such as overall accuracy (OA), F1 score, recall, and precision. The OA values of the model based on the training and testing datasets were 96% and 92%, respectively. The proposed model also achieved precision, recall, and F1 score values of 0.88, 0.99, and 0.93, respectively, for the positive (earthquake) class based on the testing dataset. The model predicted two classes and observed very-high (712,375 km2) and high probability (591,240.5 km2) areas consisting of 19.8% and 16.43% of the abovementioned zones, respectively. Results indicated that the proposed model is superior to the traditional methods for earthquake probabili...

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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Jena, R, Pradhan, B, Beydoun, G, Alamri, AM, Ardiansyah, Nizamuddin & Sofyan, H 2020, 'Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia', Science of the Total Environment, vol. 749.
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© 2020 Elsevier B.V. On 28th September 2018, a very high magnitude of earthquake Mw 7.5 struck the Palu city in the Island of Sulawesi, Indonesia. The main objective of this research is to estimate the earthquake risk based on probability and hazard in Palu region using cross-correlation among the derived parameters, Silhouette clustering (SC), pure locational clustering (PLC) based on hierarchical clustering analysis (HCA), convolutional neural network (CNN) and analytical hierarchy process (AHP) techniques. There is no specific or simple way of identifying risks as the definition of risk varies with time and space. The main aim of this study is: i) to conduct the clustering analysis to identify the earthquake-prone areas, ii) to develop a CNN model for probability estimation, and iii) to estimate and compare the risk using two calculation equations (Risk A and B). Owing to its high prediction ability, the CNN model assessed the probability while SC and PLC were implemented to understand the spatial clustering, Euclidean distance among clusters, spatial relationship and cross-correlation among the estimated Mw, PGA and intensity including events depth. Finally, AHP was implemented for the vulnerability assessment. To this end, earthquake probability assessment (EPA), susceptibility to seismic amplification (SSA) and earthquake vulnerability assessment (EVA) results were employed to generate risk A, while earthquake hazard assessment (EHA), SSA and EVA were used to generate risk B. The risk maps were compared and the differences in results were obtained. This research concludes that in the case of earthquake risk assessment (ERA), results obtained in Risk B are better than the risk A. This study achieved 89.47% accuracy for EPA while for EVA a consistency ratio of 0.07. These results have important implications for future large-scale risk assessment, land use planning and hazard mitigation.

Jena, R, Pradhan, B, Beydoun, G, Nizamuddin, Ardiansyah, Sofyan, H & Affan, M 2020, 'Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia', Geoscience Frontiers.
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© 2019 China University of Geosciences (Beijing) and Peking University Catastrophic natural hazards, such as earthquake, pose serious threats to properties and human lives in urban areas. Therefore, earthquake risk assessment (ERA) is indispensable in disaster management. ERA is an integration of the extent of probability and vulnerability of assets. This study develops an integrated model by using the artificial neural network–analytic hierarchy process (ANN–AHP) model for constructing the ERA map. The aim of the study is to quantify urban population risk that may be caused by impending earthquakes. The model is applied to the city of Banda Aceh in Indonesia, a seismically active zone of Aceh province frequently affected by devastating earthquakes. ANN is used for probability mapping, whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering. The risk map is subsequently created by combining the probability, hazard, and vulnerability maps. Then, the risk levels of various zones are obtained. The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%. Furthermore, results show that the central and southeastern regions of the city have moderate to very high risk classifications, whereas the other parts of the city fall under low to very low earthquake risk classifications. The findings of this research are useful for government agencies and decision makers, particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.

Jennifer, JJ, Saravanan, S & Pradhan, B 2020, 'Persistent Scatterer Interferometry in the post-event monitoring of the Idukki Landslides', GEOCARTO INTERNATIONAL.
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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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Jiang, J, Ji, S & Long, G 2020, 'Decentralized Knowledge Acquisition for Mobile Internet Applications', WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS.
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Jiang, P, Li, K, Chen, X, Dan, R & Yu, Y 2020, 'Magnetic and Hydrophobic Composite Polyurethane Sponge for Oil-Water Separation', APPLIED SCIENCES-BASEL, vol. 10, no. 4.
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Jiang, P, Li, R, Lu, H & Zhang, X 2020, 'Modeling of electricity demand forecast for power system', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. The emerging complex circumstances caused by economy, technology, and government policy and the requirement of low-carbon development of power grid lead to many challenges in the power system coordination and operation. However, the real-time scheduling of electricity generation needs accurate modeling of electricity demand forecasting for a range of lead times. In order to better capture the nonlinear and non-stationary characteristics and the seasonal cycles of future electricity demand data, a new concept of the integrated model is developed and successfully applied to research the forecast of electricity demand in this paper. The proposed model combines adaptive Fourier decomposition method, a new signal preprocessing technology, for extracting useful element from the original electricity demand series through filtering the noise factors. Considering the seasonal term existing in the decomposed series, it should be eliminated through the seasonal adjustment method, in which the seasonal indexes are calculated and should multiply the forecasts back to restore the final forecast. Besides, a newly proposed moth-flame optimization algorithm is used to ensure the suitable parameters of the least square support vector machine which can generate the forecasts. Finally, the case studies of Australia demonstrated the efficacy and feasibility of the proposed integrated model. Simultaneously, it can provide a better concept of modeling for electricity demand prediction over different forecasting horizons.

Jin, D, Zhang, B, Song, Y, He, D, Feng, Z, Chen, S, Li, W & Musial, K 2020, 'ModMRF: A modularity-based Markov Random Field method for community detection', Neurocomputing, vol. 405, pp. 218-228.
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© 2020 Elsevier B.V. Complex networks are widely used in the research of social and biological fields. Analyzing real community structure in networks is the key to the study of complex networks. Modularity optimization is one of the most popular techniques in community detection. However, due to its greedy characteristic, it leads to a large number of incorrect partitions and more communities than in reality. Existing methods use the modularity as a Hamiltonian at the finite temperature to solve the above problem. Nevertheless, modularity is not formalized as a statistical model in the method, which makes many statistical inference methods limited and cannot be used. Moreover, the method uses the sum-product version of belief propagation (BP) and its performance is not as good as the max-sum version, since it calculates per-variable marginal probabilities rather than the joint probability. To address these issues, we propose a novel Markov Random Field (MRF) method by formalizing modularity as an energy function based on the rich structures of MRF to represent properties and constraints of this problem, and use the max-sum BP to infer model parameters. In order to analyze our method and compare it with existing methods, we conducted experiments on both real-world and synthetic networks with ground-truth of communities, showing that the new method outperforms the state-of-the-art methods.

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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Jones, GT, Siwakoti, YP & Rogers, DJ 2020, 'Active Gate Drive to Increase the Power Capacity of Hard-Switched IGBTs', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Jothiramalingam, R, Jude, A, Patan, R, Ramachandran, M, Duraisamy, JH & Gandomi, AH 2020, 'Machine learning-based left ventricular hypertrophy detection using multi-lead ECG signal', NEURAL COMPUTING & APPLICATIONS.
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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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Ju, R, Zhou, P, Wen, S, Wei, W, Xue, Y, Huang, X & Yang, X 2020, '3D-CNN-SPP: A Patient Risk Prediction System From Electronic Health Records via 3D CNN and Spatial Pyramid Pooling', IEEE Transactions on Emerging Topics in Computational Intelligence.
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IEEE The problem of extracting useful clinical representations from longitudinal electronic health record (EHR) data, also known as the computational phenotyping problem, is an important yet challenging task in the health-care academia and industry. Recent progress in the design and applications of deep learning methods has shown promising results towards solving this problem. In this paper, we propose 3D-CNN-SPP (3D Convolutional Neural Networks and Spatial Pyramid Pooling), a novel patient risk prediction system, to investigate the application of deep neural networks in modeling longitudinal EHR data. Particularly, we propose a 3D CNN structure, which is featured by SPP. Compared with 2D CNN methods, our proposed method can capture the complex relationships in EHRs more effectively and efficiently. Furthermore, previous works handle the issue of variable length in patient records by padding zeros to all vectors so that they have a fixed length. In our work, the proposed spatial pyramid pooling divides the records into several length sections for respective pooling processing, hence handling the variable length problem easily and naturally. We take heart failure and diabetes as examples to test the performance of the system, and the experiment results demonstrate great effectiveness in patient risk prediction, compared with several strong baselines.

Jung, HS, Lee, S & Pradhan, B 2020, 'Sustainable applications of remote sensing and geospatial information systems to earth observations', Sustainability (Switzerland), vol. 12, no. 6.
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© 2020 by the authors. The Special Issue on "Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations" is published. A total of 20 qualified papers are published in this Special Issue. The topics of the papers are the application of remote sensing and geospatial information systems to Earth observations in various fields such as (1) object change detection, (2) air pollution, (3) earthquakes, (4) landslides, (5) mining, (6) biomass, (7) groundwater, and (8) urban development using the techniques of remote sensing and geospatial information systems. More than 100 researchers have participated in this Special Issue. We hope that this Special Issue is helpful for sustainable applications.

Jupp, JR & Parkes, M 2020, 'Integrated Construction Enterprise Systems: A Strategic Approach to Model-based Data and Process Management', Automation in Construction, vol. (to appear).

Karami, E, Khalilinia, A, Bali, A & Rouzbehi, K 2020, 'Monte-Carlo-based simulation and investigation of 230 kV transmission lines outage due to lightning', HIGH VOLTAGE, vol. 5, no. 1, pp. 83-91.
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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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Karimi, M, Maxit, L, Croaker, P, Robin, O, Skvortsov, A, Marburg, S, Noureddine, A & Kessissoglou, N 2020, 'Analytical and numerical prediction of acoustic radiation from a panel under turbulent boundary layer excitation', Journal of Sound and Vibration.
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The vibroacoustic response of a simply supported panel excited by turbulent flow is analytically and numerically investigated. In the analytical model, the radiated sound power is described in terms of the cross spectrum density of the wall pressure field and sensitivity functions for the acoustic pressure and fluid particle velocity. For the numerical model, a hybrid approach based on the finite element method is described in which the cross spectrum of the wall pressure field is represented by a set of uncorrelated wall plane waves. Realisations of the wall pressure field are used as deterministic input loads to the panel. The structural and acoustic responses of the panel subject to turbulent boundary layer excitation are then obtained from an ensemble average of the different realisations. Analytical and numerical results are compared with experimental data measured in an anechoic wind tunnel, showing good agreement. The effect of adding stiffeners on the vibroacoustic response of the panel is also examined using the proposed numerical approach.

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, vol. 62, no. 6, pp. 2307-2310.
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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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Kashani, AR, Gandomi, M, Camp, CV & Gandomi, AH 2020, 'Optimum design of shallow foundation using evolutionary algorithms', Soft Computing, vol. 24, no. 9, pp. 6809-6833.
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. In the current study, the performance of three evolutionary algorithms, differential algorithm (DE), evolution strategy (ES), and biogeography-based optimization algorithm (BBO), is examined for foundation design optimization. Moreover, four recent variations of evolutionary-based algorithms [i.e., improved differential evolution algorithm based on an adaptive mutation scheme, weighted differential evolution algorithm (WDE), linear population size reduction success-history-based adaptive differential evolution algorithm, and biogeography-based optimization with covariance matrix-based migration] have been tackled for handling the current problem. The objective function is based on the cost of shallow foundation designs that satisfy ACI 318-05 requirements is formulated as the objective function. This study addresses shallow footing optimization with two attitudes, routine optimization, and sensitivity analysis. As a further study, the effect of the location of the column at the top of the foundation is examined by adding two additional design variables. Three numerical case studies are used for both routine and sensitivity analysis. Moreover, the most common evolutionary-based technique, genetic algorithm (GA), is considered as a benchmark to evaluate the proposed methods’ efficiency. Based on the results, there is no algorithm which works as the most efficient solver over all the cases; while, BBO and WDE showed an acceptable performance because of satisfying records in most cases. There were several cases in which GA, DE, and ES were incapable of finding a valid solution which meets all the constraints simultaneously.

Kashani, AR, Saneirad, A & Gandomi, AH 2020, 'Optimum design of reinforced earth walls using evolutionary optimization algorithms', NEURAL COMPUTING & APPLICATIONS, vol. 32, no. 16, pp. 12079-12102.
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Kashif, M, Hossain, MJ, Fernandez, E, Taghizadeh, S, Sharma, V, Ali, SMN & Bin Irshad, U 2020, 'A Fast Time-Domain Current Harmonic Extraction Algorithm for Power Quality Improvement Using Three-Phase Active Power Filter', IEEE ACCESS, vol. 8, pp. 103539-103549.
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Kavadi, DP, Patan, R, Ramachandran, M & Gandomi, AH 2020, 'Partial derivative Nonlinear Global Pandemic Machine Learning prediction of COVID 19', Chaos, Solitons and Fractals, vol. 139.
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© 2020 The recent worldwide outbreak of the novel coronavirus disease 2019 (COVID-19) opened new challenges for the research community. Machine learning (ML)-guided methods can be useful for feature prediction, involved risk, and the causes of an analogous epidemic. Such predictions can be useful for managing and intercepting the outbreak of such diseases. The foremost advantages of applying ML methods are handling a wide variety of data and easy identification of trends and patterns of an undetermined nature.In this study, we propose a partial derivative regression and nonlinear machine learning (PDR-NML) method for global pandemic prediction of COVID-19. We used a Progressive Partial Derivative Linear Regression model to search for the best parameters in the dataset in a computationally efficient manner. Next, a Nonlinear Global Pandemic Machine Learning model was applied to the normalized features for making accurate predictions. The results show that the proposed ML method outperformed state-of-the-art methods in the Indian population and can also be a convenient tool for making predictions for other countries.

