Publications

Bhatawdekar, RM, Armaghani, DJ & Azizi, A 2022, Environmental Issues of Blasting Applications of Artificial Intelligence Techniques, Springer.
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This book gives a rigorous and up-to-date study of the various AI and machine learning algorithms for resolving environmental challenges associated with blasting.

Gill, AQ 2022, Adaptive Enterprise Architecture as Information: Architecting Intelligent Enterprises.
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This comprehensive information-driven approach uses data, analytics, and intelligence (AI/ML) for architecting intelligent enterprises.The unique reference text includes practical artefacts and vivid examples based on both practice and ...

Guan, W, Song, X, Chang, X & Nie, L 2022, Graph Learning for Fashion Compatibility Modeling, Springer International Publishing.
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Hawryszkiewycz, IT 2022, Transforming Organizations in Disruptive Environments, Springer Singapore.
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Qu, Y, Gao, L, Yu, S & Xiang, Y 2022, Privacy Preservation in IoT: Machine Learning Approaches, Springer Nature Singapore.
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Wu, K, Zhang, JA & Guo, YJ 2022, Joint Communications and Sensing, Wiley.
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Wu, K, Zhang, JA & Guo, YJ 2022, Joint Communications and Sensing From Fundamentals to Advanced Techniques, John Wiley & Sons.
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JOINT COMMUNICATIONS AND SENSING Authoritative resource systematically introducing JCAS technologies and providing valuable information and knowledge to researchers and engineers Based on over six years of dedicated research on joint ...

Wu, K, Zhang, JA & Guo, YJ 2022, Joint Communications and Sensing From Fundamentals to Advanced Techniques, Wiley-IEEE Press.

Yang, K, Shi, M, Yuan, H & Ni, Z 2022, Millimeter-Wave Communication Systems: Network Analysis and Hybrid Precoding Design, Springer Singapore.
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Zakeri, H, Nejad, FM & Gandomi, AH 2022, Automation and Computational Intelligence for Road Maintenance and Management, Wiley.
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Abd Latif, Z, Anuar, N, Mohd Zaki, NA, Omar, H, Suratman, MN & Pradhan, B 2022, 'Spatiotemporal Distribution of Mangrove at Kuala Sepetang Forest Reserve, Malaysia, Using Remotely Sensed Data' in Concepts and Applications of Remote Sensing in Forestry, Springer Nature Singapore, pp. 305-316.
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Aboulkheyr Es, H, Aref, AR & Warkiani, ME 2022, 'Generation and Culture of Organotypic Breast Carcinoma Spheroids for the Study of Drug Response in a 3D Microfluidic Device' in Methods in Molecular Biology, Springer US, pp. 49-57.
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Breast cancer (BC) is a leading cause of cancer death among women worldwide. To better understand and predict therapeutic response in BC patient developing a fast, low-cost, and reliable preclinical tumor from patient's tumor specimen is needed. Here, we describe the development of a preclinical model of BC through the generation and ex vivo culture of patient-derived organotypic tumor spheroids (PDOTS) in a 3D microfluidic device. Moreover, the real-time screening of conventional chemotherapy agents on cultured PDOTS is also described.

Abualigah, L, Al-Okbi, NK, Mirjalili, S, Alshinwan, M, Hamad, HA, Khasawneh, AM, Abu-Ulbeh, W, Elaziz, MA, Jia, H & Gandomi, AH 2022, 'Moth-Flame Optimization Algorithm, Arithmetic Optimization Algorithm, Aquila Optimizer, Gray Wolf Optimizer, and Sine Cosine Algorithm' in Handbook of Moth-Flame Optimization Algorithm, CRC Press, pp. 241-263.
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Abualigah, L, Al-Okbi, NK, Mirjalili, S, Alshinwan, M, Hamad, HA, Khasawneh, AM, Abu-Ulbeh, WAU, Elaziz, MA, Jia, H & Gandomi, AH 2022, 'Moth-Flame Optimization Algorithm, Arithmetic Optimization Algorithm, Aquila Optimizer, Gray Wolf Optimizer, and Sine Cosine Algorithm: A Comparative Analysis Using Multilevel Thresholding Image Segmentation Problems' in Handbook of Moth-Flame Optimization Algorithm: Variants, Hybrids, Improvements, and Applications, pp. 241-264.
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Abualigah, L, Mirjalili, S, Elaziz, MA, Jia, H, Şahin, CB, Khalifeh, A & Gandomi, AH 2022, 'Hybrid Aquila Optimizer with Moth-Flame Optimization Algorithm for Global Optimization' in Handbook of Moth-Flame Optimization Algorithm, CRC Press, pp. 177-208.
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Abualigah, L, Mirjalili, S, Otair, M, Sumari, P, Elaziz, MA, Jia, H & Gandomi, AH 2022, 'Boosting Moth-Flame Optimization Algorithm by Arithmetic Optimization Algorithm for Data Clustering' in Handbook of Moth-Flame Optimization Algorithm, CRC Press, pp. 209-237.
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Afsari, M, Shon, HK & Tijing, LD 2022, 'Nanocomposite membranes for wastewater treatment via membrane distillation' in Nano-Enabled Technologies for Water Remediation, Elsevier, pp. 279-309.
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Akbarzadeh, M, Oberst, S, Sepehrirahnama, S, Chiang, YK, Halkon, BJ, Melnikov, A & Powell, DA 2022, 'A Numerical Study of Acoustic Radiation Forces for the Contactless Excitation of a Microcantilever' in NODYCON Conference Proceedings Series, Springer International Publishing, pp. 335-345.
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Alsolbi, I, Agarwarl, R, Narayan, B, Bharathy, G, Samarawickrama, M, Tafavogh, S & Prasad, M 2022, 'Analyzing Donors Behaviors in Nonprofit Organizations: A Design Science Research Framework' in Lecture Notes in Electrical Engineering, Springer Nature Singapore, pp. 765-775.
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Alsolbi, I, Wu, M, Zhang, Y, Tafavogh, S, Sinha, A & Prasad, M 2022, 'Data Analytics Research in Nonprofit Organisations: A Bibliometric Analysis' in Lecture Notes in Electrical Engineering, Springer Nature Singapore, pp. 751-763.
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Angerschmid, A, Theuermann, K, Holzinger, A, Chen, F & Zhou, J 2022, 'Effects of Fairness and Explanation on Trust in Ethical AI' in Lecture Notes in Computer Science, Springer International Publishing, pp. 51-67.
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Asif, MB, Zhang, Z, Vu, MT, Mohammed, JAH, Pathak, N, Nghiem, LD & Nguyen, LN 2022, 'Membrane Bioreactor for Wastewater Treatment: Current Status, Novel Configurations and Cost Analysis' in The Handbook of Environmental Chemistry, Springer International Publishing, pp. 147-167.
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Atanassov, K, Shannon, A, Sotirova, E, Vasilev, V & Sotirov, S 2022, 'Generalized Net Model for Collecting, Evaluating and Including of Facts in the Educational Content' in Advances in Intelligent Systems Research and Innovation, Springer International Publishing, pp. 341-348.
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Bakly, S, Ibrar, I, Saleem, H, Yadav, S, Al-Juboori, R, Naji, O, Altaee, A & Zaidi, SJ 2022, 'Polymer-based nano-enhanced forward osmosis membranes' in Advancement in Polymer-Based Membranes for Water Remediation, Elsevier, pp. 471-501.
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Bakly, S, Ibrar, I, Saleem, H, Yadav, S, Al-Juboori, R, Naji, O, Altaee, A & Zaidi, SJ 2022, 'Polymer-based nano-enhanced forward osmosis membranes' in Advancement in Polymer-Based Membranes for Water Remediation, Elsevier, The Netherlands, pp. 471-501.
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Forward osmosis (FO) technology has gained tremendous attention in recent years in desalination and wastewater treatment. Developing a new state-of-the-art FO membranes is vital for advancing the FO technology to achieve commercialization status in the near future. Polymeric membranes such as cellulose triacetate and thin-film composite (TFC) membranes are the only available commercial membranes. Besides being expensive compared to the reverse osmosis membranes, these membranes exhibit lower water flux, high reverse salt flux, prone to irreversible fouling, and have a limited lifetime. The emergence of nanotechnology has enabled researchers to design new membranes with superior performance compared to the commercially available membranes. One promising field is the incorporation of nanomaterials into polymeric membranes to enhance their performance. Researching in this space has resulted in the emergence of a new class of membranes discussed in this chapter. The study covered the synthesis process of flat sheet and hollow fiber membranes modified by incorporation of nanoparticles and discussed stimuli-responsive membranes such as pH-responsive, electric field-responsive, and salt-responsive membranes for water purification. The main challenges associated with the commercialization and future research perspectives are also discussed to identify future research aspects.

Bonnis, B & McGregor, C 2022, 'Paramilitary and Military Tactical Operations as an Extreme Environment' in Engineering and Medicine in Extreme Environments, Springer Nature, Switzerland, pp. 167-184.
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Tactical Operations is by nature an exposure to the most extreme violent grotesque of societies’ situational possibilities in the widest range of environmental conditions. Although maybe not outer space or the depth of the oceans the hazards are ever real and life threatening. The threat of harm comes from a variety of physical and psychological factors while the repercussion is, at a minimum, life altering. So much so that paramilitary and military personnel spend their careers in constant training. This field has recorded an ever increasing rate of stress, post-traumatic stress disorder (PTSD), and suicides. In this chapter we present a broad introduction to paramilitary and military tactical personnel as an extreme environment. Later in the book a further chapter details opportunities and challenges for engineering, computing and information technology solutions for use with paramilitary and military tactical personnel.

Cetindamar, D & Pugalia, S 2022, 'Engaging the Technology Management for Gender Equality' in The Routledge Companion to Technology Management, Routledge, pp. 512-528.
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Chen, Z, Ngo, HH, Wang, D, Deng, L & Guo, W 2022, 'Sustainability assessment of algae-based biomaterials' in Algae-Based Biomaterials for Sustainable Development, Elsevier, pp. 237-250.
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Chen, Z, Wei, W & Ni, B-J 2022, 'Algae-based alginate biomaterial: Production and applications' in Algae-Based Biomaterials for Sustainable Development, Elsevier, pp. 37-66.
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Cheng, D, Ngo, HH, Guo, W, Pandey, A & Varjani, S 2022, 'Sustainable production and applications of biochar in circular bioeconomy' in Biomass, Biofuels, Biochemicals, Elsevier, pp. 337-361.
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Chiang, F, Agbinya, JI & Braun, R 2022, 'Risk and Vulnerability Assessment of Secure Autonomic Communication Networks' in Agbingya, JI, Sevimli, O, Reisenfeld, S, Selvadurai, K, Lal, S, Al-Jumaily, A & Li, Y (eds), Advances in Broadband Communication and Networks, River Publishers, Denmark, pp. 345-385.
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Risk and Vulnerability Analysis (RVA) plays an important role in selfprotecting and self-healing of Autonomic Communication Networks (ACNs). RVA aims at identifying the weaknesses of the networks that may be exploited to compromise the normal functions, such as service deployment, file system access permissions, applications activations and so on. ACNs are recently proposed as business-objective driven high-level self-managed telecommunication networks with the adaptation capability to cope with increasing dynamics as well as widely distributed network demands. Adaptation capability termed as adaptability becomes the premise of realizing thorough autonomy. However, statistical observations reveal that adaptability vs. autonomy and adaptability vs. vulnerability have strong similarity with respect to quantitative results of trends, while autonomy vs. vulnerability are a pair of contradicting factors in practice. In this paper, we make a delicate information-theoretic analysis on the relationship between adaptability, autonomy, and vulnerability, and propose a solution to breaking down these coherent links by maximizing systemsâ adaptability and reducing systemsâ vulnerability simultaneously.

Cibis, T & McGregorAM, C 2022, 'The Physiological and Psychological Environment in Humans' in Engineering and Medicine in Extreme Environments, Springer International Publishing, pp. 3-14.
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Deng, L, Ngo, HH & Guo, W 2022, 'Algae-based agarose biomaterials: Production and applications' in Algae-Based Biomaterials for Sustainable Development, Elsevier, pp. 81-104.
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Deuse, J, Hoffmann, F, Sick, N, Bennett, N, Lammers, T & Hernandez Moreno, V 2022, 'Digitization of the work environment for sustainable production – How could algae based sink technologies enable a neutral Product Carbon Footprint?' in Digitization of the work environment for sustainable production, GITO Verlag, pp. 193-205.
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The alarming progress of climate change requires immediate and rigorous action from our society. This also affects the manufacturing industry to a large extent. Many companies have recognised the situation and set themselves ambitious goals with regard to achieving CO2 neutrality. An important step is the detailed analysis of CO2 emissions across the entire supply chain, differentiated by Scopes 1, 2 and 3. Initiated by Siemens, industry and university partners worldwide are currently in the process of founding a non-profit, non-government organisation, called Estainium. Remarkable about the goals of the organisation is that not only CO2 sources, but in particular certified CO2 sinks are to be integrated into the emerging platform. Terrestrial and aquatic sinks without the desired CO2 neutrality could not be achieved must be connected in a clearly traceable and fraud-proof manner. The paper describes the need to create transparency with regard to a product-related carbon footprint and the development of a closed sink system for the accurate carbon capture of emissions generated by value-added processes.

Dinh, TH, Trung, NL & Lin, C-T 2022, 'Computer vision technologies for social distancing' in Enabling Technologies for Social Distancing: Fundamentals, concepts and solutions, Institution of Engineering and Technology, pp. 113-142.
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Fatimah, I, Fadillah, G, Muraza, O & Mahlia, TMI 2022, 'Diacids from Oil Producing Plant', Wiley, pp. 515-542.
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Fonseka, C, Ryu, S, Naidu, G, Kandasamy, J, Ratnaweera, H & Vigneswaran, S 2022, 'Metal-organic frameworks for recovery of valuable elements' in Nano-Enabled Technologies for Water Remediation, Elsevier, pp. 377-392.
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Gao, H, Kim, JY, Hussain, W, Iqbal, M & Duan, Y 2022, 'Preface', pp. v-vii.

Guo, J, Yuan, J & Zhang, J 2022, 'Wireless device-to-device caching networks with distributed MIMO and hierarchical cooperations' in Chen, W & Poor, HV (eds), Edge Caching for Mobile Networks, IET, UK, pp. 341-369.
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This chapter first categorizes and summarizes wireless caching on its capability of raising the wireless network throughput in centralized and decentralized network topology. More precisely, what is the throughput scaling law of the wireless caching networks, i.e., how does the network throughput scale with the caching size, the library size, and the number of nodes in different wireless networks? Then, a novel caching scheme for device-to-device (D2D) networks is presented and the throughput scaling law is derived, which shows that the average aggregate throughput of the network scales almost linearly with the number of nodes. Both analytical and numerical results shows that the proposed scheme outperforms existing ones when the local cache size is limited.

Guo, J, Yuan, J & Zhang, JA 2022, 'Wireless device-to- device caching networks with distributed MIMO and hierarchical cooperations' in Edge Caching for Mobile Networks, pp. 339-370.
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This chapter first categorizes and summarizes wireless caching on its capability of raising the wireless network throughput in centralized and decentralized network topology. More precisely, what is the throughput scaling law of the wireless caching networks, i.e., how does the network throughput scale with the caching size, the library size, and the number of nodes in different wireless networks? Then, a novel caching scheme for device-to- device (D2D) networks is presented and the throughput scaling law is derived, which shows that the average aggregate throughput of the network scales almost linearly with the number of nodes. Both analytical and numerical results show that the proposed scheme outperforms existing ones when the local cache size is limited.

Hassani, S, Mousavi, M & Sharif-Khodaei, Z 2022, 'Smart bridge monitoring' in The Rise of Smart Cities, Elsevier, pp. 343-372.
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Hussain, W & Merigó, JM 2022, 'Centralised Quality of Experience and Service Framework Using PROMETHEE-II for Cloud Provider Selection' in EAI/Springer Innovations in Communication and Computing, Springer International Publishing, pp. 79-94.
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Hussain, W, Merigó, JM, Rabhi, F & Gao, H 2022, 'Aggregating Fuzzy Sentiments with Customized QoS Parameters for Cloud Provider Selection Using Fuzzy Best Worst and Fuzzy TOPSIS' in Soft Computing and Fuzzy Methodologies in Innovation Management and Sustainability, Springer International Publishing, pp. 81-92.
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Irga, P & Torpy, F 2022, 'Understanding the impacts of air pollution on human experience: Two case studies' in Grau, U & Fernandez-Abascal, G (eds), Folk Costumes, Indo-Pacific Air, Art Paper Editions, Ghent, Begium, pp. 128-135.
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It has long been known that substances in the air can have adverse effects on human health. Today, with the impacts of climate change and human activity changing the world in untold ways, air pollution represents a considerable and global threat to our health. Between 2005 and 2010, the death rate associated with exposure to outdoor air pollution increased globally by 4 per cent, by 5 per cent in China and by a staggering 12 per cent in India (Kumar et al., 2019). The Organisation for Economic Co-operation and Development (OECD) has stated that outdoor air pollution exposure is predicted to become the leading environmental cause of premature death by 2050 (WHO Regional Office for Europe, 2015). In 2012 alone, it was estimated that approximately 7 million deaths were related to outdoor air pollution (World Health Organization, 2016). Pollution exposure also has a negative impact on economies, with a reported ~USD$ 1.7 trillion spent on health-related costs in 2010 (Kitamori et al., 2012). Whilst a proportion of air pollution results from natural processes such as bushfire smoke, pollen release and surface dust, in cities large quantities of pollutants are produced from fossil fuel emissions, which comprise a mixture of solid particulate matter (PM) and gases, including oxides of sulphur (SOx), oxides of nitrogen (NOx), carbon monoxide (CO), carbon dioxide (CO2), and ozone (Bai et al., 2018). By examining industrial pollution in China and the pollution resulting from the recent Australian bushfires, we can see how our changing planet is influencing both manmade and biomass generated sources of air pollution. But first, it is important to have an understanding of the pollutants and their risk profiles.

Jena, SR, Yadav, S, Yadav, A, Bhavya, MB, Altaee, A, Saxena, M & Samal, AK 2022, 'Advanced Functional Materials for the Detection of Perfluorinated Compounds in Water' in Polymer-Based Advanced Functional Materials for Energy and Environmental Applications, Springer Singapore, pp. 257-269.
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Kacprzyk, J, Kondratenko, YP, Merigó, JM, Hormazabal, JH, Sirbiladze, G, Bozhenyuk, A, Szmidt, E, Zadrożny, S & Owsiński, JW 2022, 'A Fuzzy Multistage Control Model for Stable Sustainable Agricultural Regional Development' in Complex Systems: Spanning Control and Computational Cybernetics: Applications, Springer International Publishing, pp. 299-329.
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Khanna, S, Cao, J, Bai, Q & Xu, G 2022, 'Preface', pp. v-vi.

Khanna, S, Cao, J, Bai, Q & Xu, G 2022, 'Preface', pp. v-vi.

Khanna, S, Cao, J, Bai, Q & Xu, G 2022, 'Preface', pp. v-vi.

Kluwak, K, Klempous, R, Ito, A, Górski, T, Nikodem, J, Wojciechowski, K, Rozenblit, J, Borowik, G, Chaczko, Z, Bożejko, W & Kulbacki, M 2022, 'Reference Datasets for Analysis of Traditional Japanese and German Martial Arts' in Computer Aided Systems Theory – EUROCAST 2022, Springer Nature Switzerland, pp. 504-511.
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Kumar, A, Dutta, S & Gandomi, AH 2022, 'Surrogate model-driven bio-inspired optimization algorithms for large-scale and high-dimensional problems' in Biomimicry for Aerospace, Elsevier, pp. 353-382.
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Kutay, C, Brown, P, Cheng, E & Lindeck, J 2022, 'Aboriginal Design Process' in Design Praxiology and Phenomenology, Springer Nature Singapore, pp. 147-170.
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Kutay, C, Goddard, G, Cheng, E, Bertei, S, Daniel, S, Lindeck, J & Boye, T 2022, 'First Nations Project Contexts in First-Year Engineering: Extending the EWB Challenge in Two Universities' in Kutay, C, Leigh, E & Kaya Prpic, J (eds), Indigenous Engineering for an Enduring Culture, Cambridge Scholars Publishing, UK, pp. 129-151.

Lalbakhsh, A, Simorangkir, RBVB, Bayat-Makou, N, Kishk, AA & Esselle, KP 2022, 'Advancements and artificial intelligence approaches in antennas for environmental sensing' in Artificial Intelligence and Data Science in Environmental Sensing, Elsevier, pp. 19-38.
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Lee, P, Crosthwaite, C, Reidsema, C, Burnett, I, Foley, B, Hargreaves, D, King, R, Lamborn, J, Symes, M & Wilson, J 2022, 'Preparing Engineers for 2035: Transforming Australia’s Engineering Education for Emerging Roles and Expectations' in Applied Degree Education and the Future of Learning, Springer Nature Singapore, pp. 29-52.
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Li, B, Guo, T, Wang, Y & Chen, F 2022, 'The Future of Transportation: How to Improve Railway Operation Performance via Advanced AI Techniques' in Humanity Driven AI, Springer International Publishing, pp. 85-110.
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LI, L, Kang, K & Sohaib, O 2022, 'An Analysis of Cultural Content on Short Video Platforms: Cultural Protection Perspective', Communications in Computer and Information Science.

Li, Z, Liang, B & Wang, Y 2022, 'Machine Learning for Efficient Water Infrastructure Management' in Humanity Driven AI, Springer International Publishing, pp. 37-61.
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Liberal, I & Ziolkowski, RW 2022, 'Quantum Antenna Arrays', Wiley, pp. 419-446.
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Liu Chung Ming, C, Ben-Sefer, E & Gentile, C 2022, 'Stem Cell-Based 3D Bioprinting for Cardiovascular Tissue Regeneration' in Advanced Technologies in Cardiovascular Bioengineering, Springer International Publishing, pp. 281-312.
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Long, G, Fang, F & Yu, H 2022, 'Preface', pp. v-vi.

Long, G, Fang, F & Yu, H 2022, 'Preface', pp. v-vi.

Long, G, Shen, T, Tan, Y, Gerrard, L, Clarke, A & Jiang, J 2022, 'Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health' in Humanity Driven AI, Springer International Publishing, pp. 113-133.
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Long, G, Yu, X & Wang, S 2022, 'Preface', pp. v-vi.

Luo, T, Dai, X & Ni, B 2022, 'Interactions between Microplastics and Contaminants in Urban Waters', Wiley, pp. 373-406.
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Ma, B, Zhao, Y, Wang, X, Liu, Z, Lin, X, Wang, Z, Ni, W & Liu, RP 2022, 'Vehicle Trajectory Obfuscation and Detection' in Cybersecurity for Smart Cities - Practices and Challenges.

McCauley, JI, Ortega, JS, Gentile, C & Ralph, PJ 2022, 'Microalgal applications in biomedicine and healthcare' in Algae-Based Biomaterials for Sustainable Development, Elsevier, pp. 133-156.
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McGregor AM, C & Bonnis, B 2022, 'Engineering and Information Technology for Paramilitary and Military Tactical Personnel' in Engineering and Medicine in Extreme Environments, Springer International Publishing, pp. 185-208.
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McGregorAM, C & Cibis, T 2022, 'Frameworks and Platforms for Extreme Environments Adaptation and Resilience Monitoring' in Engineering and Medicine in Extreme Environments, Springer International Publishing, pp. 31-53.
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Mirakhorli, F, Mohseni, SS, Bazaz, SR, Mehrizi, AA, Ralph, PJ & Warkiani, ME 2022, 'Microfluidic Platforms for Cell Sorting', Wiley, pp. 653-695.
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Mousavi, M & Gandomi, AH 2022, 'A Robust PCA-Based Framework for Long-Term Condition Monitoring of Civil Infrastructures' in Data Science in Engineering, Volume 9, Springer International Publishing, pp. 79-85.
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Ngo, HH, Nguyen, TKL, Guo, W, Zhang, J, Liang, S & Ni, B 2022, 'Life-cycle assessment on sequestration of greenhouse gases for the production of biofuels and biomaterials' in Biomass, Biofuels, Biochemicals, Elsevier, pp. 179-202.
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Ngo, HH, Nguyen, TT, Guo, W, Nguyen, DD, Pandey, A, Bui, XT, Varjani, S, Dan Nguyen, P & Nguyen, TTN 2022, 'Circular bioeconomy for resource recovery from wastewaters using algae-based technologies' in Algae-Based Biomaterials for Sustainable Development, Elsevier, pp. 217-236.
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Ngo, HH, Song, Z, Zhang, X, Guo, W, Sun, F & Bui, X-T 2022, 'Moving bed biofilm reactor for wastewater treatment' in Current Developments in Biotechnology and Bioengineering, Elsevier, pp. 119-153.
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Ngo, HH, Vo, HNP, Guo, W, Lee, D-J & Zhang, S 2022, 'Carbon dioxide fixation and phycoremediation by algae-based technologies for biofuels and biomaterials' in Biomass, Biofuels, Biochemicals, Elsevier, pp. 253-277.
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Nguyen, CT, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2022, 'Social distancing and related technologies: fundamental background' in Enabling Technologies for Social Distancing: Fundamentals, concepts and solutions, Institution of Engineering and Technology, pp. 1-21.
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Nguyen, LN, Vu, MT, Vu, HP, Zdarta, J, Mohammed, JAH, Pathak, N, Ralph, PJ & Nghiem, LD 2022, 'Seaweed carrageenans: Productions and applications' in Algae-Based Biomaterials for Sustainable Development, Elsevier, pp. 67-80.
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Nguyen, NT, Xuan Vu, T, Hoang, DT, Nguyen, DN & Dutkiewicz, E 2022, 'Open issues and future research directions' in Enabling Technologies for Social Distancing: Fundamentals, concepts and solutions, Institution of Engineering and Technology, pp. 261-278.
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Nimbalkar, S & Meena, NK 2022, 'Static and Seismic Assessment of Soil Arching in Piled Embankments' in Springer Tracts in Civil Engineering, Springer Singapore, pp. 263-281.
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Ou, Y 2022, 'AI for Real-Time Bus Travel Time Prediction in Traffic Congestion Management' in Humanity Driven AI, Springer International Publishing, pp. 63-84.
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In order to address this research problem, we propose an approach to predict bus travel time using realtime bus GPS ... can support traffic congestion management by providing the information of real-time bus arrival time and delay.

Ou, Y, Mihăiţă, A-S & Chen, F 2022, 'Big data processing and analysis on the impact of COVID-19 on public transport delay' in Data Science for COVID-19, Elsevier, pp. 257-278.
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Pradhan, B, Shit, PK, Bhunia, GS, Adhikary, PP & Pourghasemi, HR 2022, 'Introduction to Spatial Modeling of Flood Risk and Hazard: Societal Implication' in Spatial Modelling of Flood Risk and Flood Hazards, Springer International Publishing, pp. 1-13.
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Priyadharsini, C, Jagadeesh Kannan, R & Hussain, FK 2022, 'A Survey on Recent Computer‐Aided Diagnosis for Detecting Diabetic Retinopathy', Wiley, pp. 27-58.
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Prysyazhnyuk, A & McGregor AM, C 2022, 'Space as an Extreme Environment—Galactic Adventures: Exploring the Limits of Human Mind and Body, One Planet at a Time' in Engineering and Medicine in Extreme Environments, Springer International Publishing, pp. 121-141.
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Prysyazhnyuk, A & McGregorAM, C 2022, 'Space as an Extreme Environment: Technical Considerations' in Engineering and Medicine in Extreme Environments, Springer International Publishing, pp. 143-164.
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Qin, H & Stewart, MG 2022, 'Adaptation of Housing to Climate Change and Extreme Windstorms' in Springer Tracts in Civil Engineering, Springer International Publishing, pp. 119-141.
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This chapter presents a probabilistic approach to assess the long-term economic risk and cost-effectiveness of relevant adaptation measures for Australian housing exposed to non-cyclonic extreme windstorms under a changing climate. The proposed method provides decision-support for the long-term adaptation of residential communities to extreme windstorms and climate change. The cost–benefit analysis suggests that, for all considered climate scenarios, strengthening roof cladding connections is not cost-effective, while improving window resistance or installing window shutters is more cost-effective.

Qu, Y, Gao, L, Yu, S & Xiang, Y 2022, 'Current Methods of Privacy Protection in IoTs' in Privacy Preservation in IoT: Machine Learning Approaches, Springer Nature Singapore, pp. 7-18.
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In this chapter, we present the mainstream research of current privacy preservation in IoTs built upon the literature review we have done in recent years [1–3].

Qu, Y, Gao, L, Yu, S & Xiang, Y 2022, 'Future Research Directions' in Privacy Preservation in IoT: Machine Learning Approaches, Springer Nature Singapore, pp. 111-115.
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From Chaps. 2–5, we have shown the existing research status of machine learning driven privacy preservation in IoTs, especially focus on three leading directions using GAN, federated learning, and reinforcement learning. Several advanced technologies and theories are also integrated, for example, differential privacy, game theory, blockchain, etc. Nevertheless, there are still plenty of significant and prospective issues worthy investigating. The popularization of blockchain, digital twin, and artificial intelligence offers a mass of opportunities for researches on machine learning driven privacy preservation in IoTs, but meanwhile they raise new challenges such as unsatisfying data utility and limited communication and computing resources. In addition, there are various other research topics that desiderata consideration in machine learning driven privacy preservation in IoTs. To pave the way for readers and forthcoming researchers, we outline several potentially promising research directions that may be worthy of future efforts.

Qu, Y, Gao, L, Yu, S & Xiang, Y 2022, 'Introduction' in Privacy Preservation in IoT: Machine Learning Approaches, Springer Nature Singapore, pp. 1-5.
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IoT is experiencing fast proliferation with advancement of computation and communication technologies like edge computing and 5G. Along with this, a mass volume of data is collected and transmitted over IoT, and thereby raise privacy concerns. Sensitive information in the collected data are usually improperly treated without privacy-preserving techniques. Furthermore, data from different sources may be linked to reveal more sensitive information. All these issues put privacy protection under great threats.

Raad, IS, Huang, X & Raad, R 2022, 'A Tutorial on the New Higher Order Rotation Spreading Matrix for BSOFDM' in Advances in Broadband Communication and Networks, River Publishers, pp. 405-425.
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Rosowsky, DV & Stewart, MG 2022, 'Conclusions for Engineers and Policy Makers' in Springer Tracts in Civil Engineering, Springer International Publishing, pp. 449-451.
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This closing chapter provides a discussion of the political, economic and social imperatives that affect the policy making decision process for extreme events. It shows that the tools are available to inform policy makers to make calm and considered decisions based on risk assessments that considers the preferences of all stakeholders including the public. However, challenges remain if risk-based decisions are to become truly mainstream in the education of engineers and the engineering practice more broadly.

Salgotra, R, Rahimi, I & Gandomi, AH 2022, 'Artificial Intelligence for Fighting the COVID-19 Pandemic' in Humanity Driven AI, Springer International Publishing, pp. 165-177.
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Sangeetha, V, Krishankumar, R, Ravichandran, KS & Gandomi, AH 2022, 'Assessment of an ant-inspired algorithm for path planning' in Biomimicry for Materials, Design and Habitats, Elsevier, pp. 247-265.
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Saputra, YM, Rosyid, NR, Hoang, DT & Nguyen, DN 2022, 'Advanced sensing and automation technologies' in Enabling Technologies for Social Distancing: Fundamentals, concepts and solutions, Institution of Engineering and Technology, pp. 169-194.
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Saraswat, M, Roy, S, Chowdhury, C & Gandomi, AH 2022, 'Preface', pp. v-vi.

Saraswat, M, Roy, S, Chowdhury, C & Gandomi, AH 2022, 'Preface', pp. v-vi.

Seo, DH, Barclay, M, Park, MJ, Wang, C, Ostrikov, KK & Shon, HK 2022, 'Graphitic Carbon Nanomaterial-Based Membranes for Water Desalination' in The World Scientific Reference of Water Science, World Scientific, pp. 63-88.
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Sharma, N, Saqib, M, Scully-Power, P & Blumenstein, M 2022, 'SharkSpotter: Shark Detection with Drones for Human Safety and Environmental Protection' in Humanity Driven AI, Springer International Publishing, pp. 223-237.
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Sick, N, Okara, S, Bröring, S & Wambsganss, A 2022, 'Open and Closed Innovation Strategies in Converging Environments' in The Routledge Companion to Technology Management, Routledge, pp. 415-429.
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Singh, P, Manjunatha, AS, Baig, A, Dhopeshwar, P, Huo, H, Bharathy, G & Prasad, M 2022, 'Application of Artificial Intelligence in Healthcare by Industries in Australia: Opportunities and Challenges' in Proceedings of the International Conference on Intelligent Vision and Computing (ICIVC 2021), Springer International Publishing, pp. 568-580.
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Spérandio, M, Lang, L, Sabba, F, Nerenberg, R, Vanrolleghem, P, Domingo-Félez, C, Smets, BF, Duan, H, Ni, B-J & Yuan, Z 2022, 'Modelling N2O production and emissions' in Quantification and Modelling of Fugitive Greenhouse Gas Emissions from Urban Water Systems, IWA Publishing, pp. 167-196.
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Stewart, MG & Mueller, J 2022, 'Aviation Resilience to Terrorist Hijackings' in Springer Tracts in Civil Engineering, Springer International Publishing, pp. 69-91.
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Approximately $50 billion is spent annually world-wide in the quest to deter or disrupt terrorist attacks to aviation, significant expenditures that have rarely been subject to systematic cost–benefit or risk analysis. This chapter applies that approach, assessing the risks, costs, and benefits of security measures designed to disrupt terrorist hijackings of airliners assuming terrorists arrive at the airport undeterred and undetected. Under those conditions, existing security measures reduce the risk of a terrorist success by over 88%. Another security measure could be added to the existing array: secondary flight deck barriers, lightweight devices that are easy to deploy and stow, installed between the passenger cabin and the cockpit door to block access to the flight deck whenever the cockpit door is opened in flight. These barriers are highly cost-effective and raise total risk reduction to over 96%. The benefit-to-cost ratio of the measure is high at 5.1, and it remains cost effective even if risk reduction is halved and costs are doubled. On the other hand the expensive Federal Air Marshal Service fails a cost–benefit analysis, whereas the Federal Flight Deck Officer program proves to be cost-effective.

Stewart, MG & Rosowsky, DV 2022, 'Extreme Events for Infrastructure: Uncertainty and Risk' in Springer Tracts in Civil Engineering, Springer International Publishing, pp. 3-27.
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Buildings, bridges, roads, and other infrastructure essential to our economic and social well-being are at an increasing risk from hurricanes, storms, floods, earthquakes, tsunamis, heat waves, fires, terrorism, climate change and other extreme events. The timing, severity and combination of these extremes are highly uncertain, and are characterised as low probability-high consequence events. The chapter starts by introducing and reviewing basic concepts about risk and cost–benefit analysis of protective measures aim to reduce the vulnerability of infrastructure, and hence reduce the future impacts of extreme events to reveal protective measures that are cost-effective, and those that are not. This literature review justifies the introduction of risk-based decision support that integrates hazard, engineering, and fragility models, as well as economical decision tools to perform a comprehensive assessment of the cost-effectiveness of protective measures. This risk-based decision support will be illustrated with various study cases of engineering for extremes in the following chapters of this book.

Tavakoli, J & Tang, Y 2022, 'Vortex Fluidics-mediated Fluorescent Hydrogels with Aggregation-induced Emission Characteristics' in Handbook of Aggregation-Induced Emission: Emerging applications, pp. 221-242.

Tawk, M, Indraratna, B, Rujikiatkamjorn, C & Heitor, A 2022, 'A Mixture of Waste Materials as a Construction Fill in Transportation Infrastructure' in Lecture Notes in Civil Engineering, Springer International Publishing, pp. 565-577.
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The reuse of waste materials in engineering projects is a sustainable concept that is being increasingly adopted in civil projects including the transportation infrastructure sector. A mixture of coal wash (CW), a waste produced by the coal mining industry, and rubber crumbs (RC), produced by shredding waste rubber tyres, is a possible alternative to natural quarried rock aggregates that are traditionally used in sublayers of transportation corridors. These materials have different properties than traditional aggregates and their geotechnical behaviour must be investigated before they can be confidently used in transportation infrastructure projects. This study particularly addresses the compaction characteristics of four CWRC mixtures (i.e. 0, 5, 10 and 15% rubber content) with a focus on the energy absorbing nature of rubber. The compaction energy must be increased to compensate for the energy absorbed by rubber and produce a compact packing of particles whilst keeping the breakage levels to a minimum. The strength and deformation properties of the mixture are also addressed through static triaxial tests under three confining pressures to simulate field conditions (i.e. 25, 50 and 75 kPa). The preliminary results showed that the mixture has sufficient strength for transportation sublayers that are not the main load bearing layers such as the subbase layer in roads and the capping/subballast layer in railways. Despite the compressibility of rubber which induces higher settlements, for the loads applied in practice at the level of a subbase or a capping layer, the expected settlements are within the allowable limits.

Thalakotuna, D & Esselle, KP 2022, '5G Extender Antenna Systems to Enhance Indoor Millimetre-Wave Reception' in PoliTO Springer Series, Springer International Publishing, pp. 1-27.
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Extender. Antenna. Systems. to. Enhance. Indoor. Millimetre-Wave. Reception. Dushmantha Thalakotuna and Karu P. Esselle Abstract Use of millimeter-wave spectrum in 5G result in small cell coverage due to inherent high free-space ...

Thanh, HT, Li, J & Zhang, YX 2022, 'Numerical modeling of the flow of self-consolidating engineered cementitious composite (ECC) using smoothed particle hydrodynamics' in Advances in Engineered Cementitious Composites, Elsevier, pp. 321-336.
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Tian, H 2022, 'Electrical Network‐Related Incident Prediction Based on Weather Factors' in Advances in Data Science and Analytics Concepts and Paradigms, John Wiley & Sons.
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ADVANCES in DATA SCIENCE and ANALYTICS Presenting the concepts and advances of data science and analytics, this volume, written and edited by a global team of experts, also goes into the practical applications that can be utilized across ...

Toloo, M, Talatahari, S, Gandomi, AH & Rahimi, I 2022, 'Multiobjective combinatorial optimization problems: social, keywords, and journal maps' in Multi-Objective Combinatorial Optimization Problems and Solution Methods, Elsevier, pp. 1-9.
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Tong, C, Zhang, S & Sheng, D 2022, 'Particle Breakage Observed in Both Transitional and Non-transitional Carbonate Sands' in Lecture Notes in Civil Engineering, Springer International Publishing, pp. 935-943.
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Tran, LC, Tuyen Le, A, Huang, X, Lam Phung, S, Ritz, C & Bouzerdoum, A 2022, 'Background on positioning and localization for social distancing' in Enabling Technologies for Social Distancing: Fundamentals, concepts and solutions, Institution of Engineering and Technology, pp. 23-65.
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Tyagi, AK, Abraham, A, Hussain, FK, Kaklauskas, HA & Jagadeesh Kannan, R 2022, 'Conclusion to this book' in Machine Learning, Blockchain Technologies and Big Data Analytics for IoTs: Methods, technologies and applications, Institution of Engineering and Technology, pp. 615-623.
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Tyagi, AK, Abraham, A, Hussain, FK, Kaklauskas, HA & Kannan, RJ 2022, 'Introduction to machine learning, blockchain technologies, and Big Data analytics for IoTs: concepts, open issues, and critical challenges' in Machine Learning, Blockchain Technologies and Big Data Analytics for IoTs: Methods, technologies and applications, Institution of Engineering and Technology, pp. 1-16.
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Vali, M, Salimifard, K, Gandomi, AH & Chaussalet, T 2022, 'Chaotic-SCA Salp Swarm Algorithm Enhanced with Opposition Based Learning: Application to Decrease Carbon Footprint in Patient Flow' in Handbook of Nature-Inspired Optimization Algorithms: The State of the Art, Springer International Publishing, pp. 1-29.
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Vu, MT, Nguyen, LN, Mohammed, JAH, Zdarta, J, Mofijur, M, Pathak, N & Nghiem, LD 2022, 'Nutrient recovery from anaerobic digestate' in Anaerobic Digestate Management, IWA Publishing, pp. 131-150.
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Vu, MT, Nguyen, LN, Zdarta, J, Mohammed, JAH, Pathak, N & Nghiem, LD 2022, 'Wastewater to R3 – resource recovery, recycling, and reuse efficiency in urban wastewater treatment plants' in Clean Energy and Resource Recovery, Elsevier, pp. 3-16.
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Wei, W, Wu, L, Ngo, HH, Guo, W & Ni, B-J 2022, 'Sequestration of nitrous oxide for nutrient recovery and product formation' in Biomass, Biofuels, Biochemicals, Elsevier, pp. 155-177.
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Williams-Bell, FM & McGregor AM, C 2022, 'Firefighter Personnel and Their Activities in Extreme Environments' in Engineering and Medicine in Extreme Environments, Springer International Publishing, pp. 235-267.
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Wu, K & Guo, YJ 2022, 'Angle‐of‐Arrival Estimation in Large‐Scale Hybrid Antenna Arrays' in Antenna and Array Technologies for Future Wireless Ecosystems, Wiley, pp. 305-334.
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Large-scale hybrid antenna arrays are able to leverage array gains, transceiver sizes, and costs for massive multiple-input-multiple-output (MIMO) communications in the millimeter-wave and terahertz frequency bands. However, challenges of effectively applying such arrays arise from the estimation of the angle-of-arrival (AoA). In this chapter, we first review several important applications of large-scale antenna arrays in the fifth-generation (5G) and beyond 5G (B5G) communications systems. Several popular hybrid structures of large-scale arrays are then outlined. Next, we highlight the importance and challenges of the AoA estimation in large-scale hybrid arrays and their applications, along with an overview of existing estimation methods. Finally, some recent breakthroughs on fast and accurate AoA estimation are introduced, unveiling some interesting properties/rules for different types of hybrid arrays.

Wu, K, Zhang, JA, Huang, X & Guo, YJ 2022, 'Frequency-Hopping MIMO Radar-based Data Communications' in Signal Processing for Joint Radar Communications, Wiley - IEEE.
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The goal of this book is to further contribute to the diffusion of newly developed JRC tools into the radar and communications communities and to illustrate recent successes in applying modern signal processing theories to solving core ...

Yaghoubzadeh-Bavandpour, A, Bozorg-Haddad, O, Zolghadr-Asli, B & Gandomi, AH 2022, 'Improving Approaches for Meta-heuristic Algorithms: A Brief Overview' in Computational Intelligence for Water and Environmental Sciences, Springer Nature Singapore, pp. 35-61.
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Yang, Z, Ramaswamy, Y & Singh, G 2022, 'Applications of Carbon Nanotubes in Drug Delivery' in Advanced Porous Biomaterials for Drug Delivery Applications, CRC Press, pp. 395-440.
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Ye, Y, Ngo, HH, Guo, W, Chen, Z, Deng, L & Zhang, X 2022, 'Chemical looping mechanisms for sequestration of greenhouse gases for biofuel and biomaterials' in Biomass, Biofuels, Biochemicals, Elsevier, pp. 85-109.
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Yu, K, Xu, J, Ou, Y, Luo, L & Chen, F 2022, 'Learner Engagement Examination Via Computer Usage Behaviors' in Humanity Driven AI, Springer International Publishing, pp. 241-253.
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Zdarta, J, Kaźmierczak, K, Jankowska, K, Bachosz, K, Degórska, O, Bilal, M, Iqbal, HMN, Nguyen, LN, Nghiem, LD & Jesionowski, T 2022, 'Nanobiocatalysts for wastewater remediation and redefining of pollutants' in Nano-Bioremediation : Fundamentals and Applications, Elsevier, pp. 313-337.
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Zhang, J, Li, B, Fan, X, Wang, Y & Chen, F 2022, 'Sewer Corrosion Prediction for Sewer Network Sustainability' in Humanity Driven AI, Springer International Publishing, pp. 181-194.
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Zhou, J & Chen, F 2022, 'Towards Humanity-in-the-Loop in AI Lifecycle' in Humanity Driven AI, Springer International Publishing, pp. 3-13.
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Zhou, J, Li, Z, Xiao, C & Chen, F 2022, 'Does a Compromise on Fairness Exist in Using AI Models?' in AI 2022: Advances in Artificial Intelligence, Springer International Publishing, pp. 191-204.
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Ziolkowski, RW 2022, 'Electrically Small Antenna Advances for Current 5G and Evolving 6G and Beyond Wireless Systems', Wiley, pp. 335-391.
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Abdin, Z, Khalilpour, K & Catchpole, K 2022, 'Projecting the levelized cost of large scale hydrogen storage for stationary applications', Energy Conversion and Management, vol. 270, pp. 116241-116241.
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Abdollahi, A, Liu, Y, Pradhan, B, Huete, A, Dikshit, A & Nguyen Tran, N 2022, 'Short-time-series grassland mapping using Sentinel-2 imagery and deep learning-based architecture', The Egyptian Journal of Remote Sensing and Space Science, vol. 25, no. 3, pp. 673-685.
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Abdollahi, A, Pradhan, B & Alamri, A 2022, 'SC-RoadDeepNet: A New Shape and Connectivity-Preserving Road Extraction Deep Learning-Based Network From Remote Sensing Data', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15.
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Abdollahi, A, Pradhan, B & Alamri, AM 2022, 'An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images', Geocarto International, vol. 37, no. 12, pp. 3355-3370.
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Abdollahi, M, Ashtari, S, Abolhasan, M, Shariati, N, Lipman, J, Jamalipour, A & Ni, W 2022, 'Dynamic Routing Protocol Selection in Multi-Hop Device-to-Device Wireless Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8796-8809.
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Abdullah, NHB, Mijan, NA, Taufiq-Yap, YH, Ong, HC & Lee, HV 2022, 'Environment-friendly deoxygenation of non-edible Ceiba oil to liquid hydrocarbon biofuel: process parameters and optimization study', Environmental Science and Pollution Research, vol. 29, no. 34, pp. 51143-51152.
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Abedin, B 2022, 'Managing the tension between opposing effects of explainability of artificial intelligence: a contingency theory perspective', Internet Research, vol. 32, no. 2, pp. 425-453.
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PurposeResearch into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.Design/methodology/approachThe author systematically reviews the literature and synthesizes it using a contingency theory lens to develop a framework for managing the opposing effects of AI explainability.FindingsThe author finds five opposing effects of explainability: comprehensibility, conduct, confidentiality, completeness and confidence in AI (5Cs). The author also proposes six perspectives on managing the tensions between the 5Cs: pragmatism in explanation, contextualization of the explanation, cohabitation of human agency and AI agency, metrics and standardization, regulatory and ethical principles, and other emerging solutions (i.e. AI enveloping, blockchain and AI fuzzy systems).Research limitations/implicationsAs in other systematic literature review studies, the results are limited by the content of the selected papers.Practical implicationsThe findings show how AI owners and developers can manage tensions between profitability, prediction accuracy and system performance via visibility, accountabil...

Abedin, B, Meske, C, Junglas, I, Rabhi, F & Motahari-Nezhad, HR 2022, 'Designing and Managing Human-AI Interactions', Information Systems Frontiers, vol. 24, no. 3, pp. 691-697.
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Abharian, S, Sarfarazi, V, Marji, MF & Rasekh, H 2022, 'Experimental and numerical evaluation of the effects of interaction between multiple small holes and a single notch on the mechanical behavior of artificial gypsum specimens', Theoretical and Applied Fracture Mechanics, vol. 121, pp. 103462-103462.
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Abharian, S, Sarfarazi, V, Rasekh, H & Behzadinasab, M 2022, 'Effects of concrete/gypsum bedding layers and their inclination angles on the tensile failure mechanism: Experimental and numerical studies', Case Studies in Construction Materials, vol. 17, pp. e01272-e01272.
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Aboutorab, H, Hussain, OK, Saberi, M & Hussain, FK 2022, 'A reinforcement learning-based framework for disruption risk identification in supply chains', Future Generation Computer Systems, vol. 126, pp. 110-122.
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Abraham, MT, Satyam, N, Pradhan, B & Segoni, S 2022, 'Proposing an easy-to-use tool for estimating landslide dimensions using a data-driven approach', All Earth, vol. 34, no. 1, pp. 243-258.
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Abraham, MT, Satyam, N, Pradhan, B & Tian, H 2022, 'Debris flow simulation 2D (DFS 2D): Numerical modelling of debris flows and calibration of friction parameters', Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 6, pp. 1747-1760.
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Abraham, MT, Satyam, N, Pradhan, B, Segoni, S & Alamri, A 2022, 'Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring', Geosciences Journal, vol. 26, no. 2, pp. 289-301.
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Abualigah, L, Elaziz, MA, Khodadadi, N, Forestiero, A, Jia, H & Gandomi, AH 2022, 'Aquila Optimizer Based PSO Swarm Intelligence for IoT Task Scheduling Application in Cloud Computing', pp. 481-497.
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Abualigah, L, Elaziz, MA, Sumari, P, Geem, ZW & Gandomi, AH 2022, 'Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer', Expert Systems with Applications, vol. 191, pp. 116158-116158.
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Abualigah, L, Elaziz, MA, Sumari, P, Khasawneh, AM, Alshinwan, M, Mirjalili, S, Shehab, M, Abuaddous, HY & Gandomi, AH 2022, 'Black hole algorithm: A comprehensive survey', Applied Intelligence, vol. 52, no. 10, pp. 11892-11915.
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Abughalwa, M, Tuan, HD, Nguyen, DN, Poor, HV & Hanzo, L 2022, 'Finite-Blocklength RIS-Aided Transmit Beamforming', IEEE Transactions on Vehicular Technology, vol. 71, no. 11, pp. 12374-12379.
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Adhikari, M, Munusamy, A, Hazra, A, Menon, VG, Anavangot, V & Puthal, D 2022, 'Security in Edge-Centric Intelligent Internet of Vehicles: Issues and Remedies', IEEE Consumer Electronics Magazine, vol. 11, no. 6, pp. 24-31.
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Adhikari, S, Thapa, S, Naseem, U, Singh, P, Huo, H, Bharathy, G & Prasad, M 2022, 'Exploiting linguistic information from Nepali transcripts for early detection of Alzheimer's disease using natural language processing and machine learning techniques', International Journal of Human-Computer Studies, vol. 160, pp. 102761-102761.
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Adibi, T, Razavi, SE, Ahmed, SF, Amrikachi, A & Saha, SC 2022, 'Characteristic-Based Fluid Flow Modeling between Two Eccentric Cylinders in Laminar and Turbulent Regimes', Geofluids, vol. 2022, pp. 1-9.
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Determining the flow between eccentric cylinders is crucial in a wide range of industries. The governing equations for the flow between eccentric cylinders cannot be solved analytically. Therefore, three-dimensional incompressible viscous fluid flow between eccentric and concentric cylinders has numerically been simulated in this paper to investigate them using a characteristic-based approach. The first-order characteristic-based scheme is used to calculate convective terms, whereas the second-order averaging technique is used to calculate viscous fluxes. The Taylor number, eccentricity distance, Reynolds number, and radius ratio are considered the controlling parameters of fluid flow between the cylinders. The influence of flow between cylinders on flow patterns is presented in terms of velocity, pressure, and flow contours. It is found that at a constant Taylor number, the asymmetric centrifugal forces produce the Taylor vortices on the right of the internal rotating cylinder as the eccentric distance increases. When the eccentric distance increases, the magnitude of shear stress and its fluctuation on the cylinder wall, as well as the pressure on the cylinder wall, rise. The numerical results obtained were validated by comparing them to previously published experimental results, which showed a high level of agreement.

Adibi, T, Sojoudi, A & Saha, SC 2022, 'Modeling of thermal performance of a commercial alkaline electrolyzer supplied with various electrical currents', International Journal of Thermofluids, vol. 13, pp. 100126-100126.
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Aditya, L, Mahlia, TMI, Nguyen, LN, Vu, HP & Nghiem, LD 2022, 'Microalgae-bacteria consortium for wastewater treatment and biomass production', Science of The Total Environment, vol. 838, pp. 155871-155871.
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Afroz, F, Braun, R & Chaczko, Z 2022, 'XX-MAC and EX-MAC: Two Variants of X-MAC Protocol for Low Power Wireless Sensor Networks', Ad-Hoc and Sensor Wireless Networks, vol. 51, no. 4, pp. 285-314.
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The strobed preamble approach introduced in the X-MAC protocol minimises long preamble duration, overhearing, and per-hop latency in conventional wireless sensor networks (WSNs). However, it incurs high per-packet overhead and extra delay under high traffic scenarios as it operates only in the unsynchronised state. In this paper, we model a variant of X-MAC, namely XX-MAC, which employs an adaptive dutycycling algorithm to address this issue in low data rate WSNs with short, fixed inter-packet arrival time. Furthermore, we identify the shortcoming of XX-MAC as well as propose a request-based MAC protocol, namely EX-MAC, targeting WSNs in dynamic traffic scenarios. Simulations show that at optimum slot duration, XX-MAC reduces the per-packet delay by 13.53% and 48.86% than the delay experienced by X-MAC and B-MAC, respectively. XX-MAC, on average, can deliver 92.5% of packets to the receiver, whereas X-MAC and B-MAC respectively support 91.66% and 82.91% packet delivery. XX-MAC also reduces the energy consumption per received packet by 2.61% than X-MAC, and by 65.31% than the B-MAC protocol. Experimental results also demonstrate that under variable traffic conditions, EX-MAC offers the lowest packet loss (8.55%), whilst XX-MAC and X-MAC experience 13.1% and 18.3% packet loss, respectively. EX-MAC decreases per-packet network energy consumption (3.056mJ/packet) compared with XX-MAC (3.107mJ/ packet) and X-MAC (3.424mJ/packet). Furthermore, EX-MAC minimises the mean delay per received packet by 5.758% and 10.457% (approximately) than that of XX-MAC and X-MAC, respectively.

Afroz, S, Nguyen, QD, Zhang, Y, Kim, T & Castel, A 2022, 'Evaluation of cracking potential parameters for low to high grade concrete with fly ash or slag', Construction and Building Materials, vol. 350, pp. 128891-128891.
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Afroz, S, Zhang, Y, Nguyen, QD, Kim, T & Castel, A 2022, 'Effect of limestone in General Purpose cement on autogenous shrinkage of high strength GGBFS concrete and pastes', Construction and Building Materials, vol. 327, pp. 126949-126949.
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Afsari, M, Ghorbani, AH, Asghari, M, Shon, HK & Tijing, LD 2022, 'Computational fluid dynamics simulation study of hypersaline water desalination via membrane distillation: Effect of membrane characteristics and operational parameters', Chemosphere, vol. 305, pp. 135294-135294.
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Afzal, MU, Esselle, KP & Koli, MNY 2022, 'A Beam-Steering Solution With Highly Transmitting Hybrid Metasurfaces and Circularly Polarized High-Gain Radial-Line Slot Array Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 1, pp. 365-377.
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Afzali Naniz, M, Askari, M, Zolfagharian, A, Afzali Naniz, M & Bodaghi, M 2022, '4D printing: a cutting-edge platform for biomedical applications', Biomedical Materials, vol. 17, no. 6, pp. 062001-062001.
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Abstract Nature’s materials have evolved over time to be able to respond to environmental stimuli by generating complex structures that can change their functions in response to distance, time, and direction of stimuli. A number of technical efforts are currently being made to improve printing resolution, shape fidelity, and printing speed to mimic the structural design of natural materials with three-dimensional printing. Unfortunately, this technology is limited by the fact that printed objects are static and cannot be reshaped dynamically in response to stimuli. In recent years, several smart materials have been developed that can undergo dynamic morphing in response to a stimulus, thus resolving this issue. Four-dimensional (4D) printing refers to a manufacturing process involving additive manufacturing, smart materials, and specific geometries. It has become an essential technology for biomedical engineering and has the potential to create a wide range of useful biomedical products. This paper will discuss the concept of 4D bioprinting and the recent developments in smart materials, which can be actuated by different stimuli and be exploited to develop biomimetic materials and structures, with significant implications for pharmaceutics and biomedical research, as well as prospects for the future.

Agrawal, D, Minocha, S, Namasudra, S & Gandomi, AH 2022, 'A robust drug recall supply chain management system using hyperledger blockchain ecosystem', Computers in Biology and Medicine, vol. 140, pp. 105100-105100.
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Ahmad, FB, Kalam, MA, Zhang, Z & Masjuki, HH 2022, 'Sustainable production of furan-based oxygenated fuel additives from pentose-rich biomass residues', Energy Conversion and Management: X, vol. 14, pp. 100222-100222.
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Ahmadi, H, Zakertabrizi, M, Hosseini, E, Cha-Umpong, W, Abdollahzadeh, M, Korayem, AH, Chen, V, Shon, HK, Asadnia, M & Razmjou, A 2022, 'Heterogeneous asymmetric passable cavities within graphene oxide nanochannels for highly efficient lithium sieving', Desalination, vol. 538, pp. 115888-115888.
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Ahmed, F, Afzal, MU, Hayat, T, Esselle, KP & Thalakotuna, DN 2022, 'A Near-Field Meta-Steering Antenna System With Fully Metallic Metasurfaces', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10062-10075.
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Ahmed, F, Afzal, MU, Hayat, T, Esselle, KP & Thalakotuna, DN 2022, 'Self-Sustained Rigid Fully Metallic Metasurfaces to Enhance Gain of Shortened Horn Antennas', IEEE Access, vol. 10, pp. 79644-79654.
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Ahmed, N, Hoque, MA-A, Howlader, N & Pradhan, B 2022, 'Flood risk assessment: role of mitigation capacity in spatial flood risk mapping', Geocarto International, vol. 37, no. 25, pp. 8394-8416.
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Ahmed, SF, Kumar, PS, Kabir, M, Zuhara, FT, Mehjabin, A, Tasannum, N, Hoang, AT, Kabir, Z & Mofijur, M 2022, 'Threats, challenges and sustainable conservation strategies for freshwater biodiversity', Environmental Research, vol. 214, pp. 113808-113808.
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Ahmed, SF, Kumar, PS, Rozbu, MR, Chowdhury, AT, Nuzhat, S, Rafa, N, Mahlia, TMI, Ong, HC & Mofijur, M 2022, 'Heavy metal toxicity, sources, and remediation techniques for contaminated water and soil', Environmental Technology & Innovation, vol. 25, pp. 102114-102114.
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Ahmed, SF, Mehejabin, F, Momtahin, A, Tasannum, N, Faria, NT, Mofijur, M, Hoang, AT, Vo, D-VN & Mahlia, TMI 2022, 'Strategies to improve membrane performance in wastewater treatment', Chemosphere, vol. 306, pp. 135527-135527.
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Ahmed, SF, Mofijur, M, Ahmed, B, Mehnaz, T, Mehejabin, F, Maliat, D, Hoang, AT & Shafiullah, GM 2022, 'Nanomaterials as a sustainable choice for treating wastewater', Environmental Research, vol. 214, pp. 113807-113807.
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Ahmed, SF, Mofijur, M, Chowdhury, SN, Nahrin, M, Rafa, N, Chowdhury, AT, Nuzhat, S & Ong, HC 2022, 'Pathways of lignocellulosic biomass deconstruction for biofuel and value-added products production', Fuel, vol. 318, pp. 123618-123618.
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Ahmed, SF, Mofijur, M, Islam, N, Parisa, TA, Rafa, N, Bokhari, A, Klemeš, JJ & Indra Mahlia, TM 2022, 'Insights into the development of microbial fuel cells for generating biohydrogen, bioelectricity, and treating wastewater', Energy, vol. 254, pp. 124163-124163.
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Ahmed, SF, Mofijur, M, Nahrin, M, Chowdhury, SN, Nuzhat, S, Alherek, M, Rafa, N, Ong, HC, Nghiem, LD & Mahlia, TMI 2022, 'Biohydrogen production from wastewater-based microalgae: Progresses and challenges', International Journal of Hydrogen Energy, vol. 47, no. 88, pp. 37321-37342.
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Ahmed, SF, Mofijur, M, Nuzhat, S, Rafa, N, Musharrat, A, Lam, SS & Boretti, A 2022, 'Sustainable hydrogen production: Technological advancements and economic analysis', International Journal of Hydrogen Energy, vol. 47, no. 88, pp. 37227-37255.
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Ahmed, SF, Mofijur, M, Parisa, TA, Islam, N, Kusumo, F, Inayat, A, Le, VG, Badruddin, IA, Khan, TMY & Ong, HC 2022, 'Progress and challenges of contaminate removal from wastewater using microalgae biomass', Chemosphere, vol. 286, pp. 131656-131656.
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Ahmed, SF, Mofijur, M, Rafa, N, Chowdhury, AT, Chowdhury, S, Nahrin, M, Islam, ABMS & Ong, HC 2022, 'Green approaches in synthesising nanomaterials for environmental nanobioremediation: Technological advancements, applications, benefits and challenges', Environmental Research, vol. 204, pp. 111967-111967.
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Ahmed, SF, Rafa, N, Mehnaz, T, Ahmed, B, Islam, N, Mofijur, M, Hoang, AT & Shafiullah, GM 2022, 'Integration of phase change materials in improving the performance of heating, cooling, and clean energy storage systems: An overview', Journal of Cleaner Production, vol. 364, pp. 132639-132639.
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Ain, K, Rahma, O, Putra, A, Rahmatillah, A, Putri, YKA, Fajriaty, N & Chai, R 2022, 'Electrodermal activity for measuring cognitive and emotional stress level', Journal of Medical Signals & Sensors, vol. 12, no. 2, pp. 155-155.
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Stress can lead to harmful conditions in the body, such as anxiety disorders and depression. One of the promising noninvasive methods, which has been widely used in detecting stress and emotion, is electrodermal activity (EDA). EDA has a tonic and phasic component called skin conductance level and skin conductance response (SCR). However, the components of the EDA cannot be directly extracted and need to be deconvolved to obtain it. The EDA signals were collected from 18 healthy subjects that underwent three sessions - Stroop test with increasing stress levels. The EDA signals were then deconvoluted by using continuous deconvolution analysis (CDA) and convex optimization approach to electrodermal activity (cvxEDA). Four features from the result of the deconvolution process were collected, namely sample average, standard deviation, first absolute difference, and normalized first absolute difference. Those features were used as the input of the classification process using the extreme learning machine (ELM). The output of classification was the stress level; mild, moderate, and severe. The visual of the phasic component using cvxEDA is more precise or smoother than the CDA's result. However, both methods could separate SCR from the original skin conductivity raw and indicate the small peaks from the SCR. The classification process results showed that both CDA and cvxEDA methods with 50 hidden layers in ELM had a high accuracy in classifying the stress level, which was 95.56% and 94.45%, respectively. This study developed a stress level classification method using ELM and the statistical features of SCR. The result showed that EDA could classify the stress level with over 94% accuracy. This system could help people monitor their mental health during overworking, leading to anxiety and depression because of untreated stress.

Akbarzadeh, M, Oberst, S, Sepehrirahnama, S & Halkon, B 2022, 'Acoustic radiation force-induced push-pull particle oscillations', Journal of the Acoustical Society of America.

Akter, N, Fletcher, J, Perry, S, Simunovic, MP, Briggs, N & Roy, M 2022, 'Glaucoma diagnosis using multi-feature analysis and a deep learning technique', Scientific Reports, vol. 12, no. 1.
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AbstractIn this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.

Akter, S, Zakia, MA, Mofijur, M, Ahmed, SF, Vo, D-VN, Khandaker, G & Mahlia, TMI 2022, 'SARS-CoV-2 variants and environmental effects of lockdowns, masks and vaccination: a review', Environmental Chemistry Letters, vol. 20, no. 1, pp. 141-152.
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AL Hunaity, SA, Far, H & Saleh, A 2022, 'Vibration behaviour of cold-formed steel and particleboard composite flooring systems', Steel and Composite Structures, vol. 43, no. 3, pp. 403-417.
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Recently, there has been an increasing demand for buildings that allow rapid assembly of construction elements, have ample open space areas and are flexible in their final intended use. Accordingly, researchers have developed new competitive structures in terms of cost and efficiency, such as cold-formed steel and timber composite floors, to satisfy these requirements. Cold-formed steel and timber composite floors are light floors with relatively high stiffness, which allow for longer spans. As a result, they inherently have lower fundamental natural frequency and lower damping. Therefore, they are likely to undergo unwanted vibrations under the action of human activities such as walking. It is also quite expensive and complex to implement vibration control measures on problematic floors. In this study, a finite element model of a composite floor reported in the literature was developed and validated against four-point bending test results. The validated FE model was then utilised to examine the vibration behaviour of the investigated composite floor. Predictions obtained from the numerical model were compared against predictions from analytical formulas reported in the literature. Finally, the influence of various parameters on the vibration behaviour of the composite floor was studied and discussed.

Alajlouni, D, Tran, T, Bliuc, D, Blank, RD, Cawthon, PM, Orwoll, ES & Center, JR 2022, 'Muscle Strength and Physical Performance Improve Fracture Risk Prediction Beyond Garvan and FRAX : The Osteoporotic Fractures in Men ( MrOS ) Study', Journal of Bone and Mineral Research, vol. 37, no. 3, pp. 411-419.
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Alajlouni, DA, Bliuc, D, Tran, TS, Blank, RD, Cawthon, PM, Ensrud, KE, Lane, NE, Orwoll, ES, Cauley, JA & Center, JR 2022, 'Muscle Strength and Physical Performance Are Associated With Risk of Postfracture Mortality But Not Subsequent Fracture in Men', Journal of Bone and Mineral Research, vol. 37, no. 8, pp. 1571-1579.
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Alam, M, Lu, DD & Siwakoti, YP 2022, 'Time‐multiplexed hysteretic control for single‐inductor dual‐input single‐output DC‐DC power converter', International Journal of Circuit Theory and Applications, vol. 50, no. 4, pp. 1235-1249.
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Alam, MA, Wan, C, Tran, DT, Mofijur, M, Ahmed, SF, Mehmood, MA, Shaik, F, Vo, D-VN & Xu, J 2022, 'Microalgae binary culture for higher biomass production, nutrients recycling, and efficient harvesting: a review', Environmental Chemistry Letters, vol. 20, no. 2, pp. 1153-1168.
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Alam, S, Zardari, S & Bano, M 2022, 'Software engineering and 12 prominent sub‐areas: Comprehensive bibliometric assessment on 13 years (2007–2019)', IET Software, vol. 16, no. 2, pp. 125-145.
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Alderighi, T, Malomo, L, Auzinger, T, Bickel, B, Cignoni, P & Pietroni, N 2022, 'State of the Art in Computational Mould Design', Computer Graphics Forum, vol. 41, no. 6, pp. 435-452.
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Aldini, S, Singh, AK, Leong, D, Wang, Y-K, Carmichael, MG, Liu, D & Lin, C-T 2022, 'Detection and Estimation of Cognitive Conflict During Physical Human-Robot Collaboration', IEEE Transactions on Cognitive and Developmental Systems, pp. 1-1.
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Al-Doghman, F, Moustafa, N, Khalil, I, Tari, Z & Zomaya, A 2022, 'AI-enabled Secure Microservices in Edge Computing: Opportunities and Challenges', IEEE Transactions on Services Computing, pp. 1-1.
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Alharbi, SK, Ansari, AJ, Nghiem, LD & Price, WE 2022, 'New transformation products from ozonation and photolysis of diclofenac in the aqueous phase', Process Safety and Environmental Protection, vol. 157, pp. 106-114.
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Ali, O, Shrestha, A, Ghasemaghaei, M & Beydoun, G 2022, 'Assessment of Complexity in Cloud Computing Adoption: a Case Study of Local Governments in Australia', Information Systems Frontiers, vol. 24, no. 2, pp. 595-617.
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Alibeikloo, M, Khabbaz, H & Fatahi, B 2022, 'Random Field Reliability Analysis for Time-Dependent Behaviour of Soft Soils Considering Spatial Variability of Elastic Visco-Plastic Parameters', Reliability Engineering & System Safety, vol. 219, pp. 108254-108254.
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Aljarajreh, H, Lu, DD-C, Siwakoti, YP & Tse, CK 2022, 'A Nonisolated Three-Port DC–DC Converter With Two Bidirectional Ports and Fewer Components', IEEE Transactions on Power Electronics, vol. 37, no. 7, pp. 8207-8216.
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Al-Juboori, RA, Bakly, S, Bowtell, L, Alkurdi, SSA & Altaee, A 2022, 'Innovative capacitive deionization-degaussing approach for improving adsorption/desorption for macadamia nutshell biochar', Journal of Water Process Engineering, vol. 47, pp. 102786-102786.
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Allahabadi, H, Amann, J, Balot, I, Beretta, A, Binkley, C, Bozenhard, J, Bruneault, F, Brusseau, J, Candemir, S, Cappellini, LA, Chakraborty, S, Cherciu, N, Cociancig, C, Coffee, M, Ek, I, Espinosa-Leal, L, Farina, D, Fieux-Castagnet, G, Frauenfelder, T, Gallucci, A, Giuliani, G, Golda, A, van Halem, I, Hildt, E, Holm, S, Kararigas, G, Krier, SA, Kuhne, U, Lizzi, F, Madai, VI, Markus, AF, Masis, S, Mathez, EW, Mureddu, F, Neri, E, Osika, W, Ozols, M, Panigutti, C, Parent, B, Pratesi, F, Moreno-Sanchez, PA, Sartor, G, Savardi, M, Signoroni, A, Sormunen, H-M, Spezzatti, A, Srivastava, A, Stephansen, AF, Theng, LB, Tithi, JJ, Tuominen, J, Umbrello, S, Vaccher, F, Vetter, D, Westerlund, M, Wurth, R & Zicari, RV 2022, 'Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients', IEEE Transactions on Technology and Society, vol. 3, no. 4, pp. 272-289.
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Almansor, EH, Hussain, FK & Hussain, OK 2022, 'Measuring chatbot quality of service to predict human-machine hand-over using a character deep learning model', International Journal of Web and Grid Services, vol. 18, no. 4, pp. 479-479.
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Almansor, EH, Hussain, OK & Hussain, FK 2022, 'Measuring chatbot quality of service to predict human-machine hand-over using a character deep learning model', International Journal of Web and Grid Services, vol. 18, no. 4, pp. 479-479.
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Almuntashiri, A, Hosseinzadeh, A, Badeti, U, Shon, H, Freguia, S, Dorji, U & Phuntsho, S 2022, 'Removal of pharmaceutical compounds from synthetic hydrolysed urine using granular activated carbon: Column study and predictive modelling', Journal of Water Process Engineering, vol. 45, pp. 102480-102480.
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Al-Najjar, HAH, Pradhan, B, Beydoun, G, Sarkar, R, Park, H-J & Alamri, A 2022, 'A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset', Gondwana Research.
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Alotaibi, AA, Maerz, NH, Boyko, KJ, Youssef, AM & Pradhan, B 2022, 'Temporal LiDAR scanning in quantifying cumulative rockfall volume and hazard assessment: A case study at southwestern Saudi Arabia', The Egyptian Journal of Remote Sensing and Space Science, vol. 25, no. 2, pp. 435-443.
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Alsahafi, YA, Gay, V & Khwaji, AA 2022, 'Factors affecting the acceptance of integrated electronic personal health records in Saudi Arabia: The impact of e-health literacy', Health Information Management Journal, vol. 51, no. 2, pp. 98-109.
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Background: National implementation of electronic personal health record (ePHR) systems is of vital importance to governments worldwide because this type of technology promises to promote and enhance healthcare. Although there is widespread agreement as to the advantages of ePHRs, the level of awareness and acceptance of this technology among healthcare consumers has been low. Objective: The aim of this study was to identify the factors that can influence the acceptance and use of an integrated ePHR system in Saudi Arabia. Method: The unified theory of acceptance and use of technology model was extended in this study to include e-health literacy (e-HL) and tested using structural equation modelling. Data were collected via a questionnaire survey, resulting in 794 valid responses. Results: The proposed model explained 56% of the variance in behavioural intention (BI) to use the integrated ePHR system. Findings also highlighted the significance of performance expectancy, effort expectancy, social influence (SI) and e-HL as determinants of Saudi healthcare consumers’ intentions to accept and use the integrated ePHR system. Additionally, assessment of the research model moderators revealed that only gender had a moderating influence on the relationship between SI and BI. Finally, findings showed a low level of awareness among Saudi citizens about the national implementation of an integrated ePHR system, suggesting the need to promote a greater and more widespread awareness of the system and to demonstrate its usefulness. Conclusion: Findings from this study can assist governments, policymakers and developers of health information technologies and systems by identifying importan...

Alsalibi, B, Mirjalili, S, Abualigah, L, yahya, RI & Gandomi, AH 2022, 'A Comprehensive Survey on the Recent Variants and Applications of Membrane-Inspired Evolutionary Algorithms', Archives of Computational Methods in Engineering, vol. 29, no. 5, pp. 3041-3057.
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Alsawwaf, M, Chaczko, Z, Kulbacki, M & Sarathy, N 2022, 'In Your Face: Person Identification Through Ratios and Distances Between Facial Features', Vietnam Journal of Computer Science, vol. 09, no. 02, pp. 187-202.
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These days identification of a person is an integral part of many computer-based solutions. It is a key characteristic for access control, customized services, and a proof of identity. Over the last couple of decades, many new techniques were introduced for how to identify human faces. This approach investigates the human face identification based on frontal images by producing ratios from distances between the different features and their locations. Moreover, this extended version includes an investigation of identification based on side profile by extracting and diagnosing the feature sets with geometric ratio expressions which are calculated into feature vectors. The last stage involves using weighted means to calculate the resemblance. The approach considers an explainable Artificial Intelligence (XAI) approach. Findings, based on a small dataset, achieve that the used approach offers promising results. Further research could have a great influence on how faces and face-profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate, and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89. This work is an extended version of the paper submitted in ACIIDS 2020.

Alsenwi, M, Abolhasan, M & Lipman, J 2022, 'Intelligent and Reliable Millimeter Wave Communications for RIS-Aided Vehicular Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 21582-21592.
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Alshahrani, AA, Al-Zoubi, H, Alotaibi, SE, Hassan, HMA, Alsohaimi, IH, Alotaibi, KM, Alshammari, MS, Nghiem, L & Panhuis, MIH 2022, 'Assessment of commercialized nylon membranes integrated with thin layer of MWCNTs for potential use in desalination process', Journal of Materials Research and Technology, vol. 21, pp. 872-883.
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Alsufyani, N & Gill, AQ 2022, 'Digitalisation performance assessment: A systematic review', Technology in Society, vol. 68, pp. 101894-101894.
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Altulyan, M, Yao, L, Wang, X, Huang, C, Kanhere, SS & Sheng, QZ 2022, 'A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions', The Computer Journal, vol. 65, no. 8, pp. 2098-2132.
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Abstract Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, traditional recommender system approaches fail to exploit ever-growing, dynamic and heterogeneous IoT data in building recommender systems for the IoT (RSIoT). This article aims to provide a comprehensive review of state-of-the-art RSIoT, including the related techniques, applications and a discussion on the limitations of applying recommendation systems to IoT. Finally, we propose a reference framework for comparing existing studies to guide future research and practices.

Al-Zainati, N, Subbiah, S, Yadav, S, Altaee, A, Bartocci, P, Ibrar, I, Zhou, J, Samal, AK & Fantozzi, F 2022, 'Experimental and theoretical work on reverse osmosis - Dual stage pressure retarded osmosis hybrid system', Desalination, vol. 543, pp. 116099-116099.
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Two-pass reverse osmosis desalination is a common process to treat high-salinity feed solution and provides a low-salinity permeate solution. This study investigated the significance of the energy generated by the dual-stage pressure retarded osmosis (DSPRO) from the reverse osmosis (RO) brine stream. The main components of the DSPRO-RO hybrid system are RO, pressure retarded osmosis (PRO), and energy recovery device, and their models are determined. Dymola software, using Modelica modelling language, was utilized for solving the hybrid system models. Two different flowsheets were built; the first included a two-pass RO, while the second is a hybrid of a two-pass RO (2RO)-DSPRO system. Seawater salinities of 40 and 45 g/L were the RO feed solution, and 1 g/L tertiary treated wastewater was the feed solution of the DSPRO process. The net specific energy consumption was calculated for the 2RO and 2RO-DSPRO systems for 40 and 45 g/L salinities. At a 47% recovery rate and 40 g/L seawater salinity, the 2RO-DSPRO system was 14.7% more energy efficient than the 2RO system. The corresponding energy saving at a 47% recovery rate and 45 g/L seawater salinity was 17.5%. The desalination energy for the 2RO system was between 3.25 and 3.49 kWh/m3, and for the 2RO-DSPRO system was between 2.91 and 2.97 kWh/m3. The results demonstrate the great potential of integrating the 2RO with the DSPRO to reduce desalination's energy consumption and environmental impacts.