Ke, G, Li, S-N, Han, R, Li, R-Z, Liu, Z-M, Qi, Z-P & Liu, Z-Y 2020, 'Study on the propagation law of gas explosion in the space based on the goaf characteristic of coal mine', SAFETY SCIENCE, vol. 127.
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Keall, P, Nguyen, DT, O'Brien, R, Hewson, E, Ball, H, Poulsen, P, Booth, J, Greer, P, Hunter, P, Wilton, L, Bromley, R, Kipritidis, J, Eade, T, Kneebone, A, Hruby, G, Moodie, T, Hayden, A, Turner, S, Arumugam, S, Sidhom, M, Hardcastle, N, Siva, S, Tai, KH, Gebski, V & Martin, J 2020, 'Real-Time Image Guided Ablative Prostate Cancer Radiation Therapy: Results From the TROG 15.01 SPARK Trial', International Journal of Radiation Oncology Biology Physics, vol. 107, no. 3, pp. 530-538.
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© 2020 Elsevier Inc. Purpose: Kilovoltage intrafraction monitoring (KIM) is a novel software platform implemented on standard radiation therapy systems and enabling real-time image guided radiation therapy (IGRT). In a multi-institutional prospective trial, we investigated whether real-time IGRT improved the accuracy of the dose patients with prostate cancer received during radiation therapy. Methods and Materials: Forty-eight patients with prostate cancer were treated with KIM-guided SABR with 36.25 Gy in 5 fractions. During KIM-guided treatment, the prostate motion was corrected for by either beam gating with couch shifts or multileaf collimator tracking. A dose reconstruction method was used to evaluate the dose delivered to the target and organs at risk with and without real-time IGRT. Primary outcome was the effect of real-time IGRT on dose distributions. Secondary outcomes included patient-reported outcomes and toxicity. Results: Motion correction occurred in ≥1 treatment for 88% of patients (42 of 48) and 51% of treatments (121 of 235). With real-time IGRT, no treatments had prostate clinical target volume (CTV) D98% dose 5% less than planned. Without real-time IGRT, 13 treatments (5.5%) had prostate CTV D98% doses 5% less than planned. The prostate CTV D98% dose with real-time IGRT was closer to the plan by an average of 1.0% (range, –2.8% to 20.3%). Patient outcomes showed no change in the 12-month patient-reported outcomes compared with baseline and no grade ≥3 genitourinary or gastrointestinal toxicities. Conclusions: Real-time IGRT is clinically effective for prostate cancer SABR.

Kerferd, B, Eggler, D, Karimi, M & Kessissoglou, N 2020, 'Active acoustic cloaking of cylindrical shells in low Mach number flow', Journal of Sound and Vibration.
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The vibro-acoustic responses of a two-dimensional cylindrical shell in low Mach number flow are herein derived. The analytical model takes into account the structural elasticity and coupling of the shell vibration with its interior and exterior acoustic fields in the presence of a moving fluid. The cylindrical shell is modelled using Donnell-Mushtari theory. Taylor transformations are employed to transfer the convected wave equation into the ordinary wave equation which was then solved using scattering theory. Three excitation cases corresponding to a plane wave, an external monopole source and a radial point force applied directly to the shell are considered. Shell circumferential resonances and interior acoustic resonances are identified. Two active control strategies are then applied to acoustically cloak the cylindrical shell at its acoustic and structural resonances. The first control approach employs acoustic control sources in the exterior fluid domain. In the second approach, control forces are applied to directly excite the elastic shell, whereby the structural response is actively modified to manipulate the scattered and radiated acoustic fields arising from plane wave excitation of the shell. Results show that the second approach is superior in terms of both reduced control effort and cloaking of the global exterior domain. For both control approaches, the performance of the active cloak is shown to deteriorate if the convected flow field is not accounted for in the control process.

Khalilpour, KR & Lusis, P 2020, 'Network capacity charge for sustainability and energy equity: A model-based analysis', APPLIED ENERGY, vol. 266.
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Khalilpour, KR & Zafaranloo, A 2020, 'Generic techno-economic optimization methodology for concurrent design and operation of solvent-based PCC processes', INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL, vol. 99.
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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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© 2020 Hydrogen Energy Publications LLC The objective of this study was to investigate the evolution of hydrogen research and its international scientific collaboration network. From the Scopus database, 58,006 relevant articles, published from 1935 until mid-2018, were retrieved. To review this massive volume of publication records, we took a scientometric network analysis approach and investigated the social network of the publication contents based on keywords co-occurrence as well as international collaboration ties. An interesting observation is that despite publications on hydrogen occurring since 1935, the growth of this research field ignited with the Kyoto Protocol of 1997. The publication profile reveals that more than 93% of the existing records have been published over the last two decades. More recently, the accelerated growth of renewables has further motivated hydrogen research with almost 36,000 academic records having been indexed from 2010 till mid-2018. This accounts for ~62% of the total historical publications on hydrogen. The conventional hydrogen production pathway is fossil fuel-based, involving fossil fuel reforming for synthesis gas generation. The keyword analysis also shows a paradigm shift in hydrogen generation to renewables. While all components of hydrogen supply chain research are now growing, the topic areas of biohydrogen and photocatalysis seem to be growing the fastest. Analysis of international collaboration networks also reveals a strong correlation between the increase of collaboration ties on hydrogen research and the publications. Until the 1970s, only 25 countries had collaborated, while this has reached 108 countries as of 2018, with over 17,500 collaboration ties. The collaborations have also evolved into a substantially more integrated network, with a few strong clusters involving China, the United States, Germany, and Japan. The longitudinal network evolution maps also reveal a shift, over the last two decades, from ...

Khan, HA, Castel, A & Khan, MSH 2020, 'Corrosion investigation of fly ash based geopolymer mortar in natural sewer environment and sulphuric acid solution', Corrosion Science, vol. 168.
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© 2020 Elsevier Ltd The objective of this research is to estimate the durability of low-calcium fly ash based geopolymer mortar (FA-GPm) in comparison with sulphate resistant Portland cement mortars (SRPCm) exposed to natural sewer environment. Their performance is also investigated in the sulphuric acid (H2SO4) solution to highlight the difference in the corrosion mechanisms between these two exposure conditions. Mortar samples were removed from natural sewer and 1.5 % sulphuric acid solution after 12, 24 months and 6 months of exposure, respectively. Visual and physical analyses showed greater neutralization and loss in alkalinity in FA-GPm compared to SRPCm. However, mass loss and strength reduction observed for SRPCm was greater compared to FA-GPm. Microstructural analysis showed widespread gypsum crystallization within SRPCm matrix compared to FA-GPm, leading to more severe matrix deterioration. Differences in corrosion mechanism were identified between natural and sulphuric acid exposure conditions which led to the variation in estimated corrosion depth. Data collected from these microstructural and physical investigations were utilized to develop simplified linear models to express the depth of corrosion, surface pH, mass loss and neutralization depth of FA-GPm and SRPCm as a dependent of exposure time, temperature and H2S concentration in natural sewer environments.

Khan, HU, Aruya, JA & Gill, A 2020, 'Web 2.0 Technologies Adoption Barriers for External Contacts and Participation: A Case Study of Federal Establishment of Africa', International Journal of Business Information Systems.

Khan, I, Xu, T, Khan, MSH, Castel, A & Gilbert, RI 2020, 'Effect of Various Supplementary Cementitious Materials on Early-Age Concrete Cracking', JOURNAL OF MATERIALS IN CIVIL ENGINEERING, vol. 32, no. 4.
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Khan, JA, Nguyen, LN, Duong, HC & Nghiem, LD 2020, 'Acetic acid extraction from rumen fluid by forward osmosis', Environmental Technology and Innovation, vol. 20.
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© 2020 Elsevier B.V. This study evaluated the potential of extracting acetic acid from rumen fluid by forward osmosis (FO). Unlike other FO applications to extract water and reduce the feed water volume, this research used FO to mimic the ruminant intestines for extracting acetic acid from rumen fluid to a clean matrix with a minimum water flux. The FO extraction of acetic acid was optimised with a synthetic solution using cellulose triacetate (CTA) and thin film composite (TFC) polyamide membranes under different operating parameters (e.g. membrane orientation and stripping solution pH ). Under the same operating conditions the CTA membrane showed higher acetic acid transport than the TFC polyamide membrane. Increasing the stripping solution pH from 5.5–6.5 to 9.0–10.0 increased the acetic acid transport through both CTA and TFC membranes. On the other hand, the membrane orientation had no discernible effect on the transport of acetic acid. Under the optimum conditions, the FO process using the CTA membrane exhibited negligible water flux and extracted 27% of the maximum attainable acetic acid from the synthetic solution within 8 h of operation. The optimised conditions were used to elaborate the FO extraction of acetic acid from a real rumen fluid. Considerably lower extraction rate from the real rumen fluid was observed compared to the synthetic solution, suggesting the need for further research to address the complexity of the rumen matrix.

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, vol. 12, no. 14, pp. 7577-7585.
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Khan, MNH, Forouzesh, M, Siwakoti, YP, Li, L & Blaabjerg, F 2020, 'Switched Capacitor Integrated (2n+1)-Level Step-Up Single-Phase Inverter', IEEE TRANSACTIONS ON POWER ELECTRONICS, vol. 35, no. 8, pp. 8248-8260.
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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.

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

Khan, NU, Wan, W & Yu, S 2020, 'Location-Based Social Network's Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China', ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, vol. 9, no. 2.
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Khan, NU, Wan, W & Yu, S 2020, 'Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network's Data from Weibo', ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, vol. 9, no. 2.
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Khan, SA, Guo, Y, Siwakoti, YP, Lu, DD-C & Zhu, J 2020, 'A Disturbance Rejection-Based Control Strategy for Five-Level T-Type Hybrid Power Converters With Ripple Voltage Estimation Capability', IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, vol. 67, no. 9, pp. 7364-7374.
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Khan, SA, Guo, Y, Siwakoti, YP, Lu, DD-C & Zhu, J 2020, 'A Disturbance Rejection-Based Control Strategy for Five-Level T-Type Hybrid Power Converters With Ripple Voltage Estimation Capability.', IEEE Trans. Ind. Electron., vol. 67, pp. 7364-7374.

Khan, TA & Ling, SH 2020, 'A survey of the state-of-the-art swarm intelligence techniques and their application to an inverse design problem', JOURNAL OF COMPUTATIONAL ELECTRONICS.
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Khan, TA & Ling, SH 2020, 'An improved gravitational search algorithm for solving an electromagnetic design problem', JOURNAL OF COMPUTATIONAL ELECTRONICS, vol. 19, no. 2, pp. 773-779.
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Khandaker, T, Hossain, MS, Dhar, PK, Rahman, MS, Hossain, MA & Ahmed, MB 2020, 'Efficacies of Carbon-Based Adsorbents for Carbon Dioxide Capture', PROCESSES, vol. 8, no. 6.
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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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Khlaifat, N, Altaee, A, Zhou, J & Huang, Y 2020, 'A review of the key sensitive parameters on the aerodynamic performance of a horizontal wind turbine using computational fluid dynamics modelling', AIMS Energy, vol. 8, no. 3, pp. 493-524.
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© 2020 the Author(s), licensee AIMS Press. Renewable energy technologies are receiving much attention to replacing power plants operated by fossil and nuclear fuels. Of all the renewable technologies, wind power has been successfully implemented in several countries. There are several parameters in the aerodynamic characteristics and design of the horizontal wind turbine. This paper highlights the key sensitive parameters that affect the aerodynamic performance of the horizontal wind turbine, such as environmental conditions, blade shape, airfoil configuration and tip speed ratio. Different turbulence models applied to predict the flow around the horizontal wind turbine using Computational Fluid Dynamics modeling are reviewed. Finally, the challenges and concluding remarks for future research directions in wind turbine design are discussed.

Khorsand, M, Tavakoli, J, Guan, H & Tang, Y 2020, 'Artificial intelligence enhanced mathematical modeling on rotary triboelectric nanogenerators under various kinematic and geometric conditions', Nano Energy, vol. 75.
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© 2020 Elsevier Ltd The triboelectric nanogenerator (TENG) has been introduced as a revolutionary technology in the renewable electrical energy generation at micro/nanoscale. In the current study, experimental and theoretical models for augmented rotary TENGs are presented. The power generated by TENGs is found to be a function of the number of segments, rotational speed, and tribo-surface spacing. Mathematical modeling combined with artificial intelligence is applied to characterize the TENG output under various kinematics and geometric conditions. Sensitivity analysis reveals that the generated energy and the matched resistance depend highly on segmentation and angular velocity rate. It is shown that the optimized harvested energy reaches 0.369 mJ at each cycle. The TENG dynamic outputs for various structural parameters are found and described. This study enhances understanding of rotation-induced periodic TENGs and reveals optimized characteristics for disk-shaped TENG energy harvesters.

Khosravi, F, Hosseini, SA & Hayati, H 2020, 'Free and forced axial vibration of single walled carbon nanotube under linear and harmonic concentrated forces based on nonlocal theory', INTERNATIONAL JOURNAL OF MODERN PHYSICS B, vol. 34, no. 8.
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Khoury, S & Tran, N 2020, 'qPCR multiplex detection of microRNA and messenger RNA in a single reaction', PeerJ, vol. 2020, no. 6.
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Copyright 2020 Khoury and Tran. Reverse Transcription-Quantitative PCR (RT-qPCR) is one of the standards for analytical measurement of different RNA species in biological models. However, current Reverse Transcription (RT) based priming strategies are unable to synthesize differing RNAs and ncRNAs especially miRNAs, within a single tube. We present a new methodology, referred to as RNAmp, that measures in parallel miRNA and mRNA expression. We demonstrate this in various cell lines, then evaluate clinical utility by quantifying several miRNAs and mRNA simultaneously in sera. PCR efficiency in RNAmp was estimated between 1.8 and 1.9 which is comparable to standard miRNA and random primer RT approaches. Furthermore, when using RNAmp to detect selected mRNA and miRNAs, the quantification cycle (Cq) was several cycles lower. This low volume single-tube duplex protocol reduces technical variation and reagent usage and is suitable for uniform analysis of single or multiple miRNAs and/or mRNAs within a single qPCR reaction.

Khuat, TT & Le, MH 2020, 'Evaluation of Sampling-Based Ensembles of Classifiers on Imbalanced Data for Software Defect Prediction Problems', SN Computer Science, vol. 1, no. 2.
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Khuat, TT, Ruta, D & Gabrys, B 2020, 'Hyperbox-based machine learning algorithms: a comprehensive survey', SOFT COMPUTING.
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Kieu, LM, Ou, Y, Truong, LT & Cai, C 2020, 'A class-specific soft voting framework for customer booking prediction in on-demand transport', Transportation Research Part C: Emerging Technologies, vol. 114, pp. 377-390.
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© 2020 Elsevier Ltd Customer booking prediction is essential for On-Demand Transport services, especially for those in rural and suburban areas where the demand is low, variable and often regarded as unpredictable. Existing literature tends to focus more on the prediction of demand for traffic, classical public transport, and urban On-Demand Transport service such as taxi, Uber or Lyft, in areas with higher and less variable demand, in which popular time-series prediction methods can be employed. This paper proposes an ensemble learning framework to predict the customer booking behaviour and demand using the observed data of a suburban On-Demand Transport service where data scarcity is a challenge. The proposed method, which is called as Class-specific Soft Voting, is found to be the most accurate prediction method when compared to popular supervised classification methods such as Logistic Regression, Random Forest, Support Vector Machine and other ensemble techniques.

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.

Kim, DI, Gonzales, RR, Dorji, P, Gwak, G, Phuntsho, S, Hong, S & Shon, H 2020, 'Efficient recovery of nitrate from municipal wastewater via MCDI using anion-exchange polymer coated electrode embedded with nitrate selective resin', DESALINATION, vol. 484.
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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, Kříž, J, Bennett, N, Chen, B, Upadhayaya, H, Reddy, KR & Sadhu, V 2020, 'Functionalized metal oxide nanoparticles for efficient dye-sensitized solar cells (DSSCs): A review', Materials Science for Energy Technologies, vol. 3, pp. 472-481.
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Kocaballi, AB, Ijaz, K, Laranjo, L, Quiroz, JC, Rezazadegan, D, Tong, HL, Willcock, S, Berkovsky, S & Coiera, E 2020, 'Envisioning an artificial intelligence documentation assistant for future primary care consultations: A co-design study with general practitioners', Journal of the American Medical Informatics Association.
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Abstract Objective The study sought to understand the potential roles of a future artificial intelligence (AI) documentation assistant in primary care consultations and to identify implications for doctors, patients, healthcare system, and technology design from the perspective of general practitioners. Materials and Methods Co-design workshops with general practitioners were conducted. The workshops focused on (1) understanding the current consultation context and identifying existing problems, (2) ideating future solutions to these problems, and (3) discussing future roles for AI in primary care. The workshop activities included affinity diagramming, brainwriting, and video prototyping methods. The workshops were audio-recorded and transcribed verbatim. Inductive thematic analysis of the transcripts of conversations was performed. Results Two researchers facilitated 3 co-design workshops with 16 general practitioners. Three main themes emerged: professional autonomy, human-AI collaboration, and new models of care. Major implications identified within these themes included (1) concerns with medico-legal aspects arising from constant recording and accessibility of full consultation records, (2) future consultations taking place out of the exam rooms in a distributed system involving empowered patients, (3) human conversation and empathy remaining the core tasks of doctors in any future AI-enabled consultations, and (4) questioning the current focus of AI initiatives on improved efficiency as opposed to patient care. ...

Kocaballi, AB, Quiroz, JC, Rezazadegan, D, Berkovsky, S, Magrabi, F, Coiera, E & Laranjo, L 2020, 'Responses of Conversational Agents to Health and Lifestyle Prompts: Investigation of Appropriateness and Presentation Structures', JOURNAL OF MEDICAL INTERNET RESEARCH, vol. 22, no. 2.
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Koli, MNY, Afzal, MU, Esselle, KP & Hashmi, RM 2020, 'An All-Metal High-Gain Radial-Line Slot-Array Antenna for Low-Cost Satellite Communication Systems', IEEE ACCESS, vol. 8, pp. 139422-139432.
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Kong, FH & Manchester, IR 2020, 'Contraction analysis of nonlinear noncausal iterative learning control', Systems and Control Letters, vol. 136.
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© 2019 Elsevier B.V. Iterative learning control (ILC) is a method for learning input signals for repetitive control tasks. In this paper, we provide a new method based on convex optimization for certifying convergence and estimating convergence rate in ILC schemes involving a nonlinear plant and a noncausal update law, which are common in practice. Using sum-of-squares (SOS) optimization, we compute the convergence rate of an example nonlinear, noncausal ILC system and verify its accuracy in experiment.

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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Kong, L, Qu, W, Yu, J, Zuo, H, Chen, G, Xiong, F, Pan, S, Lin, S & Qiu, M 2020, 'Distributed Feature Selection for Big Data Using Fuzzy Rough Sets', IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 28, no. 5, pp. 846-857.
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Kousik, NGV, Yuvaraj, N, Suresh, K, Patan, R & Gandomi, AH 2020, 'Improving power and resource management in heterogeneous downlink OFDMA networks', Information (Switzerland), vol. 11, no. 4.
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© 2020 by the authors. In the past decade, low power consumption schemes have undergone degraded communication performance, where they fail to maintain the trade-off between the resource and power consumption. In this paper, management of resource and power consumption on small cell orthogonal frequency-division multiple access (OFDMA) networks is enacted using the sleep mode selection method. The sleep mode selection method uses both power and resource management, where the former is responsible for a heterogeneous network, and the latter is managed using a deactivation algorithm. Further, to improve the communication performance during sleep mode selection, a semi-Markov sleep mode selection decision-making process is developed. Spectrum reuse maximization is achieved using a small cell deactivation strategy that potentially identifies and eliminates the sleep mode cells. The performance of this hybrid technique is evaluated and compared against benchmark techniques. The results demonstrate that the proposed hybrid performance model shows effective power and resource management with reduced computational cost compared with benchmark techniques.

Kovaleva, M, Bulger, D & Esselle, K 2020, 'Comparative Study of Optimization Algorithms on the Design of Broadband Antennas', IEEE Journal on Multiscale and Multiphysics Computational Techniques.
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IEEE Broadband antennas find many applications in modern communication systems, such as Wi-Fi, 5G and SatCom. Multiple competing optimization methods are available for the application to antenna design, while it would be preferable to know in advance if any method is superior. Here, an application of the cross-entropy method, along with particle swarm optimization and covariance matrix adaptation evolutionary strategy, to the design of broadband antennas is presented. The first example is an aperture-coupled microstrip patch antenna that has 9.5~dBi peak directivity and 53\% bandwidth after optimization. It is then used as a feed in a high-gain broadband resonant cavity antenna. Using an all-dielectric superstrate with a transverse permittivity gradient, a compact thin resonant cavity antenna with a peak directivity of 19~dBi and 40\% 3-dB bandwidth was designed. A comparative analysis of the cross-entropy method, particle swarm optimization and covariance matrix adaptation evolutionary strategy applied to these two problems was carried out to provide the basis for further optimization of antennas in radio frequency and microwave frequency bands. We found that although all three methods reached a similar solution, the cross-entropy method has a speed advantage. It improves our ability to optimize existing designs and has wider applicability beyond antenna engineering.

Krishankumar, R, Ravichandran, KS, Gandomi, AH & Kar, S 2020, 'Interval-valued probabilistic hesitant fuzzy set-based framework for group decision-making with unknown weight information', NEURAL COMPUTING & APPLICATIONS.
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Kumar, DK, Loskot, J, Kriz, J, Bennett, N, Upadhyaya, HM, Sadhu, V, Reddy, CV & 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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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.

Laengle, S, Lobos, V, Merigó, JM, Herrera-Viedma, E, Cobo, MJ & De Baets, B 2020, 'Forty years of Fuzzy Sets and Systems: A bibliometric analysis', Fuzzy Sets and Systems.
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© 2020 Elsevier B.V. Fuzzy Sets and Systems is a leading international journal in computer science and applied mathematics that was created in 1978. In 2018, the journal celebrated its 40th anniversary. The aim of this study is to present a bibliometric overview of the leading trends occurring in the journal between 1978 and 2016 by analysing the most productive and influential authors, institutions and countries as well as the publication and citation structure. Additionally, this work presents a graphical visualization of the bibliographic data by using the visualization of similarities (VOS) viewer and the science mapping analysis tool (SciMAT) software. The results show the strong growth of fuzzy set theory over time and a huge diversity of publications from all over the world, especially from Europe, North America and East Asia.

Laengle, S, Merigó, JM, Modak, NM & Yang, JB 2020, 'Bibliometrics in operations research and management science: a university analysis', Annals of Operations Research.
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© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Many universities around the World have made important contributions in the field of operations research and management science. This article presents the most productive and influential universities between 1991 and 2015. For doing so, we use the Web of Science database in order to search for the information which is usually regarded as the most relevant for scientific research. The results show the country of origin of the leading universities being mainly from North America and Asia and especially from USA and China. The Centre National de la Recherche Scientifique (CNRS) of France is the most productive university while the Massachusetts Institute of Technology (MIT) of USA is the most influential one. The temporal evolution shows that USA is trailing its dominancy while China progressing quickly. The evaluation also reveals that Asian universities outperform North American universities during the last 5 years.

Lai, 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 Nonuniform Metallic Lattice for Rectifying Aperture Near-Field of Electromagnetic Bandgap Resonator Antennas', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 5, pp. 3328-3335.
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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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Lazaar, A, Mouazen, AM, EL Hammouti, K, Fullen, M, Pradhan, B, Memon, MS, Andich, K & Monir, A 2020, 'The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco', International Soil and Water Conservation Research, vol. 8, no. 2, pp. 195-204.
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© 2020 Soil organic matter (SOM) is a fundamental soil constituent. The estimation of this parameter in the laboratory using the classical method is complex time-consuming and requires the use of chemical reagents. The objectives of this study were to assess the accuracy of two laboratory measurement setups of the VIS-NIR spectroscopy in estimating SOM content and determine the important spectral bands in the SOM estimation model. A total of 115 soil samples were collected from the non-root zone (0–20 cm) of soil in the study area of the Triffa Plain and then analysed for SOM in the laboratory by the Walkley–Black method. The reflectance spectra of soil samples were measured by two protocols, Contact Probe (CP) and Pistol Grip (PG)) of the ASD spectroradiometer (350–2500 nm) in the laboratory. Partial least squares regression (PLSR) was used to develop the prediction models. The results of coefficient of determination (R2) and the root mean square error (RMSE) showed that the pistol grip offers reasonable accuracy with an R2 = 0.93 and RMSE = 0.13 compared to the contact probe protocol with an R2 = 0.85 and RMSE = 0.19. The near-Infrared range were more accurate than those in the visible range for predicting SOM using the both setups (CP and PG). The significant wavelengths contributing to the prediction of SOM for (PG) setup were at: 424, 597, 1432, 1484, 1830,1920, 2200, 2357 and 2430 nm, while were at 433, 587, 1380, 1431, 1929, 2200 and 2345 nm for (CP) setup.

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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© 2020 IEEE. In this letter, we present Gaussian Process Preintegration, a preintegration theory based on continuous representations of inertial measurements. A novel use of linear operators on Gaussian Process kernels is employed to generate the proposed Gaussian Preintegrated Measurements (GPMs). This formulation allows the analytical integration of inertial signals on any time interval. Consequently, GPMs are especially suited for asynchronous inertial-aided estimation frameworks. Unlike discrete preintegration approaches, the proposed method does not rely on any explicit motion-model and does not suffer from numerical integration noise. Additionally, we provide the analytical derivation of the Jacobians involved in the first-order expansion for postintegration bias and inter-sensor time-shift correction. We benchmarked the proposed method against existing preintegration methods on simulated data. Our experiments show that GPMs produce the most accurate results and their computation time allows close-to-real-time operations. We validated the suitability of GPMs for inertial-aided estimation by integrating them into a lidar-inertial localisation and mapping framework.

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, Ritz, C, Dutkiewicz, E, Phung, SL, Bouzerdoum, A & Franklin, D 2020, 'Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks', Sensors, vol. 20, no. 14, pp. 3838-3838.
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Source positioning using hybrid angle-of-arrival (AOA) estimation and received signal strength indicator (RSSI) is attractive because no synchronization is required among unknown nodes and anchors. Conventionally, hybrid AOA/RSSI localization combines the same number of these measurements to estimate the agents’ locations. However, since AOA estimation requires anchors to be equipped with large antenna arrays and complicated signal processing, this conventional combination makes the wireless sensor network (WSN) complicated. This paper proposes an unbalanced integration of the two measurements, called 1AOA/nRSSI, to simplify the WSN. Instead of using many anchors with large antenna arrays, the proposed method only requires one master anchor to provide one AOA estimation, while other anchors are simple single-antenna transceivers. By simply transforming the 1AOA/1RSSI information into two corresponding virtual anchors, the problem of integrating one AOA and N RSSI measurements is solved using the least square and subspace methods. The solutions are then evaluated to characterize the impact of angular and distance measurement errors. Simulation results show that the proposed network achieves the same level of precision as in a fully hybrid nAOA/nRSSI network with a slightly higher number of simple anchors.