AlZainati, N, Yadav, S, Altaee, A, Subbiah, S, Zaidi, SJ, Zhou, J, Al-Juboori, RA, Chen, Y & Shaheed, MH 2022, 'Impact of hydrodynamic conditions on optimum power generation in dual stage pressure retarded osmosis using spiral-wound membrane', Energy Nexus, vol. 5, pp. 100030-100030.
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Alzoubi, YI & Gill, AQ 2022, 'Can Agile Enterprise Architecture be Implemented Successfully in Distributed Agile Development? Empirical Findings', Global Journal of Flexible Systems Management, vol. 23, no. 2, pp. 221-235.
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Al-Zubi, MM, Mohan, AS, Plapper, P & Ling, SH 2022, 'Intrabody Molecular Communication via Blood-Tissue Barrier for Internet of Bio-Nano Things', IEEE Internet of Things Journal, vol. 9, no. 21, pp. 21802-21810.
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Amini, E, Mehdipour, H, Faraggiana, E, Golbaz, D, Mozaffari, S, Bracco, G & Neshat, M 2022, 'Optimization of hydraulic power take-off system settings for point absorber wave energy converter', Renewable Energy, vol. 194, pp. 938-954.
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Amiri, M, Abolhasan, M, Shariati, N & Lipman, J 2022, 'Remote Water Salinity Sensor Using Metamaterial Perfect Absorber', IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6785-6794.
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Controlling water salinity plays a key role in farming efficiency. Current sensors are mostly expensive and need regular maintenance. In addition, they require electrical connections or extra power supply that leads to difficult and costly implementation in remote sensing scenarios. In this paper, an accurate and low-profile sensor is developed using a metamaterial perfect absorber (MPA) structure. The proposed sensor works based on the level and frequency of absorbed signals. Hence, there is no need for electrical connections, which enables remote sensing applications. Square shape channels have been created in a regular FR-4 substrate to facilitate sensing of water salinity level. A 7×7 array with a total size of 140mm×160mm has been fabricated that shows a resolution of 10 MHz per percentage of water salinity. The absorption frequency shifts from f=3.12 GHz to f=3.59 GHz for salinity level from 0% to 50%. A strong correlation between measurement and simulation results validates the design procedure.

Ampah, JD, Jin, C, Rizwanul Fattah, IM, Appiah-Otoo, I, Afrane, S, Geng, Z, Yusuf, AA, Li, T, Mahlia, TMI & Liu, H 2022, 'Investigating the evolutionary trends and key enablers of hydrogen production technologies: A patent-life cycle and econometric analysis', International Journal of Hydrogen Energy.
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Anand, U, Li, X, Sunita, K, Lokhandwala, S, Gautam, P, Suresh, S, Sarma, H, Vellingiri, B, Dey, A, Bontempi, E & Jiang, G 2022, 'SARS-CoV-2 and other pathogens in municipal wastewater, landfill leachate, and solid waste: A review about virus surveillance, infectivity, and inactivation', Environmental Research, vol. 203, pp. 111839-111839.
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Andaryani, S, Nourani, V, Pradhan, B, Jalali Ansarudi, T, Ershadfath, F & Torabi Haghighi, A 2022, 'Spatiotemporal evaluation of future groundwater recharge in arid and semi-arid regions under climate change scenarios', Hydrological Sciences Journal, vol. 67, no. 6, pp. 979-995.
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Ansari, M, Jones, B & Guo, YJ 2022, 'Spherical Luneburg Lens of Layered Structure With Low Anisotropy and Low Cost', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4307-4318.
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Antoniadis, J, Arzoumanian, Z, Babak, S, Bailes, M, Bak Nielsen, A-S, Baker, PT, Bassa, CG, Bécsy, B, Berthereau, A, Bonetti, M, Brazier, A, Brook, PR, Burgay, M, Burke-Spolaor, S, Caballero, RN, Casey-Clyde, JA, Chalumeau, A, Champion, DJ, Charisi, M, Chatterjee, S, Chen, S, Cognard, I, Cordes, JM, Cornish, NJ, Crawford, F, Cromartie, HT, Crowter, K, Dai, S, DeCesar, ME, Demorest, PB, Desvignes, G, Dolch, T, Drachler, B, Falxa, M, Ferrara, EC, Fiore, W, Fonseca, E, Gair, JR, Garver-Daniels, N, Goncharov, B, Good, DC, Graikou, E, Guillemot, L, Guo, YJ, Hazboun, JS, Hobbs, G, Hu, H, Islo, K, Janssen, GH, Jennings, RJ, Johnson, AD, Jones, ML, Kaiser, AR, Kaplan, DL, Karuppusamy, R, Keith, MJ, Kelley, LZ, Kerr, M, Key, JS, Kramer, M, Lam, MT, Lamb, WG, Lazio, TJW, Lee, KJ, Lentati, L, Liu, K, Luo, J, Lynch, RS, Lyne, AG, Madison, DR, Main, RA, Manchester, RN, McEwen, A, McKee, JW, McLaughlin, MA, Mickaliger, MB, Mingarelli, CMF, Ng, C, Nice, DJ, Osłowski, S, Parthasarathy, A, Pennucci, TT, Perera, BBP, Perrodin, D, Petiteau, A, Pol, NS, Porayko, NK, Possenti, A, Ransom, SM, Ray, PS, Reardon, DJ, Russell, CJ, Samajdar, A, Sampson, LM, Sanidas, S, Sarkissian, JM, Schmitz, K, Schult, L, Sesana, A, Shaifullah, G, Shannon, RM, Shapiro-Albert, BJ, Siemens, X, Simon, J, Smith, TL, Speri, L, Spiewak, R, Stairs, IH, Stappers, BW, Stinebring, DR, Swiggum, JK, Taylor, SR, Theureau, G, Tiburzi, C, Vallisneri, M, van der Wateren, E, Vecchio, A, Verbiest, JPW, Vigeland, SJ, Wahl, H, Wang, JB, Wang, J, Wang, L, Witt, CA, Zhang, S & Zhu, XJ 2022, 'The International Pulsar Timing Array second data release: Search for an isotropic gravitational wave background', Monthly Notices of the Royal Astronomical Society, vol. 510, no. 4, pp. 4873-4887.
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ABSTRACT We searched for an isotropic stochastic gravitational wave background in the second data release of the International Pulsar Timing Array, a global collaboration synthesizing decadal-length pulsar-timing campaigns in North America, Europe, and Australia. In our reference search for a power-law strain spectrum of the form $h_c = A(f/1\, \mathrm{yr}^{-1})^{\alpha }$, we found strong evidence for a spectrally similar low-frequency stochastic process of amplitude $A = 3.8^{+6.3}_{-2.5}\times 10^{-15}$ and spectral index α = −0.5 ± 0.5, where the uncertainties represent 95 per cent credible regions, using information from the auto- and cross-correlation terms between the pulsars in the array. For a spectral index of α = −2/3, as expected from a population of inspiralling supermassive black hole binaries, the recovered amplitude is $A = 2.8^{+1.2}_{-0.8}\times 10^{-15}$. None the less, no significant evidence of the Hellings–Downs correlations that would indicate a gravitational-wave origin was found. We also analysed the constituent data from the individual pulsar timing arrays in a consistent way, and clearly demonstrate that the combined international data set is more sensitive. Furthermore, we demonstrate that this combined data set produces comparable constraints to recent single-array data sets which have more data than the constituent parts of the combination. Future international data releases will deliver increased sensitivity to gravitational wave radiation, and significantly increase the detection probability.

Anwar, MJ, Gill, AQ, Fitzgibbon, AD & Gull, I 2022, 'PESTLE+risk analysis model to assess pandemic preparedness of digital ecosystems', SECURITY AND PRIVACY, vol. 5, no. 1.
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Arachchige, CMK, Indraratna, B, Qi, Y, Vinod, JS & Rujikiatkamjorn, C 2022, 'Deformation and degradation behaviour of Rubber Intermixed Ballast System under cyclic loading', Engineering Geology, vol. 307, pp. 106786-106786.
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Arachchige, CMK, Indraratna, B, Qi, Y, Vinod, JS & Rujikiatkamjorn, C 2022, 'Geotechnical characteristics of a Rubber Intermixed Ballast System', Acta Geotechnica, vol. 17, no. 5, pp. 1847-1858.
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Arakawa, K, Kono, N, Malay, AD, Tateishi, A, Ifuku, N, Masunaga, H, Sato, R, Tsuchiya, K, Ohtoshi, R, Pedrazzoli, D, Shinohara, A, Ito, Y, Nakamura, H, Tanikawa, A, Suzuki, Y, Ichikawa, T, Fujita, S, Fujiwara, M, Tomita, M, Blamires, SJ, Chuah, J-A, Craig, H, Foong, CP, Greco, G, Guan, J, Holland, C, Kaplan, DL, Sudesh, K, Mandal, BB, Norma-Rashid, Y, Oktaviani, NA, Preda, RC, Pugno, NM, Rajkhowa, R, Wang, X, Yazawa, K, Zheng, Z & Numata, K 2022, '1000 spider silkomes: Linking sequences to silk physical properties', Science Advances, vol. 8, no. 41.
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Spider silks are among the toughest known materials and thus provide models for renewable, biodegradable, and sustainable biopolymers. However, the entirety of their diversity still remains elusive, and silks that exceed the performance limits of industrial fibers are constantly being found. We obtained transcriptome assemblies from 1098 species of spiders to comprehensively catalog silk gene sequences and measured the mechanical, thermal, structural, and hydration properties of the dragline silks of 446 species. The combination of these silk protein genotype-phenotype data revealed essential contributions of multicomponent structures with major ampullate spidroin 1 to 3 paralogs in high-performance dragline silks and numerous amino acid motifs contributing to each of the measured properties. We hope that our global sampling, comprehensive testing, integrated analysis, and open data will provide a solid starting point for future biomaterial designs.

Araujo, AM, Abaurrea, A, Azcoaga, P, López-Velazco, JI, Manzano, S, Rodriguez, J, Rezola, R, Egia-Mendikute, L, Valdés-Mora, F, Flores, JM, Jenkins, L, Pulido, L, Osorio-Querejeta, I, Fernández-Nogueira, P, Ferrari, N, Viera, C, Martín-Martín, N, Tzankov, A, Eppenberger-Castori, S, Alvarez-Lopez, I, Urruticoechea, A, Bragado, P, Coleman, N, Palazón, A, Carracedo, A, Gallego-Ortega, D, Calvo, F, Isacke, CM, Caffarel, MM & Lawrie, CH 2022, 'Stromal oncostatin M cytokine promotes breast cancer progression by reprogramming the tumor microenvironment', Journal of Clinical Investigation, vol. 132, no. 19.
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Araujo, AM, Abaurrea, A, Azcoaga, P, López-Velazco, JI, Manzano, S, Rodriguez, J, Rezola, R, Egia-Mendikute, L, Valdés-Mora, F, Flores, JM, Jenkins, L, Pulido, L, Osorio-Querejeta, I, Fernández-Nogueira, P, Ferrari, N, Viera, C, Martín-Martín, N, Tzankov, A, Eppenberger-Castori, S, Alvarez-Lopez, I, Urruticoechea, A, Bragado, P, Coleman, N, Palazón, A, Carracedo, A, Gallego-Ortega, D, Calvo, F, Isacke, CM, Caffarel, MM & Lawrie, CH 2022, 'Stromal oncostatin M cytokine promotes breast cancer progression by reprogramming the tumor microenvironment', Journal of Clinical Investigation, vol. 132, no. 7.
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Arivalagan, J, Indraratna, B, Rujikiatkamjorn, C & Warwick, A 2022, 'Effectiveness of a Geocomposite-PVD system in preventing subgrade instability and fluidisation under cyclic loading', Geotextiles and Geomembranes, vol. 50, no. 4, pp. 607-617.
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Arjmandi, A, Peyravi, M, Arjmandi, M & Altaee, A 2022, 'Taking advantage of large water-unstable Zn4O(BDC)3 nanoparticles for fabricating the PMM-based TFC FO membrane with improved water flux in desalination process', Chemical Engineering Research and Design, vol. 186, pp. 112-124.
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Arnaz, A, Lipman, J, Abolhasan, M & Hiltunen, M 2022, 'Toward Integrating Intelligence and Programmability in Open Radio Access Networks: A Comprehensive Survey', IEEE Access, vol. 10, pp. 67747-67770.
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Arora, S, Nag, A, Kalra, A, Sinha, V, Meena, E, Saxena, S, Sutaria, D, Kaur, M, Pamnani, T, Sharma, K, Saxena, S, Shrivastava, SK, Gupta, AB, Li, X & Jiang, G 2022, 'Successful application of wastewater-based epidemiology in prediction and monitoring of the second wave of COVID-19 with fragmented sewerage systems–a case study of Jaipur (India)', Environmental Monitoring and Assessment, vol. 194, no. 5.
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Arunprasad, J, Krishna, AN, Radha, D, Singh, M, Surakasi, R & Gidebo, TD 2022, 'Nanometal-Based Magnesium Oxide Nanoparticle with C. vulgaris Algae Biodiesel in Diesel Engine', Journal of Nanomaterials, vol. 2022, pp. 1-9.
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Many researchers are interested in biofuels because it isenvironmentally friendly and potentially reduce global warming. Incorporating nanoparticles into biodiesel has increased its performance and emission characteristics. The current study examines the influence of magnesium oxide nanoadditions on the performance and emissions of a diesel engine that runs on C. vulgaris algae biodiesel. The transesterification process produced methyl ester from C. vulgaris algae biodiesel.The morphology of nanoadditives was studied using scanning electron microscopy, transmission electron microscopy, and energy-dispersive X-ray spectroscopy. The fuel sample consisted of biodiesel blends with and without magnesium oxide nanoadditives. The fuel properties of the prepared C. vulgaris methyl ester were found to conform with the ASTM standards. The experimental results were determined by running a single-cylinder four-stroke diesel engine at different load conditions. When compared to B20, a B20 blend containing 100 ppm magnesium oxide nanoparticles enhanced brake thermal efficiency while reducing specific fuel consumption, according to the research. When MgO nanoparticles were introduced to B20, engine emissions of HC, CO, and smoke were decreased.

Aryal, B, Gurung, R, Camargo, AF, Fongaro, G, Treichel, H, Mainali, B, Angove, MJ, Ngo, HH, Guo, W & Puadel, SR 2022, 'Nitrous oxide emission in altered nitrogen cycle and implications for climate change', Environmental Pollution, vol. 314, pp. 120272-120272.
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Aseeri, M & Kang, K 2022, 'Big data, oriented-organizational culture, and business performance: A socio-technical approach', Problems and Perspectives in Management, vol. 20, no. 4, pp. 52-66.
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This paper experimentally examines the impact of oriented-organizational culture that could support big data analytics (BDA) in higher education institutions (HEIs) in Saudi Arabia. Specifically, this study analyzed the effect of oriented-organizational culture (OC) on big data tasks (BDTs) toward improving decision-making (DM) and organization performance (OP). The study hinged on the theory of socio-technical systems to investigate BDA elements in higher education decision-making in Saudi Arabia. The analysis was conducted using a quantitative survey research design where data were collected from 270 IT staff working in Saudi Arabian HEIs using Qualtrics. PLS-SEM was applied to validate the research data and explore the relationship between the proposed hypotheses. The findings show that oriented-organizational culture positively affected big data tasks, i.e., storing, analyzing, and visualizing. Similarly, oriented-organizational culture positively affects improving decision-making by top management in Saudi Arabian universities. OC also positively influences the performance of Saudi Arabian universities. Improving decision-making by top management has a positive impact on enhancing the overall university’s performance. However, big data tasks, i.e., storing, analyzing, and visualizing, negatively affect improving decision-making by top management in Saudi Arabian HEIs. One of the study limitations is the small sample size; future studies should include private and public universities to alter the expected outcomes. Additional technological elements, such as IT infrastructure at Saudi Arabia’s private and public HEIs, are recommended to be considered in future studies to establish the competence of respective IT infrastructure. AcknowledgmentThe authors wish to thank the Problems and Perspectives in Management Journal editors for their valuable time and assistance in improving the manuscript.

Ashtari, S, Abdollahi, M, Abolhasan, M, Shariati, N & Lipman, J 2022, 'Performance analysis of multi-hop routing protocols in SDN-based wireless networks', Computers & Electrical Engineering, vol. 97, pp. 107393-107393.
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ashtari, S, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2022, 'Joint Mobile Node Participation and Multihop Routing for Emerging Open Radio-Based Intelligent Transportation System', IEEE Access, vol. 10, pp. 85228-85242.
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Ashtari, S, Zhou, I, Abolhasan, M, Shariati, N, Lipman, J & Ni, W 2022, 'Knowledge-defined networking: Applications, challenges and future work', Array, vol. 14, pp. 100136-100136.
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Asteris, PG, Gavriilaki, E, Touloumenidou, T, Koravou, E, Koutra, M, Papayanni, PG, Pouleres, A, Karali, V, Lemonis, ME, Mamou, A, Skentou, AD, Papalexandri, A, Varelas, C, Chatzopoulou, F, Chatzidimitriou, M, Chatzidimitriou, D, Veleni, A, Rapti, E, Kioumis, I, Kaimakamis, E, Bitzani, M, Boumpas, D, Tsantes, A, Sotiropoulos, D, Papadopoulou, A, Kalantzis, IG, Vallianatou, LA, Armaghani, DJ, Cavaleri, L, Gandomi, AH, Hajihassani, M, Hasanipanah, M, Koopialipoor, M, Lourenço, PB, Samui, P, Zhou, J, Sakellari, I, Valsami, S, Politou, M, Kokoris, S & Anagnostopoulos, A 2022, 'Genetic prediction of ICU hospitalization and mortality in COVID‐19 patients using artificial neural networks', Journal of Cellular and Molecular Medicine, vol. 26, no. 5, pp. 1445-1455.
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Atamewoue Tsafack, S, Wen, S, Onasanya, BO & Feng, Y 2022, 'Skew polynomial superrings', Soft Computing, vol. 26, no. 21, pp. 11277-11286.
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Atgur, V, Manavendra, G, Desai, GP, Rao, BN, Fattah, IMR, Mohamed, BA, Sinaga, N & Masjuki, HH 2022, 'Thermogravimetric and combustion efficiency analysis of Jatropha curcas biodiesel and its derivatives', Biofuels, vol. 13, no. 9, pp. 1069-1079.
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Thermal behavior of diesel, Jatropha curcas methyl ester (JOME), and its B20 blend (20% biodiesel and 80% diesel) are examined from the profiles of thermogravimetry–differential scanning calorimetry (TG-DSC) under air. TG profiles of samples indicate the mass loss steps to volatilization and combustion of methyl esters. Due to the higher temperature combustion of the intermediate stable compounds that are formed, the peak temperature of combustion is high for JOME compared to diesel and B20 blend. DSC profiles of diesel and B20 JOME indicate an endothermic peak associated with the vaporization of methyl esters for B20 JOME and the volatilization of a small fraction of the diesel. The ignition temperature for diesel and B20 blend is 128 °C, whereas JOME has an ignition temperature of 220 °C. The burnout temperatures for the diesel, JOME, and B20 blend are 283.24, 470.02, and 376.92 °C, respectively. The ignition index for the B20 blend was found to be 73.73% more compared to diesel. The combustion index for the B20 blend was found to be 37.81% higher compared to diesel. The B20 blend exhibits high enthalpy, better thermal stability, and a reduced peak temperature of combustion, with an improved combustion index and an intensity of combustion making it nearly comparable with diesel.

Atif, Y, Soulaimani, A, Ait lamqadem, A, Pour, AB, Pradhan, B, Nouamane, EA, Abdelali, K, Muslim, AM & Hossain, MS 2022, 'Identifying hydrothermally altered rocks using ASTER satellite imageries in Eastern Anti-Atlas of Morocco: a case study from Imiter silver mine', International Journal of Image and Data Fusion, vol. 13, no. 4, pp. 337-361.
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Atique, MN, Imran, S, Razzaq, L, Mujtaba, MA, Nawaz, S, Kalam, MA, Soudagar, MEM, Hussain, A, Veza, I & Arshad, A 2022, 'Hydraulic characterization of Diesel, B50 and B100 using momentum flux', Alexandria Engineering Journal, vol. 61, no. 6, pp. 4371-4388.
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Augustine, R, S, A, Nayeem, A, Salam, SA, Augustine, P, Dan, P, Maureira, P, Mraiche, F, Gentile, C, Hansbro, PM, McClements, L & Hasan, A 2022, 'Increased complications of COVID-19 in people with cardiovascular disease: Role of the renin–angiotensin-aldosterone system (RAAS) dysregulation', Chemico-Biological Interactions, vol. 351, pp. 109738-109738.
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Aung, TWW, Wan, Y, Huo, H & Sui, Y 2022, 'Multi-triage: A multi-task learning framework for bug triage', Journal of Systems and Software, vol. 184, pp. 111133-111133.
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Awang, MSN, Mohd Zulkifli, NW, Abbas, MM, Zulkifli, MSA, Kalam, MA, Mohd Yusoff, MNA, Ahmad, MH & Wan Daud, WMA 2022, 'Effect of plastic pyrolytic oil and waste cooking biodiesel on tribological properties of palm biodiesel–diesel fuel blends', Industrial Lubrication and Tribology, vol. 74, no. 8, pp. 932-942.
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Purpose The purpose of this paper was to investigate the lubricity of palm biodiesel (PB)–diesel fuel with plastic pyrolysis oil (PPO) and waste cooking biodiesel (WCB). Design/methodology/approach Three quaternary fuels were prepared by mechanical stirring. B10 (10% PB in diesel) fuel was blended with 5%, 10% and 15% of both PPO and WCB. The results were compared to B30 (30% PB in diesel) and B10. The lubricity of fuel samples was determined using high-frequency reciprocating rig in accordance with ASTM D6079. The tribological behavior of all fuels was assessed by using scanning electron microscopy on worn steel plates to determine wear scar diameter (WSD) and surface morphology. The reported WSD is the average of the major and minor axis of the wear scar. Findings The addition of PPO and WCB to B10 had improved its lubricity while lowering wear and friction coefficients. Among the quaternary fuels, B40 showed the greatest reduction in coefficient of friction and WSD, with 7.63% and 44.5%, respectively, when compared to B10. When compared to B30a, the quaternary fuel mixes (B40, B30b and B20) exhibited significant reduction in WSD by 49.66%, 42.84% and 40.24%, respectively. Among the quaternary fuels, B40 exhibited the best overall lubricating performance, which was supported by surface morphology analysis. The evaluation of B40 indicated a reduced adhesive wear and tribo-oxidation, as well as a smoother metal surface, as compared to B20 and B30b. Originality/value Incorporation of PPO and WCB in PB–diesel blend as a quaternary fuel blend in diesel engines has not been reported. Only a few ...

Awang, MSN, Zulkifli, NWM, Abbas, MM, Zulkifli, SA, Kalam, MA, Yusoff, MNAM, Daud, WMAW & Ahmad, MH 2022, 'Effect of diesel-palm biodiesel fuel with plastic pyrolysis oil and waste cooking biodiesel on tribological characteristics of lubricating oil', Alexandria Engineering Journal, vol. 61, no. 9, pp. 7221-7231.
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Azadi, M, Emrouznejad, A, Ramezani, F & Hussain, FK 2022, 'Efficiency Measurement of Cloud Service Providers Using Network Data Envelopment Analysis', IEEE Transactions on Cloud Computing, vol. 10, no. 1, pp. 348-355.
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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.

Azam, MA, Khan, KB, Salahuddin, S, Rehman, E, Khan, SA, Khan, MA, Kadry, S & Gandomi, AH 2022, 'A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics', Computers in Biology and Medicine, vol. 144, pp. 105253-105253.
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Ba, X, Sun, X, Gong, Z, Guo, Y, Zhang, C & Zhu, J 2022, 'A Generalized Per-Phase Equivalent Circuit Model of the PMSM With Predictable Core Loss', IEEE/ASME Transactions on Mechatronics, pp. 1-10.
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Baba, AA, Hashmi, RM, Attygalle, M, Esselle, KP & Borg, D 2022, 'Ultrawideband Beam Steering at mm-Wave Frequency With Planar Dielectric Phase Transformers', IEEE Transactions on Antennas and Propagation, vol. 70, no. 3, pp. 1719-1728.
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Babakian, A, Monclus, P, Braun, R & Lipman, J 2022, 'A Retrospective on Workload Identifiers: From Data Center to Cloud-Native Networks', IEEE Access, vol. 10, pp. 105518-105527.
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Bachosz, K, Vu, MT, Nghiem, LD, Zdarta, J, Nguyen, LN & Jesionowski, T 2022, 'Enzyme-based control of membrane biofouling for water and wastewater purification: A comprehensive review', Environmental Technology & Innovation, vol. 25, pp. 102106-102106.
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Bachosz, K, Zdarta, J, Nghiem, LD & Jesionowski, T 2022, 'Multienzymatic conversion of monosaccharides from birch biomass after pretreatment', Environmental Technology & Innovation, vol. 28, pp. 102874-102874.
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Badeti, U, Jiang, J, Almuntashiri, A, Pathak, N, Dorji, U, Volpin, F, Freguia, S, Ang, WL, Chanan, A, Kumarasingham, S, Shon, HK & Phuntsho, S 2022, 'Impact of source-separation of urine on treatment capacity, process design, and capital expenditure of a decentralised wastewater treatment plant', Chemosphere, vol. 300, pp. 134489-134489.
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Baharvand, S & Pradhan, B 2022, 'Erosion and flood susceptibility evaluation in a catchment of Kopet-Dagh mountains using EPM and RFM in GIS', Environmental Earth Sciences, vol. 81, no. 20.
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Bahrami, N, Reza Nikoo, M, Al-Rawas, G, Al-Wardy, M & Gandomi, AH 2022, 'Reservoir optimal operation with an integrated approach for managing floods and droughts using NSGA-III and prospect behavioral theory', Journal of Hydrology, vol. 610, pp. 127961-127961.
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Bai, K, Zhu, X, Wen, S, Zhang, R & Zhang, W 2022, 'Broad Learning Based Dynamic Fuzzy Inference System With Adaptive Structure and Interpretable Fuzzy Rules', IEEE Transactions on Fuzzy Systems, vol. 30, no. 8, pp. 3270-3283.
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Ball, JE 2022, 'Modelling accuracy for urban design flood estimation', Urban Water Journal, vol. 19, no. 1, pp. 87-96.
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Management of flood risk remains a major problem in many urban environments. To generate the data needed for estimation of the flood risk, catchment models have been used with the reliability of the predicted catchment response for design flood estimation dependent upon the model calibration. However, the level of calibration required to achieve reliable design flood estimation remains unspecified. The purpose of this paper is to assess the event modelling accuracy needed if data from the calibrated model are to be used for continuous simulation of data for flood frequency analysis. For this purpose, a SWMM-based catchment model was investigated using 25 monitored events, while the assessment of the calibration was based on a normalised peak flow error. Alternative sets of parameter values were used to obtain estimates of the peak flow for each of the selected events. The best performing sets of these sets of parameter values were used with SWMM in a continuous simulation mode to predict flow sequences for extraction of Annual Maxima Series for an At-Site Flood Frequency Analysis. From the analysis of these At-Site Flood Frequency Analyses, it was concluded that the normalised peak flow error needed to be less than 10% if reliable design flood quantile estimates were to be obtained.

Balogun, A-L, Sheng, TY, Sallehuddin, MH, Aina, YA, Dano, UL, Pradhan, B, Yekeen, S & Tella, A 2022, 'Assessment of data mining, multi-criteria decision making and fuzzy-computing techniques for spatial flood susceptibility mapping: a comparative study', Geocarto International, vol. 37, no. 26, pp. 12989-13015.
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Banerjee, S, Lyu, J, Huang, Z, Leung, FHF, Lee, T, Yang, D, Su, S, Zheng, Y & Ling, SH 2022, 'Ultrasound spine image segmentation using multi-scale feature fusion Skip-Inception U-Net (SIU-Net)', Biocybernetics and Biomedical Engineering, vol. 42, no. 1, pp. 341-361.
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Bao, G, Wang, K, Yang, L, He, J, He, B, Xu, X & Zheng, Y 2022, 'Feasibility evaluation of a Zn-Cu alloy for intrauterine devices: In vitro and in vivo studies', Acta Biomaterialia, vol. 142, pp. 374-387.
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Bao, X, Xu, X, Zhang, P & Liu, T 2022, 'Comprehensive Formal Modeling and Automatic Vulnerability Detection for a Bitcoin-Compatible Mixing Protocol', IEEE Access, vol. 10, pp. 128847-128873.
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Bardhan, A, GuhaRay, A, Gupta, S, Pradhan, B & Gokceoglu, C 2022, 'A novel integrated approach of ELM and modified equilibrium optimizer for predicting soil compression index of subgrade layer of Dedicated Freight Corridor', Transportation Geotechnics, vol. 32, pp. 100678-100678.
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Bardhan, A, Kardani, N, Alzo’ubi, AK, Samui, P, Gandomi, AH & Gokceoglu, C 2022, 'A Comparative Analysis of Hybrid Computational Models Constructed with Swarm Intelligence Algorithms for Estimating Soil Compression Index', Archives of Computational Methods in Engineering, vol. 29, no. 7, pp. 4735-4773.
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Bardhan, A, Kardani, N, Alzo'ubi, AK, Roy, B, Samui, P & Gandomi, AH 2022, 'Novel integration of extreme learning machine and improved Harris hawks optimization with particle swarm optimization-based mutation for predicting soil consolidation parameter', Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 5, pp. 1588-1608.
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Bardhan, A, Subbiah, S, Mohanty, K, Ibrar, I & Altaee, A 2022, 'Feasibility of Poly (Vinyl Alcohol)/Poly (Diallyldimethylammonium Chloride) Polymeric Network Hydrogel as Draw Solute for Forward Osmosis Process', Membranes, vol. 12, no. 11, pp. 1097-1097.
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Forward osmosis (FO) has been identified as an emerging technology for the concentration and crystallization of aqueous solutions at low temperatures. However, the application of the FO process has been limited due to the unavailability of a suitable draw solute. An ideal draw solute should be able to generate high osmotic pressure and must be easily regenerated with less reverse solute flux (RSF). Recently, hydrogels have attracted attention as a draw solution due to their high capacity to absorb water and low RSF. This study explores a poly (vinyl alcohol)/poly (diallyldimethylammonium chloride) (PVA-polyDADMAC) polymeric network hydrogel as a draw solute in forward osmosis. A low-pressure reverse osmosis (RO) membrane was used in the FO process to study the performance of the hydrogel prepared in this study as a draw solution. The robust and straightforward gel synthesis method provides an extensive-scale application. The results indicate that incorporating cationic polyelectrolyte poly (diallyldimethylammonium chloride) into the polymeric network increases swelling capacity and osmotic pressure, thereby resulting in an average water flux of the PVA-polyDADMAC hydrogel (0.97 L m−2 h−1) that was 7.47 times higher than the PVA hydrogel during a 6 h FO process against a 5000 mg L−1 NaCl solution (as a feed solution). The effect of polymer and cross-linker composition on swelling capacity was studied to optimize the synthesized hydrogel composition. At 50 °C, the hydrogel releases nearly >70% of the water absorbed during the FO process at room temperatures, and water flux can be recovered by up to 86.6% of the initial flux after 12 hydrogel (draw solute) regenerations. Furthermore, this study suggests that incorporating cationic polyelectrolytes into the polymeric network enhances FO performances and lowers the actual energy requirements for (draw solute) regeneration. This study represents a significant step toward the commercial implementation of a hy...

Barzegarkhoo, R, Farhangi, M, Aguilera, RP, Lee, SS, Blaabjerg, F & Siwakoti, YP 2022, 'Common-Ground Grid-Connected Five-Level Transformerless Inverter With Integrated Dynamic Voltage Boosting Feature', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 6, pp. 6661-6672.
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Barzegarkhoo, R, Farhangi, M, Lee, SS, Aguilera, RP, Siwakoti, YP & Pou, J 2022, 'Nine-Level Nine-Switch Common-Ground Switched-Capacitor Inverter Suitable for High-Frequency AC-Microgrid Applications', IEEE Transactions on Power Electronics, vol. 37, no. 5, pp. 6132-6143.
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Barzegarkhoo, R, Forouzesh, M, Lee, SS, Blaabjerg, F & Siwakoti, YP 2022, 'Switched-Capacitor Multilevel Inverters: A Comprehensive Review', IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 11209-11243.
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Barzegarkhoo, R, Khan, SA, Siwakoti, YP, Aguilera, RP, Lee, SS & Khan, MNH 2022, 'Implementation and Analysis of a Novel Switched-Boost Common-Ground Five-Level Inverter Modulated With Model Predictive Control Strategy', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 1, pp. 731-744.
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Basack, S, Loganathan, MK, Goswami, G & Khabbaz, H 2022, 'Saltwater Intrusion into Coastal Aquifers and Associated Risk Management: Critical Review and Research Directives', Journal of Coastal Research, vol. 38, no. 3.
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Basack, S, Nimbalkar, S, Karakouzian, M, Bharadwaj, S, Xie, Z & Krause, N 2022, 'Field Installation Effects of Stone Columns on Load Settlement Characteristics of Reinforced Soft Ground', International Journal of Geomechanics, vol. 22, no. 4.
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Bashir, MR, Gill, AQ & Beydoun, G 2022, 'A Reference Architecture for IoT-Enabled Smart Buildings', SN Computer Science, vol. 3, no. 6.
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AbstractThe management and analytics of big data generated from IoT sensors deployed in smart buildings pose a real challenge in today’s world. Hence, there is a clear need for an IoT focused Integrated Big Data Management and Analytics framework to enable the near real-time autonomous control and management of smart buildings. The focus of this paper is on the development and evaluation of the reference architecture required to support such a framework. The applicability of the reference architecture is evaluated by taking into account various example scenarios for a smart building involving the management and analysis of near real-time IoT data from 1000 sensors. The results demonstrate that the reference architecture can guide the complex integration and orchestration of real-time IoT data management, analytics, and autonomous control of smart buildings, and that the architecture can be scaled up to address challenges for other smart environments.

Bem, NFSD, Ruppert, MG, Fleming, AJ & Yong, YK 2022, 'Simultaneous tip force and displacement sensing for AFM cantilevers with on-chip actuation: Design and characterization for off-resonance tapping mode', Sensors and Actuators A: Physical, vol. 338, pp. 113496-113496.
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Bendoy, AP, Zeweldi, HG, Park, MJ, Shon, HK, Kim, H, Chung, W-J & Nisola, GM 2022, 'Silicene nanosheets as support fillers for thin film composite forward osmosis membranes', Desalination, vol. 536, pp. 115817-115817.
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Bendoy, AP, Zeweldi, HG, Park, MJ, Shon, HK, Kim, H, Chung, W-J & Nisola, GM 2022, 'Thermo-responsive hydrogel with deep eutectic mixture co-monomer as drawing agent for forward osmosis', Desalination, vol. 542, pp. 116067-116067.
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Benedict, G & Gill, AQ 2022, 'A regulatory control framework for decentrally governed DLT systems: Action design research', Information & Management, vol. 59, no. 7, pp. 103555-103555.
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Beni, HM, Mortazavi, H & Islam, MS 2022, 'Biomedical and biophysical limits to mathematical modeling of pulmonary system mechanics: a scoping review on aerosol and drug delivery', Biomechanics and Modeling in Mechanobiology, vol. 21, no. 1, pp. 79-87.
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Bersenev, EY, Berseneva, АP, Prysyazhnyuk, A, McGregor, C, Berseneva, IА, Funtova, II & Chernikova, AG 2022, 'Cybernetic Approach to Health Assessment', CARDIOMETRY, no. 23, pp. 31-40.
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The exploration of orbital space served as a prerequisite for the creation of a new direction of medical science in relation to the very extreme conditions of life of spacecraft crews. Space medicine, relying on the most modern research methods and approaches, thanks to the development of new medical devices and the use of unique data analysis algorithms, has made a significant contribution to the development of telemedicine, medical cybernetics, and prenosological principles for assessing the state of human health. The review reflects the main stages in the development of medical cybernetics and prenosological diagnostics based on the assessment of the regulatory components of the cardiovascular system. Discussed the aspects of the application of the method of mathematical analysis of the heart rhythm in relation to the assessment and forecast of the working capacity of cosmonauts, at the simulating model of microgravity and confinement. Shown the useful methodically apply for the healthcare of manufacture teams at the plants, passenger bus driver’s employments. As the part of appliance of the new advance tools of children and adolescents public health during the educating process at schools. The created system for analyzing the current functional state of human health and mathematical models that make it possible to predict its negative changes make it possible to predetermine the vector of development of medicine in the future. The foundations of knowledge gained over the period of more than 70 years of scientific activity of Professor R.M. Bavsky are reflected in promising areas of cardiology research using computer technologies - such as Cardiometry technologies.

Beyhan, B, Akçomak, S & Cetindamar, D 2022, 'The Startup Selection Process in Accelerators: Qualitative Evidence from Turkey', Entrepreneurship Research Journal, vol. 0, no. 0, pp. 1-31.
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Abstract Startup selection is an essential mechanism of how accelerators create value. Through in-depth case studies of 10 accelerators in Turkey, our research explores the selection process in accelerators. Our findings indicate that accelerators overcome their context’s extreme uncertainty by involving various actors in the selection process and reducing the information asymmetries for investors and startups. Accelerators tend to select effortlessly coachable startups, willing to collaborate with accelerators, mentors, or other actors, and passionate enough to overcome the pressure of creating a business at a fast pace. Our research also exhibits that the selection process serves startups by directing and training them to transmit the right signals to receivers, primarily investors. Accelerators prefer to work with entrepreneurial teams that are coachable, passionate, and collaborative to vibrate the right signals. Similarly, the accelerators’ selection process helps investors by decreasing signaling noise and mitigate information asymmetry. By doing so, accelerators contribute to a well-functioning and more effective entrepreneurship ecosystem.

Bhatnagar, P, Singh, AK, Gupta, KK & Siwakoti, YP 2022, 'A Switched-Capacitors-Based 13-Level Inverter', IEEE Transactions on Power Electronics, vol. 37, no. 1, pp. 644-658.
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Bhattacharya, S, Biswas, P, Canning, J & Bandyopadhyay, S 2022, 'Realization of optical fiber regenerated gratings by rapid cooling and split annealing', Optics Letters, vol. 47, no. 24, pp. 6444-6444.
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Rapid cooling, or quenching, during regeneration of seed gratings in standard single-mode silica optical fiber is explored. It is shown that regeneration can be broken up into stages in time. The novel, to the best of our knowledge, method of “split annealing” offers a unique tool for optimizing regeneration and studying fundamental glass science within a one-dimensional bi-material system. We demonstrate regeneration at temperatures as high as T = 1200°C for the first time as well as opening up an approach suited to batch processing of regenerated gratings.

Bhol, P, Swain, S, Altaee, A, Saxena, M & Samal, AK 2022, 'Cobalt–iron decorated tellurium nanotubes for high energy density supercapacitor', Materials Today Chemistry, vol. 24, pp. 100871-100871.
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We report the synthesis of cobalt-iron (Co-Fe) decorated tellurium nanotubes (Te NTs) using semiconductive Te NTs as a sacrificial template using the wet chemical method. The Co and Fe precursor concentration incorporated into Te NT plays a significant role in obtaining various bimetallic telluride structures. The one-dimensional (1-D) structure of Co-Fe decorated Te NTs with Te NTs in the backbone provides superior conductivity and exhibits high electrochemical performance with battery type electrode behaviour. The Co-Fe decorated Te NTs electrode is combined with the electric double-layer capacitors (EDLC) type electrode activated carbon (AC) to tune the energy density performance. The asymmetric assembly shows an excellent specific capacitance of 179.2 F g-1 (48.7 mAh g-1) at a current density of 0.9 A g-1 in 4 M KOH electrolyte. More importantly, it exhibits a maximum energy density of 62.1 Wh Kg-1 at a power density of 1138.2 W Kg-1 under a potential window of 1.58 V. This potential finding shows the significant applicability of Te NTs as a template for the synthesis of bimetallic tellurides with unique morphologies. The synergistic effect from multimetals and anisotropic morphology is beneficial for energy storage applications.

Bhowmick, S, Xu, F, Molla, MM & Saha, SC 2022, 'Chaotic phenomena of natural convection for water in a V-shaped enclosure', International Journal of Thermal Sciences, vol. 176, pp. 107526-107526.
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Bi, S, Cui, J, Ni, W, Jiang, Y, Yu, S & Wang, X 2022, 'Three-Dimensional Cooperative Positioning for Internet of Things Provenance', IEEE Internet of Things Journal, vol. 9, no. 20, pp. 19945-19958.
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Bin Sawad, A, Narayan, B, Alnefaie, A, Maqbool, A, Mckie, I, Smith, J, Yuksel, B, Puthal, D, Prasad, M & Kocaballi, AB 2022, 'A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions', Sensors, vol. 22, no. 7, pp. 2625-2625.
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This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library. Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. The search retrieved 1087 articles. Twenty-six studies met the inclusion criteria. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. One agent was not specified. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies. Although many users in the studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions due to the insufficient reporting of technical implementation details.

Binh, NTM, Ngoc, NH, Binh, HTT, Van, NK & Yu, S 2022, 'A family system based evolutionary algorithm for obstacle-evasion minimal exposure path problem in Internet of Things', Expert Systems with Applications, vol. 200, pp. 116943-116943.
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Binsawad, M, Abbasi, GA & Sohaib, O 2022, 'People’s expectations and experiences of big data collection in the Saudi context', PeerJ Computer Science, vol. 8, pp. e926-e926.
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Big data and machine learning technologies facilitate various business intelligence activities for businesses. However, personal data collection can generate adverse effects on consumers. Big data collection can compromise people’s sense of autonomy, harming digital privacy, transparency and trust. This research investigates personal data collection, control, awareness, and privacy regulation on people’s autonomy in Saudi. This study used a hybrid analytical model that incorporates symmetrical and asymmetrical analysisviafuzzy set qualitative comparative analysis (fsQCA) to analyze consumer sense of autonomy regarding big data collection. The symmetrical shows that ‘Control’ had the most significant influence on people’s autonomy, followed by ‘Big data collection’ and ‘Awareness’. The fsQCA shows 84% of the variation, explaining the people’s autonomy.

Biswas, PC, Rani, S, Hossain, MA, Islam, MR & Canning, J 2022, 'Simultaneous multi-analyte sensing using a 2D quad-beam diffraction smartphone imaging spectrometer', Sensors and Actuators B: Chemical, vol. 352, pp. 130994-130994.
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Bliuc, D, Tran, T, Adachi, JD, Atkins, GJ, Berger, C, Bergh, J, Cappai, R, Eisman, JA, Geel, T, Geusens, P, Goltzman, D, Hanley, DA, Josse, R, Kaiser, S, Kovacs, CS, Langsetmo, L, Prior, JC, Nguyen, TV, Solomon, LB, Stapledon, C & Center, JR 2022, 'Reply to: The Association Between Cognitive Decline and Bone Loss and Fracture Risk Is Not Affected by Medication With Anticholinergic Effect', Journal of Bone and Mineral Research, vol. 37, no. 5, pp. 1075-1076.
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Bo, L, Li, Q, Tian, Y, Wu, D, Yu, Y, Chen, X & Gao, W 2022, 'Nonlinear dynamic investigation of the perovskite solar cell with GPLR-FGP stiffeners under blast impact', International Journal of Mechanical Sciences, vol. 213, pp. 106866-106866.
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Bobtsov, A, Ortega, R, Yi, B & Nikolaev, N 2022, 'Adaptive state estimation of state-affine systems with unknown time-varying parameters', International Journal of Control, vol. 95, no. 9, pp. 2460-2472.
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Bobtsov, A, Yi, B, Ortega, R & Astolfi, A 2022, 'Generation of New Exciting Regressors for Consistent Online Estimation of Unknown Constant Parameters', IEEE Transactions on Automatic Control, vol. 67, no. 9, pp. 4746-4753.
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Bordbar, M, Neshat, A, Javadi, S, Pradhan, B, Dixon, B & Paryani, S 2022, 'Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches', Natural Hazards, vol. 110, no. 3, pp. 1799-1820.
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The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R2, and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area. Graphic abstract: [Figure not available: see fulltext.]

Bour, H, Abolhasan, M, Jafarizadeh, S, Lipman, J & Makhdoom, I 2022, 'A multi-layered intrusion detection system for software defined networking', Computers and Electrical Engineering, vol. 101, pp. 108042-108042.
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Bourahmoune, K, Ishac, K & Amagasa, T 2022, 'Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion', Sensors, vol. 22, no. 14, pp. 5337-5337.
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We present a solution for intelligent posture training based on accurate, real-time sitting posture monitoring using the LifeChair IoT cushion and supervised machine learning from pressure sensing and user body data. We demonstrate our system’s performance in sitting posture and seated stretch recognition tasks with over 98.82% accuracy in recognizing 15 different sitting postures and 97.94% in recognizing six seated stretches. We also show that user BMI divergence significantly affects posture recognition accuracy using machine learning. We validate our method’s performance in five different real-world workplace environments and discuss training strategies for the machine learning models. Finally, we propose the first smart posture data-driven stretch recommendation system in alignment with physiotherapy standards.

Braytee, A, Naji, M & Kennedy, PJ 2022, 'Unsupervised Domain-Adaptation-Based Tensor Feature Learning With Structure Preservation', IEEE Transactions on Artificial Intelligence, vol. 3, no. 3, pp. 370-380.
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Brindha, GR, Rishiikeshwer, BS, Santhi, B, Nakendraprasath, K, Manikandan, R & Gandomi, AH 2022, 'Precise prediction of multiple anticancer drug efficacy using multi target regression and support vector regression analysis', Computer Methods and Programs in Biomedicine, vol. 224, pp. 107027-107027.
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Bryant, L, Sedlarevic, N, Stubbs, P, Bailey, B, Nguyen, V, Bluff, A, Barnett, D, Estela, M, Hayes, C, Jacobs, C, Kneebone, I, Lucas, C, Mehta, P, Power, E & Hemsley, B 2022, 'Collaborative co-design and evaluation of an immersive virtual reality application prototype for communication rehabilitation (DISCOVR prototype)', Disability and Rehabilitation: Assistive Technology, pp. 1-10.
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PURPOSE: Virtual reality (VR) lends itself to communication rehabilitation by creating safe, replicable, and authentic simulated environments in which users learn and practice communication skills. The aim of this research was to obtain the views of health professionals and technology specialists on the design characteristics and usability of a prototype VR application for communication rehabilitation. MATERIALS AND METHODS: Nine professionals from different health and technology disciplines participated in an online focus group or individual online interview to evaluate the application and use of the VR prototype. Data sources were analysed using a content thematic analysis. RESULTS: Four main themes relating to VR design and implementation in rehabilitation were identified: (i) designing rehabilitation-focused virtual worlds; (ii) understanding and using VR hardware; (iii) making room for VR in rehabilitation and training; and (iv) implementing VR will not replace the health professional's role. DISCUSSION: Health professionals and technology specialists engaged in co-design while evaluating the VR prototype. They identified software features requiring careful consideration to ensure improved usability, client safety, and success in communication rehabilitation outcomes. Continuing inclusive co-design, engaging health professionals, clients with communication disability, and their families will be essential to creating useable VR applications and integrating these successfully into rehabilitation. Implications for rehabilitationHealth and technology professionals, along with clients, are integral to the co-design of new VR technology applications.Design of VR applications needs to consider the client's communication, physical, cognitive, sensory, psychosocial, and emotional needs for greater usability of these programs.Realism and authenticity of interactions, characters, and environments are considered important factors to allow users to be fully immersed in v...

Buchlak, QD, Clair, J, Esmaili, N, Barmare, A & Chandrasekaran, S 2022, 'Clinical outcomes associated with robotic and computer-navigated total knee arthroplasty: a machine learning-augmented systematic review', European Journal of Orthopaedic Surgery & Traumatology, vol. 32, no. 5, pp. 915-931.
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Bui, HT, Hussain, OK, Prior, D, Hussain, FK & Saberi, M 2022, 'Proof by Earnestness (PoE) to determine the authenticity of subjective information in blockchains - application in supply chain risk management', Knowledge-Based Systems, vol. 250, pp. 108972-108972.
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Bui, VG, Tu Bui, TM, Ong, HC, Nižetić, S, Bui, VH, Xuan Nguyen, TT, Atabani, AE, Štěpanec, L, Phu Pham, LH & Hoang, AT 2022, 'Optimizing operation parameters of a spark-ignition engine fueled with biogas-hydrogen blend integrated into biomass-solar hybrid renewable energy system', Energy, vol. 252, pp. 124052-124052.
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Bukhari, A, Hussain, FK & Hussain, OK 2022, 'Fog node discovery and selection: A Systematic literature review', Future Generation Computer Systems, vol. 135, pp. 114-128.
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Bukhari, AA, Hussain, FK & Hussain, OK 2022, 'Intelligent context-aware fog node discovery', Internet of Things, vol. 20, pp. 100607-100607.
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Cagno, E, Accordini, D, Trianni, A, Katic, M, Ferrari, N & Gambaro, F 2022, 'Understanding the impacts of energy efficiency measures on a Company’s operational performance: A new framework', Applied Energy, vol. 328, pp. 120118-120118.
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Cagno, E, Franzò, S, Storoni, E & Trianni, A 2022, 'A characterisation framework of energy services offered by energy service companies', Applied Energy, vol. 324, pp. 119674-119674.
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Cai, B, Li, X, Kong, W, Yuan, J & Yu, S 2022, 'A Reliable and Lightweight Trust Inference Model for Service Recommendation in SIoT', IEEE Internet of Things Journal, vol. 9, no. 13, pp. 10988-11003.
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Cai, G, Wang, C, Li, J, Xu, Z, He, X & Zhao, C 2022, 'Study on Tensile Properties of Unsaturated Soil Based on Three Dimensional Discrete Element Method', Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering, vol. 30, no. 5, pp. 1228-1244.
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Based on the discrete element method for unsaturated materials proposed by the author, the PFC3D (Particle Flow Code in Three Dimensions) particle flow discrete element analysis program is improved, and a discrete element model suitable for both clay and sand under uniaxial tension is established. The relationship between uniaxial tensile stress and displacement and uniaxial tensile strength are studied. The influence of different microstructure parameters on the tensile failure of soil is explored, and the relationship between saturation and cohesive strength between particles is established by taking uniaxial tensile strength as a bridge. The uniaxial tensile test of clay and sand with different initial void ratio and saturation is studied, and the tensile properties of unsaturated soil and the applicability of discrete element model and program to simulate unsaturated soil are deeply studied. The results show that:among the five microstructure parameters of normal bond strength, shear bond strength, Young's modulus, stiffness ratio and friction coefficient, the influence of normal bond strength on uniaxial tensile simulation is the largest, followed by shear bond strength, Young's modulus and stiffness ratio, and the friction coefficient has the least influence; the uniaxial tensile strength of clay increases at first and then decreases with the increase of saturation. The results show that the increase rate of uniaxial tensile strength on the left side (dry side) is greater than that on the right side (wet side); the uniaxial tensile strength of sand shows a 'increase-decrease-increase' rule with the increase of saturation; the simulation results are in good agreement with the experimental results, which verifies the applicability of the discrete element model and the numerical analysis program in the simulation of uniaxial tensile properties of unsaturated materials.

Cai, Y, Lu, Z, Pan, Y, He, L, Guo, X & Zhang, J 2022, 'Optimal scheduling of a hybrid AC/DC multi-energy microgrid considering uncertainties and Stackelberg game-based integrated demand response', International Journal of Electrical Power & Energy Systems, vol. 142, pp. 108341-108341.
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Cai, Y, Zhu, M, Meng, X, Zhou, JL, Zhang, H & Shen, X 2022, 'The role of biochar on alleviating ammonia toxicity in anaerobic digestion of nitrogen-rich wastes: A review', Bioresource Technology, vol. 351, pp. 126924-126924.
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Canning, J, Guo, Y & Chaczko, Z 2022, '(INVITED)Sustainability, livability and wellbeing in a bionic internet-of-things', Optical Materials: X, vol. 16, pp. 100204-100204.
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Cao, L 2022, 'A New Age of AI: Features and Futures', IEEE Intelligent Systems, vol. 37, no. 1, pp. 25-37.
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Cao, L 2022, 'AI in Combating the COVID-19 Pandemic', IEEE Intelligent Systems, vol. 37, no. 2, pp. 3-13.
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Cao, L 2022, 'AI Science and Engineering', IEEE Intelligent Systems, vol. 37, no. 1, pp. 14-15.
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Cao, L 2022, 'AI Science and Engineering: A New Field', IEEE Intelligent Systems, vol. 37, no. 1, pp. 3-13.
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Cao, L 2022, 'AutoAI: Autonomous AI', IEEE Intelligent Systems, vol. 37, no. 5, pp. 3-5.
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In the AI evolution, a significant and lasting vision and mission has been on designing autonomous AI systems (AutoAI). AutoAI differs significantly from another set of movements on automated machine learning (AutoML) and automated data science (AutoDS), which are often deemed interchangeable with automated AI. AutoML and AutoDS aim to automate some of the analytical and learning tasks, processes, and pipelines. This issue highlights the theme on AutoAI: Autonomous AI with six feature articles. My editorial further clarifies various misconceptions, myths, and pitfalls about the three related and often confused areas: AutoAI, AutoML, and AutoDS. This issue also includes an article on parallel population and human in the column AI Expert, expert-machine collaboration in the column AI Focus, and another article on intelligent mobile spaces and metaverses for the AI and Cyber-Physical-Social Systems (AI-CPSS) department.

Cao, L 2022, 'Beyond AutoML: Mindful and Actionable AI and AutoAI With Mind and Action', IEEE Intelligent Systems, vol. 37, no. 5, pp. 6-18.
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Automated machine learning (AutoML), in particular, neural architecture search (NAS) for deep learning, has ignited the fast-paced development of automating data science (AutoDS) and artificial intelligence. However, in the existing literature and practice, AutoML, AutoDS, and autonomous AI (AutoAI) are highly interchangeable and primarily centered on the automation engineering of data-driven analytics and learning pipelines. This challenges the realization of the full spectrum of AI paradigms and human-like to human-level intelligent and autonomous systems. Going beyond the state-of-the-art paradigm of AutoML and their automation engineering, there is an expectation that the new age of AI and autonomous AI (or AutoAI+) will incorporate mind-to-action intelligence and integrate them with autonomy. We pave the way for this new AI and AutoAI integrating mindful AI and AutoAI with AI mind and mindfulness and actionable AI and AutoAI with AI actions and actionability and translating AI mind to AI action for autonomous, all-around AI systems.

Cao, L 2022, 'Beyond i.i.d.: Non-IID Thinking, Informatics, and Learning', IEEE Intelligent Systems, vol. 37, no. 4, pp. 5-17.
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Cao, L 2022, 'Decentralized AI: Edge Intelligence and Smart Blockchain, Metaverse, Web3, and DeSci', IEEE Intelligent Systems, vol. 37, no. 3, pp. 6-19.
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Cao, L 2022, 'Deep Learning Applications', IEEE Intelligent Systems, vol. 37, no. 3, pp. 3-5.
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Cao, L 2022, 'Non-IID Federated Learning', IEEE Intelligent Systems, vol. 37, no. 2, pp. 14-15.
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Cao, L 2022, 'Non-IID Learning', IEEE Intelligent Systems, vol. 37, no. 4, pp. 3-4.
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Cao, TN-D, Bui, X-T, Le, L-T, Dang, B-T, Tran, DP-H, Vo, T-K-Q, Tran, H-T, Nguyen, T-B, Mukhtar, H, Pan, S-Y, Varjani, S, Ngo, HH & Vo, T-D-H 2022, 'An overview of deploying membrane bioreactors in saline wastewater treatment from perspectives of microbial and treatment performance', Bioresource Technology, vol. 363, pp. 127831-127831.
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Cao, X & Tsang, IW 2022, 'Shattering Distribution for Active Learning', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 1, pp. 215-228.
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Active learning (AL) aims to maximize the learning performance of the current hypothesis by drawing as few labels as possible from an input distribution. Generally, most existing AL algorithms prune the hypothesis set via querying labels of unlabeled samples and could be deemed as a hypothesis-pruning strategy. However, this process critically depends on the initial hypothesis and its subsequent updates. This article presents a distribution-shattering strategy without an estimation of hypotheses by shattering the number density of the input distribution. For any hypothesis class, we halve the number density of an input distribution to obtain a shattered distribution, which characterizes any hypothesis with a lower bound on VC dimension. Our analysis shows that sampling in a shattered distribution reduces label complexity and error disagreement. With this paradigm guarantee, in an input distribution, a Shattered Distribution-based AL (SDAL) algorithm is derived to continuously split the shattered distribution into a number of representative samples. An empirical evaluation of benchmark data sets further verifies the effectiveness of the halving and querying abilities of SDAL in real-world AL tasks with limited labels. Experiments on active querying with adversarial examples and noisy labels further verify our theoretical insights on the performance disagreement of the hypothesis-pruning and distribution-shattering strategies. Our code is available at https://github.com/XiaofengCao-MachineLearning/Shattering-Distribution-for-Active-Learning.

Cao, X, Tsang, IW & Xu, J 2022, 'Cold-Start Active Sampling Via γ-Tube', IEEE Transactions on Cybernetics, vol. 52, no. 7, pp. 6034-6045.
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Active learning (AL) improves the generalization performance for the current classification hypothesis by querying labels from a pool of unlabeled data. The sampling process is typically assessed by an informative, representative, or diverse evaluation policy. However, the policy, which needs an initial labeled set to start, may degenerate its performance in a cold-start hypothesis. In this article, we first show that typical AL sampling can be equivalently formulated as geometric sampling over minimum enclosing balls1 (MEBs) of clusters. Following the ɣ-tube structure in geometric clustering, we then divide one MEB covering a cluster into two parts: 1) a ɣ-tube and 2) a ɣ-ball. By estimating the error disagreement between sampling in MEB and ɣ-ball, our theoretical insight reveals that ɣ-tube can effectively measure the disagreement of hypotheses in original space over MEB and sampling space over ɣ-ball. To tighten our insight, we present generalization analysis, and the results show that sampling in ɣ-tube can derive higher probability bound to achieve a nearly zero generalization error. With these analyses, we finally apply the informative sampling policy of AL over ɣ-tube to present a tube AL (TAL) algorithm against the cold-start sampling issue. As a result, the dependency between the querying process and the evaluation policy of active sampling can be alleviated. Experimental results show that by using the ɣ-tube structure to deal with cold-start sampling, TAL achieves the superior performance than standard AL evaluation baselines by presenting substantial accuracy improvements. Image edge recognition extends our theoretical results.

Cao, Y, Li, B, Wen, S & Huang, T 2022, 'Consensus tracking of stochastic multi-agent system with actuator faults and switching topologies', Information Sciences, vol. 607, pp. 921-930.
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Cao, Y, Lv, T & Ni, W 2022, 'Two-Timescale Optimization for Intelligent Reflecting Surface-Assisted MIMO Transmission in Fast-Changing Channels', IEEE Transactions on Wireless Communications, vol. 21, no. 12, pp. 10424-10437.
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Cao, Y, Zhao, L, Wen, S & Huang, T 2022, 'Lag H∞ synchronization of coupled neural networks with multiple state couplings and multiple delayed state couplings', Neural Networks, vol. 151, pp. 143-155.
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Catchpoole, DR, Gao, D & Mullins, P 2022, 'The ISBER 2022 Awards', Biopreservation and Biobanking, vol. 20, no. 3, pp. 306-307.
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Cetindamar, D & Phaal, R 2022, 'Technology Management in the Age of Digital Technologies', IEEE Transactions on Engineering Management, pp. 1-9.
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Cetindamar, D, Abedin, B & Shirahada, K 2022, 'The Role of Employees in Digital Transformation: A Preliminary Study on How Employees’ Digital Literacy Impacts Use of Digital Technologies', IEEE Transactions on Engineering Management, pp. 1-12.
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Even though digital technologies such as cloud technologies are prevalent in transforming businesses, the role of employees and their digital skills in the process is, to a large extent, neglected. This study brings forward the novel concept of digital literacy to explore the role of employees in understanding the wide variety of opportunities of digital technologies and their actualization. By treating digital literacy as the antecedent of cognitive behavior of employees in utilizing cloud technology at companies, we apply the Theory of Planned Behavior (TPB) for analyzing preliminary empirical data collected from 124 Australian employees’ technology use intentionality and behavior. The quantitative analysis shows that the TPB holds for the utilization of cloud technology and there is a positive relationship between employees' digital literacy and the utilization of cloud technology at companies. Overall, the study contributes to the technology management literature by offering a workable construct to measure the digital skills of employees in the form of digital literacy. Further, it expands the TPB framework by introducing digital literacy as a perceived behavior control variable that helps to examine the role of employees in digital transformation. The paper ends with implications and limitations of our preliminary study, followed with suggestions for future studies.

Cetindamar, D, Kitto, K, Wu, M, Zhang, Y, Abedin, B & Knight, S 2022, 'Explicating AI Literacy of Employees at Digital Workplaces', IEEE Transactions on Engineering Management, pp. 1-14.
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This paper aims to understand the definition and dimensions of artificial intelligence (AI) literacy. Digital technologies, including AI, trigger organizational affordances in workplaces, yet few studies have investigated employees’ AI literacy. This paper uses a bibliometrics analysis of 270 articles to explore the meaning of AI literacy of employees in the extant literature. Descriptive statistics, keyword co-occurrence analysis, and a hierarchical topic tree are employed to profile the research landscape and identify the core research themes and relevant papers related to AI literacy’s definition, dimensions, challenges, and future directions. Findings highlight four sets of capabilities associated with AI literacy, namely technology-related, work-related, human-machine-related, and learning-related capabilities, pointing also to the importance of operationalizing AI literacy for non AI professionals. This result contributes to the literature associated with technology management studies by offering a novel conceptualization of AI literacy and link it to the employee’s role in digital workplaces. We conclude by inviting researchers to examine the effect of employee-technology interactions on employees’ AI literacy, which might improve the design and use of AI.

Cetindamar, D, Shdifat, B & Erfani, E 2022, 'Understanding Big Data Analytics Capability and Sustainable Supply Chains', Information Systems Management, vol. 39, no. 1, pp. 19-33.
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This paper presents the knowledge available in the literature regarding big data analytics capability (BDAC) and sustainable supply chain performance (SSCP). A detailed analysis of systematic literature reviews points out the lack of studies bridging these two separate streams of work. The paper puts forward a research agenda for researchers interested in understanding the impact of big data on sustainability.

Chacon, A, Kielly, M, Rutherford, H, Franklin, DR, Caracciolo, A, Buonanno, L, D’Adda, I, Rosenfeld, A, Guatelli, S, Carminati, M, Fiorini, C & Safavi-Naeini, M 2022, 'Detection and discrimination of neutron capture events for NCEPT dose quantification', Scientific Reports, vol. 12, no. 1.
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AbstractNeutron Capture Enhanced Particle Therapy (NCEPT) boosts the effectiveness of particle therapy by capturing thermal neutrons produced by beam-target nuclear interactions in and around the treatment site, using tumour-specific $$^{10}$$ 10 B or $$^{157}$$ 157 Gd-based neutron capture agents. Neutron captures release high-LET secondary particles together with gamma photons with energies of 478 keV or one of several energies up to 7.94 MeV, for $$^{10}$$ 10 B and $$^{157}$$ 157 Gd, respectively. A key requirement for NCEPT’s translation is the development of in vivo dosimetry techniques which can measure both the direct ion dose and the dose due t...

Chai, J & Tsang, IW 2022, 'Learning With Label Proportions by Incorporating Unmarked Data', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5898-5912.
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Learning with label proportions (LLP) deals with the problem that the training data are provided as bags, where the label proportions of training bags rather than the labels of individual training instances are accessible. Existing LLP studies assume that the label proportions of all training bags are accessible. However, in many applications, it is time-consuming to mark all training bags with label proportions, which leads to the problem of learning with both marked and unmarked bags, namely, semisupervised LLP (SLLP). In this work, we propose semisupervised proportional support vector machine (SS-∝SVM), which extends the proportional SVM (∝SVM) model to its semisupervised version. To the best of our knowledge, SS-∝SVM is the first attempt to cope with the SLLP problem. Two realizations, alter-SS-∝SVM and conv-SS-∝SVM, which are based on alternating optimization and convex relaxation, respectively, are developed to solve the proposed SS-∝SVM model. Moreover, we design a cutting plane (CP) method to optimize conv-SS-∝SVM with a guaranteed convergence rate and present a fast accelerated proximal gradient method to solve the multiple kernel learning subproblem in conv-SS-∝SVM efficiently. Empirical experiments not only justify the superiority of SS-∝SVM over its supervised counterpart in classification accuracy but also demonstrate the high competitive computational efficiency of the CP optimization of conv-SS-∝SVM.

Chakraborty, S 2022, 'TOPSIS and Modified TOPSIS: A comparative analysis', Decision Analytics Journal, vol. 2, pp. 100021-100021.
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Chakraborty, S, Milner, LE, Zhu, X, Parker, A & Heimlich, M 2022, 'Analysis and Comparison of Marchand and Transformer Baluns Applied in GaAs', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 11, pp. 4278-4282.
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Chakraborty, SC, Qamruzzaman, M, Zaman, MWU, Alam, MM, Hossain, MD, Pramanik, BK, Nguyen, LN, Nghiem, LD, Ahmed, MF, Zhou, JL, Mondal, MIH, Hossain, MA, Johir, MAH, Ahmed, MB, Sithi, JA, Zargar, M & Moni, MA 2022, 'Metals in e-waste: Occurrence, fate, impacts and remediation technologies', Process Safety and Environmental Protection, vol. 162, pp. 230-252.
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Chakraborty, SC, Zaman, MWU, Hoque, M, Qamruzzaman, M, Zaman, JU, Hossain, D, Pramanik, BK, Nguyen, LN, Nghiem, LD, Mofijur, M, Mondal, MIH, Sithi, JA, Shahriar, SMS, Johir, MAH & Ahmed, MB 2022, 'Metals extraction processes from electronic waste: constraints and opportunities', Environmental Science and Pollution Research, vol. 29, no. 22, pp. 32651-32669.
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Chalmers, T & Lal, S 2022, 'Assessing cardiovascular links to depression and anxiety in Australian professional drivers', Journal of Integrative Neuroscience, vol. 21, no. 1, pp. 043-043.
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Chalmers, T, Eaves, S, Lees, T, Lin, C, Newton, PJ, Clifton‐Bligh, R, McLachlan, CS, Gustin, SM & Lal, S 2022, 'The relationship between neurocognitive performance and HRV parameters in nurses and non‐healthcare participants', Brain and Behavior, vol. 12, no. 3.
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Chandra Adhikari, S, Kumar Chanda, R, Bhowmick, S, Nath Mondal, R & Chandra Saha, S 2022, 'Pressure-Induced Instability Characteristics of a Transient Flow and Energy Distribution through a Loosely Bent Square Duct', Energy Engineering, vol. 119, no. 1, pp. 429-451.
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Chandra Shit, R, Sharma, S, Watters, P, Yelamarthi, K, Pradhan, B, Davison, R, Morgan, G & Puthal, D 2022, 'Privacy‐preserving cooperative localization in vehicular edge computing infrastructure', Concurrency and Computation: Practice and Experience, vol. 34, no. 14.
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Chandrakanthan, V, Rorimpandey, P, Zanini, F, Chacon, D, Olivier, J, Joshi, S, Kang, YC, Knezevic, K, Huang, Y, Qiao, Q, Oliver, RA, Unnikrishnan, A, Carter, DR, Lee, B, Brownlee, C, Power, C, Brink, R, Mendez-Ferrer, S, Enikolopov, G, Walsh, W, Göttgens, B, Taoudi, S, Beck, D & Pimanda, JE 2022, 'Mesoderm-derived PDGFRA+ cells regulate the emergence of hematopoietic stem cells in the dorsal aorta', Nature Cell Biology, vol. 24, no. 8, pp. 1211-1225.
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AbstractMouse haematopoietic stem cells (HSCs) first emerge at embryonic day 10.5 (E10.5), on the ventral surface of the dorsal aorta, by endothelial-to-haematopoietic transition. We investigated whether mesenchymal stem cells, which provide an essential niche for long-term HSCs (LT-HSCs) in the bone marrow, reside in the aorta–gonad–mesonephros and contribute to the development of the dorsal aorta and endothelial-to-haematopoietic transition. Here we show that mesoderm-derived PDGFRA+ stromal cells (Mesp1der PSCs) contribute to the haemogenic endothelium of the dorsal aorta and populate the E10.5–E11.5 aorta–gonad–mesonephros but by E13.5 were replaced by neural-crest-derived PSCs (Wnt1der PSCs). Co-aggregating non-haemogenic endothelial cells with Mesp1der PSCs but not Wnt1der PSCs resulted in activation of a haematopoietic transcriptional programme in endothelial cells and generation of LT-HSCs. Dose-dependent inhibition of PDGFRA or BMP, WNT and NOTCH signalling interrupted this reprogramming event. Together, aorta–gonad–mesonephros Mesp1der PSCs could potentially be harnessed to manufacture LT-HSCs from endothelium.

Chandrasekar, T, Raju, SK, Ramachandran, M, Patan, R & Gandomi, AH 2022, 'Lung cancer disease detection using service-oriented architectures and multivariate boosting classifier', Applied Soft Computing, vol. 122, pp. 108820-108820.
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Chang, L, Feng, X, Yao, K, Qin, L & Zhang, W 2022, 'Accelerating Graph Similarity Search via Efficient GED Computation', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Chang, Z, Long, G, Xie, Y & Zhou, JL 2022, 'Chemical effect of sewage sludge ash on early-age hydration of cement used as supplementary cementitious material', Construction and Building Materials, vol. 322, pp. 126116-126116.
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Chang, Z, Long, G, Xie, Y & Zhou, JL 2022, 'Pozzolanic reactivity of aluminum-rich sewage sludge ash: Influence of calcination process and effect of calcination products on cement hydration', Construction and Building Materials, vol. 318, pp. 126096-126096.
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Chang, Z, Long, G, Xie, Y & Zhou, JL 2022, 'Recycling sewage sludge ash and limestone for sustainable cementitious material production', Journal of Building Engineering, vol. 49, pp. 104035-104035.
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Che, X, Zuo, H, Lu, J & Chen, D 2022, 'Fuzzy Multioutput Transfer Learning for Regression', IEEE Transactions on Fuzzy Systems, vol. 30, no. 7, pp. 2438-2451.
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Chen, C & Jin, D 2022, 'Giant nonlinearity in upconversion nanoparticles', Nature Photonics, vol. 16, no. 8, pp. 553-554.
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Chen, C, Ding, L, Liu, B, Du, Z, Liu, Y, Di, X, Shan, X, Lin, C, Zhang, M, Xu, X, Zhong, X, Wang, J, Chang, L, Halkon, B, Chen, X, Cheng, F & Wang, F 2022, 'Exploiting Dynamic Nonlinearity in Upconversion Nanoparticles for Super-Resolution Imaging', Nano Letters, vol. 22, no. 17, pp. 7136-7143.
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Chen, C, Liu, Y, Chen, L & Zhang, C 2022, 'Bidirectional Spatial-Temporal Adaptive Transformer for Urban Traffic Flow Forecasting', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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Chen, D, Liu, Y, Li, M, Guo, P, Zeng, Z, Hu, J & Guo, YJ 2022, 'A Polarization Programmable Antenna Array', Engineering, vol. 16, pp. 100-114.
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Chen, D, Wu, C, Li, J & Liao, K 2022, 'A numerical study of gas explosion with progressive venting in a utility tunnel', Process Safety and Environmental Protection, vol. 162, pp. 1124-1138.
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Chen, F & Long, G 2022, 'FedGE: Break the scalability limitation of Graph Neural Network with Federated Graph Embedding', IEEE Transactions on Big Data, pp. 1-11.
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Chen, H, Demerdash, NAO, EL-Refaie, AM, Guo, Y, Hua, W & Lee, CHT 2022, 'Investigation of a 3D-Magnetic Flux PMSM With High Torque Density for Electric Vehicles', IEEE Transactions on Energy Conversion, vol. 37, no. 2, pp. 1442-1454.
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Chen, J, Vinod, JS, Indraratna, B, Ngo, NT, Gao, R & Liu, Y 2022, 'A discrete element study on the deformation and degradation of coal-fouled ballast', Acta Geotechnica, vol. 17, no. 9, pp. 3977-3993.
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AbstractThis paper presents the results of Discrete Element Modelling (DEM) which quantitively examine the effect of coal fouling on the deformation and degradation of ballast upon cyclic loading. The degradation model described herein considers the Weibull distribution effects in tandem with a granular medium hardening law that incorporates the maximum contact criterion to capture surface abrasion and corner breakage of angular ballast. The DEM model had been calibrated initially with laboratory data obtained from large-scale direct shear testing. Subsequently, a series of cubical shear test simulations have been carried out using DEM to understand the behaviour of fouled ballast whereby the numerical particle degradation modelling could simulate the experimental response of the ballast assembly at various fouling levels. The results show that the increased level of fouling exacerbates the sleeper settlement, while decreasing the resilient modulus and the particle breakage. Ballast beneath the sleeper experiences significant breakage compared to the crib ballast, and not surprisingly, the extent of damage decreases with depth. Rigorous microscopic analysis is also presented in relation to inter-particle contacts, particle velocity and anisotropy of the ballast assembly. This micromechanical examination highlights that the decrease in ballast breakage for fouled assemblies is predominantly attributed to the inevitable decrease in inter-particle contact pressures as effected by the coating of ballast aggregates by the coal fines.

Chen, J, Wu, Y, Yang, Y, Wen, S, Shi, K, Bermak, A & Huang, T 2022, 'An Efficient Memristor-Based Circuit Implementation of Squeeze-and-Excitation Fully Convolutional Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 4, pp. 1779-1790.
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Recently, there has been a surge of interest in applying memristors to hardware implementations of deep neural networks due to various desirable properties of the memristor, such as nonvolativity, multivalue, and nanosize. Most existing neural network circuit designs, however, are based on generic frameworks that are not optimized for memristors. Furthermore, to the best of our knowledge, there are no existing efficient memristor-based implementations of complex neural network operators, such as deconvolutions and squeeze-and-excitation (SE) blocks, which are critical for achieving high accuracy in common medical image analysis applications, such as semantic segmentation. This article proposes convolution-kernel first (CKF), an efficient scheme for designing memristor-based fully convolutional neural networks (FCNs). Compared with existing neural network circuits, CKF enables effective parameter pruning, which significantly reduces circuit power consumption. Furthermore, CKF includes the novel, memristor-optimized implementations of deconvolution layers and SE blocks. Simulation results on real medical image segmentation tasks confirm that CKF obtains up to 56.2% reduction in terms of computations and 33.62-W reduction in terms of power consumption in the circuit after weight pruning while retaining high accuracy on the test set. Moreover, the pruning results can be applied directly to existing circuits without any modification for the corresponding system.