Le, AT, Tran, LC, Huang, X, Ritz, CH, Dutkiewicz, E, Phung, SL, Bouzerdoum, A & Franklin, DR 2020, 'Unbalanced Hybrid AOA/RSSI Localization for Simplified Wireless Sensor Networks.', Sensors, vol. 20, pp. 3838-3838.

Lee, C, Nguyen, TT, Adha, RS, Shon, HK & Kim, IS 2020, 'Influence of hydrodynamic operating conditions on organic fouling of spiral-wound forward osmosis membranes: Fouling-induced performance deterioration in FO-RO hybrid system', Water Research, vol. 185.
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© 2020 Elsevier Ltd The forward osmosis-reverse osmosis (FO-RO) hybrid process has been extensively researched as part of attempts to reduce the high energy consumption of conventional seawater reverse osmosis in recent years. FO operating conditions play a substantial role in the hybrid process, dictating not only the performance of the entire system but also the propensity for fouling, which deteriorates performance in long-term field operations. Therefore, determining the optimal FO operating conditions with regard to membrane fouling may promote sustainable operation through efficient fouling control. This study thus evaluated the influence of each hydrodynamic operating condition (feed flowrate, draw flowrate, and hydraulic pressure difference) and their synergistic effects on fouling propensity in a pilot-scale FO operation under seawater and municipal wastewater conditions. Fouling-induced variation in water flux, channel pressure drop, diluted concentration, and the resulting specific energy consumption (SEC) were comparatively analyzed and utilized to project performance variation in a full-scale FO-RO system. Fouling-induced performance reduction significantly varied depending on hydrodynamic operating conditions and the resultant fouling propensity during 15 days of continuous operation. A high feed flowrate demonstrated a clear ability to mitigate fouling-induced performance deterioration in all conditions. A high draw flowrate turned out to be detrimental for fouling propensity since its high reverse solute flux accelerated fouling growth. Applying additional hydraulic pressure during FO operation caused a faster reduction of water flux, and thus feed recovery and water production; however, these drawbacks could be compensated for by a 10% reduction in the required FO membrane area and an additional reduction in RO SEC.

Lee, D, Woo, YC, Park, KH, Phuntsho, S, Tijing, LD, Yao, M, Shim, WG & 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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© 2019 The instability of polyvinylidene fluoride (PVDF) membranes in membrane distillation (MD) for seawater desalination is still a problem, despite the tremendous effort expended to resolve this issue. Here, a simple and feasible approach for improving desalination performance through the incorporation of two-dimensional boron nitride nanosheets (BNNSs) in polyvinylidene fluoride-co-hexafluoropropene (PVDF-co-HFP) electrospun nanofiber membrane (BNs-PH) is proposed as well as demonstrate its origin for fundamental understanding. The BNs-PH membrane exhibits a stable water vapor flux (18 LMH) and superior salt rejection (99.99%), even after operation for 280 h (commercial PVDF: steep decay within 28 h; neat PH: wetting within 4 h). From structural/chemical analyses, the BNNSs play a crucial role in forming favorable phases of the PH polymer crystal structure, inducing a superhydrophobic surface with greater nanoporosity and higher heterogeneity as well as enhanced mechanical properties (increase of UTS: 13.4%; modulus: 1.2%) for long-term operation. Theoretical modeling results of an air-gap MD system are consistent with our experimental results. The approach introduced in this study can be applied to other desalination systems to boost various water treatment applications.

Lee, SS, Lim, CS, Siwakoti, YP & Lee, KB 2020, 'Dual-T-Type Five-Level Cascaded Multilevel Inverter with Double Voltage Boosting Gain', IEEE Transactions on Power Electronics, vol. 35, no. 9, pp. 9524-9531.
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© 1986-2012 IEEE. The switched-capacitor-based cascaded multilevel inverters (CMI) have been emerging due to their voltage boosting capability. Unfortunately, they suffer from impulse charging current and nonuniform operation. This article presents a topology termed as dual-T-type five-level CMI to resolve these problems without compromising the desirable voltage boosting characteristic. The main idea is to integrate a half bridge and an inductor to soft charge a capacitor that is connected in series with the dc source. The capacitor enables the voltage gain boosted to two, while the control of eight power switches that constitutes a dual-T structure enables five voltage levels generation. In addition, uniform operation is achieved for cascaded extensions. The operating principle of the proposed topology is analyzed and elaborated. For validation, simulation, and experimental results of a prototype are presented.

Lee, SS, Lim, CS, Siwakoti, YP & Lee, K-B 2020, 'Hybrid 7-Level Boost Active-Neutral-Point-Clamped (H-7L-BANPC) Inverter', IEEE Transactions on Circuits and Systems II: Express Briefs, pp. 1-1.
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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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Lee, SS, Yang, Y, Siwakoti, YP & Lee, K-B 2020, 'A Novel Boost Cascaded Multilevel Inverter', IEEE Transactions on Industrial Electronics, pp. 1-1.
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Lee, XJ, Ong, HC, Gan, YY, Chen, W-H & Mahlia, TMI 2020, 'State of art review on conventional and advanced pyrolysis of macroalgae and microalgae for biochar, bio-oil and bio-syngas production', ENERGY CONVERSION AND MANAGEMENT, vol. 210.
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Lei, B, Li, W, Liu, H, Tang, Z & Tam, VWY 2020, 'Synergistic Effects of Polypropylene and Glass Fiber on Mechanical Properties and Durability of Recycled Aggregate Concrete', INTERNATIONAL JOURNAL OF CONCRETE STRUCTURES AND MATERIALS, vol. 14, no. 1.
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Lei, B, Li, W, Luo, Z, Li, X, Tam, VWY & Tang, Z 2020, 'Performance deterioration of sustainable recycled aggregate concrete under combined cyclic loading and environmental actions', Journal of Sustainable Cement-Based Materials.
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© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. The effects of strength grades, loading modes, and stress levels on the performance deterioration of sustainable recycled aggregate concrete (RAC) subjected to mechanical loading or coupled mechanical loading and environmental actions are investigated in this study. Comparison analysis of residual properties of RAC suffered from single mechanical loading, coupled actions of mechanical loading, and salt-solution freeze-thaw cycles, as well as the combined actions of mechanical loading and salt-solution corrosion were experimentally studied. The results indicate that the stress level is the most influential factor affecting the durability of RAC followed by the RAC strength grade and the number of times the alternating load applied. Moreover, applying novel intermittent loading mode to simulate the durability of RAC under the coupled actions of sustained loading and environmental factors is feasible and reasonable. Additionally, the fitting equations were proposed to discuss the effects of number of times the alternating load applied, stress level, and mechanical strength on the mechanical strength loss and durability of RAC.

Lei, F, Lv, X, Fang, J, Sun, G & Li, Q 2020, 'Multiobjective discrete optimization using the TOPSIS and entropy method for protection of pedestrian lower extremity', THIN-WALLED STRUCTURES, vol. 152.
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Lei, G, Bramerdorfer, G, Liu, C, Guo, Y & Zhu, J 2020, 'Robust Design Optimization of Electrical Machines: A Comparative Study and Space Reduction Strategy', IEEE Transactions on Energy Conversion, pp. 1-1.
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Lei, G, Bramerdorfer, G, MA, BO, Guo, Y & Zhu, J 2020, 'Robust Design Optimization of Electrical Machines: Multi-objective Approach', IEEE Transactions on Energy Conversion, pp. 1-1.
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Li, DL, Prasad, M, Liu, CL & Lin, CT 2020, 'Multi-View Vehicle Detection Based on Fusion Part Model With Active Learning', IEEE Transactions on Intelligent Transportation Systems.
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IEEE Computer vision-based vehicle detection techniques are widely used in real-world applications. However, most of these techniques aim to detect only single-view vehicles, and their performances are easily affected by partial occlusion. Therefore, this paper proposes a novel multi-view vehicle detection system that uses a part model to address the partial occlusion problem and the high variance between all types of vehicles. There are three features in this paper; firstly, different from Deformable Part Model, the construction of part models in this paper is visual and can be replaced at any time. Secondly, this paper proposes some new part models for detection of vehicles according to the appearance analysis of a large number of modern vehicles by the active learning algorithm. Finally, this paper proposes the method that contains color transformation along with the Bayesian rule to filter out the background to accelerate the detection time and increase accuracy. The proposed method outperforms other methods on given dataset.

Li, F, Cao, Z, Xu, P, Yi, C, Liao, Y, Jiang, Y, Si, Y, Song, L, Zhang, T, Yao, D & Zhang, Y 2020, 'Reconfiguration of Brain Network between Rest-ing-state and P300 task', IEEE Transactions on Cognitive and Developmental Systems.
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IEEE Previous studies explore the power spectra from the resting-state condition to the oddball task, but whether brain network existing significant difference is still unclear. Our study aims to address how the brain reconfigures its architecture from a resting-state condition (i.e., baseline) to the P300 task in the visual oddball task. In this study, electroencephalograms (EEGs) were collected from 24 subjects, who were required to only mentally count the number of target stimulus; afterwards, EEG networks constructed in different bands were compared between baseline and task to evaluate the reconfiguration of functional connectivity. Compared to the baseline, our results showed the significantly enhanced delta/theta functional connectivity and decreased alpha default mode network in the progress of brain reconfiguration to the task. Furthermore, the reconfigured coupling strengths were found to relate to P300 amplitudes, which were then regarded as features to train a classifier to differentiate the brain states and the high and low P300 groups with an accuracy of 100% and 77.78%, respectively. The findings of our study help us to under-stand the updates in functional connectivity from resting-state to the oddball task, and the reconfigured network structure has the potential for the selection of good subjects for P300-based brain-computer interface.

Li, G, Feng, B, Zhou, H, Zhang, Y, Sood, K & Yu, S 2020, 'Adaptive service function chaining mappings in 5G using deep Q-learning', Computer Communications, vol. 152, pp. 305-315.
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© 2020 Elsevier B.V. With introduction of Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies, mobile network operators are able to provide on-demand Service Function Chaining (SFC) to meet various needs from users. However, it is challenging to map multiple SFCs to substrate networks efficiently, particularly in a number of key scenarios of forthcoming 5G, where user requests have different priorities and various resource demands. To this end, we first formulate the mapping of multiple SFCs with priorities as a multi-step Linear Integer Programming (ILP) problem, of which the mapping strategy (i.e., the objective function) in each step is configurable to improve overall CPU and bandwidth resource utilization rates. Secondly, to solve the strategy selection problem in each step and alleviate the complexity of ILP, we propose an adaptive deep Q-learning based SFC mapping approach (ADAP), where an agent is learned to make decisions from two low-complexity heuristic SFC mapping algorithms. Finally, we conduct extensive simulations using multiple SFC requests with randomly generated CPU and bandwidth demands in a real-world substrate network topology. Related results demonstrate that compared with a single strategy or random selections of strategies under the ILP-based approach or the proposed heuristic algorithms, our ADAP approach can improve whole-system resource efficiency by scheduling this two simply designed heuristic algorithms properly after limited training episodes.

Li, G, Zhou, H, Feng, B, Zhang, Y & Yu, S 2020, 'Efficient Provision of Service Function Chains in Overlay Networks using Reinforcement Learning', IEEE Transactions on Cloud Computing.
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IEEE Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies facilitate deploying Service Function Chains (SFCs) at clouds in efficiency and flexibility. However, it is still challenging to efficiently chain Virtualized Network Functions (VNFs) in overlay networks without knowledge of underlying network configurations. Although there are many deterministic approaches for VNF placement and chaining, they have high complexity and depend on state information of substrate networks. Fortunately, Reinforcement Learning (RL) brings opportunities to alleviate this challenge as it can learn to make suitable decisions without prior knowledge. Therefore, in this paper, we propose an RL approach for efficient SFC provision in overlay networks, where the same VNFs provided by multiple vendors are with different performance. Specifically, we first formulate the problem into an Integer Linear Programming (ILP) model for benchmarking. Then, we present the online SFC path selection into a Markov Decision Process (MDP) and propose a corresponding policy-gradient-based solution. Finally, we evaluate our proposed approach with extensive simulations with randomly generated SFC requests and a real-world video streaming dataset, and implement an emulation system for feasibility verification. Related results demonstrate that performance of our approach is close to the ILP-based method and better than deep Q-learning, random, and load-least-greedy methods.

Li, H, Li, Y & Li, J 2020, 'Negative stiffness devices for vibration isolation applications: A review', Advances in Structural Engineering, vol. 23, no. 8, pp. 1739-1755.
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© The Author(s) 2020. In recent years, negative stiffness vibration isolation device with nonlinear characteristic has become an emerging research area and attracted a significant amount of attentions in the community due to the promising potentials it brought into the field. Its high-static-low-dynamic stiffness property endows the capacity to realize effective vibration isolation and in the meantime to maintain the system stability. This article presents a comprehensive review of the recent research and developments on negative stiffness vibration isolation device. It begins with an introduction on the concept of negative stiffness and then provides a summary and discussion regarding the realization and characteristics of negative stiffness vibration isolation device. The article places its special interest on the principles, structure design, and device characterisation of different types of negative stiffness vibration isolation devices, including spring type, pre-bucked beam type, magnetism type, geometrically nonlinear structural type, and composite structural type. Besides, the applications of negative stiffness vibration isolation device, as well as negative stiffness damper, are summarized and discussed based on the current state-of-the-art. Finally, the conclusions and further discussion provide highlights of the investigation.