Chen, L, Chen, L, Ge, Z, Sun, Y, Hamilton, T & Zhu, X 2022, 'A W-Band SPDT Switch With 15-dBm P1dB in 55-nm Bulk CMOS', IEEE Microwave and Wireless Components Letters, vol. 32, no. 7, pp. 879-882.
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Chen, L, Liu, Y, Ren, Y, Zhu, C, Yang, S & Guo, YJ 2022, 'Synthesizing Wideband Frequency-Invariant Shaped Patterns by Linear Phase Response-Based Iterative Spatiotemporal Fourier Transform', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10378-10390.
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Chen, L, Zhu, H, Gomez-Garcia, R & Zhu, X 2022, 'Miniaturized On-Chip Notch Filter With Sharp Selectivity and >35-dB Attenuation in 0.13-μm Bulk CMOS Technology', IEEE Electron Device Letters, vol. 43, no. 8, pp. 1175-1178.
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Chen, Q, Guo, D, Ke, W, Xu, C & Nimbalkar, S 2022, 'Novel Open Trench Techniques in Mitigating Ground-Borne Vibrations due to Traffic under a Wide Range of Ground Conditions', International Journal of Geomechanics, vol. 22, no. 6.
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Chen, S, Eager, D & Zhao, L 2022, 'Enhanced frequency synchronization for concurrent aeroelastic and base vibratory energy harvesting using a softening nonlinear galloping energy harvester', Journal of Intelligent Material Systems and Structures, vol. 33, no. 5, pp. 687-702.
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This paper proposes a softening nonlinear aeroelastic galloping energy harvester for enhanced energy harvesting from concurrent wind flow and base vibration. Traditional linear aeroelastic energy harvesters have poor performance with quasi-periodic oscillations when the base vibration frequency deviates from the aeroelastic frequency. The softening nonlinearity in the proposed harvester alters the self-excited galloping frequency and simultaneously extends the large-amplitude base-excited oscillation to a wider frequency range, achieving frequency synchronization over a remarkably broadened bandwidth with periodic oscillations for efficient energy conversion from dual sources. A fully coupled aero-electro-mechanical model is built and validated with measurements on a devised prototype. At a wind speed of 5.5 m/s and base acceleration of 0.1 g, the proposed harvester improves the performance by widening the effective bandwidth by 300% compared to the linear counterpart without sacrificing the voltage level. The influences of nonlinearity configuration, excitation magnitude, and electromechanical coupling strength on the mechanical and electrical behavior are examined. The results of this paper form a baseline for future efficiency enhancement of energy harvesting from concurrent wind and base vibration utilizing monostable stiffness nonlinearities.

Chen, S-L, Liu, Y, Zhu, H, Chen, D & Guo, YJ 2022, 'Millimeter-Wave Cavity-Backed Multi-Linear Polarization Reconfigurable Antenna', IEEE Transactions on Antennas and Propagation, vol. 70, no. 4, pp. 2531-2542.
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Chen, S-L, Liu, Y, Ziolkowski, RW, Li, Z & Guo, YJ 2022, 'High-Gain Single-Feed Overmoded Cavity Antenna With Closely Spaced Phased Patch Surface', IEEE Transactions on Antennas and Propagation, vol. 70, no. 1, pp. 229-239.
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Chen, S-L, Wu, G-B, Wong, H, Chen, B-J, Chan, CH & Guo, YJ 2022, 'Millimeter-Wave Slot-Based Cavity Antennas With Flexibly-Chosen Linear Polarization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 6604-6616.
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Slot-based cavity antennas are hailed as promising candidates for millimeter-wave applications. Nevertheless, the linear-polarization (LP) angle of their broadside main beam is limited by the slots etched on the cavity’s top surface. In this work, an innovative technique is developed to significantly improve the selection flexibility of their LP inclination angle. It is attained by an integration of a single-layer, closely-spaced C-shaped patch surface. A TE710-mode slot-based cavity antenna is employed as the base configuration, which radiates a broadside beam with its LP along ϕ=90°. To effectively predict and monitor the polarization conversion of the surface-integrated TE710-mode cavity antenna, an analysis method using a unit cavity extracted from its original cavity antenna is presented. A subsequent surface-integrated system with the specified 45°-LP was then simulated, fabricated, and measured. The measured results validate that a 45°-LP state is achieved with an operating bandwidth from 33.3 to 36.5 GHz. Further investigation is conducted to flexibly choose the LP direction from ϕ=15° to 165°. Two more examples with the fabricated antenna prototypes successfully radiate the specified ϕ=15° and 75° LP beam, respectively. This near-field polarization conversion surface can be generalized to cavities with different resonant modes.

Chen, S-L, Ziolkowski, RW, Jones, B & Guo, YJ 2022, 'Analysis, Design, and Measurement of Directed-Beam Toroidal Waveguide-Based Leaky-Wave Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10141-10155.
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Chen, T, Xie, G-S, Yao, Y, Wang, Q, Shen, F, Tang, Z & Zhang, J 2022, 'Semantically Meaningful Class Prototype Learning for One-Shot Image Segmentation', IEEE Transactions on Multimedia, vol. 24, pp. 968-980.
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Chen, W-H, Chen, K-H, Chein, R-Y, Ong, HC & Arunachalam, KD 2022, 'Optimization of hydrogen enrichment via palladium membrane in vacuum environments using Taguchi method and normalized regression analysis', International Journal of Hydrogen Energy, vol. 47, no. 100, pp. 42280-42292.
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Chen, W-H, Hoang, AT, Nižetić, S, Pandey, A, Cheng, CK, Luque, R, Ong, HC, Thomas, S & Nguyen, XP 2022, 'Biomass-derived biochar: From production to application in removing heavy metal-contaminated water', Process Safety and Environmental Protection, vol. 160, pp. 704-733.
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Chen, X, Chen, S, Yao, J, Zheng, H, Zhang, Y & Tsang, IW 2022, 'Learning on Attribute-Missing Graphs', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 2, pp. 740-757.
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Chen, X, Feng, Z, Zhang, JA, Wei, Z, Yuan, X & Zhang, P 2022, 'Sensing-aided Uplink Channel Estimation for Joint Communication and Sensing', IEEE Wireless Communications Letters, pp. 1-1.
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Chen, X, Gao, C, Li, C, Yang, Y & Meng, D 2022, 'Infrared Action Detection in the Dark via Cross-Stream Attention Mechanism', IEEE Transactions on Multimedia, vol. 24, pp. 288-300.
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Action detection plays an important role in the field of video understanding and attracts considerable attention in the last decade. However, current action detection methods are mainly based on visible videos, and few of them consider scenes with low-light, where actions are difficult to be detected by existing methods, or even by human eyes. Compared with visible videos, infrared videos are more suitable for the dark environment and resistant to background clutter. In this paper, we investigate the temporal action detection problem in the dark by using infrared videos, which is, to the best of our knowledge, the first attempt in the action detection community. Our model takes the whole video as input, a Flow Estimation Network (FEN) is employed to generate the optical flow for infrared data, and it is optimized with the whole network to obtain action-related motion representations. After feature extraction, the infrared stream and flow stream are fed into a Selective Cross-stream Attention (SCA) module to narrow the performance gap between infrared and visible videos. The SCA emphasizes informative snippets and focuses on the more discriminative stream automatically. Then we adopt a snippet-level classifier to obtain action scores for all snippets and link continuous snippets into final detection results. All these modules are trained in an end-to-end manner. We collect an Infrared action Detection (InfDet) dataset obtained in the dark and conduct extensive experiments to verify the effectiveness of the proposed method. Experimental results show that our proposed method surpasses the state-of-the-art temporal action detection methods designed for visible videos, and it also achieves the best performance compared with other infrared action recognition methods on both InfAR and Infrared-Visible datasets.

Chen, X, Han, Y, Wang, X, Sun, Y & Yang, Y 2022, 'Action Keypoint Network for Efficient Video Recognition', IEEE Transactions on Image Processing, vol. 31, pp. 4980-4993.
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Chen, X, Huo, P, Yang, L, Wei, W, Yang, L, Wei, W & Ni, B-J 2022, 'Influences of Granule Properties on the Performance of Autotrophic Nitrogen Removal Granular Reactor: A Model-Based Evaluation', Bioresource Technology, vol. 356, pp. 127307-127307.
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Chen, X, Li, F, Huo, P, Liu, J, Yang, L, Li, X, Wei, W & Ni, B-J 2022, 'Influences of longitudinal gradients on methane-driven membrane biofilm reactor for complete nitrogen removal: A model-based investigation', Water Research, vol. 220, pp. 118665-118665.
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Chen, X, Li, Y, Yao, L, Adeli, E, Zhang, Y & Wang, X 2022, 'Generative adversarial U-Net for domain-free few-shot medical diagnosis', Pattern Recognition Letters, vol. 157, pp. 112-118.
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Chen, X, Wen, H, Ni, W, Zhang, S, Wang, X, Xu, S & Pei, Q 2022, 'Distributed Online Optimization of Edge Computing With Mixed Power Supply of Renewable Energy and Smart Grid', IEEE Transactions on Communications, vol. 70, no. 1, pp. 389-403.
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Chen, X, Yao, L, Wang, X, Sun, A & Sheng, QZ 2022, 'Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference', IEEE Transactions on Knowledge and Data Engineering, pp. 1-12.
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Chen, XC, Hellmann, A & Sood, S 2022, 'A framework for analyst economic incentives and cognitive biases: Origination of the walk-down in earnings forecasts', Journal of Behavioral and Experimental Finance, vol. 36, pp. 100759-100759.
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Chen, Y, Lin, S, Liang, Z, Surawski, NC & Huang, X 2022, 'Smouldering organic waste removal technology with smoke emissions cleaned by self-sustained flame', Journal of Cleaner Production, vol. 362, pp. 132363-132363.
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Chen, Y, Shimoni, O, Huang, G, Wen, S, Liao, J, Duong, HTT, Maddahfar, M, Su, QP, Ortega, DG, Lu, Y, Campbell, DH, Walsh, BJ & Jin, D 2022, 'Upconversion nanoparticle‐assisted single‐molecule assay for detecting circulating antigens of aggressive prostate cancer', Cytometry Part A, vol. 101, no. 5, pp. 400-410.
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Chen, Y, Su, Y, Zhang, M, Chai, H, Wei, Y & Yu, S 2022, 'FedTor: An Anonymous Framework of Federated Learning in Internet of Things', IEEE Internet of Things Journal, vol. 9, no. 19, pp. 18620-18631.
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Chen, Y, Wu, D, Dai, K & Gao, W 2022, 'A numerically efficient framework in failure mode evaluation of a wind turbine tower under cyclones', Marine Structures, vol. 86, pp. 103303-103303.
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Chen, Y, Wu, D, Li, H & Gao, W 2022, 'Quantifying the fatigue life of wind turbines in cyclone-prone regions', Applied Mathematical Modelling, vol. 110, pp. 455-474.
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Chen, Y, Zhao, L, Zhang, Y, Huang, S & Dissanayake, G 2022, 'Anchor Selection for SLAM Based on Graph Topology and Submodular Optimization', IEEE Transactions on Robotics, vol. 38, no. 1, pp. 329-350.
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Chen, Y, Zhu, S, Shen, M, Liu, X & Wen, S 2022, 'Event-Based Output Quantized Synchronization Control for Multiple Delayed Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-11.
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Chen, Z, Fang, J, Wei, W, Ngo, HH, Guo, W & Ni, B-J 2022, 'Emerging adsorbents for micro/nanoplastics removal from contaminated water: Advances and perspectives', Journal of Cleaner Production, vol. 371, pp. 133676-133676.
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Chen, Z, Liu, X, Wei, W, Chen, H & Ni, B-J 2022, 'Removal of microplastics and nanoplastics from urban waters: Separation and degradation', Water Research, vol. 221, pp. 118820-118820.
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The omnipresent micro/nanoplastics (MPs/NPs) in urban waters arouse great public concern. To build a MP/NP-free urban water system, enormous efforts have been made to meet this goal via separating and degrading MPs/NPs in urban waters. Herein, we comprehensively review the recent developments in the separation and degradation of MPs/NPs in urban waters. Efficient MP/NP separation techniques, such as adsorption, coagulation/flocculation, flotation, filtration, and magnetic separation are first summarized. The influence of functional materials/reagents, properties of MPs/NPs, and aquatic chemistry on the separation efficiency is analyzed. Then, MP/NP degradation methods, including electrochemical degradation, advanced oxidation processes (AOPs), photodegradation, photocatalytic degradation, and biological degradation are detailed. Also, the effects of critical functional materials/organisms and operational parameters on degradation performance are discussed. At last, the current challenges and prospects in the separation, degradation, and further upcycling of MPs/NPs in urban waters are outlined. This review will potentially guide the development of next-generation technologies for MP/NP pollution control in urban waters.

Chen, Z, Ren, Z, Zheng, R, Gao, H & Ni, B-J 2022, 'Migration behavior of impurities during the purification of waste graphite powders', Journal of Environmental Management, vol. 315, pp. 115150-115150.
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Chen, Z, Tang, M-C, Li, M, Yi, D, Mu, D & Ziolkowski, RW 2022, 'Wideband, High-Density Circularly Polarized Array With Reduced Mutual Coupling and Enhanced Realized Gain', IEEE Transactions on Antennas and Propagation, vol. 70, no. 2, pp. 1132-1143.
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Chen, Z, Wang, S, Fu, A, Gao, Y, Yu, S & Deng, RH 2022, 'LinkBreaker: Breaking the Backdoor-Trigger Link in DNNs via Neurons Consistency Check', IEEE Transactions on Information Forensics and Security, vol. 17, pp. 2000-2014.
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Chen, Z, Wei, W & Ni, B-J 2022, 'Transition metal chalcogenides as emerging electrocatalysts for urea electrolysis', Current Opinion in Electrochemistry, vol. 31, pp. 100888-100888.
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Chen, Z, Wei, W, Chen, H & Ni, B-J 2022, 'Recent advances in waste-derived functional materials for wastewater remediation', Eco-Environment & Health, vol. 1, no. 2, pp. 86-104.
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Chen, Z, Wei, W, Liu, X & Ni, B-J 2022, 'Emerging electrochemical techniques for identifying and removing micro/nanoplastics in urban waters', Water Research, vol. 221, pp. 118846-118846.
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The ubiquitous micro/nanoplastics (MPs/NPs) in urban waters are priority pollutants due to their toxic effects on living organisms. Currently, great efforts have been made to realize a plastic-free urban water system, and the identification and removal of MPs/NPs are two primary issues. Among diverse methods, emerging electrochemical techniques have gained growing interests owing to their facile implementation, high efficiency, eco-compatibility, onsite operation, etc. Herein, recent progress in the electrochemical identification and removal of MPs/NPs in urban waters are comprehensively reviewed. The electrochemical sensing of MPs/NPs and their released pollutants (e.g., bisphenol A (BPA)) has been analyzed, and the sensing principles and the featured electrochemical devices/electrodes are examined. Afterwards, recent applications of electrochemical methods (i.e., electrocoagulation, electroadsorption, electrokinetic separation and electrochemical degradation) in MPs/NPs removal are discussed in detail. The influences of critical parameters (e.g., plastics' property, current density and electrolyte) in the electrochemical identification and removal of MPs/NPs are also analyzed. Finally, the current challenges and prospects in electrochemical sensing and removal of MPs/NPs in urban waters are elaborated. This review would advance efficient electrochemical technologies for future MPs/NPs pollutions management in urban waters.

Chen, Z, Wei, W, Ni, B-J & Chen, H 2022, 'Plastic wastes derived carbon materials for green energy and sustainable environmental applications', Environmental Functional Materials, vol. 1, no. 1, pp. 34-48.
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Chen, Z, Wei, W, Song, L & Ni, B-J 2022, 'Hybrid Water Electrolysis: A New Sustainable Avenue for Energy-Saving Hydrogen Production', Sustainable Horizons, vol. 1, pp. 100002-100002.
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Chen, Z, Yuan, L, Lin, X, Qin, L & Zhang, W 2022, 'Balanced Clique Computation in Signed Networks: Concepts and Algorithms', IEEE Transactions on Knowledge and Data Engineering, pp. 1-14.
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Chen, Z, Zheng, R, Li, S, Wang, R, Wei, W, Wei, W, Ni, B-J & Chen, H 2022, 'Dual-anion etching induced in situ interfacial engineering for high-efficiency oxygen evolution', Chemical Engineering Journal, vol. 431, pp. 134304-134304.
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Chen, Z, Zheng, R, Wei, W, Wei, W, Ni, B-J & Chen, H 2022, 'Unlocking the electrocatalytic activity of natural chalcopyrite using mechanochemistry', Journal of Energy Chemistry, vol. 68, pp. 275-283.
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Chen, Z, Zheng, R, Wei, W, Wei, W, Zou, W, Li, J, Ni, B-J & Chen, H 2022, 'Recycling spent water treatment adsorbents for efficient electrocatalytic water oxidation reaction', Resources, Conservation and Recycling, vol. 178, pp. 106037-106037.
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Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Bui, XT, Wei, W, Ni, B, Varjani, S & Hoang, NB 2022, 'Enhanced photo-fermentative biohydrogen production from biowastes: An overview', Bioresource Technology, vol. 357, pp. 127341-127341.
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Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Deng, L, Chen, Z, Ye, Y, Bui, XT & Hoang, NB 2022, 'Advanced strategies for enhancing dark fermentative biohydrogen production from biowaste towards sustainable environment', Bioresource Technology, vol. 351, pp. 127045-127045.
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Cheng, D, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Zhang, S, Deng, S, An, D & Hoang, NB 2022, 'Impact factors and novel strategies for improving biohydrogen production in microbial electrolysis cells', Bioresource Technology, vol. 346, pp. 126588-126588.
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Cheng, D, Wang, X, Zhang, Y & Zhang, L 2022, 'Graph Neural Network for Fraud Detection via Spatial-Temporal Attention', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 8, pp. 3800-3813.
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Cheng, H-C, Gao, L & Hsieh, M-H 2022, 'Properties of Noncommutative Rényi and Augustin Information', Communications in Mathematical Physics, vol. 390, no. 2, pp. 501-544.
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Cheng, H-C, Hanson, EP, Datta, N & Hsieh, M-H 2022, 'Duality Between Source Coding With Quantum Side Information and Classical-Quantum Channel Coding', IEEE Transactions on Information Theory, vol. 68, no. 11, pp. 7315-7345.
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Cheng, J, You, H, Tian, M, Kuang, S, Liu, S, Chen, H, Li, X, Liu, H & Liu, T 2022, 'Occurrence of nitrite-dependent anaerobic methane oxidation bacteria in the continental shelf sediments', Process Safety and Environmental Protection, vol. 168, pp. 626-632.
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Cheng, X, Nie, X, Li, N, Wang, H, Zheng, Z & Sui, Y 2022, 'How About Bug-Triggering Paths? - Understanding and Characterizing Learning-Based Vulnerability Detectors', IEEE Transactions on Dependable and Secure Computing, pp. 1-18.
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Cheng, Z, Ye, D, Zhu, T, Zhou, W, Yu, PS & Zhu, C 2022, 'Multi‐agent reinforcement learning via knowledge transfer with differentially private noise', International Journal of Intelligent Systems, vol. 37, no. 1, pp. 799-828.
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Chepurin, D, Chamoli, U & Diwan, AD 2022, 'Bony Stress and Its Association With Intervertebral Disc Degeneration in the Lumbar Spine: A Systematic Review of Clinical and Basic Science Studies', Global Spine Journal, vol. 12, no. 5, pp. 964-979.
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Study Design: Translational review encompassing basic science and clinical evidence. Objectives: Multiple components of the lumbar spine interact during its normal and pathological function. Bony stress in the lumbar spine is recognized as a factor in the development of pars interarticularis defect and stress fractures, but its relationship with intervertebral disc (IVD) degeneration is not well understood. Therefore, we conducted a systematic review to examine the relationship between bony stress and IVD degeneration. Methods: Online databases Scopus, PubMed and MEDLINE via OVID were searched for relevant studies published between January 1980-February 2020, using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. Two authors independently analyzed the data, noting characteristics and biases in various studies. Results: Thirty-two articles were included in the review: 8 clinical studies, 9 finite element modeling studies, 3 in-vivo biomechanical testing studies, and 12 in-vitro biomechanical testing studies. Of the 32 articles, 19 supported, 4 rejected and 9 made no conclusion on the hypothesis that there is a positive associative relationship between IVD degeneration and bony stress. However, sufficient evidence was not available to confirm or reject a causal relationship. Conclusions: Most studies suggest that the prevalence of IVD degeneration increases in the presence of bony stress; whether a causal relationship exists is unclear. The literature recommends early diagnosis and clinical suspicion of IVD degeneration and bony stress. Longitudinal studies are required to explore causal relationships between IVD degeneration and bony st...

Chiniforush, AA, Gharehchaei, M, Akbar Nezhad, A, Castel, A, Moghaddam, F, Keyte, L, Hocking, D & Foster, S 2022, 'Numerical simulation of risk mitigation strategies for early-age thermal cracking and DEF in concrete', Construction and Building Materials, vol. 322, pp. 126478-126478.
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Choi, PJ, Lim, S, Shon, H & An, AK 2022, 'Incorporation of negatively charged silver nanoparticles in outer-selective hollow fiber forward osmosis (OSHF-FO) membrane for wastewater dewatering', Desalination, vol. 522, pp. 115402-115402.
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Choo, Y, Hwa, Y & Cairns, EJ 2022, 'A review of the rational interfacial designs and characterizations for solid‐state lithium/sulfur cells', Electrochemical Science Advances, vol. 2, no. 6.
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Choo, Y, Snyder, RL, Shah, NJ, Abel, BA, Coates, GW & Balsara, NP 2022, 'Complete Electrochemical Characterization and Limiting Current of Polyacetal Electrolytes', Journal of The Electrochemical Society, vol. 169, no. 2, pp. 020538-020538.
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We investigate a polyacetal-based electrolyte, poly(1,3,6-trioxocane) (P(2EO-MO)) mixed with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) salt, and report full electrochemical characterization of the transport parameters and a thermodynamic property in comparison to the previously reported poly(ethylene oxide) (PEO) electrolyte data [D. Gribble et al., J. Electrochem. Soc., 166, A3228 (2019)]. While the steady-state current fraction (ρ +) of P(2EO-MO) electrolyte is greater than that of PEO electrolyte in the entire salt concentration window we explored, the rigorously defined transference number using Newman’s concentrated solution theory ( t + 0 ) appears to be similar to that of PEO electrolyte. On the basis of full electrochemical characterization, we calculate the salt concentration profile as a function of position in the cell and predict limiting current density (i L ...

Chowdhury, RR, Chattopadhyay, S & Adak, C 2022, 'CAHPHF: Context-Aware Hierarchical QoS Prediction With Hybrid Filtering', IEEE Transactions on Services Computing, vol. 15, no. 4, pp. 2232-2247.
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IEEE With the proliferation of Internet-of-Things and continuous growth in the number of web-services at the Internet-scale, service-recommendation is becoming a challenge nowadays. One of the prime aspects influencing the service-recommendation is the Quality-of-Service(QoS) parameter, which depicts the performance of a web-service. In general, the service provider furnishes the QoS values before service deployment. In reality, the QoS values of service vary across different users, time, locations, etc. Therefore, estimating the QoS value of service before its execution is an important task. Thus, QoS-prediction has gained significant attention. Multiple approaches are available in the literature for predicting QoS. However, these approaches are yet to reach the desired accuracy level. Here, we study the QoS-prediction problem across different users and propose a novel solution by considering the contextual information of both services and users. Our proposal includes two key-steps: (a)hybrid-filtering, (b)hierarchical-prediction-mechanism. On one hand, the hybrid-filtering aims to obtain a set of similar users and services, given a target user and a service. On the other hand, the goal of the hierarchical-prediction-mechanism is to estimate the QoS value accurately by leveraging hierarchical-neural-regression. We evaluated our framework on WS-DREAM datasets. The experimental results show our framework outperformed the major state-of-the-art approaches.

Chu, NH, Hoang, DT, Nguyen, DN, Van Huynh, N & Dutkiewicz, E 2022, 'Joint Speed Control and Energy Replenishment Optimization for UAV-assisted IoT Data Collection with Deep Reinforcement Transfer Learning', IEEE Internet of Things Journal, pp. 1-1.
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Chu, Y, Fu, X, Luo, Y, Canning, J, Wang, J, Ren, J, Zhang, J & Peng, G-D 2022, 'Additive Manufacturing Fiber Preforms for Structured Silica Fibers with Bismuth and Erbium Dopants', Light: Advanced Manufacturing, vol. 3, no. 2, pp. 1-1.
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Chu, Y, Zhao, S, He, L & Niu, F 2022, 'Wind noise suppression in filtered-x least mean squares-based active noise control systems', The Journal of the Acoustical Society of America, vol. 152, no. 6, pp. 3340-3345.
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Wind noise is notorious for its detrimental impacts on audio devices. This letter evaluates the influence of wind noise on the active noise control performance of headphones in a wind tunnel, and the noise reduction is found to decrease with wind speeds. To improve the performance of noise control systems in windy environments, the filtered-x least mean squares algorithm is modified based on the total least squares technique, taking the characteristics of wind noise into account. Computer simulations with real-recorded data demonstrate that the proposed algorithm could improve the noise reduction by approximately 3 dB in windy conditions.

Clemon, LM 2022, 'Rapid estimation of viral emission source location via genetic algorithm', Computational Mechanics, vol. 69, no. 5, pp. 1213-1224.
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AbstractIndoor spread of infectious diseases is well-studied as a common transmission route. For highly infectious diseases, like Sars-CoV-2, considering poorly or semi ventilated areas outdoors is increasingly important. This is important in communities with high proportions of infected people, highly infectious variants, or where spread is difficult to manage. This work develops a simulation framework based on probabilistic distributions of viral particles, decay, and infection. The methodology reduces the computational cost of generating rapid estimations of a wide variety of scenarios compared to other simulation methods with high computational cost and more fidelity. Outdoor predictions are provided in example applications for a gathering of five people with oscillating wind and a public speaking event. The results indicate that infection is sensitive to population density and outdoor transmission is plausible and likely locations of a virtual super-spreader are identified. Outdoor gatherings should consider precautions to reduce infection spread.

Cousin, E, Duncan, BB, Stein, C, Ong, KL, Vos, T, Abbafati, C, Abbasi-Kangevari, M, Abdelmasseh, M, Abdoli, A, Abd-Rabu, R, Abolhassani, H, Abu-Gharbieh, E, Accrombessi, MMK, Adnani, QES, Afzal, MS, Agarwal, G, Agrawaal, KK, Agudelo-Botero, M, Ahinkorah, BO, Ahmad, S, Ahmad, T, Ahmadi, K, Ahmadi, S, Ahmadi, A, Ahmed, A, Ahmed Salih, Y, Akande-Sholabi, W, Akram, T, Al Hamad, H, Al-Aly, Z, Alcalde-Rabanal, JE, Alipour, V, Aljunid, SM, Al-Raddadi, RM, Alvis-Guzman, N, Amini, S, Ancuceanu, R, Andrei, T, Andrei, CL, Anjana, RM, Ansar, A, Antonazzo, IC, Antony, B, Anyasodor, AE, Arabloo, J, Arizmendi, D, Armocida, B, Artamonov, AA, Arulappan, J, Aryan, Z, Asgari, S, Ashraf, T, Astell-Burt, T, Atorkey, P, Atout, MMW, Ayanore, MA, Badiye, AD, Baig, AA, Bairwa, M, Baker, JL, Baltatu, OC, Banik, PC, Barnett, A, Barone, MTU, Barone-Adesi, F, Barrow, A, Bedi, N, Belete, R, Belgaumi, UI, Bell, AW, Bennett, DA, Bensenor, IM, Beran, D, Bhagavathula, AS, Bhaskar, S, Bhattacharyya, K, Bhojaraja, VS, Bijani, A, Bikbov, B, Birara, S, Bodolica, V, Bonny, A, Brenner, H, Briko, NI, Butt, ZA, Caetano dos Santos, FL, Cámera, LA, Campos-Nonato, IR, Cao, Y, Cao, C, Cerin, E, Chakraborty, PA, Chandan, JS, Chattu, VK, Chen, S, Choi, J-YJ, Choudhari, SG, Chowdhury, EK, Chu, D-T, Corso, B, Dadras, O, Dai, X, Damasceno, AAM, Dandona, L, Dandona, R, Dávila-Cervantes, CA, De Neve, J-W, Denova-Gutiérrez, E, Dhamnetiya, D, Diaz, D, Ebtehaj, S, Edinur, HA, Eftekharzadeh, S, El Sayed, I, Elgendy, IY, Elhadi, M, Elmonem, MA, Faisaluddin, M, Farooque, U, Feng, X, Fernandes, E, Fischer, F, Flood, D, Freitas, M, Gaal, PA, Gad, MM, Gaewkhiew, P, Getacher, L, Ghafourifard, M, Ghanei Gheshlagh, R, Ghashghaee, A, Ghith, N, Ghozali, G, Gill, PS, Ginawi, IA, Glushkova, EV, Golechha, M, Gopalani, SV, Guimarães, RA, Gupta, RD, Gupta, R, Gupta, VK, Gupta, VB, Gupta, S, Habtewold, TD, Hafezi-Nejad, N, Halwani, R, Hanif, A, Hankey, GJ, Haque, S, Hasaballah, AI, Hasan, SS, Hashi, A, Hassanipour, S, Hay, SI, Hayat, K, Heidari, M, Hossain, MBH, Hossain, S, Hosseini, M, Hoveidamanesh, S, Huang, J, Humayun, A, Hussain, R, Hwang, B-F, Ibitoye, SE, Ikuta, KS, Inbaraj, LR, Iqbal, U, Islam, MS, Islam, SMS, Islam, RM, Ismail, NE, Isola, G, Itumalla, R, Iwagami, M, Iyamu, IO, Jahani, MA, Jakovljevic, M, Jayawardena, R, Jha, RP, John, O, Jonas, JB, Joo, T, Kabir, A, Kalhor, R & et al. 2022, 'Diabetes mortality and trends before 25 years of age: an analysis of the Global Burden of Disease Study 2019', The Lancet Diabetes & Endocrinology, vol. 10, no. 3, pp. 177-192.
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Crowther, CA, Samuel, D, McCowan, LME, Edlin, R, Tran, T & McKinlay, CJ 2022, 'Lower versus Higher Glycemic Criteria for Diagnosis of Gestational Diabetes', New England Journal of Medicine, vol. 387, no. 7, pp. 587-598.
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Cu Thi, P, Ball, JE & Dao, NH 2022, 'Early stopping technique using a genetic algorithm for calibration of an urban runoff model', International Journal of River Basin Management, vol. 20, no. 4, pp. 545-554.
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Identifying suitable parameter sets for use in catchment modelling remains a critical issue in hydrology. This paper describes an early stopping technique (EST) for use during calibration of a multi-parameter urban catchment modelling system. The proposed method takes advantage of MODE and lower confidence limit (LCL) functions in statistical analysis of spanning set of objective function values. The paper also introduces a monitoring process and regularization techniques to avoid under/overfitting during the calibration and to enhance generalisation performance. The methodology is assessed using SWMM and linked with a Genetic Algorithm for calibration of a Powells Creek catchment model in Sydney, Australia. Results demonstrate that the statistical spanning set analysis approach overcomes issues of poor interpretation and deterioration in the model’s generalisation properties. By stopping early, the calibration process avoided overfitting; this was indicated by too closely fitting to the calibration dataset and a failure to fit to the monitoring dataset.

Cui, L, Guo, L, Gao, L, Cai, B, Qu, Y, Zhou, Y & Yu, S 2022, 'A Covert Electricity-Theft Cyberattack Against Machine Learning-Based Detection Models', IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 7824-7833.
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Cui, L, Qu, Y, Xie, G, Zeng, D, Li, R, Shen, S & Yu, S 2022, 'Security and Privacy-Enhanced Federated Learning for Anomaly Detection in IoT Infrastructures', IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3492-3500.
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Cui, Q, Zhang, Z, Yanpeng, S, Ni, W, Zeng, M & Zhou, M 2022, 'Dynamic Multichannel Access Based on Deep Reinforcement Learning in Distributed Wireless Networks', IEEE Systems Journal, vol. 16, no. 4, pp. 5831-5834.
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Cui, Q, Zhao, X, Ni, W, Hu, Z, Tao, X & Zhang, P 2022, 'Multi-Agent Deep Reinforcement Learning-Based Interdependent Computing for Mobile Edge Computing-Assisted Robot Teams', IEEE Transactions on Vehicular Technology, pp. 1-12.
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Cui, Z, Chen, H, Cui, L, Liu, S, Liu, X, Xu, G & Yin, H 2022, 'Reinforced KGs reasoning for explainable sequential recommendation', World Wide Web, vol. 25, no. 2, pp. 631-654.
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Cui, Z, Wang, X, Ngo, H & Zhu, G 2022, 'In-situ monitoring of membrane fouling migration and compression mechanism with improved ultraviolet technique in membrane bioreactors', Bioresource Technology, vol. 347, pp. 126684-126684.
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da Rocha, CG, Saldanha, RB, Tonini de Araújo, M & Consoli, NC 2022, 'Social and environmental assessments of Eco-friendly Pavement alternatives', Construction and Building Materials, vol. 325, pp. 126736-126736.
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Dai, J, Yang, C, Xu, D, Wen, S, Jian, M & Yang, D 2022, 'Leaderless Consensus of Semilinear Hyperbolic Multiagent Systems with Semipositive or Seminegative Definite Convection', Discrete Dynamics in Nature and Society, vol. 2022, pp. 1-8.
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This paper deals with a leaderless consensus of semilinear first-order hyperbolic partial differential equation-based multiagent systems (HPDEMASs). A consensus controller under an undirected graph is designed. Dealing with different convection assumptions, two different boundary conditions are presented, one right endpoint and the other left endpoint. Two sufficient conditions for leaderless consensus of HPDEMAS are presented by giving the gain range in the case of the symmetric seminegative definite convection coefficient and the semipositive definite convection coefficient, respectively. Two examples are presented to show the effectiveness of the control methods.

Dai, W, Mu, J, Chen, Z, Zhang, J, Pei, X, Luo, W & Ni, B-J 2022, 'Design of Few-Layer Carbon Nitride/Bifeo3 Composites for Efficient Organic Pollutant Photodegradation', Environmental Research, vol. 215, pp. 114190-114190.
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Dai, Y, Zhang, X, Liu, Y, Yu, H, Su, W, Zhou, J, Ye, Q & Huang, Z 2022, '1,6;2,3-Bis-BN Cyclohexane: Synthesis, Structure, and Hydrogen Release', Journal of the American Chemical Society, vol. 144, no. 19, pp. 8434-8438.
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Dang, B-T, Bui, X-T, Tran, DPH, Hao Ngo, H, Nghiem, LD, Hoang, T-K-D, Nguyen, P-T, Nguyen, HH, Vo, T-K-Q, Lin, C, Yi Andrew Lin, K & Varjani, S 2022, 'Current application of algae derivatives for bioplastic production: A review', Bioresource Technology, vol. 347, pp. 126698-126698.
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Dang, B-T, Nguyen, T-T, Bui, X-T, Hao Ngo, H, Andrew Lin, K-Y, Tomoaki, I, Saunders, T, Huynh, T-N, Ngoc-Dan Cao, T, Visvanathan, C, Varjani, S & Rene, ER 2022, 'Non-submerged attached growth process for domestic wastewater treatment: Influence of media types and internal recirculation ratios', Bioresource Technology, vol. 343, pp. 126125-126125.
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Dang, B-T, Nguyen, T-T, Ngo, HH, Pham, M-D-T, Le, LT, Nguyen, N-K-Q, Vo, T-D-H, Varjani, S, You, S-J, Lin, KA, Huynh, K-P-H & Bui, X-T 2022, 'Influence of C/N ratios on treatment performance and biomass production during co-culture of microalgae and activated sludge', Science of The Total Environment, vol. 837, pp. 155832-155832.
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Dang, B-T, Tran, DPH, Nguyen, N-K-Q, Cao, HTN, Tomoaki, I, Huynh, K-P-H, Pham, T-T, Varjani, S, Hao Ngo, H, Wang, Y-F, You, S-J & Bui, X-T 2022, 'Comparison of degradation kinetics of tannery wastewater treatment using a nonlinear model by salt-tolerant Nitrosomonas sp. and Nitrobacter sp.', Bioresource Technology, vol. 351, pp. 127000-127000.
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Dang, KB, Phan, TTH, Nguyen, TT, Pham, TPN, Nguyen, MH, Dang, VB, Hoang, TTH & Ngo, VL 2022, 'Economic valuation of wetland ecosystem services in northeastern part of Vietnam', Knowledge & Management of Aquatic Ecosystems, no. 423, pp. 12-12.
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Coastal wetlands have been heavily exploited in the world. Valuation of ecosystem services help to provide the necessary improvements in coastal policy and management to monitor the driving forces of ecological changes in wetland ecosystems. In this study, the monetary values of wetland ecosystem services (WES) in the northeastern part of Vietnam were evaluated based on the integration of different quantitative methods, including interview, remote sensing, ecological modeling, statistic, and cost-benefit analyses. Particularly, seven wetland ecosystems and eleven services obtained from them were identified. As a result, the annual net WES value is evaluated at more than 390 million USD. The intensive and industrial aquaculture ecosystems in the northeastern part represent the highest economic value with more than 2100 USD/ha/year. A “planning” scenario was formulated to predict WES for the next ten years based on policy changes published by local managers. The framework developed here can serve as a decision support tool for environmental and economic managers in wetlands planning.

Dang, TD, Hoang, D & Nguyen, DN 2022, 'Trust-Based Scheduling Framework for Big Data Processing with MapReduce', IEEE Transactions on Services Computing, vol. 15, no. 1, pp. 279-293.
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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, VM, Nguyen, VD, Van, HT, Nguyen, VQ, Nguyen, TN & Nghiem, LD 2022, 'Removal of Cr(VI) and Pb(II) from aqueous solution using Mg/Al layered double hydroxides-mordenite composite', Separation Science and Technology, vol. 57, no. 15, pp. 2432-2445.
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Dang-Ngoc, H, Nguyen, DN, Ho-Van, K, Hoang, DT, Dutkiewicz, E, Pham, Q-V & Hwang, W-J 2022, 'Secure Swarm UAV-Assisted Communications With Cooperative Friendly Jamming', IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25596-25611.
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Darwish, A, Halkon, B & Oberst, S 2022, 'Non-Contact Vibro-Acoustic Object Recognition Using Laser Doppler Vibrometry and Convolutional Neural Networks', Sensors, vol. 22, no. 23, pp. 9360-9360.
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Laser Doppler vibrometers (LDVs) have been widely adopted due to their large number of benefits in comparison to traditional contacting vibration transducers. Their high sensitivity, among other unique characteristics, has also led to their use as optical microphones, where the measurement of object vibration in the vicinity of a sound source can act as a microphone. Recent work enabling full correction of LDV measurement in the presence of sensor head vibration unlocks new potential applications, including integration within autonomous vehicles (AVs). In this paper, the common AV challenge of object classification is addressed by presenting and evaluating a novel, non-contact vibro-acoustic object recognition technique. This technique utilises a custom set-up involving a synchronised loudspeaker and scanning LDV to simultaneously remotely solicit and record responses to a periodic chirp excitation in various objects. The 864 recorded signals per object were pre-processed into spectrograms of various forms, which were used to train a ResNet-18 neural network via transfer learning to accurately recognise the objects based only on their vibro-acoustic characteristics. A five-fold cross-validation optimisation approach is described, through which the effects of data set size and pre-processing type on classification accuracy are assessed. A further assessment of the ability of the CNN to classify never-before-seen objects belonging to groups of similar objects on which it has been trained is then described. In both scenarios, the CNN was able to obtain excellent classification accuracy of over 99.7%. The work described here demonstrates the significant promise of such an approach as a viable non-contact object recognition technique suitable for various machine automation tasks, for example, defect detection in production lines or even loose rock identification in underground mines.

Darwish, A, Halkon, B, Rothberg, S, Oberst, S & Fitch, R 2022, 'A comparison of time and frequency domain-based approaches to laser Doppler vibrometer instrument vibration correction', Journal of Sound and Vibration, vol. 520, pp. 116607-116607.
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Dayarathne, HNP, Angove, MJ, Paudel, SR, Ngo, HH, Guo, W & Mainali, B 2022, 'Optimisation of dual coagulation process for the removal of turbidity in source water using streaming potential', Groundwater for Sustainable Development, vol. 16, pp. 100714-100714.
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De Carvalho Gomes, S, Zhou, JL, Zeng, X & Long, G 2022, 'Water treatment sludge conversion to biochar as cementitious material in cement composite', Journal of Environmental Management, vol. 306, pp. 114463-114463.
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Deady, M, Glozier, N, Calvo, R, Johnston, D, Mackinnon, A, Milne, D, Choi, I, Gayed, A, Peters, D, Bryant, R, Christensen, H & Harvey, SB 2022, 'Preventing depression using a smartphone app: a randomized controlled trial', Psychological Medicine, vol. 52, no. 3, pp. 457-466.
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AbstractBackgroundThere is evidence that depression can be prevented; however, traditional approaches face significant scalability issues. Digital technologies provide a potential solution, although this has not been adequately tested. The aim of this study was to evaluate the effectiveness of a new smartphone app designed to reduce depression symptoms and subsequent incident depression amongst a large group of Australian workers.MethodsA randomized controlled trial was conducted with follow-up assessments at 5 weeks and 3 and 12 months post-baseline. Participants were employed Australians reporting no clinically significant depression. The intervention group (N = 1128) was allocated to use HeadGear, a smartphone app which included a 30-day behavioural activation and mindfulness intervention. The attention-control group (N = 1143) used an app which included a 30-day mood monitoring component. The primary outcome was the level of depressive symptomatology (PHQ-9) at 3-month follow-up. Analyses were conducted within an intention-to-treat framework using mixed modelling.ResultsThose assigned to the HeadGear arm had fewer depressive symptoms over the course of the trial compared to those assigned to the control (F3,734.7 = 2.98, p = 0.031). Prevalence of depression over the 12-month period was 8.0% and 3.5% for controls and HeadGear recipients, respectively, with odds of depression caseness amongst the intervention group of 0.43 (p<...

Dean, T, Whitfield, J, Miller, J & Brady, P 2022, 'An automated survey design process to reduce the environmental impact of onshore seismic surveys', The Leading Edge, vol. 41, no. 11, pp. 786-791.
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Land seismic surveys acquired in anything but the barest landscapes encounter obstacles that cause the planned survey points to be either shifted or skipped. Besides these forced changes, an increased awareness of the environmental impact of seismic surveys, coupled with a general lack of trust and even opposition to the traditional energy industry, has resulted in a demand to minimize the impacts of the clearing that is necessary to allow access to the survey lines. We present a method to reduce the environmental impact of seismic surveys without compromising seismic imaging. The process is straightforward and can be applied automatically using standard mathematical software (in this case, MATLAB). For the example shown here, where the line spacing was particularly dense, the impact on vegetation greater than 2 m in height was reduced by 55%. For less dense geometries, where there is more opportunity to offset points, the impact is likely to be larger.

Dehghanimadvar, M, Shirmohammadi, R, Ahmadi, F, Aslani, A & Khalilpour, KR 2022, 'Mapping the development of various solar thermal technologies with hype cycle analysis', Sustainable Energy Technologies and Assessments, vol. 53, pp. 102615-102615.
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Deng, J, Chen, X, Jiang, R, Song, X & Tsang, IW 2022, 'A Multi-View Multi-Task Learning Framework for Multi-Variate Time Series Forecasting', IEEE Transactions on Knowledge and Data Engineering, pp. 1-16.
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Deng, K, Zhu, S, Dai, W, Yang, C & Wen, S 2022, 'New Criteria on Stability of Dynamic Memristor Delayed Cellular Neural Networks', IEEE Transactions on Cybernetics, vol. 52, no. 6, pp. 5367-5379.
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IEEE Dynamic memristor (DM)-cellular neural networks (CNNs), which replace a linear resistor with flux-controlled memristor in the architecture of each cell of traditional CNNs, have attracted researchers' attention. Compared with common neural networks, the DM-CNNs have an outstanding merit: when a steady state is reached, all voltages, currents, and power consumption of DM-CNNs disappeared, in the meantime, the memristor can store the computation results by serving as nonvolatile memories. The previous study on stability of DM-CNNs rarely considered time delay, while delay is quite common and highly impacts the stability of the system. Thus, taking the time delay effect into consideration, we extend the original system to DM-D(delay)CNNs model. By using the Lyapunov method and the matrix theory, some new sufficient conditions for the global asymptotic stability and global exponential stability with a known convergence rate of DM-DCNNs are obtained. These criteria generalized some known conclusions and are easily verified. Moreover, we find DM-DCNNs have 3ⁿ equilibrium points (EPs) and 2ⁿ of them are locally asymptotically stable. These results are obtained via a given constitutive relation of memristor and the appropriate division of state space. Combine with these theoretical results, the applications of DM-DCNNs can be extended to other fields, such as associative memory, and its advantage can be used in a better way. Finally, numerical simulations are offered to illustrate the effectiveness of our theoretical results.

Deng, L, Guo, W, Ngo, HH, Zhang, X, Chen, C, Chen, Z, Cheng, D, Ni, S-Q & Wang, Q 2022, 'Recent advances in attached growth membrane bioreactor systems for wastewater treatment', Science of The Total Environment, vol. 808, pp. 152123-152123.
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Deng, L, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Pandey, A, Varjani, S & Hoang, NB 2022, 'Recent advances in circular bioeconomy based clean technologies for sustainable environment', Journal of Water Process Engineering, vol. 46, pp. 102534-102534.
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Deng, S, Ji, J, Wen, G & Xu, H 2022, 'Two-parameter dynamics of an autonomous mechanical governor system with time delay', Nonlinear Dynamics, vol. 107, no. 1, pp. 641-663.
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Deng, S, Peng, S, Ngo, HH, Oh, SJ-A, Hu, Z, Yao, H & Li, D 2022, 'Characterization of nitrous oxide and nitrite accumulation during iron (Fe(0))- and ferrous iron (Fe(II))-driven autotrophic denitrification: mechanisms, environmental impact factors and molecular microbial characterization', Chemical Engineering Journal, vol. 438, pp. 135627-135627.
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The iron (Fe(0))-/ferrous iron (Fe(II))-driven autotrophic denitrification processes have been alternative methods for nitrogen removal from low organic carbon (OC) wastewater, but the accumulation of nitrous oxide (N2O) and nitrite (NO2−) along with these processes remains unclear. This research aimed to systematically characterize the N2O/NO2− accumulation in Fe(0)-/Fe(II)-ADN processes through investigating the mechanisms, impact factors, and molecular biological characteristics. Results showed that Fe(II)-ADN was effective in NO3− reduction but was less efficient in N2O reduction (k = 0.50 h−1) than Fe(0)-ADN (k = 1.82 h−1). NO2−/N2O accumulation in Fe(II)-ADN (28.6%/30.7%) was much higher than that in Fe(0)-ADN (12.6%/1.5%). Introducing hydrogenotrophic denitrification (H-ADN) into Fe(II)-ADN system significantly (p < 0.05) reduced NO2−/N2O accumulation. Fe(0)-ADN was proved a coupled process of Fe(II)- and H-ADN by in-situ generating Fe(II)/H2, and Fe(II)-ADN and H-ADN mainly contributed to NO3− and NO2−/N2O reduction, respectively. Optimum pH (7.5) and temperature (30–35 °C) were confirmed with controlled NO2–/N2O accumulation and effective denitrification. Dosing inorganic carbon (IC) and OC enhanced denitrification and reduced NO2–/N2O accumulation, where OC was more efficient with an optimum dosage of 0.25 mmol C/mmol N. 16S rRNA high-throughput sequencing and Pearson Correlation Coefficients verified that Thiobacillus was the main contributor to NO3− reduction, whereas Thauera and Acidovorax possessed high NO2−/N2O reduction capability. Real-time quantitative polymerase chain reaction and enzyme activity assay demonstrated that the nitrite reductase encoded by gene nirK and the nitrous oxide reductase encoded by gene nosZ were efficient in catalyzing the further reduction of NO2− and N2O, respectively. This study could provide an in-depth understanding of NO2−/N2O accumulation in Fe(II)-/Fe(0)-ADN processes and contribute to their application, optimiza...

Deng, Y & Feng, Y 2022, 'Formal semantics of a classical-quantum language', Theoretical Computer Science, vol. 913, pp. 73-93.
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Deng, Z, Mu, H, Jiang, L, Xi, W, Xu, X & Zheng, W 2022, 'Preparation and characterization of electrospun PLGA-SF nanofibers as a potential drug delivery system', Materials Chemistry and Physics, vol. 289, pp. 126452-126452.
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Deng, Z, Zhao, L, Mu, H, Jiang, L, Xi, W, Xu, X & Zheng, W 2022, 'High selective property of gelatin/MWCNTs functionalized carbon fiber microelectrode: Toward real-time monitoring of ascorbate', Journal of Electroanalytical Chemistry, vol. 914, pp. 116315-116315.
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Deng, Z, Zhao, L, Zhou, H, Xu, X & Zheng, W 2022, 'Recent advances in electrochemical analysis of hydrogen peroxide towards in vivo detection', Process Biochemistry, vol. 115, pp. 57-69.
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Deutsch, F, Regina Bullen, I, Nguyen, K, Tran, N-H, Elliott, M & Tran, N 2022, 'Current state of play for HPV-positive oropharyngeal cancers', Cancer Treatment Reviews, vol. 110, pp. 102439-102439.
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Deveci, O & Shannon, AG 2022, 'On The Complex-Type Catalan Transform of the k-Fibonacci Numbers', Journal of Integer Sequences, vol. 25, no. 4.
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We define a type of complex Catalan number and find some its properties. We also produce a complex Catalan transform and its inverse, together with associated generating functions and related matrices. These lead to connections with complex Catalan transforms of the k-Fibonacci numbers and the determinants of their Hankel matrices. The paper finishes with a conjecture.

Deveci, Ö, Shannon, AG & Karaduman, E 2022, 'The complex-type Fibonacci p-Sequences', Annals of the University of Craiova - Mathematics and Computer Science Series, vol. 49, no. 2, pp. 260-269.
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In this paper, we define a new sequence which is called the complex-type Fibonacci p-sequence and we obtain the generating matrix of this complex-type Fibonacci p-sequence. We also derive the determinantal and the permanental representations. Then, using the roots of the characteristic polynomial of the complex-type Fibonacci p-sequence, we produce the Binet formula for this defined sequence. In addition, we give the combinatorial representations, the generating function, the exponential representation and the sums of the complex-type Fibonacci p-numbers.

Dhana Raju, V, Nair, JN, Venu, H, Subramani, L, M. Soudagar, ME, Mujtaba, MA, Khan, TMY, Ismail, KA, Elfasakhany, A, Yusuf, AA, Mohamed, BA & Fattah, IMR 2022, 'Combined assessment of injection timing and exhaust gas recirculation strategy on the performance, emission and combustion characteristics of algae biodiesel powered diesel engine', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 44, no. 4, pp. 8554-8571.
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Dhandapani, Y, Joseph, S, Bishnoi, S, Kunther, W, Kanavaris, F, Kim, T, Irassar, E, Castel, A, Zunino, F, Machner, A, Talakokula, V, Thienel, K-C, Wilson, W, Elsen, J, Martirena, F & Santhanam, M 2022, 'Durability performance of binary and ternary blended cementitious systems with calcined clay: a RILEM TC 282-CCL, review', Materials and Structures, vol. 55, no. 5.
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Dhandapani, Y, Joseph, S, Geddes, DA, Zhao, Z, Boustingorry, P, Bishnoi, S, Vieira, M, Martirena, F, Castel, A, Kanavaris, F & Riding, KA 2022, 'Fresh properties of concrete containing calcined clays: a review by RILEM TC-282 CCL', Materials and Structures, vol. 55, no. 6.
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Dharmapriya, S, Kiridena, S & Shukla, N 2022, 'Multiagent Optimization Approach to Supply Network Configuration Problems With Varied Product-Market Profiles', IEEE Transactions on Engineering Management, vol. 69, no. 6, pp. 2707-2722.
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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.

Dhull, P, Guevara, AP, Ansari, M, Pollin, S, Shariati, N & Schreurs, D 2022, 'Internet of Things Networks: Enabling Simultaneous Wireless Information and Power Transfer', IEEE Microwave Magazine, vol. 23, no. 3, pp. 39-54.
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Di, X, Wang, D, Su, QP, Liu, Y, Liao, J, Maddahfar, M, Zhou, J & Jin, D 2022, 'Spatiotemporally mapping temperature dynamics of lysosomes and mitochondria using cascade organelle-targeting upconversion nanoparticles', Proceedings of the National Academy of Sciences, vol. 119, no. 45, p. e2207402119.
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The intracellular metabolism of organelles, like lysosomes and mitochondria, is highly coordinated spatiotemporally and functionally. The activities of lysosomal enzymes significantly rely on the cytoplasmic temperature, and heat is constantly released by mitochondria as the byproduct of adenosine triphosphate (ATP) generation during active metabolism. Here, we developed temperature-sensitive LysoDots and MitoDots to monitor the in situ thermal dynamics of lysosomes and mitochondria. The design is based on upconversion nanoparticles (UCNPs) with high-density surface modifications to achieve the exceptionally high sensitivity of 2.7% K −1 and low uncertainty of 0.8 K for nanothermometry to be used in living cells. We show the measurement is independent of the ion concentrations and pH values. With Ca 2+ ion shock, the temperatures of both lysosomes and mitochondria increased by ∼2 to 4 °C. Intriguingly, with chloroquine (CQ) treatment, the lysosomal temperature was observed to decrease by up to ∼3 °C, while mitochondria remained relatively stable. Lastly, with oxidative phosphorylation inhibitor treatment, we observed an ∼3 to 7 °C temperature increase and a thermal transition from mitochondria to lysosomes. These observations indicate different metabolic pathways and thermal transitions between lysosomes and mitochondria inside HeLa cells. The nanothermometry probes provide a powerful tool for multimodality functional imaging of subcellular organelles and interactions with high spatial, temporal, and thermal dynamics resolutions.

Diao, K, Sun, X, Bramerdorfer, G, Cai, Y, Lei, G & Chen, L 2022, 'Design optimization of switched reluctance machines for performance and reliability enhancements: A review', Renewable and Sustainable Energy Reviews, vol. 168, pp. 112785-112785.
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Dikshit, A, Pradhan, B & Santosh, M 2022, 'Artificial neural networks in drought prediction in the 21st century–A scientometric analysis', Applied Soft Computing, vol. 114, pp. 108080-108080.
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Dikshit, A, Pradhan, B, Assiri, ME, Almazroui, M & Park, H-J 2022, 'Solving transparency in drought forecasting using attention models', Science of The Total Environment, vol. 837, pp. 155856-155856.
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Dikshit, A, Pradhan, B, Huete, A & Park, H-J 2022, 'Spatial based drought assessment: Where are we heading? A review on the current status and future', Science of The Total Environment, vol. 844, pp. 157239-157239.
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Droughts are the most spatially complex natural hazards that exert global impacts and are further aggravated by climate change. The investigation of drought events is challenging as it involves numerous factors ranging from detection and assessment to modelling, management and mitigation. The analysis of these factors and their quantitative assessments have significantly evolved in recent times. In this paper, we review recent methods used to examine and model droughts from a spatial viewpoint. Our analysis was conducted at three spatial scales (point-wise, regional and global) and we evaluated how recent spatial methods have advanced our understanding of drought through case study examples. Further, we also examine and provide a broad overview of relevant case studies related to future drought occurrences under climate change. This study is a comprehensive synthesis of the various quantitative techniques used to assess the spatial characteristics of droughts at different spatial scales, and not an exhaustive review of all drought aspects. However, this serves as a basis for understanding the key milestones and advances accomplished through new spatial concepts relative to the traditional approaches to study drought. This work also aims to address the gaps in knowledge that are in need of further attention and provides recommendations to improve our understanding of droughts.

Ding, A, Lin, W, Chen, R, Ngo, HH, Zhang, R, He, X, Nan, J, Li, G & Ma, J 2022, 'Improvement of sludge dewaterability by energy uncoupling combined with chemical re-flocculation: Reconstruction of floc, distribution of extracellular polymeric substances, and structure change of proteins', Science of The Total Environment, vol. 816, pp. 151646-151646.
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Ding, A, Ren, Z, Hu, L, Zhang, R, Ngo, HH, Lv, D, Nan, J, Li, G & Ma, J 2022, 'Oxidation and coagulation/adsorption dual effects of ferrate (VI) pretreatment on organics removal and membrane fouling alleviation in UF process during secondary effluent treatment', Science of The Total Environment, vol. 850, pp. 157986-157986.
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Ding, L, Razavi Bazaz, S, Asadniaye Fardjahromi, M, McKinnirey, F, Saputro, B, Banerjee, B, Vesey, G & Ebrahimi Warkiani, M 2022, 'A modular 3D printed microfluidic system: a potential solution for continuous cell harvesting in large-scale bioprocessing', Bioresources and Bioprocessing, vol. 9, no. 1.
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AbstractMicrofluidic devices have shown promising applications in the bioprocessing industry. However, the lack of modularity and high cost of testing and error limit their implementation in the industry. Advances in 3D printing technologies have facilitated the conversion of microfluidic devices from research output to applicable industrial systems. Here, for the first time, we presented a 3D printed modular microfluidic system consisting of two micromixers, one spiral microfluidic separator, and one microfluidic concentrator. We showed that this system can detach and separate mesenchymal stem cells (MSCs) from microcarriers (MCs) in a short time while maintaining the cell’s viability and functionality. The system can be multiplexed and scaled up to process large volumes of the industry. Importantly, this system is a closed system with no human intervention and is promising for current good manufacturing practices. Graphical Abstract

Ding, L, Razavi Bazaz, S, Hall, T, Vesey, G & Ebrahimi Warkiani, M 2022, 'Giardia purification from fecal samples using rigid spiral inertial microfluidics', Biomicrofluidics, vol. 16, no. 1, pp. 014105-014105.
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Giardia is one of the most common waterborne pathogens causing around 200 × 106 diarrheal infections annually. It is of great interest to microbiological research as it is among the oldest known eukaryotic cells. Purifying Giardia from fecal samples for both research and diagnostic purposes presents one of the most difficult challenges. Traditional purification methods rely on density gradient centrifugation, membrane-based filtration, and sedimentation methods, which suffer from low recovery rates, high costs, and poor efficiency. Here, we report on the use of microfluidics to purify Giardia cysts from mouse feces. We propose a rigid spiral microfluidic device with a trapezoidal cross section to effectively separate Giardia from surrounding debris. Our characterizations reveal that the recovery rate is concentration-dependent, and our proposed device can achieve recovery rates as high as 75% with 0.75 ml/min throughput. Moreover, this device can purify Giardia from extremely turbid samples to a level where cysts are visually distinguishable with just one round of purification. This highly scalable and versatile 3D printed microfluidic device is then capable of further purifying or enhancing the recovery rate of the samples by recirculation. This device also has the potential to purify other gastrointestinal pathogens of similar size, and throughput can be significantly increased by parallelization.

Ding, L, Shan, X, Wang, D, Liu, B, Du, Z, Di, X, Chen, C, Maddahfar, M, Zhang, L, Shi, Y, Reece, P, Halkon, B, Aharonovich, I, Xu, X & Wang, F 2022, 'Lanthanide Ion Resonance‐Driven Rayleigh Scattering of Nanoparticles for Dual‐Modality Interferometric Scattering Microscopy', Advanced Science, vol. 9, no. 32, pp. 2203354-2203354.
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Ding, W, Ming, Y, Cao, Z & Lin, C-T 2022, 'A Generalized Deep Neural Network Approach for Digital Watermarking Analysis', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 3, pp. 613-627.
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Technology advancement has facilitated digital content, such as images, being acquired in large volumes. However, requirement from the privacy or legislation perspective still demands the need for intellectual content protection. In this paper, we propose a deep neural network (DNN) based watermarking method to achieve this goal. Instead of training a neural network for protecting a specific image, we train the network on an image dataset and generalize the trained model to protect distinct test images in a bulk manner. Respective evaluations from both the subjective and objective aspects confirm the generality and practicality of our proposed method. To demonstrate the robustness of this general neural watermarking approach, commonly used attacks are applied to the watermarked images to examine the corresponding extracted watermarks, which still retain sufficient recognizable traits for some occasions. Testing on distinctive dataset shows the satisfying generalization of our proposed method, and practice such as loss function adjustment can cater to the capacity requirement of complicated watermark. We also discuss some traits of the trained model, which incur the vulnerability to JPEG compression attack. However, remedy seeking for this can potentially open a window to understand the underlying working principle of DNN in future work. Considering its performance and economy, it is concluded that subsequent studies that generalize our work on utilizing DNN for intellectual content protection might be a promising research trend.

Ding, W, Sun, Y, Li, M, Liu, J, Ju, H, Huang, J & Lin, C-T 2022, 'A Novel Spark-Based Attribute Reduction and Neighborhood Classification for Rough Evidence', IEEE Transactions on Cybernetics, pp. 1-14.
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Ding, Y, Wu, Y, Huang, C, Tang, S, Wu, F, Yang, Y, Zhu, W & Zhuang, Y 2022, 'NAP: Neural architecture search with pruning', Neurocomputing, vol. 477, pp. 85-95.
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Ding, Z, Chen, C, Wen, S, Li, S & Wang, L 2022, 'Lag projective synchronization of nonidentical fractional delayed memristive neural networks', Neurocomputing, vol. 469, pp. 138-150.
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Ding, Z, Chen, X, Dong, Y, Yu, S & Herrera, F 2022, 'Consensus Convergence Speed in Social Network DeGroot Model: The Effects of the Agents With High Self-Confidence Levels', IEEE Transactions on Computational Social Systems, pp. 1-11.
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Dinh, TH, Singh, AK, Linh Trung, N, Nguyen, DN & Lin, C-T 2022, 'EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-Based Ranking', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 1548-1556.
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Dinh, TQ, Nguyen, DN, Hoang, DT, Pham, TV & Dutkiewicz, E 2022, 'In-network Computation for Large-scale Federated Learning over Wireless Edge Networks', IEEE Transactions on Mobile Computing, pp. 1-15.
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Do, MH, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Q, Nghiem, DL, Thanh, BX, Zhang, X & Hoang, NB 2022, 'Performance of a dual-chamber microbial fuel cell as a biosensor for in situ monitoring Bisphenol A in wastewater', Science of The Total Environment, vol. 845, pp. 157125-157125.
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This research explores the possibilities of a dual-chamber microbial fuel cell as a biosensor to measure Bisphenol A (BPA) in wastewater. BPA is an organic compound and is considered to be an endocrine disruptor, affecting exposed organisms, the environment, and human health. The performance of the microbial fuel cells (MFCs) was first controlled with specific operational conditions (pH, temperature, fuel feeding rate, and organic loading rate) to obtain the best accuracy of the sensor signal. After that, BPA concentrations varying from 50 to 1000 μg L-1 were examined under the biosensor's cell voltage generation. The outcome illustrates that MFC generates the most power under the best possible conditions of neutral pH, 300 mg L-1 of COD, R 1000 Ω, and ambient temperature. In general, adding BPA improved the biosensor's cell voltage generation. A slight linear trend between voltage output generation and BPA concentration was observed with R2 0.96, which indicated that BPA in this particular concentration range did not real harm to the MFC's electrogenic bacteria. Scanning electron microscope (SEM) images revealed a better cover biofilm after BPA injection on the surface electrode compared to it without BPA. These results confirmed that electroactive biofilm-based MFCs can serve to detect BPA found in wastewaters.

Do, MH, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Pandey, A, Sharma, P, Varjani, S, Nguyen, TAH & Hoang, NB 2022, 'A dual chamber microbial fuel cell based biosensor for monitoring copper and arsenic in municipal wastewater', Science of The Total Environment, vol. 811, pp. 152261-152261.
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Do, NTT, Lin, C-T & Gramann, K 2022, 'Human brain dynamics in active spatial navigation', Scientific Reports, vol. 11, no. 1, pp. 1-12.
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Dogan, S, Barua, PD, Baygin, M, Chakraborty, S, Ciaccio, EJ, Tuncer, T, Abd Kadir, KA, Md Shah, MN, Azman, RR, Lee, CC, Ng, KH & Acharya, UR 2022, 'Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts', Biocybernetics and Biomedical Engineering, vol. 42, no. 3, pp. 815-828.
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Dolmark, T, Sohaib, O, Beydoun, G, Wu, K & Taghikhah, F 2022, 'The Effect of Technology Readiness on Individual Absorptive Capacity Toward Learning Behavior in Australian Universities', Journal of Global Information Management, vol. 30, no. 1, pp. 1-21.
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Recipient's absorptive capacity (ACAP) is a barrier to knowledge transfer in organizations. The technology readiness (TR) dimensions measure an individual's technological beliefs and aligns with the individual's ACAP. The purpose of this research is to study if technological beliefs have a causal effect onto individual learning capability and behaviour. University's knowledge transfer makes them an ideal context for this research. Through surveying individuals and conducting statistical analysis, the authors provide empirical evidence that there is a causal effect from the TR dimensions to individuals ACAP and their technological learning behaviour at the individual level. The findings could potentially help leverage technology to address said recipient's ACAP. It would also benefit the development of new technologies, in particular in e-learning and tailoring pedagogy.

Dong, F, Lu, J, Song, Y, Liu, F & Zhang, G 2022, 'A Drift Region-Based Data Sample Filtering Method', IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 9377-9390.
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Concept drift refers to changes in the underlying data distribution of data streams over time. A well-trained model will be outdated if concept drift occurs. Once concept drift is detected, it is necessary to understand where the drift occurs to support the drift adaptation strategy and effectively update the outdated models. This process, called drift understanding, has rarely been studied in this area. To fill this gap, this article develops a drift region-based data sample filtering method to update the obsolete model and track the new data pattern accurately. The proposed method can effectively identify the drift region and utilize information on the drift region to filter the data sample for training models. The theoretical proof guarantees the identified drift region converges uniformly to the real drift region as the sample size increases. Experimental evaluations based on four synthetic datasets and two real-world datasets demonstrate our method improves the learning accuracy when dealing with data streams involving concept drift.

Dong, L, Yang, Y, Liu, Z, Ren, Q, Li, J, Zhang, Y & Wu, C 2022, 'Microstructure and mechanical behaviour of 3D printed ultra-high performance concrete after elevated temperatures', Additive Manufacturing, vol. 58, pp. 103032-103032.
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Dong, M, Yuan, F, Yao, L, Wang, X, Xu, X & Zhu, L 2022, 'A survey for trust-aware recommender systems: A deep learning perspective', Knowledge-Based Systems, vol. 249, pp. 108954-108954.
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Dong, W, Li, W, Guo, Y, Qu, F, Wang, K & Sheng, D 2022, 'Piezoresistive performance of hydrophobic cement-based sensors under moisture and chloride-rich environments', Cement and Concrete Composites, vol. 126, pp. 104379-104379.
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Dong, W, Li, W, Guo, Y, Sun, Z, Qu, F, Liang, R & Shah, SP 2022, 'Application of intrinsic self-sensing cement-based sensor for traffic detection of human motion and vehicle speed', Construction and Building Materials, vol. 355, pp. 129130-129130.
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Dong, W, Li, W, Guo, Y, Wang, K & Sheng, D 2022, 'Mechanical properties and piezoresistive performances of intrinsic graphene nanoplate/cement-based sensors subjected to impact load', Construction and Building Materials, vol. 327, pp. 126978-126978.
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Dong, W, Li, W, Sun, Z, Ibrahim, I & Sheng, D 2022, 'Intrinsic graphene/cement-based sensors with piezoresistivity and superhydrophobicity capacities for smart concrete infrastructure', Automation in Construction, vol. 133, pp. 103983-103983.
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Dong, W, Li, W, Wang, K, Shah, SP & Sheng, D 2022, 'Multifunctional cementitious composites with integrated self-sensing and self-healing capacities using carbon black and slaked lime', Ceramics International, vol. 48, no. 14, pp. 19851-19863.
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Dong, W, Lu, Z, He, L, Geng, L, Guo, X & Zhang, J 2022, 'Low-carbon optimal planning of an integrated energy station considering combined power-to-gas and gas-fired units equipped with carbon capture systems', International Journal of Electrical Power & Energy Systems, vol. 138, pp. 107966-107966.
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Dong, Y, Guo, S, Wang, Q, Yu, S & Yang, Y 2022, 'Content Caching-Enhanced Computation Offloading in Mobile Edge Service Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 872-886.
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Dorji, P, Phuntsho, S, Kim, DI, Lim, S, Park, MJ, Hong, S & Shon, HK 2022, 'Electrode for selective bromide removal in membrane capacitive deionisation', Chemosphere, vol. 287, pp. 132169-132169.
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Dorji, U, Dorji, P, Shon, H, Badeti, U, Dorji, C, Wangmo, C, Tijing, L, Kandasamy, J, Vigneswaran, S, Chanan, A & Phuntsho, S 2022, 'On-site domestic wastewater treatment system using shredded waste plastic bottles as biofilter media: Pilot-scale study on effluent standards in Bhutan', Chemosphere, vol. 286, pp. 131729-131729.
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Dou, Y, Cheng, X, Miao, M, Wang, T, Hao, T, Zhang, Y, Li, Y, Ning, X & Wang, Q 2022, 'The impact of chlorination on the tetracycline sorption behavior of microplastics in aqueous solution', Science of The Total Environment, vol. 849, pp. 157800-157800.
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Dou, Y, Li, Y, Lin, Z, Yue, S & Zhu, J 2022, 'An Improved Cross-Yoke SST for Accurate 1-D and 2-D Magnetic Testing of Fe-Si Sheets', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10.
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Du, T, Chen, J, Qu, F, Li, C, Zhao, H, Xie, B, Yuan, M & Li, W 2022, 'Degradation prediction of recycled aggregate concrete under sulphate wetting–drying cycles using BP neural network', Structures, vol. 46, pp. 1837-1850.
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Du, T, Li, C, Wang, X, Ma, L, Qu, F, Wang, B, Peng, J & Li, W 2022, 'Effects of pipe diameter, curing age and exposure temperature on chloride diffusion of concrete with embedded PVC pipe', Journal of Building Engineering, vol. 57, pp. 104957-104957.
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Du, X, Sui, Y, Liu, Z & Ai, J 2022, 'An Empirical Study of Fault Triggers in Deep Learning Frameworks', IEEE Transactions on Dependable and Secure Computing, pp. 1-1.
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Du, Y, Hsieh, M-H, Liu, T, You, S & Tao, D 2022, 'Quantum Differentially Private Sparse Regression Learning', IEEE Transactions on Information Theory, vol. 68, no. 8, pp. 5217-5233.
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Du, Y, Huang, T, You, S, Hsieh, M-H & Tao, D 2022, 'Quantum circuit architecture search for variational quantum algorithms', npj Quantum Information, vol. 8, no. 1.
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AbstractVariational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS cannot only alleviate the influence of quantum noise and barren plateaus but also outperforms VQAs with pre-selected ansatze.

Du, Y, Ma, R, Wang, L, Qian, J & Wang, Q 2022, '2D/1D BiOI/g-C3N4 nanotubes heterostructure for photoelectrochemical overall water splitting', Science of The Total Environment, vol. 838, pp. 156166-156166.
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Duan, N, Xu, W, Ma, X, Wang, S & Zhu, J 2022, 'Magnetic Characteristic Analysis of High Temperature Superconductors by the Elemental Operator Model', IEEE Transactions on Magnetics, vol. 58, no. 2, pp. 1-4.
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In this paper, to accurately analysis the magnetic properties of high temperature superconductor, a superconductor hysteresis model is proposed. Based on the physical mechanism, the hysteresis loop of high temperature superconductor can be decomposed into an antimagnetic curve and a paramagnetic loop. The calculated and measured results of silver-sheathed Bi2223 superconducting tape samples under external magnetic field are compared to show the effectiveness and accuracy of the presented method.

Duan, Y, Chen, N, Shen, S, Zhang, P, Qu, Y & Yu, S 2022, 'FDSA-STG: Fully Dynamic Self-Attention Spatio-Temporal Graph Networks for Intelligent Traffic Flow Prediction', IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9250-9260.
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Duan, Y, Wang, Z, Wang, J, Wang, Y-K & Lin, C-T 2022, 'Position-aware image captioning with spatial relation', Neurocomputing, vol. 497, pp. 28-38.
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Duong, HC, Nghiem, LD, Ansari, AJ, Vu, TD & Nguyen, KM 2022, 'Assessment of pilot direct contact membrane distillation regeneration of lithium chloride solution in liquid desiccant air-conditioning systems using computer simulation', Environmental Science and Pollution Research, vol. 29, no. 28, pp. 41941-41952.
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Duong, TD, Li, Q & Xu, G 2022, 'Stochastic intervention for causal inference via reinforcement learning', Neurocomputing, vol. 482, pp. 40-49.
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Dwivedi, KA, Kumar, V, Wang, C-T, Chong, WT & Ong, HC 2022, 'Design and feasibility study of novel swirler incorporated microbial fuel cell for enhancing power generation and domestic wastewater treatment', Journal of Cleaner Production, vol. 337, pp. 130382-130382.
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Ejegwa, PA, Wen, S, Feng, Y, Zhang, W & Tang, N 2022, 'Novel Pythagorean Fuzzy Correlation Measures Via Pythagorean Fuzzy Deviation, Variance, and Covariance With Applications to Pattern Recognition and Career Placement', IEEE Transactions on Fuzzy Systems, vol. 30, no. 6, pp. 1660-1668.
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El Hassan, M, Assoum, H, Bukharin, N, Al Otaibi, H, Mofijur, M & Sakout, A 2022, 'A review on the transmission of COVID-19 based on cough/sneeze/breath flows', The European Physical Journal Plus, vol. 137, no. 1.
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El‐Hawat, O, Fatahi, B & Taciroglu, E 2022, 'Novel post‐tensioned rocking piles for enhancing the seismic resilience of bridges', Earthquake Engineering & Structural Dynamics, vol. 51, no. 2, pp. 393-417.
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Elsemary, MT, Maritz, MF, Smith, LE, Warkiani, M, Bandara, V, Napoli, S, Barry, SC, Coombs, JT & Thierry, B 2022, 'Inertial Microfluidic Purification of CAR‐T‐Cell Products', Advanced Biology, vol. 6, no. 1, pp. 2101018-2101018.
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Eshkevari, M, Jahangoshai Rezaee, M, Saberi, M & Hussain, OK 2022, 'An end-to-end ranking system based on customers reviews: Integrating semantic mining and MCDM techniques', Expert Systems with Applications, vol. 209, pp. 118294-118294.
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Eskandari, M, Huang, H, Savkin, AV & Ni, W 2022, 'Model Predictive Control-based 3D Navigation of a RIS-Equipped UAV for LoS Wireless Communication with a Ground Intelligent Vehicle', IEEE Transactions on Intelligent Vehicles, pp. 1-13.
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Eskandari, M, Savkin, AV & Ni, W 2022, 'Consensus-Based Autonomous Navigation of a Team of RIS-Equipped UAVs for LoS Wireless Communication With Mobile Nodes in High-Density Areas', IEEE Transactions on Automation Science and Engineering, pp. 1-13.
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Eslahi, H, Hamilton, TJ & Khandelwal, S 2022, 'Compact and Energy Efficient Neuron With Tunable Spiking Frequency in 22-nm FDSOI', IEEE Transactions on Nanotechnology, vol. 21, pp. 189-195.
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Esselle, K, Matekovits, L, Yang, Y, Thalakotuna, D, Afzal, M, Kovaleva, M & Singh, K 2022, 'Guest Editorial Disruptive Beam-Steering Antenna Technologies for Emerging and Future Satellite Services', IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 11, pp. 2211-2218.
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Etaati, B, Dehkordi, AA, Sadollah, A, El-Abd, M & Neshat, M 2022, 'A Comparative State-of-the-Art Constrained Metaheuristics Framework for TRUSS Optimisation on Shape and Sizing', Mathematical Problems in Engineering, vol. 2022, pp. 1-13.
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In order to develop the dynamic effectiveness of the structures such as trusses, the application of optimisation methods plays a significant role in improving the shape and size of elements. However, conjoining two heterogeneous variables, nodal coordinates and cross-sectional elements, makes a challenging optimisation problem that is nonlinear, multimodal, large-scale with dynamic constraints. To handle these challenges, evolutionary and swarm optimisation algorithms can be robust and practical tools and show great potential to solve such complex problems. This paper proposed a comparative truss optimisation framework to solve two large-scale structures, including 314-bar and 260-bar trusses. The proposed framework consists of twelve state-of-the-art bio-inspired algorithms. The experimental results show that the marine predators algorithm (MPA) performed best compared with other algorithms in terms of convergence speed and the quality of the proposed designs of the trusses.