Li, H, Ma, B, Zhang, S & Sheng, D 2020, 'Mechanical behaviors of soft rocks based on the fractional thermal elastic-plastic theory', Yanshilixue Yu Gongcheng Xuebao/Chinese Journal of Rock Mechanics and Engineering, vol. 39, no. 7, pp. 1311-1320.
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© 2020, Science Press. All right reserved. Soft rocks have complex thermodynamic behaviors and their strength maybe increase or decrease with temperature. In this paper, the soft rocks were considered as heavily over-consolidated clays and their preconsolidation pressure was assumed to be the uniaxial compression strength. Based on the fractional thermal elastoplastic theory, a fractional sub-loading surface model for soft rocks considering the effects of temperature was proposed, which can decribe associated and non-associated flow rules without introducing the plastic potential. The analysis results show that the phenomena of heat-increase and heat-decrease are closely related to the angle of the plastic flow direction and the loading direction. Application of the non-associated flow rule in the undrained triaxial test results in that stress paths of soft rocks cross through the critical state line and finally reach the critical state, and the undrained shear strength of soft rocks will increase as temperature increases. On the other hand, the associated flow rule will do the opposite. In addition, the preconsolidation pressure of soft rocks will decrease as temperature increases, leading that the sub-loading surface is located outside the temperature loading surface in the p-q plane and that the overconsolidated ratio OCR is smaller than 1. Comparisons between the proposed model with test results indicate that soft rocks show the features of strain-softening and dilatancy, which can be captured by the proposed model, and that, increasing temperature will decrease the fragility of soft rocks as well as the dilatancy. Compared with the modified Cam-clay model, the proposed model introduces two extra parameters including the coefficient of linear expansion αT and dilative related parameter m, which have clear physical meanings and can be determined through conventional tests directly.

Li, H, Yang, Y, Dai, Y, Yu, S & Xiang, Y 2020, 'Achieving Secure and Efficient Dynamic Searchable Symmetric Encryption over Medical Cloud Data', IEEE Transactions on Cloud Computing, vol. 8, no. 2, pp. 484-494.
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© 2013 IEEE. In medical cloud computing, a patient can remotely outsource her medical data to the cloud server. In this case, only authorized doctors are allowed to access the data since the medical data is highly sensitive. Before outsourcing, the data is commonly encrypted, where the corresponding secret key is sent to authorized doctors. However, performing searches on encrypted medical data is difficult without decryption. In this paper, we propose two Secure and Efficient Dynamic Searchable Symmetric Encryption (SEDSSE) schemes over medical cloud data. First, we utilize the secure k-Nearest Neighbor (kNN) and Attribute-Based Encryption (ABE) techniques to construct a dynamic searchable symmetric encryption scheme, which can achieve forward privacy and backward privacy simultaneously. These tow security properties are vital and very challenging in the area of dynamic searchable symmetric encryption. Then, we propose an enhanced scheme to solve the key sharing problem which widely exists in the kNN based searchable encryption scheme. Compared with existing proposals, our schemes are better in terms of storage, search and updating complexity. Extensive experiments demonstrate the efficiency of our schemes on storage overhead, index building, trapdoor generating and query.

Li, H-Y, Xu, J-X & Zhang, XY 2020, 'Single-to-Balanced and Balanced-to-Balanced Dual-channel Filters Using Multilayer Substrate Integrated Waveguide Cavities', IEEE Transactions on Industrial Electronics, pp. 1-1.
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Li, HY, Xu, JX, Yang, Y & Zhang, XY 2020, 'Novel Switchable Filtering Circuit With Function Reconfigurability Between SPQT Filtering Switch and Four-Way Filtering Power Divider', IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 3, pp. 867-876.
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© 2019 IEEE. In this article, we propose a circuit model for designing the switchable filtering circuit with reconfigurable functions between a single-pole quad-throw (SPQT) filtering switch (State 1) and a four-way filtering power divider (State 2) without additional switchable impedance matching network (IMN). The proposed circuit model can be utilized as the feeding network of a switchable directive and omni-directional antenna system to eliminate the omni-directional antenna in conventional solutions to reduce the size and cost. It consists of four filter channels with a common port. In State 1, it works as a SPQT filtering switch, where only one channel is turned on to realize good filtering responses and the impedances at the other three channels are adapted to infinity to realize high isolation. The four channels are turned on simultaneously in State 2 to work as a four-way filtering power divider, and the input impedance of the common port is the same as that in State 1. Thus, a switchable IMN is not required for port matching when switching between the two states. Transmission zeros are generated to enhance the skirt selectivity. For verification, the filtering circuit is constructed using high- Q coaxial resonators. Experimental results show excellent performance in both states acting as a SPQT filtering switch and a four-way filtering power divider.

Li, J & Little, C 2020, 'AN IN VITRO STUDY TO INVESTIGATE THE EFFECTS OF STEM CELL THERAPY FOR TREATING OSTEOARTHRITIS', OSTEOARTHRITIS AND CARTILAGE, vol. 28, pp. S503-S504.

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, Pan, Y, Sui, Y & Tsang, IW 2020, 'Secure Metric Learning via Differential Pairwise Privacy', IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 15, pp. 3640-3652.
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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.
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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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Li, P, Guo, S, Yu, S & Zhuang, W 2020, 'Cross-Cloud MapReduce for Big Data', IEEE Transactions on Cloud Computing, vol. 8, no. 2, pp. 375-386.
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© 2013 IEEE. MapReduce plays a critical role as a leading framework for big data analytics. In this paper, we consider a geo-distributed cloud architecture that provides MapReduce services based on the big data collected from end users all over the world. Existing work handles MapReduce jobs by a traditional computation-centric approach that all input data distributed in multiple clouds are aggregated to a virtual cluster that resides in a single cloud. Its poor efficiency and high cost for big data support motivate us to propose a novel data-centric architecture with three key techniques, namely, cross-cloud virtual cluster, data-centric job placement, and network coding based traffic routing. Our design leads to an optimization framework with the objective of minimizing both computation and transmission cost for running a set of MapReduce jobs in geo-distributed clouds. We further design a parallel algorithm by decomposing the original large-scale problem into several distributively solvable subproblems that are coordinated by a high-level master problem. Finally, we conduct real-world experiments and extensive simulations to show that our proposal significantly outperforms the existing works.

Li, P, Li, W, Yu, T, Qu, F & Tam, VWY 2020, 'Investigation on early-age hydration, mechanical properties and microstructure of seawater sea sand cement mortar', Construction and Building Materials, vol. 249.
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© 2020 Elsevier Ltd Using seawater for concrete manufacturing promisingly provides significant economical and environmental benefits. In this study, ordinary Portland cement (OPC) hydration in distilled water and seawater and the corresponding evolution of solid phases was investigated by heat evolution, hydrated phase, hydration kinetics, and microstructure characterization. The results show that seawater can promote the early hydration of tricalcium silicate (C3S) during the hydration acceleration period. The hydrated phase assemblage was affected by the dissolved ions in seawater. Friedel's salt was detected as a specific hydration phase in seawater, which was formed by chemical combination between the aluminate ferrite monosulfate (AFm) phase and chloride ions. The monocarboaluminate can be converted into a stable phase as Friedel's salt in the seawater, due to the reaction with chloride ions. Furthermore, the ettringite becomes more stable when coexists with Friedel's salt than that with monocarboaluminate, and thus ettringite formed in seawater remains 67% higher than that formed in distilled water at the later curing age. Moreover, additional unhydrated cement and less amorphous calcium silicate hydrate (C-S-H) were formed in seawater, which might be responsible for the slightly lower compressive strength of cement mortar prepared by seawater and sea sand. A modeled evolution of the solid phase and pore solution have been established, which agrees well with the characteristics of the dissolution of mineral phase, precipitation of hydration products and changes of pore solution. The related results can provide an insight into the applications of seawater and sea sand concrete for marine infrastructures.

Li, Q, Cao, Z, Ding, W & Li, Q 2020, 'A multi-objective adaptive evolutionary algorithm to extract communities in networks', SWARM AND EVOLUTIONARY COMPUTATION, vol. 52.
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Li, Q, Cao, Z, Tanveer, M, Pandey, HM & Wang, C 2020, 'A Semantic Collaboration Method Based on Uniform Knowledge Graph', IEEE INTERNET OF THINGS JOURNAL, vol. 7, no. 5, pp. 4473-4484.
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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.
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© 2019 The present study employs the modified strain gradient theory (MSGT) in conjunction with the refined shear deformation plate theory to explore the buckling behaviour of simply supported and clamped OSC. The Winkler-Pasternak elastic foundation is implemented to idealise the foundation. The size-dependent effect of the OSC is captured by the three length scale parameters within the MSGT. The Hamilton principle is used to derive the equations of motion and the boundary conditions, and the Galerkin procedure is subsequently implemented to obtain the critical buckling load. Subsequently, the framework is extended to the thermally induced buckling behaviour, and three types of temperature rise patterns, namely uniform, linear and nonlinear temperature variations, along the thickness of the OSC are considered. Several verification studies are conducted to illustrate the accuracy of the present method. Besides, size-dependent material properties are taken into consideration during the numerical experiments. Thorough studies are conducted to demonstrate the difference between critical buckling loads obtained from the MSGT, the modified couple stress theory (MCST), and the classical plate theory (CPT) models. Furthermore, the effects of length scale parameter (h/l), the aspect ratio (a/b), the length-to-thickness ratio (a/h) and the Winkler-Pasternak elastic foundation parameters on the buckling behaviour of the OSC are also revealed by the numerical results.

Li, Q, Zhong, J, Cao, Z & Li, X 2020, 'Optimizing streaming graph partitioning via a heuristic greedy method and caching strategy', Optimization Methods and Software.
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© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Graph partitioning is an important method for accelerating large distributed graph computation. Streaming graph partitioning is more efficient than offline partitioning, and it has been developed continuously in the application of graph partitioning in recent years. In this work, we first introduce a heuristic greedy streaming partitioning method and show that it outperforms the state-of-the-art streaming partitioning methods, leading to exact balance and fewer cut edges. Second, we propose a cache structure for streaming partitioning, called an adjacent edge structure, which can improve the partition efficiency several times on a single commodity type computer without affecting the partition quality. Regardless as to whether the memory capacity is limited (local cache) or not (global cache), our strategy can also improve the partition quality by restreaming partitioning. Taking linear weight greedy streaming algorithm as an example, the experimental results on 19 real-world graphs show that the average partitioning time of the new method is 4.9 times faster than that of the original method, which proves the effectiveness and superiority of the cache structure mentioned in this paper.

Li, 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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© 1990-2012 IEEE. With the rapid progress made by industry and academia, quantum computers with dozens of qubits or even larger size are being realized. However, the fidelity of existing quantum computers often sharply decreases as the circuit depth increases. Thus, an ideal quantum circuit simulator on classical computers, especially on high-performance computers, is needed for benchmarking and validation. We design a large-scale simulator of universal random quantum circuits, often called 'quantum supremacy circuits', and implement it on Sunway TaihuLight. The simulator can be used to accomplish the following two tasks: 1) Computing a complete output state-vector; 2) Calculating one or a few amplitudes. We target the simulation of 49-qubit circuits. For task 1), we successfully simulate such a circuit of depth 39, and for task 2) we reach the 55-depth level. To the best of our knowledge, both of the simulation results reach the largest depth for 49-qubit quantum supremacy circuits.

Li, S, Liang, Y, Li, Y, Li, J & Zhou, Y 2020, 'Investigation of dynamic properties of isotropic and anisotropic magnetorheological elastomers with a hybrid magnet shear test rig', Smart Materials and Structures.
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Li, S, Tian, T, Wang, H, Li, Y, Li, J, Zhou, Y & Wu, J 2020, 'Development of a four-parameter phenomenological model for the nonlinear viscoelastic behaviour of magnetorheological gels', Materials & Design, pp. 108935-108935.
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Li, S, Watterson, P, Li, Y, Wen, Q & Li, J 2020, 'Improved magnetic circuit analysis of a laminated magnetorheological elastomer devices featuring both permanent magnets and electromagnets', Smart Materials and Structures.
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Li, T, Fong, S, Li, X, Lu, Z & Gandomi, AH 2020, 'Swarm Decision Table and Ensemble Search Methods in Fog Computing Environment: Case of Day-Ahead Prediction of Building Energy Demands Using IoT Sensors', IEEE INTERNET OF THINGS JOURNAL, vol. 7, no. 3, pp. 2321-2342.
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Li, W, Dong, W, Shen, L, Castel, A & Shah, SP 2020, 'Conductivity and piezoresistivity of nano-carbon black (NCB) enhanced functional cement-based sensors using polypropylene fibres', Materials Letters, vol. 270.
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© 2020 Elsevier B.V. The cement-based sensors have a great potential for structural health monitoring, especially functional sensors filled with nano-carbon black (NCB) particles. To improve the sensing efficiency of NCB filled cementitious composite, polypropylene (PP) fibres were premixed with NCB during the manufacturing of cement-based sensor. Although the compressive strength is slightly decreased, the electrical conductivity and piezoresistivity of the NCB filled cementitious composite are improved by PP fibres. Microstructural characterization indicated that NCB attached to the surface of PP fibres significantly promotes the generation of conductive paths and contact points in cement-based sensors. The results can provide a new insight into the application of nonconductive fibres to enhance the conductivity and piezoresistivity of spherical conductors filled cement-based sensor.

Li, W, Huang, L & Ji, J 2020, 'GLOBALLY EXPONENTIALLY STABLE PERIODIC SOLUTION IN A GENERAL DELAYED PREDATOR-PREY MODEL UNDER DISCONTINUOUS PREY CONTROL STRATEGY', DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS-SERIES B, vol. 25, no. 7, pp. 2639-2664.
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Li, W, Huang, L, Guo, Z & Ji, J 2020, 'Global dynamic behavior of a plant disease model with ratio dependent impulsive control strategy', MATHEMATICS AND COMPUTERS IN SIMULATION, vol. 177.
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Li, W, Ji, J & Huang, L 2020, 'DYNAMICS OF A CONTROLLED DISCONTINUOUS COMPUTER WORM SYSTEM', PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY, vol. 148, no. 10, pp. 4389-4403.
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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, vol. 99, no. 4, pp. 3351-3371.
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Li, W, Tang, Z, Tam, VWY, Zhao, X & Wang, K 2020, 'A Review on Durability of Alkali-activated System from Sustainable Construction Materials to Infrastructures', ES Materials & Manufacturing.
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Li, X, Cao, Y, Li, Q, Shang, Y, Li, Y, Liu, Y & Xu, G 2020, 'RLINK: Deep reinforcement learning for user identity linkage', World Wide Web.
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© 2020, The Author(s). User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based on the similarity scores. However, most of these methods fail to utilize the results of previously matched identities, which could contribute to the subsequent linkages in following matching steps. To address this problem, we transform user identity linkage into a sequence decision problem and propose a reinforcement learning model to optimize the linkage strategy from the global perspective. Our method makes full use of both the social network structure and the history matched identities, meanwhile explores the long-term influence of processing matching on subsequent decisions. We conduct extensive experiments on real-world datasets, the results show that our method outperforms the state-of-the-art methods.

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, D-VN, Van, QP & Ngoc, HT 2020, 'A novel red mud adsorbent for phosphorus and diclofenac removal from wastewater', JOURNAL OF MOLECULAR LIQUIDS, vol. 303.
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Li, X, Li, J, Zhang, X, Gao, J & Zhang, C 2020, 'Simplified analysis of cable-stayed bridges with longitudinal viscous dampers', Engineering, Construction and Architectural Management.
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© 2020, Emerald Publishing Limited. Purpose: Viscous dampers are commonly used in large span cable-stayed bridges to mitigate seismic effects and have achieved great success. Design/methodology/approach: However, the nonlinear analysis on damper parameters is usually computational intensive and nonobjective. To address these issues, this paper proposes a simplified method to determine the viscous damper parameters for double-tower cable-stayed bridges. An empirical formula of the equivalent damping ratio of viscous dampers is established through decoupling nonclassical damping structures and linearization of nonlinear viscous dampers. Shaking table tests are conducted to verify the feasibility of the proposed method. Moreover, this simplified method has been proved in long-span cable-stayed bridges. Findings: The feasibility of this method is verified by the simplified model shaking table test. This simplified method for determining the parameters of viscous dampers is verified in cable-stayed bridges with different spans. Originality/value: This simplified method has been validated in cable-stayed bridges with various spans.

Li, X, Li, X, Kuang, Z, Kuang, Z, Zhang, J, Zhang, J, Liu, X, Liu, X, Hu, J, Hu, J, Xu, Q, Wang, D, Wang, D, Liu, Y, Wang, Q, Yang, Q, Yang, Q & Li, H 2020, 'Performance and Mechanism of Potassium Ferrate(VI) Enhancing Dark Fermentative Hydrogen Accumulation from Waste Activated Sludge', ACS Sustainable Chemistry and Engineering, vol. 8, no. 23, pp. 8681-8691.
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© 2020 American Chemical Society. Potassium ferrate (K2FeO4, PF), as a multifunctional green oxidant, has been used for oxidative degradation of pollutants and recovery of resources in sludge. However, its impact on the generation of hydrogen in anaerobic fermentation of waste activated sludge (WAS) is still unclear. The purpose of this work is to study the influence of PF on the dark fermentative hydrogen production. Experimental result suggested that as PF increased from 0 to 0.09 g/g of TSS (total suspended solids), the maximal hydrogen production increased from 1.47 to 8.35 mL/g VSS (volatile suspended solids). A further increase to 0.12 g/g of TSS resulted in a decrease in hydrogen yield. Mechanism studies revealed that that the addition of PF not only facilitated the disruption of sludge cell and extracellular polymeric substances (EPS) but also increased the proportion of biodegradable organics, providing more bioavailable organics for subsequent reactions involved in hydrogen accumulation. Although the activities of microorganisms relevant to dark fermentation were suppressed to a certain extent in the presence of PF, the induced suppression to hydrogen consumers was more severe. Microbial studies indicated that the relative abundances of hydrogen producers (such as Petrimonas and Proteiniborus) were augmented while hydrogen consumers (such as Methanosaeta and Methylocaldum) decreased in the presence of PF.

Li, X, Ling, SH & Su, S 2020, 'A Hybrid Feature Selection and Extraction Methods for Sleep Apnea Detection Using Bio-Signals', SENSORS, vol. 20, no. 15.
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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, Zeng, X, Zhou, J, Liu, H, Gu, Y, Pan, Z, Zeng, Y & Zeng, Y 2020, 'Incorporation of disposed oil-contaminated soil in cement-based materials', RESOURCES CONSERVATION AND RECYCLING, vol. 160.
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Li, Y, Zhu, B, Zhang, N, Peng, H & Chen, Y 2020, 'Parameters optimization of two-speed powertrain of electric vehicle based on genetic algorithm', Advances in Mechanical Engineering, vol. 12, no. 1.
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© The Author(s) 2020. Aiming at the shortcomings of only optimizing the gear ratios of two-speed transmission in the optimization process of two-speed powertrain parameters of electric vehicles, the optimization of two-speed powertrain parameters of electric vehicles based on genetic algorithm is proposed. The optimization process is to optimize the main performance parameters of the drive motor and the gear ratios of two-speed transmission. That is, taking the economy and dynamic of the electric vehicle as the fitness function, the gear ratios of two-speed transmission is optimized under the main performance parameters of different drive motors, so as to find the powertrain parameter with the best fitness function value. Among them, the AMESim software is used to build the vehicle optimization model, the genetic algorithm is improved by MATLAB, and the improved genetic algorithm is used to optimize the vehicle optimization model. The results show that the optimization of the vehicle’s economic and dynamic performance has been improved, indicating that this optimization method is effective.

Li, Y, Zhu, J, Zhu, L, Li, Y & Lei, G 2020, 'A Dynamic Magnetostriction Model of Grain-Oriented Sheet Steels Based on Becker-Doring Crystal Magnetization Model and Jiles-Atherton Theory of Magnetic Hysteresis', IEEE TRANSACTIONS ON MAGNETICS, vol. 56, no. 3.
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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, Tang, X, Yang, Y, Lu, D & Cai, Z 2020, 'Three-pole wide-tuning-range balanced frequency-agile bandpass filter with constant absolute bandwidth and 3 transmission zeros', Microwave and Optical Technology Letters.
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© 2020 Wiley Periodicals, Inc. This paper proposes a 3-pole wide-tuning-range, balanced frequency-agile bandpass filter (BAL-FA-BPF) with constant absolute bandwidth (CABW) and 3 transmission zeros (TZs). Under the differential-mode (DM) operation, the cascaded trisection coupling topology with source-load coupling is constructed. Thus, 3 adaptive TZs are generated for the DM responses, which improves the passband selectivity and stopband suppression. Under the common-mode (CM) operation, the short stubs and lumped elements are added at the symmetric plane which effectively improves the CM suppression. Using the advanced matrix synthesis, a demonstrative BAL-FA-BPF is designed with stable CABW and controlled by single-DC-bias. The measured frequency tuning range of the DM passband is 2.0 to 3.0 GHz (40%) and 3 dB-bandwidth is kept at 174 ± 3 MHz (±1.7%). The CM suppression is better than 25 dB.

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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Lian, J-W, Ban, Y-L, Zhu, H & Guo, YJ 2020, 'Uniplanar Beam-Forming Network Employing Eight-Port Hybrid Couplers and Crossovers for 2-D Multibeam Array Antennas', IEEE Transactions on Microwave Theory and Techniques, pp. 1-1.
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Lian, J-W, Zhu, H, Ban, Y-L, Karmokar, DK & Guo, YJ 2020, 'Uniplanar High-Gain 2-D Scanning Leaky-Wave Multibeam Array Antenna at Fixed Frequency', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 7, pp. 5257-5268.
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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-BASEL, vol. 10, no. 3.
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Lim, S, Akther, N, Van, HT, Bae, T-H, Phuntsho, S, Merenda, A, Dumee, LF & Shon, HK 2020, 'Covalent organic framework incorporated outer-selective hollow fiber thin-film nanocomposite membranes for osmotically driven desalination', DESALINATION, vol. 485.
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Lim, S, Park, KH, Van, HT, Akther, N, Phuntsho, S, Choi, JY & Shon, HK 2020, 'Size -controlled graphene oxide for highly permeable and fouling -resistant outer -selective hollow fiber thin-film composite membranes for forward osmosis', JOURNAL OF MEMBRANE SCIENCE, vol. 609.
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Lim, WYB, Luong, NC, Hoang, DT, Jiao, Y, Liang, YC, Yang, Q, Niyato, D & Miao, C 2020, 'Federated Learning in Mobile Edge Networks: A Comprehensive Survey', IEEE Communications Surveys and Tutorials, vol. 22, no. 3, pp. 2031-2063.
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© 1998-2012 IEEE. In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications, e.g., for medical purposes and in vehicular networks. Traditional cloud-based Machine Learning (ML) approaches require the data to be centralized in a cloud server or data center. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislations and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges and future research directions in FL.

Lin, A, Lu, J, Xuan, J, Zhu, F & Zhang, G 2020, 'A Causal Dirichlet Mixture Model for Causal Inference from Observational Data', ACM Transactions on Intelligent Systems and Technology, vol. 11, no. 3.
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© 2020 ACM. Estimating causal effects by making causal inferences from observational data is common practice in scientific studies, business decision-making, and daily life. In today's data-driven world, causal inference has become a key part of the evaluation process for many purposes, such as examining the effects of medicine or the impact of an economic policy on society. However, although the literature contains some excellent models, there is room to improve their representation power and their ability to capture complex relationships. For these reasons, we propose a novel prior called Causal DP and a model called CDP. The prior captures the complex relationships between covariates, treatments, and outcomes in observational data using a rational probabilistic dependency structure. The model is Bayesian, nonparametric, and generative and is not based on the assumption of any parametric distribution. CDP is designed to estimate various kinds of causal effects - average, conditional average, average treated, quantile, and so on. It performs well with missing covariates and does not suffer from overfitting. Comparative experiments on synthetic datasets against several state-of-the-art methods demonstrate that CDP has a superior ability to capture complex relationships. Further, a simple evaluation to infer the effect of a job training program on trainee earnings from real-world data shows that CDP is both effective and useful for causal inference.

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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Lin, W & Ziolkowski, RW 2020, 'High-Directivity, Compact, Omnidirectional Horizontally Polarized Antenna Array', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 8, pp. 6049-6058.
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Lin, W, Yin, X, Wang, S & Khosravi, MR 2020, 'A Blockchain-enabled decentralized settlement model for IoT data exchange services', Wireless Networks.
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© 2020, Springer Science+Business Media, LLC, part of Springer Nature. IoT data exchange services are emerging to connect various and distributed IoT data sources, which facilitate data owners exchange their IoT data flexibly. Traditional IoT data exchange services rely on a centralized third-party to negotiate the settlement of trades between data consumers and providers. Such settlement model suffers from issues including single point of failure, extra settlement fee, un-transparency of settlement details, to name a few. New settlement model is imperative to overcome such limitations. Blockchain is an innovative technology that is competent in governing the decentralized network, which poses great opportunity to implement fair settlement in decentralized manner. In this paper, we propose a Blockchain-enabled settlement model for decentralized IoT data exchange services. Firstly, Bitcoin-based time commitment scheme is adopted to build fair and autonomous settlement model. Furthermore, to enhance the accountability of all transactions, an optimized practical Byzantine fault tolerant consensus protocol named ReBFT, is proposed to enable all members involved in the IoT data exchange application achieve identical shared ledger recording all transactions. Finally, experiments are conducted to verify the feasibility of our proposal.

Lin, Y, Wu, Y, Yan, C, Xu, M & Yang, Y 2020, 'Unsupervised person re-identification via cross-camera similarity exploration', IEEE Transactions on Image Processing, vol. 29, pp. 5481-5490.
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© 1992-2012 IEEE. Most person re-identification (re-ID) approaches are based on supervised learning, which requires manually annotated data. However, it is not only resource-intensive to acquire identity annotation but also impractical for large-scale data. To relieve this problem, we propose a cross-camera unsupervised approach that makes use of unsupervised style-transferred images to jointly optimize a convolutional neural network (CNN) and the relationship among the individual samples for person re-ID. Our algorithm considers two fundamental facts in the re-ID task, i.e., variance across diverse cameras and similarity within the same identity. In this paper, we propose an iterative framework which overcomes the camera variance and achieves across-camera similarity exploration. Specifically, we apply an unsupervised style transfer model to generate style-transferred training images with different camera styles. Then we iteratively exploit the similarity within the same identity from both the original and the style-transferred data. We start with considering each training image as a different class to initialize the Convolutional Neural Network (CNN) model. Then we measure the similarity and gradually group similar samples into one class, which increases similarity within each identity. We also introduce a diversity regularization term in the clustering to balance the cluster distribution. The experimental results demonstrate that our algorithm is not only superior to state-of-the-art unsupervised re-ID approaches, but also performs favorably compared with other competing unsupervised domain adaptation methods (UDA) and semi-supervised learning methods.

Lin, Z, Lv, T, Ni, W, Zhang, JA & Liu, RP 2020, 'Nested Hybrid Cylindrical Array Design and DoA Estimation for Massive IoT Networks', IEEE Journal on Selected Areas in Communications.
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IEEE Reducing cost and power consumption while maintaining high network access capability is a key physical-layer requirement of massive Internet of Things (mIoT) networks. Deploying a hybrid array is a cost-and energy-efficient way to meet the requirement, but would penalize system degree of freedom (DoF) and channel estimation accuracy. This is because signals from multiple antennas are combined by a radio frequency (RF) network of the hybrid array. This paper presents a novel hybrid uniform circular cylindrical array (UCyA) for mIoT networks. We design a nested hybrid beamforming structure based on sparse array techniques and propose the corresponding channel estimation method based on the second-order channel statistics. As a result, only a small number of RF chains are required to preserve the DoF of the UCyA. We also propose a new tensor-based two-dimensional (2-D) direction-of-arrival (DoA) estimation algorithm tailored for the proposed hybrid array. The algorithm suppresses the noise components in all tensor modes and operates on the signal data model directly, hence improving estimation accuracy with an affordable computational complexity. Corroborated by a Cramér-Rao lower bound (CRLB) analysis, simulation results show that the proposed hybrid UCyA array and the DoA estimation algorithm can accurately estimate the 2-D DoAs of a large number of IoT devices.