Ezugwu, AE, Agushaka, JO, Abualigah, L, Mirjalili, S & Gandomi, AH 2022, 'Prairie Dog Optimization Algorithm', Neural Computing and Applications, vol. 34, no. 22, pp. 20017-20065.
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Fahmideh, M, Grundy, J, Beydoun, G, Zowghi, D, Susilo, W & Mougouei, D 2022, 'A model-driven approach to reengineering processes in cloud computing', Information and Software Technology, vol. 144, pp. 106795-106795.
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Faisal, SN & Iacopi, F 2022, 'Thin-Film Electrodes Based on Two-Dimensional Nanomaterials for Neural Interfaces', ACS Applied Nano Materials, vol. 5, no. 8, pp. 10137-10150.
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Fallahpoor, M, Chakraborty, S, Heshejin, MT, Chegeni, H, Horry, MJ & Pradhan, B 2022, 'Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection', Computers in Biology and Medicine, vol. 145, pp. 105464-105464.
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Fan, H, Liu, P, Xu, M & Yang, Y 2022, 'Unsupervised Visual Representation Learning via Dual-Level Progressive Similar Instance Selection', IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 8851-8861.
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Fan, H, Yu, X, Yang, Y & Kankanhalli, M 2022, 'Deep Hierarchical Representation of Point Cloud Videos via Spatio-Temporal Decomposition', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 12, pp. 9918-9930.
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Fan, H, Zhuo, T, Yu, X, Yang, Y & Kankanhalli, M 2022, 'Understanding Atomic Hand-Object Interaction With Human Intention', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 1, pp. 275-285.
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Hand-object interaction plays a very important role when humans manipulate objects. While existing methods focus on improving hand-object recognition with fully automatic methods, human intention has been largely neglected in the recognition process, thus leading to undesirable interaction descriptions. To better interpret human-object interaction that is aligned to human intention, we argue that a reference specifying human intention should be taken into account. Thus, we propose a new approach to represent interactions while reflecting human purpose with three key factors, i.e., hand, object and reference. Specifically, we design a pattern of <hand-object, object-reference, hand, object, reference> (HOR) to recognize intention based atomic hand-object interactions. This pattern aims to model interactions with the states of hand, object, reference and their relationships. Furthermore, we design a simple yet effective Spatially Part-based (3+1)D convolutional neural network, namely SP(3+1)D, which leverages 3D and 1D convolutions to model visual dynamics and object position changes based on our HOR, respectively. With the help of our SP(3+1)D network, the recognition results are able to indicate human purposes accurately. To evaluate the proposed method, we annotate a Something-1.3k dataset, which contains 10 atomic hand-object interactions and about 130 videos for each interaction. Experimental results on Something-1.3k demonstrate the effectiveness of our SP(3+1)D network.

Fan, J, Li, J, Zhou, Y, Hsieh, M-H & Poor, HV 2022, 'Entanglement-assisted concatenated quantum codes', Proceedings of the National Academy of Sciences, vol. 119, no. 24.
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Entanglement-assisted concatenated quantum codes (EACQCs), constructed by concatenating two quantum codes, are proposed. These EACQCs show significant advantages over standard concatenated quantum codes (CQCs). First, we prove that, unlike standard CQCs, EACQCs can beat the nondegenerate Hamming bound for entanglement-assisted quantum error-correction codes (EAQECCs). Second, we construct families of EACQCs with parameters better than the best-known standard quantum error-correction codes (QECCs) and EAQECCs. Moreover, these EACQCs require very few Einstein–Podolsky–Rosen (EPR) pairs to begin with. Finally, it is shown that EACQCs make entanglement-assisted quantum communication possible, even if the ebits are noisy. Furthermore, EACQCs can outperform CQCs in entanglement fidelity over depolarizing channels if the ebits are less noisy than the qubits. We show that the error-probability threshold of EACQCs is larger than that of CQCs when the error rate of ebits is sufficiently lower than that of qubits. Specifically, we derive a high threshold of 47% when the error probability of the preshared entanglement is 1% to that of qubits.

Fan, J, Yao, J, Yu, Y & Li, Y 2022, 'A macroscopic viscoelastic model of magnetorheological elastomer with different initial particle chain orientation angles based on fractional viscoelasticity', Smart Materials and Structures, vol. 31, no. 2, pp. 025025-025025.
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Abstract In this paper, a macroscopic viscoelastic modeling method for magnetorheological elastomer (MRE) based on fractional derivative model is presented to describe the dynamic viscoelastic properties of MRE with different initial particle chain orientation angles. The angle between the particle chain and the applied magnetic field is used as an indicator to describe the directionality of the particle chain. MRE samples with different initial inclination angles have been designed and fabricated. The dynamic viscoelastic properties of different MRE samples under shear working mode were measured using a parallel plate rheometer. The dynamic viscoelastic properties of MRE with different initial inclination angles are analyzed under the test conditions of different strain amplitude, frequency and magnetic flux density. The test results show that the initial inclination angle of the particle chain in the MRE has a significant effect on the dynamic viscoelastic properties of the MRE. A polynomial function is used to describe the relationship between the initial particle chain orientation angle and the magneto-induced modulus of MRE. A phenomenological model of magneto-induced modulus is established based on the fractional derivative model. The model parameters are identified using the nonlinear least square method. The predicted values of the model are in good agreement with the experimental results, indicating that the model can well describe the dynamic viscoelastic properties of MRE.

Fan, S, Ni, W, Tian, H, Huang, Z & Zeng, R 2022, 'Carrier Phase-Based Synchronization and High-Accuracy Positioning in 5G New Radio Cellular Networks', IEEE Transactions on Communications, vol. 70, no. 1, pp. 564-577.
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Fan, Y, Liu, D & Ye, L 2022, 'A Novel Continuum Robot With Stiffness Variation Capability Using Layer Jamming: Design, Modeling, and Validation', IEEE Access, vol. 10, pp. 130253-130263.
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Fang, C, Meng, X, Hu, Z, Xu, F, Zeng, D, Dong, M & Ni, W 2022, 'AI-Driven Energy-Efficient Content Task Offloading in Cloud-Edge-End Cooperation Networks', IEEE Open Journal of the Computer Society, vol. 3, pp. 162-171.
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Fang, J, Ge, Y, Chen, Z, Xing, B, Bao, S, Yong, Q, Chi, R, Yang, S & Ni, B-J 2022, 'Flotation purification of waste high-silica phosphogypsum', Journal of Environmental Management, vol. 320, pp. 115824-115824.
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Fani, A, Golroo, A, Ali Mirhassani, S & Gandomi, AH 2022, 'Pavement maintenance and rehabilitation planning optimisation under budget and pavement deterioration uncertainty', International Journal of Pavement Engineering, vol. 23, no. 2, pp. 414-424.
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Fani, A, Naseri, H, Golroo, A, Mirhassani, SA & Gandomi, AH 2022, 'A progressive hedging approach for large-scale pavement maintenance scheduling under uncertainty', International Journal of Pavement Engineering, vol. 23, no. 7, pp. 2460-2472.
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Farah, N, Lei, G, Zhu, J & Guo, Y 2022, 'Two-vector Dimensionless Model Predictive Control of PMSM Drives Based on Fuzzy Decision Making', CES Transactions on Electrical Machines and Systems, vol. 6, no. 4, pp. 393-403.
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Farhangi, M, Barzegarkhoo, R, Aguilera, RP, Lee, SS, Lu, DD-C & Siwakoti, YP 2022, 'A Single-Source Single-Stage Switched-Boost Multilevel Inverter: Operation, Topological Extensions, and Experimental Validation', IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 11258-11271.
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Faro, B, Abedin, B & Cetindamar, D 2022, 'Hybrid organizational forms in public sector’s digital transformation: a technology enactment approach', Journal of Enterprise Information Management, vol. 35, no. 6, pp. 1742-1763.
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PurposeThe purpose of this paper is to examine how public sector organizations become nimbler while retaining their resilience during digital transformation.Design/methodology/approachThe study adopts a hermeneutic approach in conducting deep expert interviews with 22 senior executives and managers of multiple organizations. The method blends theory and expert views to study digital transformation in the context of enterprise information management.FindingsDrawing on technology enactment framework (TEF), this research poses that organizational form is critical in the enactment of technologies in digital transformation. By extending the TEF, the authors claim that organizations are not in pure bureaucratic or network organizational form during digital transformation; instead, they need a hybrid combination in order to support competing strategic needs for nimbleness and resilience simultaneously. The four hybrid organizational forms presented in this model (4R) allow for networks and bureaucracy to coexist, though at different levels depending on the level of resiliency and nimbleness required at each point in the continuous digital transformation journey.Research limitations/implicationsThe main theoretical contribution of this research is to extend the TEF to illustrate that the need for coexistence of nimbleness with stability in a digital transformation results in a hybrid of networks and bureaucratic organization forms. This research aims to guide public sector organizations' digital transformation with extended the TEF as a tool for building the required organizational forms to influence the t...

Fathipour, H, Payan, M, Jamshidi Chenari, R & Fatahi, B 2022, 'General failure envelope of eccentrically and obliquely loaded strip footings resting on an inherently anisotropic granular medium', Computers and Geotechnics, vol. 146, pp. 104734-104734.
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Faust, O, Hong, W, Loh, HW, Xu, S, Tan, R-S, Chakraborty, S, Barua, PD, Molinari, F & Acharya, UR 2022, 'Heart rate variability for medical decision support systems: A review', Computers in Biology and Medicine, vol. 145, pp. 105407-105407.
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Fayaz, H, Khan, SA, Saleel, CA, Shaik, S, Yusuf, AA, Veza, I, Fattah, IMR, Rawi, NFM, Asyraf, MRM & Alarifi, IM 2022, 'Developments in Nanoparticles Enhanced Biofuels and Solar Energy in Malaysian Perspective: A Review of State of the Art', Journal of Nanomaterials, vol. 2022, pp. 1-22.
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The rapid rise in global oil prices, the scarcity of petroleum sources, and environmental concerns have all created severe issues. As a result of the country’s rapid expansion and financial affluence, Malaysia’s energy consumption has skyrocketed. Biodiesel and solar power are currently two of the most popular alternatives to fossil fuels in Malaysia. These two types of renewable energy sources appear to be viable options because of their abundant availability together with environmental and performance competence to highly polluting and fast depleting fossil fuels. The purpose of adopting renewable technology is to expand the nation’s accessibility to a reliable and secure power supply. The current review article investigates nonconventional energy sources added with nanosized metal particles called as nanomaterials including biodiesel and solar, as well as readily available renewable energy options. Concerning the nation’s energy policy agenda, the sources of energy demand are also investigated. The article evaluates Malaysia’s existing position in renewable energy industries, such as biodiesel and solar, as well as the impact of nanomaterials. This review article discusses biodiesel production, applications, and government policies in Malaysia, as well as biodiesel consumption and recent developments in the bioenergy sector, such as biodiesel property modifications utilizing nanoparticle additions. In addition, the current review study examines the scope of solar energy, different photovoltaic concentrators, types of solar energy harvesting systems, photovoltaic electricity potential in Malaysia, and the experimental setup of solar flat plate collectors (FPC) with nanotechnology.

Fazal, MAU, Ferguson, S & Saeed, Z 2022, 'Investigating cognitive workload in concurrent speech-based information communication', International Journal of Human-Computer Studies, vol. 157, pp. 102728-102728.
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Fazeli, A, Nguyen, HH, Tuan, HD & Poor, HV 2022, 'Non-Coherent Multi-Level Index Modulation', IEEE Transactions on Communications, vol. 70, no. 4, pp. 2240-2255.
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Feng, A, Akther, N, Duan, X, Peng, S, Onggowarsito, C, Mao, S, Fu, Q & Kolev, SD 2022, 'Recent Development of Atmospheric Water Harvesting Materials: A Review', ACS Materials Au, vol. 2, no. 5, pp. 576-595.
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Feng, B, Huang, Y, Tian, A, Wang, H, Zhou, H, Yu, S & Zhang, H 2022, 'DR-SDSN: An Elastic Differentiated Routing Framework for Software-Defined Satellite Networks', IEEE Wireless Communications, vol. 29, no. 6, pp. 80-86.
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Feng, B, Tian, A, Yu, S, Li, J, Zhou, H & Zhang, H 2022, 'Efficient Cache Consistency Management for Transient IoT Data in Content-Centric Networking', IEEE Internet of Things Journal, vol. 9, no. 15, pp. 12931-12944.
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Feng, C, Huang, XL, Li, Y, Wang, Y, Li, C, Qiu, W, Zhang, S, Liu, H, Zhang, Y, Liu, HK, Dou, SX & Wang, Z 2022, 'Towards rechargeable Na-SexSy batteries: From fundamental insights to improvement strategies', Chemical Engineering Journal, vol. 442, pp. 136189-136189.
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Feng, G, Fang, Z, Jie, L, Zhen, F & Guangquan, Z 2022, 'Neighbor-Searching Discrepancy-based Real Concept Drift', IEEE Transactions on Pattern Analysis and Machine Intelligence.

Feng, K, Ji, JC, Li, Y, Ni, Q, Wu, H & Zheng, J 2022, 'A novel cyclic-correntropy based indicator for gear wear monitoring', Tribology International, vol. 171, pp. 107528-107528.
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Feng, L, Huang, Y, Tsang, IW, Gupta, A, Tang, K, Tan, KC & Ong, Y-S 2022, 'Towards Faster Vehicle Routing by Transferring Knowledge From Customer Representation', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 2, pp. 952-965.
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Feng, S, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Zhang, S, Phong Vo, HN, Bui, XT & Ngoc Hoang, B 2022, 'Volatile fatty acids production from waste streams by anaerobic digestion: A critical review of the roles and application of enzymes', Bioresource Technology, vol. 359, pp. 127420-127420.
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Feng, S, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Liu, Y, Zhang, X, Bui, XT, Varjani, S & Hoang, BN 2022, 'Wastewater-derived biohydrogen: Critical analysis of related enzymatic processes at the research and large scales', Science of The Total Environment, vol. 851, pp. 158112-158112.
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Feng, Y, Li, L & Zhao, A 2022, 'A Cognitive-Emotional Model From Mobile Short-Form Video Addiction to Intermittent Discontinuance: The Moderating Role of Neutralization', International Journal of Human–Computer Interaction, pp. 1-13.
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Feng, Z-K, Huang, Q-Q, Niu, W-J, Yang, T, Wang, J-Y & Wen, S-P 2022, 'Multi-step-ahead solar output time series prediction with gate recurrent unit neural network using data decomposition and cooperation search algorithm', Energy, vol. 261, pp. 125217-125217.
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Ferdowsi, A, Mousavi, S-F, Mohamad Hoseini, S, Faramarzpour, M & Gandomi, AH 2022, 'A Survey of PSO Contributions to Water and Environmental Sciences', pp. 85-102.
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Ferguson, BM, Entezari, A, Fang, J & Li, Q 2022, 'Optimal placement of fixation system for scaffold-based mandibular reconstruction', Journal of the Mechanical Behavior of Biomedical Materials, vol. 126, pp. 104855-104855.
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Figiela, M, Wysokowski, M, Stanisz, E, Hao, D & Ni, B 2022, 'Highly Sensitive, Fast Response and Selective Glucose Detection Based on CuO/Nitrogen‐doped Carbon Non‐enzymatic Sensor', Electroanalysis, vol. 34, no. 11, pp. 1725-1734.
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Figuerola-Wischke, A, Gil-Lafuente, AM & Merigó, JM 2022, 'The uncertain ordered weighted averaging adequacy coefficient operator', International Journal of Approximate Reasoning, vol. 148, pp. 68-79.
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Fleck, R, Gill, R, Pettit, TJ, Torpy, FR & Irga, PJ 2022, 'Bio-solar green roofs increase solar energy output: The sunny side of integrating sustainable technologies', Building and Environment, vol. 226, pp. 109703-109703.
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Fleck, R, Gill, RL, Saadeh, S, Pettit, T, Wooster, E, Torpy, F & Irga, P 2022, 'Urban green roofs to manage rooftop microclimates: A case study from Sydney, Australia', Building and Environment, vol. 209, pp. 108673-108673.
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Fleck, R, Westerhausen, MT, Killingsworth, N, Ball, J, Torpy, FR & Irga, PJ 2022, 'The hydrological performance of a green roof in Sydney, Australia: A tale of two towers', Building and Environment, vol. 221, pp. 109274-109274.
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Flores‐Sosa, M, Avilés‐Ochoa, E & Merigó, JM 2022, 'Exchange rate and volatility: A bibliometric review', International Journal of Finance & Economics, vol. 27, no. 1, pp. 1419-1442.
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Flores-Sosa, M, Avilés-Ochoa, E, Merigó, JM & Kacprzyk, J 2022, 'The OWA operator in multiple linear regression', Applied Soft Computing, vol. 124, pp. 108985-108985.
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Flores-Sosa, M, León-Castro, E, Merigó, JM & Yager, RR 2022, 'Forecasting the exchange rate with multiple linear regression and heavy ordered weighted average operators', Knowledge-Based Systems, vol. 248, pp. 108863-108863.
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Fonseka, C, Ryu, S, Naidu, G, Kandasamy, J & Vigneswaran, S 2022, 'Recovery of water and valuable metals using low pressure nanofiltration and sequential adsorption from acid mine drainage', Environmental Technology & Innovation, vol. 28, pp. 102753-102753.
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Acid mine drainage (AMD) contains an array of valuable resources such as Rare Earth Elements (REE) and Copper (Cu) which can be recovered along with fresh water. Low pressure nanofiltration with NF90 membrane was first studied to recover fresh water from synthetic AMD and concentration of dissolved metals for subsequent efficient selective recovery. Organic matter (OM) present in AMD was found to cause membrane fouling which resulted in significant flux decline. Powdered eggshell was investigated as a low-cost adsorbent for OM removal. The study showed that a 0.2 mg/l dose of powdered eggshell adsorbed 100% of OM and Fe with no significant loss of other dissolved metals. A steady permeate flux of 15.5 ± 0.2 L/m2h (LMH) was achieved for pre-treated AMD with a solute rejection rate of more than 98%. A chromium-based metal organic framework (MOF) modified with N- (phosphonomethyl) iminodiacetic acid (PMIDA) and an amine-grafted mesoporous silica (SBA15) material was synthesized for selective recovery of REE and Cu, respectively. The two adsorbents were used sequentially to selectively adsorb REE (91%) and Cu (90%) from pH adjusted concentrated feed. The formation of coordinating complexes with carboxylate and phosphonic groups on MOF was found to be the primary driving force for selective REE adsorption. Selective uptake of Cu onto amine-grafted SBA15 was due to the formation of strong chelating bonds between Cu and amine ligands. Both adsorbents remained structurally stable over 5 regeneration cycles. The findings here highlight the practical potential of membrane/adsorption hybrid systems for water and valuable metal (REE) recovery from AMD.

Francis, I & Saha, SC 2022, 'Computational fluid dynamics and machine learning algorithms analysis of striking particle velocity magnitude, particle diameter, and impact time inside an acinar region of the human lung', Physics of Fluids, vol. 34, no. 10, pp. 101904-101904.
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Complementing computational fluid dynamics (CFD) simulations with machine learning algorithms is becoming increasingly popular as the combination reduces the computational time of the CFD simulations required for classifying, predicting, or optimizing the impact of geometrical and physical variables of a specific study. The main target of drug delivery studies is indicating the optimum particle diameter for targeting particular locations in the lung to achieve a desired therapeutic effect. In addition, the main goal of molecular dynamics studies is to investigate particle–lung interaction through given particle properties. Therefore, this study combines the two by numerically determining the optimum particle diameter required to obtain an ideal striking velocity magnitude (velocity at the time of striking the alveoli, i.e., deposition by sedimentation/diffusion) and impact time (time from release until deposition) inside an acinar part of the lung. At first, the striking velocity magnitudes and time for impact (two independent properties) of three different particle diameters ([Formula: see text]) are computed using CFD simulations. Then, machine learning classifiers determine the particle diameter corresponding to these two independent properties. In this study, two cases are compared: A healthy acinus where a surfactant layer covers the inner surface of the alveoli providing low air–liquid surface tension values [Formula: see text]), and a diseased acinus where only a water layer covers the surface causing high surface tension values [Formula: see text]). In this study, the airflow velocity throughout the breathing cycle corresponds to a person with a respiratory rate of 13 breaths per minute and a volume flow rate of [Formula: see text]. Accurate machine learning results showed that all three particle diameters attain larger velocities and smaller impact times in a diseased acinus compared to a healthy one. In both cases, the [Formula: see text] parti...

Francis, I & Saha, SC 2022, 'Surface tension effects on flow dynamics and alveolar mechanics in the acinar region of human lung', Heliyon, vol. 8, no. 10, pp. e11026-e11026.
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Francis, I, Shrestha, J, Paudel, KR, Hansbro, PM, Warkiani, ME & Saha, SC 2022, 'Recent advances in lung-on-a-chip models', Drug Discovery Today, vol. 27, no. 9, pp. 2593-2602.
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Fu, A, Yu, S, Zhang, Y, Wang, H & Huang, C 2022, 'NPP: A New Privacy-Aware Public Auditing Scheme for Cloud Data Sharing with Group Users', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 14-24.
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Fu, D, Zhao, X & Zhu, J 2022, 'A Novel Robust Super-Twisting Nonsingular Terminal Sliding Mode Controller for Permanent Magnet Linear Synchronous Motors', IEEE Transactions on Power Electronics, vol. 37, no. 3, pp. 2936-2945.
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Fu, J, Huang, C-H, Dang, C & Wang, Q 2022, 'A review on treatment of disinfection byproduct precursors by biological activated carbon process', Chinese Chemical Letters, vol. 33, no. 10, pp. 4495-4504.
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Fu, K, Cai, X, Yuan, B, Yang, Y & Yao, X 2022, 'An Efficient Surrogate Assisted Particle Swarm Optimization for Antenna Synthesis', IEEE Transactions on Antennas and Propagation, vol. 70, no. 7, pp. 4977-4984.
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Fumanal-Idocin, J, Takac, Z, Fernandez, J, Sanz, JA, Goyena, H, Lin, C-T, Wang, Y-K & Bustince, H 2022, 'Interval-Valued Aggregation Functions Based on Moderate Deviations Applied to Motor-Imagery-Based Brain–Computer Interface', IEEE Transactions on Fuzzy Systems, vol. 30, no. 7, pp. 2706-2720.
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Fumanal-Idocin, J, Wang, Y-K, Lin, C-T, Fernandez, J, Sanz, JA & Bustince, H 2022, 'Motor-Imagery-Based Brain–Computer Interface Using Signal Derivation and Aggregation Functions', IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7944-7955.
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Gadipudi, N, Elamvazuthi, I, Lu, C-K, Paramasivam, S & Su, S 2022, 'Lightweight spatial attentive network for vehicular visual odometry estimation in urban environments', Neural Computing and Applications, vol. 34, no. 21, pp. 18823-18836.
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Galvão, N, Matos, JC, Hajdin, R, Ferreira, L & Stewart, MG 2022, 'Impact of construction errors on the structural safety of a post-tensioned reinforced concrete bridge', Engineering Structures, vol. 267, pp. 114650-114650.
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The ageing of bridge stock in developed countries worldwide and the increasing number of recorded bridge collapses have underlined the need for more sophisticated and comprehensive assessment procedures concerning the safety and serviceability of structures. In many recent failures, construction errors or deficiencies have contributed to the unfortunate outcome either by depleting the safety margin or speeding up the deterioration rate of structures. This research aims to quantify the impact of construction errors on the structural safety of a bridge considering corresponding models available in the literature that probabilistically characterise the occurrence rate and severity of some of these errors. The nominal probability of failure of structures, neglecting construction errors, is typically computed in numerous works in the literature. Therefore, the novelty of this paper lies in the consideration of an additional source of uncertainty (i.e., construction errors) combined with sophisticated numerical methods leading to a more refined estimation of the probability of failure of structures. Accordingly, some benchmark results focussing on error-free and error-included scenarios are established, providing useful information to close the gap between the nominal and the actual probability of failure of a railway bridge.

Gan, L, Teng, Z, Zhang, Y, Zhu, L, Wu, F & Yang, Y 2022, 'SemGloVe: Semantic Co-Occurrences for GloVe From BERT', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 30, pp. 2696-2704.
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Ganaie, MA, Tanveer, M & Lin, C-T 2022, 'Large-Scale Fuzzy Least Squares Twin SVMs for Class Imbalance Learning', IEEE Transactions on Fuzzy Systems, vol. 30, no. 11, pp. 4815-4827.
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Ganbat, N, Altaee, A, Zhou, JL, Lockwood, T, Al-Juboori, RA, Hamdi, FM, Karbassiyazdi, E, Samal, AK, Hawari, A & Khabbaz, H 2022, 'Investigation of the effect of surfactant on the electrokinetic treatment of PFOA contaminated soil', Environmental Technology & Innovation, vol. 28, pp. 102938-102938.
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Gandomi, AH & Roke, DA 2022, 'A Multiobjective Evolutionary Framework for Formulation of Nonlinear Structural Systems', IEEE Transactions on Industrial Informatics, vol. 18, no. 9, pp. 5795-5803.
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Gao, F, Zhang, S, He, X & Sheng, D 2022, 'Experimental Study on Migration Behavior of Sandy Silt under Cyclic Load', Journal of Geotechnical and Geoenvironmental Engineering, vol. 148, no. 5.
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Gao, H, Fang, D, Xiao, J, Hussain, W & Kim, JY 2022, 'CAMRL: A Joint Method of Channel Attention and Multidimensional Regression Loss for 3D Object Detection in Automated Vehicles', IEEE Transactions on Intelligent Transportation Systems, pp. 1-15.
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Gao, H, Huang, J, Tao, Y, Hussain, W & Huang, Y 2022, 'The Joint Method of Triple Attention and Novel Loss Function for Entity Relation Extraction in Small Data-Driven Computational Social Systems', IEEE Transactions on Computational Social Systems, vol. 9, no. 6, pp. 1725-1735.
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Gao, H, Luo, X, Barroso, RJD & Hussain, W 2022, 'Guest editorial: Smart communications and networking: architecture, applications, and future challenges', IET Communications, vol. 16, no. 10, pp. 1021-1024.
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Gao, H, Qin, X, Barroso, RJD, Hussain, W, Xu, Y & Yin, Y 2022, 'Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 1, pp. 66-76.
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Gao, H, Qiu, B, Duran Barroso, RJ, Hussain, W, Xu, Y & Wang, X 2022, 'TSMAE: A Novel Anomaly Detection Approach for Internet of Things Time Series Data Using Memory-Augmented Autoencoder', IEEE Transactions on Network Science and Engineering, pp. 1-1.
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Gao, H, Zhang, Y & Hussain, W 2022, 'Special issue on intelligent software engineering', Expert Systems, vol. 39, no. 6.
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Gao, J, Yu, H & Zhang, S 2022, 'Joint event causality extraction using dual-channel enhanced neural network', Knowledge-Based Systems, vol. 258, pp. 109935-109935.
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Gao, L, Li, S, Xu, X, Zou, C & Zhang, G 2022, 'Highly Sensitive H2 Sensors Based on Co3O4/PEI-CNTs at Room Temperature', Journal of Nanomaterials, vol. 2022, pp. 1-8.
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The highly dispersed Co3O4 on the surface of CNTs modified with polyethylenimine (PEI) was synthesized using the hydrothermal method. In the CNT-Co3O4 composite materials, CNTs not only provide the substrate for the Co3O4 nanoparticles but also prevent their aggregation. Furthermore, the interaction between Co3O4 and CNTs modified with polyethylenimine (PEI) helps to improve the gas sensing performance. In particular, the CNT-Co3O4 composite synthesized at 190°C shows the outstanding sensitive characteristics to H2 with a lower detection limit of 30 ppm at room temperature. The obtained CNT-Co3O4 sensor displays excellent selectivity and stability to H2. The energy band model of the conductive mechanism has been built to explain the resistance change when the gas sensor is exposed to the H2. Hence, the CNT-Co3O4 composite material presents highly promising applications in H2 gas sensing.

Gao, S, Guo, YJ, Safavi-Naeini, SA, Hong, W & Yang, X-X 2022, 'Guest Editorial Low-Cost Wide-Angle Beam-Scanning Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7378-7383.
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Gao, S, Yu, S, Wu, L, Yao, S & Zhou, X 2022, 'Detecting adversarial examples by additional evidence from noise domain', IET Image Processing, vol. 16, no. 2, pp. 378-392.
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Gao, W, Wu, J & Xu, G 2022, 'Detecting Duplicate Questions in Stack Overflow via Source Code Modeling', International Journal of Software Engineering and Knowledge Engineering, vol. 32, no. 02, pp. 227-255.
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Stack Overflow is one of the most popular Question-Answering sites for programmers. However, it faces the problem of question duplication, where newly created questions are identical to previous questions. Existing works on duplicate question detection in Stack Overflow extract a set of textual features on the question pairs and use supervised learning approaches to classify duplicate question pairs. However, they do not consider the source code information in the questions. While in some cases, the intention of a question is mainly represented by the source code. In this paper, we aim to learn the semantics of a question by combining both text features and source code features. We use word embedding and convolutional neural networks to extract textual features from questions to overcome the lexical gap issue. We use tree-based convolutional neural networks to extract structural and semantic features from source code. In addition, we perform multi-task learning by combining the duplication question detection task with a question tag prediction side task. We conduct extensive experiments on the Stack Overflow dataset and show that our approach can detect duplicate questions with higher recall and MRR compared with baseline approaches on Python and Java programming languages.

Gao, X & Zhang, Y 2022, 'What is behind the globalization of technology? Exploring the interplay of multi-level drivers of international patent extension in the solar photovoltaic industry', Renewable and Sustainable Energy Reviews, vol. 163, pp. 112510-112510.
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Gao, X, Yang, F, Yan, Z, Zhao, J, Li, S, Nghiem, L, Li, G & Luo, W 2022, 'Humification and maturation of kitchen waste during indoor composting by individual households', Science of The Total Environment, vol. 814, pp. 152509-152509.
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García, MDPR, Alejandro, KAC, Merigó, JM, Terceño-Gómez, A, Forradellas, MTS & Kacprzyk, J 2022, 'Preface', Lecture Notes in Networks and Systems, vol. 384 LNNS, pp. v-vii.

García-Orozco, D, Alfaro-García, VG, Merigó, JM, Espitia Moreno, IC & Gómez Monge, R 2022, 'An overview of the most influential journals in fuzzy systems research', Expert Systems with Applications, vol. 200, pp. 117090-117090.
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Garg, H, Gandomi, AH, Ali, Z & Mahmood, T 2022, 'Neutrality aggregation operators based on complex q‐rung orthopair fuzzy sets and their applications in multiattribute decision‐making problems', International Journal of Intelligent Systems, vol. 37, no. 1, pp. 1010-1051.
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Gaur, VK, Gautam, K, Sharma, P, Gupta, P, Dwivedi, S, Srivastava, JK, Varjani, S, Ngo, HH, Kim, S-H, Chang, J-S, Bui, X-T, Taherzadeh, MJ & Parra-Saldívar, R 2022, 'Sustainable strategies for combating hydrocarbon pollution: Special emphasis on mobil oil bioremediation', Science of The Total Environment, vol. 832, pp. 155083-155083.
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Gaur, VK, Sharma, P, Gupta, S, Varjani, S, Srivastava, JK, Wong, JWC & Ngo, HH 2022, 'Opportunities and challenges in omics approaches for biosurfactant production and feasibility of site remediation: Strategies and advancements', Environmental Technology & Innovation, vol. 25, pp. 102132-102132.
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Gautam, S, Lu, Y, Taghizadeh, S, Xiao, W & Lu, DD-C 2022, 'An Enhanced Time-Delay-Based Reference Current Identification Method for Single-Phase System', IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 3, no. 3, pp. 683-693.
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Gautam, S, Xiao, W, Ahmed, H & Lu, DD-C 2022, 'Enhanced Single-Phase Phase Locked Loop Based on Complex-Coefficient Filter', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-8.
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Gautam, S, Xiao, W, Lu, DD-C, Ahmed, H & Guerrero, JM 2022, 'Development of Frequency-Fixed All-Pass Filter-Based Single-Phase Phase-Locked Loop', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 1, pp. 506-517.
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Ghasemi, M, Khedri, M, Didandeh, M, Taheri, M, Ghasemy, E, Maleki, R, Shon, HK & Razmjou, A 2022, 'Removal of Pharmaceutical Pollutants from Wastewater Using 2D Covalent Organic Frameworks (COFs): An In Silico Engineering Study', Industrial & Engineering Chemistry Research, vol. 61, no. 25, pp. 8809-8820.
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Gholami, K, Azizivahed, A, Li, L & Zhang, J 2022, 'Accuracy enhancement of second-order cone relaxation for AC optimal power flow via linear mapping', Electric Power Systems Research, vol. 212, pp. 108646-108646.
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Gholami, K, Islam, MR, Rahman, MM, Azizivahed, A & Fekih, A 2022, 'State-of-the-art technologies for volt-var control to support the penetration of renewable energy into the smart distribution grids', Energy Reports, vol. 8, pp. 8630-8651.
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Gil Lafuente, AM, Reverter, SB, Merigó, JM & Martínez, AT 2022, 'Preface', Lecture Notes in Networks and Systems, vol. 388 LNNS, pp. v-vi.

Goh, BHH, Chong, CT, Ong, HC, Milano, J, Shamsuddin, AH, Lee, XJ & Ng, J-H 2022, 'Strategies for fuel property enhancement for second-generation multi-feedstock biodiesel', Fuel, vol. 315, pp. 123178-123178.
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Goh, BHH, Chong, CT, Ong, HC, Seljak, T, Katrašnik, T, Józsa, V, Ng, J-H, Tian, B, Karmarkar, S & Ashokkumar, V 2022, 'Recent advancements in catalytic conversion pathways for synthetic jet fuel produced from bioresources', Energy Conversion and Management, vol. 251, pp. 114974-114974.
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Golbaz, D, Asadi, R, Amini, E, Mehdipour, H, Nasiri, M, Etaati, B, Naeeni, STO, Neshat, M, Mirjalili, S & Gandomi, AH 2022, 'Layout and design optimization of ocean wave energy converters: A scoping review of state-of-the-art canonical, hybrid, cooperative, and combinatorial optimization methods', Energy Reports, vol. 8, pp. 15446-15479.
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Gong, S, Ball, J & Surawski, N 2022, 'Urban land-use land-cover extraction for catchment modelling using deep learning techniques', Journal of Hydroinformatics, vol. 24, no. 2, pp. 388-405.
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Abstract Throughout the world, the likelihood of floods and managing the associated risk are a concern to many catchment managers and the population residing in those catchments. Catchment modelling is a popular approach to predicting the design flood quantiles of a catchment with complex spatial characteristics and limited monitoring data to obtain the necessary information for preparing the flood risk management plan. As an important indicator of urbanisation, land use land cover (LULC) plays a critical role in catchment parameterisation and modelling the rainfall–runoff process. Digitising LULC from remote sensing imagery of urban catchment is becoming increasingly difficult and time-consuming as the variability and diversity of land uses occur during urban development. In recent years, deep learning neural networks (DNNs) have achieved remarkable image classification and segmentation outcomes with the powerful capacity to process complex workflow and features, learn sophisticated relationships and produce superior results. This paper describes end-to-end data assimilation and processing path using U-net and DeepLabV3+, also proposes a novel approach integrated with the clustering algorithm MeanShift. These methods were developed to generate pixel-based LULC semantic segmentation from high-resolution satellite imagery of the Alexandria Canal catchment, Sydney, Australia, and assess the applicability of their outputs as inputs to different catchment modelling systems. A significant innovation is using the MeanShift clustering algorithm to reduce the spatial noise in the raw image and propagate it to the deep learning network to improve prediction. All three methods achieved excellent classification performance, where the MeanShift+U-net has the highest accuracy and consistency on the test imagery. The final suitability assessment illustrates that all three methods are more suitable for the parameterisation of semi...

Gong, S, Guo, Z, Liu, M, Wen, S & Huang, T 2022, 'Aperiodic Event-triggered Synchronization Control for Neural Networks with Stochastic Perturbations and Time Delay', IEEE Transactions on Circuits and Systems II: Express Briefs, pp. 1-1.
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Gong, S, Guo, Z, Wen, S & Huang, T 2022, 'Stabilization Analysis for Linear Disturbed Event-Triggered Control System With Packet Losses', IEEE Transactions on Control of Network Systems, vol. 9, no. 3, pp. 1339-1347.
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Gong, S, Zou, Y, Xu, J, Hoang, DT, Lyu, B & Niyato, D 2022, 'Optimization-Driven Hierarchical Learning Framework for Wireless Powered Backscatter-Aided Relay Communications', IEEE Transactions on Wireless Communications, vol. 21, no. 2, pp. 1378-1391.
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Gong, Y, Li, Z, Zhang, J, Liu, W & Zheng, Y 2022, 'Online Spatio-Temporal Crowd Flow Distribution Prediction for Complex Metro System', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 2, pp. 865-880.
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Gonzalez de Vega, R, Lockwood, TE, Xu, X, Gonzalez de Vega, C, Scholz, J, Horstmann, M, Doble, PA & Clases, D 2022, 'Analysis of Ti- and Pb-based particles in the aqueous environment of Melbourne (Australia) via single particle ICP-MS', Analytical and Bioanalytical Chemistry, vol. 414, no. 18, pp. 5671-5681.
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AbstractThe analysis of natural and anthropogenic nanomaterials (NMs) in the environment is challenging and requires methods capable to identify and characterise structures on the nanoscale regarding particle number concentrations (PNCs), elemental composition, size, and mass distributions. In this study, we employed single particle inductively coupled plasma-mass spectrometry (SP ICP-MS) to investigate the occurrence of NMs in the Melbourne area (Australia) across 63 locations. Poisson statistics were used to discriminate between signals from nanoparticulate matter and ionic background. TiO2-based NMs were frequently detected and corresponding NM signals were calibated with an automated data processing platform. Additionally, a method utilising a larger mass bandpass was developed to screen for particulate high-mass elements. This procedure identified Pb-based NMs in various samples. The effects of different environmental matrices consisting of fresh, brackish, or seawater were mitigated with an aerosol dilution method reducing the introduction of salt into the plasma and avoiding signal drift. Signals from TiO2- and Pb-based NMs were counted, integrated, and subsequently calibrated to determine PNCs as well as mass and size distributions. PNCs, mean sizes, particulate masses, and ionic background levels were compared across different locations and environments. Graphical abstract

Goodswen, SJ, Kennedy, PJ & Ellis, JT 2022, 'Compilation of parasitic immunogenic proteins from 30 years of published research using machine learning and natural language processing', Scientific Reports, vol. 12, no. 1.
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AbstractThe World Health Organisation reported in 2020 that six of the top 10 sources of death in low-income countries are parasites. Parasites are microorganisms in a relationship with a larger organism, the host. They acquire all benefits at the host’s expense. A disease develops if the parasitic infection disrupts normal functioning of the host. This disruption can range from mild to severe, including death. Humans and livestock continue to be challenged by established and emerging infectious disease threats. Vaccination is the most efficient tool for preventing current and future threats. Immunogenic proteins sourced from the disease-causing parasite are worthwhile vaccine components (subunits) due to reliable safety and manufacturing capacity. Publications with ‘subunit vaccine’ in their title have accumulated to thousands over the last three decades. However, there are possibly thousands more reporting immunogenicity results without mentioning ‘subunit’ and/or ‘vaccine’. The exact number is unclear given the non-standardised keywords in publications. The study aim is to identify parasite proteins that induce a protective response in an animal model as reported in the scientific literature within the last 30 years using machine learning and natural language processing. Source code to fulfil this aim and the vaccine candidate list obtained is made available.

Goudarzi, S, Ahmad Soleymani, S, Hossein Anisi, M, Ciuonzo, D, Kama, N, Abdullah, S, Abdollahi Azgomi, M, Chaczko, Z & Azmi, A 2022, 'Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network', Computers, Materials & Continua, vol. 70, no. 1, pp. 715-738.
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Gravina da Rocha, C, Korb, S & Sacks, R 2022, 'Work structuring and product design for customized repetitive projects', Construction Management and Economics, vol. 40, no. 7-8, pp. 526-547.
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Grigorev, A, Mihaita, A-S, Lee, S & Chen, F 2022, 'Incident duration prediction using a bi-level machine learning framework with outlier removal and intra–extra joint optimisation', Transportation Research Part C: Emerging Technologies, vol. 141, pp. 103721-103721.
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Grosser, M, Lin, H, Wu, M, Zhang, Y, Tipper, S, Venter, D, Lu, J & dos Remedios, CG 2022, 'A bibliometric review of peripartum cardiomyopathy compared to other cardiomyopathies using artificial intelligence and machine learning', Biophysical Reviews, vol. 14, no. 1, pp. 381-401.
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Grzybowska, H, Wijayaratna, K, Shafiei, S, Amini, N & Travis Waller, S 2022, 'Ramp Metering Strategy Implementation: A Case Study Review', Journal of Transportation Engineering, Part A: Systems, vol. 148, no. 5.
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Guan, R, Zheng, H, Liu, Q, Ou, K, Li, D-S, Fan, J, Fu, Q & Sun, Y 2022, 'DIW 3D printing of hybrid magnetorheological materials for application in soft robotic grippers', Composites Science and Technology, vol. 223, pp. 109409-109409.
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Guan, S, Lu, H, Zhu, L & Fang, G 2022, 'AFE-CNN: 3D Skeleton-based Action Recognition with Action Feature Enhancement', Neurocomputing, vol. 514, pp. 256-267.
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Guan, W, Wen, H, Song, X, Wang, C, Yeh, C-H, Chang, X & Nie, L 2022, 'Partially Supervised Compatibility Modeling', IEEE Transactions on Image Processing, vol. 31, pp. 4733-4745.
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Gui, L, Xu, S, Xiao, F, Shu, F & Yu, S 2022, 'Non-Line-of-Sight Localization of Passive UHF RFID Tags in Smart Storage Systems', IEEE Transactions on Mobile Computing, vol. 21, no. 10, pp. 3731-3743.
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Gunatilake, A, Kodagoda, S & Thiyagarajan, K 2022, 'A Novel UHF-RFID Dual Antenna Signals Combined With Gaussian Process and Particle Filter for In-Pipe Robot Localization', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 6005-6011.
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Gunatilake, A, Kodagoda, S & Thiyagarajan, K 2022, 'Battery-Free UHF-RFID Sensors-Based SLAM for In-Pipe Robot Perception', IEEE Sensors Journal, vol. 22, no. 20, pp. 20019-20026.
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Guo, CA & Guo, YJ 2022, 'A General Approach for Synthesizing Multibeam Antenna Arrays Employing Generalized Joined Coupler Matrix', IEEE Transactions on Antennas and Propagation, vol. 70, no. 9, pp. 7556-7564.
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Guo, E, Li, P, Yu, S & Wang, H 2022, 'Efficient Video Privacy Protection Against Malicious Face Recognition Models', IEEE Open Journal of the Computer Society, vol. 3, pp. 271-280.
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Guo, H, Dai, R, Xie, M, Peng, LE, Yao, Z, Yang, Z, Nghiem, LD, Snyder, SA, Wang, Z & Tang, CY 2022, 'Tweak in Puzzle: Tailoring Membrane Chemistry and Structure toward Targeted Removal of Organic Micropollutants for Water Reuse', Environmental Science & Technology Letters, vol. 9, no. 4, pp. 247-257.
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Guo, H, Wang, J, Li, Z, Lu, H & Zhang, L 2022, 'A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory', Resources Policy, vol. 79, pp. 102975-102975.
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Guo, J, Cao, L & Gong, Z 2022, 'Recurrent Coupled Topic Modeling over Sequential Documents', ACM Transactions on Knowledge Discovery from Data, vol. 16, no. 1, pp. 1-32.
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The abundant sequential documents such as online archival, social media, and news feeds are streamingly updated, where each chunk of documents is incorporated with smoothly evolving yet dependent topics. Such digital texts have attracted extensive research on dynamic topic modeling to infer hidden evolving topics and their temporal dependencies. However, most of the existing approaches focus on single-topic-thread evolution and ignore the fact that a current topic may be coupled with multiple relevant prior topics. In addition, these approaches also incur the intractable inference problem when inferring latent parameters, resulting in a high computational cost and performance degradation. In this work, we assume that a current topic evolves from all prior topics with corresponding coupling weights, forming the multi-topic-thread evolution . Our method models the dependencies between evolving topics and thoroughly encodes their complex multi-couplings across time steps. To conquer the intractable inference challenge, a new solution with a set of novel data augmentation techniques is proposed, which successfully discomposes the multi-couplings between evolving topics. A fully conjugate model is thus obtained to guarantee the effectiveness and efficiency of the inference technique. A novel Gibbs sampler with a backward–forward filter algorithm efficiently learns latent time-evolving parameters in a closed-form. In addition, the latent Indian Buffet Process compound distribution is exploited to automatically infer the overall topic number and customize the sparse topic proportions for each sequential document without bias. The proposed method is evaluated on both synthetic and real-world datasets against the competitive baselines, demonstrating its superiority over the baselines in terms of the low per-word perplexity, high coherent topics, and better document time prediction.

Guo, K & Guo, Y 2022, 'Design and Analysis of an Outer Mover Linear-Rotary Vernier Machine', Journal of Electrical Engineering & Technology, vol. 17, no. 2, pp. 1087-1095.
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Guo, K, Guo, Y & Fang, S 2022, 'Flux Leakage Analytical Calculation in the E-Shape Stator of Linear Rotary Motor With Interlaced Permanent Magnet Poles', IEEE Transactions on Magnetics, vol. 58, no. 8, pp. 1-6.
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Guo, X, Zhang, H, Tang, W, Lu, Z, Hua, C, Siwakoti, YP, Malinowski, M & Blaabjerg, F 2022, 'Overview of Recent Advanced Topologies for Transformerless Dual-Grounded Inverters', IEEE Transactions on Power Electronics, vol. 37, no. 10, pp. 12679-12704.
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Guo, Y, Li, W, Dong, W, Luo, Z, Qu, F, Yang, F & Wang, K 2022, 'Self-sensing performance of cement-based sensor with carbon black and polypropylene fibre subjected to different loading conditions', Journal of Building Engineering, vol. 59, pp. 105003-105003.
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Guo, Y, Li, W, Dong, W, Wang, K, He, X, Vessalas, K & Sheng, D 2022, 'Self-sensing cement-based sensors with superhydrophobic and self-cleaning capacities after silane-based surficial treatments', Case Studies in Construction Materials, vol. 17, pp. e01311-e01311.
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A novel cement-based sensors was developed with integrated self-sensing superhydrophobicity, and self-cleaning functions in this paper. The synthesis was carried out by penetrating precast graphene nanoplate/cement-based sensors with silane/isopropanol solutions. The silane-treated cement-based sensors showed satisfactory stress/strain sensing performance with an average gauge factor of 141.8, and exhibited excellent hydrophobic behaviour with the highest water contact angle of 163° on the intact surface. The contact angle decreased to 148° and 142°, for the surface with scratches and for the inner part of sensors, respectively. The reduction was due to the spalling and less amount of silane particles within the scratches and the harder entry of silane to the inner part of sensor. The self-cleaning properties of silane-treated cement-based sensor were evaluated by the visual observation of removing efficiency of hydrophilic carbon black dust and lipophilic sauces after water rinsing. It was found that the silane-treated cement-based sensor showed excellent self-cleaning performance using hydrophilic carbon dust. Despite the removing efficiency decreased for the lipophilic sauces, the silane-treated cement-based sensors maintained much less stain than that of untreated ones on the surface. The related results will promote the synthesis and practical applications of multifunctional cement-based sensors for the application of intrisic structural health monitoring.

Guo, Y, Li, X, Luby, S & Jiang, G 2022, 'Vertical outbreak of COVID-19 in high-rise buildings: The role of sewer stacks and prevention measures', Current Opinion in Environmental Science & Health, vol. 29, pp. 100379-100379.
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Guo, Z, Lian, M, Wen, S & Huang, T 2022, 'An Adaptive Multi-Agent System With Duplex Control Laws for Distributed Resource Allocation', IEEE Transactions on Network Science and Engineering, vol. 9, no. 2, pp. 389-400.
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Guo, Z, Xiao, F, Sheng, B, Sun, L & Yu, S 2022, 'TWCC: A Robust Through-the-Wall Crowd Counting System Using Ambient WiFi Signals', IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 4198-4211.
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Gupta, BB, Chaudhary, P, Chang, X & Nedjah, N 2022, 'Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers', Computers & Electrical Engineering, vol. 98, pp. 107726-107726.
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Gupta, BB, Tewari, A, Cvitić, I, Peraković, D & Chang, X 2022, 'Artificial intelligence empowered emails classifier for Internet of Things based systems in industry 4.0', Wireless Networks, vol. 28, no. 1, pp. 493-503.
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Haakenstad, A, Yearwood, JA, Fullman, N, Bintz, C, Bienhoff, K, Weaver, MR, Nandakumar, V, Joffe, JN, LeGrand, KE, Knight, M, Abbafati, C, Abbasi-Kangevari, M, Abdoli, A, Abeldaño Zuñiga, RA, Adedeji, IA, Adekanmbi, V, Adetokunboh, OO, Afzal, MS, Afzal, S, Agudelo-Botero, M, Ahinkorah, BO, Ahmad, S, Ahmadi, A, Ahmadi, S, Ahmed, A, Ahmed Rashid, T, Aji, B, Akande-Sholabi, W, Alam, K, Al Hamad, H, Alhassan, RK, Ali, L, Alipour, V, Aljunid, SM, Ameyaw, EK, Amin, TT, Amu, H, Amugsi, DA, Ancuceanu, R, Andrade, PP, Anjum, A, Arabloo, J, Arab-Zozani, M, Ariffin, H, Arulappan, J, Aryan, Z, Ashraf, T, Atnafu, DD, Atreya, A, Ausloos, M, Avila-Burgos, L, Ayano, G, Ayanore, MA, Azari, S, Badiye, AD, Baig, AA, Bairwa, M, Bakkannavar, SM, Baliga, S, Banik, PC, Bärnighausen, TW, Barra, F, Barrow, A, Basu, S, Bayati, M, Belete, R, Bell, AW, Bhagat, DS, Bhagavathula, AS, Bhardwaj, P, Bhardwaj, N, Bhaskar, S, Bhattacharyya, K, Bhutta, ZA, Bibi, S, Bijani, A, Bikbov, B, Biondi, A, Bolarinwa, OA, Bonny, A, Brenner, H, Buonsenso, D, Burkart, K, Busse, R, Butt, ZA, Butt, NS, Caetano dos Santos, FL, Cahuana-Hurtado, L, Cámera, LA, Cárdenas, R, Carneiro, VLA, Catalá-López, F, Chandan, JS, Charan, J, Chavan, PP, Chen, S, Chen, S, Choudhari, SG, Chowdhury, EK, Chowdhury, MAK, Cirillo, M, Corso, B, Dadras, O, Dahlawi, SMA, Dai, X, Dandona, L, Dandona, R, Dangel, WJ, Dávila-Cervantes, CA, Davletov, K, Deuba, K, Dhimal, M, Dhimal, ML, Djalalinia, S, Do, HP, Doshmangir, L, Duncan, BB, Effiong, A, Ehsani-Chimeh, E, Elgendy, IY, Elhadi, M, El Sayed, I, El Tantawi, M, Erku, DA, Eskandarieh, S, Fares, J, Farzadfar, F, Ferrero, S, Ferro Desideri, L, Fischer, F, Foigt, NA, Foroutan, M, Fukumoto, T, Gaal, PA, Gaihre, S, Gardner, WM, Garg, T, Getachew Obsa, A, Ghafourifard, M, Ghashghaee, A, Ghith, N, Gilani, SA, Gill, PS, Goharinezhad, S, Golechha, M, Guadamuz, JS, Guo, Y, Gupta, RD, Gupta, R, Gupta, VK, Gupta, VB, Hamiduzzaman, M, Hanif, A, Haro, JM, Hasaballah, AI, Hasan, MM, Hasan, MT, Hashi, A, Hay, SI, Hayat, K, Heidari, M, Heidari, G, Henry, NJ, Herteliu, C, Holla, R, Hossain, S, Hossain, SJ, Hossain, MBH, Hosseinzadeh, M, Hostiuc, S, Hoveidamanesh, S, Hsieh, VC-R, Hu, G, Huang, J, Huda, MM, Ifeagwu, SC, Ikuta, KS, Ilesanmi, OS, Irvani, SSN, Islam, RM, Islam, SMS, Ismail, NE, Iso, H, Isola, G, Itumalla, R, Iwagami, M, Jahani, MA, Jahanmehr, N, Jain, R & et al. 2022, 'Assessing performance of the Healthcare Access and Quality Index, overall and by select age groups, for 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019', The Lancet Global Health, vol. 10, no. 12, pp. e1715-e1743.
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Hagos, FY, Abd Aziz, AR, Zainal, EZ, Mofijur, M & Ahmed, SF 2022, 'Recovery of gas waste from the petroleum industry: a review', Environmental Chemistry Letters, vol. 20, no. 1, pp. 263-281.
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Haider, JB, Haque, MI, Hoque, M, Hossen, MM, Mottakin, M, Khaleque, MA, Johir, MAH, Zhou, JL, Ahmed, MB & Zargar, M 2022, 'Efficient extraction of silica from openly burned rice husk ash as adsorbent for dye removal', Journal of Cleaner Production, vol. 380, pp. 135121-135121.
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Rice is the staple food in many countries including Bangladesh. In Bangladesh, >80% of the total irrigated area is planted with rice, which generates a huge amount of rice husk (RH) as a solid waste which requires proper management. This study, therefore, aimed to extract amorphous silica from openly burned rice husk ash (RHA) using a simple method by avoiding calcination or combustion processes. The extracted silica was then applied for the removal of environmental contaminants i.e., methylene blue dye from an aqueous solution. It was found that the yield of silica produced from sulfuric acid-pretreated RHA was 72.4%. The FTIR absorption peaks at 1057 and 783 cm−1 indicate the presence of a highly condensed silica-containing asymmetric and symmetric siloxane (Si–O–Si) network mixture. The broad maximum bond peak intensity at 2θ = 22° by x-ray diffraction analysis also indicates that the produced silica was amorphous with a mesoporous structure. The surface area of sulfuric acid treated RHA-based silica was 183 m2/g. This silica resulted in a maximum adsorption capacity of 107 mg/g of methylene blue at pH 8 with a faster equilibrium reached at 60 min. The mechanistic study indicated that both Langmuir and Freundlich adsorption isotherms were both fitted well which suggested homogeneous adsorbent surfaces involving monolayer and multilayer adsorption processes.

Hamdi, AMA, Hussain, FK & Hussain, OK 2022, 'Task offloading in vehicular fog computing: State-of-the-art and open issues', Future Generation Computer Systems, vol. 133, pp. 201-212.
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Han, C, Li, W, Wang, J & Huang, Z 2022, 'Boron leaching: Creating vacancy-rich Ni for enhanced hydrogen evolution', Nano Research, vol. 15, no. 3, pp. 1868-1873.
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Creating vacancy is often highly effective in enhancing the hydrogen evolution performance of transition metal-based catalysts. Vacancy-rich Ni nanosheets have been fabricated via topochemical formation of two-dimentional (2D) Ni2B on graphene precursor followed by boron leaching. Anchored on graphene, a few atomic layered Ni2B nanosheets are first obtained by reduction and annealing. Large number of atomic vacancies are then generated in the Ni2B layer via leaching boron atoms. When used for hydrogen evolution reaction (HER), the vacancy-rich Ni/Ni(OH)2 heterostructure nanosheets demonstrate remarkable performance with a low overpotential of 159 mV at a current density of 10 mA·cm−2 in alkaline solution, a dramatic improvement over 262 mV of its precursor. This enhancement is associated with the formation of vacancies which introduce more active sites for HER along Ni/Ni(OH)2 heterointerfaces. This work offers a facile and universal route to introduce vacancies and improve catalytic activity. [Figure not available: see fulltext.]

Han, C, Yu, X, Gao, C, Sang, N & Yang, Y 2022, 'Single image based 3D human pose estimation via uncertainty learning', Pattern Recognition, vol. 132, pp. 108934-108934.
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Han, C, Zheng, Z, Su, K, Yu, D, Yuan, Z, Gao, C, Sang, N & Yang, Y 2022, 'DMRNet++: Learning Discriminative Features with Decoupled Networks and Enriched Pairs for One-Step Person Search', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-18.
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Han, Z, Huo, J, Zhang, X, Ngo, HH, Guo, W, Du, Q, Zhang, Y, Li, C & Zhang, D 2022, 'Characterization and flocculation performance of a newly green flocculant derived from natural bagasse cellulose', Chemosphere, vol. 301, pp. 134615-134615.
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Hanaei, S, Takian, A, Majdzadeh, R, Maboloc, CR, Grossmann, I, Gomes, O, Milosevic, M, Gupta, M, Shamshirsaz, AA, Harbi, A, Burhan, AM, Uddin, LQ, Kulasinghe, A, Lam, C-M, Ramakrishna, S, Alavi, A, Nouwen, JL, Dorigo, T, Schreiber, M, Abraham, A, Shelkovaya, N, Krysztofiak, W, Ebrahimi Warkiani, M, Sellke, F, Ogino, S, Barba, FJ, Brand, S, Vasconcelos, C, Salunke, DB & Rezaei, N 2022, 'Emerging Standards and the Hybrid Model for Organizing Scientific Events During and After the COVID-19 Pandemic', Disaster Medicine and Public Health Preparedness, vol. 16, no. 3, pp. 1172-1177.
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AbstractSince the beginning of 2020, the coronavirus disease (COVID-19) pandemic has dramatically influenced almost every aspect of human life. Activities requiring human gatherings have either been postponed, canceled, or held completely virtually. To supplement lack of in-person contact, people have increasingly turned to virtual settings online, advantages of which include increased inclusivity and accessibility and a reduced carbon footprint. However, emerging online technologies cannot fully replace in-person scientific events. In-person meetings are not susceptible to poor Internet connectivity problems, and they provide novel opportunities for socialization, creating new collaborations and sharing ideas. To continue such activities, a hybrid model for scientific events could be a solution offering both in-person and virtual components. While participants can freely choose the mode of their participation, virtual meetings would most benefit those who cannot attend in-person due to the limitations. In-person portions of meetings should be organized with full consideration of prevention and safety strategies, including risk assessment and mitigation, venue and environmental sanitation, participant protection and disease prevention, and promoting the hybrid model. This new way of interaction between scholars can be considered as a part of a resilience system, which was neglected previously and should become a part of routine practice in the scientific community.

Hang, J, Wu, Y, Li, Y, Lai, T, Zhang, J & Li, Y 2022, 'A deep learning semantic segmentation network with attention mechanism for concrete crack detection', Structural Health Monitoring, pp. 147592172211261-147592172211261.
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In this research, an attention-based feature fusion network (AFFNet), with a backbone residual network (ResNet101) enhanced with two attention mechanism modules, is proposed for automatic pixel-level detection of concrete crack. In particular, the inclusion of attention mechanism modules, for example, the vertical and horizontal compression attention module (VH-CAM) and the efficient channel attention upsample module (ECAUM), is to enable selective concentration on the crack feature. The VH-CAM generates a feature map integrating pixel-level information in vertical and horizontal directions. The ECAUM applied on each decoder layer combines efficient channel attention (ECA) and feature fusion, which can provide rich contextual information as guidance to help low-level features recover crack localization. The proposed model is evaluated on the test dataset and the results reach 84.49% for mean intersection over union (MIoU). Comparison with other state-of-the-art models proves high efficiency and accuracy of the proposed method.

Hao, D, Ma, T, Jia, B, Wei, Y, Bai, X, Wei, W & Ni, B-J 2022, 'Small molecule π-conjugated electron acceptor for highly enhanced photocatalytic nitrogen reduction of BiOBr', Journal of Materials Science & Technology, vol. 109, pp. 276-281.
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Hao, D, Wei, Y, Mao, L, Bai, X, Liu, Y, Xu, B, Wei, W & Ni, B-J 2022, 'Boosted selective catalytic nitrate reduction to ammonia on carbon/bismuth/bismuth oxide photocatalysts', Journal of Cleaner Production, vol. 331, pp. 129975-129975.
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Hao, Y, Xiao, D, Hao, H, Li, J & Li, J 2022, 'Experimental study of reinforced concrete beams reinforced with hybrid spiral-hooked end steel fibres under static and impact loads', Advances in Structural Engineering, vol. 25, no. 15, pp. 3019-3030.
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Discrete short steel fibres were proposed to be mixed with concrete for arresting cracks and enhancing the post-cracking resistance. It has been proven in previous tests that spiral steel fibres possessed markedly higher bonding to concrete matrix, leading to significantly improved performance of steel fibre reinforced concrete (SFRC) in terms of crack controllability, impact resistance, deformability and energy absorption capability. However, at the initial stage of cracking, SFRC reinforced with spiral fibres has relatively lower resistance to crack opening as compared to that reinforced with other types of steel fibres because of spiral shape stretching. To overcome this shortcoming, in the present study, short hooked-end steel fibres that exhibit high pull-out resistance at the crack initiation stage were mixed with spiral steel fibres in the normal-strength concrete matrix. A total volume fraction of 1% of hybrid steel fibres was mixed to cast SFRC specimens. With various mix ratios between spiral and hooked-end fibres considered, five batches of SFRC specimens were tested. Uniaxial compressive tests and four-point bending tests were carried out to compare the mechanical properties of SFRC materials with hybrid fibres while three-point bending tests on SFRC structural beams under static, drop-weight impact and post-impact static loading tests were conducted to investigate the structural performances. An equal dosage of hooked-end and spiral fibres was found to outperform other blend proportions to provide synergetic reinforcement to concrete matrix in terms of post-cracking resistance, energy absorption capacity and post-impact performance.

Hao, Y, Zhang, X, Du, Q, Wang, H, Ngo, HH, Guo, W, Zhang, Y, Long, T & Qi, L 2022, 'A new integrated single-chamber air-cathode microbial fuel cell - Anaerobic membrane bioreactor system for improving methane production and membrane fouling mitigation', Journal of Membrane Science, vol. 655, pp. 120591-120591.
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Haribabu, K, Sivasubramanian, V, Deepanraj, B & Ong, HC 2022, 'Thematic issue: Bioenergy and biorefinery approaches for environmental sustainability', Biomass Conversion and Biorefinery, vol. 12, no. 5, pp. 1433-1433.
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Haris, A, Sepehrirahnama, S, Lee, HP & Lim, K-M 2022, 'Mitigation of vibration of ship structure via local structural modifications', Ships and Offshore Structures, vol. 17, no. 8, pp. 1684-1694.
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Hasan, H 2022, 'NUMERICAL SIMULATION OF PERVIOUS CONCRETE PILE IN LOOSE AND SILTY SAND AFTER TREATING WITH MICROBIALLY INDUCED CALCITE PRECIPITATION', International Journal of GEOMATE, vol. 22, no. 90.
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Hassani, S, Mousavi, M & Gandomi, AH 2022, 'A Hilbert transform sensitivity-based model-updating method for damage detection of structures with closely-spaced eigenvalues', Engineering Structures, vol. 268, pp. 114761-114761.
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Hassani, S, Mousavi, M & Gandomi, AH 2022, 'A mode shape sensitivity-based method for damage detection of structures with closely-spaced eigenvalues', Measurement, vol. 190, pp. 110644-110644.
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Hassani, S, Mousavi, M & Gandomi, AH 2022, 'Damage detection of composite laminate structures using VMD of FRF contaminated by high percentage of noise', Composite Structures, vol. 286, pp. 115243-115243.
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Hassanpour, M, Cai, G, Cooper, T, Wang, Q, O'Hara, IM & Zhang, Z 2022, 'Triple action of FeCl3-assisted hydrothermal treatment of digested sludge for deep dewatering', Science of The Total Environment, vol. 848, pp. 157727-157727.
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He, F, Parvez Mahmud, MA, Kouzani, AZ, Anwar, A, Jiang, F & Ling, SH 2022, 'An Improved SLIC Algorithm for Segmentation of Microscopic Cell Images', Biomedical Signal Processing and Control, vol. 73, pp. 103464-103464.
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He, HS, Teng, JD, Zhang, S & Sheng, DC 2022, 'Rationality of frost susceptibility of soils', Yantu Gongcheng Xuebao/Chinese Journal of Geotechnical Engineering, vol. 44, no. 2, pp. 224-234.
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The frost heave and thaw weakening are the critical issues for the infrastructures in cold regions. How to reasonably assess the frost susceptibility of soils has been a hotspot in cold-region geotechnics. The frost susceptibility has been studied for about one hundred years since Casagrande (1931) proposed fine content as a main index to evaluate the frost susceptibility of soils. In most cases, the frost characteristics defined by fines content are clear and very simple, and work well in guiding the engineering construction in cold regions. However, the recent studies show that: (1) The frost heave occurs frequently in the subgrade which is designed and constructed absolutely according to the existing frost susceptibility criteria. (2) The current frost susceptibility criteria vary greatly in different countries and regions with different accuracies. (3) The vapour flow can lead to considerable frost heave in coarse-grained soils, which is not considered in the existing frost susceptibility. The above issues challenge the existing frost susceptibility. It is worth to analyze whether the concept of frost susceptibility is reasonable or not as well as its evaluation system. This study tries to analyze the advantages and disadvantages of the existing frost susceptibility criteria. The main findings are: (1) The reliability of the existing frost susceptibility is generally low, within the range of 50% to 80%. (2) The existing frost susceptibility criteria are not suitable to the case that the frost heave in coarse-grained soils is caused by vapour transfer. The freezing environmental factors should be considered in defining the frost susceptibility. (3) The existing frost susceptibility may be suitable to indicate the thaw weakening property of soils. The outcome of this study is helpful to replenishing the classification of frost susceptibility criteria. It would be of great significance to frost disaster prevention in cold regions.

He, L, Wang, X, Chen, H & Xu, G 2022, 'Online Spam Review Detection: A Survey of Literature', Human-Centric Intelligent Systems, vol. 2, no. 1-2, pp. 14-30.
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AbstractThe increasingly developed online platform generates a large amount of online reviews every moment, e.g., Yelp and Amazon. Consumers gradually develop the habit of reading previous reviews before making a decision of buying or choosing various products. Online reviews play an vital part in determining consumers’ purchase choices in e-commerce, yet many online reviews are intentionally created to confuse or mislead potential consumers. Moreover, driven by product reputations and merchants’ profits, more and more spam reviews were inserted into online platform. This kind of reviews can be positive, negative or neutral, but they had common features: misleading consumers or damaging reputations. In the past decade, many people conducted research on detecting spam reviews using statistical or deep learning method with various datasets. In view of that, this article first introduces the task of spam online reviews detection and makes a common definition of spam reviews. Then, we comprehensively conclude the existing method and available datasets. Third, we summarize the existing network-based approaches in dealing with this task and propose some direction for future research.

He, L, Xu, G, Jameel, S, Wang, X & Chen, H 2022, 'Graph-Aware Deep Fusion Networks for Online Spam Review Detection', IEEE Transactions on Computational Social Systems, pp. 1-9.
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He, N & Ferguson, S 2022, 'Music emotion recognition based on segment-level two-stage learning', International Journal of Multimedia Information Retrieval, vol. 11, no. 3, pp. 383-394.
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AbstractIn most Music Emotion Recognition (MER) tasks, researchers tend to use supervised learning models based on music features and corresponding annotation. However, few researchers have considered applying unsupervised learning approaches to labeled data except for feature representation. In this paper, we propose a segment-based two-stage model combining unsupervised learning and supervised learning. In the first stage, we split each music excerpt into contiguous segments and then utilize an autoencoder to generate segment-level feature representation. In the second stage, we feed these time-series music segments to a bidirectional long short-term memory deep learning model to achieve the final music emotion classification. Compared with the whole music excerpts, segments as model inputs could be the proper granularity for model training and augment the scale of training samples to reduce the risk of overfitting during deep learning. Apart from that, we also apply frequency and time masking to segment-level inputs in the unsupervised learning part to enhance training performance. We evaluate our model on two datasets. The results show that our model outperforms state-of-the-art models, some of which even use multimodal architectures. And the performance comparison also evidences the effectiveness of audio segmentation and the autoencoder with masking in an unsupervised way.

He, T, Wu, M, Lu, DD-C, Song, K & Zhu, J 2022, 'Model Predictive Sliding Control for Cascaded H-Bridge Multilevel Converters With Dynamic Current Reference Tracking', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 2, pp. 1409-1421.
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He, T-X, Shannon, AG & Shiue, PJ-S 2022, 'Some identities of Gaussian binomial coefficients', Annales Mathematicae et Informaticae, vol. Accepted manuscript.
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In this paper, we present some identities of Gaussian binomial coefficients with respect to recursive sequences, Fibonomial coefficients, and complete functions by use of their relationships.

He, X, Wang, F, Li, W & Sheng, D 2022, 'Deep learning for efficient stochastic analysis with spatial variability', Acta Geotechnica, vol. 17, no. 4, pp. 1031-1051.
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He, Y, Liu, P, Zhu, L & Yang, Y 2022, 'Filter Pruning by Switching to Neighboring CNNs With Good Attributes', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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He, Y, Liu, Y, Yan, M, Zhao, T, Liu, Y, Zhu, T & Ni, B-J 2022, 'Insights into N2O turnovers under polyethylene terephthalate microplastics stress in mainstream biological nitrogen removal process', Water Research, vol. 224, pp. 119037-119037.
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He, Y, Zhang, X, Xia, Z, Liu, Y, Sood, K & Yu, S 2022, 'Joint optimization of Service Chain Graph Design and Mapping in NFV-enabled networks', Computer Networks, vol. 202, pp. 108626-108626.
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He, Z, Luo, Q, Li, Q, Zheng, G & Sun, G 2022, 'Fatigue behavior of CFRP/Al adhesive joints — Failure mechanisms study using digital image correlation (DIC) technique', Thin-Walled Structures, vol. 174, pp. 109075-109075.
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He, Z, Zhang, L, Yang, Y & Gao, X 2022, 'Partial Alignment for Object Detection in the Wild', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 8, pp. 5238-5251.
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Heggart, K & Dickson-Deane, C 2022, 'What should learning designers learn?', Journal of Computing in Higher Education, vol. 34, no. 2, pp. 281-296.
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There is widespread interest in employing designers who focus on learning, performance and education technology in many industries at a global level. In Australia, learning designers are in demand in Education, Corporate Training, Finance, Charity, Non-Government Sectors, and also in Start-Ups and Entrepreneurial arenas. This demand is despite the fact that the role of the Learning Designer is incredibly varied, contextually-based, and also unclear to many employers - and students! This suggests that there is currently an opportunity for learning designers and academics who deliver learning design content to define what it means to be a learning designer. This paper presents an Australian case study which uses design-based research methods in a pre-production mode to identify the key principles that informed the development of a course of study (what others may refer to as a program). How those principles were operationalised within the course design and more are discussed in an effort to reposition understandings of knowledge, skills and abilities for this field.

Heidari, A, Bansal, RC, Hossain, J & Zhu, J 2022, 'Strategic risk aversion of smart energy hubs in the joined energy markets applying a stochastic game approach', Journal of Cleaner Production, vol. 349, pp. 131386-131386.
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Hemsley, B, Dann, S, Reddacliff, C, Smith, R, Given, F, Gay, V, Leong, TW, Josserand, E, Skellern, K, Bull, C, Palmer, S & Balandin, S 2022, 'Views on the usability, design, and future possibilities of a 3D food printer for people with dysphagia: outcomes of an immersive experience', Disability and Rehabilitation: Assistive Technology, pp. 1-10.
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PURPOSE: Although 3D food printing is expected to enable the creation of visually appealing pureed food for people with disability and dysphagia, little is known about the user experience in engaging with 3D food printing or the feasibility of use with populations who need texture-modified foods. The aim of this study was to explore the feasibility and usability of using domestic-scale 3D food printer as an assistive technology to print pureed food into attractive food shapes for people with dysphagia. MATERIALS AND METHODS: In total, 16 participants engaged in the unfamiliar, novel process of using a domestic-scale 3D food printer (choosing, printing, tasting), designed for printing pureed food, and discussed their impressions in focus group or individual interviews. RESULTS AND CONCLUSIONS: Overall, results demonstrated that informed experts who were novice users perceived the 3D food printing process to be fun but time consuming, and that 3D food printers might not yet be suitable for people with dysphagia or their supporters. Slow response time, lack of user feedback, scant detail on the appropriate recipes for the pureed food to create a successful print, and small font on the user panel interface were perceived as barriers to accessibility for people with disability and older people. Participants expected more interactive elements and feedback from the device, particularly in relation to resolving printer or user errors. This study will inform future usability trials and food safety research into 3D printed foods for people with disability and dysphagia. IMPLICATIONS FOR REHABILITATION3D food printers potentially have a role as an assistive technology in the preparation of texture-modified foods for people with disability and dysphagia.To increase feasibility, 3D food printers should be co-designed with people with disability and their supporters and health professionals working in the field of dysphagia and rehabilitation.Experts struggled to be able to pr...

Henke, T & Deuse, J 2022, 'Application of heuristics for packing problems to optimise throughput time in fixed position assembly islands', International Journal of Productivity and Quality Management, vol. 36, no. 1, pp. 150-150.
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Hesam‐Shariati, N, Chang, W, Wewege, MA, McAuley, JH, Booth, A, Trost, Z, Lin, C, Newton‐John, T & Gustin, SM 2022, 'The analgesic effect of electroencephalographic neurofeedback for people with chronic pain: A systematic review and meta‐analysis', European Journal of Neurology, vol. 29, no. 3, pp. 921-936.
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Hidayat, A, Cheema, MA, Lin, X, Zhang, W & Zhang, Y 2022, 'Continuous monitoring of moving skyline and top-k queries', The VLDB Journal, vol. 31, no. 3, pp. 459-482.
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Hieu, NQ, Hoang, DT, Niyato, D, Wang, P, Kim, DI & Yuen, C 2022, 'Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles With Joint Radar-Data Communications', IEEE Transactions on Communications, vol. 70, no. 8, pp. 5164-5180.
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Hill, M & Tran, N 2022, 'miRNA:miRNA Interactions: A Novel Mode of miRNA Regulation and Its Effect On Disease', pp. 241-257.
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Ho, W-HJ, Law, AMK, Masle-Farquhar, E, Castillo, LE, Mawson, A, O’Bryan, MK, Goodnow, CC, Gallego-Ortega, D, Oakes, SR & Ormandy, CJ 2022, 'Activation of the viral sensor oligoadenylate synthetase 2 (Oas2) prevents pregnancy-driven mammary cancer metastases', Breast Cancer Research, vol. 24, no. 1.
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Abstract Background The interferon response can influence the primary and metastatic activity of breast cancers and can interact with checkpoint immunotherapy to modulate its effects. Using N-ethyl-N-nitrosourea mutagenesis, we found a mouse with an activating mutation in oligoadenylate synthetase 2 (Oas2), a sensor of viral double stranded RNA, that resulted in an interferon response and prevented lactation in otherwise healthy mice. Methods To determine if sole activation of Oas2 could alter the course of mammary cancer, we combined the Oas2 mutation with the MMTV-PyMT oncogene model of breast cancer and examined disease progression and the effects of checkpoint immunotherapy using Kaplan–Meier survival analysis with immunohistochemistry and flow cytometry. Results Oas2 mutation prevented pregnancy from increasing metastases to lung. Checkpoint immunotherapy with antibodies against programmed death-ligand 1 was more effective when the Oas2 mutation was present. Conclusions These data establish OAS2 as a therapeutic target for agents designed to reduce metastases and increase the effectiveness of checkpoint immunotherapy.

Hoang, AT, Foley, AM, Nižetić, S, Huang, Z, Ong, HC, Ölçer, AI, Pham, VV & Nguyen, XP 2022, 'Energy-related approach for reduction of CO2 emissions: A critical strategy on the port-to-ship pathway', Journal of Cleaner Production, vol. 355, pp. 131772-131772.
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Hoang, AT, Huang, Z, Nižetić, S, Pandey, A, Nguyen, XP, Luque, R, Ong, HC, Said, Z, Le, TH & Pham, VV 2022, 'Characteristics of hydrogen production from steam gasification of plant-originated lignocellulosic biomass and its prospects in Vietnam', International Journal of Hydrogen Energy, vol. 47, no. 7, pp. 4394-4425.
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Hoang, D & Hoang, S 2022, 'Deep learning - cancer genetics and application of deep learning to cancer oncology', Vietnam Journal of Science and Technology, vol. 60, no. 6, pp. 885-928.
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Arguably the human body has been one of the most sophisticated systems we encounter but until now we are still far from understanding its complexity. We have been trying to replicate human intelligence by way of artificial intelligence but with limited success. We have discovered the molecular structure in terms of genetics, performed gene editing to change an organism’s DNA and much more, but their translatability into the field of oncology has remained limited. Conventional machine learning methods achieved some degree of success in solving problems that we do not have an explicit algorithm. However, they are basically shallow learning methods, not rich enough to discover and extract intricate features that represent patterns in the real environment. Deep learning has exceeded human performance in pattern recognition as well as strategic games and are powerful for dealing with many complex problems. High-throughput sequencing and microarray techniques have generated vast amounts of data and allowed the comprehensive study of gene expression in tumor cells. The application of deep learning with molecular data enables applications in oncology with information not available from clinical diagnosis. This paper provides fundamental concepts of deep learning, an essential knowledge of cancer genetics, and a review of applications of deep learning to cancer oncology. Importantly, it provides an insightful knowledge of deep learning and an extensive discussion on its challenges. The ultimate purpose is to germinate ideas and facilitate collaborations between cancer biologists and deep learning researchers to address challenging oncological problems using advanced deep learning technologies.

Hoang, LM, Andrew Zhang, J, Nguyen, DN & Thai Hoang, D 2022, 'Frequency Hopping Joint Radar-Communications With Hybrid Sub-Pulse Frequency and Duration Modulation', IEEE Wireless Communications Letters, vol. 11, no. 11, pp. 2300-2304.
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Hoang, PM, Tuan, HD, Son, TT, Poor, HV & Hanzo, L 2022, 'Learning Unbalanced and Sparse Low-Order Tensors', IEEE Transactions on Signal Processing, vol. 70, pp. 5624-5638.
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Holloway, CL, Prajapati, N, Artusio-Glimpse, AB, Berweger, S, Simons, MT, Kasahara, Y, Alù, A & Ziolkowski, RW 2022, 'Rydberg atom-based field sensing enhancement using a split-ring resonator', Applied Physics Letters, vol. 120, no. 20, pp. 204001-204001.
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We investigate the use of a split-ring resonator (SRR) incorporated with an atomic-vapor cell to improve the sensitivity and the minimal detectable electric (E) field of Rydberg atom-based sensors. In this approach, a sub-wavelength SRR is placed around an atomic vapor-cell filled with cesium atoms for E-field measurements at 1.3 GHz. The SRR provides a factor of 100 in the enhancement of the E-field measurement sensitivity. Using electromagnetically induced transparency (EIT) with Aulter–Townes splitting, E-field measurements down to 5 mV/m are demonstrated with the SRR, while in the absence of the SRR, the minimal detectable field is 500 mV/m. We demonstrate that by combining EIT with a heterodyne Rydberg atom-based mixer approach, the SRR allows for a sensitivity of 5.5  μV/m[Formula: see text], which is two-orders of magnitude improvement in sensitivity than when the SRR is not used.

Horng, S-J, Supardi, J, Zhou, W, Lin, C-T & Jiang, B 2022, 'Recognizing Very Small Face Images Using Convolution Neural Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 2103-2115.
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Horng, S-J, Vu, D-T, Nguyen, T-V, Zhou, W & Lin, C-T 2022, 'Recognizing Palm Vein in Smartphones Using RGB Images', IEEE Transactions on Industrial Informatics, vol. 18, no. 9, pp. 5992-6002.
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Hossain, SM, Ibrahim, I, Choo, Y, Razmjou, A, Naidu, G, Tijing, L, Kim, J-H & Shon, HK 2022, 'Preparation of effective lithium-ion sieve from sludge-generated TiO2', Desalination, vol. 525, pp. 115491-115491.
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Hossain, SM, Tijing, L, Suzuki, N, Fujishima, A, Kim, J-H & Shon, HK 2022, 'Visible light activation of photocatalysts formed from the heterojunction of sludge-generated TiO2 and g-CN towards NO removal', Journal of Hazardous Materials, vol. 422, pp. 126919-126919.
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The feasibility of preparing TiO₂/g-CN heterojunction from Ti-incorporated dried dye wastewater sludge is explored in this study. Two reaction routes of composite formation were evaluated. In the initial approach, one-step calcination of dried sludge and melamine mixture @600 °C was carried out. Detailed morphological and chemical characterizations showed that the one-step calcination route did not create TiO₂/g-CN composites; instead, only N-doped anatase TiO₂ composites were formed. Moreover, due to the non-uniform composition of organic content in the dried sludge, it was not easy to control the N doping level by varying melamine content (0-100%) in the precursor mix. However, successful formation of anatase TiO₂ and g-CN was observed when a two-step calcination route was followed, i.e., via synthesis of anatase TiO₂ from dried sludge, and later development of heterojunction by calcining (@550 °C) the TiO₂ and melamine mixture. X-ray diffraction along with infrared and X-ray photoelectron spectroscopy verified the effective heterojunction. In addition, maximum atmospheric NO removal under UV and visible light were observed for the prepared composite when the melamine content in the precursor mixture was 70%. After 1 h of UV and visible light irradiation, the best TiO₂/g-CN composite removed 25.71% and 13.50% of NO, respectively. Optical characterization suggested that the enhanced NO oxidation under UV/visible light was due to the bandgap narrowing and diminished photogenerated electron-hole recombination.

Hosseinzadeh, A, Zhou, JL, Altaee, A & Li, D 2022, 'Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process', Bioresource Technology, vol. 343, pp. 126111-126111.
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Hosseinzadeh, A, Zhou, JL, Li, X, Afsari, M & Altaee, A 2022, 'Techno-economic and environmental impact assessment of hydrogen production processes using bio-waste as renewable energy resource', Renewable and Sustainable Energy Reviews, vol. 156, pp. 111991-111991.
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Hosseinzadeh, A, Zhou, JL, Zyaie, J, AlZainati, N, Ibrar, I & Altaee, A 2022, 'Machine learning-based modeling and analysis of PFOS removal from contaminated water by nanofiltration process', Separation and Purification Technology, vol. 289, pp. 120775-120775.
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Hou, F, Gao, Q, Song, Y, Wang, Z, Bai, Z, Yang, Y & Tian, Z 2022, 'Deep feature pyramid network for EEG emotion recognition', Measurement, vol. 201, pp. 111724-111724.
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Hu, S, Ni, W, Wang, X & Jamalipour, A 2022, 'Disguised Tailing and Video Surveillance With Solar-Powered Fixed-Wing Unmanned Aerial Vehicle', IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5507-5518.
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Hu, S, Yu, S, Li, H & Piuri, V 2022, 'Guest Editorial Special Issue on Security, Privacy, and Trustworthiness in Intelligent Cyber–Physical Systems and Internet of Things', IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22044-22047.
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Hu, S, Yuan, X, Ni, W & Wang, X 2022, 'Trajectory Planning of Cellular-Connected UAV for Communication-Assisted Radar Sensing', IEEE Transactions on Communications, vol. 70, no. 9, pp. 6385-6396.
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Hu, X, Jin, Z, Zhang, L, Zhou, A & Ye, D 2022, 'Privacy preservation auction in a dynamic social network', Concurrency and Computation: Practice and Experience, vol. 34, no. 16.
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Hu, X, Zhu, T, Zhai, X, Wang, H, Zhou, W & Zhao, W 2022, 'Privacy Data Diffusion Modeling and Preserving in Online Social Network', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Hu, Z, Tebyetekerwa, M, Elkholy, AE, Xia, Q, Hussain, T, Liu, H & Zhao, XS 2022, 'Synthesis of carbon-modified cobalt disphosphide as anode for sodium-ion storage', Electrochimica Acta, vol. 423, pp. 140611-140611.
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Huang, C, Yao, L, Wang, X, Sheng, QZ, Dustdar, S, Wang, Z & Xu, X 2022, 'Intent-Aware Interactive Internet of Things for Enhanced Collaborative Ambient Intelligence', IEEE Internet Computing, vol. 26, no. 5, pp. 68-75.
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Huang, G, Zhu, Y, Wen, S, Mei, H, Liu, Y, Wang, D, Maddahfar, M, Su, QP, Lin, G, Chen, Y & Jin, D 2022, 'Single Small Extracellular Vesicle (sEV) Quantification by Upconversion Nanoparticles', Nano Letters, vol. 22, no. 9, pp. 3761-3769.
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Huang, H, Savkin, AV & Ni, W 2022, 'Decentralized Navigation of a UAV Team for Collaborative Covert Eavesdropping on a Group of Mobile Ground Nodes', IEEE Transactions on Automation Science and Engineering, vol. 19, no. 4, pp. 3932-3941.
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Huang, H, Savkin, AV & Ni, W 2022, 'Online UAV Trajectory Planning for Covert Video Surveillance of Mobile Targets', IEEE Transactions on Automation Science and Engineering, vol. 19, no. 2, pp. 735-746.
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This article considers the use of an unmanned aerial vehicle (UAV) for covert video surveillance of a mobile target on the ground and presents a new online UAV trajectory planning technique with a balanced consideration of the energy efficiency, covertness, and aeronautic maneuverability of the UAV. Specifically, a new metric is designed to quantify the covertness of the UAV, based on which a multiobjective UAV trajectory planning problem is formulated to maximize the disguising performance and minimize the trajectory length of the UAV. A forward dynamic programming method is put forth to solve the problem online and plan the trajectory for the foreseeable future. In addition, the kinematic model of the UAV is considered in the planning process so that it can be tracked without any later adjustment. Extensive computer simulations are conducted to demonstrate the effectiveness of the proposed technique.

Huang, H, Zhang, J, Yu, L, Zhang, J, Wu, Q & Xu, C 2022, 'TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization With Few Labeled Samples', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 2, pp. 853-866.
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Huang, J, Luo, K, Cao, L, Wen, Y & Zhong, S 2022, 'Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 20681-20695.
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Huang, L, Chen, X, Zhang, Y, Wang, C, Cao, X & Liu, J 2022, 'Identification of topic evolution: network analytics with piecewise linear representation and word embedding', Scientometrics, vol. 127, no. 9, pp. 5353-5383.
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Huang, L, Liu, Z, Wu, C, Liang, J & Pei, Q 2022, 'A three-dimensional indirect boundary integral equation method for the scattering of seismic waves in a poroelastic layered half-space', Engineering Analysis with Boundary Elements, vol. 135, pp. 167-181.
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Huang, S & Zhao, L 2022, '2021 IEEE RAS Winter School on Simultaneous Localization and Mapping in Deformable Environments [Education]', IEEE Robotics & Automation Magazine, vol. 29, no. 1, pp. 120-122.
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Huang, S, Shi, W, Xu, Z, Tsang, IW & Lv, J 2022, 'Efficient federated multi-view learning', Pattern Recognition, vol. 131, pp. 108817-108817.
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Huang, S, Tsang, IW, Xu, Z & Lv, J 2022, 'CGDD: Multiview Graph Clustering via Cross-Graph Diversity Detection', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Huang, S, Tsang, IW, Xu, Z & Lv, J 2022, 'Latent Representation Guided Multi-view Clustering', IEEE Transactions on Knowledge and Data Engineering, pp. 1-6.
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Huang, S, Tsang, IW, Xu, Z & Lv, J 2022, 'Measuring Diversity in Graph Learning: A Unified Framework for Structured Multi-View Clustering', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 12, pp. 5869-5883.
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Graph Learning has emerged as a promising technique for multi-view clustering due to its efficiency of learning a unified graph from multiple views. Previous multi-view graph learning methods mainly try to exploit the multi-view consistency to boost learning performance. However, these methods ignore the prevalent multi-view diversity which may be induced by noise, corruptions, or even view-specific attributes. In this paper, we propose to simultaneously and explicitly leverage the multi-view consistency and the multi-view diversity in a unified framework. The consistent parts are further fused to our target graph with a clear clustering structure, on which the cluster label to each instance can be directly allocated without any postprocessing such as k-means in classical spectral clustering. In addition, our model can automatically assign suitable weight for each view based on its clustering capacity. By leveraging the subtasks of measuring the diversity of graphs, integrating the consistent parts with automatically learned weights, allocating cluster label to each instance in a joint framework, each subtask can be alternately boosted by utilizing the results of the others towards an overall optimal solution. Extensive experimental results on several benchmark multi-view datasets demonstrate the effectiveness of our model in comparison to several state-of-the-art algorithms.

Huang, T, Ben, X, Gong, C, Zhang, B, Yan, R & Wu, Q 2022, 'Enhanced Spatial-Temporal Salience for Cross-View Gait Recognition', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 10, pp. 6967-6980.
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Huang, W, Zhou, S, Zhu, T & Liao, Y 2022, 'Privately Publishing Internet of Things Data: Bring Personalized Sampling Into Differentially Private Mechanisms', IEEE Internet of Things Journal, vol. 9, no. 1, pp. 80-91.
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Huang, X, Clemon, LM, Islam, MS & C. Saha, S 2022, 'Optimization of fluid characteristics in the main nozzle of an air-jet loom', Textile Research Journal, vol. 92, no. 3-4, pp. 525-538.
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As part of the propulsion system, the fluid dynamic features of the main nozzle can immediately affect the stability and efficiency of an air-jet loom. This study aims to optimize the fluid characteristics in the main nozzle of an air-jet loom. To investigate ways of weakening the effect of airflow congestion and backflow phenomenon occurring in the sudden expansion region, the computational fluid dynamics method is employed. Three-dimensional turbulence flow models for a regular main nozzle and 12 prototypes with different nozzle core tip geometry are built, simulated, and analyzed to get the optimum performance. Furthermore, a set of modified equations that consider the direction of airflow are proposed for better estimation of the friction force applied by the nozzle. The result shows that the nozzle core tip's geometry has a significant influence on the internal airflow, affecting the acceleration tube airflow velocity, turbulence intensity, and backflow strength of the sudden expansion region, and other critical fluid characteristics as well. Several proposed models have succeeded in reducing the backflow and outperforming the original design in many different aspects. Models A-60 and C-P, in particular, manage to increase the propulsion force by 37.6% and 20.2% in the acceleration tube while reducing the maximum backflow by 57.1% and 52.2%, respectively. These simulation results can provide invaluable information for the future optimization of the main nozzle.

Huang, X, Le, AT & Jay Guo, Y 2022, 'Joint Analog and Digital Self-Interference Cancellation for Full Duplex Transceiver with Frequency-Dependent I/Q Imbalance', IEEE Transactions on Wireless Communications, pp. 1-1.
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Huang, X, Li, S, Zuo, Y, Fang, Y, Zhang, J & Zhao, X 2022, 'Unsupervised Point Cloud Registration by Learning Unified Gaussian Mixture Models', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7028-7035.
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Huang, X, Nan, Y & Guo, YJ 2022, 'Radio Frequency Camera: A Noncoherent Circular Array SAR With Uncoordinated Illuminations', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-14.
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Huang, X, Wang, Y, Li, S, Mei, G, Xu, Z, Wang, Y, Zhang, J & Bennamoun, M 2022, 'Robust real-world point cloud registration by inlier detection', Computer Vision and Image Understanding, vol. 224, pp. 103556-103556.
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Huang, Y, Lee, CKC, Yam, Y-S, Mok, W-C, Zhou, JL, Zhuang, Y, Surawski, NC, Organ, B & Chan, EFC 2022, 'Rapid detection of high-emitting vehicles by on-road remote sensing technology improves urban air quality', Science Advances, vol. 8, no. 5.
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Vehicle emissions are the most important source of air pollution in the urban environment worldwide, and their detection and control are critical for protecting public health. Here, we report the use of on-road remote sensing (RS) technology for fast, accurate, and cost-effective identification of high-emitting vehicles as an enforcement program for improving urban air quality. Using large emission datasets from chassis dynamometer testing, RS, and air quality monitoring, we found that significant percentages of in-use petrol and LPG vehicles failed the emission standards, particularly the high-mileage fleets. The RS enforcement program greatly cleaned these fleets, in terms of high-emitter percentages, fleet average emissions, roadside and ambient pollutant concentrations, and emission inventory. The challenges of the current enforcement program are conservative setting of cut points, single-lane measurement sites, and lack of application experience in diesel vehicles. Developing more accurate and vertical RS systems will improve and extend their applications.

Huang, Y, Lee, CKC, Yam, Y-S, Zhou, JL, Surawski, NC, Organ, B, Lei, C & Shon, HK 2022, 'Effective emissions reduction of high-mileage fleets through a catalytic converter and oxygen sensor replacement program', Science of The Total Environment, vol. 850, pp. 158004-158004.
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High-mileage vehicles such as taxis make disproportionately large contributions to urban air pollution due to their accelerated engine deterioration rates and high operation intensities despite their small proportions of the total fleet. Controlling emissions from these high-mileage fleets is thus important for improving urban air quality. This study evaluates the effectiveness of a pilot repair program in reducing emissions from taxis in Hong Kong which account for about 2 % of the total licensed vehicles. The emission factors of a large sample of 684 in-service taxis (including 121 for an emission survey program and 563 for a pilot repair program) were measured on transient chassis dynamometers. The results showed that 63 % of the sampled taxis failed the driving cycle test before the pilot repair program. Most of failed taxis were NO related and 91 % of failed taxis exceeded the emission limits of at least two regulated pollutants simultaneously. After the pilot repair program by replacing catalytic converters and oxygen sensors, the failure rate was significantly reduced to only 7 %. In addition, the fleet average NO, HC and CO emission factors were reduced by 85 %, 82 % and 56 %, respectively. In addition, on-road remote sensing measurements confirmed the real-world emission reductions from the taxis that participated in the pilot repair program. These findings led to the implementation of a large-scale replacement program for all taxis in Hong Kong during 2013-2014, which was estimated to have reduced the total HC, CO and NO emissions by about 420, 2570 and 1000 t per year, respectively (equivalent to 5-8 % emission reductions from the whole road transport sector). Therefore, reducing emissions from the small high-mileage fleets is a highly cost-effective measure to improve urban air quality.

Huang, Y, Li, Y, Heyes, T, Jourjon, G, Cheng, A, Seneviratne, S, Thilakarathna, K, Webb, D & Xu, RYD 2022, 'Task adaptive siamese neural networks for open-set recognition of encrypted network traffic with bidirectional dropout', Pattern Recognition Letters, vol. 159, pp. 132-139.
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Huang, Y, Ng, ECY, Surawski, NC, Zhou, JL, Wang, X, Gao, J, Lin, W & Brown, RJ 2022, 'Effect of diesel particulate filter regeneration on fuel consumption and emissions performance under real-driving conditions', Fuel, vol. 320, pp. 123937-123937.
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Huang, Y, Wang, X, Zhang, Y, Chiavetta, D & Porter, AL 2022, '“Big data” driven tech mining and ST&I management: an introduction', Scientometrics, vol. 127, no. 9, pp. 5227-5231.
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Huang, Y, Wu, Q, Xu, J, Zhong, Y, Zhang, P & Zhang, Z 2022, 'Alleviating Modality Bias Training for Infrared-Visible Person Re-Identification', IEEE Transactions on Multimedia, vol. 24, pp. 1570-1582.
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Huang, Z, Zhao, R, Leung, FHF, Banerjee, S, Lee, TT-Y, Yang, D, Lun, DPK, Lam, K-M, Zheng, Y-P & Ling, SH 2022, 'Joint Spine Segmentation and Noise Removal From Ultrasound Volume Projection Images With Selective Feature Sharing', IEEE Transactions on Medical Imaging, vol. 41, no. 7, pp. 1610-1624.
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Hunt, H, Indraratna, B & Qi, Y 2022, 'Ductility and energy absorbing behaviour of coal wash – rubber crumb mixtures', International Journal of Rail Transportation, pp. 1-21.
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The reuse of waste materials, such as coal wash (CW) and rubber crumbs (RC), is becoming increasingly popular in large-scale civil engineering applications, which is environmentally friendly and economically attractive. In this study, the ductility and strain energy density of CW-RC mixtures with different RC contents compacted to the same initial void ratio and subjected to triaxial shearing are evaluated. As expected, the ductility and energy absorbing capacity of the waste mixture are improved with RC addition. This makes the use of CW-RC mixtures in substructure applications a promising development for future rail design where loads are expected to increase. Furthermore, empirical models for the shear strength and strain energy density based on the RC content are proposed. These models may be used as a guide to approximate the sheacr strength and strain energy density of these compacted CW-RC mixtures prior to the undertaking of extensive triaxial tests.

Huo, C, Wang, Y, Liu, C & Lei, G 2022, 'Study on the residual flux density measurement method for power transformer cores based on magnetising inductance', IET Electric Power Applications, vol. 16, no. 2, pp. 224-235.
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Huo, P, Chen, X, Yang, L, Wei, W & Ni, B-J 2022, 'Modeling of Sulfur-Driven Autotrophic Denitrification Coupled with Anammox Process', Bioresource Technology, vol. 349, pp. 126887-126887.
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Huo, X, Jiang, Z, Luo, Q, Li, Q & Sun, G 2022, 'Mechanical characterization and numerical modeling on the yield and fracture behaviors of polymethacrylimide (PMI) foam materials', International Journal of Mechanical Sciences, vol. 218, pp. 107033-107033.
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Huo, X, Luo, Q, Li, Q, Zheng, G & Sun, G 2022, 'On characterization of cohesive zone model (CZM) based upon digital image correlation (DIC) method', International Journal of Mechanical Sciences, vol. 215, pp. 106921-106921.
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Huq, T, Ong, HC, Chew, BT, Leong, KY & Kazi, SN 2022, 'Review on aqueous graphene nanoplatelet Nanofluids: Preparation, Stability, thermophysical Properties, and applications in heat exchangers and solar thermal collectors', Applied Thermal Engineering, vol. 210, pp. 118342-118342.
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Hussain, W 2022, 'Approaching a Large Defect on the Lower Nasal Sidewall—A Twist on a Classic Reconstruction', Dermatologic Surgery, vol. 48, no. 2, pp. 239-241.
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Hussain, W, Gao, H, Raza, MR, Rabhi, FA & Merigó, JM 2022, 'Assessing cloud QoS predictions using OWA in neural network methods', Neural Computing and Applications, vol. 34, no. 17, pp. 14895-14912.
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AbstractQuality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods—Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy.

Hussain, W, Merigó, JM & Raza, MR 2022, 'Predictive intelligence using ANFIS‐induced OWAWA for complex stock market prediction', International Journal of Intelligent Systems, vol. 37, no. 8, pp. 4586-4611.
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Hussain, W, Merigó, JM, Raza, MR & Gao, H 2022, 'A new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioning', Information Sciences, vol. 584, pp. 280-300.
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Hussain, W, Raza, MR, Jan, MA, Merigo, JM & Gao, H 2022, 'Cloud Risk Management With OWA-LSTM and Fuzzy Linguistic Decision Making', IEEE Transactions on Fuzzy Systems, vol. 30, no. 11, pp. 4657-4666.
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Huyen Vu, T, Dang, LC, Kang, G & Sirivivatnanon, V 2022, 'Chloride induced corrosion of steel reinforcement in alkali activated slag concretes: A critical review', Case Studies in Construction Materials, vol. 16, pp. e01112-e01112.
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Iacopi, F & Lin, C-T 2022, 'A perspective on electroencephalography sensors for brain-computer interfaces', Progress in Biomedical Engineering, vol. 4, no. 4, pp. 043002-043002.
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Abstract This Perspective offers a concise overview of the current, state-of-the-art, neural sensors for brain-machine interfaces, with particular attention towards brain-controlled robotics. We first describe current approaches, decoding models and associated choice of common paradigms, and their relation to the position and requirements of the neural sensors. While implanted intracortical sensors offer unparalleled spatial, temporal and frequency resolution, the risks related to surgery and post-surgery complications pose a significant barrier to deployment beyond severely disabled individuals. For less critical and larger scale applications, we emphasize the need to further develop dry scalp electroencephalography (EEG) sensors as non-invasive probes with high sensitivity, accuracy, comfort and robustness for prolonged and repeated use. In particular, as many of the employed paradigms require placing EEG sensors in hairy areas of the scalp, ensuring the aforementioned requirements becomes particularly challenging. Nevertheless, neural sensing technologies in this area are accelerating thanks to the advancement of miniaturised technologies and the engineering of novel biocompatible nanomaterials. The development of novel multifunctional nanomaterials is also expected to enable the integration of redundancy by probing the same type of information through different mechanisms for increased accuracy, as well as the integration of complementary and synergetic functions that could range from the monitoring of physiological states to incorporating optical imaging.

Ibrahim, I, Hossain, SM, Seo, DH, McDonagh, A, Foster, T, Shon, HK & Tijing, L 2022, 'Insight into the role of polydopamine nanostructures on nickel foam-based photothermal materials for solar water evaporation', Separation and Purification Technology, vol. 293, pp. 121054-121054.
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Ibrahim, I, Seo, DH, Park, MJ, Angeloski, A, McDonagh, A, Bendavid, A, Shon, HK & Tijing, L 2022, 'Highly stable gold nanolayer membrane for efficient solar water evaporation under a harsh environment', Chemosphere, vol. 299, pp. 134394-134394.
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Ibrahim, IA & Hossain, MJ 2022, 'A benchmark model for low voltage distribution networks with PV systems and smart inverter control techniques', Renewable and Sustainable Energy Reviews, vol. 166, pp. 112571-112571.
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Ibrahim, IA, Hossain, MJ & Duck, BC 2022, 'A hybrid wind driven-based fruit fly optimization algorithm for identifying the parameters of a double-diode photovoltaic cell model considering degradation effects', Sustainable Energy Technologies and Assessments, vol. 50, pp. 101685-101685.
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Ibrar, I, Yadav, S, Altaee, A, Safaei, J, Samal, AK, Subbiah, S, Millar, G, Deka, P & Zhou, J 2022, 'Sodium docusate as a cleaning agent for forward osmosis membranes fouled by landfill leachate wastewater', Chemosphere, vol. 308, pp. 136237-136237.
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Ibrar, I, Yadav, S, Braytee, A, Altaee, A, HosseinZadeh, A, Samal, AK, Zhou, JL, Khan, JA, Bartocci, P & Fantozzi, F 2022, 'Evaluation of machine learning algorithms to predict internal concentration polarization in forward osmosis', Journal of Membrane Science, vol. 646, pp. 120257-120257.
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Ibrar, I, Yadav, S, Naji, O, Alanezi, AA, Ghaffour, N, Déon, S, Subbiah, S & Altaee, A 2022, 'Development in forward Osmosis-Membrane distillation hybrid system for wastewater treatment', Separation and Purification Technology, vol. 286, pp. 120498-120498.
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Ilahi, I, Usama, M, Qadir, J, Janjua, MU, Al-Fuqaha, A, Hoang, DT & Niyato, D 2022, 'Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning', IEEE Transactions on Artificial Intelligence, vol. 3, no. 2, pp. 90-109.
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Inan, DI, Beydoun, G & Pradhan, B 2022, 'Disaster Management Knowledge Analysis Framework Validated', Information Systems Frontiers, vol. 24, no. 6, pp. 2077-2097.
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Indraratna, B, Haq, S, Rujikiatkamjorn, C & Israr, J 2022, 'Microscale boundaries of internally stable and unstable soils', Acta Geotechnica, vol. 17, no. 5, pp. 2037-2046.
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This study presents a microscale approach for evaluating the internal instability of natural granular soils using the discrete element method. The coordination number and the stress reduction factor are combined to assess the internal instability of soil. Distinct boundaries are identified between various soils that are internally stable and unstable. The microscale investigations are then compared with constriction and particle size-based criteria. The findings reveal that the constriction-based criterion predicts internal instability with significantly better accuracy. The relationship between microscale parameters and the constriction-based retention ratio is also examined for practical purposes.

Indraratna, B, Medawela, S, Athuraliya, S, Heitor, A & Baral, P 2022, 'Chemical clogging of granular media under acidic groundwater', Environmental Geotechnics, vol. 9, no. 7, pp. 450-462.
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Generation of acidic groundwater attributed to pyrite oxidation in low-lying acid sulfate soil has caused substantial damage to the soil-water environment and civil infrastructure in coastal Australia. The installation of permeable reactive barriers (PRBs) is a frontier technology in the field of acid neutralisation and removal of toxic heavy metal cations – for example, soluble iron (Fe) and aluminium (Al). This study aims to assess the potential of limestone (calcite) aggregates as the PRB’s main reactive material in low-lying pyritic land. During long-term laboratory column experiments, a significant capacity of limestone for removing contaminant chemical species was observed. Nevertheless, the formation of secondary mineral precipitates upon geochemical reactivity within the granular media in the PRB caused armouring and chemical clogging, which diminished the rate of reactivity – that is, the treatment capacity of calcite aggregates – mainly at the entrance zone of the porous media. Flow properties were altered due to blockage of pores; for instance, hydraulic conductivity was reduced by 25% at the inlet zone. Non-homogeneous clogging towards the outlet was analysed, and the time-dependent effect on the longevity of a limestone column was studied and quantified.

Indraratna, B, Medawela, SK, Athuraliya, S, Heitor, A & Baral, P 2022, 'Chemical clogging of granular media under acidic groundwater conditions', Environmental Geotechnics, vol. 9, no. 7, pp. 450-462.
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Generation of acidic groundwater attributed to pyrite oxidation in low-lying acid sulfate soil has caused substantial damage to the soil-water environment and civil infrastructure in coastal Australia. The installation of permeable reactive barriers (PRBs) is a frontier technology in the field of acid neutralisation and removal of toxic heavy metal cations – for example, soluble iron (Fe) and aluminium (Al). This study aims to assess the potential of limestone (calcite) aggregates as the PRB’s main reactive material in low-lying pyritic land. During long-term laboratory column experiments, a significant capacity of limestone for removing contaminant chemical species was observed. Nevertheless, the formation of secondary mineral precipitates upon geochemical reactivity within the granular media in the PRB caused armouring and chemical clogging, which diminished the rate of reactivity – that is, the treatment capacity of calcite aggregates – mainly at the entrance zone of the porous media. Flow properties were altered due to blockage of pores; for instance, hydraulic conductivity was reduced by 25% at the inlet zone. Non-homogeneous clogging towards the outlet was analysed, and the time-dependent effect on the longevity of a limestone column was studied and quantified.

Indraratna, B, Mehmood, F, Mishra, S, Ngo, T & Rujikiatkamjorn, C 2022, 'The role of recycled rubber inclusions on increased confinement in track substructure', Transportation Geotechnics, vol. 36, pp. 100829-100829.
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Indraratna, B, Qi, Y, Malisetty, RS, Navaratnarajah, SK, Mehmood, F & Tawk, M 2022, 'Recycled materials in railroad substructure: an energy perspective', Railway Engineering Science, vol. 30, no. 3, pp. 304-322.
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AbstractGiven that the current ballasted tracks in Australia may not be able to support faster and significantly heavier freight trains as planned for the future, the imminent need for innovative and sustainable ballasted tracks for transport infrastructure is crucial. Over the past two decades, a number of studies have been conducted by the researchers of Transport Research Centre (TRC) at the University of Technology Sydney (UTS) to investigate the ability of recycled rubber mats, as well as waste tyre cells and granulated rubber to improve the stability of track substructure including ballast and subballast layers. This paper reviews four applications of these novel methods, including using recycled rubber products such as CWRC mixtures (i.e., mixtures of coal wash (CW) and rubber crumbs (RC)) and SEAL mixtures (i.e., mixtures of steel furnace slag, CW and RC) to replace subballast/capping materials, tyre cells reinforcements for subballast/capping layer and under ballast mats; and investigates the energy dissipation capacity for each application based on small-scale cyclic triaxial tests and large-scale track model tests. It has been found that the inclusion of these rubber products increases the energy dissipation effect of the track, hence reducing the ballast degradation efficiently and increasing the track stability. Moreover, a rheological model is also proposed to investigate the effect of different rubber inclusions on their efficiency to reduce the transient motion of rail track under dynamic loading. The outcomes elucidated in this paper will lead to a better understanding of the performance of ballast tracks upgraded with resilient rubber products, while promoting environmentally sustainable and more affordable ballasted tracks for greater passenger comfort and increased safety.

Indraratna, B, Qi, Y, Tawk, M, Heitor, A, Rujikiatkamjorn, C & Navaratnarajah, SK 2022, 'Advances in ground improvement using waste materials for transportation infrastructure', Proceedings of the Institution of Civil Engineers - Ground Improvement, vol. 175, no. 1, pp. 3-22.
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Recycling waste materials for transport infrastructure such as coal wash (CW), steel furnace slag (SFS), fly ash (FA) and recycled tyre products is an efficient way of minimising the stockpiles of waste materials while offering significant economic and environmental benefits, as well as improving the stability and longevity of infrastructure foundations. This paper presents some of the most recent state-of-the-art studies undertaken at the University of Wollongong, Australia on the use of waste materials such as (a) CW-based granular mixtures (i.e. SFS + CW, CW + FA) for port reclamation and road base/subbase and (b) using recycled tyre products (i.e. rubber crumbs, tyre cell, under-sleeper pads and under-ballast mats) to increase track stability and reduce ballast degradation. Typical methods of applying these waste materials for different infrastructure conditions are described and the results of comprehensive laboratory and field tests are presented and discussed.

Indraratna, B, Singh, M, Nguyen, TT, Rujikiatkamjorn, C, Malisetty, RS, Arivalagan, J & Nair, L 2022, 'Internal Instability and Fluidisation of Subgrade Soil under Cyclic Loading', Indian Geotechnical Journal, vol. 52, no. 5, pp. 1226-1243.
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AbstractRapid globalisation and the rise in population have substantially increased the demand for rail infrastructure which have been critical in transporting passengers and freight across landmasses for over a century. The surge in demand often leads to the construction of railway lines along with unfavourable soil conditions which result in different forms of substructure challenges such as uneven track deformations, ballast degradation, and subgrade mud pumping. A widespread site investigation along the eastern coast of New South Wales, Australia, indicated the prevalence of mud holes or bog holes along the tracks. The field studies suggest that low-to-medium plasticity soils are highly susceptible to mud pump when subjected to heavy axle loads under impeding drainage conditions. Subsequent laboratory investigations conducted on the remoulded soil samples collected from the sites indicated the sharp rise in cyclic axial strains and excess pore pressures along with the internal redistribution of moisture content as the governing mechanism for mud pumping. Numerical simulations performed using discrete element method coupled with computational fluid dynamics show that at a high hydraulic gradient, there is a substantial loss of soil contact network which leads to the upward migration of soil particles. The role of plastic fines and the inclusion of geosynthetic layer between the ballast and subgrade are also discussed in this paper. It was observed that the addition of 10% of cohesive fines increased the resistance of subgrade soils to mud pumping. On the other hand, geosynthetic inclusions not only assist in dissipating high cyclic excess pore pressures but also inhibit the upward migration of fine particles.

Irfan, S, Khan, SB, Lam, SS, Ong, HC, Aizaz Ud Din, M, Dong, F & Chen, D 2022, 'Removal of persistent acetophenone from industrial waste-water via bismuth ferrite nanostructures', Chemosphere, vol. 302, pp. 134750-134750.
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Irga, PJ, Fleck, R, Arsenteva, E & Torpy, FR 2022, 'Biosolar green roofs and ambient air pollution in city centres: Mixed results', Building and Environment, vol. 226, pp. 109712-109712.
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Irmawati, Chai, R, Basari & Gunawan, D 2022, 'Optimizing CNN Hyperparameters for Blastocyst Quality Assessment in Small Datasets', IEEE Access, vol. 10, pp. 88621-88631.
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Islam, MR, Lu, H, Hossain, MJ & Li, L 2022, 'Coordinating Electric Vehicles and Distributed Energy Sources Constrained by User’s Travel Commitment', IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5307-5317.
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Islam, MS, Rahman, MM, Arsalanloo, A, Beni, HM, Larpruenrudee, P, Bennett, NS, Collins, R, Gemci, T, Taylor, M & Gu, Y 2022, 'How SARS-CoV-2 Omicron droplets transport and deposit in realistic extrathoracic airways', Physics of Fluids, vol. 34, no. 11, pp. 113320-113320.
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The SARS-CoV-2 Omicron variant is more highly transmissible and causes a higher mortality rate compared to the other eleven variants despite the high vaccination rate. The Omicron variant also establishes a local infection at the extrathoracic airway level. For better health risk assessment of the infected patients, it is essential to understand the transport behavior and the toxicity of the Omicron variant droplet deposition in the extrathoracic airways, which is missing in the literature. Therefore, this study aims to develop a numerical model for the Omicron droplet transport to the extrathoracic airways and to analyze that transport behavior. The finite volume method and ANSYS Fluent 2020 R2 solver were used for the numerical simulation. The Lagrangian approach, the discrete phase model, and the species transport model were employed to simulate the Omicron droplet transport and deposition. Different breathing rates, the mouth and nose inhalation methods were employed to analyze the viral toxicity at the airway wall. The results from this study indicated that there was a 33% of pressure drop for a flow rate at 30 l/min, while there was only a 3.5% of pressure drop for a 7.5 l/min. The nose inhalation of SARS-CoV-2 Omicron droplets is significantly more harmful than through the mouth due to a high deposition rate at the extrathoracic airways and high toxicity in the nasal cavities. The findings of this study would potentially improve knowledge of the health risk assessment of Omicron-infected patients.

Islam, MZ, Hossain, SI, Deplazes, E & Saha, SC 2022, 'The steroid mometasone alters protein containing lung surfactant monolayers in a concentration-dependent manner', Journal of Molecular Graphics and Modelling, vol. 111, pp. 108084-108084.
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Jaafari, A, Panahi, M, Mafi-Gholami, D, Rahmati, O, Shahabi, H, Shirzadi, A, Lee, S, Bui, DT & Pradhan, B 2022, 'Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides', Applied Soft Computing, vol. 116, pp. 108254-108254.
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Jamshaid, M, Masjuki, HH, Kalam, MA, Zulkifli, NWM, Arslan, A & Qureshi, AA 2022, 'Experimental investigation of performance, emissions and tribological characteristics of B20 blend from cottonseed and palm oil biodiesels', Energy, vol. 239, pp. 121894-121894.
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Javed, AR, Shahzad, F, Rehman, SU, Zikria, YB, Razzak, I, Jalil, Z & Xu, G 2022, 'Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects', Cities, vol. 129, pp. 103794-103794.
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Jawahar, M, H, S, L, JA & Gandomi, AH 2022, 'ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification', Computers in Biology and Medicine, vol. 148, pp. 105894-105894.
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Jayaraman, S, Ramachandran, M, Patan, R, Daneshmand, M & Gandomi, AH 2022, 'Fuzzy Deep Neural Learning Based on Goodman and Kruskal's Gamma for Search Engine Optimization', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 268-277.
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IEEE Search engine optimization (SEO) is a significant problem for enhancing a website's visibility with search engine results. SEO issues, such as Site Popularity, Content Quality, Keyword Density, and Publicity, were not considered during the search engine optimization process. Therefore, the retrieval rate of the existing techniques is inadequate. In this study, Triangular Fuzzy Deep Structured Learning-Based Predictive Page Ranking (TFDSL-PPR) Technique is proposed to solve these limitations. First, the TFDSL-PPR technique takes a number of user queries as input in the input layer, and then it employs four hidden layers in order to deeply analyze the web pages based on an input query. The first hidden layer determines the keywords from the user query. The second hidden layer measures the site popularity, content quality, keyword density and publicity of all web pages in the search engine. It then accomplishes Goodman and Kruskal's Gamma Predictive Ranking process in the third hidden layer, where it ranks the web pages by considering their similarities. The proposed TFDSL-PPR technique is applied to the ClueWeb09 Dataset with respect to a variety of user queries. The results are benchmarked by existing methods based on several metrics such as retrieval rate, time, and false-positive rate.

Jayawickrama, BA & He, Y 2022, 'Improved Layered Normalized Min-Sum Algorithm for 5G NR LDPC', IEEE Wireless Communications Letters, vol. 11, no. 9, pp. 2015-2018.
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Jena, KK, Bhoi, SK, Prasad, M & Puthal, D 2022, 'A fuzzy rule-based efficient hospital bed management approach for coronavirus disease-19 infected patients', Neural Computing and Applications, vol. 34, no. 14, pp. 11361-11382.
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Jena, R, Pradhan, B, Beydoun, G, Alamri, A & Shanableh, A 2022, 'Spatial earthquake vulnerability assessment by using multi-criteria decision making and probabilistic neural network techniques in Odisha, India', Geocarto International, vol. 37, no. 25, pp. 8080-8099.
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Jena, R, Pradhan, B, Gite, S, Alamri, A & Park, H-J 2022, 'A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model', Gondwana Research.
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Machine learning (ML) models have been extensively used in several geological applications. Owing to the increase in model complexity, interpreting the outputs becomes quite challenging. Shapley additive explanation (SHAP) measures the importance of each input attribute on the model's output. This study implemented SHAP to estimate earthquake probability using two different types of ML approaches, namely, artificial neural network (ANN) and random forest (RF). The two algorithms were first compared to evaluate the importance and effect of the factors. SHAP was then carried out to interpret the output of the models designed for the earthquake probability. This study aims not only to achieve high accuracy in probability estimation but also to rank the input parameters and select appropriate features for classification. SHAP was tested on earthquake probability assessment using eight factors for the Indian subcontinent. The models obtained an overall accuracy of 96 % for ANN and 98 % for RF. SHAP identified the high contributing factors as epicenter distance, depth density, intensity variation, and magnitude density in a sequential order for ANN. Finally, the authors argued that an explainable artificial intelligence (AI) model can help in earthquake probability estimation, which then open avenues to building a transferable AI model.

Jennifer, JJ, Saravanan, S & Pradhan, B 2022, 'Persistent Scatterer Interferometry in the post-event monitoring of the Idukki Landslides', Geocarto International, vol. 37, no. 5, pp. 1514-1528.
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Ji, J, Sun, X, He, W, Liu, Y, Duan, J, Liu, W, Nghiem, LD, Wang, Q & Cai, Z 2022, 'Built-in electric field enabled in carbon-doped Bi3O4Br nanocrystals for excellent photodegradation of PAHs', Separation and Purification Technology, vol. 302, pp. 122066-122066.
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Ji, Z, Natarajan, A, Vidick, T, Wright, J & Yuen, H 2022, 'Quantum Soundness of Testing Tensor Codes', Discrete Analysis, vol. 2022.
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A locally testable code is an error-correcting code that admits very efficient probabilistic tests of membership. Tensor codes provide a simple family of combinatorial constructions of locally testable codes that generalize the family of Reed-Muller codes. The natural test for tensor codes, the axis-parallel line vs. point test, plays an essential role in constructions of probabilistically checkable proofs. We analyze the axis-parallel line vs. point test as a two-prover game and show that the test is sound against quantum provers sharing entanglement. Our result implies the quantum-soundness of the low individual degree test, which is an essential component of the MIP = RE theorem. Our proof generalizes to the infinite-dimensional commuting-operator model of quantum provers.

Jia, M, Gabrys, B & Musial, K 2022, 'Measuring Quadrangle Formation in Complex Networks', IEEE Transactions on Network Science and Engineering, vol. 9, no. 2, pp. 538-551.
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Jiang, C, Ni, B-J, Zheng, X, Lu, B, Chen, Z, Gao, Y, Zhang, Y, Zhang, S & Luo, G 2022, 'The changes of microplastics’ behavior in adsorption and anaerobic digestion of waste activated sludge induced by hydrothermal pretreatment', Water Research, vol. 221, pp. 118744-118744.
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Jiang, G, Wu, J, Weidhaas, J, Li, X, Chen, Y, Mueller, J, Li, J, Kumar, M, Zhou, X, Arora, S, Haramoto, E, Sherchan, S, Orive, G, Lertxundi, U, Honda, R, Kitajima, M & Jackson, G 2022, 'Artificial neural network-based estimation of COVID-19 case numbers and effective reproduction rate using wastewater-based epidemiology', Water Research, vol. 218, pp. 118451-118451.
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Jiang, M, Wu, T, Wang, Z, Gong, Y, Zhang, L & Liu, RP 2022, 'A Multi-Intersection Vehicular Cooperative Control Based on End-Edge-Cloud Computing', IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 2459-2471.
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Jiang, S, Li, K & Da Xu, RY 2022, 'Magnitude Bounded Matrix Factorisation for Recommender Systems', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 4, pp. 1856-1869.
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Jifroudi, HM, Mansor, SB, Pradhan, B, Halin, AA, Ahmad, N & Abdullah, AFB 2022, 'A new approach to derive buildings footprint from light detection and ranging data using rule-based learning techniques and decision tree', Measurement, vol. 192, pp. 110781-110781.
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Jin, JX, Zhou, Q, Yang, RH, Li, YJ, Li, H, Guo, YG & Zhu, JG 2022, 'A superconducting magnetic energy storage based current-type interline dynamic voltage restorer for transient power quality enhancement of composited data center and renewable energy source power system', Journal of Energy Storage, vol. 52, pp. 105003-105003.
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Jin, L, Ruckin, J, Kiss, SH, Vidal-Calleja, T & Popovic, M 2022, 'Adaptive-Resolution Field Mapping Using Gaussian Process Fusion With Integral Kernels', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7471-7478.
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Jin, P, Zhang, J, Lu, Y, Guo, Y, Lei, G & Zhu, J 2022, 'Variable Frequency Isolated Bidirectional CLLC Resonant Converter with Voltage Controlled Variable Capacitors', IEEE Transactions on Industrial Electronics, pp. 1-10.
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Jin, X, Kaw, HY, Liu, Y, Zhao, J, Piao, X, Jin, D, He, M, Yan, X-P, Zhou, JL & Li, D 2022, 'One-step integrated sample pretreatment technique by gas-liquid microextraction (GLME) to determine multi-class pesticide residues in plant-derived foods', Food Chemistry, vol. 367, pp. 130774-130774.
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Jin, Y, Jiang, W, Yang, Y & Mu, Y 2022, 'Zero-Shot Video Event Detection With High-Order Semantic Concept Discovery and Matching', IEEE Transactions on Multimedia, vol. 24, pp. 1896-1908.
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Jin, Z, Sun, X, Lei, G, Guo, Y & Zhu, J 2022, 'Sliding Mode Direct Torque Control of SPMSMs Based on a Hybrid Wolf Optimization Algorithm', IEEE Transactions on Industrial Electronics, vol. 69, no. 5, pp. 4534-4544.
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Direct torque control has been widely employed to control surface-mounted permanent magnet synchronous motors (SPMSMs). However, the large torque ripple and accuracy of the flux tracking affect its performance. As a promising improvement of conventional direct torque control, sliding mode direct torque control (SMDTC) can handle these problems to a certain extent. Nevertheless, the optimal performance is hardly obtained by trial and error tuning. Hence, in this paper, a hybrid wolf optimization algorithm (HWOA) is proposed to automatically adjust the parameters of the controllers of SMDTC for SPMSMs. This algorithm combines the grey wolf optimization algorithm (GWOA) and coyote optimization algorithm (COA) and retains their respective advantages. A conversion probability is designed to realize the simultaneous use of these two algorithms. The convergence is relatively fast, and the locally optimal problem can be avoided effectively. Meanwhile, a special fitness index with penalty terms is designed to enhance flux tracking and reduce the torque ripple. Comparison among the GWOA-based, COA-based and HWOA-based controllers is implemented both in simulation and experiment to show the superiority of the proposed control method.

Johansen, MD, Mahbub, RM, Idrees, S, Nguyen, DH, Miemczyk, S, Pathinayake, P, Nichol, K, Hansbro, NG, Gearing, LJ, Hertzog, PJ, Gallego-Ortega, D, Britton, WJ, Saunders, BM, Wark, PA, Faiz, A & Hansbro, PM 2022, 'Increased SARS-CoV-2 Infection, Protease, and Inflammatory Responses in Chronic Obstructive Pulmonary Disease Primary Bronchial Epithelial Cells Defined with Single-Cell RNA Sequencing', American Journal of Respiratory and Critical Care Medicine, vol. 206, no. 6, pp. 712-729.
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John, AR, Singh, AK, Do, T-TN, Eidels, A, Nalivaiko, E, Gavgani, AM, Brown, S, Bennett, M, Lal, S, Simpson, AM, Gustin, SM, Double, K, Walker, FR, Kleitman, S, Morley, J & Lin, C-T 2022, 'Unraveling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 770-781.
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John, CB, Raja, SA, Deepanraj, B & Ong, HC 2022, 'Palm stearin biodiesel: preparation, characterization using spectrometric techniques and the assessment of fuel properties', Biomass Conversion and Biorefinery, vol. 12, no. 5, pp. 1679-1693.
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Joshi, S & Sharma, M 2022, 'Digital technologies (DT) adoption in agri-food supply chains amidst COVID-19: an approach towards food security concerns in developing countries', Journal of Global Operations and Strategic Sourcing, vol. 15, no. 2, pp. 262-282.
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Purpose This study aims to explore the critical factors for digital technologies (DT) adoption to develop a sustainable agri-food supply chain (AFSC). As the developing countries are struggling to survive during COVID-19, DT adoption in AFSC can bring resilience and minimizes the food security concerns. Design/methodology/approach The study has used Fuzzy Delphi and fuzzy decision-making trial and evaluation laboratory (DEMATEL) methods for identifying the critical success factors (CSFs) for DT adoption and inter-relationship among them to explore the crucial factors for food security across AFSC. Findings The research reveals that “Digital Technologies, Logistics and infrastructure” is the most crucial CSF for managing food security in developing economy during the COVID-19 situation. This factor supports the decision-makers to manage data for demand and supply management and helps to survive and sustain in the disruptive environment. The findings of the study will help farmers and supply chain partners to manage the smooth flow of food items from source to end-users during a disruptive environment. The sourcing, manufacturing and delivery methods are needed to be changed with DT inclusion and may support to redesign their internal systems for improvisation. This shorter AFSC will enhance the resilience in AFSCs. Research limitations/implications The emergency situation raised by the COVID-19 pandemic has brought global food security concerns. Adoption of DT across AFSCs can strategically reduce food waste and optimize the demand and supply balance.

Joshi, S & Sharma, M 2022, 'Impact of sustainable Supply Chain Management on the Performance of SMEs during COVID-19 Pandemic: An Indian Perspective', International Journal of Logistics Economics and Globalisation.

Joshi, S & Sharma, M 2022, 'Prolonging retailer-supplier relationship: A study of retail firms during pandemic COVID-19', International Journal of Logistics Economics and Globalisation.

Joshua Tapas, M, Thomas, P, Vessalas, K & Sirivivatnanon, V 2022, 'Mechanisms of Alkali-Silica Reaction Mitigation in AMBT Conditions: Comparative Study of Traditional Supplementary Cementitious Materials', Journal of Materials in Civil Engineering, vol. 34, no. 3.
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Ju, M, Ding, C, Ren, W & Yang, Y 2022, 'IDBP: Image Dehazing Using Blended Priors Including Non-Local, Local, and Global Priors', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 7, pp. 4867-4871.
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Juang, C-F, Chou, C-Y & Lin, C-T 2022, 'Navigation of a Fuzzy-Controlled Wheeled Robot Through the Combination of Expert Knowledge and Data-Driven Multiobjective Evolutionary Learning', IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7388-7401.
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Jung, MC, Chai, R, Zheng, J & Nguyen, H 2022, 'Enhanced myoelectric control against arm position change with weighted recursive Gaussian process', Neural Computing and Applications, vol. 34, no. 7, pp. 5015-5028.
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Jupp, JR & Parkes, M 2022, 'Integrated Construction Enterprise Systems: A Strategic Approach to Model-based Data and Process Management', Automation in Construction, vol. (to appear).

Kabir, MM, Alam, F, Akter, MM, Gilroyed, BH, Didar-ul-Alam, M, Tijing, L & Shon, HK 2022, 'Highly effective water hyacinth (Eichhornia crassipes) waste-based functionalized sustainable green adsorbents for antibiotic remediation from wastewater', Chemosphere, vol. 304, pp. 135293-135293.
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Kacprzak, S & Tijing, LD 2022, 'Microplastics in indoor environment: Sources, mitigation and fate', Journal of Environmental Chemical Engineering, vol. 10, no. 2, pp. 107359-107359.
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Kajikawa, Y, Mejia, C, Wu, M & Zhang, Y 2022, 'Academic landscape of Technological Forecasting and Social Change through citation network and topic analyses', Technological Forecasting and Social Change, vol. 182, pp. 121877-121877.
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Kamal, MS, Dey, N, Chowdhury, L, Hasan, SI & Santosh, KC 2022, 'Explainable AI for Glaucoma Prediction Analysis to Understand Risk Factors in Treatment Planning', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-9.
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Kang, G, Jiang, L, Wei, Y, Yang, Y & Hauptmann, A 2022, 'Contrastive Adaptation Network for Single- and Multi-Source Domain Adaptation', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 4, pp. 1793-1804.
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Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy neglecting the class information, which may lead to misalignment and poor generalization performance. To tackle this issue, this paper proposes Contrastive Adaptation Network (CAN) that optimizes a new metric named Contrastive Domain Discrepancy explicitly modeling the intra-class domain discrepancy and the inter-class domain discrepancy. To optimize CAN, two technical issues need to be addressed: 1) the target labels are not available and 2) the conventional mini-batch sampling is imbalanced. Thus we design an alternating update strategy to optimize both the target label estimations and the feature representations. Moreover, we develop class-aware sampling to enable more efficient and effective training. Our framework can be generally applied to the single-source and multi-source domain adaptation scenarios. In particular, to deal with multiple source domain data, we propose 1) multi-source clustering ensemble which exploits the complementary knowledge of distinct source domains to make more accurate and robust target label estimations, and 2) boundary-sensitive alignment to make the decision boundary better fitted to the target. Experiments conducted on three real-world benchmarks, demonstrating CAN performs favorably against previous state-of-the-arts.

Kapeleris, J, Müller Bark, J, Ranjit, S, Irwin, D, Hartel, G, Warkiani, ME, Leo, P, O'Leary, C, Ladwa, R, O'Byrne, K, Hughes, BGM & Punyadeera, C 2022, 'Prognostic value of integrating circulating tumour cells and cell-free DNA in non-small cell lung cancer', Heliyon, vol. 8, no. 7, pp. e09971-e09971.
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Kaplan, E, Ekinci, T, Kaplan, S, Barua, PD, Dogan, S, Tuncer, T, Tan, R-S, Arunkumar, N & Acharya, UR 2022, 'PFP-LHCINCA: Pyramidal Fixed-Size Patch-Based Feature Extraction and Chi-Square Iterative Neighborhood Component Analysis for Automated Fetal Sex Classification on Ultrasound Images', Contrast Media & Molecular Imaging, vol. 2022, pp. 1-10.
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Objectives. Fetal sex determination with ultrasound (US) examination is indicated in pregnancies at risk of X-linked genetic disorders or ambiguous genitalia. However, misdiagnoses often arise due to operator inexperience and technical difficulties while acquiring diagnostic images. We aimed to develop an efficient automated US-based fetal sex classification model that can facilitate efficient screening and reduce misclassification. Methods. We have developed a novel feature engineering model termed PFP-LHCINCA that employs pyramidal fixed-size patch generation with average pooling-based image decomposition, handcrafted feature extraction based on local phase quantization (LPQ), and histogram of oriented gradients (HOG) to extract directional and textural features and used Chi-square iterative neighborhood component analysis feature selection (CINCA), which iteratively selects the most informative feature vector for each image that minimizes calculated feature parameter-derived k-nearest neighbor-based misclassification rates. The model was trained and tested on a sizeable expert-labeled dataset comprising 339 males’ and 332 females’ fetal US images. One transverse fetal US image per subject zoomed to the genital area and standardized to 256 × 256 size was used for analysis. Fetal sex was annotated by experts on US images and confirmed postnatally. Results. Standard model performance metrics were compared using five shallow classifiers—k-nearest neighbor (kNN), decision tree, naïve Bayes, linear discriminant, and support vector machine (SVM)—with the hyperparameters tuned using a Bayesian optimizer. The PFP-LHCINCA model achieved a sex classification accuracy of ≥88% with all five classifiers and the best accuracy rates (>98%) with kNN and SVM classifiers. Conclusions. US-based fetal sex classification is feasible and accurate using the presented PFP-LHCINCA model. The salutary results support its clinical use for fetal US image screening for sex class...

Karacan, I, Ben‐Nissan, B, Santos, J, Yiu, S, Bradbury, P, Valenzuela, SM & Chou, J 2022, 'In vitro testing and efficacy of poly‐lactic acid coating incorporating antibiotic loaded coralline bioceramic on Ti6Al4V implant against Staphylococcus aureus', Journal of Tissue Engineering and Regenerative Medicine, vol. 16, no. 12, pp. 1149-1162.
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Karbassiyazdi, E, Fattahi, F, Yousefi, N, Tahmassebi, A, Taromi, AA, Manzari, JZ, Gandomi, AH, Altaee, A & Razmjou, A 2022, 'XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions', Environmental Research, vol. 215, pp. 114286-114286.
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Karki, D, Al-Hunaity, S, Far, H & Saleh, A 2022, 'Composite connections between CFS beams and plywood panels for flooring systems: Testing and analysis', Structures, vol. 40, pp. 771-785.
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Kashani, AR, Camp, CV, Rostamian, M, Azizi, K & Gandomi, AH 2022, 'Population-based optimization in structural engineering: a review', Artificial Intelligence Review, vol. 55, no. 1, pp. 345-452.
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Katzmarek, DA, Pradeepkumar, A, Ziolkowski, RW & Iacopi, F 2022, 'Review of graphene for the generation, manipulation, and detection of electromagnetic fields from microwave to terahertz', 2D Materials, vol. 9, no. 2, pp. 022002-022002.
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AbstractGraphene has attracted considerable attention ever since the discovery of its unprecedented properties, including its extraordinary and tunable electronic and optical properties. In particular, applications within the microwave to terahertz frequency spectrum can benefit from graphene’s high electrical conductivity, mechanical flexibility and robustness, transparency, support of surface-plasmon-polaritons, and the possibility of dynamic tunability with direct current to light sources. This review aims to provide an in-depth analysis of current trends, challenges, and prospects within the research areas of generating, manipulating, and detecting electromagnetic fields using graphene-based devices that operate from microwave to terahertz frequencies. The properties of and models describing graphene are reviewed first, notably those of importance to electromagnetic applications. State-of-the-art graphene-based antennas, such as resonant and leaky-wave antennas, are discussed next. A critical evaluation of the performance and limitations within each particular technology is given. Graphene-based metasurfaces and devices used to manipulate electromagnetic fields, e.g. wavefront engineering, are then examined. Lastly, the state-of-the-art of detecting electromagnetic fields using graphene-based devices is discussed.

Keshavarz, R & Shariati, N 2022, 'Highly Sensitive and Compact Quad-Band Ambient RF Energy Harvester', IEEE Transactions on Industrial Electronics, vol. 69, no. 4, pp. 3609-3621.
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Keshavarz, R & Shariati, N 2022, 'High-Sensitivity and Compact Time Domain Soil Moisture Sensor Using Dispersive Phase Shifter for Complex Permittivity Measurement', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10.
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Key, S, Demir, S, Gurger, M, Yilmaz, E, Barua, PD, Dogan, S, Tuncer, T, Arunkumar, N, Tan, R-S & Acharya, UR 2022, 'ViVGG19: Novel exemplar deep feature extraction-based shoulder rotator cuff tear and biceps tendinosis detection using magnetic resonance images', Medical Engineering & Physics, vol. 110, pp. 103864-103864.
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Khaliliboroujeni, S, He, X, Jia, W & Amirgholipour, S 2022, 'End-to-end metastasis detection of breast cancer from histopathology whole slide images', Computerized Medical Imaging and Graphics, vol. 102, pp. 102136-102136.
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Worldwide breast cancer is one of the most frequent and mortal diseases across women. Early, accurate metastasis cancer detection is a significant factor in raising the survival rate among patients. Diverse Computer-Aided Diagnostic (CAD) systems applying medical imaging modalities, have been designed for breast cancer detection. The impact of deep learning in improving CAD systems' performance is undeniable. Among all of the medical image modalities, histopathology (HP) images consist of richer phenotypic details and help keep track of cancer metastasis. Nonetheless, metastasis detection in whole slide images (WSIs) is still problematic because of the enormous size of these images and the massive cost of labelling them. In this paper, we develop a reliable, fast and accurate CAD system for metastasis detection in breast cancer while applying only a small amount of annotated data with lower resolution. This saves considerable time and cost. Unlike other works which apply patch classification for tumor detection, we employ the benefits of attention modules adding to regression and classification, to extract tumor parts simultaneously. Then, we use dense prediction for mask generation and identify individual metastases in WSIs. Experimental outcomes demonstrate the efficiency of our method. It provides more accurate results than other methods that apply the total dataset. The proposed method is about seven times faster than an expert pathologist, while producing even more accurate results than an expert pathologist in tumor detection.

Khan, HA, Yasir, M & Castel, A 2022, 'Performance of cementitious and alkali-activated mortars exposed to laboratory simulated microbially induced corrosion test', Cement and Concrete Composites, vol. 128, pp. 104445-104445.
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Khan, MNH, Barzegarkhoo, R, Siwakoti, YP, Khan, SA, Li, L & Blaabjerg, F 2022, 'A new switched-capacitor multilevel inverter with soft start and quasi resonant charging capabilities', International Journal of Electrical Power & Energy Systems, vol. 135, pp. 107412-107412.
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Khan, MNH, Hasan, SU & Siwakoti, YP 2022, 'New PWM Strategy to Enable Dual-Mode Operation Capability in Common-Grounded Transformerless Inverters', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 6, pp. 7361-7370.
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Khan, R, Tao, X, Anjum, A, Malik, SR, Yu, S, Khan, A, Rehman, W & Malik, H 2022, '( τ ,  m )‐ slicedBucket privacy model for sequential anonymization for improving privacy and utility', Transactions on Emerging Telecommunications Technologies, vol. 33, no. 6.
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Khan, S, Wilson, A & Hussain, FK 2022, 'Contextual information aware optimal communication in radio networks in considering the pervasive computing -A literature review', Procedia Computer Science, vol. 203, pp. 127-134.
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Khezri, M, Hu, Y, Luo, Q, Bambach, MR, Tong, L & Rasmussen, KJR 2022, 'Structural morphing induced by functionalising buckling', Thin-Walled Structures, vol. 181, pp. 110103-110103.
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Khoa, TV, Hoang, DT, Trung, NL, Nguyen, CT, Quynh, TTT, Nguyen, DN, Ha, NV & Dutkiewicz, E 2022, 'Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks', IEEE Internet of Things Journal, pp. 1-1.
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Khodasevych, I, Rufangura, P & Iacopi, F 2022, 'Designing concentric nanoparticles for surface-enhanced light-matter interaction in the mid-infrared', Optics Express, vol. 30, no. 13, pp. 24118-24118.
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Nanosized particles with high responsivity in the infrared spectrum are of great interest for biomedical applications. We derive a closed-form expression for the polarizability of nanoparticles made of up to three concentric nanolayers consisting of a frequency dependent polar dielectric core, low permittivity dielectric spacer shell and conductive graphene outer shell, using the electrostatic Mie theory in combination with conductive layer in a dipole approximation. We use the obtained formula to investigate SiC, GaN and hBN as core materials, and graphene as conductive shell, separated by a low-permittivity dielectric spacer. Three-layer nanoparticles demonstrate up to a 12-fold increased mid-infrared (MIR) absorption as compared to their monolithic polar dielectrics, and up to 1.7 as compared to two-layer (no spacer) counterparts. They also show orders of magnitude enhancement of the nanoparticle scattering efficiency. The enhancement originates from the phonon-plasmon hybridization thanks to the graphene and polar dielectric combination, assisted by coupling via the low permittivity spacer, resulting in the splitting of the dielectric resonance into two modes. Those modes extend beyond the dielectric’s Reststrahlen band and can be tuned by tailoring the nanoparticles characteristics as they can be easily calculated through the closed-form expression. Nanoparticles with dual band resonances and enhanced absorption and scattering efficiencies in the MIR are of high technological interest for biomedical applications, such as surface -enhanced vibrational spectroscopies allowing simultaneous imaging and spectroscopy of samples, as well as assisting guided drug delivery.

Khokher, MR, Little, LR, Tuck, GN, Smith, DV, Qiao, M, Devine, C, O’Neill, H, Pogonoski, JJ, Arangio, R & Wang, D 2022, 'Early lessons in deploying cameras and artificial intelligence technology for fisheries catch monitoring: where machine learning meets commercial fishing', Canadian Journal of Fisheries and Aquatic Sciences, vol. 79, no. 2, pp. 257-266.
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Electronic monitoring (EM) is increasingly used to monitor catch and bycatch in wild capture fisheries. EM video data are still manually reviewed and adds to ongoing management costs. Computer vision, machine learning, and artificial intelligence-based systems are seen to be the next step in automating EM data workflows. Here we show some of the obstacles we have confronted and approaches taken as we develop a system to automatically identify and count target and bycatch species using cameras deployed to an industry vessel. A Convolutional Neural Network was trained to detect and classify target and bycatch species groups, and a visual tracking system was developed to produce counts. The multiclass detector achieved a mean average precision of 53.42%. Based on the detection results, the visual tracking system provided automatic fish counts for the test video data. Automatic counts were within two standard deviations of the manual counts for the target species and most times for the bycatch species. Unlike other recent attempts, weather and lighting conditions were largely controlled by mounting cameras under cover.

Kiani, M, Andreu-Perez, J, Hagras, H, Papageorgiou, EI, Prasad, M & Lin, C-T 2022, 'Effective Brain Connectivity for fNIRS With Fuzzy Cognitive Maps in Neuroergonomics', IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 1, pp. 50-63.
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Kim, J, Charbel-Salloum, A, Perry, S & Palmisano, S 2022, 'Effects of display lag on vection and presence in the Oculus Rift HMD', Virtual Reality, vol. 26, no. 2, pp. 425-436.
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Kim, J, Kim, H-W, Tijing, LD, Shon, HK & Hong, S 2022, 'Elucidation of physicochemical scaling mechanisms in membrane distillation (MD): Implication to the control of inorganic fouling', Desalination, vol. 527, pp. 115573-115573.
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Kim, J, Yun, E-T, Tijing, L, Shon, HK & Hong, S 2022, 'Mitigation of fouling and wetting in membrane distillation by electrical repulsion using a multi-layered single-wall carbon nanotube/polyvinylidene fluoride membrane', Journal of Membrane Science, vol. 653, pp. 120519-120519.
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King, J-T, John, AR, Wang, Y-K, Shih, C-K, Zhang, D, Huang, K-C & Lin, C-T 2022, 'Brain Connectivity Changes During Bimanual and Rotated Motor Imagery', IEEE Journal of Translational Engineering in Health and Medicine, vol. 10, pp. 1-8.
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Kiss, SH, Katuwandeniya, K, Alempijevic, A & Vidal-Calleja, T 2022, 'Constrained Gaussian Processes With Integrated Kernels for Long-Horizon Prediction of Dense Pedestrian Crowd Flows', IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 7343-7350.
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Kiyani, A, Nasimuddin, N, Hashmi, RM, Baba, AA, Abbas, SM, Esselle, KP & Mahmoud, A 2022, 'A Single-Feed Wideband Circularly Polarized Dielectric Resonator Antenna Using Hybrid Technique With a Thin Metasurface', IEEE Access, vol. 10, pp. 90244-90253.
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Kocaballi, AB, Laranjo, L, Clark, L, Kocielnik, R, Moore, RJ, Liao, QV & Bickmore, T 2022, 'Special Issue on Conversational Agents for Healthcare and Wellbeing', ACM Transactions on Interactive Intelligent Systems, vol. 12, no. 2, pp. 1-3.
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Koli, MNY, Afzal, MU & Esselle, KP 2022, 'Increasing the Gain of Beam-Tilted Circularly Polarized Radial Line Slot Array Antennas', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4392-4403.
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Kolli, MK, Opp, C, Karthe, D & Pradhan, B 2022, 'Automatic extraction of large-scale aquaculture encroachment areas using Canny Edge Otsu algorithm in Google earth engine – the case study of Kolleru Lake, South India', Geocarto International, vol. 37, no. 26, pp. 11173-11189.
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Kong, W, Li, X, Hou, L, Yuan, J, Gao, Y & Yu, S 2022, 'A Reliable and Efficient Task Offloading Strategy Based on Multifeedback Trust Mechanism for IoT Edge Computing', IEEE Internet of Things Journal, vol. 9, no. 15, pp. 13927-13941.
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Kong, X, Luo, J, Luo, Q, Li, Q & Sun, G 2022, 'Experimental study on interface failure behavior of 3D printed continuous fiber reinforced composites', Additive Manufacturing, vol. 59, pp. 103077-103077.
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Kotobuki, M, Zhou, C, Su, Z, Yang, L, Wang, Y, Jason, CJJ, Liu, Z & Lu, L 2022, 'Importance of substrate materials for sintering Li1.5Al0.5Ge1.5(PO4)3 solid electrolyte', Journal of Solid State Chemistry, vol. 310, pp. 123043-123043.
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Koul, Y, Devda, V, Varjani, S, Guo, W, Ngo, HH, Taherzadeh, MJ, Chang, J-S, Wong, JWC, Bilal, M, Kim, S-H, Bui, X-T & Parra-Saldívar, R 2022, 'Microbial electrolysis: a promising approach for treatment and resource recovery from industrial wastewater', Bioengineered, vol. 13, no. 4, pp. 8115-8134.
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Kouretzis, G, Sheng, D & Thomas, HR 2022, 'In memory of Scott William Sloan (1954–2019)', Computers and Geotechnics, vol. 143, pp. 104593-104593.
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Kozanoglu, DC, Daim, TU & Contreras-Cruz, A 2022, 'Unraveling the Dynamics of Immigrant Engineers’ Full-Utilization in Australia', IEEE Transactions on Engineering Management, pp. 1-16.
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Kridalukmana, R, Lu, H & Naderpour, M 2022, 'Self-Explaining Abilities of an Intelligent Agent for Transparency in a Collaborative Driving Context', IEEE Transactions on Human-Machine Systems, vol. 52, no. 6, pp. 1155-1165.
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Krishankumar, R, Ecer, F, Mishra, AR, Ravichandran, KS, Gandomi, AH & Kar, S 2022, 'A SWOT-Based Framework for Personalized Ranking of IoT Service Providers With Generalized Fuzzy Data for Sustainable Transport in Urban Regions', IEEE Transactions on Engineering Management, pp. 1-14.
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Krishankumar, R, Supraja Nimmagadda, S, Mishra, AR, Pamucar, D, Ravichandran, KS & Gandomi, AH 2022, 'An integrated decision model for cloud vendor selection using probabilistic linguistic information and unknown weights', Engineering Applications of Artificial Intelligence, vol. 114, pp. 105114-105114.
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Kuang, B, Fu, A, Susilo, W, Yu, S & Gao, Y 2022, 'A survey of remote attestation in Internet of Things: Attacks, countermeasures, and prospects', Computers & Security, vol. 112, pp. 102498-102498.
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Kumar, A, Esmaili, N & Piccardi, M 2022, 'Neural Topic Model Training with the REBAR Gradient Estimator', ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 21, no. 5, pp. 1-18.
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Topic modelling is an important approach of unsupervised machine learning that allows automatically extracting the main “topics” from large collections of documents. In addition, topic modelling is able to identify the topic proportions of each individual document, which can be helpful for organizing the collections. Many topic modelling algorithms have been proposed to date, including several that leverage advanced techniques such as variational inference and deep autoencoders. However, to date topic modelling has made limited use of reinforcement learning, a framework that has obtained vast success in many other unsupervised learning tasks. For this reason, in this article we propose training a neural topic model using a reinforcement learning objective and minimizing the objective with the recently-proposed REBAR gradient estimator. Experiments performed over two probing datasets have shown that the proposed model has achieved improvements over all the compared models in terms of both model perplexity and topic coherence, and produced topics that appear qualitatively informative and consistent.

Kumar, A, Joshi, S, Sharma, M & Vishvakarma, N 2022, 'Digital humanitarianism and crisis management: an empirical study of antecedents and consequences', Journal of Humanitarian Logistics and Supply Chain Management, vol. 12, no. 4, pp. 570-593.
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PurposeThis study proposes a digital humanitarianism dynamic capability (DHDC) paradigm that explores the direct effects of DHDC on disaster risk reduction (DRR) and the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between DHDC and DRR.Design/methodology/approachTo validate the proposed model, the authors used an offline survey to gather data from 260 district magistrates in India managing the COVID-19 pandemic.FindingsThe results affirm the importance of the DHDC system for DRR. The findings depict that the impact of PODC on DRR in the DHDC system is negligible. This study can help policymakers in planning during emergencies.Research limitations/implicationsTechnological innovation has reshaped the way humanitarian organizations (HOs) respond to humanitarian crises. These organizations are able to provide immediate aid to affected communities through digital humanitarianism (DH), which involves significant innovations to match the specific needs of people in real-time through online platforms. Despite the growing need for DH, there is still limited know-how regarding how to leverage such technological concepts into disaster management. Moreover, the impact of DH on DRR is rarely examined.Originality/valueThe present study examines the impact of the dynamic capabilities of HOs on DRR by applying the resource-based view (RBV) and dynamic capability theory (DCT).