Ling, Y, Wang, K, Wang, X & Li, W 2020, 'Prediction of engineering properties of fly ash-based geopolymer using artificial neural networks', Neural Computing and Applications.
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© 2019, Springer-Verlag London Ltd., part of Springer Nature. Fly ash-based geopolymer has been studied extensively in recent years due to its comparable properties to Portland cement and its environmental benefits. However, the uncertainty and complexity of design parameters, such as the SiO2/Na2O mole ratio in alkaline solution, the alkaline solution concentration in liquid phase, and the liquid-to-fly ash mass ratio (L/F), have made it very difficult to create a systematic approach for geopolymer mix design. These mix design parameters, along with fly ash properties and curing conditions (temperature and time), significantly influence key properties of the material, such as setting time and compressive strength. In this study, an artificial neural network (ANN) was used to develop models for predicting the key properties of high-calcium fly ash-based geopolymer according to its mix design parameters. The correlations between experimental measurements and ANN model predictions of setting time, compressive strength, and heat of geopolymerization were established based on the results of tests on 36, 273, and 72 geopolymer mixes, respectively. The results show that the correlations between the experimental measurements and ANN model predictions of the properties studied are all strong. ANN modeling was found to be a suitable computing method to analyze the effects of design parameters on geopolymer properties and showed that L/F exhibited the greatest effect on setting time, alkaline solution concentration had the greatest influence on compressive strength, and a mole ratio larger than 1.5 significantly impacted heat at the geopolymerization peak. The developed ANN models can be used as guidance for mix design of high-calcium fly ash geopolymer in engineering applications.

Liu, A, Lu, J & Zhang, G 2020, 'Concept Drift Detection via Equal Intensity k-Means Space Partitioning.', IEEE transactions on cybernetics.
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The data stream poses additional challenges to statistical classification tasks because distributions of the training and target samples may differ as time passes. Such a distribution change in streaming data is called concept drift. Numerous histogram-based distribution change detection methods have been proposed to detect drift. Most histograms are developed on the grid-based or tree-based space partitioning algorithms which makes the space partitions arbitrary, unexplainable, and may cause drift blind spots. There is a need to improve the drift detection accuracy for the histogram-based methods with the unsupervised setting. To address this problem, we propose a cluster-based histogram, called equal intensity k-means space partitioning (EI-kMeans). In addition, a heuristic method to improve the sensitivity of drift detection is introduced. The fundamental idea of improving the sensitivity is to minimize the risk of creating partitions in distribution offset regions. Pearson's chi-square test is used as the statistical hypothesis test so that the test statistics remain independent of the sample distribution. The number of bins and their shapes, which strongly influence the ability to detect drift, are determined dynamically from the sample based on an asymptotic constraint in the chi-square test. Accordingly, three algorithms are developed to implement concept drift detection, including a greedy centroids initialization algorithm, a cluster amplify-shrink algorithm, and a drift detection algorithm. For drift adaptation, we recommend retraining the learner if a drift is detected. The results of experiments on the synthetic and real-world datasets demonstrate the advantages of EI-kMeans and show its efficacy in detecting concept drift.

Liu, A, Lu, J & Zhang, G 2020, 'Concept Drift Detection: Dealing with Missing Values via Fuzzy Distance Estimations', IEEE Transactions on Fuzzy Systems, pp. 1-1.
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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, vol. PP.
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Concept drift refers to changes in the distribution of underlying data and is an inherent property of evolving data streams. Ensemble learning, with dynamic classifiers, has proved to be an efficient method of handling concept drift. However, the best way to create and maintain ensemble diversity with evolving streams is still a challenging problem. In contrast to estimating diversity via inputs, outputs, or classifier parameters, we propose a diversity measurement based on whether the ensemble members agree on the probability of a regional distribution change. In our method, estimations over regional distribution changes are used as instance weights. Constructing different region sets through different schemes will lead to different drift estimation results, thereby creating diversity. The classifiers that disagree the most are selected to maximize diversity. Accordingly, an instance-based ensemble learning algorithm, called the diverse instance-weighting ensemble (DiwE), is developed to address concept drift for data stream classification problems. Evaluations of various synthetic and real-world data stream benchmarks show the effectiveness and advantages of the proposed algorithm.

Liu, B, Chen, C, Di, X, Liao, J, Wen, S, Su, QP, Shan, X, Xu, Z-Q, Ju, LA, Mi, C, Wang, F & Jin, D 2020, 'Upconversion Nonlinear Structured Illumination Microscopy', NANO LETTERS, vol. 20, no. 7, pp. 4775-4781.
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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, F, Liu, Y, Han, F, Ban, Y-L & Guo, YJ 2020, 'Synthesis of Large Unequally Spaced Planar Arrays Utilizing Differential Evolution With New Encoding Mechanism and Cauchy Mutation', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 6, pp. 4406-4416.
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Liu, F, Zhang, G & Lu, J 2020, 'Heterogeneous Domain Adaptation: An Unsupervised Approach.', IEEE transactions on neural networks and learning systems.
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Domain adaptation leverages the knowledge in one domain--the source domain--to improve learning efficiency in another domain--the target domain. Existing heterogeneous domain adaptation research is relatively well-progressed but only in situations where the target domain contains at least a few labeled instances. In contrast, heterogeneous domain adaptation with an unlabeled target domain has not been well-studied. To contribute to the research in this emerging field, this article presents: 1) an unsupervised knowledge transfer theorem that guarantees the correctness of transferring knowledge and 2) a principal angle-based metric to measure the distance between two pairs of domains: one pair comprises the original source and target domains and the other pair comprises two homogeneous representations of two domains. The theorem and the metric have been implemented in an innovative transfer model, called a Grassmann-linear monotonic maps-geodesic flow kernel (GLG), which is specifically designed for heterogeneous unsupervised domain adaptation (HeUDA). The linear monotonic maps (LMMs) meet the conditions of the theorem and are used to construct homogeneous representations of the heterogeneous domains. The metric shows the extent to which the homogeneous representations have preserved the information in the original source and target domains. By minimizing the proposed metric, the GLG model learns the homogeneous representations of heterogeneous domains and transfers knowledge through these learned representations via a geodesic flow kernel (GFK). To evaluate the model, five public data sets were reorganized into ten HeUDA tasks across three applications: cancer detection, the credit assessment, and text classification. The experiments demonstrate that the proposed model delivers superior performance over the existing baselines.

Liu, F, Zhang, G & Lu, J 2020, 'Multi-source heterogeneous unsupervised domain adaptation via fuzzy-relation neural networks', IEEE Transactions on Fuzzy Systems, pp. 1-1.
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Liu, J, Hou, J, Huang, X, Xiang, Y & Zhu, T 2020, 'Secure and efficient sharing of authenticated energy usage data with privacy preservation', Computers and Security, vol. 92.
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© 2020 Elsevier Ltd As a technological innovation, smart grid improves electricity services in terms of substantiality, economics, efficiency, and reliability. This owes to the bi-directional communication property which not only enables the fine-grained energy usage data to be available for different entities but also facilitates the automated grid management. However, sharing such energy usage data with other parties could potentially cause the leakage of customers’ sensitive information. In addition, the data are vulnerable to be tampered with by any internal or external attacker such that the value of data could be destroyed. Therefore, it is crucial to preserve customers’ privacy and provide authenticity and verifiability while sharing energy usage data with other parties. In this paper, we propose a bilinear-map accumulator based redactable signature scheme (RSS-BMA) which allows customers to safeguard their privacy while guaranteeing the verifiability of shared data. Furthermore, the batch-data-block verification property of our design enhances not only efficiency but also security by prohibiting additional redaction to a batch of data blocks. We analyze the efficiency and security of our proposed scheme extensively, and the results indicate our construction is more practical than others.

Liu, J, Wu, C, Liu, Z, Li, J, Xu, S, Liu, K, Su, Y & Chen, G 2020, 'Investigations on the Response of Ceramic Ball Aggregated and Steel Fibre Reinforced Geopolymer-Based Ultra-High Performance Concrete (G-UHPC) to Projectile Penetration', Composite Structures, pp. 112983-112983.
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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, K, Wu, C, Li, X, Li, Q, Fang, J & Liu, J 2020, 'A modified HJC model for improved dynamic response of brittle materials under blasting loads', COMPUTERS AND GEOTECHNICS, vol. 123.
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Liu, M, Luo, Y, Yang, C, Pang, S, Puthal, D, Ren, K & Zhang, X 2020, 'Privacy-preserving matrix product based static mutual exclusive roles constraints violation detection in interoperable role-based access control', Future Generation Computer Systems.
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© 2018 Elsevier B.V. Secure interoperation is an important technology to protect shared data in multi-domain environments. IRBAC (Interoperable Role-based Access Control) 2000 model has been proposed to achieve security interoperation between two or more RBAC administrative domains. Static Separation of Duties (SSoD) is an important security policy in RBAC, but it has not been enforced in the IRBAC 2000 model. As a result, some previous works have studied the problem of SMER (Statically Mutually Exclusive Roles) constraints violation between two RBAC domains in the IRBAC 2000 model. However all of them do not enforce how to preserve privacy of RBAC policies, such as roles, roles hierarchies and user-role assignment while detecting SMER constraints violation, if the two interoperable domains do not want to disclose them each other and to others. In order to enforce privacy-preserving detection of SMER constraints violation, we first introduce a solution without privacy-preserving mechanism using matrix product. Then a privacy-preserving solution is proposed to securely detect SMER constraints violation without disclosing any RBAC policy based on a secure three-party protocol to matrix product computation. By efficiency analysis and experimental results comparison, the secure three-party computation protocol to matrix product based on the Paillier cryptosystem is more efficient and practical.

Liu, M, Nothling, MD, Tan, SSL, Webley, PA, Qiao, GG & Fu, Q 2020, 'Polyrotaxane-based thin film composite membranes for enhanced nanofiltration performance', SEPARATION AND PURIFICATION TECHNOLOGY, vol. 246.
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Liu, M, Nothling, MD, Webley, PA, Jin, J, Fu, Q & Qiao, GG 2020, 'High-throughput CO2 capture using PIM-1@MOF based thin film composite membranes', CHEMICAL ENGINEERING JOURNAL, vol. 396.
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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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The probe-type and substrate-type ultrasonic micro/nano manipulation systems have proven to be two kinds of powerful tools for manipulating micro/nanoscale materials. Numerical simulations of acoustofluidic fields in these two kinds of systems can not only be used to explain and analyze the physical mechanisms of experimental phenomena, but also provide guidelines for optimization of device parameters and working conditions. However, in-depth quantitative study and analysis of acoustofluidic fields in the two ultrasonic micro/nano manipulation systems have scarcely been reported. In this paper, based on the finite element method (FEM), we numerically investigated the two-dimensional (2D) axisymmetric acoustofluidic fields in the probe-type and substrate-type ultrasonic micro/nano manipulation systems by the perturbation method (PM) and Reynolds stress method (RSM), respectively. Through comparing the simulation results computed by the two methods and the experimental verifications, the feasibility and reasonability of the two methods in simulating the acoustofluidic fields in these two ultrasonic micro/nano manipulation systems have been validated. Moreover, the effects of device parameters and working conditions on the acoustofluidic fields are clarified by the simulation results and qualitatively verified by the experiments.

Liu, Q, Do, TDT & Cao, L 2020, 'Answer Keyword Generation for Community Question Answering by Multi-aspect Gamma-Poisson Matrix Completion', IEEE Intelligent Systems.
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IEEE Community question answering (CQA) recommends appropriate answers to existing and new questions. Such answer recommendation is challenging since CQA data is often sparse and decentralized and lacks sufficient information to generate suitable answers to existing questions. Matching answers to new questions is more challenging in modeling Q/A sparsity, generating answers to cold-start/novel questions, and integrating metadata about Q/A into models, etc. This paper addresses these issues by a novel statistical model to automatically generate answer keywords in CQA with multi-aspect Gamma-Poisson matrix completion (MAGIC). MAGIC is the first trial in CQA to model multiple aspects of Q/A sentence information in CQA by involving Q/A metadata, Q/A sparsity, and both lexical and semantic Q/A information in a hierarchical Gamma-Poisson model. MAGIC can efficiently generate answer keywords for both existing and new questions against nonnegative matrix factorization (MF), probability MF, and relevant Poisson factorization models w.r.t. recommending appropriate and informative answer keywords.

Liu, Q, Huang, H, Xuan, J, Zhang, G, Gao, Y & Lu, J 2020, 'A Fuzzy Word Similarity Measure for Selecting Top-k Similar Words in Query Expansion', IEEE Transactions on Fuzzy Systems, pp. 1-1.
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Liu, Q, Kang, B, Yu, K, Qi, X, Li, J, Wang, S & Li, HA 2020, 'Contour-Maintaining-Based Image Adaption for an Efficient Ambulance Service in Intelligent Transportation Systems', IEEE Access, vol. 8, pp. 12644-12654.
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© 2013 IEEE. Ambulance services play a vital role in intelligent transportation systems (ITS). In an intelligent ambulance system, the medical images can help doctors quickly and accurately understand the patients' condition during first aid. On various display devices in different kinds of ambulances, content-aware image adaption can be used to better present the medical image among different display resolutions and aspect ratios. Most existing methods mainly focus on visual protection of salient areas, such as specific organ parts of the human body, with less attention paid to the visual effect of unimportant areas. However, the human visual system is more sensitive to the edge and contour of images, which are important for ambulance services. To improve the visual effect of adapted images, a contour-maintaining-based image adaption method for an efficient ambulance service in ITS is proposed here. Firstly, the proposed method innovatively combines the weighted gradient, saliency, and edge maps into an importance map. Secondly, energy is optimized for reducing contour distortion and interruption according to the visual slope and curvature of contours and edges in non-salient areas. Finally, applying the sub-procedure of a forward seam carving method, the optimal seams can more evenly pass through the contour areas. The experimental results demonstrate that the proposed method is more effective than other similar methods.