Kumar, AN, Ashok, B, Nanthagopal, K, Ong, HC, Geca, MJ, Victor, J, Vignesh, R, Jeevanantham, AK, Kannan, C & Kishore, PS 2022, 'Experimental analysis of higher alcohol–based ternary biodiesel blends in CI engine parameters through multivariate and desirability approaches', Biomass Conversion and Biorefinery, vol. 12, no. 5, pp. 1525-1540.
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The present work has enabled to aspire the enhancement of palm biodiesel viability for compression ignition (CI) engine applications using reformulation strategy by the addition of higher alcohols. In this work, 20% and 30% of 1-decanol and n-hexanol were used for ternary blend preparation along with palm biodiesel concentration as 20–30% and 50% diesel fuels for combustion, emission, and performance behaviour investigation in CI engine. Furthermore, the experimental results were also compared with 100% palm biodiesel (P100) and pure diesel (D100) and a binary blend of D50B50 fuels. The experimental study has revealed that the presence of higher alcohols in the ternary blends has improved the cylinder pressure and heat release rate whereas the same trend was not evident in binary biodiesel blend. All the ternary blends of higher alcohols-biodiesel and diesels have shown higher brake thermal efficiency and reduction in brake-specific fuel consumption. At the same time, decanol and hexanol addition in the palm biodiesel-diesel blends has favoured in all exhaust emission reductions with slight exemption in NO emission. The experimental results are optimized through multivariate and desirability analyses for identifying the effective composition of blend. Multivariate analysis has revealed that the higher alcohol proposition in the ternary blend was more influential than the type of higher alcohol. Furthermore, the desirability study has also validated the prescribed proportion with the maximum error of 6.17% for D50P22DC28 and 4.84% for D50P26HE24. Finally, the research concludes that decanol would be the preferable choice for ternary blend preparation than hexanol due to its overall better performance. x

Kumar, M, Jiang, G, Kumar Thakur, A, Chatterjee, S, Bhattacharya, T, Mohapatra, S, Chaminda, T, Kumar Tyagi, V, Vithanage, M, Bhattacharya, P, Nghiem, LD, Sarkar, D, Sonne, C & Mahlknecht, J 2022, 'Lead time of early warning by wastewater surveillance for COVID-19: Geographical variations and impacting factors', Chemical Engineering Journal, vol. 441, pp. 135936-135936.
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Kumar, S, Lyalin, A, Huang, Z & Taketsugu, T 2022, 'Catalytic Oxidative Dehydrogenation of Light Alkanes over Oxygen Functionalized Hexagonal Boron Nitride', ChemistrySelect, vol. 7, no. 1.
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Kurdkandi, NV, Marangalu, MG, Mohammadsalehian, S, Tarzamni, H, Siwakoti, YP, Islam, MR & Muttaqi, KM 2022, 'A New Six-Level Transformer-Less Grid-Connected Solar Photovoltaic Inverter With Less Leakage Current', IEEE Access, vol. 10, pp. 63736-63753.
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Kusumo, F, Mahlia, TMI, Pradhan, S, Ong, HC, Silitonga, AS, Fattah, IMR, Nghiem, LD & Mofijur, M 2022, 'A framework to assess indicators of the circular economy in biological systems', Environmental Technology & Innovation, vol. 28, pp. 102945-102945.
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Kusumo, F, Mahlia, TMI, Shamsuddin, AH, Ahmad, AR, Silitonga, AS, Dharma, S, Mofijur, M, Ideris, F, Ong, HC, Sebayang, R, Milano, J, Hassan, MH & Varman, M 2022, 'Optimisation of biodiesel production from mixed Sterculia foetida and rice bran oil', International Journal of Ambient Energy, vol. 43, no. 1, pp. 4380-4390.
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Kuzhiumparambil, U, Labeeuw, L, Commault, A, Vu, HP, Nguyen, LN, Ralph, PJ & Nghiem, LD 2022, 'Effects of harvesting on morphological and biochemical characteristics of microalgal biomass harvested by polyacrylamide addition, pH-induced flocculation, and centrifugation', Bioresource Technology, vol. 359, pp. 127433-127433.
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La, DD, Ngo, HH, Nguyen, DD, Tran, NT, Vo, HT, Nguyen, XH, Chang, SW, Chung, WJ & Nguyen, MD-B 2022, 'Advances and prospects of porphyrin-based nanomaterials via self-assembly for photocatalytic applications in environmental treatment', Coordination Chemistry Reviews, vol. 463, pp. 214543-214543.
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Laccone, F, Malomo, L, Callieri, M, Alderighi, T, Muntoni, A, Ponchio, F, Pietroni, N & Cignoni, P 2022, 'Design And Construction Of a Bending-Active Plywood Structure: The Flexmaps Pavilion', Journal of the International Association for Shell and Spatial Structures, vol. 63, no. 2, pp. 98-114.
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Mesostructured patterns are a modern and efficient concept based on designing the geometry of structural material at the meso-scale to achieve desired mechanical performances. In the context of bending-active structures, such a concept can be used to control the flexibility of the panels forming a surface without changing the constituting material. These panels undergo a formation process of deformation by bending, and application of internal restraints. This paper describes a new constructional system, FlexMaps, that has initiated the adoption of bending-active mesostructures at the architectural scale. Here, these modules are in the form of four-arms spirals made of CNC-milled plywood and are designed to reach the desired target shape once assembled. All phases from the conceptual design to the fabrication are seamlessly linked within an automated workflow. To illustrate the potential of the system, the paper discusses the results of a demonstrator project entitled FlexMaps Pavilion (3.90x3.96x3.25 meters) that has been exhibited at the IASS Symposium in 2019 and more recently at the 2021 17th International Architecture Exhibition, La Biennale di Venezia. The structural response is investigated through a detailed structural analysis, and the long-term behavior is assessed through a photogrammetric survey.

Lai, Y, Paul, G, Cui, Y & Matsubara, T 2022, 'User intent estimation during robot learning using physical human robot interaction primitives', Autonomous Robots, vol. 46, no. 2, pp. 421-436.
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AbstractAs robotic systems transition from traditional setups to collaborative work spaces, the prevalence of physical Human Robot Interaction has risen in both industrial and domestic environments. A popular representation for robot behavior is movement primitives which learn, imitate, and generalize from expert demonstrations. While there are existing works in context-aware movement primitives, they are usually limited to contact-free human robot interactions. This paper presents physical Human Robot Interaction Primitives (pHRIP), which utilize only the interaction forces between the human user and robot to estimate user intent and generate the appropriate robot response during physical human robot interactions. The efficacy of pHRIP is evaluated through multiple experiments based on target-directed reaching and obstacle avoidance tasks using a real seven degree of freedom robot arm. The results are validated against Interaction Primitives which use observations of robotic trajectories, with discussions of future pHRI applications utilizing pHRIP.

Lakomy, K, Madonski, R, Dai, B, Yang, J, Kicki, P, Ansari, M & Li, S 2022, 'Active Disturbance Rejection Control Design With Suppression of Sensor Noise Effects in Application to DC–DC Buck Power Converter', IEEE Transactions on Industrial Electronics, vol. 69, no. 1, pp. 816-824.
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Lalbakhsh, A, Afzal, MU, Esselle, KP & Smith, SL 2022, 'All-Metal Wideband Frequency-Selective Surface Bandpass Filter for TE and TM Polarizations', IEEE Transactions on Antennas and Propagation, vol. 70, no. 4, pp. 2790-2800.
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Lalbakhsh, A, Pitcairn, A, Mandal, K, Alibakhshikenari, M, Esselle, KP & Reisenfeld, S 2022, 'Darkening Low-Earth Orbit Satellite Constellations: A Review', IEEE Access, vol. 10, pp. 24383-24394.
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Lammers, T, Guertler, M & Skirde, H 2022, 'Can product modularization approaches help address challenges in technical project portfolio management? – Laying the foundations for a methodology transfer', International Journal of Information Systems and Project Management, vol. 10, no. 2, pp. 26-42.
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Formalized Project Portfolio Management (PPM) models struggle to provide comprehensive solutions to project selection, resource allocation and adaptability to dynamic technology project environments. In this article, we introduce a vision for a novel Modular Project Portfolio Management (MPPM) approach by drawing on well-established engineering methods for designing modular product architectures. We show how systems theory can be used to enable a transfer of methods from the area of engineering design and manufacturing to the area of PPM and how the concept of product modularity could help address challenges of existing PPM approaches. This lays the groundwork for the possible development of MPPM as a new and innovative methodology for managing complex technology and engineering project landscapes.

Lammers, T, Rashid, L, Kratzer, J & Voinov, A 2022, 'An analysis of the sustainability goals of digital technology start-ups in Berlin', Technological Forecasting and Social Change, vol. 185, pp. 122096-122096.
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Lan, T, Hutvagner, G, Zhang, X, Liu, T, Wong, L & Li, J 2022, 'Density-based detection of cell transition states to construct disparate and bifurcating trajectories', Nucleic Acids Research, vol. 50, no. 21, pp. e122-e122.
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Abstract Tree- and linear-shaped cell differentiation trajectories have been widely observed in developmental biologies and can be also inferred through computational methods from single-cell RNA-sequencing datasets. However, trajectories with complicated topologies such as loops, disparate lineages and bifurcating hierarchy remain difficult to infer accurately. Here, we introduce a density-based trajectory inference method capable of constructing diverse shapes of topological patterns including the most intriguing bifurcations. The novelty of our method is a step to exploit overlapping probability distributions to identify transition states of cells for determining connectability between cell clusters, and another step to infer a stable trajectory through a base-topology guided iterative fitting. Our method precisely re-constructed various benchmark reference trajectories. As a case study to demonstrate practical usefulness, our method was tested on single-cell RNA sequencing profiles of blood cells of SARS-CoV-2-infected patients. We not only re-discovered the linear trajectory bridging the transition from IgM plasmablast cells to developing neutrophils, and also found a previously-undiscovered lineage which can be rigorously supported by differentially expressed gene analysis.

Lau, CW, Qu, Z, Draper, D, Quan, R, Braytee, A, Bluff, A, Zhang, D, Johnston, A, Kennedy, PJ, Simoff, S, Nguyen, QV & Catchpoole, D 2022, 'Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts', Scientific Reports, vol. 12, no. 1.
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AbstractThe significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine.

Law, AMK, Chen, J, Colino‐Sanguino, Y, Fuente, LRDL, Fang, G, Grimes, SM, Lu, H, Huang, RJ, Boyle, ST, Venhuizen, J, Castillo, L, Tavakoli, J, Skhinas, JN, Millar, EKA, Beretov, J, Rossello, FJ, Tipper, JL, Ormandy, CJ, Samuel, MS, Cox, TR, Martelotto, L, Jin, D, Valdes‐Mora, F, Ji, HP & Gallego‐Ortega, D 2022, 'ALTEN: A High‐Fidelity Primary Tissue‐Engineering Platform to Assess Cellular Responses Ex Vivo', Advanced Science, vol. 9, no. 21, pp. 2103332-2103332.
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Le, A, Nimbalkar, S, Zobeiry, N & Malek, S 2022, 'An efficient multi-scale approach for viscoelastic analysis of woven composites under bending', Composite Structures, vol. 292, pp. 115698-115698.
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Le, AT, Huang, X & Guo, YJ 2022, 'A Two-Stage Analog Self-Interference Cancelation Structure for High Transmit Power In-Band Full-Duplex Radios', IEEE Wireless Communications Letters, vol. 11, no. 11, pp. 2425-2429.
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Le, AT, Huang, X, Tran, LC & Guo, YJ 2022, 'On the Impacts of I/Q Imbalance in Analog Least Mean Square Adaptive Filter for Self-Interference Cancellation in Full-Duplex Radios', IEEE Transactions on Vehicular Technology, vol. 71, no. 10, pp. 10683-10693.
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Le, L-T, Nguyen, K-QN, Nguyen, P-T, Duong, HC, Bui, X-T, Hoang, NB & Nghiem, LD 2022, 'Microfibers in laundry wastewater: Problem and solution', Science of The Total Environment, vol. 852, pp. 158412-158412.
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Le, T-S, Nguyen, P-D, Ngo, HH, Bui, X-T, Dang, B-T, Diels, L, Bui, H-H, Nguyen, M-T & Le Quang, D-T 2022, 'Two-stage anaerobic membrane bioreactor for co-treatment of food waste and kitchen wastewater for biogas production and nutrients recovery', Chemosphere, vol. 309, pp. 136537-136537.
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Lee, SS, Lim, RJS, Barzegarkhoo, R, Lim, CS, Grigoletto, FB & Siwakoti, YP 2022, 'A Family of Single-Phase Single-Stage Boost Inverters', IEEE Transactions on Industrial Electronics, pp. 1-9.
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H-bridge inverter is a common topology used for single-phase applications. Due to its limited voltage gain, a two-stage power conversion with a front-end dc-dc converter is usually adopted to accommodate the low dc source voltage. Recently, single-stage boost inverters are gaining significant interest due to their higher power efficiency and compactness. This paper presents a family of boost inverters with continuous dc source current. By the incorporation of merely a power switch and a boost inductor to the first leg of H-bridge, voltage-boosting and 3-level generation can be simultaneously achieved within a single-stage operation. All potential topologies using the same number of components are derived. An extension to generate five voltage levels with voltage gain enhancement is also proposed. The operation of the proposed boost inverters is thoroughly analyzed. Simulation and experimental results are presented for verification.

Lee, SS, Siwakoti, YP, Barzegarkhoo, R & Blaabjerg, F 2022, 'A Novel Common-Ground-Type Nine-Level Dynamic Boost Inverter', IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 4, pp. 4435-4442.
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Lee, T, Min, C, Naidu, G, Huang, Y, Shon, HK & Kim, S-H 2022, 'Optimizing the performance of sweeping gas membrane distillation for treating naturally heated saline groundwater', Desalination, vol. 532, pp. 115736-115736.
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Lee, XJ, Ong, HC, Ooi, J, Yu, KL, Tham, TC, Chen, W-H & Ok, YS 2022, 'Engineered macroalgal and microalgal adsorbents: Synthesis routes and adsorptive performance on hazardous water contaminants', Journal of Hazardous Materials, vol. 423, pp. 126921-126921.
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Lei, Y, Sui, Y, Ding, S & Zhang, Q 2022, 'Taming transitive redundancy for context-free language reachability', Proceedings of the ACM on Programming Languages, vol. 6, no. OOPSLA2, pp. 1556-1582.
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Given an edge-labeled graph, context-free language reachability (CFL-reachability) computes reachable node pairs by deriving new edges and adding them to the graph. The redundancy that limits the scalability of CFL-reachability manifests as redundant derivations, i.e., identical edges can be derived multiple times due to the many paths between two reachable nodes. We observe that most redundancy arises from the derivations involving transitive relations of reachable node pairs. Unfortunately, existing techniques for reducing redundancy in transitive-closure-based problems are either ineffective or inapplicable to identifying and eliminating redundant derivations during on-the-fly CFL-reachability solving. This paper proposes a scalable yet precision-preserving approach to all-pairs CFL-reachability analysis by taming its transitive redundancy. Our key insight is that transitive relations are intrinsically ordered, and utilizing the order for edge derivation can avoid most redundancy. To address the challenges in determining the derivation order from the dynamically changed graph during CFL-reachability solving, we introduce a hybrid graph representation by combining spanning trees and adjacency lists, together with a dynamic construction algorithm. Based on this representation, we propose a fast and effective partially ordered algorithm POCR to boost the performance of CFL-reachability analysis by reducing its transitive redundancy during on-the-fly solving. Our experiments on context-sensitive value-flow analysis and field-sensitive alias analysis for C/C++ demonstrate the promising performance of POCR. On average, POCR eliminates 98.50% and 97.26% redundant derivations respectively for the value-flow and alias analysis, achieving speedups of 21.48× and 19.57× over the standard CFL-reachability algorithm. We also compare POCR with two recent open-source tools, Graspan (a CFL-reachability solver) and Soufflé (a Datalog engine). ...

Lei, Y, Wu, C, Lu, X, Hua, W, Li, S, Liang, Y, Liu, H, Lai, W, Gu, Q, Cai, X, Wang, N, Wang, Y, Chou, S, Liu, H, Wang, G & Dou, S 2022, 'Streamline Sulfur Redox Reactions to Achieve Efficient Room‐Temperature Sodium–Sulfur Batteries', Angewandte Chemie, vol. 134, no. 16.
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Lei, Y, Yang, H, Liang, Y, Liu, H, Zhang, B, Wang, L, Lai, W, Wang, Y, Liu, H & Dou, S 2022, 'Progress and Prospects of Emerging Potassium–Sulfur Batteries', Advanced Energy Materials, vol. 12, no. 46, pp. 2202523-2202523.
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Leng, D, Zhu, Z, Liu, G & Li, Y 2022, 'Neuro fuzzy logic control of magnetorheological elastomer isolation system for vibration mitigation of offshore jacket platforms', Ocean Engineering, vol. 253, pp. 111293-111293.
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Leon-Castro, E, Blanco-Mesa, F, Alfaro-Garcia, V, Gil-Lafuente, AM, Merigó, JM & Kacprzyk, J 2022, 'Preface', Lecture Notes in Networks and Systems, vol. 337, pp. V-XIII.

Leong, KY, Hasbi, S, Ku Ahmad, KZ, Mat Jali, N, Ong, HC & Md Din, MF 2022, 'Thermal properties evaluation of paraffin wax enhanced with carbon nanotubes as latent heat thermal energy storage', Journal of Energy Storage, vol. 52, pp. 105027-105027.
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Lezana, P, Norambuena, M & Aguilera, RP 2022, 'Dual-Stage Control Strategy for a Flying Capacitor Converter Based on Model Predictive and Linear Controllers', IEEE Transactions on Industrial Informatics, vol. 18, no. 4, pp. 2203-2212.
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Li, A, Yang, B, Huo, H, Chen, H, Xu, G & Wang, Z 2022, 'Hyperbolic Neural Collaborative Recommender', IEEE Transactions on Knowledge and Data Engineering, pp. 1-12.
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Li, A, Yang, B, Hussain, FK & Huo, H 2022, 'HSR: Hyperbolic Social Recommender', Information Sciences, vol. 585, pp. 275-288.
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With the prevalence of online social media, users’ social connections have been widely studied and utilized to enhance the performance of recommender systems. In this paper, we explore the use of hyperbolic geometry for social recommendation. We present the Hyperbolic Social Recommender (HSR), a novel social recommendation framework that utilizes hyperbolic geometry to boost the performance. With the help of hyperbolic space, HSR can learn high-quality user and item representations to better model user-item interaction and user-user social relations. Through extensive experiments on four real-world datasets, we show that our proposed HSR outperforms its Euclidean counterpart and state-of-the-art social recommenders in click-through rate prediction and top-K recommendation, demonstrating the effectiveness of social recommendation in the hyperbolic space.

Li, C, Wang, G, Wang, B, Liang, X, Li, Z & Chang, X 2022, 'DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Vision Transformers', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-16.
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Li, C, Yang, L, Yu, S, Qin, W & Ma, J 2022, 'SEMMI: Multi-party security decision-making scheme for linear functions in the internet of medical things', Information Sciences, vol. 612, pp. 151-167.
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Li, D, Qing, L, Li, M, Cheng, H, Yang, G, Fu, Q & Sun, Y 2022, 'Ultra-fast self-repairing of anti-corrosive coating based on synergistic effect between cobalt octoate and linseed oil', Progress in Organic Coatings, vol. 166, pp. 106776-106776.
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Li, D, Tang, M-C, Wang, Y, Hu, K-Z & Ziolkowski, RW 2022, 'Dual-Band, Differentially-Fed Filtenna With Wide Bandwidth, High Selectivity, and Low Cross-Polarization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4872-4877.
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Li, F, Chen, W & Yang, Y 2022, 'Research on bearing fault diagnosis based on sparse adaptive S-transform and deep residual network', Dianji yu Kongzhi Xuebao/Electric Machines and Control, vol. 26, no. 8, pp. 112-119.
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The vibration signals of complex rolling bearings are nonlinear and non-stationary, and the traditional signal processing methods are difficult to achieve effective extraction of fault features and high-precision fault classification. To address this problem, a bearing fault diagnosis method based on sparse adaptive fault diagnosis S transform and deep residual network is proposed considering of the time-frequency characteristics of bearing vibration signals. Firstly, sparse adaptive S transform was applied to the collected vibration signals to obtain the time-frequency image characteristics of bearings under different working conditions. Then, the structure of deep residual network was constructed, and network parameters such as optimizer and initial learning rate were reasonably selected,and a bearing fault diagnosis model based on deep residual network was proposed. The calculation results of a rolling bearing vibration data sets show that time-frequency analysis based on sparse adaptive S transform method has a high time-frequency resolution, and the construction of the depth of the residual network model can accurately identify under different fault conditions and severity of bearing fault state.

Li, H, Huang, X, Zhang, JA, Zhang, H & Cheng, Z 2022, 'Dual pulse shaping transmission with sinc‐function based complementary Nyquist pulses', IET Communications, vol. 16, no. 17, pp. 2091-2104.
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Li, H, Li, J & Bi, K 2022, 'A quasi-active negative stiffness damper for structural vibration control under earthquakes', Mechanical Systems and Signal Processing, vol. 173, pp. 109071-109071.
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Li, J, Guo, J, Zhu, X & Yu, Y 2022, 'Nonlinear characteristics of damaged bridges under moving loads using parameter optimization variational mode decomposition', Journal of Civil Structural Health Monitoring, vol. 12, no. 5, pp. 1009-1026.
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Li, J, Han, L, Wang, Y, Yuan, B, Yuan, X, Yang, Y & Yan, H 2022, 'Combined angular margin and cosine margin softmax loss for music classification based on spectrograms', Neural Computing and Applications, vol. 34, no. 13, pp. 10337-10353.
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Li, J, Hlavka-Zhang, J, Shrimp, JH, Piper, C, Dupéré-Richér, D, Roth, JS, Jing, D, Casellas Román, HL, Troche, C, Swaroop, A, Kulis, M, Oyer, JA, Will, CM, Shen, M, Riva, A, Bennett, RL, Ferrando, AA, Hall, MD, Lock, RB & Licht, JD 2022, 'PRC2 Inhibitors Overcome Glucocorticoid Resistance Driven by NSD2 Mutation in Pediatric Acute Lymphoblastic Leukemia', Cancer Discovery, vol. 12, no. 1, pp. 186-203.
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Abstract Mutations in epigenetic regulators are common in relapsed pediatric acute lymphoblastic leukemia (ALL). Here, we uncovered the mechanism underlying the relapse of ALL driven by an activating mutation of the NSD2 histone methyltransferase (p.E1099K). Using high-throughput drug screening, we found that NSD2-mutant cells were specifically resistant to glucocorticoids. Correction of this mutation restored glucocorticoid sensitivity. The transcriptional response to glucocorticoids was blocked in NSD2-mutant cells due to depressed glucocorticoid receptor (GR) levels and the failure of glucocorticoids to autoactivate GR expression. Although H3K27me3 was globally decreased by NSD2 p.E1099K, H3K27me3 accumulated at the NR3C1 (GR) promoter. Pretreatment of NSD2 p.E1099K cell lines and patient-derived xenograft samples with PRC2 inhibitors reversed glucocorticoid resistance in vitro and in vivo. PRC2 inhibitors restored NR3C1 autoactivation by glucocorticoids, increasing GR levels and allowing GR binding and activation of proapoptotic genes. These findings suggest a new therapeutic approach to relapsed ALL associated with NSD2 mutation. Significance: NSD2 histone methyltransferase mutations observed in relapsed pediatric ALL drove glucocorticoid resistance by repression of the GR and abrogation of GR gene autoactivation due to accumulation of K3K27me3 at its promoter. Pretreatment with PRC2 inhibitors reversed resistance, suggesting a new therapeutic approach to these patients with ALL. This article is highlighted in the In This Issue feature, p. 1

Li, J, Ou, R, Liao, H, Ma, J, Sun, L, Jin, Q, He, D & Wang, Q 2022, 'Natural lighting enhancing the algae proliferation and nitrogen removal in membrane-aerated bacterial-algal biofilm reactor', Science of The Total Environment, vol. 851, pp. 158063-158063.
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Li, J, Wang, W, Wu, C, Liu, Z & Wu, P 2022, 'Impact response of ultra-high performance fiber-reinforced concrete filled square double-skin steel tubular columns', Steel and Composite Structures, vol. 42, no. 3, pp. 325-351.
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This paper studies the lateral impact behavior of ultra-high performance fiber-reinforced concrete (UHPFRC) filled double-skin steel tubular (UHPFRCFDST) columns. The impact force, midspan deflection, and strain histories were recorded. Based on the test results, the influences of drop height, axial load, concrete type, and steel tube wall thickness on the impact resistance of UHPFRCFDST members were analyzed. LS-DYNA software was used to establish a finite element (FE) model of UHPFRC filled steel tubular members. The failure modes and histories of impact force and midspan deflection of specimens were obtained. The simulation results were compared to the test results, which demonstrated the accuracy of the finite element analysis (FEA) model. Finally, the effects of the steel tube thickness, impact energy, type of concrete and impact indenter shape, and void ratio on the lateral impact performances of the UHPFRCFDST columns were analyzed.

Li, J, Yu, E, Ma, J, Chang, X, Zhang, H & Sun, J 2022, 'Discrete Fusion Adversarial Hashing for cross-modal retrieval', Knowledge-Based Systems, vol. 253, pp. 109503-109503.
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Li, J, Zhu, X & Guo, J 2022, 'Bridge modal identification based on successive variational mode decomposition using a moving test vehicle', Advances in Structural Engineering, vol. 25, no. 11, pp. 2284-2300.
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Bridge modal identification using an instrumented test vehicle as a moving sensor is promising but challenging. A key factor is to extract bridge dynamic components from vehicle responses measured when the bridge is operating. A new method based on an advanced adaptive signal decomposition technique, the successive variational mode decomposition (SVMD), has been developed to estimate the bridge modal parameters from the dynamic responses of a passing test vehicle. When bridge-related dynamic components are extracted from the decomposition, the natural excitation technique and/or random-decrement technique based fitting methods are used to estimate the modal frequencies and damping ratios of the bridge. Effects of measurement noise, moving speed and vehicle properties on the decomposition are investigated numerically. The superiority of SVMD in the decomposition is verified by comparing to another adaptive decomposition technique, the singular spectrum decomposition. The results of the proposed method confirm that the bridge modal frequencies can be identified from bridge related components with high accuracy, while damping ratio is more sensitive to the random operational load. Finally, the feasibility of the proposed method for bridge monitoring using a moving test vehicle is further verified by an in-situ experimental test on a cable-stayed bridge. The components related to the bridge dynamic responses are successfully extracted from vehicle responses.

Li, J, Zhu, X & Guo, J 2022, 'Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis', Structural Control and Health Monitoring, vol. 29, no. 5.
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Li, JJ, Liu, H, Zhu, Y, Yan, L, Liu, R, Wang, G, Wang, B & Zhao, B 2022, 'Animal Models for Treating Spinal Cord Injury Using Biomaterials-Based Tissue Engineering Strategies', Tissue Engineering Part B: Reviews, vol. 28, no. 1, pp. 79-100.
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Li, K, Duan, H, Liu, L, Qiu, R, van den Akker, B, Ni, B-J, Chen, T, Yin, H, Yuan, Z & Ye, L 2022, 'An Integrated First Principal and Deep Learning Approach for Modeling Nitrous Oxide Emissions from Wastewater Treatment Plants', Environmental Science & Technology, vol. 56, no. 4, pp. 2816-2826.
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Li, K, Lu, J, Zuo, H & Zhang, G 2022, 'Dynamic Classifier Alignment for Unsupervised Multi-Source Domain Adaptation', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Li, K, Lu, J, Zuo, H & Zhang, G 2022, 'Multi-Source Contribution Learning for Domain Adaptation', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5293-5307.
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Transfer learning becomes an attractive technology to tackle a task from a target domain by leveraging previously acquired knowledge from a similar domain (source domain). Many existing transfer learning methods focus on learning one discriminator with single-source domain. Sometimes, knowledge from single-source domain might not be enough for predicting the target task. Thus, multiple source domains carrying richer transferable information are considered to complete the target task. Although there are some previous studies dealing with multi-source domain adaptation, these methods commonly combine source predictions by averaging source performances. Different source domains contain different transferable information; they may contribute differently to a target domain compared with each other. Hence, the source contribution should be taken into account when predicting a target task. In this article, we propose a novel multi-source contribution learning method for domain adaptation (MSCLDA). As proposed, the similarities and diversities of domains are learned simultaneously by extracting multi-view features. One view represents common features (similarities) among all domains. Other views represent different characteristics (diversities) in a target domain; each characteristic is expressed by features extracted in a source domain. Then multi-level distribution matching is employed to improve the transferability of latent features, aiming to reduce misclassification of boundary samples by maximizing discrepancy between different classes and minimizing discrepancy between the same classes. Concurrently, when completing a target task by combining source predictions, instead of averaging source predictions or weighting sources using normalized similarities, the original weights learned by normalizing similarities between source and target domains are adjusted using pseudo target labels to increase the disparities of weight values, which is desired to improve the perfo...

Li, K, Ni, W & Dressler, F 2022, 'Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks', IEEE Transactions on Mobile Computing, vol. 21, no. 8, pp. 2732-2744.
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Li, K, Ni, W & Dressler, F 2022, 'LSTM-Characterized Deep Reinforcement Learning for Continuous Flight Control and Resource Allocation in UAV-Assisted Sensor Network', IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4179-4189.
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Li, K, Ni, W, Yuan, X, Noor, A & Jamalipour, A 2022, 'Deep-Graph-Based Reinforcement Learning for Joint Cruise Control and Task Offloading for Aerial Edge Internet of Things (EdgeIoT)', IEEE Internet of Things Journal, vol. 9, no. 21, pp. 21676-21686.
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Li, L & Kang, K 2022, 'Impact of opportunity and capability on e-entrepreneurial motivation: a comparison of urban and rural perspectives', Journal of Entrepreneurship in Emerging Economies.
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Purpose E-entrepreneurship is developed based on digital platforms, having specific technical opportunities, such as the interactive ecosystem, fast payment method and online store function, without strict requirements for online entrepreneurs. Considering China’s e-entrepreneurship environment and cultural background, this paper aims to analyse individuals’ e-entrepreneurship motivation based on the capability–opportunity–motivation–behaviour (COM-B) behaviour changing theory. Design/methodology/approach Through testing 602 samples based on the partial least squares path modelling and variance-based structural equation modelling, the factors from the opportunity and capability units positively affect individuals’ e-entrepreneurship motivation. Meanwhile, because of the economic and social environmental differences between China’s urban and rural regions, this study promotes the multi-group analysis based on individuals’ regional backgrounds. Findings First, as opportunity factors, technical and policy opportunities have significantly positive relationships with individuals’ e-entrepreneurship motivation. Second, entrepreneurial and cultural capabilities are essential for Chinese entrepreneurs while making an entrepreneurial decision. Third, because of the e-entrepreneurial environment difference and educational system gap, entrepreneurial capability exerts a greater influence on the e-entrepreneurship motivation for Chinese individuals from urban regions, and cultural capability exerts a higher impact on the e-entrepreneurship motivation for Chinese individuals from rural regions. Originality/value...

Li, L & Kang, K 2022, 'Understanding the real-time interaction between middle-aged consumers and online experts based on the COM-B model', Journal of Marketing Analytics.
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AbstractThis paper presents a study of middle-aged online consumers’ specific shopping behaviour on live streaming platforms and analyses the distinct marketing strategy provided by online experts. Influenced by unique social and cultural backgrounds, middle-aged online consumers lack related shopping experience and keep counterfeiting concerns to live streaming shopping, making them prefer to interact with online experts before making final decisions. Based on the COM-B Behaviour Changing theory and the Emotional attachment theory, the research model has been established in this study, and it divides influencing factors into the Emotion unit, Opportunity unit and Capability unit. To test the relationships between influencing factors and middle-aged online consumers’ interactive motivation, the partial least-squares path modelling and variance-based structural equation modelling (PLS-SEM) have been applied on the SmartPLS. By analysing 450 samples, the study shows that the counterfeiting concern and ease of use factors positively impact online consumers’ motivation to interact with online experts, and self-efficacy plays a negative role.

Li, M, Chen, S, Shen, Y, Liu, G, Tsang, IW & Zhang, Y 2022, 'Online Multi-Agent Forecasting With Interpretable Collaborative Graph Neural Networks', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Li, M, Huang, P-Y, Chang, X, Hu, J, Yang, Y & Hauptmann, A 2022, 'Video Pivoting Unsupervised Multi-Modal Machine Translation', IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-15.
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Li, M, Liu, Y, Chen, S-L, Hu, J & Guo, YJ 2022, 'Synthesizing Shaped-Beam Cylindrical Conformal Array Considering Mutual Coupling Using Refined Rotation/Phase Optimization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 11, pp. 10543-10553.
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Li, N, Asteris, PG, Tran, TT, Pradhan, B & Nguyen, H 2022, 'Modelling the deflection of reinforced concrete beams using the improved artificial neural network by imperialist competitive optimization', Steel and Composite Structures, vol. 42, no. 6, pp. 733-745.
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This study proposed a robust artificial intelligence (AI) model based on the social behaviour of the imperialist competitive algorithm (ICA) and artificial neural network (ANN) for modelling the deflection of reinforced concrete beams, abbreviated as ICA-ANN model. Accordingly, the ICA was used to adjust and optimize the parameters of an ANN model (i.e., weights and biases) aiming to improve the accuracy of the ANN model in modelling the deflection reinforced concrete beams. A total of 120 experimental datasets of reinforced concrete beams were employed for this aim. Therein, applied load, tensile reinforcement strength and the reinforcement percentage were used to simulate the deflection of reinforced concrete beams. Besides, five other AI models, such as ANN, SVM (support vector machine), GLMNET (lasso and elastic-net regularized generalized linear models), CART (classification and regression tree) and KNN (k-nearest neighbours), were also used for the comprehensive assessment of the proposed model (i.e., ICA-ANN). The comparison of the derived results with the experimental findings demonstrates that among the developed models the ICA-ANN model is that can approximate the reinforced concrete beams deflection in a more reliable and robust manner.

Li, N, Nguyen, H, Rostami, J, Zhang, W, Bui, X-N & Pradhan, B 2022, 'Predicting rock displacement in underground mines using improved machine learning-based models', Measurement, vol. 188, pp. 110552-110552.
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Li, P, Li, C, Bore, JC, Si, Y, Li, F, Cao, Z, Zhang, Y, Wang, G, Zhang, Z, Yao, D & Xu, P 2022, 'L1-norm based time-varying brain neural network and its application to dynamic analysis for motor imagery', Journal of Neural Engineering, vol. 19, no. 2, pp. 026019-026019.
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Abstract Objective . Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interface offers a promising way to improve the efficiency of motor rehabilitation and motor skill learning. In recent years, the power of dynamic network analysis for MI classification has been proved. In fact, its usability mainly depends on the accurate estimation of brain connection. However, traditional dynamic network estimation strategies such as adaptive directed transfer function (ADTF) are designed in the L2-norm. Usually, they estimate a series of pseudo connections caused by outliers, which results in biased features and further limits its online application. Thus, how to accurately infer dynamic causal relationship under outlier influence is urgent. Approach . In this work, we proposed a novel ADTF, which solves the dynamic system in the L1-norm space (L1-ADTF), so as to restrict the outlier influence. To enhance its convergence, we designed an iteration strategy with the alternating direction method of multipliers, which could be used for the solution of the dynamic state-space model restricted in the L1-norm space. Furthermore, we compared L1-ADTF to traditional ADTF and its dual extension across both simulation and real EEG experiments. Main results . A quantitative comparison between L1-ADTF and other ADTFs in simulation studies demonstrates that fewer bias errors and more desirable dynamic state transformation patterns can be captured by the L1-ADTF. Application to real MI EEG datasets seriously noised by ocular artifacts also reveals the efficiency of the proposed L1-ADTF approach to extract the time-varying brain neural network patterns, even when more complex noises are involved.

Li, P, Xu, Y, Wei, Y & Yang, Y 2022, 'Self-Correction for Human Parsing', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 6, pp. 3260-3271.
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Li, Q, Yuan, X, Hu, X, Meers, E, Ong, HC, Chen, W-H, Duan, P, Zhang, S, Lee, KB & Ok, YS 2022, 'Co-liquefaction of mixed biomass feedstocks for bio-oil production: A critical review', Renewable and Sustainable Energy Reviews, vol. 154, pp. 111814-111814.
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Li, S, Show, PL, Ngo, HH & Ho, S-H 2022, 'Algae-mediated antibiotic wastewater treatment: A critical review', Environmental Science and Ecotechnology, vol. 9, pp. 100145-100145.
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Li, T, Sun, X, Lei, G, Guo, Y, Yang, Z & Zhu, J 2022, 'Finite-Control-Set Model Predictive Control of Permanent Magnet Synchronous Motor Drive Systems—An Overview', IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 12, pp. 2087-2105.
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Li, W, Cao, L, Yue, P, Wen, S, Liao, J, Xia, J & Feng, X 2022, 'VAVM: A Flexible Technique for Variable-Angle Around View Monitor System Towards Articulated Engineering Vehicle', IEEE Transactions on Vehicular Technology, vol. 71, no. 4, pp. 3556-3568.
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Li, W, Dong, W, Guo, Y, Wang, K & Shah, SP 2022, 'Advances in multifunctional cementitious composites with conductive carbon nanomaterials for smart infrastructure', Cement and Concrete Composites, vol. 128, pp. 104454-104454.
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Li, W, Ji, J & Huang, L 2022, 'Global dynamics analysis of a water hyacinth fish ecological system under impulsive control', Journal of the Franklin Institute, vol. 359, no. 18, pp. 10628-10652.
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Li, W, Li, S, Liu, C, Lu, L, Shi, Z & Wen, S 2022, 'Span identification and technique classification of propaganda in news articles', Complex & Intelligent Systems, vol. 8, no. 5, pp. 3603-3612.
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AbstractPropaganda is a rhetorical technique designed to serve a specific topic, which is often used purposefully in news article to achieve our intended purpose because of its specific psychological effect. Therefore, it is significant to be clear where and what propaganda techniques are used in the news for people to understand its theme efficiently during our daily lives. Recently, some relevant researches are proposed for propaganda detection but unsatisfactorily. As a result, detection of propaganda techniques in news articles is badly in need of research. In this paper, we are going to introduce our systems for detection of propaganda techniques in news articles, which is split into two tasks, Span Identification and Technique Classification. For these two tasks, we design a system based on the popular pretrained BERT model, respectively. Furthermore, we adopt the over-sampling and EDA strategies, propose a sentence-level feature concatenating method in our systems. Experiments on the dataset of about 550 news articles offered by SEMEVAL show that our systems perform state-of-the-art.

Li, W, Qiao, M, Qin, L, Zhang, Y, Chang, L & Lin, X 2022, 'Distance labeling: on parallelism, compression, and ordering', The VLDB Journal, vol. 31, no. 1, pp. 129-155.
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Distance labeling approaches are widely adopted to speed up the online performance of shortest-distance queries. The construction of the distance labeling, however, can be exhaustive, especially on big graphs. For a major category of large graphs, small-world networks, the state-of-the-art approach is pruned landmark labeling (PLL). PLL prunes distance labels based on a node order and directly constructs the pruned labels by performing breadth-first searches in the node order. The pruning technique, as well as the index construction, has a strong sequential nature which hinders PLL from being parallelized. It becomes an urgent issue on massive small-world networks whose index can hardly be constructed by a single thread within a reasonable time. This paper first scales distance labeling on small-world networks by proposing a parallel shortest-distance labeling (PSL) scheme. PSL insightfully converts the PLL’s node-order dependency to a shortest-distance dependence, which leads to a propagation-based parallel labeling in D rounds where D denotes the diameter of the graph. To further scale up PSL, it is critical to reduce the index size. This paper proposes effective index compression techniques based on graph properties as well as label properties; it also explores best practices in using betweenness-based node order to reduce the index size. The efficient betweenness estimation of the graph nodes proposed may be of independent interest to graph practitioners. Extensive experimental results verify our efficiency on billion-scale graphs, near-linear speedup in a multi-core environment, and up to 94 % reduction in the index size.

Li, W, Qu, F, Dong, W, Mishra, G & Shah, SP 2022, 'A comprehensive review on self-sensing graphene/cementitious composites: A pathway toward next-generation smart concrete', Construction and Building Materials, vol. 331, pp. 127284-127284.
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Li, W, Wen, S, Shi, K, Yang, Y & Huang, T 2022, 'Neural Architecture Search With a Lightweight Transformer for Text-to-Image Synthesis', IEEE Transactions on Network Science and Engineering, vol. 9, no. 3, pp. 1567-1576.
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Li, W, Yuan, H, Li, S & Zhu, J 2022, 'A Revisit to Model-Free Control', IEEE Transactions on Power Electronics, vol. 37, no. 12, pp. 14408-14421.
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Li, X, Cui, Y, Zhang, JA, Liu, F, Zhang, D & Hanzo, L 2022, 'Integrated Human Activity Sensing and Communications', IEEE Communications Magazine, pp. 1-8.
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Li, X, Johnson, I, Mueller, K, Wilkie, S, Hanzic, L, Bond, PL, O'Moore, L, Yuan, Z & Jiang, G 2022, 'Corrosion mitigation by nitrite spray on corroded concrete in a real sewer system', Science of The Total Environment, vol. 806, pp. 151328-151328.
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Li, X, Kulandaivelu, J, Guo, Y, Zhang, S, Shi, J, O’Brien, J, Arora, S, Kumar, M, Sherchan, SP, Honda, R, Jackson, G, Luby, SP & Jiang, G 2022, 'SARS-CoV-2 shedding sources in wastewater and implications for wastewater-based epidemiology', Journal of Hazardous Materials, vol. 432, pp. 128667-128667.
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Li, X, Lu, L, Ni, W, Jamalipour, A, Zhang, D & Du, H 2022, 'Federated Multi-Agent Deep Reinforcement Learning for Resource Allocation of Vehicle-to-Vehicle Communications', IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8810-8824.
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Li, X, Ye, P, Li, J, Liu, Z, Cao, L & Wang, F-Y 2022, 'From Features Engineering to Scenarios Engineering for Trustworthy AI: I&I, C&C, and V&V', IEEE Intelligent Systems, vol. 37, no. 4, pp. 18-26.
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Li, X, Zhang, JA, Wu, K, Cui, Y & Jing, X 2022, 'CSI-Ratio-Based Doppler Frequency Estimation in Integrated Sensing and Communications', IEEE Sensors Journal, vol. 22, no. 21, pp. 20886-20895.
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Li, XL, Tse, CK & Lu, DD-C 2022, 'Synthesis of Reconfigurable and Scalable Single-Inductor Multiport Converters With No Cross Regulation', IEEE Transactions on Power Electronics, vol. 37, no. 9, pp. 10889-10902.
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Li, Y, Fan, X, Chen, L, Li, B & Sisson, SA 2022, 'Smoothing graphons for modelling exchangeable relational data', Machine Learning, vol. 111, no. 1, pp. 319-344.
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Li, Y, Huang, Y, Seneviratne, S, Thilakarathna, K, Cheng, A, Jourjon, G, Webb, D, Smith, DB & Xu, RYD 2022, 'From traffic classes to content: A hierarchical approach for encrypted traffic classification', Computer Networks, vol. 212, pp. 109017-109017.
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Li, Y, Liu, Z, Yao, L, Wang, X, McAuley, J & Chang, X 2022, 'An Entropy-Guided Reinforced Partial Convolutional Network for Zero-Shot Learning', IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 8, pp. 5175-5186.
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Li, Y, Tan, VYF & Tomamichel, M 2022, 'Optimal Adaptive Strategies for Sequential Quantum Hypothesis Testing', Communications in Mathematical Physics, vol. 392, no. 3, pp. 993-1027.
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Li, Y, Wang, Q & Li, S 2022, 'On quotients of formal power series', Information and Computation, vol. 285, pp. 104874-104874.
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Li, Y, Zeng, X, Shi, Y, Yang, K, Zhou, J, Umar, HA, Long, G & Xie, Y 2022, 'A comparative study on mechanical properties and environmental impact of UHPC with belite cement and portland cement', Journal of Cleaner Production, vol. 380, pp. 135003-135003.
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Li, Y, Zhang, X, Ngo, HH, Guo, W, Zhang, D, Wang, H & Long, T 2022, 'Magnetic spent coffee biochar (Fe-BC) activated peroxymonosulfate system for humic acid removal from water and membrane fouling mitigation', Journal of Water Process Engineering, vol. 49, pp. 103185-103185.
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Li, Y, Zhu, J, Li, Y & Zhu, L 2022, 'A hybrid Jiles–Atherton and Preisach model of dynamic magnetic hysteresis based on backpropagation neural networks', Journal of Magnetism and Magnetic Materials, vol. 544, pp. 168655-168655.
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Li, Z, Gao, W, Yu Wang, M & Luo, Z 2022, 'Design of multi-material isotropic auxetic microlattices with zero thermal expansion', Materials & Design, vol. 222, pp. 111051-111051.
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Li, Z, Tang, Y, Huang, T & Wen, S 2022, 'Formation Control of Multiagent Networks: Cooperative and Antagonistic Interactions', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 5, pp. 2809-2818.
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This article studies the formation control problem of second-order multiagent networks, in which cooperative and antagonistic interactions of the agents spontaneously coexist in the communication process. Based on the convex analysis theory, several convex polytopes that do not require some kinds of system constraints are constructed in the presence of these interactions. Then, the matrix perturbation theory and some mathematical techniques are utilized to analyze these convex polytopes. The obtained results show that the agents with cooperative interactions monotonously converge to their own specified formation shape while maintaining the desired relative position of the other agents with antagonistic interactions. Subsequently, two numerical examples are presented to illustrate the obtained results.

Liang, M, Huang, S, Pan, S, Gong, M & Liu, W 2022, 'Learning multi-level weight-centric features for few-shot learning', Pattern Recognition, vol. 128, pp. 108662-108662.
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Liang, R, Zhang, Q, Wang, J & Lu, J 2022, 'A Hierarchical Attention Network for Cross-Domain Group Recommendation', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15.
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Liang, Y, Liu, Y, Zhou, Y, Shi, Q, Zhang, M, Li, Y, Wen, W, Feng, L & Wu, J 2022, 'Efficient and stable electrorheological fluids based on chestnut-like cobalt hydroxide coupled with surface-functionalized carbon dots', Soft Matter, vol. 18, no. 20, pp. 3845-3855.
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The synergistic effect of the lipophilic groups on the surface of CDs and the biomimetic chestnut-like structure give Co(OH)2@CDs good wettability with silicone oil, great electrorheological efficiency and dynamic shear stress stability.

Liang, Y, Zhu, L, Wang, X & Yang, Y 2022, 'Penalizing the Hard Example But Not Too Much: A Strong Baseline for Fine-Grained Visual Classification', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Liao, Q, Wang, D & Xu, M 2022, 'Category attention transfer for efficient fine-grained visual categorization', Pattern Recognition Letters, vol. 153, pp. 10-15.
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Liao, W, Zhang, Q, Yuan, B, Zhang, G & Lu, J 2022, 'Heterogeneous Multidomain Recommender System Through Adversarial Learning', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-13.
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Lim, S-M, Indraratna, B, Heitor, A, Yao, K, Jin, D, Albadri, WM & Liu, X 2022, 'Influence of matric suction on resilient modulus and CBR of compacted Ballina clay', Construction and Building Materials, vol. 359, pp. 129482-129482.
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Lin, A, Cheng, J, Rutkowski, L, Wen, S, Luo, M & Cao, J 2022, 'Asynchronous Fault Detection for Memristive Neural Networks With Dwell-Time-Based Communication Protocol', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-12.
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Lin, C, Cheruiyot, NK, Bui, X-T & Ngo, HH 2022, 'Composting and its application in bioremediation of organic contaminants', Bioengineered, vol. 13, no. 1, pp. 1073-1089.
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Lin, C-T, Tian, Y, Wang, Y-K, Do, T-TN, Chang, Y-L, King, J-T, Huang, K-C & Liao, L-D 2022, 'Effects of Multisensory Distractor Interference on Attentional Driving', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 10395-10403.
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Lin, C-T, Wang, Y-K, Huang, P-L, Shi, Y & Chang, Y-C 2022, 'Spatial-temporal attention-based convolutional network with text and numerical information for stock price prediction', Neural Computing and Applications, vol. 34, no. 17, pp. 14387-14395.
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AbstractIn the financial market, the stock price prediction is a challenging task which is influenced by many factors. These factors include economic change, politics and global events that are usually recorded in text format, such as the daily news. Therefore, we assume that real-world text information can be used to forecast stock market activity. However, only a few works considered both text and numerical information to predict or analyse stock trends. These works used preprocessed text features as the model inputs; therefore, latent information in text may be lost because the relationships between the text and stock price are not considered. In this paper, we propose a fusion network, i.e. a spatial-temporal attention-based convolutional network (STACN) that can leverage the advantages of an attention mechanism, a convolutional neural network and long short-term memory to extract text and numerical information for stock price prediction. Benefiting from the utilisation of an attention mechanism, reliable text features that are highly relevant to stock value can be extracted, which improves the overall model performance. The experimental results on real-world stock data demonstrate that our STACN model and training scheme can handle both text and numerical data and achieve high accuracy on stock regression tasks. The STACN is compared with CNNs and LSTMs with different settings, e.g. a CNN with only stock data, a CNN with only news titles and LSTMs with only stock data. CNNs considering only stock data and news titles have mean squared errors of 28.3935 and 0.1814, respectively. The accuracy of LSTMs is 0.0763. The STACN can achieve an accuracy of 0.0304, outperforming CNNs and LSTMs in stock regression tasks.

Lin, J, Ma, J & Zhu, J 2022, 'A Privacy-Preserving Federated Learning Method for Probabilistic Community-Level Behind-the-Meter Solar Generation Disaggregation', IEEE Transactions on Smart Grid, vol. 13, no. 1, pp. 268-279.
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Lin, J, Ma, J & Zhu, J 2022, 'Hierarchical Federated Learning for Power Transformer Fault Diagnosis', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-11.
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Lin, J, Ma, J & Zhu, J 2022, 'Privacy-Preserving Household Characteristic Identification With Federated Learning Method', IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1088-1099.
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Lin, J, Ma, J & Zhu, JG 2022, 'Estimation of household characteristics with uncertainties from smart meter data', International Journal of Electrical Power & Energy Systems, vol. 143, pp. 108440-108440.
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The knowledge of household characteristics can help energy providers carry out more personalized demand-side management programs. Obtaining such information through surveys is costly and time-consuming in practice. This paper proposes a novel estimation method for household characteristics with uncertainties using the residential electricity consumption data. To alleviate the class imbalance problem in the dataset, a dynamic time warping sampling (DTWS) method is proposed to generate synthetic data for the minority class. To overcome the problem that the existing methods for identifying household characteristics cannot provide the confidence level of the results, a Bayesian convolutional neural network (BCNN) model is developed for feature extraction and characteristic identification with uncertainties. These quantified uncertainties can be regarded as a measure of confidence and can be used to target customers more effectively for energy efficiency and demand response programs. The effectiveness of the proposed model is validated by experiments on ground truth data.

Lin, J, Ma, J, Zhu, J & Cui, Y 2022, 'Short-term load forecasting based on LSTM networks considering attention mechanism', International Journal of Electrical Power & Energy Systems, vol. 137, pp. 107818-107818.
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Lin, J, Ma, J, Zhu, J & Liang, H 2022, 'Deep Domain Adaptation for Non-Intrusive Load Monitoring Based on a Knowledge Transfer Learning Network', IEEE Transactions on Smart Grid, vol. 13, no. 1, pp. 280-292.
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Lin, J, Ma, J, Zhu, JG & Cui, Y 2022, 'A Transfer Ensemble Learning Method for Evaluating Power Transformer Health Conditions with Limited Measurement Data', IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-1.
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Health condition evaluation of power transformers is of great importance for the safe and reliable operation of power grids. The data-driven methods have been widely studied and applied. However, in practical applications, collecting high-quality data is difficult and expensive. Therefore, it is still challenging to build effective diagnostic models with insufficient labeled training data. This article proposes a transfer ensemble model to address this data scarcity problem. First, a 1-D convolutional neural network is pretrained using the large source-domain dataset. Then, the target model parameters are initialized with those of the source model and fine-tuned using the target dataset. A transfer strategy is proposed to decide what and where the diagnostic knowledge should be transferred from the source domain to the target domain. Finally, an ensemble model is built on the basis of a series of target models with different transfer strategies, which can further alleviate the overfitting issue and improve the generalization ability. The experimental results validate the effectiveness of the proposed method and show the feasibility of practical applications.

Lin, J, Sun, G, Beydoun, G & Li, L 2022, 'Applying Machine Translation and Language Modelling Strategies for the Recommendation Task of Micro Learning Service', Educational Technology and Society, vol. 25, no. 1, pp. 205-212.
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A newly emerged micro learning service offers a flexible formal, informal, or non-formal online learning opportunity to worldwide users with different backgrounds in real-time. With the assist of big data technology and cloud computing service, online learners can access tremendous fine-grained learning resources through micro learning service. However, big data also causes serious information overload during online learning activities. Hence, an intelligent recommender system is required to filter out not-suitable learning resources and pick the one that matches the learner’s learning requirement and academic background. From the perspective of natural language processing (NLP), this study proposed a novel recommender system that utilises machine translation and language modelling. The proposed model aims to overcome the defects of conventional recommender systems and further enhance distinguish ability of the recommender system for different learning resources.

Lin, J, Sun, G, Shen, J, Pritchard, DE, Yu, P, Cui, T, Xu, D, Li, L & Beydoun, G 2022, 'From computer vision to short text understanding: Applying similar approaches into different disciplines', Intelligent and Converged Networks, vol. 3, no. 2, pp. 161-172.
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Lin, J, Zheng, Z, Zhong, Z, Luo, Z, Li, S, Yang, Y & Sebe, N 2022, 'Joint Representation Learning and Keypoint Detection for Cross-View Geo-Localization', IEEE Transactions on Image Processing, vol. 31, pp. 3780-3792.
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Lin, Q, Tang, M-C, Chen, X, Yi, D, Li, M & Ziolkowski, RW 2022, 'Low-Profile, Electrically Small, Ultrawideband Antenna Enabled With an Inductive Grid Array Metasurface', IEEE Transactions on Antennas and Propagation, vol. 70, no. 8, pp. 7152-7157.
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Lin, W & Ziolkowski, RW 2022, 'High Performance Electrically Small Huygens Rectennas Enable Wirelessly Powered Internet of Things Sensing Applications: A Review', Engineering, vol. 11, pp. 42-59.
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Lin, W, Chen, R, Liu, X, Hao Ngo, H, Nan, J, Li, G, Ma, J, He, X & Ding, A 2022, 'Deep mechanism of enhanced dewaterability of residual sludge by Na+: Comprehensive analyses of intermolecular forces, hydrophilicity and water-holding capacity of EPS', Chemical Engineering Journal, vol. 450, pp. 138505-138505.
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Lin, W, Ding, A, Ngo, HH, Ren, Z, Nan, J, Li, G & Ma, J 2022, 'Effects of the metabolic uncoupler TCS on residual sludge treatment: Analyses of the microbial community and sludge dewaterability potential', Chemosphere, vol. 288, pp. 132473-132473.
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Lin, W, Fan, H, Hu, C, Yang, Y, Yu, S & Ni, L 2022, 'Improved Medical Image Segmentation Model Based on 3D U-Net', Journal of Donghua University (English Edition), vol. 39, no. 4, pp. 311-316.
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With the widespread application of deep learning in the field of computer vision, gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance. Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction, model over-fitting, and low degree of semantic information fusion, an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images. In this model, we make full use of the residual network (ResNet) to solve the over-fitting problem. In order to process and aggregate data at different scales, the inception network is used instead of the traditional convolutional layer, and the dilated convolution is used to increase the receptive field. The conditional random field(CRF) can complete the contour refinement work. Compared with the traditional 3D U-Net network, the segmentation accuracy of the improved liver and tumor images increases by 2.89% and 7.66%, respectively. As a part of the image processing process, the method in this paper not only can be used for medical image segmentation, but also can lay the foundation for subsequent image 3D reconstruction work.

Lin, W, Liu, X, Ding, A, Ngo, HH, Zhang, R, Nan, J, Ma, J & Li, G 2022, 'Advanced oxidation processes (AOPs)-based sludge conditioning for enhanced sludge dewatering and micropollutants removal: A critical review', Journal of Water Process Engineering, vol. 45, pp. 102468-102468.
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Lin, W, Zeng, J, Zhang, R, He, X, Nan, J, Li, G, Ma, J, Ngo, HH & Ding, A 2022, 'Selection of metal ions in different valence states on sludge conditioning: Analysis of hydrophobicity and evaluation of resource recovery capacity', Journal of Water Process Engineering, vol. 50, pp. 103297-103297.
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Lin, W-T, Chen, G & Huang, Y 2022, 'Incentive edge-based federated learning for false data injection attack detection on power grid state estimation: A novel mechanism design approach', Applied Energy, vol. 314, pp. 118828-118828.
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Lin, Y, Huo, P, Li, F, Chen, X, Yang, L, Jiang, Y, Zhang, Y, Ni, B-J & Zhou, M 2022, 'A critical review on cathode modification methods for efficient Electro-Fenton degradation of persistent organic pollutants', Chemical Engineering Journal, vol. 450, pp. 137948-137948.
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Lin, Y, Zhang, J & Li, L 2022, 'A model predictive control approach to a water pumping system in an Australian cotton farm microgrid', Cleaner Energy Systems, vol. 3, pp. 100026-100026.
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Ling, L, Yelland, N, Hatzigianni, M & Dickson-Deane, C 2022, 'The use of Internet of Things devices in early childhood education: A systematic review', Education and Information Technologies, vol. 27, no. 5, pp. 6333-6352.
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Linghu, Q, Zhang, F, Lin, X, Zhang, W & Zhang, Y 2022, 'Anchored coreness: efficient reinforcement of social networks', The VLDB Journal, vol. 31, no. 2, pp. 227-252.
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Lionnie, R, Apriono, C, Chai, R & Gunawan, D 2022, 'Curvature Best Basis: A Novel Criterion to Dynamically Select a Single Best Basis as the Extracted Feature for Periocular Recognition', IEEE Access, vol. 10, pp. 113523-113542.
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Litov, N, Falkner, B, Zhou, H, Mehta, A, Gondwe, W, Thalakotuna, DN, Mirshekar-Syahkal, D, Esselle, K & Nakano, H 2022, 'Radar Cross Section Analysis of Two Wind Turbines via a Novel Millimeter-Wave Technique and Scale Model Measurements', IEEE Access, vol. 10, pp. 17897-17907.
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Liu, A, Lu, J, Song, Y, Xuan, J & Zhang, G 2022, 'Concept Drift Detection Delay Index', IEEE Transactions on Knowledge and Data Engineering, pp. 1-1.
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Liu, B, Chang, X, Yuan, D & Yang, Y 2022, 'HCDC-SRCF tracker: Learning an adaptively multi-feature fuse tracker in spatial regularized correlation filters framework', Knowledge-Based Systems, vol. 238, pp. 107913-107913.
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Liu, B, Ding, M, Shaham, S, Rahayu, W, Farokhi, F & Lin, Z 2022, 'When Machine Learning Meets Privacy', ACM Computing Surveys, vol. 54, no. 2, pp. 1-36.
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The newly emerged machine learning (e.g., deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems. Meanwhile, privacy has emerged as a big concern in this machine learning-based artificial intelligence era. It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both friend and foe. Currently, the work on the preservation of privacy and machine learning are still in an infancy stage, as most existing solutions only focus on privacy problems during the machine learning process. Therefore, a comprehensive study on the privacy preservation problems and machine learning is required. This article surveys the state of the art in privacy issues and solutions for machine learning. The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy attack and corresponding protection schemes. The current research progress in each category is reviewed and the key challenges are identified. Finally, based on our in-depth analysis of the area of privacy and machine learning, we point out future research directions in this field.

Liu, B, Ni, W, Liu, RP, Zhu, Q, Guo, YJ & Zhu, H 2022, 'Novel Integrated Framework of Unmanned Aerial Vehicle and Road Traffic for Energy-Efficient Delay-Sensitive Delivery', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 8, pp. 10692-10707.
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Unmanned aerial vehicle (UAV) has demonstrated its usefulness in goods delivery. However, the delivery distances are often restrained by the battery capacity of UAVs. This paper integrates UAVs into intelligent transportation systems for energy-efficient, delay-sensitive goods delivery. Dynamic programming (DP) is first applied to minimize the energy consumption of a UAV and ensure its timely arrival at its destination, by optimizing the control policy of the UAV. The control policy involves decisions including flight speed, hitchhiking (on collaborative ground vehicles), or recharging at roadside charging stations. Another key aspect is that we reveal the conditions of the remaining flight distance or the elapsed time, only under which the optimal action of the UAV changes. Accordingly, thresholds are derived, and the optimal control policy can be instantly made by comparing the remaining flight distance and the elapsed time with the thresholds. Simulations show that the proposed algorithms can improve the flight distance by 48%, as compared with existing alternatives. The proposed threshold-based technique can achieve the same performance as the DP-based solution, while significantly reducing the computational complexity.

Liu, C, Wang, X, Wang, S, Wang, Y, Lei, G & Zhu, J 2022, 'Magnetothermal Coupling Analysis of Permanent Magnet Claw Pole Machine Using Combined 3D Magnetic and Thermal Network Method', IEEE Transactions on Applied Superconductivity, vol. 32, no. 6, pp. 1-5.
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Liu, C, Wang, X, Wang, Y, Lei, G, Guo, Y & Zhu, J 2022, 'Comparative study of rotor PM transverse flux machine and stator PM transverse flux machine with SMC cores', Electrical Engineering, vol. 104, no. 3, pp. 1153-1161.
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Liu, G, Liu, L, Huo, Y, Dai, Z, Zhang, L & Wang, Q 2022, 'Enhanced two-phase anaerobic digestion of waste activated sludge by combined free nitrous acid and manganese dioxide', Journal of Cleaner Production, vol. 379, pp. 134777-134777.
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Liu, G, Zhang, W, Li, X, Fan, K & Yu, S 2022, 'VulnerGAN: a backdoor attack through vulnerability amplification against machine learning-based network intrusion detection systems', Science China Information Sciences, vol. 65, no. 7.
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Liu, H, Lai, W, Lei, Y, Yang, H, Wang, N, Chou, S, Liu, HK, Dou, SX & Wang, Y 2022, 'Electrolytes/Interphases: Enabling Distinguishable Sulfur Redox Processes in Room‐Temperature Sodium‐Sulfur Batteries', Advanced Energy Materials, vol. 12, no. 6, pp. 2103304-2103304.
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Liu, H, Li, X, Zhang, Z, Nghiem, LD & Wang, Q 2022, 'Urine pretreatment significantly promotes methane production in anaerobic waste activated sludge digestion', Science of The Total Environment, vol. 853, pp. 158684-158684.
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Liu, H, Zhang, C, Yao, Y, Wei, X-S, Shen, F, Tang, Z & Zhang, J 2022, 'Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Open-Set Noise and Utilizing Hard Examples', IEEE Transactions on Multimedia, vol. 24, pp. 546-557.
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Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images for fine-grained recognition has attracted broad attention. However, the presence of label noise and hard examples in web images are two obstacles for training robust fine-grained recognition models. To this end, in this paper, we propose a novel approach to remove irrelevant samples from real-world web images during training, while employing useful hard examples to update the network. Thus, our approach can alleviate the harmful effects of irrelevant noisy web images and hard examples to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods.

Liu, J, Li, J, Fang, J, Liu, K, Su, Y & Wu, C 2022, 'Investigation of ultra-high performance concrete slabs under contact explosions with a calibrated K&C model', Engineering Structures, vol. 255, pp. 113958-113958.
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Liu, J, Li, J, Fang, J, Su, Y & Wu, C 2022, 'Ultra-high performance concrete targets against high velocity projectile impact – a-state-of-the-art review', International Journal of Impact Engineering, vol. 160, pp. 104080-104080.
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Liu, J, Liu, C, Qu, K, Li, J & Wu, C 2022, 'Calibration of Holmquist Johnson Cook (HJC) model for projectile penetration of geopolymer-based ultra-high performance concrete (G-UHPC)', Structures, vol. 43, pp. 149-163.
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Liu, J, Liu, C, Xu, S, Li, J, Fang, J, Su, Y & Wu, C 2022, 'G-UHPC slabs strengthened with high toughness and lightweight energy absorption materials under contact explosions', Journal of Building Engineering, vol. 50, pp. 104138-104138.
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Liu, J, Singh, AK & Lin, C-T 2022, 'Corrections to “Predicting the Quality of Spatial Learning via Virtual Global Landmarks”', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2971-2971.
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Liu, J, Singh, AK & Lin, C-T 2022, 'Predicting the Quality of Spatial Learning via Virtual Global Landmarks', IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2418-2425.
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Liu, J, Singh, AK & Lin, C-T 2022, 'Using virtual global landmark to improve incidental spatial learning', Scientific Reports, vol. 12, no. 1.
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AbstractTo reduce the decline of spatial cognitive skills caused by the increasing use of automated GPS navigation, the virtual global landmark (VGL) system is proposed to help people naturally improve their sense of direction. Designed to accompany a heads-up navigation system, VGL system constantly displays silhouette of global landmarks in the navigator’s vision as a notable frame of reference. This study exams how VGL system impacts incidental spatial learning, i.e., subconscious spatial knowledge acquisition. We asked 55 participants to explore a virtual environment and then draw a map of what they had explored while capturing electroencephalogram (EEG) signals and eye activity. The results suggest that, with the VGL system, participants paid more attention during exploration and performed significantly better at the map drawing task—a result that indicates substantially improved incidental spatial learning. This finding might kickstart a redesigning navigation aids, to teach users to learn a route rather than simply showing them the way.

Liu, J, Singh, AK, Wunderlich, A, Gramann, K & Lin, C-T 2022, 'Redesigning navigational aids using virtual global landmarks to improve spatial knowledge retrieval', npj Science of Learning, vol. 7, no. 1.
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AbstractAlthough beacon- and map-based spatial strategies are the default strategies for navigation activities, today’s navigational aids mostly follow a beacon-based design where one is provided with turn-by-turn instructions. Recent research, however, shows that our reliance on these navigational aids is causing a decline in our spatial skills. We are processing less of our surrounding environment and relying too heavily on the instructions given. To reverse this decline, we need to engage more in map-based learning, which encourages the user to process and integrate spatial knowledge into a cognitive map built to benefit flexible and independent spatial navigation behaviour. In an attempt to curb our loss of skills, we proposed a navigation assistant to support map-based learning during active navigation. Called the virtual global landmark (VGL) system, this augmented reality (AR) system is based on the kinds of techniques used in traditional orienteering. Specifically, a notable landmark is always present in the user’s sight, allowing the user to continuously compute where they are in relation to that specific location. The efficacy of the unit as a navigational aid was tested in an experiment with 27 students from the University of Technology Sydney via a comparison of brain dynamics and behaviour. From an analysis of behaviour and event-related spectral perturbation, we found that participants were encouraged to process more spatial information with a map-based strategy where a silhouette of the compass-like landmark was perpetually in view. As a result of this technique, they consistently navigated with greater efficiency and better accuracy.

Liu, J, Wang, X, Shen, S, Fang, Z, Yu, S, Yue, G & Li, M 2022, 'Intelligent Jamming Defense Using DNN Stackelberg Game in Sensor Edge Cloud', IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4356-4370.
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Liu, K, Song, R, Li, J, Guo, T, Li, X, Yang, J & Yan, Z 2022, 'Effect of steel fiber type and content on the dynamic tensile properties of ultra-high performance cementitious composites (UHPCC)', Construction and Building Materials, vol. 342, pp. 127908-127908.
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Liu, K, Wu, C, Li, X, Tao, M, Li, J, Liu, J & Xu, S 2022, 'Fire damaged ultra-high performance concrete (UHPC) under coupled axial static and impact loading', Cement and Concrete Composites, vol. 126, pp. 104340-104340.
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Liu, L, Ji, J, Li, B, Miao, Z & Zhou, J 2022, 'Distributed Stochastic Consensus of Networked Nonholonomic Mobile Robots and Its Formation Application', Journal of Dynamic Systems, Measurement, and Control, vol. 144, no. 11.
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Abstract This paper proposes a novel distributed stochastic consensus seeking algorithm for networked nonholonomic wheeled mobile robots (NNWMRs) and its application to consensus-based formation. Time-varying delays and noisy measurement are incorporated into the dynamic model to represent two key issues inherently appearing in the communication and information exchange process among robots. Based on backstepping technique and sliding mode approach, the proposed consensus algorithm integrates kinematic controller and dynamic torque controller into the control protocol. A key feature of the proposed consensus algorithm is the introduction of the consensus gains, which characterizes the effects of time delays and noisy measurement. A unified methodology is provided for the convergence analysis of the networked system by using the generalized stochastic delayed Halanay inequality. It is shown that time delays and noisy measurement can play crucial roles in distributed consensus seeking in collaborative multirobot systems. Illustrative examples and simulations are provided to demonstrate and validate the theoretical results.

Liu, L, Yang, R, Cui, J, Chen, P, Ri, HC, Sun, H, Piao, X, Li, M, Pu, Q, Quinto, M, Zhou, JL, Shang, H-B & Li, D 2022, 'Circular Nonuniform Electric Field Gel Electrophoresis for the Separation and Concentration of Nanoparticles', Analytical Chemistry, vol. 94, no. 23, pp. 8474-8482.
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Liu, L-Y, Xie, G-J, Ding, J, Liu, B-F, Xing, D-F, Ren, N-Q & Wang, Q 2022, 'Microbial methane emissions from the non-methanogenesis processes: A critical review', Science of The Total Environment, vol. 806, pp. 151362-151362.
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Liu, M, Blankenship, JR, Levi, AE, Fu, Q, Hudson, ZM & Bates, CM 2022, 'Miktoarm Star Polymers: Synthesis and Applications', Chemistry of Materials, vol. 34, no. 14, pp. 6188-6209.
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Liu, M, Nothling, MD, Zhang, S, Fu, Q & Qiao, GG 2022, 'Thin film composite membranes for postcombustion carbon capture: Polymers and beyond', Progress in Polymer Science, vol. 126, pp. 101504-101504.
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Liu, P, Lin, Y, Meng, Z, Lu, L, Deng, W, Zhou, JT & Yang, Y 2022, 'Point Adversarial Self-Mining: A Simple Method for Facial Expression Recognition', IEEE Transactions on Cybernetics, vol. 52, no. 12, pp. 12649-12660.
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Liu, Q & Cao, L 2022, 'Modeling time evolving COVID-19 uncertainties with density dependent asymptomatic infections and social reinforcement', Scientific Reports, vol. 12, no. 1.
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AbstractThe COVID-19 pandemic has posed significant challenges in modeling its complex epidemic transmissions, infection and contagion, which are very different from known epidemics. The challenges in quantifying COVID-19 complexities include effectively modeling its process and data uncertainties. The uncertainties are embedded in implicit and high-proportional undocumented infections, asymptomatic contagion, social reinforcement of infections, and various quality issues in the reported data. These uncertainties become even more apparent in the first 2 months of the COVID-19 pandemic, when the relevant knowledge, case reporting and testing were all limited. Here we introduce a novel hybrid approach SUDR by expanding the foundational compartmental epidemic Susceptible-Infected-Recovered (SIR) model with two compartments to a Susceptible-Undocumented infected-Documented infected-Recovered (SUDR) model. First, SUDR (1) characterizes and distinguishes Undocumented (U) and Documented (D) infections commonly seen during COVID-19 incubation periods and asymptomatic infections. Second, SUDR characterizes the probabilistic density of infections by capturing exogenous processes like clustering contagion interactions, superspreading, and social reinforcement. Lastly, SUDR approximates the density likelihood of COVID-19 prevalence over time by incorporating Bayesian inference into SUDR. Different from existing COVID-19 models, SUDR characterizes the undocumented infections during unknown transmission processes. To capture the uncertainties of temporal transmission and social reinforcement during COVID-19 contagion, the transmission rate is modeled by a time-varying density function of undocumented infectious cases. By sampling from the mean-field posterior distribution with reasonable priors, SUDR handles the randomness, noise and sparsity of COVID-19 observations widely seen in the public COVID-19 case data. The results demonstrate a deeper...

Liu, Q, Zhang, Q, Jiang, S, Du, Z, Zhang, X, Chen, H, Cao, W, Nghiem, LD & Ngo, HH 2022, 'Enhancement of lead removal from soil by in-situ release of dissolved organic matters from biochar in electrokinetic remediation', Journal of Cleaner Production, vol. 361, pp. 132294-132294.
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Liu, T, Hu, X, Xu, H, Shu, T & Nguyen, DN 2022, 'High-accuracy low-cost privacy-preserving federated learning in IoT systems via adaptive perturbation', Journal of Information Security and Applications, vol. 70, pp. 103309-103309.
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Liu, T, Lu, J, Yan, Z & Zhang, G 2022, 'Robust Gaussian Process Regression With Input Uncertainty: A PAC-Bayes Perspective', IEEE Transactions on Cybernetics, pp. 1-12.
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Liu, T, Xia, J, Ling, Z, Fu, X, Yu, S & Chen, M 2022, 'Efficient Federated Learning for AIoT Applications Using Knowledge Distillation', IEEE Internet of Things Journal, pp. 1-1.
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Liu, T, Zhang, W, Li, J, Ueland, M, Forbes, SL, Zheng, WX & Su, SW 2022, 'A Multiscale Wavelet Kernel Regularization-Based Feature Extraction Method for Electronic Nose', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 11, pp. 7078-7089.
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Liu, W, Wang, H, Shen, X & Tsang, IW 2022, 'The Emerging Trends of Multi-Label Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 7955-7974.
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Liu, X, Deng, Q, Zheng, Y, Wang, D & Ni, B-J 2022, 'Microplastics aging in wastewater treatment plants: Focusing on physicochemical characteristics changes and corresponding environmental risks', Water Research, vol. 221, pp. 118780-118780.
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Liu, X, Duan, X, Bao, T, Hao, D, Chen, Z, Wei, W, Wang, D, Wang, S & Ni, B-J 2022, 'High-performance photocatalytic decomposition of PFOA by BiOX/TiO2 heterojunctions: Self-induced inner electric fields and band alignment', Journal of Hazardous Materials, vol. 430, pp. 128195-128195.
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Liu, X, Li, S & Zhu, J 2022, 'Optimal Coordination for Multiple Network-Constrained VPPs via Multi-Agent Deep Reinforcement Learning', IEEE Transactions on Smart Grid, pp. 1-1.
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Liu, X, Wang, D, Chen, Z, Wei, W, Mannina, G & Ni, B-J 2022, 'Advances in pretreatment strategies to enhance the biodegradability of waste activated sludge for the conversion of refractory substances', Bioresource Technology, vol. 362, pp. 127804-127804.
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Liu, X, Zhu, T, Jiang, C, Ye, D & Zhao, F 2022, 'Prioritized Experience Replay based on Multi-armed Bandit', Expert Systems with Applications, vol. 189, pp. 116023-116023.
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Liu, Y, Luo, G, Ngo, HH & Zhang, S 2022, 'New approach of bioprocessing towards lignin biodegradation', Bioresource Technology, vol. 361, pp. 127730-127730.
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Liu, Y, Yao, L, Li, B, Sammut, C & Chang, X 2022, 'Interpolation graph convolutional network for 3D point cloud analysis', International Journal of Intelligent Systems, vol. 37, no. 12, pp. 12283-12304.
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Liu, Y, Zhang, S, Fang, H, Wang, Q, Jiang, S, Zhang, C & Qiu, P 2022, 'Inactivation of antibiotic resistant bacterium Escherichia coli by electrochemical disinfection on molybdenum carbide electrode', Chemosphere, vol. 287, pp. 132398-132398.
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Liu, Z, Gao, Y, Yang, J, Xu, X, Fang, J & Xie, F 2022, 'Multi-objective optimization framework of a vehicle door design in the slamming event for optimal dynamic performances', Applied Acoustics, vol. 187, pp. 108526-108526.
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Liu, Z, Li, Y, Yao, L, Wang, X & Nie, F 2022, 'Agglomerative Neural Networks for Multiview Clustering', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 7, pp. 2842-2852.
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Conventional multiview clustering methods seek a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, pairwise comparison cannot portray the interview relationship precisely if some of the subviews can be further agglomerated. To address the above challenge, we propose the agglomerative analysis to approximate the optimal consensus view, thereby describing the subview relationship within a view structure. We present an agglomerative neural network (ANN) based on constrained Laplacian rank to cluster multiview data directly without a dedicated postprocessing step (e.g., using K-means). We further extend ANN with a learnable data space to handle data of complex scenarios. Our evaluations against several state-of-the-art multiview clustering approaches on four popular data sets show the promising view-consensus analysis ability of ANN. We further demonstrate ANN's capability in analyzing complex view structures, extensibility through our case study and robustness and effectiveness of data-driven modifications.

Liu, Z, Wang, X, Li, Y, Yao, L, An, J, Bai, L & Lim, E-P 2022, 'Face to purchase: Predicting consumer choices with structured facial and behavioral traits embedding', Knowledge-Based Systems, vol. 235, pp. 107665-107665.
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Lloret-Cabot, M & Sheng, D 2022, 'Assessing the accuracy and efficiency of different order implicit and explicit integration schemes', Computers and Geotechnics, vol. 141, pp. 104531-104531.
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Loengbudnark, W, Khalilpour, K, Bharathy, G, Taghikhah, F & Voinov, A 2022, 'Battery and hydrogen-based electric vehicle adoption: A survey of Australian consumers perspective', Case Studies on Transport Policy, vol. 10, no. 4, pp. 2451-2463.
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Loengbudnark, W, Khalilpour, K, Bharathy, G, Voinov, A & Thomas, L 2022, 'Impact of occupant autonomy on satisfaction and building energy efficiency', Energy and Built Environment.
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Loganathan, P, Kandasamy, J, Jamil, S, Ratnaweera, H & Vigneswaran, S 2022, 'Ozonation/adsorption hybrid treatment system for improved removal of natural organic matter and organic micropollutants from water – A mini review and future perspectives', Chemosphere, vol. 296, pp. 133961-133961.
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Loh, HW, Xu, S, Faust, O, Ooi, CP, Barua, PD, Chakraborty, S, Tan, R-S, Molinari, F & Acharya, UR 2022, 'Application of photoplethysmography signals for healthcare systems: An in-depth review', Computer Methods and Programs in Biomedicine, vol. 216, pp. 106677-106677.
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Lou, B, Barbieri, DM, Passavanti, M, Hui, C, Gupta, A, Hoff, I, Lessa, DA, Sikka, G, Chang, K, Fang, K, Lam, L, Maharaj, B, Ghasemi, N, Qiao, Y, Adomako, S, Foroutan Mirhosseini, A, Naik, B, Banerjee, A, Wang, F, Tucker, A, Liu, Z, Wijayaratna, K, Naseri, S, Yu, L, Chen, H, Shu, B, Goswami, S, Peprah, P, Hessami, A, Abbas, M & Agarwal, N 2022, 'Air pollution perception in ten countries during the COVID-19 pandemic', Ambio, vol. 51, no. 3, pp. 531-545.
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AbstractAs largely documented in the literature, the stark restrictions enforced worldwide in 2020 to curb the COVID-19 pandemic also curtailed the production of air pollutants to some extent. This study investigates the perception of the air pollution as assessed by individuals located in ten countries: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the USA. The perceptions towards air quality were evaluated by employing an online survey administered in May 2020. Participants (N = 9394) in the ten countries expressed their opinions according to a Likert-scale response. A reduction in pollutant concentration was clearly perceived, albeit to a different extent, by all populations. The survey participants located in India and Italy perceived the largest drop in the air pollution concentration; conversely, the smallest variation was perceived among Chinese and Norwegian respondents. Among all the demographic indicators considered, only gender proved to be statistically significant.

Lowe, D, Goldfinch, T, Kadi, A, Willey, K & Wilkinson, T 2022, 'Engineering graduates professional formation: the connection between activity types and professional competencies', European Journal of Engineering Education, vol. 47, no. 1, pp. 8-29.
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Lu, L, Xiao, Y, Chang, X, Wang, X, Ren, P & Ren, Z 2022, 'Deformable attention-oriented feature pyramid network for semantic segmentation', Knowledge-Based Systems, vol. 254, pp. 109623-109623.
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Lu, W, Wang, R, Wang, S, Peng, X, Wu, H & Zhang, Q 2022, 'Aspect-Driven User Preference and News Representation Learning for News Recommendation', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 25297-25307.
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Lu, W, Zhao, P, Li, Y, Wang, S, Huang, H, Shi, S & Wu, H 2022, 'Chinese sentence semantic matching based on multi-level relevance extraction and aggregation for intelligent human–robot interaction', Applied Soft Computing, vol. 131, pp. 109795-109795.
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Lu, X, Cong Luong, N, Hoang, DT, Niyato, D, Xiao, Y & Wang, P 2022, 'Secure Wirelessly Powered Networks at the Physical Layer: Challenges, Countermeasures, and Road Ahead', Proceedings of the IEEE, vol. 110, no. 1, pp. 193-209.
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Lu, X, Wang, C, Xu, X, Yang, H, Zhang, S, Tan, K, Bao, X, Su, W & Gu, H 2022, 'Automatic RFI Identification for Sentinel-1 Based on Siamese-Type Deep CNN Using Repeat-Pass Images', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-16.
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Lu, Y, Xiao, W & Lu, DD-C 2022, 'Optimal Dynamic and Steady-State Performance of PV-Interfaced Converters Using Adaptive Observers', IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 12, pp. 4909-4913.
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Lu, Z, Qi, S, Zhang, J, Cai, Y, Guo, X & Luo, S 2022, 'An improved multi-objective bacterial colony chemotaxis algorithm based on Pareto dominance', Soft Computing, vol. 26, no. 1, pp. 69-87.
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Lu, Z, Xu, Y, Peng, L, Liang, C, Liu, Y & Ni, B-J 2022, 'A two-stage degradation coupling photocatalysis to microalgae enhances the mineralization of enrofloxacin', Chemosphere, vol. 293, pp. 133523-133523.
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Lukomskyj, AO, Rao, N, Yan, L, Pye, JS, Li, H, Wang, B & Li, JJ 2022, 'Stem Cell-Based Tissue Engineering for the Treatment of Burn Wounds: A Systematic Review of Preclinical Studies', Stem Cell Reviews and Reports, vol. 18, no. 6, pp. 1926-1955.
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AbstractBurn wounds are a devastating type of skin injury leading to severe impacts on both patients and the healthcare system. Current treatment methods are far from ideal, driving the need for tissue engineered solutions. Among various approaches, stem cell-based strategies are promising candidates for improving the treatment of burn wounds. A thorough search of the Embase, Medline, Scopus, and Web of Science databases was conducted to retrieve original research studies on stem cell-based tissue engineering treatments tested in preclinical models of burn wounds, published between January 2009 and June 2021. Of the 347 articles retrieved from the initial database search, 33 were eligible for inclusion in this review. The majority of studies used murine models with a xenogeneic graft, while a few used the porcine model. Thermal burn was the most commonly induced injury type, followed by surgical wound, and less commonly radiation burn. Most studies applied stem cell treatment immediately post-burn, with final endpoints ranging from 7 to 90 days. Mesenchymal stromal cells (MSCs) were the most common stem cell type used in the included studies. Stem cells from a variety of sources were used, most commonly from adipose tissue, bone marrow or umbilical cord, in conjunction with an extensive range of biomaterial scaffolds to treat the skin wounds. Overall, the studies showed favourable results of skin wound repair in animal models when stem cell-based tissue engineering treatments were applied, suggesting that such strategies hold promise as an improved therapy for burn wounds. Graphical abstract

Luo, J, Luo, Q, Zhang, G, Li, Q & Sun, G 2022, 'On strain rate and temperature dependent mechanical properties and constitutive models for additively manufactured polylactic acid (PLA) materials', Thin-Walled Structures, vol. 179, pp. 109624-109624.
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Luo, Q, Tong, L, Bambach, M, Rasmussen, KJR & Khezri, M 2022, 'Active nonlinear buckling control of optimally designed laminated plates using SMA and PZT actuators', Thin-Walled Structures, vol. 181, pp. 110134-110134.
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Luo, Q, Tong, L, Khezri, M, Rasmussen, KJR & Bambach, MR 2022, 'Optimal design of thin laminate plates for efficient airflow in ventilation via buckling', Thin-Walled Structures, vol. 170, pp. 108582-108582.
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Luo, T, Xu, Q, Wei, W, Sun, J, Dai, X & Ni, B-J 2022, 'Performance and Mechanism of Fe₃O₄ Improving Biotransformation of Waste Activated Sludge into Liquid High-Value Products', Environmental Science & Technology, vol. 56, no. 6, pp. 3658-3668.
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Luo, Z, Li, W, Wang, K, Shah, SP & Sheng, D 2022, 'Nano/micromechanical characterisation and image analysis on the properties and heterogeneity of ITZs in geopolymer concrete', Cement and Concrete Research, vol. 152, pp. 106677-106677.
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Luthra, S, Sharma, M, Kumar, A, Joshi, S, Collins, E & Mangla, S 2022, 'Overcoming barriers to cross-sector collaboration in circular supply chain management: a multi-method approach', Transportation Research Part E: Logistics and Transportation Review, vol. 157, pp. 102582-102582.
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Luu, MH, Walsum, TV, Mai, HS, Franklin, D, Nguyen, TTT, Le, TM, Moelker, A, Le, VK, Vu, DL, Le, NH, Tran, QL, Chu, DT & Trung, NL 2022, 'Automatic scan range for dose-reduced multiphase CT imaging of the liver utilizing CNNs and Gaussian models', Medical Image Analysis, vol. 78, pp. 102422-102422.
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Ly, QV, Truong, VH, Ji, B, Nguyen, XC, Cho, KH, Ngo, HH & Zhang, Z 2022, 'Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants', Science of The Total Environment, vol. 832, pp. 154930-154930.
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Lyu, B, Hamdi, M, Yang, Y, Cao, Y, Yan, Z, Li, K, Wen, S & Huang, T 2022, 'Efficient Spectral Graph Convolutional Network Deployment on Memristive Crossbars', IEEE Transactions on Emerging Topics in Computational Intelligence, pp. 1-11.
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Lyu, B, Ramezani, P, Hoang, DT & Jamalipour, A 2022, 'IRS-Assisted Downlink and Uplink NOMA in Wireless Powered Communication Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 1083-1088.
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Lyu, C, Shi, Y, Sun, L & Lin, C-T 2022, 'Community detection in multiplex networks based on evolutionary multi-task optimization and evolutionary clustering ensemble', IEEE Transactions on Evolutionary Computation, pp. 1-1.
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M. B., B, B., RP, Tripathi, A, Yadav, S, John, NS, Thapa, R, Altaee, A, Saxena, M & Samal, AK 2022, 'A Unique Bridging Facet Assembly of Gold Nanorods for the Detection of Thiram through Surface-Enhanced Raman Scattering', ACS Sustainable Chemistry & Engineering, vol. 10, no. 22, pp. 7330-7340.
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Ma, B, Teng, J, Li, H, Zhang, S, Cai, G & Sheng, D 2022, 'A New Strength Criterion for Frozen Soil Considering Pore Ice Content', International Journal of Geomechanics, vol. 22, no. 7.
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Ma, B, Wang, X, Ni, W & Liu, RP 2022, 'Personalized Location Privacy With Road Network-Indistinguishability', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 20860-20872.
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Ma, F, Wu, Y, Yu, X & Yang, Y 2022, 'Learning With Noisy Labels via Self-Reweighting From Class Centroids', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 11, pp. 6275-6285.
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Although deep neural networks have been proved effective in many applications, they are data hungry, and training deep models often requires laboriously labeled data. However, when labeled data contain erroneous labels, they often lead to model performance degradation. A common solution is to assign each sample with a dynamic weight during optimization, and the weight is adjusted in accordance with the loss. However, those weights are usually unreliable since they are measured by the losses of corrupted labels. Thus, this scheme might impede the discriminative ability of neural networks trained on noisy data. To address this issue, we propose a novel reweighting method, dubbed self-reweighting from class centroids (SRCC), by assigning sample weights based on the similarities between the samples and our online learned class centroids. Since we exploit statistical class centers in the image feature space to reweight data samples in learning, our method is robust to noise caused by corrupted labels. In addition, even after reweighting the noisy data, the decision boundaries might still suffer distortions. Thus, we leverage mixed inputs that are generated by linearly interpolating two random images and their labels to further regularize the boundaries. We employ the learned class centroids to evaluate the confidence of our generated mixed data via measuring feature similarities. During the network optimization, the class centroids are updated as more discriminative feature representations of original images are learned. In doing so, SRCC will generate more robust weighting coefficients for noisy and mixed data and facilitates our feature representation learning in return. Extensive experiments on both the synthetic and real image recognition tasks demonstrate that our method SRCC outperforms the state of the art on learning with noisy data.

Ma, F, Zhu, L & Yang, Y 2022, 'Weakly Supervised Moment Localization with Decoupled Consistent Concept Prediction', International Journal of Computer Vision, vol. 130, no. 5, pp. 1244-1258.
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Ma, G, Lu, J, Liu, F, Fang, Z & Zhang, G 2022, 'Multiclass Classification With Fuzzy-Feature Observations: Theory and Algorithms', IEEE Transactions on Cybernetics, pp. 1-14.
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Ma, T, Mafi, R, Cami, B, Javankhoshdel, S & Gandomi, AH 2022, 'NURBS Surface-Altering Optimization for Identifying Critical Slip Surfaces in 3D Slopes', International Journal of Geomechanics, vol. 22, no. 9.
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Ma, Y, Huang, Y, Wu, J, E, J, Zhang, B, Han, D & Ong, HC 2022, 'A review of atmospheric fine particulate matters: chemical composition, source identification and their variations in Beijing', Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 44, no. 2, pp. 4783-4807.
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Ma, Y, Wu, N, Zhang, JA, Li, B & Hanzo, L 2022, 'Generalized Approximate Message Passing Equalization for Multi-Carrier Faster-Than-Nyquist Signaling', IEEE Transactions on Vehicular Technology, vol. 71, no. 3, pp. 3309-3314.
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Mądzik, MT, Asaad, S, Youssry, A, Joecker, B, Rudinger, KM, Nielsen, E, Young, KC, Proctor, TJ, Baczewski, AD, Laucht, A, Schmitt, V, Hudson, FE, Itoh, KM, Jakob, AM, Johnson, BC, Jamieson, DN, Dzurak, AS, Ferrie, C, Blume-Kohout, R & Morello, A 2022, 'Precision tomography of a three-qubit donor quantum processor in silicon', Nature, vol. 601, no. 7893, pp. 348-353.
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Mahmood, AH, Babaee, M, Foster, SJ & Castel, A 2022, 'Capturing the early-age physicochemical transformations of alkali-activated fly ash and slag using ultrasonic pulse velocity technique', Cement and Concrete Composites, vol. 130, pp. 104529-104529.
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Mahmud, MA, Zheng, J, Tang, S, Wang, G, Bing, J, Bui, AD, Qu, J, Yang, L, Liao, C, Chen, H, Bremner, SP, Nguyen, HT, Cairney, J & Ho‐Baillie, AWY 2022, 'Cation‐Diffusion‐Based Simultaneous Bulk and Surface Passivations for High Bandgap Inverted Perovskite Solar Cell Producing Record Fill Factor and Efficiency', Advanced Energy Materials, vol. 12, no. 36, pp. 2201672-2201672.
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Mahmud, MAP, Bazaz, SR, Dabiri, S, Mehrizi, AA, Asadnia, M, Warkiani, ME & Wang, ZL 2022, 'Advances in MEMS and Microfluidics‐Based Energy Harvesting Technologies', Advanced Materials Technologies, vol. 7, no. 7, pp. 2101347-2101347.
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Mahmudul, HM, Akbar, D, Rasul, MG, Narayanan, R & Mofijur, M 2022, 'Estimation of the sustainable production of gaseous biofuels, generation of electricity, and reduction of greenhouse gas emissions using food waste in anaerobic digesters', Fuel, vol. 310, pp. 122346-122346.
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Mahmudul, HM, Rasul, MG, Akbar, D, Narayanan, R & Mofijur, M 2022, 'Food waste as a source of sustainable energy: Technical, economical, environmental and regulatory feasibility analysis', Renewable and Sustainable Energy Reviews, vol. 166, pp. 112577-112577.
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Majidi Nezhad, M, Heydari, A, Neshat, M, Keynia, F, Piras, G & Garcia, DA 2022, 'A Mediterranean Sea Offshore Wind classification using MERRA-2 and machine learning models', Renewable Energy, vol. 190, pp. 156-166.
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Majidi Nezhad, M, Neshat, M, Piras, G & Astiaso Garcia, D 2022, 'Sites exploring prioritisation of offshore wind energy potential and mapping for wind farms installation: Iranian islands case studies', Renewable and Sustainable Energy Reviews, vol. 168, pp. 112791-112791.
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Makhanbet, M, Lv, T, Ni, W & Orynbet, M 2022, 'Energy-Delay-Aware Power Control for Reliable Transmission of Dynamic Cell-Free Massive MIMO', IEEE Transactions on Communications, vol. 70, no. 1, pp. 276-290.
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Makhdoom, I, Abolhasan, M & Lipman, J 2022, 'A comprehensive survey of covert communication techniques, limitations and future challenges', Computers & Security, vol. 120, pp. 102784-102784.
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Makhdoom, I, Lipman, J, Abolhasan, M & Challen, D 2022, 'Science and Technology Parks: A Futuristic Approach', IEEE Access, vol. 10, pp. 31981-32021.
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Malik, K, Kumar, D, Perissin, D & Pradhan, B 2022, 'Estimation of ground subsidence of New Delhi, India using PS-InSAR technique and Multi-sensor Radar data', Advances in Space Research, vol. 69, no. 4, pp. 1863-1882.
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Mannina, G, Gulhan, H & Ni, B-J 2022, 'Water reuse from wastewater treatment: The transition towards circular economy in the water sector', Bioresource Technology, vol. 363, pp. 127951-127951.
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Mao, T, Mihaita, A-S, Chen, F & Vu, HL 2022, 'Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 7112-7141.
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Marjanovic, O 2022, 'A novel mechanism for business analytics value creation: improvement of knowledge-intensive business processes', Journal of Knowledge Management, vol. 26, no. 1, pp. 17-44.
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Purpose This paper focuses on the “how” of business analytics (BA) value creation, which remains an open research problem and a practical challenge. The main purpose of this paper is to propose a novel BA value creation mechanism that is BA-enabled improvement of Knowledge-intensive Business Processes (KIBPs), with experiential knowledge of decision makers as the key to a more sustainable BA-enabled competitive differentiation. Design/methodology/approach This research uses a qualitative research case study, conducted in a large retail distribution company. The research insights were observed through a combined lens of work systems theory and the knowledge-based view (KBV) of the firm, using an interpretive approach. Findings The proposed theoretical model identifies three stages of KIBP improvement through BA and explains how they lead to a sustainable BA-enabled competitive differentiation. Stage 1 focusses on BA support for individual knowledge-intensive tasks, Stage 2 focusses on individual decision makers and their ability to gain KIBP-related analytical insights and turn them into action; and Stage 3 on sharing of the acquired experiential knowledge amongst decision makers using BA. Originality/value In addition to proposing a novel mechanism for BA value creation, this research demonstrates the importance of leveraging experiential knowledge of decision makers as a pathway to a more sustainable competitive differentiation through BA. This, in turn, creates new opportunities for knowledge management researchers to engage in BA-related research. It also opens a new approach for BA resea...

Marjanovic, O & Murthy, V 2022, 'The Emerging Liquid IT Workforce: Theorizing Their Personal Competitive Advantage', Information Systems Frontiers, vol. 24, no. 6, pp. 1775-1793.
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In this paper we aim to contribute to a better understanding of an emerging phenomenon of ‘liquid workforce’, which according to industry press, is rapidly growing. Our specific focus is on the broad research questions: How do liquid IT workers remain competitive? What are suitable strategies for their management and engagement? Using the research insights from the interviews with independent liquid IT professionals working on the same mission-critical compliance program in a large financial institution, we propose a new conceptual model of their ‘personal competitive advantage’ (PCA). Drawing from the theories of human capital and social capital, we theorize PCA as a complex, mutually enhancing (a triple-helix-like) interplay of three highly-intertwined components of ‘Doing’, ‘Relating’ and ‘Becoming’. Based on the proposed model, we then articulate an initial set of strategies for management and engagement of the liquid workforce. In doing so, we expand and challenge the current IS research on IT workforce that remains focused on its retention and prevention of turnover. Instead, we propose to focus on specific management strategies for building and maintaining social capital within and beyond organizational boundaries.

Marjanovic, O, Cecez-Kecmanovic, D & Vidgen, R 2022, 'Theorising Algorithmic Justice', European Journal of Information Systems, vol. 31, no. 3, pp. 269-287.
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Martin, K, Arbour, S, McGregor, C & Rice, M 2022, 'Silver linings: Observed reductions in aggression and use of restraints and seclusion in psychiatric inpatient care during COVID‐19', Journal of Psychiatric and Mental Health Nursing, vol. 29, no. 2, pp. 381-385.
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Masangkay, J, Munasinghe, N, Watterson, P & Paul, G 2022, 'Simulation and experimental characterisation of a 3D-printed electromagnetic vibration sensor', Sensors and Actuators A: Physical, vol. 338, pp. 113470-113470.
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Additive manufacturing, also known as 3D printing has already transformed from a rapid prototyping tool to a final end-product manufacturing technique. 3D printing can be used to develop various types of sensors. This paper investigates the ability to use the electromagnetic induction properties of 3D printed carbon-based filament for developing sensors. The paper presents a novel prototype vibration sensor which is 3D-printable, except for an included NdFeB magnet. Motion is detected from the voltage induced by the relative motion of the magnet. The devised vibration sensor is simulated using ANSYS, and a novel prototype is 3D-printed for physical testing to characterise and understand its electromagnetic properties. Simulation helped establish constraints for the design. Two types of experimental setups were physically tested, one setup with a magnet freely sliding inside a cylindrical cavity within an oscillating coil, and the other setup with a stationary coil and oscillating magnet. At a frequency of 10 Hz and a motion travel of about 12 mm, the induced voltage for the moving coil case varied from 5.4 mV RMS for pure sliding motion of the internal magnet to 22.1 mV RMS. The findings of this paper suggest that future sensors can be developed using the electromagnetic induction properties of the carbon-based filament.

Mathew, M, Rad, MA, Mata, JP, Mahmodi, H, Kabakova, IV, Raston, CL, Tang, Y, Tipper, JL & Tavakoli, J 2022, 'Hyperbranched polymers tune the physicochemical, mechanical, and biomedical properties of alginate hydrogels', Materials Today Chemistry, vol. 23, pp. 100656-100656.
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Matin, SS & Pradhan, B 2022, 'Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review', Geocarto International, vol. 37, no. 21, pp. 6186-6212.
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Matta, SM, Selam, MA, Manzoor, H, Adham, S, Shon, HK, Castier, M & Abdel-Wahab, A 2022, 'Predicting the performance of spiral-wound membranes in pressure-retarded osmosis processes', Renewable Energy, vol. 189, pp. 66-77.
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Mayer, JU, Hilligan, KL, Chandler, JS, Eccles, DA, Old, SI, Domingues, RG, Yang, J, Webb, GR, Munoz-Erazo, L, Hyde, EJ, Wakelin, KA, Tang, S-C, Chappell, SC, von Daake, S, Brombacher, F, Mackay, CR, Sher, A, Tussiwand, R, Connor, LM, Gallego-Ortega, D, Jankovic, D, Le Gros, G, Hepworth, MR, Lamiable, O & Ronchese, F 2022, 'Author Correction: Homeostatic IL-13 in healthy skin directs dendritic cell differentiation to promote TH2 and inhibit TH17 cell polarization', Nature Immunology, vol. 23, no. 6, pp. 985-985.
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McCourt, LR, Routley, BS, Ruppert, MG, Keast, VJ, Sathish, CI, Borah, R, Goreham, RV & Fleming, AJ 2022, 'Single-Walled Carbon Nanotubes as One-Dimensional Scattering Surfaces for Measuring Point Spread Functions and Performance of Tip-Enhanced Raman Spectroscopy Probes', ACS Applied Nano Materials, vol. 5, no. 7, pp. 9024-9033.
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Mckie, I, Narayan, B & Kocaballi, B 2022, 'Conversational Voice Assistants and a Case Study of Long-Term Users: A Human Information Behaviours Perspective', Journal of the Australian Library and Information Association, vol. 71, no. 3, pp. 233-255.
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Medawela, S, Indraratna, B, Athuraliya, S, Lugg, G & Nghiem, LD 2022, 'Monitoring the performance of permeable reactive barriers constructed in acid sulfate soils', Engineering Geology, vol. 296, pp. 106465-106465.
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Meena, MS, Pare, S, Singh, P, Rana, A & Prasad, M 2022, 'A Robust Illumination and Intensity invariant Face Recognition System', International Journal of Circuits, Systems and Signal Processing, vol. 16, pp. 974-984.
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Face recognition has achieved more attention in computer vision with the focus on modelling the expression variations of human. However, in computer vision system, face recognition is a challenging task, due to variation in expressions, poses, and lighting conditions. This paper proposes a facial recognition technique based on 2D Hybrid Markov Model (2D HMM), Cat Swam Optimization (CSO), Local Directional Pattern (LDP), and Tetrolet Transform. Skin segmentation method is used for pre-processing followed by filtering to extract the region of interest. Resultant image is fed to proposed feature extraction method comprising of Tetrolet Transform and LDP. Extracted features are classified using proposed classifier “CSO trained 2D-HMM classification method”. To prove the superiority of method, four face datasets are used, and comparative results are presented. Quantitively results are measured by False Acceptance Rate (FAR), False Rejection Rate (FRR) and Accuracy and the values are 0.0025, 0.0035 and 99.65% respectively

Mehami, J, Falque, R, Vidal-Calleja, T & Alempijevic, A 2022, 'Multi-Modal Non-Isotropic Light Source Modelling for Reflectance Estimation in Hyperspectral Imaging', IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10336-10343.
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Mehrabi, N & Khabbaz, H 2022, 'A trustful transition zone for high-speed rail using stone columns', Australian Journal of Civil Engineering, vol. 20, no. 1, pp. 56-66.
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The high-speed railway projects have encountered several geotechnical challenges. One of the most important challenges is the differential settlement control in transition zones. Cement-treated soil is a common method to prevent the differential settlement at transition zones. An alternative method uses stone columns for controlling the differential settlement in approaching embankment of bridges. In this study, numerical modelling using PLAXIS 2D is selected for the assessment of stone columns in the reduction of total and differential settlements. One of the overpass bridges of the track constructed for the Tehran–Isfahan railway, the first high-speed railway in the country, is chosen as the case study. Three models are created based on the properties of the selected case study. The first one is a typical approaching embankment. The second one is the bridge abutment section, and the last one is a typical reinforced approaching embankment with stone columns.

Mehraj, S, Mushtaq, S, Parah, SA, Giri, KJ, Sheikh, JA, Gandomi, AH, Hijji, M & Muhammad, K 2022, 'Spatial Domain-Based Robust Watermarking Framework for Cultural Images', IEEE Access, vol. 10, pp. 117248-117260.
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Mehraj, S, Mushtaq, S, Parah, SA, Giri, KJ, Sheikh, JA, Gandomi, AH, Hijji, M, Gupta, BB & Muhammad, K 2022, 'RBWCI: Robust and Blind Watermarking Framework for Cultural Images', IEEE Transactions on Consumer Electronics, pp. 1-1.
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Mei, F, Li, J, Zhang, L, Gao, J, Li, H, Zhou, D, Xing, D & Lin, J 2022, 'Posterior-Stabilized Versus Cruciate-Retaining Prostheses for Total Knee Arthroplasty: An Overview of Systematic Reviews and Risk of Bias Considerations', Indian Journal of Orthopaedics, vol. 56, no. 11, pp. 1858-1870.
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Mei, F, Li, J, Zhang, L, Gao, J, Wang, B, Zhou, Q, Xu, Y, Zhou, C, Zhao, J, Li, P, Zhao, Y, Yuan, T, Fu, W, Li, C, Jin, Y, Yang, P, Xing, D & Lin, J 2022, 'Preference of Orthopedic Practitioners Toward the Use of Topical Medicine for Musculoskeletal Pain Management in China: A National Survey', Orthopaedic Surgery, vol. 14, no. 10, pp. 2470-2479.
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Meng, X, Li, X, Nghiem, LD, Ruiz, E, Johir, MA, Gao, L & Wang, Q 2022, 'Improved stormwater management through the combination of the conventional water sensitive urban design and stormwater pipeline network', Process Safety and Environmental Protection, vol. 159, pp. 1164-1173.
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Merino-Arteaga, I, Alfaro-García, VG & Merigó, JM 2022, 'Fuzzy systems research in the United States of America and Canada: A bibliometric overview', Information Sciences, vol. 617, pp. 277-292.
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Meske, C, Abedin, B, Klier, M & Rabhi, F 2022, 'Explainable and responsible artificial intelligence', Electronic Markets, vol. 32, no. 4, pp. 2103-2106.
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Miao, J, Wu, Y & Yang, Y 2022, 'Identifying Visible Parts via Pose Estimation for Occluded Person Re-Identification', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 9, pp. 4624-4634.
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We focus on the occlusion problem in person re-identification (re-id), which is one of the main challenges in real-world person retrieval scenarios. Previous methods on the occluded re-id problem usually assume that only the probes are occluded, thereby removing occlusions by manually cropping. However, this may not always hold in practice. This article relaxes this assumption and investigates a more general occlusion problem, where both the probe and gallery images could be occluded. The key to this challenging problem is depressing the noise information by identifying bodies and occlusions. We propose to incorporate the pose information into the re-id framework, which benefits the model in three aspects. First, it provides the location of the body. We then design a Pose-Masked Feature Branch to make our model focus on the body region only and filter those noise features brought by occlusions. Second, the estimated pose reveals which body parts are visible, giving us a hint to construct more informative person features. We propose a Pose-Embedded Feature Branch to adaptively re-calibrate channel-wise feature responses based on the visible body parts. Third, in testing, the estimated pose indicates which regions are informative and reliable for both probe and gallery images. Then we explicitly split the extracted spatial feature into parts. Only part features from those commonly visible parts are utilized in the retrieval. To better evaluate the performances of the occluded re-id, we also propose a large-scale data set for the occluded re-id with more than 35 000 images, namely Occluded-DukeMTMC. Extensive experiments show our approach surpasses previous methods on the occluded, partial, and non-occluded re-id data sets.

Min, C, Kim, JE, Shon, HK & Kim, S-H 2022, 'Low energy resonance vibration submerged membrane system for microalgae harvesting: Performance and feasibility', Desalination, vol. 539, pp. 115895-115895.
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Mir, T, Waqas, M, Tu, S, Fang, C, Ni, W, MacKenzie, R, Xue, X & Han, Z 2022, 'Relay Hybrid Precoding in UAV-Assisted Wideband Millimeter-Wave Massive MIMO System', IEEE Transactions on Wireless Communications, vol. 21, no. 9, pp. 7040-7054.
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Mishra, A, Alzoubi, YI, Anwar, MJ & Gill, AQ 2022, 'Attributes impacting cybersecurity policy development: An evidence from seven nations', Computers & Security, vol. 120, pp. 102820-102820.
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Mishra, DK, Ghadi, MJ, Li, L, Zhang, J & Hossain, MJ 2022, 'Active distribution system resilience quantification and enhancement through multi-microgrid and mobile energy storage', Applied Energy, vol. 311, pp. 118665-118665.
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Mishra, DK, Ray, PK, Li, L, Zhang, J, Hossain, MJ & Mohanty, A 2022, 'Resilient control based frequency regulation scheme of isolated microgrids considering cyber attack and parameter uncertainties', Applied Energy, vol. 306, pp. 118054-118054.
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Cyber-physical attacks and parameter uncertainties are becoming a compelling issue on load frequency control, directly affecting the resilience (i.e., reliability plus security) of the microgrid and multi-microgrid systems enabled by internet of things and the fifth generation communication system. A resilient system aims to endure and quickly restore a system's transients during extreme events. Therefore, it is critically important to have a resilient system to evade the total system failure or blackout in order to make them attack-resilient. With this objective, this paper presents a resilience-based frequency regulation scheme in a microgrid under different operating conditions, such as, step and random change in load and different wind speed patterns. Furthermore, a cyber-attack model is considered in the problem formulation to make the system robust against external attacks. To protect against the cyber-attack and parameter uncertainties in the system, different control schemes are employed, and their robustness characteristics are compared through various performance indices. Besides, the proposed control schemes are validated through a real-time software synchronisation environment, i.e., OPAL-RT. As noted, the proposed type-2 fuzzy proportional-integral-derivative based controller provides the most significant improvement in the dynamic performance for frequency regulation compared to that of the others under the cyber-attack and uncertainties.

Mishra, M, Chaudhuri, S, Kshetrimayum, RS, Alphones, A & Esselle, KP 2022, 'Space Efficient Meta-Grid Lines for Mutual Coupling Reduction in Two-Port Planar Monopole and DRA Array', IEEE Access, vol. 10, pp. 49829-49838.
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Mittal, A, Shivakumara, P, Pal, U, Lu, T & Blumenstein, M 2022, 'A new method for detection and prediction of occluded text in natural scene images', Signal Processing: Image Communication, vol. 100, pp. 116512-116512.
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Text detection from natural scene images is an active research area for computer vision, signal, and image processing because of several real-time applications such as driving vehicles automatically and tracing person behaviors during sports or marathon events. In these situations, there is a high probability of missing text information due to the occlusion of different objects/persons while capturing images. Unlike most of the existing methods, which focus only on text detection by ignoring the effect of missing texts, this work detects and predicts missing texts so that the performance of the OCR improves. The proposed method exploits the property of DCT for finding significant information in images by selecting multiple channels. For chosen DCT channels, the proposed method studies texture distribution based on statistical measurement to extract features. We propose to adopt Bayesian classifier for categorizing text pixels using extracted features. Then a deep learning model is proposed for eliminating false positives to improve text detection performance. Further, the proposed method employs a Natural Language Processing (NLP) model for predicting missing text information by using detected and recognition texts. Experimental results on our dataset, which contains texts occluded by objects, show that the proposed method is effective in predicting missing text information. To demonstrate the effectiveness and objectiveness of the proposed method, we also tested it on the standard datasets of natural scene images, namely, ICDAR 2017-MLT, Total-Text, and CTW1500.

Mofijur, M, Ashrafur Rahman, SM, Nguyen, LN, Mahlia, TMI & Nghiem, LD 2022, 'Selection of microalgae strains for sustainable production of aviation biofuel', Bioresource Technology, vol. 345, pp. 126408-126408.
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Mohamed, BA, Bilal, M, Salama, E-S, Periyasamy, S, Fattah, IMR, Ruan, R, Awasthi, MK & Leng, L 2022, 'Phenolic-rich bio-oil production by microwave catalytic pyrolysis of switchgrass: Experimental study, life cycle assessment, and economic analysis', Journal of Cleaner Production, vol. 366, pp. 132668-132668.
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Mohamed, BA, Fattah, IMR, Yousaf, B & Periyasamy, S 2022, 'Effects of the COVID-19 pandemic on the environment, waste management, and energy sectors: a deeper look into the long-term impacts', Environmental Science and Pollution Research, vol. 29, no. 31, pp. 46438-46457.
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Mohammadi, E, Jahanandish, M, Ghahramani, A, Nikoo, MR, Javankhoshdel, S & Gandomi, AH 2022, 'Stochastic optimization model for determining support system parameters of a subway station', Expert Systems with Applications, vol. 203, pp. 117509-117509.
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Mojiri, A, Zhou, JL, Nazari V, M, Rezania, S, Farraji, H & Vakili, M 2022, 'Biochar enhanced the performance of microalgae/bacteria consortium for insecticides removal from synthetic wastewater', Process Safety and Environmental Protection, vol. 157, pp. 284-296.
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Mojiri, A, Zhou, JL, Ratnaweera, H, Rezania, S & Nazari V, M 2022, 'Pharmaceuticals and personal care products in aquatic environments and their removal by algae-based systems', Chemosphere, vol. 288, pp. 132580-132580.
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Monjurul Hasan, ASM, Trianni, A, Shukla, N & Katic, M 2022, 'A novel characterization based framework to incorporate industrial energy management services', Applied Energy, vol. 313, pp. 118891-118891.
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Morgan, AL, Torpy, FR, Irga, PJ, Fleck, R, Gill, RL & Pettit, T 2022, 'The botanical biofiltration of volatile organic compounds and particulate matter derived from cigarette smoke', Chemosphere, vol. 295, pp. 133942-133942.
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Morshedi Rad, D, Rezaei, M, Radfar, P & Ebrahimi Warkiani, M 2022, 'Microengineered filters for efficient delivery of nanomaterials into mammalian cells', Scientific Reports, vol. 12, no. 1.
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AbstractIntracellular delivery of nanomaterials into the cells of interest has enabled cell manipulation for numerous applications ranging from cell-based therapies to biomedical research. To date, different carriers or membrane poration-based techniques have been developed to load nanomaterials to the cell interior. These biotools have shown promise to surpass the membrane barrier and provide access to the intracellular space followed by passive diffusion of exogenous cargoes. However, most of them suffer from inconsistent delivery, cytotoxicity, and expensive protocols, somewhat limiting their utility in a variety of delivery applications. Here, by leveraging the benefits of microengineered porous membranes with a suitable porosity, we demonstrated an efficient intracellular loading of diverse nanomaterials to different cell types based on inducing mechanical disruption to the cell membrane. In this work, for the first time, we used ultra-thin silicon nitride (SiN) filter membranes with uniform micropores smaller than the cell diameter to load impermeable nanomaterials into adherent and non-adherent cell types. The delivery performance using SiN microsieves has been validated through the loading of functional nanomaterials from a few nanometers to hundreds of nanometers into mammalian cells with minimal undesired impacts. Besides the high delivery efficiency and improved cell viability, this simple and low-cost approach offers less clogging and higher throughput (107 cell min−1). Therefore, it yields to the efficient introduction of exogenous nanomaterials into the large population of cells, illustrating the potential of these microengineered filters to be widely used in the microfiltroporation (MFP) setup.

Mortazavi, H, Mortazavy Beni, H & Islam, MS 2022, 'Thermal/fluid characteristics of the inline stacked plain‐weave screen as solar‐powered Stirling engine heat regenerators', IET Renewable Power Generation, vol. 16, no. 5, pp. 956-965.
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Morvan, A, Andersen, TI, Mi, X, Neill, C, Petukhov, A, Kechedzhi, K, Abanin, DA, Michailidis, A, Acharya, R, Arute, F, Arya, K, Asfaw, A, Atalaya, J, Bardin, JC, Basso, J, Bengtsson, A, Bortoli, G, Bourassa, A, Bovaird, J, Brill, L, Broughton, M, Buckley, BB, Buell, DA, Burger, T, Burkett, B, Bushnell, N, Chen, Z, Chiaro, B, Collins, R, Conner, P, Courtney, W, Crook, AL, Curtin, B, Debroy, DM, Del Toro Barba, A, Demura, S, Dunsworth, A, Eppens, D, Erickson, C, Faoro, L, Farhi, E, Fatemi, R, Flores Burgos, L, Forati, E, Fowler, AG, Foxen, B, Giang, W, Gidney, C, Gilboa, D, Giustina, M, Grajales Dau, A, Gross, JA, Habegger, S, Hamilton, MC, Harrigan, MP, Harrington, SD, Hoffmann, M, Hong, S, Huang, T, Huff, A, Huggins, WJ, Isakov, SV, Iveland, J, Jeffrey, E, Jiang, Z, Jones, C, Juhas, P, Kafri, D, Khattar, T, Khezri, M, Kieferová, M, Kim, S, Kitaev, AY, Klimov, PV, Klots, AR, Korotkov, AN, Kostritsa, F, Kreikebaum, JM, Landhuis, D, Laptev, P, Lau, K-M, Laws, L, Lee, J, Lee, KW, Lester, BJ, Lill, AT, Liu, W, Locharla, A, Malone, F, Martin, O, McClean, JR, McEwen, M, Meurer Costa, B, Miao, KC, Mohseni, M, Montazeri, S, Mount, E, Mruczkiewicz, W, Naaman, O, Neeley, M, Nersisyan, A, Newman, M, Nguyen, A, Nguyen, M, Niu, MY, O’Brien, TE, Olenewa, R, Opremcak, A, Potter, R, Quintana, C, Rubin, NC, Saei, N, Sank, D, Sankaragomathi, K, Satzinger, KJ, Schurkus, HF, Schuster, C, Shearn, MJ, Shorter, A, Shvarts, V, Skruzny, J, Smith, WC, Strain, D, Sterling, G, Su, Y, Szalay, M, Torres, A, Vidal, G, Villalonga, B, Vollgraff-Heidweiller, C, White, T, Xing, C, Yao, Z, Yeh, P, Yoo, J, Zalcman, A, Zhang, Y, Zhu, N, Neven, H, Bacon, D, Hilton, J, Lucero, E, Babbush, R, Boixo, S, Megrant, A, Kelly, J, Chen, Y, Smelyanskiy, V, Aleiner, I, Ioffe, LB & Roushan, P 2022, 'Formation of robust bound states of interacting microwave photons', Nature, vol. 612, no. 7939, pp. 240-245.
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AbstractSystems of correlated particles appear in many fields of modern science and represent some of the most intractable computational problems in nature. The computational challenge in these systems arises when interactions become comparable to other energy scales, which makes the state of each particle depend on all other particles1. The lack of general solutions for the three-body problem and acceptable theory for strongly correlated electrons shows that our understanding of correlated systems fades when the particle number or the interaction strength increases. One of the hallmarks of interacting systems is the formation of multiparticle bound states2–9. Here we develop a high-fidelity parameterizable fSim gate and implement the periodic quantum circuit of the spin-½ XXZ model in a ring of 24 superconducting qubits. We study the propagation of these excitations and observe their bound nature for up to five photons. We devise a phase-sensitive method for constructing the few-body spectrum of the bound states and extract their pseudo-charge by introducing a synthetic flux. By introducing interactions between the ring and additional qubits, we observe an unexpected resilience of the bound states to integrability breaking. This finding goes against the idea that bound states in non-integrable systems are unstable when their energies overlap with the continuum spectrum. Our work provides experimental evidence for bound states of interacting photons and discovers their stability beyond the integrability limit.

Mousavi, M, Gandomi, AH, Abdel Wahab, M & Glisic, B 2022, 'Monitoring onsite‐temperature prediction error for condition monitoring of civil infrastructures', Structural Control and Health Monitoring, vol. 29, no. 12.
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Mousavi, M, Gandomi, AH, Holloway, D, Berry, A & Chen, F 2022, 'Machine learning analysis of features extracted from time–frequency domain of ultrasonic testing results for wood material assessment', Construction and Building Materials, vol. 342, pp. 127761-127761.
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Mousavi, M, Holloway, D, Olivier, JC & Gandomi, AH 2022, 'Quaternion analysis of beam multi‐type vibration data for damage detection', Structural Control and Health Monitoring, vol. 29, no. 2.
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Muhammad, K, Del Ser, J, Magaia, N, Fonseca, R, Hussain, T, Gandomi, AH, Daneshmand, M & de Albuquerque, VHC 2022, 'Communication Technologies for Edge Learning and Inference: A Novel Framework, Open Issues, and Perspectives', IEEE Network, pp. 1-7.
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Muhit, IB, Masia, MJ & Stewart, MG 2022, 'Monte-Carlo laboratory testing of unreinforced masonry veneer wall system under out-of-plane loading', Construction and Building Materials, vol. 321, pp. 126334-126334.
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This paper presents the results of a probabilistic experimental study into the behaviour of full-scale unreinforced masonry (URM) veneer walls with flexible backup subjected to out-of-plane loading. The actual safety and reliability of the contemporary Australian URM structures are unknown due to the absence of information regarding the probabilistic behaviour of the whole veneer wall system and material characterisation of the wall constituent materials. The study focused on masonry typologies representative of modern URM buildings in the Australian context. In this study, 18 full-scale URM veneer wall systems with theoretically identical geometries and properties were tested under inward and outward out-of-plane loading. For each loading type, one specimen was tested under semi-cyclic loading to check whether the monotonic loading can capture the overall behaviour of the cyclic response. For each mortar batch mixed, bond wrench testing was conducted at the same age as the test for the associated wall constructed using that mix. Batch to batch variabilities were statistically analysed, and probability distributions for flexural tensile strength were established. Lognormal distributions with aggregated means of 0.40 MPa and 0.42 MPa for inward and outward loading, respectively, were estimated for flexural tensile strengths. After the wall tests, all timber studs used to build the veneer walls were tested to evaluate the probabilistic characterisation of timber stiffness. This probabilistic information is essential for a stochastic finite element analysis (FEA) to conduct the reliability analysis. From the wall tests, veneer wall system behaviour was observed and measured until the collapse or 20% post-peak drop of the peak load. Outward loaded specimens exhibited higher variabilities for masonry cracking and system peak load compared to inward loading due to variabilities from materials, testing arrangements and failure mechanism. The true coefficient of variatio...

Muhit, IB, Masia, MJ, Stewart, MG & Isfeld, AC 2022, 'Spatial variability and stochastic finite element model of unreinforced masonry veneer wall system under Out-of-plane loading', Engineering Structures, vol. 267, pp. 114674-114674.
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Mukund Deshpande, N, Gite, S, Pradhan, B & Ebraheem Assiri, M 2022, 'Explainable Artificial Intelligence–A New Step towards the Trust in Medical Diagnosis with AI Frameworks: A Review', Computer Modeling in Engineering & Sciences, vol. 133, no. 3, pp. 843-872.
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Naderi, E, Azizivahed, A & Asrari, A 2022, 'A step toward cleaner energy production: A water saving-based optimization approach for economic dispatch in modern power systems', Electric Power Systems Research, vol. 204, pp. 107689-107689.
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Nahar, K & Gill, AQ 2022, 'Integrated identity and access management metamodel and pattern system for secure enterprise architecture', Data & Knowledge Engineering, vol. 140, pp. 102038-102038.
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Naik, D, Ramesh, D, Gandomi, AH & Babu Gorojanam, N 2022, 'Parallel and distributed paradigms for community detection in social networks: A methodological review', Expert Systems with Applications, vol. 187, pp. 115956-115956.
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Nama, S, Sharma, S, Saha, AK & Gandomi, AH 2022, 'A quantum mutation-based backtracking search algorithm', Artificial Intelligence Review, vol. 55, no. 4, pp. 3019-3073.
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Namdarpour, F, Mesbah, M, Gandomi, AH & Assemi, B 2022, 'Using genetic programming on GPS trajectories for travel mode detection', IET Intelligent Transport Systems, vol. 16, no. 1, pp. 99-113.
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Namisango, F, Kang, K & Beydoun, G 2022, 'How the Structures Provided by Social Media Enable Collaborative Outcomes: A Study of Service Co-creation in Nonprofits', Information Systems Frontiers, vol. 24, no. 2, pp. 517-535.
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Nan, Y, Huang, X & Guo, YJ 2022, '3-D Millimeter-Wave Helical Imaging', IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 4, pp. 2499-2511.
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Nan, Y, Huang, X & Guo, YJ 2022, 'A Panoramic Synthetic Aperture Radar', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13.
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Nan, Y, Huang, X & Guo, YJ 2022, 'An Universal Circular Synthetic Aperture Radar', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15.
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Nandanwar, L, Shivakumara, P, Jalab, HA, Ibrahim, RW, Raghavendra, R, Pal, U, Lu, T & Blumenstein, M 2022, 'A Conformable Moments-Based Deep Learning System for Forged Handwriting Detection', IEEE Transactions on Neural Networks and Learning Systems, pp. 1-14.
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Nascimben, M, Wang, Y-K, King, J-T, Jung, T-P, Touryan, J, Lance, BJ & Lin, C-T 2022, 'Alpha Correlates of Practice During Mental Preparation for Motor Imagery', IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 1, pp. 146-155.
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IEEE In this study we quantified performance variations of motor imagery (MI)-based brain-computer interface (BCI) systems induced by practice. Two experimental sessions were recorded from ten healthy subjects while playing a BCI-oriented videogame for two weeks. The analysis focused on the exploration of electroencephalographic changes during mental preparation between novice and practiced subjects. EEG changes were quantified using global field power (GFP), dynamic time warping (TW) and mutual information (MutInf): GFP represents the strength of the electric field, TW measures signal similarities and MutInf signals inter-dependency. Each metric was selected to relate insights extracted from mental preparation to the three experimental hypotheses associating practice with BCI performance. Significant results were identified in lower alpha for GFP and upper alpha for TW and MutInf. GFP in lower alpha during mental preparation assessed not only novice vs practiced variations but also “intra-session” differences. Findings suggest that EEG changes during mental preparation provide a quantitative measure of practice level. These metrics extracted before motor intention could be applied to BCI models targeting MI to monitor a user’s degree of training.

Naseri, H, Shokoohi, M, Jahanbakhsh, H, Golroo, A & Gandomi, AH 2022, 'Evolutionary and swarm intelligence algorithms on pavement maintenance and rehabilitation planning', International Journal of Pavement Engineering, vol. 23, no. 13, pp. 4649-4663.
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Nasir, AA, Tuan, HD, Dutkiewicz, E & Hanzo, L 2022, 'Finite-Resolution Digital Beamforming for Multi-User Millimeter-Wave Networks', IEEE Transactions on Vehicular Technology, vol. 71, no. 9, pp. 9647-9662.
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Nasir, AA, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Low-Resolution RIS-Aided Multiuser MIMO Signaling', IEEE Transactions on Communications, vol. 70, no. 10, pp. 6517-6531.
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Nasir, AA, Tuan, HD, Dutkiewicz, E, Poor, HV & Hanzo, L 2022, 'Relay-Aided Multi-User OFDM Relying on Joint Wireless Power Transfer and Self-Interference Recycling', IEEE Transactions on Communications, vol. 70, no. 1, pp. 291-305.
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Naveed Arif, M, Waqas, A, Ahmed Butt, F, Mahmood, M, Hussain Khoja, A, Ali, M, Ullah, K, Mujtaba, MA & Kalam, MA 2022, 'Techno-economic assessment of solar water heating systems for sustainable tourism in northern Pakistan', Alexandria Engineering Journal, vol. 61, no. 7, pp. 5485-5499.
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Nayak, RP, Sethi, S, Bhoi, SK, Mohapatra, D, Sahoo, RR, Sharma, PK & Puthal, D 2022, 'TFMD-SDVN: a trust framework for misbehavior detection in the edge of software-defined vehicular network', The Journal of Supercomputing, vol. 78, no. 6, pp. 7948-7981.
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Neha, B, Panda, SK, Sahu, PK, Sahoo, KS & Gandomi, AH 2022, 'A Systematic Review on Osmotic Computing', ACM Transactions on Internet of Things, vol. 3, no. 2, pp. 1-30.
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Osmotic computing in association with related computing paradigms (cloud, fog, and edge) emerges as a promising solution for handling bulk of security-critical as well as latency-sensitive data generated by the digital devices. It is a growing research domain that studies deployment, migration, and optimization of applications in the form of microservices across cloud/edge infrastructure. It presents dynamically tailored microservices in technology-centric environments by exploiting edge and cloud platforms. Osmotic computing promotes digital transformation and furnishes benefits to transportation, smart cities, education, and healthcare. In this article, we present a comprehensive analysis of osmotic computing through a systematic literature review approach. To ensure high-quality review, we conduct an advanced search on numerous digital libraries to extracting related studies. The advanced search strategy identifies 99 studies, from which 29 relevant studies are selected for a thorough review. We present a summary of applications in osmotic computing build on their key features. On the basis of the observations, we outline the research challenges for the applications in this research field. Finally, we discuss the security issues resolved and unresolved in osmotic computing.

Nematollahi, B, Nikoo, MR, Gandomi, AH, Talebbeydokhti, N & Rakhshandehroo, GR 2022, 'A Multi-criteria Decision-making Optimization Model for Flood Management in Reservoirs', Water Resources Management, vol. 36, no. 13, pp. 4933-4949.
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Neshat, M, Majidi Nezhad, M, Mirjalili, S, Piras, G & Garcia, DA 2022, 'Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting: North aegean islands case studies', Energy Conversion and Management, vol. 259, pp. 115590-115590.
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Neshat, M, Nezhad, MM, Sergiienko, NY, Mirjalili, S, Piras, G & Garcia, DA 2022, 'Wave power forecasting using an effective decomposition-based convolutional Bi-directional model with equilibrium Nelder-Mead optimiser', Energy, vol. 256, pp. 124623-124623.
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Ng, BYS, Ong, HC, Lau, HLN, Ishak, NS, Elfasakhany, A & Lee, HV 2022, 'Production of sustainable two-stroke engine biolubricant ester base oil from palm fatty acid distillate', Industrial Crops and Products, vol. 175, pp. 114224-114224.
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Ngo, QT, Phan, KT, Xiang, W, Mahmood, A & Slay, J 2022, 'Two-Tier Cache-Aided Full-Duplex Hybrid Satellite–Terrestrial Communication Networks', IEEE Transactions on Aerospace and Electronic Systems, vol. 58, no. 3, pp. 1753-1765.
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Ngo, T, Indraratna, B & Ferreira, F 2022, 'Influence of synthetic inclusions on the degradation and deformation of ballast under heavy-haul cyclic loading', International Journal of Rail Transportation, vol. 10, no. 4, pp. 413-435.
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Nguyen, A, Long Nguyen, C, Gharehbaghi, V, Perera, R, Brown, J, Yu, Y & Kalbkhani, H 2022, 'A computationally efficient crack detection approach based on deep learning assisted by stockwell transform and linear discriminant analysis', Structures, vol. 45, pp. 1962-1970.
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Nguyen, AQ, Nguyen, LN, Johir, MAH, Ngo, HH & Nghiem, LD 2022, 'Linking endogenous decay and sludge bulking in the microbial community to membrane fouling at sub-critical flux', Journal of Membrane Science Letters, vol. 2, no. 1, pp. 100023-100023.
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Nguyen, AQ, Nguyen, LN, McDonald, JA, Nghiem, LD, Leusch, FDL, Neale, PA & Khan, SJ 2022, 'Chiral inversion of 2-arylpropionoic acid (2-APA) enantiomers during simulated biological wastewater treatment', Water Research, vol. 209, pp. 117871-117871.
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Nguyen, AQ, Nguyen, LN, Xu, Z, Luo, W & Nghiem, LD 2022, 'New insights to the difference in microbial composition and interspecies interactions between fouling layer and mixed liquor in a membrane bioreactor', Journal of Membrane Science, vol. 643, pp. 120034-120034.
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Nguyen, B-P, Nguyen, TT, Nguyen, THY & Tran, T-D 2022, 'Performance of Composite PVD–SC Column Foundation under Embankment through Plane-Strain Numerical Analysis', International Journal of Geomechanics, vol. 22, no. 9.
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Nguyen, CT, Nguyen, DN, Hoang, DT, Phan, KT, Niyato, D, Pham, H-A & Dutkiewicz, E 2022, 'Elastic Resource Allocation for Coded Distributed Computing over Heterogeneous Wireless Edge Networks', IEEE Transactions on Wireless Communications, pp. 1-1.
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Coded distributed computing (CDC) has recently emerged to be a promising solution to address the straggling effects in conventional distributed computing systems. By assigning redundant workloads to the computing nodes, CDC can significantly enhance the performance of the whole system. However, since the core idea of CDC is to introduce redundancies to compensate for uncertainties, it may lead to a large amount of wasted energy at the edge nodes. It can be observed that the more redundant workload added, the less impact the straggling effects have on the system. However, at the same time, the more energy is needed to perform redundant tasks. In this work, we develop a novel framework, namely CERA, to elastically allocate computing resources for CDC processes. Particularly, CERA consists of two stages. In the first stage, we model a joint coding and node selection optimization problem to minimize the expected processing time for a CDC task. Since the problem is NP-hard, we propose a linearization approach and a hybrid algorithm to quickly obtain the optimal solutions. In the second stage, we develop a smart online approach based on Lyapunov optimization to dynamically turn off straggling nodes based on their actual performance. As a result, wasteful energy consumption can be significantly reduced with minimal impact on the total processing time. Simulations using real-world datasets have shown that our proposed approach can reduce the system’s total processing time by more than 200% compared to that of the state-of-the-art approach, even when the nodes’ actual performance is not known in advance. Moreover, the results have shown that CERA’s online optimization stage can reduce the energy consumption by up to 37.14% without affecting the total processing time.

Nguyen, CT, Van Huynh, N, Chu, NH, Saputra, YM, Hoang, DT, Nguyen, DN, Pham, Q-V, Niyato, D, Dutkiewicz, E & Hwang, W-J 2022, 'Transfer Learning for Wireless Networks: A Comprehensive Survey', Proceedings of the IEEE, vol. 110, no. 8, pp. 1073-1115.
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Nguyen, D-A, Tran, X-T, Dang, KN & Iacopi, F 2022, 'A low-power, high-accuracy with fully on-chip ternary weight hardware architecture for Deep Spiking Neural Networks', Microprocessors and Microsystems, vol. 90, pp. 104458-104458.
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Nguyen, HAD & Ha, QP 2022, 'Wireless Sensor Network Dependable Monitoring for Urban Air Quality', IEEE Access, vol. 10, pp. 40051-40062.
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Nguyen, KT, Navidpour, AH, Ahmed, MB, Mojiri, A, Huang, Y & Zhou, JL 2022, 'Adsorption and desorption behavior of arsenite and arsenate at river sediment-water interface', Journal of Environmental Management, vol. 317, pp. 115497-115497.
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Nguyen, LN, Vu, HP, Fu, Q, Abu Hasan Johir, M, Ibrahim, I, Mofijur, M, Labeeuw, L, Pernice, M, Ralph, PJ & Nghiem, LD 2022, 'Synthesis and evaluation of cationic polyacrylamide and polyacrylate flocculants for harvesting freshwater and marine microalgae', Chemical Engineering Journal, vol. 433, pp. 133623-133623.
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Nguyen, M-D, Lee, S-M, Pham, Q-V, Hoang, DT, Nguyen, DN & Hwang, W-J 2022, 'HCFL: A High Compression Approach for Communication-Efficient Federated Learning in Very Large Scale IoT Networks', IEEE Transactions on Mobile Computing, pp. 1-13.
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Nguyen, NHT, Nguyen, TT & Phan, QT 2022, 'Dynamics and runout distance of saturated particle-fluid mixture flow on a horizontal plane: A coupled VOF-DEM study', Powder Technology, vol. 408, pp. 117759-117759.
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Nguyen, NHT, Perry, S, Bone, D, Le Thanh, H, Xu, M & Nguyen, TT 2022, 'Combination of Images and Point Clouds in a Generative Adversarial Network for Upsampling Crack Point Clouds', IEEE Access, vol. 10, pp. 67198-67209.
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Nguyen, NPT, Sultana, A, Areerachakul, N & Kandasamy, J 2022, 'Evaluating the Field Performance of Permeable Concrete Pavers', Water, vol. 14, no. 14, pp. 2143-2143.
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The benefits of using permeable interlocking concrete pavement systems (PICPs) have not translated into widespread adoption in Australia, where their uptake has been slow. This paper communicates the actual performance of PICPs installed in the field by providing evidence of their long-term efficiency. There are currently no Australian standards for design, specification and installation of PICPs. In this study, field measurements were conducted to determine the infiltration capacity of PICPs in Sydney and Wollongong, New South Wales, applying the single ring infiltrometer test (SRIT) and the stormwater infiltration field test (SWIFT). A strong correlation was found between the results of the two tests in a previous study, which was verified in this study. The long-term performance of PICPs is demonstrated by their high infiltration rates (ranging from 125 mm/h to 25,000 mm/h) measured in this study at field sites under a diverse range of conditions. The influences of conditions such as age of installation, slope and tree cover on infiltration rates were explored.

Nguyen, PM, Do, PT, Pham, YB, Doan, TO, Nguyen, XC, Lee, WK, Nguyen, DD, Vadiveloo, A, Um, M-J & Ngo, HH 2022, 'Roles, mechanism of action, and potential applications of sulfur-oxidizing bacteria for environmental bioremediation', Science of The Total Environment, vol. 852, pp. 158203-158203.
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Nguyen, QA, Vu, HP, McDonald, JA, Nguyen, LN, Leusch, FDL, Neale, PA, Khan, SJ & Nghiem, LD 2022, 'Chiral Inversion of 2-Arylpropionic Acid Enantiomers under Anaerobic Conditions', Environmental Science & Technology, vol. 56, no. 12, pp. 8197-8208.
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Nguyen, QD, Afroz, S, Zhang, Y, Kim, T, Li, W & Castel, A 2022, 'Autogenous and total shrinkage of limestone calcined clay cement (LC3) concretes', Construction and Building Materials, vol. 314, pp. 125720-125720.
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Nguyen, TAH, Le, TV, Ngo, HH, Guo, WS, Vu, ND, Tran, TTT, Nguyen, THH, Nguyen, XC, Nguyen, VH & Pham, TT 2022, 'Hybrid use of coal slag and calcined ferralsol as wetland substrate for improving phosphorus removal from wastewater', Chemical Engineering Journal, vol. 428, pp. 132124-132124.
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Nguyen, TG, Phan, TV, Hoang, DT, Nguyen, HH & Le, DT 2022, 'DeepPlace: Deep reinforcement learning for adaptive flow rule placement in Software-Defined IoT Networks', Computer Communications, vol. 181, pp. 156-163.
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Nguyen, TH, Loganathan, P, Nguyen, TV, Vigneswaran, S, Ha Nguyen, TH, Tran, HN & Nguyen, QB 2022, 'Arsenic removal by pomelo peel biochar coated with iron', Chemical Engineering Research and Design, vol. 186, pp. 252-265.
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Nguyen, TH, Ryu, S, Loganathan, P, Kandasamy, J, Nguyen, TV & Vigneswaran, S 2022, 'Arsenic adsorption by low-cost laterite column: Long-term experiments and dynamic column modeling', Process Safety and Environmental Protection, vol. 160, pp. 868-875.
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Nguyen, TH, Tran, HN, Nguyen, TV, Vigneswaran, S, Trinh, VT, Nguyen, TD, Ha Nguyen, TH, Mai, TN & Chao, H-P 2022, 'Single-step removal of arsenite ions from water through oxidation-coupled adsorption using Mn/Mg/Fe layered double hydroxide as catalyst and adsorbent', Chemosphere, vol. 295, pp. 133370-133370.
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Nguyen, TK, Nguyen, HH, Tuan, HD & Ngo, HQ 2022, 'Improved Pilot Designs for Enhancing Connectivity in Multicarrier Massive MIMO Systems', IEEE Wireless Communications Letters, vol. 11, no. 5, pp. 1057-1061.
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Nguyen, TN, Sanchez, LFM, Li, J, Fournier, B & Sirivivatnanon, V 2022, 'Correlating alkali-silica reaction (ASR) induced expansion from short-term laboratory testings to long-term field performance: A semi-empirical model', Cement and Concrete Composites, vol. 134, pp. 104817-104817.
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Nguyen, TT & Indraratna, B 2022, 'Fluidization of soil under increasing seepage flow: an energy perspective through CFD-DEM coupling', Granular Matter, vol. 24, no. 3.
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AbstractIncreasing seepage flow causes soil particles to migrate, i.e., from local piping to complete fluidization, resulting in reduced effectives stress and degraded shear stiffness of the soil foundation. This process has received considerable attention in the past years, however, majority of them concentrate on macro-aspects such as the internal erosion and soil deformation, while there is a lack of fundamental studies addressing the energy transport at micro-scale of fluid-soil systems during soil approaching fluidization. In this regard, the current study presents an assessment of the energy evolution in soil fluidization based on the discrete element method (DEM) coupled with computation fluid dynamics (CFD). In this paper, an upward seepage flow of fluid is modelled by CFD based on the modified Navier–Stokes equations, while soil particles are governed by DEM with their mutual interactions being computed through fluid-particle force models. The energy transformation from the potential state to kinetic forms during fluid flowing is discussed with respect to numerical (CFD-DEM) results and the energy conservation concepts. The results show that majority of the potential energy induced by fluid flows has lost due to frictional mechanisms, while only a small amount of energy is needed to cause the soil to fluidize completely. The contribution of rotational and translational components to the total kinetic energy of particles, and their changing roles during soil fluidization is also presented. The effect of boundary condition on the energy transformation and fluidization of soil is also investigated and discussed. Graphical abstract

Nguyen, TT & Indraratna, B 2022, 'Rail track degradation under mud pumping evaluated through site and laboratory investigations', International Journal of Rail Transportation, vol. 10, no. 1, pp. 44-71.
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Nguyen, TT, Indraratna, B & Leroueil, S 2022, 'Localized behaviour of fluidized subgrade soil subjected to cyclic loading', CANADIAN GEOTECHNICAL JOURNAL, vol. 59, no. 5.
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Nguyen, TT, Indraratna, B & Leroueil, S 2022, 'Localized behaviour of fluidized subgrade soil subjected to cyclic loading', Canadian Geotechnical Journal, vol. 59, no. 10, pp. 1844-1849.
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Recent investigations have shown that under adverse cyclic triaxial loading, the upper part of soil specimens can turn into a fluid-like state with increased water content (i.e., fluidization), whereas the lower layers can maintain a relatively high stiffness. This paper aims to gain further insight into this behaviour by monitoring the development in excess pore water pressure (EPWP) at the top and bottom of the test specimens, followed by post-analysis of water content distribution along the specimen. The results show that the EPWP at the uppermost part of the specimen develops rapidly and approaches the zero-effective stress level, whereas the EPWP at the bottom part of the specimen tends to stabilize while undergoing densification. Accompanied with this process is a redistribution of the water content along the specimen height where the water content at the upper soil layer increases to approach the liquid limit while increasing the void ratio.

Nguyen, TT, Indraratna, B & Rujikiatkamjorn, C 2022, 'A numerical approach to modelling biodegradable vertical drains', Environmental Geotechnics, vol. 9, no. 8, pp. 515-523.
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Because of their distinct features such as biodegradability and favourable engineering properties, naturally occurring materials including jute and coconut fibres have been used increasingly in numerous geoengineering applications in recent years. However, these materials can sometimes decompose rapidly when subjected to adverse environmental conditions, resulting in severe degradation of their engineering characteristics and consequently causing damage to the design target. This paper presents a numerical approach where the finite-element method (FEM) is used to estimate the influence that the degradation of natural fibre drains can have on soil consolidation. A subroutine which can describe the reduction in drain discharge capacity over time is incorporated into the FEM model. Different cases including those varying the rate and time-dependent form of biodegradation are examined in this paper. The results of this investigation indicate that the dissipation of excess pore pressure can be hampered significantly if drains decay too early and speedily, particularly when the discharge capacity falls below 0·03 m3/d. Different rates of decay can impose different consolidation responses in the surrounding soft soil. Application of the proposed FEM to compare with laboratory data indicates an acceptable agreement between the predictions and the measurements.

Nguyen, TT, Ngo, HH, Guo, W, Chang, SW, Nguyen, DD, Nguyen, CT, Zhang, J, Liang, S, Bui, XT & Hoang, NB 2022, 'A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm', Science of The Total Environment, vol. 833, pp. 155066-155066.
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Nguyen, TT, Pham, TD, Nguyen, CT, Delfos, J, Archibald, R, Dang, KB, Hoang, NB, Guo, W & Ngo, HH 2022, 'A novel intelligence approach based active and ensemble learning for agricultural soil organic carbon prediction using multispectral and SAR data fusion', Science of The Total Environment, vol. 804, pp. 150187-150187.
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Nguyen, T-T-D, Bui, X-T, Nguyen, T-T, Hao Ngo, H, Yi Andrew Lin, K, Lin, C, Le, L-T, Dang, B-T, Bui, M-H & Varjani, S 2022, 'Co-culture of microalgae-activated sludge in sequencing batch photobioreactor systems: Effects of natural and artificial lighting on wastewater treatment', Bioresource Technology, vol. 343, pp. 126091-126091.
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Nguyen, XC, Nguyen, TTH, Le, QV, Le, PC, Srivastav, AL, Pham, QB, Nguyen, PM, La, DD, Rene, ER, Ngo, HH, Chang, SW & Nguyen, DD 2022, 'Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms', Journal of Environmental Management, vol. 301, pp. 113868-113868.
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Ni, M, Wang, C, Zhu, T, Yu, S & Liu, W 2022, 'Attacking neural machine translations via hybrid attention learning', Machine Learning, vol. 111, no. 11, pp. 3977-4002.
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AbstractDeep-learning based natural language processing (NLP) models are proven vulnerable to adversarial attacks. However, there is currently insufficient research that studies attacks to neural machine translations (NMTs) and examines the robustness of deep-learning based NMTs. In this paper, we aim to fill this critical research gap. When generating word-level adversarial examples in NLP attacks, there is a conventional trade-off in existing methods between the attacking performance and the amount of perturbations. Although some literature has studied such a trade-off and successfully generated adversarial examples with a reasonable amount of perturbations, it is still challenging to generate highly successful translation attacks while concealing the changes to the texts. To this end, we propose a novel Hybrid Attentive Attack method to locate language-specific and sequence-focused words, and make semantic-aware substitutions to attack NMTs. We evaluate the effectiveness of our attack strategy by attacking three high-performing translation models. The experimental results show that our method achieves the highest attacking performance compared with other existing attacking strategies.

Ni, Q, Ji, JC, Feng, K & Halkon, B 2022, 'A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis', Mechanical Systems and Signal Processing, vol. 164, pp. 108216-108216.
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Ni, Z, Zhang, JA, Yang, K, Huang, X & Tsiftsis, TA 2022, 'Multi-Metric Waveform Optimization for Multiple-Input Single-Output Joint Communication and Radar Sensing', IEEE Transactions on Communications, vol. 70, no. 2, pp. 1276-1289.
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Nichols, E, Steinmetz, JD, Vollset, SE, Fukutaki, K, Chalek, J, Abd-Allah, F, Abdoli, A, Abualhasan, A, Abu-Gharbieh, E, Akram, TT, Al Hamad, H, Alahdab, F, Alanezi, FM, Alipour, V, Almustanyir, S, Amu, H, Ansari, I, Arabloo, J, Ashraf, T, Astell-Burt, T, Ayano, G, Ayuso-Mateos, JL, Baig, AA, Barnett, A, Barrow, A, Baune, BT, Béjot, Y, Bezabhe, WMM, Bezabih, YM, Bhagavathula, AS, Bhaskar, S, Bhattacharyya, K, Bijani, A, Biswas, A, Bolla, SR, Boloor, A, Brayne, C, Brenner, H, Burkart, K, Burns, RA, Cámera, LA, Cao, C, Carvalho, F, Castro-de-Araujo, LFS, Catalá-López, F, Cerin, E, Chavan, PP, Cherbuin, N, Chu, D-T, Costa, VM, Couto, RAS, Dadras, O, Dai, X, Dandona, L, Dandona, R, De la Cruz-Góngora, V, Dhamnetiya, D, Dias da Silva, D, Diaz, D, Douiri, A, Edvardsson, D, Ekholuenetale, M, El Sayed, I, El-Jaafary, SI, Eskandari, K, Eskandarieh, S, Esmaeilnejad, S, Fares, J, Faro, A, Farooque, U, Feigin, VL, Feng, X, Fereshtehnejad, S-M, Fernandes, E, Ferrara, P, Filip, I, Fillit, H, Fischer, F, Gaidhane, S, Galluzzo, L, Ghashghaee, A, Ghith, N, Gialluisi, A, Gilani, SA, Glavan, I-R, Gnedovskaya, EV, Golechha, M, Gupta, R, Gupta, VB, Gupta, VK, Haider, MR, Hall, BJ, Hamidi, S, Hanif, A, Hankey, GJ, Haque, S, Hartono, RK, Hasaballah, AI, Hasan, MT, Hassan, A, Hay, SI, Hayat, K, Hegazy, MI, Heidari, G, Heidari-Soureshjani, R, Herteliu, C, Househ, M, Hussain, R, Hwang, B-F, Iacoviello, L, Iavicoli, I, Ilesanmi, OS, Ilic, IM, Ilic, MD, Irvani, SSN, Iso, H, Iwagami, M, Jabbarinejad, R, Jacob, L, Jain, V, Jayapal, SK, Jayawardena, R, Jha, RP, Jonas, JB, Joseph, N, Kalani, R, Kandel, A, Kandel, H, Karch, A, Kasa, AS, Kassie, GM, Keshavarz, P, Khan, MAB, Khatib, MN, Khoja, TAM, Khubchandani, J, Kim, MS, Kim, YJ, Kisa, A, Kisa, S, Kivimäki, M, Koroshetz, WJ, Koyanagi, A, Kumar, GA, Kumar, M, Lak, HM, Leonardi, M, Li, B, Lim, SS, Liu, X, Liu, Y, Logroscino, G, Lorkowski, S, Lucchetti, G, Lutzky Saute, R, Magnani, FG, Malik, AA, Massano, J, Mehndiratta, MM, Menezes, RG, Meretoja, A, Mohajer, B, Mohamed Ibrahim, N, Mohammad, Y, Mohammed, A, Mokdad, AH, Mondello, S, Moni, MAA, Moniruzzaman, M, Mossie, TB, Nagel, G, Naveed, M, Nayak, VC, Neupane Kandel, S, Nguyen, TH, Oancea, B, Otstavnov, N, Otstavnov, SS, Owolabi, MO, Panda-Jonas, S, Pashazadeh Kan, F, Pasovic, M, Patel, UK, Pathak, M, Peres, MFP, Perianayagam, A, Peterson, CB, Phillips, MR & et al. 2022, 'Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019', The Lancet Public Health, vol. 7, no. 2, pp. e105-e125.
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Nie, X, Zhang, A, Chen, J, Qu, Y & Yu, S 2022, 'Time-Enabled and Verifiable Secure Search for Blockchain-Empowered Electronic Health Record Sharing in IoT', Security and Communication Networks, vol. 2022, pp. 1-15.
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The collection and sharing of electronic health records (EHRs) via the Internet of Things (IoT) can enhance the accuracy of disease diagnosis. However, it is challenging to guarantee the secure search of EHR during the sharing process. The advent of blockchain is a promising solution to address the issues, owing to its remarkable features such as immutability and anonymity. In this paper, we propose a novel blockchain-based secure sharing system over searchable encryption and hidden data structure via IoT devices. EHR ciphertexts of data owners are stored in the interplanetary file system (IPFS). A user with proper access permissions can search for the desired data with the data owner’s time-bound authorization and verify the authenticity of the search result. After that, the data user can access the relevant EHR ciphertext from IPFS using a symmetric key. The scheme jointly uses searchable encryption and smart contract to realize secure search, time control, verifiable keyword search, fast search, and forward privacy in IoT scenarios. Performance analysis and proof demonstrate that the proposed protocol can satisfy the design goals. In addition, performance evaluation shows the high scalability and feasibility of the proposed scheme.

Nimmy, SF, Hussain, OK, Chakrabortty, RK, Hussain, FK & Saberi, M 2022, 'Explainability in supply chain operational risk management: A systematic literature review', Knowledge-Based Systems, vol. 235, pp. 107587-107587.
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It is important to manage operational disruptions to ensure the success of supply chain operations. To achieve this aim, researchers have developed techniques that determine the occurrence of operational risk events which assists supply chain operational risk managers develop plans to manage them by detection/monitoring, mitigation/management, or optimization techniques. Various artificial intelligence (AI) approaches have been used to develop such techniques in the broad activities of operational risk management. However, all of these techniques are black box in their working nature. This means that the chosen technique cannot explain why it has given that output and whether it is correct and free from bias. To address this, researchers argue the need for supply chain management professionals to move towards using explainable AI methods for operational risk management. In this paper, we conduct a systematic literature review on the techniques used to determine operational risks and analyse whether they satisfy the requirement of them being explainable. The findings highlight the shortcomings and inspires directions for future research. From a managerial perspective, the paper encourages risk managers to choose techniques for supply chain operational risk management that can be auditable as this will ensure that the risk managers know why they should take a particular risk management action rather than just what they should do to manage the operational risks.