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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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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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, W, Gong, D, Tan, M, Shi, JQ, Yang, Y & Hauptmann, AG 2020, 'Learning Distilled Graph for Large-Scale Social Network Data Clustering', IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 32, no. 7, pp. 1393-1404.
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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, vol. 195.
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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, X, Wei, W, Xu, J, Wang, D, Song, L & Ni, B-J 2020, 'Photochemical decomposition of perfluorochemicals in contaminated water.', Water research, vol. 186, p. 116311.
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Perfluorochemicals (PFCs) are a set of chemicals containing C-F bonds, which are concerned due to their bioaccumulation property, persistent and toxicological properties. Photocatalytic approaches have been widely studied for the effective removal of PFCs due to the mild operation conditions. This review aims to provide a comprehensive and up-to-date summary on the homogenous and heterogeneous photocatalytic processes for PFCs removal. Specifically, the homogenous photocatalytic methods for remediating PFCs are firstly discussed, including generation of hydrated electrons (eaq‒) and its performance and mechanisms for photo-reductive destruction of PFCs, the active species responsible for photo-oxidative degradation of PFCs and the corresponding mechanisms, and metal-ion-mediated (Fe(III) mainly used) processes for the remediation of PFCs. The influences of molecular structures of PFCs and water matrix, such as dissolved oxygen, humic acid, nitrate, chloride on the homogenous photocatalytic degradation of PFCs are also discussed. For heterogeneous photocatalytic processes, various semiconductor photocatalysts used for the decomposition of perfluorooctanoic acid (PFOA) are then discussed in terms of their specific properties benefiting photocatalytic performances. The preparation methods for optimizing the performance of photocatalysts are also overviewed. Moreover, the photo-oxidative and photo-reductive pathways are summarized for remediating PFOA in the presences of different semiconductor photocatalysts, including active species responsible for the degradation. We finally put forward several key perspectives for the photocatalytic removal of PFCs to promote its practical application in PFCs-containing wastewater treatment, including the treatment of PFCs degradation products such as fluoride ion, and the development of noble-metal free photocatalysts that could efficiently remove PFCs under solar light irradiation.

Liu, X, Zhou, A, Shen, S-L, Li, J & Sheng, D 2020, 'A micro-mechanical model for unsaturated soils based on DEM', COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, vol. 368.
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Liu, Y, Chen, L, Zhu, C, Ban, Y-L & Guo, YJ 2020, 'Efficient and Accurate Frequency-Invariant Beam Pattern Synthesis Utilizing Iterative Spatiotemporal Fourier Transform', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 8, pp. 6069-6079.
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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, Li, M, Haupt, RL & Guo, YJ 2020, 'Synthesizing Shaped Power Patterns for Linear and Planar Antenna Arrays Including Mutual Coupling by Refined Joint Rotation/Phase Optimization', IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, vol. 68, no. 6, pp. 4648-4657.
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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, Wang, F, Lu, H, Fang, G, Wen, S, Chen, C, Shan, X, Xu, X, Zhang, L, Stenzel, M & Jin, D 2020, 'Super-Resolution Mapping of Single Nanoparticles inside Tumor Spheroids', SMALL, vol. 16, no. 6.
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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, vol. 68, no. 7, pp. 5696-5701.
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Liu, Y, Yang, Y, Wu, P, Ma, X, Li, M, Xu, KD & Guo, YJ 2020, 'Synthesis of Multibeam Sparse Circular-Arc Antenna Arrays Employing Refined Extended Alternating Convex Optimization', IEEE Transactions on Antennas and Propagation.
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IEEE A refined extended alternating convex optimization (REACO) method is presented to synthesize multibeam sparse circular-arc antenna arrays with minimum element spacing control by considering real antenna array structure characteristics. This method consists of initial step and a few refining steps. At the initial step, an initial array with dense elements distributed on a circular-arc is considered, and its array manifold vector is described by rotating a simulated isolated element pattern (IEP) without considering element mutual coupling. The collective excitation coefficient vector (CECV) and its energy bound are introduced for each element, and consequently the common element positions for generating desired multibeam patterns can be found by minimizing the number of active CECVs under multiple constraints. This minimization problem is further formulated as performing a sequence of alternating convex optimization (ACO) in which the CECV and an auxiliary weighting vector are alternately chosen as the optimization variables, so that the mimimum element spacing constraint can be easily dealt with. Once the initial optimization step is finished, a few refining steps are performed in which the element positions and excitations are successively updated in each step by renewing the array manifold vector through rotating the simulated nearby active element patterns (AEPs) of the antenna array obtained at the previous step. In such a way, the mutual coupling can be incorporated into the multibeam sparse array synthesis. An example of synthesizing a sparse circular-arc conformal array with 23 beams covering the space from–63.25° to 63.25° is conducted to validate the effectiveness and advantage of the proposed method.

Liu, 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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Liu, Z, Zhang, L, Ni, W & Collings, IB 2020, 'Uncoordinated Pseudonym Changes for Privacy Preserving in Distributed Networks', IEEE Transactions on Mobile Computing, vol. 19, no. 6, pp. 1465-1477.
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© 2002-2012 IEEE. Pseudonyms have been adopted to preserve identity privacy of nodes in distributed networks. Frequent and unlinkable changes of pseudonyms need to be enabled by having at least $k$k nodes change together to confuse potential eavesdroppers. Existing approaches either depend on the coordination from central controllers, or involve interactive signaling between the nodes. This can potentially compromise privacy. This paper proposes a fully uncoordinated approach to change pseudonyms in distributed networks, where each node uses a pseudonym until its expiration and then changes after a random delay. We develop a new model to analyse the time-varying population of changing pseudonyms. Critical conditions are analytically established, under which individual nodes can independently change their pseudonyms while their identity privacy is preserved. The conditions are validated by illustrative examples. Corroborated by simulations, the accuracy of the analytical model improves, as the number of nodes increases. The analysis confirms that, the $k$k-anonymity can be achieved at a negligible throughput loss in the case of large networks.

Livesu, M, Pietroni, N, Puppo, E, Sheffer, A & Cignoni, P 2020, 'LoopyCuts: Practical Feature-Preserving Block Decomposition for Strongly Hex-Dominant Meshing', ACM Transactions on Graphics, vol. 39, no. 4.
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© 2020 ACM. We present a new fully automatic block-decomposition algorithm for feature-preserving, strongly hex-dominant meshing, that yields results with a drastically larger percentage of hex elements than prior art. Our method is guided by a surface field that conforms to both surface curvature and feature lines, and exploits an ordered set of cutting loops that evenly cover the input surface, defining an arrangement of loops suitable for hex-element generation. We decompose the solid into coarse blocks by iteratively cutting it with surfaces bounded by these loops. The vast majority of the obtained blocks can be turned into hexahedral cells via simple midpoint subdivision. Our method produces pure hexahedral meshes in approximately 80% of the cases, and hex-dominant meshes with less than 2% non-hexahedral cells in the remaining cases. We demonstrate the robustness of our method on 70+ models, including CAD objects with features of various complexity, organic and synthetic shapes, and provide extensive comparisons to prior art, demonstrating its superiority.

Liyanaarachchi, S, Jegatheesan, V, Shu, L, Shon, HK, Muthukumaran, S & Li, CQ 2020, 'Evaluating the feasibility of forward osmosis in diluting ro concentrate using pretreatment backwash water', Membranes, vol. 10, no. 3.
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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Forward osmosis (FO) is an excellent membrane process to dilute seawater (SW) reverse osmosis (RO) concentrate for either to increase the water recovery or for safe disposal. However, the low fluxes through FO membranes as well the biofouling/scaling of FO membranes are bottlenecks of this process requiring larger membrane area and membranes with anti‐fouling properties. This study evaluates the performance of hollow fibre and flat sheet membranes with respect to flux and biofouling. Ferric hydroxide sludge was used as impaired water mimicking the backwash water of a filter that is generally employed as pretreatment in a SWRO plant and RO concentrate was used as draw solution for the studies. Synthetic salts are also used as draw solutions to compare the flux produced. The study found that cellulose triacetate (CTA) flat sheet FO membrane produced higher flux (3–6 L m−2 h−1) compared to that produced by polyamide (PA) hollow fibre FO membrane (less than 2.5 L m−2 h−1) under the same experimental conditions. Therefore, long‐term studies conducted on the flat sheet FO membranes showed that fouling due to ferric hydroxide sludge did not allow the water flux to increase more than 3.15 L m−2 h−1.

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.

Logeswaran, J, Shamsuddin, AH, Silitonga, AS & Mahlia, TMI 2020, 'Prospect of using rice straw for power generation: a review', Environmental Science and Pollution Research, vol. 27, no. 21, pp. 25956-25969.
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© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. With the ever-increasing energy demands, fossil fuels are gradually depleting and eventually, these nonrenewable sources of energy will be exhausted. Hence, there is an urgent need to formulate alternative fuels that are both renewable and sustainable. Biomass is one of the reliable sources of energy because it is replenishable. Rice is the staple food in many countries, particularly in Asia. The number of paddy fields has increased tremendously over the years and is expected to increase in the future in response to the growing world population. This will lead to significant amounts of agricultural wastes annually, particularly rice straw. In some countries, open burning and soil incorporation are used to manage agricultural wastes. Open burning is the preferred method because it is inexpensive. However, this method is highly undesirable because of its detrimental impact on the environment resulting from the release of carbon dioxide and methane gas. Hence, it is important to develop an energy-harvesting method from rice straw for power generation. More studies need to be carried out on the availability and characteristics of rice straw as well as logistic analysis to assess the potential of rice straw for power generation. This paper is focused on reviewing studies pertaining to the characteristics and potential of rice straw for power generation, current rice straw management practices, and logistic analysis in order to develop a suitable energy-harvesting method from rice straw in Malaysia.

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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© 2019 Elsevier Ltd The development of an efficient process to simultaneously extract oxygen and metals from lunar regolith by way of in-situ resource utilisation (ISRU) has the potential to enable sustainable activities beyond Earth. The Metalysis-FFC (Fray, Farthing, Chen) process has recently been proven for the industrial-scale production of metals and alloys, leading to the present investigation into the potential application of this process to regolith-like materials. This paper provides a proof-of-concept for the electro-deoxidation of powdered solid-state lunar regolith simulant using an oxygen-evolving SnO2 anode, and constitutes the first in-depth study of regolith reduction by this process that fully characterises and quantifies both the anodic and cathodic products. Analysis of the resulting metallic powder shows that 96% of the total oxygen was successfully extracted to give a mixed metal alloy product. Approximately a third of the total oxygen in the sample was detected in the off-gas, with the remaining oxygen being lost to corrosion of the reactor vessel. We anticipate, with appropriate adjustments to the experimental set-up and operating parameters, to be able to isolate essentially all of the oxygen from lunar regolith simulants using this process, leading to the exciting possibility of concomitant oxygen generation and metal alloy production on the lunar surface.

Long, G, Ding, F, Zhang, N, Zhang, J & Qin, A 2020, 'Regenerative active suspension system with residual energy for in-wheel motor driven electric vehicle', APPLIED ENERGY, vol. 260.
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Long, G, Xie, Y, Luo, Z, Qu, L, Zhou, JL & Li, W 2020, 'Deterioration mechanism of steam-cured concrete subjected to coupled environmental acid and drying action', Journal of Infrastructure Preservation and Resilience, vol. 1, no. 1.
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Long, Z, Meng, H, Li, T & Li, S 2020, 'Compact geometric representation of qualitative directional knowledge', KNOWLEDGE-BASED SYSTEMS, vol. 195.
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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, W & Liu, D 2020, 'A²: Extracting cyclic switchings from DOB-nets for rejecting excessive disturbances: A²: Extracting cyclic switchings from DOB-nets for rejecting excessive disturbances', Neurocomputing, vol. 400, pp. 161-172.
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© 2020 Reinforcement Learning (RL) is limited in practice by its poor explainability, which is responsible for insufficient trustiness from users, unsatisfied interpretation for human intervention, inadequate analysis for future improvement, etc. This paper seeks to partially characterize the interplay between dynamical environments and a previously-proposed Disturbance OBserver net (DOB-net). The DOB-net is trained via RL and offers optimal control for a set of Partially Observable Markovian Decision Processes (POMDPs). The transition function of each POMDP is largely determined by the environments (excessive external disturbances). This paper proposes an Attention-based Abstraction (A2) approach to extract a finite-state automaton, referred to as a Key Moore Machine Network (KMMN), to capture the switching mechanisms exhibited by the DOB-net in dealing with multiple such POMDPs. A2 first quantizes the controlled platform by learning continuous-discrete interfaces. Then it extracts the KMMN by finding the key hidden states and transitions that attract sufficient attention from the DOB-net. Within the resultant KMMN, three patterns of cyclic switchings (between key hidden states) are found, and saturated controls are shown synchronized with unknown disturbances. Interestingly, the found switchings have previously appeared in the control design for often-saturated systems. They are interpreted via an analogy to the discrete-event subsystem of hybrid control.

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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© 201