Niu, K, Guo, Z, Peng, X & Pei, S 2022, 'P-ResUnet: Segmentation of brain tissue with Purified Residual Unet', Computers in Biology and Medicine, vol. 151, pp. 106294-106294.
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Niu, K, Lu, Y, Peng, X & Zeng, J 2022, 'Fusion of sequential visits and medical ontology for mortality prediction', Journal of Biomedical Informatics, vol. 127, pp. 104012-104012.
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Nizami, S, Tushar, W, Hossain, MJ, Yuen, C, Saha, T & Poor, HV 2022, 'Transactive energy for low voltage residential networks: A review', Applied Energy, vol. 323, pp. 119556-119556.
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Norouzian-Maleki, P, Izadbakhsh, H, Saberi, M, Hussain, O, Jahangoshai Rezaee, M & GhanbarTehrani, N 2022, 'An integrated approach to system dynamics and data envelopment analysis for determining efficient policies and forecasting travel demand in an urban transport system', Transportation Letters, vol. 14, no. 2, pp. 157-173.
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Nouhi, B, Khodadadi, N, Azizi, M, Talatahari, S & Gandomi, AH 2022, 'Multi-Objective Material Generation Algorithm (MOMGA) for Optimization Purposes', IEEE Access, vol. 10, pp. 107095-107115.
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Nuruzzaman, M, Liu, Y, Ren, J, Rahman, MM, Zhang, H, Hasan Johir, MA, Shon, HK & Naidu, R 2022, 'Capability of Organically Modified Montmorillonite Nanoclay as a Carrier for Imidacloprid Delivery', ACS Agricultural Science & Technology, vol. 2, no. 1, pp. 57-68.
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Nuvoli, S, Pietroni, N, Cignoni, P, Scateni, R & Tarini, M 2022, 'SkinMixer', ACM Transactions on Graphics, vol. 41, no. 6, pp. 1-15.
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We propose a novel technique to compose new 3D animated models, such as videogame characters, by combining pieces from existing ones. Our method works on production-ready rigged, skinned, and animated 3D models to reassemble new ones. We exploit mix-and-match operations on the skeletons to trigger the automatic creation of a new mesh, linked to the new skeleton by a set of skinning weights and complete with a set of animations. The resulting model preserves the quality of the input meshings (which can be quad-dominant and semi-regular), skinning weights (inducing believable deformation), and animations, featuring coherent movements of the new skeleton. Our method enables content creators to reuse valuable, carefully designed assets by assembling new ready-to-use characters while preserving most of the hand-crafted subtleties of models authored by digital artists. As shown in the accompanying video, it allows for drastically cutting the time needed to obtain the final result.

O’Brien, K, Sood, S & Shete, R 2022, 'Big Data Approach to Visualising, Analysing and Modelling Company Culture: A New Paradigm and Tool for Exploring Toxic Cultures and the Way We Work', THE INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION, vol. 8, no. 2, pp. 48-61.
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This paper explores the use of big data to measure company culture, good and bad, including toxic culture. Culture is a central factor driving employee experiences and contributing to the “great resignation”. Harnessing the key Artificial Intelligence (AI) technology of neural networks using deep learning methodology for NLP provides the capability to extract cultural meanings from a diverse array of organizational information and cultural artefacts ( texts, images, speech and video) available online. Using big data and AI provides a predictive capability surpassing the value of employee survey instruments of the last century providing a rear view of insights. Big data helps break free from the paradigm of only thinking about culture moving at a glacial pace. An innovative methodology and AI technologies help measure and visually plot the organizational culture trajectory within a company cultural landscape. Cultural values, inclusive of toxicity, have the potential for detection across all forms of communications media. A non-invasive approach using a broad range of open data sources overcomes limitations of the traditional survey instruments and approaches for achieving a culture read. The benefits of the approach and the AI technology are the real-time ingestion of ongoing executive and managerial feedback while entirely sidestepping the issues of survey biases and viable samples. The methodology under study for reading a culture moves well beyond traditional text-centric searches, content analyses, dictionaries and text mining, delivering an understanding of the meanings of words, phrases, sentences or even concepts comprising company culture. Embeddings are an ideal neural network breakthrough technology enabling the computation of text as data through creating a meaningful space in which similar word meanings exist in close proximity. Vector algebra in a multidimensional space helps unpack the cultural nuances and biases pent up within the unstr...

Olszak, CM, Zurada, J & Cetindamar, D 2022, 'Business Intelligence & Big Data for Innovative and Sustainable Development of Organizations', Information Systems Management, vol. 39, no. 1, pp. 2-2.
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Omar, KR, Fatahi, B & Nguyen, LD 2022, 'Impacts of Pre-contamination Moisture Content on Mechanical Properties of High-Plasticity Clay Contaminated with Used Engine Oil', Journal of Testing and Evaluation, vol. 50, no. 6, pp. 20210477-20210477.
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Oner, O & Khalilpour, K 2022, 'Evaluation of green hydrogen carriers: A multi-criteria decision analysis tool', Renewable and Sustainable Energy Reviews, vol. 168, pp. 112764-112764.
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Onggowarsito, C, Feng, A, Mao, S, Nguyen, LN, Xu, J & Fu, Q 2022, 'Water Harvesting Strategies through Solar Steam Generator Systems', ChemSusChem, vol. 15, no. 23.
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Onggowarsito, C, Feng, A, Mao, S, Zhang, S, Ibrahim, I, Tijing, L, Fu, Q & Ngo, HH 2022, 'Development of an innovative MnO2 nanorod for efficient solar vapor generator', Environmental Functional Materials, vol. 1, no. 2, pp. 196-203.
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Orcesi, A, O'Connor, A, Bastidas-Arteaga, E, Stewart, MG, Imam, B, Kreislova, K, Schoefs, F, Markogiannaki, O, Wu, T, Li, Y, Salman, A, Hawchar, L & Ryan, PC 2022, 'Investigating the Effects of Climate Change on Material Properties and Structural Performance', Structural Engineering International, vol. 32, no. 4, pp. 577-588.
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Ortega, JS, Corrales-Orovio, R, Ralph, P, Egaña, JT & Gentile, C 2022, 'Photosynthetic microorganisms for the oxygenation of advanced 3D bioprinted tissues', Acta Biomaterialia.
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Ortega-Delgado, B, Palenzuela, P, Altaee, A, Alarcón-Padilla, D-C, Hawari, AH & Zaragoza, G 2022, 'Thermo-economic assessment of forward osmosis as pretreatment to boost the performance and sustainability of multi-effect distillation for seawater desalination', Desalination, vol. 541, pp. 115989-115989.
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Ottenhaus, L-M, Li, Z & Crews, K 2022, 'Half hole and full hole dowel embedment Strength: A review of international developments and recommendations for Australian softwoods', Construction and Building Materials, vol. 344, pp. 128130-128130.
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Ouchchen, M, Boutaleb, S, Abia, EH, El Azzab, D, Miftah, A, Dadi, B, Echogdali, FZ, Mamouch, Y, Pradhan, B, Santosh, M & Abioui, M 2022, 'Exploration targeting of copper deposits using staged factor analysis, geochemical mineralization prospectivity index, and fractal model (Western Anti-Atlas, Morocco)', Ore Geology Reviews, vol. 143, pp. 104762-104762.
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Oyelade, ON, Ezugwu, AE, Venter, HS, Mirjalili, S & Gandomi, AH 2022, 'Abnormality classification and localization using dual-branch whole-region-based CNN model with histopathological images', Computers in Biology and Medicine, vol. 149, pp. 105943-105943.
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Pal, PK, Jana, KC, Siwakoti, YP, Majumdar, S & Blaabjerg, F 2022, 'An Active-Neutral-Point-Clamped Switched-Capacitor Multilevel Inverter With Quasi-Resonant Capacitor Charging', IEEE Transactions on Power Electronics, vol. 37, no. 12, pp. 14888-14901.
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Pan, T, Jiang, Z, Han, J, Wen, S, Men, A & Wang, H 2022, 'Taylor saves for later: Disentanglement for video prediction using Taylor representation', Neurocomputing, vol. 472, pp. 166-174.
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Pang, G, Shen, C, Cao, L & Hengel, AVD 2022, 'Deep Learning for Anomaly Detection', ACM Computing Surveys, vol. 54, no. 2, pp. 1-38.
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Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection , has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.

Pang, S, Du, A, Orgun, MA, Wang, Y, Sheng, QZ, Wang, S, Huang, X & Yu, Z 2022, 'Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation', IEEE Transactions on Cybernetics, pp. 1-12.
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Park, MJ, Akther, N, Phuntsho, S, Naidu, G, Razmjou, A, An, AK & Shon, HK 2022, 'Development of highly permeable self-standing nanocomposite sulfonated poly ether ketone membrane using covalent organic frameworks', Desalination, vol. 538, pp. 115935-115935.
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Park, MJ, Wang, C, Gonzales, RR, Phuntsho, S, Matsuyama, H, Drioli, E & Shon, HK 2022, 'Fabrication of thin film composite polyamide membrane for water purification via inkjet printing of aqueous and solvent inks', Desalination, vol. 541, pp. 116027-116027.
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Patan, R, Kallam, S, Gandomi, AH, Hanne, T & Ramachandran, M 2022, 'Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling', Journal of the Operational Research Society, vol. 73, no. 10, pp. 2204-2215.
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Paudel, KR, Patel, V, Vishwas, S, Gupta, S, Sharma, S, Chan, Y, Jha, NK, Shrestha, J, Imran, M, Panth, N, Shukla, SD, Jha, SK, Devkota, HP, Warkiani, ME, Singh, SK, Ali, MK, Gupta, G, Chellappan, DK, Hansbro, PM & Dua, K 2022, 'Nutraceuticals and COVID‐19: A mechanistic approach toward attenuating the disease complications', Journal of Food Biochemistry, vol. 46, no. 12.
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Peden, AE, Cullen, P, Francis, KL, Moeller, H, Peden, MM, Ye, P, Tian, M, Zou, Z, Sawyer, SM, Aali, A, Abbasi-Kangevari, Z, Abbasi-Kangevari, M, Abdelmasseh, M, Abdoun, M, Abd-Rabu, R, Abdulah, DM, Abebe, G, Abebe, AM, Abedi, A, Abidi, H, Aboagye, RG, Abubaker Ali, H, Abu-Gharbieh, E, Adane, DE, Adane, TD, Addo, IY, Adewole, OG, Adhikari, S, Adnan, M, Adnani, QES, Afolabi, AAB, Afzal, S, Afzal, MS, Aghdam, ZB, Ahinkorah, BO, Ahmad, AR, Ahmad, T, Ahmad, S, Ahmadi, A, Ahmed, H, Ahmed, MB, Ahmed, A, Ahmed, A, Ahmed, JQ, Ahmed Rashid, T, Aithala, JP, Aji, B, Akhlaghdoust, M, Alahdab, F, Alanezi, FM, Alemayehu, A, Al Hamad, H, Ali, SS, Ali, L, Alimohamadi, Y, Alipour, V, Aljunid, SM, Almidani, L, Almustanyir, S, Altirkawi, KA, Alvis-Zakzuk, NJ, Ameyaw, EK, Amin, TT, Amir-Behghadami, M, Amiri, S, Amiri, H, Anagaw, TF, Andrei, T, Andrei, CL, Anvari, D, Anwar, SL, Anyasodor, AE, Arabloo, J, Arab-Zozani, M, Arja, A, Arulappan, J, Arumugam, A, Aryannejad, A, Asgary, S, Ashraf, T, Athari, SS, Atreya, A, Attia, S, Aujayeb, A, Awedew, AF, Azadnajafabad, S, Azangou-Khyavy, M, Azari, S, Azari Jafari, A, Azizi, H, Azzam, AY, Badiye, AD, Baghcheghi, N, Bagherieh, S, Baig, AA, Bakkannavar, SM, Balta, AB, Banach, M, Banik, PC, Bansal, H, Bardhan, M, Barone-Adesi, F, Barrow, A, Bashiri, A, Baskaran, P, Basu, S, Bayileyegn, NS, Bekel, AA, Bekele, AB, Bendak, S, Bensenor, IM, Berhie, AY, Bhagat, DS, Bhagavathula, AS, Bhardwaj, P, Bhardwaj, N, Bhaskar, S, Bhat, AN, Bhattacharyya, K, Bhutta, ZA, Bibi, S, Bintoro, BS, Bitaraf, S, Bodicha, BBA, Boloor, A, Bouaoud, S, Brown, J, Burkart, K, Butt, NS, Butt, MH, Cámera, LA, Campuzano Rincon, JC, Cao, C, Carvalho, AF, Carvalho, M, Chakraborty, PA, Chandrasekar, EK, Chang, J-C, Charalampous, P, Charan, J, Chattu, VK, Chekole, BM, Chitheer, A, Cho, DY, Chopra, H, Christopher, DJ, Chukwu, IS, Cruz-Martins, N, Dadras, O, Dahlawi, SMA, Dai, X, Damiani, G, Darmstadt, GL, Darvishi Cheshmeh Soltani, R, Darwesh, AM, Das, S, Dastiridou, A, Debela, SA, Dehghan, A, Demeke, GM, Demetriades, AK, Demissie, S, Dessalegn, FN, Desta, AA, Dianatinasab, M, Diao, N, Dias da Silva, D, Diaz, D, Digesa, LE, Diress, M, Djalalinia, S, Doan, LP, Dodangeh, M, Doku, PN, Dongarwar, D, Dsouza, HL, Eini, E, Ekholuenetale, M, Ekundayo, TC, Elagali, AEM, Elbahnasawy, MA, Elhabashy, HR, Elhadi, M, El Sayed Zaki, M & et al. 2022, 'Adolescent transport and unintentional injuries: a systematic analysis using the Global Burden of Disease Study 2019', The Lancet Public Health, vol. 7, no. 8, pp. e657-e669.
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BACKGROUND: Globally, transport and unintentional injuries persist as leading preventable causes of mortality and morbidity for adolescents. We sought to report comprehensive trends in injury-related mortality and morbidity for adolescents aged 10-24 years during the past three decades. METHODS: Using the Global Burden of Disease, Injuries, and Risk Factors 2019 Study, we analysed mortality and disability-adjusted life-years (DALYs) attributed to transport and unintentional injuries for adolescents in 204 countries. Burden is reported in absolute numbers and age-standardised rates per 100 000 population by sex, age group (10-14, 15-19, and 20-24 years), and sociodemographic index (SDI) with 95% uncertainty intervals (UIs). We report percentage changes in deaths and DALYs between 1990 and 2019. FINDINGS: In 2019, 369 061 deaths (of which 214 337 [58%] were transport related) and 31·1 million DALYs (of which 16·2 million [52%] were transport related) among adolescents aged 10-24 years were caused by transport and unintentional injuries combined. If compared with other causes, transport and unintentional injuries combined accounted for 25% of deaths and 14% of DALYs in 2019, and showed little improvement from 1990 when such injuries accounted for 26% of adolescent deaths and 17% of adolescent DALYs. Throughout adolescence, transport and unintentional injury fatality rates increased by age group. The unintentional injury burden was higher among males than females for all injury types, except for injuries related to fire, heat, and hot substances, or to adverse effects of medical treatment. From 1990 to 2019, global mortality rates declined by 34·4% (from 17·5 to 11·5 per 100 000) for transport injuries, and by 47·7% (from 15·9 to 8·3 per 100 000) for unintentional injuries. However, in low-SDI nations the absolute number of deaths increased (by 80·5% to 42 774 for transport injuries and by 39·4% to 31 961 for unintentional injuries). In the high-SDI quintile in 2010-...

Peellage, WH, Fatahi, B & Rasekh, H 2022, 'Experimental investigation for vibration characteristics of jointed rocks using cyclic triaxial tests', Soil Dynamics and Earthquake Engineering, vol. 160, pp. 107377-107377.
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Peng, M, Tian, Y, Gaudin, C, Zhang, L & Sheng, D 2022, 'Application of a coupled hydro‐mechanical interface model in simulating uplifting problems', International Journal for Numerical and Analytical Methods in Geomechanics, vol. 46, no. 17, pp. 3256-3280.
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Peng, S, Cao, L, Zhou, Y, Ouyang, Z, Yang, A, Li, X, Jia, W & Yu, S 2022, 'A survey on deep learning for textual emotion analysis in social networks', Digital Communications and Networks, vol. 8, no. 5, pp. 745-762.
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Peng, Y, Azeem, M, Li, R, Xing, L, Li, Y, Zhang, Y, Guo, Z, Wang, Q, Ngo, HH, Qu, G & Zhang, Z 2022, 'Zirconium hydroxide nanoparticle encapsulated magnetic biochar composite derived from rice residue: Application for As(III) and As(V) polluted water purification', Journal of Hazardous Materials, vol. 423, pp. 127081-127081.
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Finding a low-cost and suitable adsorbent is still in urgent need for efficient decontamination of As(III) and As(V) elements from the polluted waters. A novel zirconium hydroxide nanoparticle encapsulated magnetic biochar composite (ZBC) derived from rice residue was synthesized for the adsorptive capture of As(III) and As(V) from aqueous solutions. The results revealed that ZBC showed an acceptable magnet separation ability and its surface was encapsulated with lots of hydrous zirconium oxide nanoparticles. Compared to As(III), the adsorption of As(V) onto ZBC was mainly dependent on the pH of the solution. The intraparticle diffusion model described the adsorption process. ZBC showed satisfactory adsorption performances to As(III) and As(V) with the highest adsorption quantity of 107.6 mg/g and 40.8 mg/g at pH 6.5 and 8.5, respectively. The adsorption of As(III) and As(V) on ZBC was almost impervious with the ionic strength while the presence of coexisting ions, especially phosphate, significantly affected the adsorption process. The processes of complexation reaction and electrostatic attraction contributed to the adsorption of As(III) and As(V) onto ZBC. ZBC prepared from kitchen rice residue was found to be a low cost environmentally friendly promising adsorbent with high removal capacity for As(III) and As(V) and could be recycled easily from contaminated waters.

Peng, Y, Liu, Y, Li, M, Liu, H & Guo, YJ 2022, 'Synthesizing Circularly Polarized Multi-Beam Planar Dipole Arrays With Sidelobe and Cross-Polarization Control by Two-Step Element Rotation and Phase Optimization', IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4379-4391.
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Pérez-Escamilla, B, Benrimoj, SI, Martínez-Martínez, F, Gastelurrutia, MÁ, Varas-Doval, R, Musial-Gabrys, K & Garcia-Cardenas, V 2022, 'Using network analysis to explore factors moderating the implementation of a medication review service in community pharmacy', Research in Social and Administrative Pharmacy, vol. 18, no. 3, pp. 2432-2443.
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Background

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

Objectives

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

Methods

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

Results

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

Conclusion

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

Perrier, E, Youssry, A & Ferrie, C 2022, 'QDataSet, quantum datasets for machine learning', Scientific Data, vol. 9, no. 1, p. 582.
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AbstractThe availability of large-scale datasets on which to train, benchmark and test algorithms has been central to the rapid development of machine learning as a discipline. Despite considerable advancements, the field of quantum machine learning has thus far lacked a set of comprehensive large-scale datasets upon which to benchmark the development of algorithms for use in applied and theoretical quantum settings. In this paper, we introduce such a dataset, the QDataSet, a quantum dataset designed specifically to facilitate the training and development of quantum machine learning algorithms. The QDataSet comprises 52 high-quality publicly available datasets derived from simulations of one- and two-qubit systems evolving in the presence and/or absence of noise. The datasets are structured to provide a wealth of information to enable machine learning practitioners to use the QDataSet to solve problems in applied quantum computation, such as quantum control, quantum spectroscopy and tomography. Accompanying the datasets on the associated GitHub repository are a set of workbooks demonstrating the use of the QDataSet in a range of optimisation contexts.

Petrik, LF, Ngo, HH, Varjani, S, Osseweijer, P, Xevgenos, D, van Loosdrecht, M, Smol, M, Yang, X & Mateo-Sagasta, J 2022, 'From wastewater to resource', One Earth, vol. 5, no. 2, pp. 122-125.
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Pham, DX, Phung, AHT, Nguyen, HD, Bui, TD, Mai, LD, Tran, BNH, Tran, TS, Nguyen, TV & Ho-Pham, LT 2022, 'Trends in colorectal cancer incidence in Ho Chi Minh City, Vietnam (1996–2015): Joinpoint regression and age–period–cohort analyses', Cancer Epidemiology, vol. 77, pp. 102113-102113.
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Pham, HN, Dang, KB, Nguyen, TV, Tran, NC, Ngo, XQ, Nguyen, DA, Phan, TTH, Nguyen, TT, Guo, W & Ngo, HH 2022, 'A new deep learning approach based on bilateral semantic segmentation models for sustainable estuarine wetland ecosystem management', Science of The Total Environment, vol. 838, pp. 155826-155826.
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Pietroni, N, Dumery, C, Falque, R, Liu, M, Vidal-Calleja, T & Sorkine-Hornung, O 2022, 'Computational pattern making from 3D garment models', ACM Transactions on Graphics, vol. 41, no. 4, pp. 1-14.
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We propose a method for computing a sewing pattern of a given 3D garment model. Our algorithm segments an input 3D garment shape into patches and computes their 2D parameterization, resulting in pattern pieces that can be cut out of fabric and sewn together to manufacture the garment. Unlike the general state-of-the-art approaches for surface cutting and flattening, our method explicitly targets garment fabrication. It accounts for the unique properties and constraints of tailoring, such as seam symmetry, the usage of darts, fabric grain alignment, and a flattening distortion measure that models woven fabric deformation, respecting its anisotropic behavior. We bootstrap a recent patch layout approach developed for quadrilateral remeshing and adapt it to the purpose of computational pattern making, ensuring that the deformation of each pattern piece stays within prescribed bounds of cloth stress. While our algorithm can automatically produce the sewing patterns, it is fast enough to admit user input to creatively iterate on the pattern design. Our method can take several target poses of the 3D garment into account and integrate them into the sewing pattern design. We demonstrate results on both skintight and loose garments, showcasing the versatile application possibilities of our approach.

Poostchi, H & Piccardi, M 2022, 'BiLSTM-SSVM: Training the BiLSTM with a Structured Hinge Loss for Named-Entity Recognition', IEEE Transactions on Big Data, vol. 8, no. 1, pp. 203-212.
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Building on the achievements of the BiLSTM-CRF in named-entity recognition (NER), this paper introduces the BiLSTM-SSVM, an equivalent neural model where training is performed using a structured hinge loss. The typical loss functions used for evaluating NER are entity-level variants of the F1 score such as the CoNLL and MUC losses. Unfortunately, the common loss function used for training NER - the cross entropy - is only loosely related to the evaluation losses. For this reason, in this paper we propose a training approach for the BiLSTM-CRF that leverages a hinge loss bounding the CoNLL loss from above. In addition, we present a mixed hinge loss that bounds either the CoNLL loss or the Hamming loss based on the density of entity tokens in each sentence. The experimental results over four benchmark languages (English, German, Spanish and Dutch) show that training with the mixed hinge loss has led to small but consistent improvements over the cross entropy across all languages and four different evaluation measures

Poursafar, N, Hossain, MJ & Taghizadeh, S 2022, 'Distributed DC-Bus Signaling Control of Photovoltaic Systems in Islanded DC Microgrid', CSEE Journal of Power and Energy Systems, vol. 8, no. 6, pp. 1741-1750.
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The stability of an islanded DC microgrid (DCMG) is highly dependent on the presence and performance of the backup energy storage system (BESS), due to the lack of main grid support. This condition makes the DCMG vulnerable to the critical situation of absence of the BESS, which could be caused by a fault or being fully charged or flat. This paper presents an enhanced distributed DC-bus signaling control strategy for converters of photovoltaic systems (PVs) to make the islanded DCMG less dependent on the BESS. Unlike a conventional control approach that utilizes PVs to operate in maximum power point tracking (MPPT) mode and the BESS solely regulating DC-bus voltage, the proposed control method maintains DC-bus voltage via intelligently managing output powers of the PVs. The proposed control method continuously monitors DC-bus voltage and regulates the output powers of all the PVs via switching between MPPT mode and voltage regulating mode. Accordingly, if the DC-bus voltage level is less than a predefined maximum level, the PVs work in MPPT mode; otherwise, the PVs work in voltage regulating mode to maintain DC-bus voltage at an acceptable range. Such switching between MPPT and voltage regulating control operations results in protecting the DCMG from unavoidable shutdowns conventionally necessary during the absence of the BESS unit. Moreover, the proposed control method reduces oscillations on the DC-bus voltage during existence of the BESS. The performance and effectiveness of the proposed control strategy are validated through different case studies in MATLAB/Simulink.

Poursafar, N, Taghizadeh, S, J. Hossain, M & M. Guerrero, J 2022, 'An Optimized Distributed Cooperative Control to Improve the Charging Performance of Battery Energy Storage in a Multiphotovoltaic Islanded DC Microgrid', IEEE Systems Journal, vol. 16, no. 1, pp. 1170-1181.
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Priharsari, D & Abedin, B 2022, 'Orchestrating value co-creation in online communities as fluid organisations: firm roles and value creation mechanisms', Information Technology & People, vol. 35, no. 7, pp. 2393-2417.
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PurposeThe lack of authority of the sponsoring firm in online communities raises questions about how to orchestrate members of an online community in value co-creation. Hence, this study aims to examine how online communities co-create value with community members. The authors draw upon service-dominant logic (SDL) to study two comparable, and yet different, Indonesian firm-sponsored online communities.Design/methodology/approachThe authors build on an earlier systematic literature review and triangulate it with semi-structured interviews of 28 community members and content analysis of over 35,000 online comments. The data collection was conducted from February to October 2018.FindingsThe findings revealed that (1) value co-creation in online communities is orchestrated through the fluidity of the online community, which is represented by three mechanisms: consensus-making, consensus settlement and changing boundaries, and (2) the mechanisms can be conditioned by switching firm roles (as a co-creator and facilitator).Research limitations/implicationsThe study has enriched the body of knowledge in fluid organisations by explicating three mechanisms, consensus-making, consensus settlement and changing boundaries, that explain the coordination efforts between individuals who have options to participate or not and changing boundaries, that reveals actors' responses in online communities. The mechanisms demonstrate the dynamics of a service ecosystem.Originality/valueThis study offers valuable insights into...

Prior, DD, Saberi, M, Janjua, NK & Jie, F 2022, 'Can i trust you? incorporating supplier trustworthiness into supplier selection criteria', Enterprise Information Systems, vol. 16, no. 8-9, pp. 1-28.
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Pu, Y, Tang, J, Zeng, T, Hu, Y, Wang, Q, Huang, J, Pan, S, Wang, XC, Li, Y, Hao Ngo, H & Abomohra, A 2022, 'Enhanced energy production and biological treatment of swine wastewater using anaerobic membrane bioreactor: Fouling mechanism and microbial community', Bioresource Technology, vol. 362, pp. 127850-127850.
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Pugalia, S & Cetindamar, D 2022, 'Insights on the glass ceiling for immigrant women entrepreneurs in the technology sector', International Journal of Gender and Entrepreneurship, vol. 14, no. 1, pp. 44-68.
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PurposeTechnology sector is the pivotal element for innovation and economic development of any country. Hence, the present article explores past researches looking into challenges faced by immigrant women entrepreneurs in technology sector and their corresponding response strategies.Design/methodology/approachThis study employs a systematic literature review (SLR) technique to collate all the relevant literature looking into the challenges and strategies from immigrant women entrepreneur's perspective and provide a comprehensive picture. Overall, 49 research articles are included in this SLR.FindingsFindings indicate that immigrant status further escalates the human, financial and network disadvantages faced by women who want to start a technology-based venture.Originality/valueThis paper contributes to the literature by categorizing the barriers and strategies on a 3 × 2 matrix reflecting the origins of the barrier or strategy (taking place at the individual, firm or institutional level) versus the type of the barrier or strategy (arising from being an immigrant woman and being a woman in the technology sector). After underlining the dearth of studies in the literature about the complex phenomenon of immigrant WEs in the technology sector, the paper points out several neglected themes for future research.

Punetha, P & Nimbalkar, S 2022, 'Geotechnical rheological modeling of ballasted railway tracks considering the effect of principal stress rotation', Canadian Geotechnical Journal, vol. 59, no. 10, pp. 1793-1818.
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The rotation of principal stress direction experienced by the soil elements in a railway track substructure during a train passage influences the magnitude of accumulated settlement. However, the existing methods to evaluate the track response under repeated train loads disregard the influence of principal stress rotation (PSR). This article presents a novel approach for assessing the behavior of ballasted railway tracks incorporating the contribution of PSR on track deformation. The proposed technique employs a geotechnical rheological model to evaluate the track behavior, in which the material plasticity is captured through plastic slider elements. The influence of PSR is accounted for by extending an existing constitutive relationship for the slider elements for the substructure layers, which is successfully validated against experimental data reported in the literature. The results reveal that PSR causes significant cumulative deformation in the substructure layers, and disregarding it in the analysis leads to inaccurate predictions. The proposed approach is then applied to an open track-bridge transition with heterogeneous support conditions, in which the differential settlement is found to be largely influenced by PSR. The findings from this study highlight the importance of including the effect of PSR in predictive models for a reliable evaluation of track performance.

Punetha, P & Nimbalkar, S 2022, 'Performance improvement of ballasted railway tracks using three-dimensional cellular geoinclusions', Geotextiles and Geomembranes, vol. 50, no. 6, pp. 1061-1082.
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Punitha, S, Stephan, T & Gandomi, AH 2022, 'A Novel Breast Cancer Diagnosis Scheme With Intelligent Feature and Parameter Selections', Computer Methods and Programs in Biomedicine, vol. 214, pp. 106432-106432.
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Puthal, D, Wilson, S, Nanda, A, Liu, M, Swain, S, Sahoo, BPS, Yelamarthi, K, Pillai, P, El-Sayed, H & Prasad, M 2022, 'Decision tree based user-centric security solution for critical IoT infrastructure', Computers and Electrical Engineering, vol. 99, pp. 107754-107754.
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Qi, C, Yin, R, Cheng, J, Xu, Z, Chen, J, Gao, X, Li, G, Nghiem, L & Luo, W 2022, 'Bacterial dynamics for gaseous emission and humification during bio-augmented composting of kitchen waste with lime addition for acidity regulation', Science of The Total Environment, vol. 848, pp. 157653-157653.
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Qi, Y & Indraratna, B 2022, 'Influence of Rubber Inclusion on the Dynamic Response of Rail Track', Journal of Materials in Civil Engineering, vol. 34, no. 2.
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Qian, H, Li, J, Pan, Y, Zong, Z & Wu, C 2022, 'Numerical derivation of P-I diagrams for shallow buried RC box structures', Tunnelling and Underground Space Technology, vol. 124, pp. 104454-104454.
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Qian, J, Zhang, Y, Bai, L, Yan, X, Du, Y, Ma, R & Ni, B-J 2022, 'Revealing the mechanisms of polypyrrole (Ppy) enhancing methane production from anaerobic digestion of waste activated sludge (WAS)', Water Research, vol. 226, pp. 119291-119291.
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Anaerobic digestion (AD) is a promising method for treating waste activated sludge (WAS), but the low methane yield limits its large-scale application. The addition of conductive nanomaterials has been demonstrated to enhance the activity of AD via promoting the direct interspecies electron transfer (DIET). In this study, novel conductive polypyrrole (Ppy) was prepared to effectively improve the AD performance of WAS. The results showed that the accumulative methane production was enhanced by 27.83% by Ppy, with both acidogenesis and methanogenesis being efficiently accelerated. The microbial community analysis indicated that the abundance of bacteria associated with acidogenesis process was significantly elevated by Ppy. Further investigation by metatranscriptomics revealed that fadE and fadN genes (to express the key enzymes in fatty acid metabolism) were highly expressed in the Ppy-driven AD, suggesting that Ppy promoted electron generation during acid production. For methanogenesis metabolism, genes related to acetate utilization and CO2 utilization methanogenesis were also up-regulated by Ppy, illustrating that Ppy facilitates the utilization of acetate and electrons by methanogenic archaea, thus potentially promoting the methanogenesis through DIET.

Qin, H 2022, 'Decision-making under uncertainty for buildings exposed to environmental hazards', Journal of Safety Science and Resilience, vol. 3, no. 1, pp. 1-14.
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Qin, H, Li, R-H, Yuan, Y, Wang, G, Qin, L & Zhang, Z 2022, 'Mining Bursting Core in Large Temporal Graphs', Proceedings of the VLDB Endowment, vol. 15, no. 13, pp. 3911-3923.
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Temporal graphs are ubiquitous. Mining communities that are bursting in a period of time is essential for seeking real emergency events in temporal graphs. Unfortunately, most previous studies on community mining in temporal networks ignore the bursting patterns of communities. In this paper, we study the problem of seeking bursting communities in a temporal graph. We propose a novel model, called the ( l , δ)-maximal bursting core, to represent a bursting community in a temporal graph. Specifically, an ( l , δ)-maximal bursting core is a temporal subgraph in which each node has an average degree no less than δ in a time segment with length no less than l. To compute the ( l , δ)-maximal bursting core, we first develop a novel dynamic programming algorithm that can reduce time complexity of calculating the segment density from O (| T |) 2 to O (| T |). Then, we propose an efficient updating algorithm which can update the segment density in O ( l ) time. In addition, we develop an efficient algorithm to enumerate all ( l , δ)-maximal bursting cores that are not dominated by the others in terms of l and δ. The results of extensive experiments on 9 real-life datasets demonstrate the effectiveness, efficiency and scalability of our algorithms.

Qin, H, Li, R-H, Yuan, Y, Wang, G, Yang, W & Qin, L 2022, 'Periodic Communities Mining in Temporal Networks: Concepts and Algorithms', IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 8, pp. 3927-3945.
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Qin, L, Yang, G, Li, D, Ou, K, Zheng, H, Fu, Q & Sun, Y 2022, 'High area energy density of all-solid-state supercapacitor based on double-network hydrogel with high content of graphene/PANI fiber', Chemical Engineering Journal, vol. 430, pp. 133045-133045.
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Qin, P-Y, Song, L-Z & Guo, YJ 2022, 'Conformal Transmitarrays for Unmanned Aerial Vehicles Aided 6G Networks', IEEE Communications Magazine, vol. 60, no. 1, pp. 14-20.
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Qin, S, Yang, N, Zhu, X & Wang, Z 2022, 'Analytical Approach for Load-Carrying Capacity Evaluation of Tibetan Timber Beam-column Joint', International Journal of Architectural Heritage, pp. 1-17.
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Queti is an important component of Tibetan timber beam-column joint to transfer compression, shear, and bending moment from one structural component to another. The inclination of Queti is a common type of damage in Tibetan heritage buildings and it significantly reduces the load-carrying capacity and safety of the joint under vertical load. In this paper, an analytical model of the joint with Queti-inclination is proposed to predict the yield and ultimate loads of the joint and the corresponding failure modes. Laboratory tests have been conducted on typical Tibetan beam-column joints to verify the proposed model. A parametric study is also conducted on the effects of material property, Queti width and height, as well as the dowel height on the load-carrying capacity of the joint. Results obtained show that a weaker material property will significantly reduce the capacity of the joint. An increase in Queti width and dowel height have an ameliorative effect on the yield and ultimate loads, while the Queti height has the opposite effect.

Qiu, N, Zhang, J, Yuan, F, Jin, Z, Zhang, Y & Fang, J 2022, 'Mechanical performance of triply periodic minimal surface structures with a novel hybrid gradient fabricated by selective laser melting', Engineering Structures, vol. 263, pp. 114377-114377.
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Qiu, Y-X, Wen, D, Li, R-H, Qin, L, Yu, M & Lin, X 2022, 'Computing Significant Cliques in Large Labeled Networks', IEEE Transactions on Big Data, pp. 1-13.
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Qiu, Y-X, Wen, D, Qin, L, Li, W, Li, R-H & Zhang, Y 2022, 'Efficient shortest path counting on large road networks', Proceedings of the VLDB Endowment, vol. 15, no. 10, pp. 2098-2110.
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The shortest path distance and related concepts lay the foundations of many real-world applications in road network analysis. The shortest path count has drawn much research attention in academia, not only as a closeness metric accompanying the shorted distance but also serving as a building block of centrality computation. This paper aims to improve the efficiency of counting the shortest paths between two query vertices on a large road network. We propose a novel index solution by organizing all vertices in a tree structure and propose several optimizations to speed up the index construction. We conduct extensive experiments on 14 real-world networks. Compared with the state-of-the-art solution, we achieve much higher efficiency on both query processing and index construction with a more compact index.

Qu, F, Li, W, Guo, Y, Zhang, S, Zhou, JL & Wang, K 2022, 'Chloride-binding capacity of cement-GGBFS-nanosilica composites under seawater chloride-rich environment', Construction and Building Materials, vol. 342, pp. 127890-127890.
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Qu, X, Zou, Z, Su, X, Zhou, P, Wei, W, Wen, S & Wu, D 2022, 'Attend to Where and When: Cascaded Attention Network for Facial Expression Recognition', IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 6, no. 3, pp. 580-592.
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Qu, Y, Gao, L, Xiang, Y, Shen, S & Yu, S 2022, 'FedTwin: Blockchain-Enabled Adaptive Asynchronous Federated Learning for Digital Twin Networks', IEEE Network, vol. 36, no. 6, pp. 183-190.
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Qu, Y, Xu, C, Gao, L, Xiang, Y & Yu, S 2022, 'FL-SEC: Privacy-Preserving Decentralized Federated Learning Using SignSGD for the Internet of Artificially Intelligent Things', IEEE Internet of Things Magazine, vol. 5, no. 1, pp. 85-90.
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Qu, Z, Lau, CW, Simoff, SJ, Kennedy, PJ, Nguyen, QV & Catchpoole, DR 2022, 'Review of Innovative Immersive Technologies for Healthcare Applications', Innovations in Digital Health, Diagnostics, and Biomarkers, vol. 2, no. 2022, pp. 27-39.
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ABSTRACT Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications' usability, innovation, and future work in the healthcare domain, and digital health data visual analytics.

R., J, Gurunathan, B, K, S, Varjani, S, Ngo, HH & Gnansounou, E 2022, 'Advancements in heavy metals removal from effluents employing nano-adsorbents: Way towards cleaner production', Environmental Research, vol. 203, pp. 111815-111815.
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Rad, HS, Shiravand, Y, Radfar, P, Ladwa, R, Perry, C, Han, X, Warkiani, ME, Adams, MN, Hughes, BGM, O'Byrne, K & Kulasinghe, A 2022, 'Understanding the tumor microenvironment in head and neck squamous cell carcinoma', Clinical & Translational Immunology, vol. 11, no. 6.
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Rad, MA, Mahmodi, H, Filipe, EC, Cox, TR, Kabakova, I & Tipper, JL 2022, 'Micromechanical characterisation of 3D bioprinted neural cell models using Brillouin microspectroscopy', Bioprinting, vol. 25, pp. e00179-e00179.
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Radfar, P, Aboulkheyr Es, H, Salomon, R, Kulasinghe, A, Ramalingam, N, Sarafraz-Yazdi, E, Thiery, JP & Warkiani, ME 2022, 'Single-cell analysis of circulating tumour cells: enabling technologies and clinical applications', Trends in Biotechnology, vol. 40, no. 9, pp. 1041-1060.
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Rafique, S, Nizami, MSH, Irshad, UB, Hossain, MJ & Mukhopadhyay, SC 2022, 'A two-stage multi-objective stochastic optimization strategy to minimize cost for electric bus depot operators', Journal of Cleaner Production, vol. 332, pp. 129856-129856.
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Rafique, S, Nizami, MSH, Irshad, UB, Hossain, MJ & Mukhopadhyay, SC 2022, 'EV Scheduling Framework for Peak Demand Management in LV Residential Networks', IEEE Systems Journal, vol. 16, no. 1, pp. 1520-1528.
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Ragazzon, MRP, Messineo, S, Gravdahl, JT, Harcombe, DM & Ruppert, MG 2022, 'The Generalized Lyapunov Demodulator: High-Bandwidth, Low-Noise Amplitude and Phase Estimation', IEEE Open Journal of Control Systems, vol. 1, pp. 69-84.
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Rahman, M, Zhao, M, Islam, MS, Dong, K & Saha, SC 2022, 'Numerical study of nano and micro pollutant particle transport and deposition in realistic human lung airways', Powder Technology, vol. 402, pp. 117364-117364.
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Rahman, MM, Zhao, M, Islam, MS, Dong, K & Saha, SC 2022, 'Nanoparticle transport and deposition in a heterogeneous human lung airway tree: An efficient one path model for CFD simulations', European Journal of Pharmaceutical Sciences, vol. 177, pp. 106279-106279.
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Rahman, T, Paul, SK, Shukla, N, Agarwal, R & Taghikhah, F 2022, 'Supply chain resilience initiatives and strategies: A systematic review', Computers & Industrial Engineering, vol. 170, pp. 108317-108317.
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Rajamohan, D, Kim, J, Garratt, M & Pickering, M 2022, 'Image based Localization under large perspective difference between Sfm and SLAM using split sim(3) optimization', Autonomous Robots, vol. 46, no. 3, pp. 437-449.
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AbstractImage based Localization (IbL) uses both Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM) data for accurate pose estimation. However, under conditions where there is a large perspective difference between the SfM images and SLAM keyframes, the SfM-SLAM co-visibility graph becomes sparse. As a result, the scale drift can increase especially when using monocular SLAM as part of the IbL framework. The drift rarely gets corrected at loop closure due to its large magnitude. We propose a split affine transformation approach that uses SfM-SLAM information along with Sim(3) optimization to minimize the scale drift. Experiments are performed using an image dataset collected in a campus environment with different trajectories, showing the improvement in scale drift correction with the proposed method. The SLAM data was collected close to plainly textured structures like buildings while SfM images were captured from a larger distance from the building facade which leads to a challenging navigation scenario in the context of IbL. Localizing mobile platforms moving close to buildings is an example of such a case. The paper positively impacts the widespread use of small autonomous robotic platforms, which is to perform an accurate outdoor localization under urban conditions using only a monocular camera.

Ramachandran, M, Patan, R, Kumar, A, Hosseini, S & Gandomi, AH 2022, 'Mutual Informative MapReduce and Minimum Quadrangle Classification for Brain Tumor Big Data', IEEE Transactions on Engineering Management, pp. 1-12.
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Ran, H, Sun, L, Cheng, S, Ma, Y, Yan, S, Meng, S, Shi, K & Wen, S 2022, 'A novel cooperative searching architecture for multi‐unmanned aerial vehicles under restricted communication', Asian Journal of Control, vol. 24, no. 2, pp. 510-516.
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Ran, H, Wen, S, Li, Q, Yang, Y, Shi, K, Feng, Y, Zhou, P & Huang, T 2022, 'Memristor-Based Edge Computing of Blaze Block for Image Recognition', IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 5, pp. 2121-2131.
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In this article, a novel edge computing system is proposed for image recognition via memristor-based blaze block circuit, which includes a memristive convolutional neural network (MCNN) layer, two single-memristive blaze blocks (SMBBs), four double-memristive blaze blocks (DMBBs), a global Avg-pooling (GAP) layer, and a memristive full connected (MFC) layer. SMBBs and DMBBs mainly utilize the depthwise separable convolution neural network (DwCNN) that can be implemented with a much smaller memristor crossbar (MC). In the backward propagation, we use batch normalization (BN) layers to accelerate the convergence. In the forward propagation, this circuit combines DwCNN layers/CNN layers with nonseparate BN layers, which means that the required number of operational amplifiers is cut by half as long as the greatly reduced power consumption. A diode is added after the rectified linear unit (ReLU) layer to limit the output of the circuit below the threshold voltage Vt of the memristor; thus, the circuit is more stable. Experiments show that the proposed memristor-based circuit achieves an accuracy of 84.38% on the CIFAR-10 data set with advantages in computing resources, calculation time, and power consumption. Experiments also show that, when the number of multistate conductance is 2⁸ and the quantization bit of the data is 8, the circuit can achieve its best balance between power consumption and production cost.

Rana, AK, Thakur, MK, Saini, AK, Mokhta, SK, Moradi, O, Rydzkowski, T, Alsanie, WF, Wang, Q, Grammatikos, S & Thakur, VK 2022, 'Recent developments in microbial degradation of polypropylene: Integrated approaches towards a sustainable environment', Science of The Total Environment, vol. 826, pp. 154056-154056.
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Rani, P, Mishra, AR, Krishankumar, R, Ravichandran, KS & Gandomi, AH 2022, 'A New Pythagorean Fuzzy Based Decision Framework for Assessing Healthcare Waste Treatment', IEEE Transactions on Engineering Management, vol. 69, no. 6, pp. 2915-2929.
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Ranjbar, E, Suratgar, AA, Menhaj, MB & Prasad, M 2022, 'Design of a Fuzzy Adaptive Sliding Mode Control System for MEMS Tunable Capacitors in Voltage Reference Applications', IEEE Transactions on Fuzzy Systems, vol. 30, no. 6, pp. 1838-1852.
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Rao, A, Elder, E, Center, JR, Tran, T, Pocock, N & Elder, GJ 2022, 'Improving Bone Mineral Density Screening by Using Digital X‐Radiogrammetry Combined With Mammography', JBMR Plus, vol. 6, no. 5.
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Rao, P, Ouyang, P, Wu, J, Li, P, Nimbalkar, S & Chen, Q 2022, 'Seismic Stability of Heterogeneous Slopes with Tensile Strength Cutoff Using Discrete-Kinematic Mechanism and a Pseudostatic Approach', International Journal of Geomechanics, vol. 22, no. 12.
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Rao, P, Xiang, Y, Ouyang, P, Nimbalkar, S & Chen, Q 2022, 'Finite Element Analysis of Electro-Thermal Coupling of Sandstone Under Lightning Currents', Geotechnical and Geological Engineering, vol. 40, no. 5, pp. 2593-2604.
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Rao, P-P, Ouyang, P-H, Nimbalkar, S, Chen, Q-S, Wu, Z-L & Cui, J-F 2022, 'Analytical modelling of the mechanical damage of soil induced by lightning strikes capturing electro-thermal, thermo-osmotic, and electro-osmotic effects', Journal of Mountain Science, vol. 19, no. 7, pp. 2027-2043.
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Rasal, AS, Yadav, S, Kashale, AA, Altaee, A & Chang, J-Y 2022, 'Stability of quantum dot-sensitized solar cells: A review and prospects', Nano Energy, vol. 94, pp. 106854-106854.
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Rasouli, H & Fatahi, B 2022, 'Liquefaction and post-liquefaction resistance of sand reinforced with recycled geofibre', Geotextiles and Geomembranes, vol. 50, no. 1, pp. 69-81.
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Rasouli, H, Fatahi, B & Nimbalkar, S 2022, 'Re-liquefaction resistance of lightly cemented sands', Canadian Geotechnical Journal, vol. 59, no. 12, pp. 2085-2101.
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The re-liquefaction resistance of cemented sands under multiple liquefaction events such as pre-shock, main-shock, and after-shock earthquakes is a complex phenomenon because the response may alter due to bond breakage. A series of multistage liquefaction–re-consolidation soil element tests under undrained stress-controlled cyclic loading condition using cyclic triaxial were carried out to assess the liquefaction and re-liquefaction resistance of cemented sands with varying degrees of cementation. Lightly cemented specimens were reconstituted using Sydney sand and high early strength Portland cement with cement content ranging from 0.25% to 1% and unconfined compression strength from 15 to 80 kPa. The results showed that the re-liquefaction resistance of cemented sands with different amounts of cement decreased after the first liquefaction event and then increased for succeeding liquefaction events. While the trend of residual excess pore water pressure ratio and cyclic stiffness degradation index of untreated sand under successive liquefaction events remained consistent, the corresponding responses for cemented sands altered for the second to the fifth liquefaction events. In fact, the residual excess pore water pressure ratio and cyclic stiffness of cemented sand increased and degraded faster during the early cycles of loading for the second to fifth liquefaction events.

Ravindran, MXY, Asikin-Mijan, N, Ong, HC, Derawi, D, Yusof, MR, Mastuli, MS, Lee, HV, Wan Mahmood, WNAS, Razali, MS, Abdulkareem Al-Sultan, G & Taufiq-Yap, YH 2022, 'Feasibility of advancing the production of bio-jet fuel via microwave reactor under low reaction temperature', Journal of Analytical and Applied Pyrolysis, vol. 168, pp. 105772-105772.
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Raza, M, Ali, L, Inayat, A, Rocha‐Meneses, L, Ahmed, SF, Mofijur, M, Jamil, F & Azimoh, CL 2022, 'Sustainability of biodiesel production using immobilized enzymes: A strategy to meet future bio‐economy challenges', International Journal of Energy Research, vol. 46, no. 13, pp. 19090-19108.
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Raza, MA, Abolhasan, M, Lipman, J, Shariati, N, Ni, W & Jamalipour, A 2022, 'Statistical Learning-Based Grant-Free Access for Delay-Sensitive Internet of Things Applications', IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 5492-5506.
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Razmjoo, A, Gandomi, AH, Pazhoohesh, M, Mirjalili, S & Rezaei, M 2022, 'The key role of clean energy and technology in smart cities development', Energy Strategy Reviews, vol. 44, pp. 100943-100943.
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Razzak, I, Eklund, P & Xu, G 2022, 'Improving healthcare outcomes using multimedia big data analytics', Neural Computing and Applications, vol. 34, no. 17, pp. 15095-15097.
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Razzak, I, Eklund, P & Xu, G 2022, 'Introduction to the special section on securing IoT-based critical infrastructure (VSI-cei)', Computers and Electrical Engineering, vol. 101, pp. 108118-108118.
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Razzak, I, Moustafa, N, Mumtaz, S & Xu, G 2022, 'One‐class tensor machine with randomized projection for large‐scale anomaly detection in high‐dimensional and noisy data', International Journal of Intelligent Systems, vol. 37, no. 8, pp. 4515-4536.
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Razzak, I, Xu, G & Khan, MK 2022, 'Guest Editorial: Privacy-Preserving Federated Machine Learning Solutions for Enhanced Security of Critical Energy Infrastructures', IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3449-3451.
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Razzaq, L, Mujtaba, MA, Shahbaz, MA, Nawaz, S, Mahmood Khan, H, Hussain, A, Ishtiaq, U, Kalam, MA, M. Soudagar, ME, Ismail, KA, Elfasakhany, A & Rizwan, HM 2022, 'Effect of biodiesel-dimethyl carbonate blends on engine performance, combustion and emission characteristics', Alexandria Engineering Journal, vol. 61, no. 7, pp. 5111-5121.
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Rees, N, Thiyagarajan, K, Wickramanayake, S & Kodagoda, S 2022, 'Ground-Penetrating Radar Signal Characterization for Non-destructive Evaluation of Low-Range Concrete Sub-surface Boundary Conditions', IEEE Sensors Letters, vol. 6, no. 4, pp. 1-4.
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Reja, VK, Varghese, K & Ha, QP 2022, 'Computer vision-based construction progress monitoring', Automation in Construction, vol. 138, pp. 104245-104245.
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Ren, H, Lu, W, Xiao, Y, Chang, X, Wang, X, Dong, Z & Fang, D 2022, 'Graph convolutional networks in language and vision: A survey', Knowledge-Based Systems, vol. 251, pp. 109250-109250.
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Ren, J, Zhang, B, Zhu, X & Li, S 2022, 'Damaged cable identification in cable-stayed bridge from bridge deck strain measurements using support vector machine', Advances in Structural Engineering, vol. 25, no. 4, pp. 754-771.
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A new two-step approach is developed for damaged cable identification in a cable-stayed bridge from deck bending strain responses using Support Vector Machine. A Damaged Cable Identification Machine (DCIM) based on support vector classification is constructed to determine the damaged cable and a Damage Severity Identification Machine (DSIM) based on support vector regression is built to estimate the damage severity. A field cable-stayed bridge with a long-term monitoring system is used to verify the proposed method. The three-dimensional Finite Element Model (FEM) of the cable-stayed bridge is established using ANSYS, and the model is validated using the field testing results, such as the mode shape, natural frequencies and its bending strain responses of the bridge under a moving vehicle. Then the validated FEM is used to simulate the bending strain responses of the longitude deck near the cable anchors when the vehicle is passing over the bridge. Different damage scenarios are simulated for each cable with various severities. Based on damage indexes vector, the training datasets and testing datasets are acquired, including single damaged cable scenarios and double damaged cable scenarios. Eventually, DCIM is trained using Support Vector Classification Machine and DSIM is trained using Support Vector Regression Machine. The testing datasets are input in DCIM and DSIM to check their accuracy and generalization capability. Different noise levels including 5%, 10%, and 20% are considered to study their anti-noise capability. The results show that DCIM and DSIM both have good generalization capability and anti-noise capability.

Ren, P, Xiao, Y, Chang, X, Huang, P-Y, Li, Z, Chen, X & Wang, X 2022, 'A Comprehensive Survey of Neural Architecture Search', ACM Computing Surveys, vol. 54, no. 4, pp. 1-34.
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Deep learning has made substantial breakthroughs in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is crucial to the feature representation of data and the final performance. However, the design of the neural architecture heavily relies on the researchers’ prior knowledge and experience. And due to the limitations of humans’ inherent knowledge, it is difficult for people to jump out of their original thinking paradigm and design an optimal model. Therefore, an intuitive idea would be to reduce human intervention as much as possible and let the algorithm automatically design the neural architecture. Neural Architecture Search ( NAS ) is just such a revolutionary algorithm, and the related research work is complicated and rich. Therefore, a comprehensive and systematic survey on the NAS is essential. Previously related surveys have begun to classify existing work mainly based on the key components of NAS: search space, search strategy, and evaluation strategy. While this classification method is more intuitive, it is difficult for readers to grasp the challenges and the landmark work involved. Therefore, in this survey, we provide a new perspective: beginning with an overview of the characteristics of the earliest NAS algorithms, summarizing the problems in these early NAS algorithms, and then providing solutions for subsequent related research work. In addition, we conduct a detailed and comprehensive analysis, comparison, and summary of these works. Finally, we provide some possible future research directions.

Ren, P, Xiao, Y, Chang, X, Huang, P-Y, Li, Z, Gupta, BB, Chen, X & Wang, X 2022, 'A Survey of Deep Active Learning', ACM Computing Surveys, vol. 54, no. 9, pp. 1-40.
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Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted significant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have a relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely according the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to a large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in fields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.). Therefore, AL is gradually coming to receive the attention it is due. It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DeepAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DeepAL-related works; accordingly, this article aims to fill this gap. We provide a formal classification method for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DeepAL from an application perspective. Finally, we discuss the confusion and p...

Ren, S, Guo, B, Li, K, Wang, Q, Yu, Z & Cao, L 2022, 'CoupledMUTS: Coupled Multivariate Utility Time-Series Representation and Prediction', IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22972-22982.
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Ren, Z, Zhang, X, Huang, Z, Hu, J, Li, Y, Zheng, S, Gao, M, Pan, H & Liu, Y 2022, 'Controllable synthesis of 2D TiH2 nanoflakes with superior catalytic activity for low-temperature hydrogen cycling of NaAlH4', Chemical Engineering Journal, vol. 427, pp. 131546-131546.
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Rene, ER, Kennes, C, Nghiem, LD & Varjani, S 2022, 'New insights in biodegradation of organic pollutants', Bioresource Technology, vol. 347, pp. 126737-126737.
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Rezazadegan, D, Berkovsky, S, Quiroz, JC, Kocaballi, AB, Wang, Y, Laranjo, L & Coiera, E 2022, 'Symbolic and Statistical Learning Approaches to Speech Summarization: A Scoping Review', Computer Speech & Language, vol. 72, pp. 101305-101305.
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Ricafrente, A, Cwiklinski, K, Nguyen, H, Dalton, JP, Tran, N & Donnelly, S 2022, 'Stage-specific miRNAs regulate gene expression associated with growth, development and parasite-host interaction during the intra-mammalian migration of the zoonotic helminth parasite Fasciola hepatica', BMC Genomics, vol. 23, no. 1.
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Abstract Background MiRNAs are small non-coding RNAs that post-transcriptionally regulate gene expression in organisms ranging from viruses to mammals. There is great relevance in understanding how miRNAs regulate genes involved in the growth, development, and maturation of the many parasitic worms (helminths) that together afflict more than 2 billion people. Results Here, we describe the miRNAs expressed by each of the predominant intra-mammalian development stages of Fasciola hepatica, a foodborne flatworm that infects a wide range of mammals worldwide, most importantly humans and their livestock. A total of 124 miRNAs were profiled, 72 of which had been previously reported and three of which were conserved miRNA sequences described here for the first time. The remaining 49 miRNAs were novel sequences of which, 31 were conserved with F. gigantica and the remaining 18 were specific to F. hepatica. The newly excysted juveniles express 22 unique miRNAs while the immature liver and mature bile duct stages each express 16 unique miRNAs. We discovered several sequence variant miRNAs (IsomiRs) as well as miRNA clusters that exhibit strict temporal expression paralleling parasite development. Target analysis revealed the close association between miRNA expression and stage-specific changes in the transcriptome; for example, we identified specific miRNAs that target parasite proteases known to be essential for intestinal wall penetration (cathepsin L3). Moreover, we demonstrate that miRNAs fine-tune the expression of genes involved in the metabolic pathways that allow the parasites to move from an aerobic external environment to the anerobic environment of the host. ...

Roberts, AGK, Catchpoole, DR & Kennedy, PJ 2022, 'Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability', NAR Genomics and Bioinformatics, vol. 4, no. 1.
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ABSTRACT There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour–normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.

Rodd, J & Castel, A 2022, 'Structural considerations to minimise the risk of horizontal cracks in the wall of circular concrete tanks', Structures, vol. 40, pp. 1091-1106.
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Rodriguez, J, Garcia, C, Mora, A, Flores-Bahamonde, F, Acuna, P, Novak, M, Zhang, Y, Tarisciotti, L, Davari, SA, Zhang, Z, Wang, F, Norambuena, M, Dragicevic, T, Blaabjerg, F, Geyer, T, Kennel, R, Khaburi, DA, Abdelrahem, M, Zhang, Z, Mijatovic, N & Aguilera, RP 2022, 'Latest Advances of Model Predictive Control in Electrical Drives—Part I: Basic Concepts and Advanced Strategies', IEEE Transactions on Power Electronics, vol. 37, no. 4, pp. 3927-3942.
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Rodríguez-Antón, JM, Rubio-Andrada, L, Celemín-Pedroche, MS & Ruíz-Peñalver, SM 2022, 'From the circular economy to the sustainable development goals in the European Union: an empirical comparison', International Environmental Agreements: Politics, Law and Economics, vol. 22, no. 1, pp. 67-95.
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AbstractThe European Union (EU) is trying to accelerate the transition from the current linear economy to a circular economy (CE). In fact, the CE is considered a tool to attain sustainable development goals (SDGs). In this sense, this paper aims at analysing the interaction between the CE and SDGs in the context of the new 2030 Agenda and the European CE strategy; thus contributing to the scarce empirical literature that links the potential of the European CE strategy to the achievement of the SDGs set by the 2030 Agenda. Three specific research questions have been formulated. First, could the objectives defined in the 2030 Agenda be considered homogeneous, and could they uniquely measure the concept of sustainability? Second, are there significant correlations between the implementation of a CE in the EU and the SDGs? Finally, is the behaviour of the 28 countries that make up the EU homogeneous in terms of the results of the initiatives aimed at the implementation of a CE? From these questions, nine hypotheses are put forward concerning the possible relationships between a CE implementation and the fulfilment of SDGs in the EU. Using a correlation analysis, an exploratory factor analysis, and a cluster analysis, it has been demonstrated that (a) SDGs do not univocally measure the concept of sustainability; (b) there are significant relationships between CE and SDGs in the EU; (c) the behaviour of these European countries is not homogeneous.

Romeijn, T, Behrens, M, Paul, G & Wei, D 2022, 'Experimental analysis of water and slurry flows in gravity-driven helical mineral separators', Powder Technology, vol. 405, pp. 117538-117538.
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Romeijn, T, Behrens, M, Paul, G & Wei, D 2022, 'Instantaneous and long-term mechanical properties of Polyethylene Terephthalate Glycol (PETG) additively manufactured by pellet-based material extrusion', Additive Manufacturing, vol. 59, pp. 103145-103145.
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Romero, JG, Gandarilla, I, Santibanez, V & Yi, B 2022, 'A Constructive Procedure for Orbital Stabilization of a Class of Underactuated Mechanical Systems', IEEE Transactions on Control Systems Technology, vol. 30, no. 6, pp. 2698-2706.
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Roslan, MF, Hannan, MA, Ker, PJ, Mannan, M, Muttaqi, KM & Mahlia, TMI 2022, 'Microgrid control methods toward achieving sustainable energy management: A bibliometric analysis for future directions', Journal of Cleaner Production, vol. 348, pp. 131340-131340.
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Rout, JK, Dalmia, A, Rath, SK, Mohanta, BK, Ramasubbareddy, S & H. Gandomi, A 2022, 'Detecting Product Review Spammers Using Principles of Big Data', IEEE Transactions on Engineering Management, pp. 1-12.
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Rout, RK, Hassan, SS, Sheikh, S, Umer, S, Sahoo, KS & Gandomi, AH 2022, 'Feature-extraction and analysis based on spatial distribution of amino acids for SARS-CoV-2 Protein sequences', Computers in Biology and Medicine, vol. 141, pp. 105024-105024.
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Rozali, S, Abd Latif, Z, Adnan, NA, Hussin, Y, Blackburn, A & Pradhan, B 2022, 'Estimating feature extraction changes of Berkelah Forest, Malaysia from multisensor remote sensing data using and object-based technique', Geocarto International, vol. 37, no. 11, pp. 3247-3264.
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Rozina, Ahmad, M, Asif, S, Klemeš, JJ, Mubashir, M, Bokhari, A, Sultana, S, Mukhtar, A, Zafar, M, Bazmi, AA, Ullah, S, Khan, MS, Koyande, AK, Mofijur, M & Show, P-L 2022, 'Conversion of the toxic and hazardous Zanthoxylum armatum seed oil into methyl ester using green and recyclable silver oxide nanoparticles', Fuel, vol. 310, pp. 122296-122296.
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Ruan, Z, Song, W, Zhang, Y, Yao, G & Guo, Y 2022, 'A Variable Switching Frequency Space Vector Pulse Width Modulation Technique Using Virtual Flux Ripple', IEEE Journal of Emerging and Selected Topics in Power Electronics, pp. 1-1.
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Rush, A, Catchpoole, DR, Reaiche-Miller, G, Gilbert, T, Ng, W, Watson, PH & Byrne, JA 2022, 'What Do Biomedical Researchers Want from Biobanks? Results of an Online Survey', Biopreservation and Biobanking, vol. 20, no. 3, pp. 271-282.
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Rutherford, H, Saha Turai, R, Chacon, A, Franklin, DR, Mohammadi, A, Tashima, H, Yamaya, T, Parodi, K, Rosenfeld, AB, Guatelli, S & Safavi-Naeini, M 2022, 'An inception network for positron emission tomography based dose estimation in carbon ion therapy', Physics in Medicine & Biology, vol. 67, no. 19, pp. 194001-194001.
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Abstract Objective. We aim to evaluate a method for estimating 1D physical dose deposition profiles in carbon ion therapy via analysis of dynamic PET images using a deep residual learning convolutional neural network (CNN). The method is validated using Monte Carlo simulations of 12C ion spread-out Bragg peak (SOBP) profiles, and demonstrated with an experimental PET image. Approach. A set of dose deposition and positron annihilation profiles for monoenergetic 12C ion pencil beams in PMMA are first generated using Monte Carlo simulations. From these, a set of random polyenergetic dose and positron annihilation profiles are synthesised and used to train the CNN. Performance is evaluated by generating a second set of simulated 12C ion SOBP profiles (one 116 mm SOBP profile and ten 60 mm SOBP profiles), and using the trained neural network to estimate the dose profile deposited by each beam and the position of the distal edge of the SOBP. Next, the same methods are used to evaluate the network using an experimental PET image, obtained after irradiating a PMMA phantom with a 12C ion beam at QST’s Heavy Ion Medical Accelerator in Chiba facility in Chiba, Japan. The performance of the CNN is compared to that of a recently published iterative technique using the same simulated and experimental 12C SOBP profiles. Main results. The CNN estimated the simulated dose profiles with a mean relative error (MRE) of 0.7% ± 1.0% and the distal edge position with an accuracy of 0.1 mm ± 0.2 mm, and estimate the dose delivered by the experimental 12C ion beam with a MRE of 3.7%, and the distal edge with an accuracy of 1.7 mm. Significance. The CNN was able to produce estimates of the dos...

Sabetamal, H, Sheng, D & Carter, JP 2022, 'Coupled hydro-mechanical modelling of unsaturated soils; numerical implementation and application to large deformation problems', Computers and Geotechnics, vol. 152, pp. 105044-105044.
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Sadeghian, F, Jahandari, S, Haddad, A, Rasekh, H & Li, J 2022, 'Effects of variations of voltage and pH value on the shear strength of soil and durability of different electrodes and piles during electrokinetic phenomenon', Journal of Rock Mechanics and Geotechnical Engineering, vol. 14, no. 2, pp. 625-636.
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Saha, SC, Francis, I, Huang, X & Paul, AR 2022, 'Heat transfer and fluid flow analysis in a realistic 16-generation lung', Physics of Fluids, vol. 34, no. 6, pp. 061906-061906.
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Heat transfer between inhaled hot/cool air and the lung surface within the human respiratory system is an intriguing topic that has not received enough attention. The lung can be considered an in vivo heat exchanger, balancing the inhaled air temperature by lowering the hot air temperature and increasing the cool air temperature. The current work studies the unsteady and incompressible airflow motion and heat transfer during inhalation between the surface of the lungs (37 °C) and the inhaled cool air (25 °C) in one case and inhaled hot air (43 °C) in another. Computerized tomography scan (CT-scan) images of the lung of a 39-year-old male patient were processed to generate the airway geometry consisting of 16 generations. The geometry was further modified in UG NX 12.0, and the mesh generation was carried out using Ansys Meshing. The shear stress transport (SST) [Formula: see text] turbulent model was employed in Ansys Fluent 20.2 to model the air/lung convective volume heat transfer utilizing a realistic breathing velocity profile. Temperature streamlines, lung volume temperatures, surface heat flux, and surface temperatures on all 16 generations were produced for both cases during the breathing cycle of 4.75 s. Several conclusions were made by studying and comparing the two cases. First, heat transfer between inhaled hot or cool air and the lung surface mainly occurred in the first few generations. Second, airflow temperature patterns are dependent on the inlet breathing velocity profile. Third, the lung volume temperature change directly correlates with the temperature difference between air and the lung surface. Finally, the surface heat flux strongly depended on the heat transfer coefficient. The density, viscosity, thermal conductivity, and specific heat of hot/cool air affected the Reynolds number, Nusselt number, heat transfer coefficient, and surface heat flux.

Saki, M, Abolhasan, M, Lipman, J & Jamalipour, A 2022, 'Mobility Model for Contact-Aware Data Offloading Through Train-to-Train Communications in Rail Networks', IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 597-609.
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Saleem, R, Ni, W, Ikram, M & Jamalipour, A 2022, 'Deep Reinforcement Learning-Driven Secrecy Design for Intelligent Reflecting Surface-Based 6G-IoT Networks', IEEE Internet of Things Journal, pp. 1-1.
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Saleem, S, Amin, J, Sharif, M, Mallah, GA, Kadry, S & Gandomi, AH 2022, 'Leukemia segmentation and classification: A comprehensive survey', Computers in Biology and Medicine, vol. 150, pp. 106028-106028.
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Salehi, Y, Shafaghat, A & Khabbaz, H 2022, 'A REVIEW ON PERFORMANCE OF STONE COLUMNS AS A GROUND IMPROVEMENT TECHNIQUE: LESSONS LEARNT FROM PAST EXPERIENCES AND PROSPECT FOR FUTURE DEVELOPMENT', Australian Geomechanics Journal, vol. 57, no. 1, pp. 71-91.
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Since the population growth is creating a strong demand for urban development, the need for construction in soft soils is dramatically increasing. Accordingly, ground improvement is an important requirement to avoid problems such as nonuniform settlements, failure due to low bearing capacity or liquefaction. Stone columns are used as one of the ground improvement techniques to stabilize the soil through increasing soil stiffness and shear resistance while decreasing the compressibility and settlement. Predicting the behaviour of a stone column needs to meet technical challenges, particularly in soft cohesive soils. Therefore, the aim of this paper is to make a broad assessment of the performance characteristics of stone columns in clayey soils as a review. In this study, the stone columns behaviour has been studied through analytical, experimental and numerical techniques, and failure modes and design of stone columns and their installation techniques are discussed. Based on previous investigations, it is gathered that in very soft soils, the dry-bottom feed vibro replacement technique is preferred to other methods and usage of geosynthetic encasement is very efficient where insufficient lateral confinement of the soil is problematic. According to past findings, the friction angle of the stone material and the diameter of the column are significant parameters for the design of the bearing capacity of the column. Furthermore, apart from ground improvement benefits, stone columns are used as vertical drains, which can decrease the pore water pressure during earthquakes and therefore mitigate the liquefaction potential. In addition, the cost-effectiveness of using low priced materials instead of aggregates without disturbing the overall performance of stone columns seems to be viable and can be explored further in future. This review can give an enhanced viewpoint to engineers and practitioners considering the use of stone columns in their projects.

Samy, I, Han, X, Lazos, L, Li, M, Xiao, Y & Krunz, M 2022, 'Misbehavior Detection in Wi-Fi/LTE Coexistence over Unlicensed Bands', IEEE Transactions on Mobile Computing, pp. 1-1.
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Sanahuja-Embuena, V, Lim, S, Górecki, R, Trzaskus, K, Hélix-Nielsen, C & Kyong Shon, H 2022, 'Enhancing selectivity of novel outer-selective hollow fiber forward osmosis membrane by polymer nanostructures', Chemical Engineering Journal, vol. 433, pp. 133634-133634.
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Sansom, TM, Oberst, S, Richter, A, Lai, JCS, Saadatfar, M, Nowotny, M & Evans, TA 2022, 'Low radiodensity μCT scans to reveal detailed morphology of the termite leg and its subgenual organ', Arthropod Structure & Development, vol. 70, pp. 101191-101191.
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Santoro, S, Aquino, M, Han Seo, D, Van Der Laan, T, Lee, M, Sung Yun, J, Jun Park, M, Bendavid, A, Kyong Shon, H, Halil Avci, A & Curcio, E 2022, 'Dimensionally controlled graphene-based surfaces for photothermal membrane crystallization', Journal of Colloid and Interface Science, vol. 623, pp. 607-616.
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Saputra, YM, Nguyen, D, Dinh, HT, Pham, Q-V, Dutkiewicz, E & Hwang, W-J 2022, 'Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services', IEEE Transactions on Mobile Computing, pp. 1-1.
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Saravanakumar, A, Chen, W-H, Arunachalam, KD, Park, Y-K & Chyuan Ong, H 2022, 'Pilot-scale study on downdraft gasification of municipal solid waste with mass and energy balance analysis', Fuel, vol. 315, pp. 123287-123287.
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Sarker, PC, Guo, Y, Lu, H & Zhu, JG 2022, 'Improvement on parameter identification of modified Jiles-Atherton model for iron loss calculation', Journal of Magnetism and Magnetic Materials, vol. 542, pp. 168602-168602.
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Sayem, ASM, Lalbakhsh, A, Esselle, KP, Buckley, JL, O'Flynn, B & Simorangkir, RBVB 2022, 'Flexible Transparent Antennas: Advancements, Challenges, and Prospects', IEEE Open Journal of Antennas and Propagation, vol. 3, pp. 1109-1133.
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Schlegl, T, Schlegl, S, Tomaselli, D, West, N & Deuse, J 2022, 'Adaptive similarity search for the retrieval of rare events from large time series databases', Advanced Engineering Informatics, vol. 52, pp. 101629-101629.
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Schlegl, T, Tomaselli, D, Schlegl, S, West, N & Deuse, J 2022, 'Automated search of process control limits for fault detection in time series data', Journal of Process Control, vol. 117, pp. 52-64.
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Schwenken, J, Schallow, J, Sollik, D, Richter, R & Deuse, J 2022, 'Identifikation und Prognose dynamischer Engpässe', Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 117, no. 5, pp. 294-299.
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Abstract Eine erhöhte geplante und ungeplante Variabilität innerhalb der Produktion begünstigt das vermehrte Auftreten dynamischer Engpässe. Deren Beherrschung in Form eines zielgerichteten Engpassmanagements gelingt in der Unternehmenspraxis derzeit häufig nur sehr unzureichend und weitestgehend reaktiv. Dieser Beitrag stellt wesentliche Anwenderanforderungen sowie eine Bewertungssystematik und deren Anwendung für ausgewählte Verfahren der Engpassidentifikation und -prognose vor.

Sebayang, AH, Milano, J, Shamsuddin, AH, Alfansuri, M, Silitonga, AS, Kusumo, F, Prahmana, RA, Fayaz, H & Zamri, MFMA 2022, 'Modelling and prediction approach for engine performance and exhaust emission based on artificial intelligence of sterculia foetida biodiesel', Energy Reports, vol. 8, pp. 8333-8345.
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Seethaler, R, Mansour, SZ, Ruppert, MG & Fleming, AJ 2022, 'Piezoelectric benders with strain sensing electrodes: Sensor design for position control and force estimation', Sensors and Actuators A: Physical, vol. 335, pp. 113384-113384.
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Sekaran, R, Munnangi, AK, Ramachandran, M & Gandomi, AH 2022, '3D brain slice classification and feature extraction using Deformable Hierarchical Heuristic Model', Computers in Biology and Medicine, vol. 149, pp. 105990-105990.
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Senanayake, S & Pradhan, B 2022, 'Predicting soil erosion susceptibility associated with climate change scenarios in the Central Highlands of Sri Lanka', Journal of Environmental Management, vol. 308, pp. 114589-114589.
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Senanayake, S, Pradhan, B, Alamri, A & Park, H-J 2022, 'A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction', Science of The Total Environment, vol. 845, pp. 157220-157220.
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Rainfall variation causes frequent unexpected disasters all over the world. Increasing rainfall intensity significantly escalates soil erosion and soil erosion related hazards. Forecasting accurate rainfall helps early detection of soil erosion vulnerability and can minimise the damages by taking appropriate measures caused by severe storms, droughts and floods. This study aims to predict soil erosion probability using the deep learning approach: long short-term memory neural network model (LSTM) and revised universal soil loss equation (RUSLE) model. Daily rainfall data were gathered from five agro-meteorological stations in the Central Highlands of Sri Lanka from 1990 to 2021 and fed into the LSTM model simulation. The LSTM model was forecasted with the time-series monthly rainfall data for a long lead time period, rainfall values for next 36 months in each station. Geo-informatics tools were used to create the rainfall erosivity map layer for the year 2024. The RUSLE model prediction indicates the average annual soil erosion over the Highlands will be 11.92 t/ha/yr. Soil erosion susceptibility map suggests around 30 % of the land area will be categorised as moderate to very-high soil erosion susceptible classes. The resulted map layer was validated using past soil erosion map layers developed for 2000, 2010 and 2019. The soil erosion susceptibility map indicates an accuracy of 0.93 with the area under the receiver operator characteristic curve (AUC-ROC), showing a satisfactory prediction performance. These findings will be helpful in policy-level decision making and researchers can further tested different deep learning models with the RUSLE model to enhance the prediction capability of soil erosion probability.

Senanayake, S, Pradhan, B, Huete, A & Brennan, J 2022, 'Spatial modeling of soil erosion hazards and crop diversity change with rainfall variation in the Central Highlands of Sri Lanka', Science of The Total Environment, vol. 806, pp. 150405-150405.
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Septiadi, WN, Iswari, GA, Sudarsana, PB, Putra, GJP, Febraldo, D, Putra, N & Mahlia, TMI 2022, 'Effect of Al2O3 and TiO2 nano-coated wick on the thermal performance of heat pipe', Journal of Thermal Analysis and Calorimetry, vol. 147, no. 11, pp. 6193-6205.
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A heat pipe is a passive two-phase heat exchanger technology, as a capillary-driven structure that allows heat transport by maintaining temperature difference. Heat pipe performance can be determined from the value of heat resistance, and nanoparticle can be applied to increase heat pipe performance. This research uses Al2O3 and TiO2 as a coating material for the heat pipe. The methods used in this research were by giving the heat pipe a nano-coating treatment using the electrophoretic deposition process and doing a pool boiling experiment by giving the heat pipe some heat loads. The main data of this research are temperature and bubble growt